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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16432">arXiv:2410.16432</a> <span> [<a href="https://arxiv.org/pdf/2410.16432">pdf</a>, <a href="https://arxiv.org/format/2410.16432">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Rajabi%2C+A">AmirArsalan Rajabi</a>, <a href="/search/cs?searchtype=author&query=Tayebi%2C+A">Aida Tayebi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16432v2-abstract-short" style="display: inline;"> The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in information loss and an inadequate balance between accuracy and fairness. To address this, we propose a novel methodology grounded in bilevel optimization princ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16432v2-abstract-full').style.display = 'inline'; document.getElementById('2410.16432v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16432v2-abstract-full" style="display: none;"> The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in information loss and an inadequate balance between accuracy and fairness. To address this, we propose a novel methodology grounded in bilevel optimization principles. Our deep learning-based approach concurrently optimizes for both accuracy and fairness objectives, and under certain assumptions, achieving proven Pareto optimal solutions while mitigating bias in the trained model. Theoretical analysis indicates that the upper bound on the loss incurred by this method is less than or equal to the loss of the Lagrangian approach, which involves adding a regularization term to the loss function. We demonstrate the efficacy of our model primarily on tabular datasets such as UCI Adult and Heritage Health. When benchmarked against state-of-the-art fairness methods, our model exhibits superior performance, advancing fairness-aware machine learning solutions and bridging the accuracy-fairness gap. The implementation of FairBiNN is available on https://github.com/yazdanimehdi/FairBiNN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16432v2-abstract-full').style.display = 'none'; document.getElementById('2410.16432v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to NeurIPS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.09657">arXiv:2407.09657</a> <span> [<a href="https://arxiv.org/pdf/2407.09657">pdf</a>, <a href="https://arxiv.org/format/2407.09657">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Analyzing X's Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Baekey%2C+A">Alexander Baekey</a>, <a href="/search/cs?searchtype=author&query=Jayalath%2C+C">Chathura Jayalath</a>, <a href="/search/cs?searchtype=author&query=Mantzaris%2C+A">Alexander Mantzaris</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.09657v1-abstract-short" style="display: inline;"> The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.09657v1-abstract-full').style.display = 'inline'; document.getElementById('2407.09657v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.09657v1-abstract-full" style="display: none;"> The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). This study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. The methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. This asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.09657v1-abstract-full').style.display = 'none'; document.getElementById('2407.09657v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the Advances in Social Networks Analysis and Mining (ASONAM) - 2024, Annual Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11656">arXiv:2401.11656</a> <span> [<a href="https://arxiv.org/pdf/2401.11656">pdf</a>, <a href="https://arxiv.org/format/2401.11656">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11656v1-abstract-short" style="display: inline;"> Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the lim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11656v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11656v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11656v1-abstract-full" style="display: none;"> Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives. Our results indicate that, among various factors, the frequency of cleaning intervals is the most critical element for controlling the spread of CDI in a hospital environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11656v1-abstract-full').style.display = 'none'; document.getElementById('2401.11656v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted and presented at the Computational Social Science Society of the Americas Conference (CSS 2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11524">arXiv:2401.11524</a> <span> [<a href="https://arxiv.org/pdf/2401.11524">pdf</a>, <a href="https://arxiv.org/format/2401.11524">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11524v1-abstract-short" style="display: inline;"> The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-bas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11524v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11524v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11524v1-abstract-full" style="display: none;"> The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11524v1-abstract-full').style.display = 'none'; document.getElementById('2401.11524v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at The Computational Social Science Society of the Americas (CSS) - 2023, Annual Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.00540">arXiv:2202.00540</a> <span> [<a href="https://arxiv.org/pdf/2202.00540">pdf</a>, <a href="https://arxiv.org/format/2202.00540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Oghaz%2C+T+A">Toktam A. Oghaz</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.00540v1-abstract-short" style="display: inline;"> Recent advances in natural language processing (NLP) in online social media are evidently owed to large-scale datasets. However, labeling, storing, and processing a large number of textual data points, e.g., tweets, has remained challenging. On top of that, in applications such as hate speech detection, labeling a sufficiently large dataset containing offensive content can be mentally and emotiona… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00540v1-abstract-full').style.display = 'inline'; document.getElementById('2202.00540v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.00540v1-abstract-full" style="display: none;"> Recent advances in natural language processing (NLP) in online social media are evidently owed to large-scale datasets. However, labeling, storing, and processing a large number of textual data points, e.g., tweets, has remained challenging. On top of that, in applications such as hate speech detection, labeling a sufficiently large dataset containing offensive content can be mentally and emotionally taxing for human annotators. Thus, NLP methods that can make the best use of significantly less labeled data points are of great interest. In this paper, we present a novel pool-based active learning method that can be used for the training of large unlabeled corpus with minimum annotation cost. For that, we propose to find the dominant sets of local clusters in the feature space. These sets represent maximally cohesive structures in the data. Then, the samples that do not belong to any of the dominant sets are selected to be used to train the model, as they represent the boundaries of the local clusters and are more challenging to classify. Our proposed method does not have any parameters to be tuned, making it dataset-independent, and it can approximately achieve the same classification accuracy as full training data, with significantly fewer data points. Additionally, our method achieves a higher performance in comparison to the state-of-the-art active learning strategies. Furthermore, our proposed algorithm is able to incorporate conventional active learning scores, such as uncertainty-based scores, into its selection criteria. We show the effectiveness of our method on different datasets and using different neural network architectures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00540v1-abstract-full').style.display = 'none'; document.getElementById('2202.00540v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 5 tables, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.10272">arXiv:2111.10272</a> <span> [<a href="https://arxiv.org/pdf/2111.10272">pdf</a>, <a href="https://arxiv.org/format/2111.10272">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Resilience from Diversity: Population-based approach to harden models against adversarial attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.10272v2-abstract-short" style="display: inline;"> Traditional deep learning networks (DNN) exhibit intriguing vulnerabilities that allow an attacker to force them to fail at their task. Notorious attacks such as the Fast Gradient Sign Method (FGSM) and the more powerful Projected Gradient Descent (PGD) generate adversarial samples by adding a magnitude of perturbation $蔚$ to the input's computed gradient, resulting in a deterioration of the effec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.10272v2-abstract-full').style.display = 'inline'; document.getElementById('2111.10272v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.10272v2-abstract-full" style="display: none;"> Traditional deep learning networks (DNN) exhibit intriguing vulnerabilities that allow an attacker to force them to fail at their task. Notorious attacks such as the Fast Gradient Sign Method (FGSM) and the more powerful Projected Gradient Descent (PGD) generate adversarial samples by adding a magnitude of perturbation $蔚$ to the input's computed gradient, resulting in a deterioration of the effectiveness of the model's classification. This work introduces a model that is resilient to adversarial attacks. Our model leverages an established mechanism of defense which utilizes randomness and a population of DNNs. More precisely, our model consists of a population of $n$ diverse submodels, each one of them trained to individually obtain a high accuracy for the task at hand, while forced to maintain meaningful differences in their weights. Each time our model receives a classification query, it selects a submodel from its population at random to answer the query. To counter the attack transferability, diversity is introduced and maintained in the population of submodels. Thus introducing the concept of counter linking weights. A Counter-Linked Model (CLM) consists of a population of DNNs of the same architecture where a periodic random similarity examination is conducted during the simultaneous training to guarantee diversity while maintaining accuracy. Though the randomization technique proved to be resilient against adversarial attacks, we show that by retraining the DNNs ensemble or training them from the start with counter linking would enhance the robustness by around 20\% when tested on the MNIST dataset and at least 15\% when tested on the CIFAR-10 dataset. When CLM is coupled with adversarial training, this defense mechanism achieves state-of-the-art robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.10272v2-abstract-full').style.display = 'none'; document.getElementById('2111.10272v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures, 5 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.5.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.14046">arXiv:2107.14046</a> <span> [<a href="https://arxiv.org/pdf/2107.14046">pdf</a>, <a href="https://arxiv.org/format/2107.14046">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.14046v1-abstract-short" style="display: inline;"> Algorithms are becoming more widely used in business, and businesses are becoming increasingly concerned that their algorithms will cause significant reputational or financial damage. We should emphasize that any of these damages stem from situations in which the United States lacks strict legislative prohibitions or specified protocols for measuring damages. As a result, governments are enacting… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14046v1-abstract-full').style.display = 'inline'; document.getElementById('2107.14046v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.14046v1-abstract-full" style="display: none;"> Algorithms are becoming more widely used in business, and businesses are becoming increasingly concerned that their algorithms will cause significant reputational or financial damage. We should emphasize that any of these damages stem from situations in which the United States lacks strict legislative prohibitions or specified protocols for measuring damages. As a result, governments are enacting legislation and enforcing prohibitions, regulators are fining businesses, and the judiciary is debating whether or not to make artificially intelligent computer models as the decision-makers in the eyes of the law. From autonomous vehicles and banking to medical care, housing, and legal decisions, there will soon be enormous amounts of algorithms that make decisions with limited human interference. Governments, businesses, and society would have an algorithm audit, which would have systematic verification that algorithms are lawful, ethical, and secure, similar to financial audits. A modern market, auditing, and assurance of algorithms developed to professionalize and industrialize AI, machine learning, and related algorithms. Stakeholders of this emerging field include policymakers and regulators, along with industry experts and entrepreneurs. In addition, we foresee audit thresholds and frameworks providing valuable information to all who are concerned with governance and standardization. This paper aims to review the critical areas required for auditing and assurance and spark discussion in this novel field of study and practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14046v1-abstract-full').style.display = 'none'; document.getElementById('2107.14046v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Conference on Human-Computer Interaction 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.14044">arXiv:2107.14044</a> <span> [<a href="https://arxiv.org/pdf/2107.14044">pdf</a>, <a href="https://arxiv.org/format/2107.14044">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Ethical AI for Social Good </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.14044v1-abstract-short" style="display: inline;"> The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community. Through all the advancement of AI-based solutions, it can solve societal issues effectively. To date, however, there is only a rudimentary grasp of what constitutes AI socially beneficial in principle, what constitutes AI4SG in reality, and what are the policies and regulations needed t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14044v1-abstract-full').style.display = 'inline'; document.getElementById('2107.14044v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.14044v1-abstract-full" style="display: none;"> The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community. Through all the advancement of AI-based solutions, it can solve societal issues effectively. To date, however, there is only a rudimentary grasp of what constitutes AI socially beneficial in principle, what constitutes AI4SG in reality, and what are the policies and regulations needed to ensure it. This paper fills the vacuum by addressing the ethical aspects that are critical for future AI4SG efforts. Some of these characteristics are new to AI, while others have greater importance due to its usage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14044v1-abstract-full').style.display = 'none'; document.getElementById('2107.14044v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Conference on Human-Computer Interaction, 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.05875">arXiv:2101.05875</a> <span> [<a href="https://arxiv.org/pdf/2101.05875">pdf</a>, <a href="https://arxiv.org/format/2101.05875">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/e23040394">10.3390/e23040394 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.05875v1-abstract-short" style="display: inline;"> Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.05875v1-abstract-full').style.display = 'inline'; document.getElementById('2101.05875v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.05875v1-abstract-full" style="display: none;"> Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.05875v1-abstract-full').style.display = 'none'; document.getElementById('2101.05875v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.01188">arXiv:2009.01188</a> <span> [<a href="https://arxiv.org/pdf/2009.01188">pdf</a>, <a href="https://arxiv.org/format/2009.01188">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+%C3%87">Ece 脟i臒dem Mutlu</a>, <a href="/search/cs?searchtype=author&query=Oghaz%2C+T+A">Toktam A. Oghaz</a>, <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=T%C3%BCt%C3%BCnc%C3%BCler%2C+E">Ege T眉t眉nc眉ler</a>, <a href="/search/cs?searchtype=author&query=Rajabi%2C+A">Amirarsalan Rajabi</a>, <a href="/search/cs?searchtype=author&query=Tayebi%2C+A">Aida Tayebi</a>, <a href="/search/cs?searchtype=author&query=Ozmen%2C+O">Ozlem Ozmen</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.01188v2-abstract-short" style="display: inline;"> At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, specially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.01188v2-abstract-full').style.display = 'inline'; document.getElementById('2009.01188v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.01188v2-abstract-full" style="display: none;"> At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, specially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the opinions of the tweet initiators regarding the use of "chloroquine" and "hydroxychloroquine" for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users' stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users' stances towards a claim, in any domain. We have made this data set available to the research community via GitHub. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.01188v2-abstract-full').style.display = 'none'; document.getElementById('2009.01188v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.12723">arXiv:2008.12723</a> <span> [<a href="https://arxiv.org/pdf/2008.12723">pdf</a>, <a href="https://arxiv.org/format/2008.12723">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> CD-SEIZ: Cognition-Driven SEIZ Compartmental Model for the Prediction of Information Cascades on Twitter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+%C3%87">Ece 脟i臒dem Mutlu</a>, <a href="/search/cs?searchtype=author&query=Rajabi%2C+A">Amirarsalan Rajabi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.12723v1-abstract-short" style="display: inline;"> Information spreading social media platforms has become ubiquitous in our lives due to viral information propagation regardless of its veracity. Some information cascades turn out to be viral since they circulated rapidly on the Internet. The uncontrollable virality of manipulated or disorientated true information (fake news) might be quite harmful, while the spread of the true news is advantageou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.12723v1-abstract-full').style.display = 'inline'; document.getElementById('2008.12723v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.12723v1-abstract-full" style="display: none;"> Information spreading social media platforms has become ubiquitous in our lives due to viral information propagation regardless of its veracity. Some information cascades turn out to be viral since they circulated rapidly on the Internet. The uncontrollable virality of manipulated or disorientated true information (fake news) might be quite harmful, while the spread of the true news is advantageous, especially in emergencies. We tackle the problem of predicting information cascades by presenting a novel variant of SEIZ (Susceptible/ Exposed/ Infected/ Skeptics) model that outperforms the original version by taking into account the cognitive processing depth of users. We define an information cascade as the set of social media users' reactions to the original content which requires at least minimal physical and cognitive effort; therefore, we considered retweet/ reply/ quote (mention) activities and tested our framework on the Syrian White Helmets Twitter data set from April 1st, 2018 to April 30th, 2019. In the prediction of cascade pattern via traditional compartmental models, all the activities are grouped, and their summation is taken into account; however, transition rates between compartments should vary according to the activity type since their requirements of physical and cognitive efforts are not same. Based on this assumption, we design a cognition-driven SEIZ (CD-SEIZ) model in the prediction of information cascades on Twitter. We tested SIS, SEIZ, and CD-SEIZ models on 1000 Twitter cascades and found that CD-SEIZ has a significantly low fitting error and provides a statistically more accurate estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.12723v1-abstract-full').style.display = 'none'; document.getElementById('2008.12723v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.13991">arXiv:2006.13991</a> <span> [<a href="https://arxiv.org/pdf/2006.13991">pdf</a>, <a href="https://arxiv.org/format/2006.13991">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/s42001-021-00121-z">10.1007/s42001-021-00121-z <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Controversial information spreads faster and further in Reddit </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Scheinert%2C+S">Steve Scheinert</a>, <a href="/search/cs?searchtype=author&query=Mantzaris%2C+A+V">Alexander V. Mantzaris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.13991v1-abstract-short" style="display: inline;"> Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, cont… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.13991v1-abstract-full').style.display = 'inline'; document.getElementById('2006.13991v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.13991v1-abstract-full" style="display: none;"> Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, controversiality within social cascades is of high interest to us. It is known that controversiality is bound to happen within online discussions. The online social network platform Reddit has the feature to tag comments as controversial if the users have mixed opinions about that comment. The difference between this study and previous attempts at understanding controversiality on social networks is that we do not investigate topics that are known to be controversial. On the contrary, we examine typical cascades with comments that the readers deemed to be controversial concerning the matter discussed. This work asks whether controversially initiated information cascades have distinctive characteristics than those not controversial in Reddit. We used data collected from Reddit consisting of around 17 million posts and their corresponding comments related to cybersecurity issues to answer these emerging questions. From the comparative analyses conducted, controversial content travels faster and further from its origin. Understanding this phenomenon would shed light on how users or organization might use it to their help in controlling and spreading a specific beneficiary message. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.13991v1-abstract-full').style.display = 'none'; document.getElementById('2006.13991v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.12624">arXiv:2006.12624</a> <span> [<a href="https://arxiv.org/pdf/2006.12624">pdf</a>, <a href="https://arxiv.org/format/2006.12624">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf Students: An Agent-Based Modeling Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alaghband%2C+M">Marie Alaghband</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.12624v1-abstract-short" style="display: inline;"> Post-secondary education persistence is the likelihood of a student remaining in post-secondary education. Although statistics show that post-secondary persistence for deaf students has increased recently, there are still many obstacles obstructing students from completing their post-secondary degree goals. Therefore, increasing the persistence rate is crucial to increase education and work goals… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.12624v1-abstract-full').style.display = 'inline'; document.getElementById('2006.12624v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.12624v1-abstract-full" style="display: none;"> Post-secondary education persistence is the likelihood of a student remaining in post-secondary education. Although statistics show that post-secondary persistence for deaf students has increased recently, there are still many obstacles obstructing students from completing their post-secondary degree goals. Therefore, increasing the persistence rate is crucial to increase education and work goals for deaf students. In this work, we present an agent-based model using NetLogo software for the persistence phenomena of deaf students. We consider four non-cognitive factors: having clear goals, social integration, social skills, and academic experience, which influence the departure decision of deaf students. Progress and results of this work suggest that agent-based modeling approaches promise to give better understanding of what will increase persistence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.12624v1-abstract-full').style.display = 'none'; document.getElementById('2006.12624v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.13142">arXiv:2004.13142</a> <span> [<a href="https://arxiv.org/pdf/2004.13142">pdf</a>, <a href="https://arxiv.org/format/2004.13142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Quantifying Latent Moral Foundations in Twitter Narratives: The Case of the Syrian White Helmets Misinformation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+%C3%87">Ece 脟i臒dem Mutlu</a>, <a href="/search/cs?searchtype=author&query=Oghaz%2C+T">Toktam Oghaz</a>, <a href="/search/cs?searchtype=author&query=T%C3%BCt%C3%BCnc%C3%BCler%2C+E">Ege T眉t眉nc眉ler</a>, <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.13142v1-abstract-short" style="display: inline;"> For years, many studies employed sentiment analysis to understand the reasoning behind people's choices and feelings, their communication styles, and the communities which they belong to. We argue that gaining more in-depth insight into moral dimensions coupled with sentiment analysis can potentially provide superior results. Understanding moral foundations can yield powerful results in terms of p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.13142v1-abstract-full').style.display = 'inline'; document.getElementById('2004.13142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.13142v1-abstract-full" style="display: none;"> For years, many studies employed sentiment analysis to understand the reasoning behind people's choices and feelings, their communication styles, and the communities which they belong to. We argue that gaining more in-depth insight into moral dimensions coupled with sentiment analysis can potentially provide superior results. Understanding moral foundations can yield powerful results in terms of perceiving the intended meaning of the text data, as the concept of morality provides additional information on the unobservable characteristics of information processing and non-conscious cognitive processes. Therefore, we studied latent moral loadings of Syrian White Helmets-related tweets of Twitter users from April 1st, 2018 to April 30th, 2019. For the operationalization and quantification of moral rhetoric in tweets, we use Extended Moral Foundations Dictionary in which five psychological dimensions (Harm/Care, Fairness/Reciprocity, In-group/Loyalty, Authority/Respect and Purity/Sanctity) are considered. We show that people tend to share more tweets involving the virtue moral rhetoric than the tweets involving the vice rhetoric. We observe that the pattern of the moral rhetoric of tweets among these five dimensions are very similar during different time periods, while the strength of the five dimension is time-variant. Even though there is no significant difference between the use of Fairness/Reciprocity, In-group/Loyalty or Purity/Sanctity rhetoric, the less use of Harm/Care rhetoric is significant and remarkable. Besides, the strength of the moral rhetoric and the polarization in morality across people are mostly observed in tweets involving Harm/Care rhetoric despite the number of tweets involving the Harm/Care dimension is low. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.13142v1-abstract-full').style.display = 'none'; document.getElementById('2004.13142v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.13131">arXiv:2004.13131</a> <span> [<a href="https://arxiv.org/pdf/2004.13131">pdf</a>, <a href="https://arxiv.org/format/2004.13131">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> Effects of Assortativity on Consensus Formation with Heterogeneous Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+%C3%87">Ece 脟i臒dem Mutlu</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.13131v1-abstract-short" style="display: inline;"> Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi networks of which degree correlation (assortativity) is neutral, numerous studies demonstrated that online social networks tend to show assortative mixing (positive degree correlation), while non-social networks show a disassortative mixing (negative degree correlation). First, we analyzed the variability in the assortati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.13131v1-abstract-full').style.display = 'inline'; document.getElementById('2004.13131v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.13131v1-abstract-full" style="display: none;"> Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi networks of which degree correlation (assortativity) is neutral, numerous studies demonstrated that online social networks tend to show assortative mixing (positive degree correlation), while non-social networks show a disassortative mixing (negative degree correlation). First, we analyzed the variability in the assortativity coefficients of different groups of the same platform by using three different subreddits in Reddit. Our data analysis results showed that Reddit is disassortative, and assortativity coefficients of the aforementioned subreddits are computed as -0.0384, -0.0588 and -0.1107, respectively. Motivated by the variability in the results even in the same platform, we decided to investigate the sensitivity of dynamics of consensus formation to the assortativity of the network. We concluded that the system is more likely to reach a consensus when the network is disassortatively mixed or neutral; however, the likelihood of the consensus significantly decreases when the network is assortatively mixed. Surprisingly, the time elapsed until all nodes fix their opinions is slightly lower when the network is neutral compared to either assortative or disassortative networks. These results are more pronounced when the thresholds of agents are more heterogeneously distributed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.13131v1-abstract-full').style.display = 'none'; document.getElementById('2004.13131v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.06793">arXiv:2004.06793</a> <span> [<a href="https://arxiv.org/pdf/2004.06793">pdf</a>, <a href="https://arxiv.org/format/2004.06793">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3372923.3404790">10.1145/3372923.3404790 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Oghaz%2C+T+A">Toktam A. Oghaz</a>, <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+C">Ece C. Mutlu</a>, <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.06793v1-abstract-short" style="display: inline;"> Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.06793v1-abstract-full').style.display = 'inline'; document.getElementById('2004.06793v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.06793v1-abstract-full" style="display: none;"> Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.06793v1-abstract-full').style.display = 'none'; document.getElementById('2004.06793v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.00379">arXiv:2004.00379</a> <span> [<a href="https://arxiv.org/pdf/2004.00379">pdf</a>, <a href="https://arxiv.org/format/2004.00379">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Resistance of communities against disinformation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rajabi%2C+A">Amirarsalan Rajabi</a>, <a href="/search/cs?searchtype=author&query=Talebzadehhosseini%2C+S">Seyyedmilad Talebzadehhosseini</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.00379v1-abstract-short" style="display: inline;"> The spread of disinformation is considered a big threat to societies and has recently received unprecedented attention. In this paper we propose an agent-based model to simulate dissemination of a conspiracy in a population. The model is able to compare the resistance of different network structures against the activity of conspirators. Results show that connectedness of network structure and cent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00379v1-abstract-full').style.display = 'inline'; document.getElementById('2004.00379v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.00379v1-abstract-full" style="display: none;"> The spread of disinformation is considered a big threat to societies and has recently received unprecedented attention. In this paper we propose an agent-based model to simulate dissemination of a conspiracy in a population. The model is able to compare the resistance of different network structures against the activity of conspirators. Results show that connectedness of network structure and centrality of conspirators are of crucial importance in preventing conspiracies from becoming widespread. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00379v1-abstract-full').style.display = 'none'; document.getElementById('2004.00379v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.11671">arXiv:2003.11671</a> <span> [<a href="https://arxiv.org/pdf/2003.11671">pdf</a>, <a href="https://arxiv.org/format/2003.11671">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> The Degree-Dependent Threshold Model: Towards a Better Understanding of Opinion Dynamics on Online Social Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+C">Ece C. Mutlu</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.11671v1-abstract-short" style="display: inline;"> With the rapid growth of online social media, people become increasingly overwhelmed by the volume and the content of the information present in the environment. The threshold model is currently one of the most common methods to capture the effect of people on others' opinions and emotions. Although many studies employ and try to improve upon the threshold model, the search for an appropriate thre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.11671v1-abstract-full').style.display = 'inline'; document.getElementById('2003.11671v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.11671v1-abstract-full" style="display: none;"> With the rapid growth of online social media, people become increasingly overwhelmed by the volume and the content of the information present in the environment. The threshold model is currently one of the most common methods to capture the effect of people on others' opinions and emotions. Although many studies employ and try to improve upon the threshold model, the search for an appropriate threshold function for defining human behavior is an essential and yet unattained quest. The definition of heterogeneity in thresholds of individuals is oftentimes poorly defined, which leads to the rather simplistic use of uniform and binary functions, albeit they are far from representing the reality. In this study, we use Twitter data of size 30,704,025 tweets to mimic the adoption of a new opinion. Our results show that the threshold is not only correlated with the out-degree of nodes, which contradicts other studies but also correlated with nodes' in-degree. Therefore, we simulated two cases in which thresholds are out-degree and in-degree dependent, separately. We concluded that the system is more likely to reach a consensus when thresholds are in-degree dependent; however, the time elapsed until all nodes fix their opinions is significantly higher in this case. Additionally, we did not observe a notable effect of mean-degree on either the average opinion or the fixation time of opinions for both cases, and increasing seed size has a negative effect on reaching a consensus. Although threshold heterogeneity has a slight influence on the average opinion, the positive effect of heterogeneity on reaching a consensus is more pronounced when thresholds are in-degree dependent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.11671v1-abstract-full').style.display = 'none'; document.getElementById('2003.11671v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.11611">arXiv:2003.11611</a> <span> [<a href="https://arxiv.org/pdf/2003.11611">pdf</a>, <a href="https://arxiv.org/format/2003.11611">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-030-77517-9_11">10.1007/978-3-030-77517-9_11 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Oghaz%2C+T+A">Toktam A. Oghaz</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+C">Ece C. Mutlu</a>, <a href="/search/cs?searchtype=author&query=Schiappa%2C+M">Madeline Schiappa</a>, <a href="/search/cs?searchtype=author&query=Scheinert%2C+S">Steven Scheinert</a>, <a href="/search/cs?searchtype=author&query=Anagnostopoulos%2C+G+C">Georgios C. Anagnostopoulos</a>, <a href="/search/cs?searchtype=author&query=Bouwens%2C+C">Christina Bouwens</a>, <a href="/search/cs?searchtype=author&query=Fiore%2C+S+M">Stephen M. Fiore</a>, <a href="/search/cs?searchtype=author&query=Mantzaris%2C+A">Alexander Mantzaris</a>, <a href="/search/cs?searchtype=author&query=Murphy%2C+J+T">John T. Murphy</a>, <a href="/search/cs?searchtype=author&query=Rand%2C+W">William Rand</a>, <a href="/search/cs?searchtype=author&query=Salter%2C+A">Anastasia Salter</a>, <a href="/search/cs?searchtype=author&query=Stanfill%2C+M">Mel Stanfill</a>, <a href="/search/cs?searchtype=author&query=Sukthankar%2C+G">Gita Sukthankar</a>, <a href="/search/cs?searchtype=author&query=Baral%2C+N">Nisha Baral</a>, <a href="/search/cs?searchtype=author&query=Fair%2C+G">Gabriel Fair</a>, <a href="/search/cs?searchtype=author&query=Gunaratne%2C+C">Chathika Gunaratne</a>, <a href="/search/cs?searchtype=author&query=Hajiakhoond%2C+N+B">Neda B. Hajiakhoond</a>, <a href="/search/cs?searchtype=author&query=Jasser%2C+J">Jasser Jasser</a>, <a href="/search/cs?searchtype=author&query=Jayalath%2C+C">Chathura Jayalath</a>, <a href="/search/cs?searchtype=author&query=Newton%2C+O">Olivia Newton</a>, <a href="/search/cs?searchtype=author&query=Saadat%2C+S">Samaneh Saadat</a>, <a href="/search/cs?searchtype=author&query=Senevirathna%2C+C">Chathurani Senevirathna</a>, <a href="/search/cs?searchtype=author&query=Winter%2C+R">Rachel Winter</a> , et al. (1 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.11611v2-abstract-short" style="display: inline;"> This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeli… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.11611v2-abstract-full').style.display = 'inline'; document.getElementById('2003.11611v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.11611v2-abstract-full" style="display: none;"> This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.11611v2-abstract-full').style.display = 'none'; document.getElementById('2003.11611v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.08759">arXiv:2003.08759</a> <span> [<a href="https://arxiv.org/pdf/2003.08759">pdf</a>, <a href="https://arxiv.org/format/2003.08759">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alaghband%2C+M">Marie Alaghband</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.08759v2-abstract-short" style="display: inline;"> Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over $3000$ facial images extracted fro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08759v2-abstract-full').style.display = 'inline'; document.getElementById('2003.08759v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.08759v2-abstract-full" style="display: none;"> Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over $3000$ facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image's facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08759v2-abstract-full').style.display = 'none'; document.getElementById('2003.08759v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.02629">arXiv:1912.02629</a> <span> [<a href="https://arxiv.org/pdf/1912.02629">pdf</a>, <a href="https://arxiv.org/ps/1912.02629">ps</a>, <a href="https://arxiv.org/format/1912.02629">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Alaghband%2C+M">Marie Alaghband</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.02629v1-abstract-short" style="display: inline;"> With the increase of credit card usage, the volume of credit card misuse also has significantly increased. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.02629v1-abstract-full').style.display = 'inline'; document.getElementById('1912.02629v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.02629v1-abstract-full" style="display: none;"> With the increase of credit card usage, the volume of credit card misuse also has significantly increased. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part,we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.02629v1-abstract-full').style.display = 'none'; document.getElementById('1912.02629v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.12589">arXiv:1910.12589</a> <span> [<a href="https://arxiv.org/pdf/1910.12589">pdf</a>, <a href="https://arxiv.org/format/1910.12589">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Forecasting the Success of Television Series using Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Wieselthier%2C+Z">Zachary Wieselthier</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+L">Laura Martin</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.12589v1-abstract-short" style="display: inline;"> Television is an ever-evolving multi billion dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being bo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.12589v1-abstract-full').style.display = 'inline'; document.getElementById('1910.12589v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.12589v1-abstract-full" style="display: none;"> Television is an ever-evolving multi billion dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being box office failures and complete disappointments. In current studies, linguistic exploration is being performed on the relationship between Television series and target community of viewers. Having a decision support system that can display sound and predictable results would be needed to build confidence in the investment of a new TV series. The models presented in this study use data to study and determine what makes a sitcom successful. In this paper, we use descriptive and predictive modeling techniques to assess the continuing success of television comedies: The Office, Big Bang Theory, Arrested Development, Scrubs, and South Park. The factors that are tested for statistical significance on episode ratings are character presence, director, and writer. These statistics show that while characters are indeed crucial to the shows themselves, the creation and direction of the shows pose implication upon the ratings and therefore the success of the shows. We use machine learning based forecasting models to accurately predict the success of shows. The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows. Due to the many factors that go into a series, the empirical analysis in this work shows that there is no one-fits-all model to forecast the rating or success of a television show. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.12589v1-abstract-full').style.display = 'none'; document.getElementById('1910.12589v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 Pages, 10 Figures and 2 Tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.09686">arXiv:1910.09686</a> <span> [<a href="https://arxiv.org/pdf/1910.09686">pdf</a>, <a href="https://arxiv.org/format/1910.09686">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> The Effects of Information Overload on Online Conversation Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gunaratne%2C+C">Chathika Gunaratne</a>, <a href="/search/cs?searchtype=author&query=Baral%2C+N">Nisha Baral</a>, <a href="/search/cs?searchtype=author&query=Rand%2C+W">William Rand</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Jayalath%2C+C">Chathura Jayalath</a>, <a href="/search/cs?searchtype=author&query=Senevirathna%2C+C">Chathurani Senevirathna</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.09686v2-abstract-short" style="display: inline;"> The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.09686v2-abstract-full').style.display = 'inline'; document.getElementById('1910.09686v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.09686v2-abstract-full" style="display: none;"> The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that conversation volume and participation are lowest under high information overload thresholds and mid-range rates of information loss. Calibrating the model to user responsiveness data on Twitter, we replicate and explain several observed phenomena: 1) Responsiveness is sensitive to information overload threshold at high rates of information loss; 2) Information overload threshold and rate of information loss are Pareto-optimal and users may experience overload at inflows exceeding 30 notifications per hour; 3) Local abundance of small cascades of modest global popularity and local scarcity of larger cascades of high global popularity explains why overloaded users receive, but do not respond to large, highly popular cascades; 4) Users typically work with 7 notifications per hour; 5) Over-exposure to information can suppress the likelihood of response by overloading users, contrary to analogies to biologically-inspired viral spread. Reconceptualizing information spread with the mechanisms of information overload creates a richer representation of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.09686v2-abstract-full').style.display = 'none'; document.getElementById('1910.09686v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.09356">arXiv:1910.09356</a> <span> [<a href="https://arxiv.org/pdf/1910.09356">pdf</a>, <a href="https://arxiv.org/format/1910.09356">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+N">Ni Nguyen</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.09356v1-abstract-short" style="display: inline;"> According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in life. People can live healthy and happy lives while living with diabetes, but earl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.09356v1-abstract-full').style.display = 'inline'; document.getElementById('1910.09356v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.09356v1-abstract-full" style="display: none;"> According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in life. People can live healthy and happy lives while living with diabetes, but early detection produces a better overall outcome on most patient's health. Thus, to test the accurate prediction of Type 2 diabetes, we use the patients' information from an electronic health records company called Practice Fusion, which has about 10,000 patient records from 2009 to 2012. This data contains individual key biometrics, including age, diastolic and systolic blood pressure, gender, height, and weight. We use this data on popular machine learning algorithms and for each algorithm, we evaluate the performance of every model based on their classification accuracy, precision, sensitivity, specificity/recall, negative predictive value, and F1 score. In our study, we find that all algorithms other than Naive Bayes suffered from very low precision. Hence, we take a step further and incorporate all the algorithms into a weighted average or soft voting ensemble model where each algorithm will count towards a majority vote towards the decision outcome of whether a patient has diabetes or not. The accuracy of the Ensemble model on Practice Fusion is 85\%, by far our ensemble approach is new in this space. We firmly believe that the weighted average ensemble model not only performed well in overall metrics but also helped to recover wrong predictions and aid in accurate prediction of Type 2 diabetes. Our accurate novel model can be used as an alert for the patients to seek medical evaluation in time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.09356v1-abstract-full').style.display = 'none'; document.getElementById('1910.09356v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 Pages, # Tables and 8 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.07999">arXiv:1910.07999</a> <span> [<a href="https://arxiv.org/pdf/1910.07999">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> DeepFork: Supervised Prediction of Information Diffusion in GitHub </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akula%2C+R">Ramya Akula</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.07999v1-abstract-short" style="display: inline;"> Information spreads on complex social networks extremely fast, in other words, a piece of information can go viral within no time. Often it is hard to barricade this diffusion prior to the significant occurrence of chaos, be it a social media or an online coding platform. GitHub is one such trending online focal point for any business to reach their potential contributors and customers, simultaneo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07999v1-abstract-full').style.display = 'inline'; document.getElementById('1910.07999v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.07999v1-abstract-full" style="display: none;"> Information spreads on complex social networks extremely fast, in other words, a piece of information can go viral within no time. Often it is hard to barricade this diffusion prior to the significant occurrence of chaos, be it a social media or an online coding platform. GitHub is one such trending online focal point for any business to reach their potential contributors and customers, simultaneously. By exploiting such software development paradigm, millions of free software emerged lately in diverse communities. To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features. In our empirical studies, we observed that information diffusion can be detected by link prediction using supervised learning. DeepFork outperforms other machine learning models as it better learns the discriminative patterns from the input features. DeepFork aids in understanding information spread and evolution through a bipartite network of users and repositories i.e., information flow from a user to repository to user. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07999v1-abstract-full').style.display = 'none'; document.getElementById('1910.07999v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 Pages, 7 Figures, 2 Tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.05216">arXiv:1902.05216</a> <span> [<a href="https://arxiv.org/pdf/1902.05216">pdf</a>, <a href="https://arxiv.org/format/1902.05216">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> A Cross-Repository Model for Predicting Popularity in GitHub </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bidoki%2C+N+H">Neda Hajiakhoond Bidoki</a>, <a href="/search/cs?searchtype=author&query=Sukthankar%2C+G">Gita Sukthankar</a>, <a href="/search/cs?searchtype=author&query=Keathley%2C+H">Heather Keathley</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.05216v1-abstract-short" style="display: inline;"> Social coding platforms, such as GitHub, can serve as natural laboratories for studying the diffusion of innovation through tracking the pattern of code adoption by programmers. This paper focuses on the problem of predicting the popularity of software repositories over time; our aim is to forecast the time series of popularity-related events (code forks and watches). In particular, we are interes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05216v1-abstract-full').style.display = 'inline'; document.getElementById('1902.05216v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.05216v1-abstract-full" style="display: none;"> Social coding platforms, such as GitHub, can serve as natural laboratories for studying the diffusion of innovation through tracking the pattern of code adoption by programmers. This paper focuses on the problem of predicting the popularity of software repositories over time; our aim is to forecast the time series of popularity-related events (code forks and watches). In particular, we are interested in cross-repository patterns-how do events on one repository affect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrent neural network integrates events across multiple active repositories, outperforming a standard ARIMA (Auto-Regressive Integrated Moving Average) time series prediction based on the single repository. The ability of the LSTM to leverage cross-repository information gives it a significant edge over standard time series forecasting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05216v1-abstract-full').style.display = 'none'; document.getElementById('1902.05216v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 91D30 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> CSCI 2018: Computational Science & Computational Intelligence </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.03425">arXiv:1901.03425</a> <span> [<a href="https://arxiv.org/pdf/1901.03425">pdf</a>, <a href="https://arxiv.org/format/1901.03425">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/make2040036">10.3390/make2040036 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Review on Learning and Extracting Graph Features for Link Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutlu%2C+E+C">Ece C. Mutlu</a>, <a href="/search/cs?searchtype=author&query=Oghaz%2C+T+A">Toktam A. Oghaz</a>, <a href="/search/cs?searchtype=author&query=Rajabi%2C+A">Amirarsalan Rajabi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.03425v5-abstract-short" style="display: inline;"> Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.03425v5-abstract-full').style.display = 'inline'; document.getElementById('1901.03425v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.03425v5-abstract-full" style="display: none;"> Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.03425v5-abstract-full').style.display = 'none'; document.getElementById('1901.03425v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.03292">arXiv:1808.03292</a> <span> [<a href="https://arxiv.org/pdf/1808.03292">pdf</a>, <a href="https://arxiv.org/format/1808.03292">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gunaratne%2C+C">Chathika Gunaratne</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1808.03292v5-abstract-short" style="display: inline;"> External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.03292v5-abstract-full').style.display = 'inline'; document.getElementById('1808.03292v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.03292v5-abstract-full" style="display: none;"> External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed, scalability, and simplicity of use. NL4Py provides access to the large number of open-source machine learning and analytics libraries of Python and enables convenient and efficient parallelization of NetLogo simulations with minimal coding expertise by domain scientists. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.03292v5-abstract-full').style.display = 'none'; document.getElementById('1808.03292v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.10732">arXiv:1805.10732</a> <span> [<a href="https://arxiv.org/pdf/1805.10732">pdf</a>, <a href="https://arxiv.org/format/1805.10732">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> How polarization can provide an increase in content dissemination amongst the highly ranked influencers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Taylor%2C+C+E">Cameron E. Taylor</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Mantzaris%2C+A+V">Alexander V. Mantzaris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1805.10732v1-abstract-short" style="display: inline;"> This work extends a model of simulating influence in a network of stochastic edge dynamics to account for polarization. The model built upon is termed Dynamic Communicators and seeks to understand the process which produces low volume, high influence amongst users. This model is extended to introduce the effects polarization. The fundamental assumption of the model is that a parameter of importanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.10732v1-abstract-full').style.display = 'inline'; document.getElementById('1805.10732v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.10732v1-abstract-full" style="display: none;"> This work extends a model of simulating influence in a network of stochastic edge dynamics to account for polarization. The model built upon is termed Dynamic Communicators and seeks to understand the process which produces low volume, high influence amongst users. This model is extended to introduce the effects polarization. The fundamental assumption of the model is that a parameter of importance governs the rate of message responsiveness. With the introduction of relative incremental changes according to the response incurred in adjacent nodes receiving content, the changes in the power brokerage of a network can be examined. This provides a content agnostic interpretation for the desire to proliferate content amongst peers. From the results of the simulations, the analysis shows that a lack of polarization incrementally develops a more level discussion network with more even response rates whereas the polarization introduction leads to a gradual increase in response rate disparity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.10732v1-abstract-full').style.display = 'none'; document.getElementById('1805.10732v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 6 figures, Conference on Complexity and Policy Studies 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.00435">arXiv:1802.00435</a> <span> [<a href="https://arxiv.org/pdf/1802.00435">pdf</a>, <a href="https://arxiv.org/ps/1802.00435">ps</a>, <a href="https://arxiv.org/format/1802.00435">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the ancestral Pueblo </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gunaratne%2C+C">Chathika Gunaratne</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Dang%2C+N">Nguyen Dang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.00435v2-abstract-short" style="display: inline;"> Agent-based modeling of artificial societies offers a platform to test human-interpretable, causal explanations of human behavior that generate society-scale phenomena. However, parameter calibration is insufficient to conduct an adequate data-driven exploration of the importance of causal factors that constitute agent rules, resulting in models with limited causal accuracy and robustness. We intr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.00435v2-abstract-full').style.display = 'inline'; document.getElementById('1802.00435v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.00435v2-abstract-full" style="display: none;"> Agent-based modeling of artificial societies offers a platform to test human-interpretable, causal explanations of human behavior that generate society-scale phenomena. However, parameter calibration is insufficient to conduct an adequate data-driven exploration of the importance of causal factors that constitute agent rules, resulting in models with limited causal accuracy and robustness. We introduce evolutionary model discovery, a framework that combines genetic programming and random forest regression to evaluate the importance of a set of causal factors hypothesized to affect the individual's decision-making process. We investigated the farm plot seeking behavior of the ancestral Pueblo of the Long House Valley simulated in the Artificial Anasazi model our proposed framework. We evaluated the importance of causal factors not considered in the original model that we hypothesized to have affected the decision-making process. Contrary to the original model, where closeness was the sole factor driving farm plot selection, selection of higher quality land and desire for social presence are shown to be more important. In fact, model performance is improved when agents select farm plots further away from their failed farm plot. Farm selection strategies designed using these insights into the socio-agricultural behavior of the ancestral Pueblo significantly improved the model's accuracy and robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.00435v2-abstract-full').style.display = 'none'; document.getElementById('1802.00435v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1604.06121">arXiv:1604.06121</a> <span> [<a href="https://arxiv.org/pdf/1604.06121">pdf</a>, <a href="https://arxiv.org/format/1604.06121">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Evaluation of Zika Vector Control Strategies Using Agent-Based Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gunaratne%2C+C">Chathika Gunaratne</a>, <a href="/search/cs?searchtype=author&query=Akbas%2C+M+I">Mustafa Ilhan Akbas</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Ozmen%2C+O">Ozlem Ozmen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1604.06121v2-abstract-short" style="display: inline;"> Aedes Aegypti is the vector of several deadly diseases, including Zika. Effective and sustainable vector control measures must be deployed to keep A. aegypti numbers under control. The distribution of A. Aegypti is subject to spatial and climatic constraints. Using agent-based modeling, we model the population dynamics of A. aegypti subjected to the spatial and climatic constraints of a neighborho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.06121v2-abstract-full').style.display = 'inline'; document.getElementById('1604.06121v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1604.06121v2-abstract-full" style="display: none;"> Aedes Aegypti is the vector of several deadly diseases, including Zika. Effective and sustainable vector control measures must be deployed to keep A. aegypti numbers under control. The distribution of A. Aegypti is subject to spatial and climatic constraints. Using agent-based modeling, we model the population dynamics of A. aegypti subjected to the spatial and climatic constraints of a neighborhood in the Key West. Satellite imagery was used to identify vegetation, houses (CO2 zones) both critical to the mosquito lifecycle. The model replicates the seasonal fluctuation of adult population sampled through field studies and approximates the population at a high of 986 (95% CI: [979, 993]) females and 1031 (95% CI: [1024, 1039]) males in the fall and a low of 316 (95% CI: [313, 319]) females and 333 (95% CI: [330, 336]) males during the winter. We then simulate two biological vector control strategies: 1) Wolbachia infection and 2) Release of Insects carrying a Dominant Lethal gene (RIDL). Our results support the probability of sustained Wolbachia infection within the population for two years after the year of release. egies, our approach provides a realistic simulation environment consisting of male and female Aedes aegypti, breeding spots, vegetation and CO2 sources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.06121v2-abstract-full').style.display = 'none'; document.getElementById('1604.06121v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 August, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 6 figures, 1 table, conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1112.4708">arXiv:1112.4708</a> <span> [<a href="https://arxiv.org/pdf/1112.4708">pdf</a>, <a href="https://arxiv.org/format/1112.4708">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Transformation Networks: How Innovation and the Availability of Technology can Increase Economic Performance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hollander%2C+C+D">Christopher D. Hollander</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1112.4708v1-abstract-short" style="display: inline;"> A transformation network describes how one set of resources can be transformed into another via technological processes. Transformation networks in economics are useful because they can highlight areas for future innovations, both in terms of new products, new production techniques, or better efficiency. They also make it easy to detect areas where an economy might be fragile. In this paper, we us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.4708v1-abstract-full').style.display = 'inline'; document.getElementById('1112.4708v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1112.4708v1-abstract-full" style="display: none;"> A transformation network describes how one set of resources can be transformed into another via technological processes. Transformation networks in economics are useful because they can highlight areas for future innovations, both in terms of new products, new production techniques, or better efficiency. They also make it easy to detect areas where an economy might be fragile. In this paper, we use computational simulations to investigate how the density of a transformation network affects the economic performance, as measured by the gross domestic product (GDP), of an artificial economy. Our results show that on average, the GDP of our economy increases as the density of the transformation network increases. We also find that while the average performance increases, the maximum possible performance decreases and the minimum possible performance increases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.4708v1-abstract-full').style.display = 'none'; document.getElementById('1112.4708v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 December, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages. Submitted to CompleNet 2012</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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