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</div> <div class="control"> <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/2502.13830">arXiv:2502.13830</a> <span> [<a href="https://arxiv.org/pdf/2502.13830">pdf</a>, <a href="https://arxiv.org/format/2502.13830">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> The Round Complexity of Black-Box Post-Quantum Secure Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rohit Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Liang%2C+X">Xiao Liang</a>, <a href="/search/cs?searchtype=author&query=Pandey%2C+O">Omkant Pandey</a>, <a href="/search/cs?searchtype=author&query=Yamakawa%2C+T">Takashi Yamakawa</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="2502.13830v1-abstract-short" style="display: inline;"> We study the round complexity of secure multi-party computation (MPC) in the post-quantum regime. Our focus is on the fully black-box setting, where both the construction and security reduction are black-box. Chia, Chung, Liu, and Yamakawa [FOCS'22] demonstrated the infeasibility of achieving standard simulation-based security within constant rounds unless $\mathbf{NP} \subseteq \mathbf{BQP}$. Thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13830v1-abstract-full').style.display = 'inline'; document.getElementById('2502.13830v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.13830v1-abstract-full" style="display: none;"> We study the round complexity of secure multi-party computation (MPC) in the post-quantum regime. Our focus is on the fully black-box setting, where both the construction and security reduction are black-box. Chia, Chung, Liu, and Yamakawa [FOCS'22] demonstrated the infeasibility of achieving standard simulation-based security within constant rounds unless $\mathbf{NP} \subseteq \mathbf{BQP}$. This leaves crucial feasibility questions unresolved. Specifically, it remains unknown whether black-box constructions are achievable within polynomial rounds; also, the existence of constant-round constructions with respect to $蔚$-simulation, a relaxed yet useful alternative to standard simulation, remains unestablished. This work provides positive answers. We introduce the first black-box construction for PQ-MPC in polynomial rounds, from the minimal assumption of post-quantum semi-honest oblivious transfers. In the two-party scenario, our construction requires only $蠅(1)$ rounds. These results have already been applied in the oracle separation between classical-communication quantum MPC and $\mathbf{P} = \mathbf{NP}$ in Kretschmer, Qian, and Tal [STOC'25]. As for $蔚$-simulation, Chia, Chung, Liang, and Yamakawa [CRYPTO'22] resolved the issue for the two-party setting, leaving the multi-party case open. We complete the picture by presenting the first black-box, constant-round construction in the multi-party setting, instantiable using various standard post-quantum primitives. En route, we obtain a black-box, constant-round post-quantum commitment achieving a weaker version of 1-many non-malleability, from post-quantum one-way functions. Besides its role in our MPC construction, this commitment also reduces the assumption used in the quantum parallel repetition lower bound by Bostanci, Qian, Spooner, and Yuen [STOC'24]. We anticipate further applications in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13830v1-abstract-full').style.display = 'none'; document.getElementById('2502.13830v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.17295">arXiv:2501.17295</a> <span> [<a href="https://arxiv.org/pdf/2501.17295">pdf</a>, <a href="https://arxiv.org/format/2501.17295">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tang%2C+Z">Zilu Tang</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rajen Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Garg%2C+S">Sarthak Garg</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="2501.17295v1-abstract-short" style="display: inline;"> Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-decoder models trained specifically for translation tasks. However, LLM-based systems are at a higher risk of generating hallucinations, which can severely undermine user's trust and safety. Most prior research on hallucina… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17295v1-abstract-full').style.display = 'inline'; document.getElementById('2501.17295v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17295v1-abstract-full" style="display: none;"> Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-decoder models trained specifically for translation tasks. However, LLM-based systems are at a higher risk of generating hallucinations, which can severely undermine user's trust and safety. Most prior research on hallucination mitigation focuses on traditional MT models, with solutions that involve post-hoc mitigation - detecting hallucinated translations and re-translating them. While effective, this approach introduces additional complexity in deploying extra tools in production and also increases latency. To address these limitations, we propose a method that intrinsically learns to mitigate hallucinations during the model training phase. Specifically, we introduce a data creation framework to generate hallucination focused preference datasets. Fine-tuning LLMs on these preference datasets reduces the hallucination rate by an average of 96% across five language pairs, while preserving overall translation quality. In a zero-shot setting our approach reduces hallucinations by 89% on an average across three unseen target languages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17295v1-abstract-full').style.display = 'none'; document.getElementById('2501.17295v1-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, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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">NAACL 2025 Main Conference Long paper (9 pages)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> NAACL 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09609">arXiv:2501.09609</a> <span> [<a href="https://arxiv.org/pdf/2501.09609">pdf</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"> Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Goswami%2C+M">Mitul Goswami</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Romit Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Mahato%2C+S">Somnath Mahato</a>, <a href="/search/cs?searchtype=author&query=Pattnaik%2C+P+K">Prasant Kumar Pattnaik</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="2501.09609v1-abstract-short" style="display: inline;"> The research presents a study on enhancing the robustness of Wi-Fi-based indoor positioning systems against adversarial attacks. The goal is to improve the positioning accuracy and resilience of these systems under two attack scenarios: Wi-Fi Spoofing and Signal Strength Manipulation. Three models are developed and evaluated: a baseline model (M_Base), an adversarially trained robust model (M_Rob)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09609v1-abstract-full').style.display = 'inline'; document.getElementById('2501.09609v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09609v1-abstract-full" style="display: none;"> The research presents a study on enhancing the robustness of Wi-Fi-based indoor positioning systems against adversarial attacks. The goal is to improve the positioning accuracy and resilience of these systems under two attack scenarios: Wi-Fi Spoofing and Signal Strength Manipulation. Three models are developed and evaluated: a baseline model (M_Base), an adversarially trained robust model (M_Rob), and an ensemble model (M_Ens). All models utilize a Kolmogorov-Arnold Network (KAN) architecture. The robust model is trained with adversarially perturbed data, while the ensemble model combines predictions from both the base and robust models. Experimental results show that the robust model reduces positioning error by approximately 10% compared to the baseline, achieving 2.03 meters error under Wi-Fi spoofing and 2.00 meters under signal strength manipulation. The ensemble model further outperforms with errors of 2.01 meters and 1.975 meters for the respective attack types. This analysis highlights the effectiveness of adversarial training techniques in mitigating attack impacts. The findings underscore the importance of considering adversarial scenarios in developing indoor positioning systems, as improved resilience can significantly enhance the accuracy and reliability of such systems in mission-critical environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09609v1-abstract-full').style.display = 'none'; document.getElementById('2501.09609v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.07555">arXiv:2501.07555</a> <span> [<a href="https://arxiv.org/pdf/2501.07555">pdf</a>, <a href="https://arxiv.org/format/2501.07555">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"> Dynamic Prototype Rehearsal for Continual Learning in ECG Arrhythmia Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rahmani%2C+S">Sana Rahmani</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Reetam Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Etemad%2C+A">Ali Etemad</a>, <a href="/search/cs?searchtype=author&query=Hashemi%2C+J">Javad Hashemi</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="2501.07555v1-abstract-short" style="display: inline;"> Continual Learning (CL) methods aim to learn from a sequence of tasks while avoiding the challenge of forgetting previous knowledge. We present DREAM-CL, a novel CL method for ECG arrhythmia detection that introduces dynamic prototype rehearsal memory. DREAM-CL selects representative prototypes by clustering data based on learning behavior during each training session. Within each cluster, we appl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07555v1-abstract-full').style.display = 'inline'; document.getElementById('2501.07555v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.07555v1-abstract-full" style="display: none;"> Continual Learning (CL) methods aim to learn from a sequence of tasks while avoiding the challenge of forgetting previous knowledge. We present DREAM-CL, a novel CL method for ECG arrhythmia detection that introduces dynamic prototype rehearsal memory. DREAM-CL selects representative prototypes by clustering data based on learning behavior during each training session. Within each cluster, we apply a smooth sorting operation that ranks samples by training difficulty, compressing extreme values and removing outliers. The more challenging samples are then chosen as prototypes for the rehearsal memory, ensuring effective knowledge retention across sessions. We evaluate our method on time-incremental, class-incremental, and lead-incremental scenarios using two widely used ECG arrhythmia datasets, Chapman and PTB-XL. The results demonstrate that DREAM-CL outperforms the state-of-the-art in CL for ECG arrhythmia detection. Detailed ablation and sensitivity studies are performed to validate the different design choices of our method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07555v1-abstract-full').style.display = 'none'; document.getElementById('2501.07555v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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 2025 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16012">arXiv:2410.16012</a> <span> [<a href="https://arxiv.org/pdf/2410.16012">pdf</a>, <a href="https://arxiv.org/format/2410.16012">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> <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="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Massimo: Public Queue Monitoring and Management using Mass-Spring Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kumar%2C+A">Abhijeet Kumar</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+U">Unnati Singh</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rajdeep Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Bandyopadhyay%2C+T">Tathagata Bandyopadhyay</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="2410.16012v1-abstract-short" style="display: inline;"> An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning al… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16012v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16012v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16012v1-abstract-full" style="display: none;"> An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning algorithms, deep learning our system provide accurate information about the place is crowded or not and the necessary efforts to be taken. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16012v1-abstract-full').style.display = 'none'; document.getElementById('2410.16012v1-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 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">8 pages, 6 figures, 3 algorithms, 3 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/2408.11374">arXiv:2408.11374</a> <span> [<a href="https://arxiv.org/pdf/2408.11374">pdf</a>, <a href="https://arxiv.org/format/2408.11374">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"> A Unified Framework for Continual Learning and Unlearning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Romit Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Chundawat%2C+V">Vikram Chundawat</a>, <a href="/search/cs?searchtype=author&query=Tarun%2C+A">Ayush Tarun</a>, <a href="/search/cs?searchtype=author&query=Mali%2C+A">Ankur Mali</a>, <a href="/search/cs?searchtype=author&query=Mandal%2C+M">Murari Mandal</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="2408.11374v2-abstract-short" style="display: inline;"> Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unlearning involves selectively forgetting specific subsets of data. In this paper, we introduce a new framework that jointly tackles both tasks by leveraging controlled knowledge dis… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11374v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11374v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11374v2-abstract-full" style="display: none;"> Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unlearning involves selectively forgetting specific subsets of data. In this paper, we introduce a new framework that jointly tackles both tasks by leveraging controlled knowledge distillation. Our approach enables efficient learning with minimal forgetting and effective targeted unlearning. By incorporating a fixed memory buffer, the system supports learning new concepts while retaining prior knowledge. The distillation process is carefully managed to ensure a balance between acquiring new information and forgetting specific data as needed. Experimental results on benchmark datasets show that our method matches or exceeds the performance of existing approaches in both continual learning and machine unlearning. This unified framework is the first to address both challenges simultaneously, paving the way for adaptable models capable of dynamic learning and forgetting while maintaining strong overall performance. Source code: \textcolor{blue}{https://respailab.github.io/CLMUL} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11374v2-abstract-full').style.display = 'none'; document.getElementById('2408.11374v2-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.04664">arXiv:2309.04664</a> <span> [<a href="https://arxiv.org/pdf/2309.04664">pdf</a>, <a href="https://arxiv.org/format/2309.04664">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Compact: Approximating Complex Activation Functions for Secure Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M">Mazharul Islam</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+S+S">Sunpreet S. Arora</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Rindal%2C+P">Peter Rindal</a>, <a href="/search/cs?searchtype=author&query=Shirvanian%2C+M">Maliheh Shirvanian</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="2309.04664v2-abstract-short" style="display: inline;"> Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions such as ReLU. However, these techniques are ineffective and/or inefficient for the complex and highly non-linear activation functi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.04664v2-abstract-full').style.display = 'inline'; document.getElementById('2309.04664v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.04664v2-abstract-full" style="display: none;"> Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions such as ReLU. However, these techniques are ineffective and/or inefficient for the complex and highly non-linear activation functions used in cutting-edge DNN models. We present Compact, which produces piece-wise polynomial approximations of complex AFs to enable their efficient use with state-of-the-art MPC techniques. Compact neither requires nor imposes any restriction on model training and results in near-identical model accuracy. To achieve this, we design Compact with input density awareness and use an application-specific simulated annealing type optimization to generate computationally more efficient approximations of complex AFs. We extensively evaluate Compact on four different machine-learning tasks with DNN architectures that use popular complex AFs silu, gelu, and mish. Our experimental results show that Compact incurs negligible accuracy loss while being 2x-5x computationally more efficient than state-of-the-art approaches for DNN models with large number of hidden layers. Our work accelerates easy adoption of MPC techniques to provide user data privacy even when the queried DNN models consist of a number of hidden layers and trained over complex AFs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.04664v2-abstract-full').style.display = 'none'; document.getElementById('2309.04664v2-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </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 Proceedings on Privacy Enhancing Technologies (PoPETs)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.09735">arXiv:2304.09735</a> <span> [<a href="https://arxiv.org/pdf/2304.09735">pdf</a>, <a href="https://arxiv.org/format/2304.09735">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> <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"> Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abedi%2C+A">Ali Abedi</a>, <a href="/search/cs?searchtype=author&query=Bisht%2C+P">Paritosh Bisht</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Riddhi Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Agrawal%2C+R">Rachit Agrawal</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+V">Vyom Sharma</a>, <a href="/search/cs?searchtype=author&query=Jayagopi%2C+D+B">Dinesh Babu Jayagopi</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+S+S">Shehroz S. Khan</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="2304.09735v1-abstract-short" style="display: inline;"> Physical exercise is an essential component of rehabilitation programs that improve quality of life and reduce mortality and re-hospitalization rates. In AI-driven virtual rehabilitation programs, patients complete their exercises independently at home, while AI algorithms analyze the exercise data to provide feedback to patients and report their progress to clinicians. To analyze exercise data, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09735v1-abstract-full').style.display = 'inline'; document.getElementById('2304.09735v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.09735v1-abstract-full" style="display: none;"> Physical exercise is an essential component of rehabilitation programs that improve quality of life and reduce mortality and re-hospitalization rates. In AI-driven virtual rehabilitation programs, patients complete their exercises independently at home, while AI algorithms analyze the exercise data to provide feedback to patients and report their progress to clinicians. To analyze exercise data, the first step is to segment it into consecutive repetitions. There has been a significant amount of research performed on segmenting and counting the repetitive activities of healthy individuals using raw video data, which raises concerns regarding privacy and is computationally intensive. Previous research on patients' rehabilitation exercise segmentation relied on data collected by multiple wearable sensors, which are difficult to use at home by rehabilitation patients. Compared to healthy individuals, segmenting and counting exercise repetitions in patients is more challenging because of the irregular repetition duration and the variation between repetitions. This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients, based on their skeletal body joints. Skeletal body joints can be acquired through depth cameras or computer vision techniques applied to RGB videos of patients. Various sequential neural networks are designed to analyze the sequences of skeletal body joints and perform repetition segmentation and counting. Extensive experiments on three publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and IntelliRehabDS, demonstrate the superiority of the proposed method compared to previous methods. The proposed method enables accurate exercise analysis while preserving privacy, facilitating the effective delivery of virtual rehabilitation programs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09735v1-abstract-full').style.display = 'none'; document.getElementById('2304.09735v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </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">8 pages, 1 figure, 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/2303.09150">arXiv:2303.09150</a> <span> [<a href="https://arxiv.org/pdf/2303.09150">pdf</a>, <a href="https://arxiv.org/format/2303.09150">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> MASCARA: Systematically Generating Memorable And Secure Passphrases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mukherjee%2C+A">Avirup Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Murali%2C+K">Kousshik Murali</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+S+K">Shivam Kumar Jha</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+N">Niloy Ganguly</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Mondal%2C+M">Mainack Mondal</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="2303.09150v1-abstract-short" style="display: inline;"> Passwords are the most common mechanism for authenticating users online. However, studies have shown that users find it difficult to create and manage secure passwords. To that end, passphrases are often recommended as a usable alternative to passwords, which would potentially be easy to remember and hard to guess. However, as we show, user-chosen passphrases fall short of being secure, while stat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.09150v1-abstract-full').style.display = 'inline'; document.getElementById('2303.09150v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.09150v1-abstract-full" style="display: none;"> Passwords are the most common mechanism for authenticating users online. However, studies have shown that users find it difficult to create and manage secure passwords. To that end, passphrases are often recommended as a usable alternative to passwords, which would potentially be easy to remember and hard to guess. However, as we show, user-chosen passphrases fall short of being secure, while state-of-the-art machine-generated passphrases are difficult to remember. In this work, we aim to tackle the drawbacks of the systems that generate passphrases for practical use. In particular, we address the problem of generating secure and memorable passphrases and compare them against user chosen passphrases in use. We identify and characterize 72, 999 user-chosen in-use unique English passphrases from prior leaked password databases. Then we leverage this understanding to create a novel framework for measuring memorability and guessability of passphrases. Utilizing our framework, we design MASCARA, which follows a constrained Markov generation process to create passphrases that optimize for both memorability and guessability. Our evaluation of passphrases shows that MASCARA-generated passphrases are harder to guess than in-use user-generated passphrases, while being easier to remember compared to state-of-the-art machine-generated passphrases. We conduct a two-part user study with crowdsourcing platform Prolific to demonstrate that users have highest memory-recall (and lowest error rate) while using MASCARA passphrases. Moreover, for passphrases of length desired by the users, the recall rate is 60-100% higher for MASCARA-generated passphrases compared to current system-generated ones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.09150v1-abstract-full').style.display = 'none'; document.getElementById('2303.09150v1-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </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">This paper appeared on ACM ASIACCS '23 conference. The pdf includes Github repository with all data and code</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.04427">arXiv:2203.04427</a> <span> [<a href="https://arxiv.org/pdf/2203.04427">pdf</a>, <a href="https://arxiv.org/format/2203.04427">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Experimental Security Analysis of the App Model in Business Collaboration Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yunang Chen</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Y">Yue Gao</a>, <a href="/search/cs?searchtype=author&query=Ceccio%2C+N">Nick Ceccio</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Fawaz%2C+K">Kassem Fawaz</a>, <a href="/search/cs?searchtype=author&query=Fernandes%2C+E">Earlence Fernandes</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="2203.04427v3-abstract-short" style="display: inline;"> Business Collaboration Platforms like Microsoft Teams and Slack enable teamwork by supporting text chatting and third-party resource integration. A user can access online file storage, make video calls, and manage a code repository, all from within the platform, thus making them a hub for sensitive communication and resources. The key enabler for these productivity features is a third-party applic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04427v3-abstract-full').style.display = 'inline'; document.getElementById('2203.04427v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.04427v3-abstract-full" style="display: none;"> Business Collaboration Platforms like Microsoft Teams and Slack enable teamwork by supporting text chatting and third-party resource integration. A user can access online file storage, make video calls, and manage a code repository, all from within the platform, thus making them a hub for sensitive communication and resources. The key enabler for these productivity features is a third-party application model. We contribute an experimental security analysis of this model and the third-party apps. Performing this analysis is challenging because commercial platforms and their apps are closed-source systems. Our analysis methodology is to systematically investigate different types of interactions possible between apps and users. We discover that the access control model in these systems violates two fundamental security principles: least privilege and complete mediation. These violations enable a malicious app to exploit the confidentiality and integrity of user messages and third-party resources connected to the platform. We construct proof-of-concept attacks that can: (1) eavesdrop on user messages without having permission to read those messages; (2) launch fake video calls; (3) automatically merge code into repositories without user approval or involvement. Finally, we provide an analysis of countermeasures that systems like Slack and Microsoft Teams can adopt today. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04427v3-abstract-full').style.display = 'none'; document.getElementById('2203.04427v3-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 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.04292">arXiv:2201.04292</a> <span> [<a href="https://arxiv.org/pdf/2201.04292">pdf</a>, <a href="https://arxiv.org/ps/2201.04292">ps</a>, <a href="https://arxiv.org/format/2201.04292">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 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/10.1371/journal.pone.0270681">10.1371/journal.pone.0270681 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Predicting Terrorist Attacks in the United States using Localized News Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Krieg%2C+S+J">Steven J. Krieg</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+C+W">Christian W. Smith</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rusha Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Chawla%2C+N+V">Nitesh V. Chawla</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="2201.04292v2-abstract-short" style="display: inline;"> Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. Toward the end of better understanding and mitigating these attacks, we present a set of machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model--a Random For… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04292v2-abstract-full').style.display = 'inline'; document.getElementById('2201.04292v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.04292v2-abstract-full" style="display: none;"> Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. Toward the end of better understanding and mitigating these attacks, we present a set of machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model--a Random Forest that learns from a novel variable-length moving average representation of the feature space--achieves area under the receiver operating characteristic scores $> .667$ on four of the five states that were impacted most by terrorism between 2015 and 2018. Our key findings include that modeling terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach--especially when the events are sparse and dissimilar. Additionally, our results highlight the need for localized models that account for differences between locations. From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context. We also show that its predictions are relatively robust to time gaps between attacks and observed characteristics of the attacks. Finally, we analyze factors that limit model performance, which include a noisy feature space and small amount of available data. These contributions provide an important foundation for the use of machine learning in efforts against terrorism in the United States and beyond. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04292v2-abstract-full').style.display = 'none'; document.getElementById('2201.04292v2-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.06078">arXiv:2112.06078</a> <span> [<a href="https://arxiv.org/pdf/2112.06078">pdf</a>, <a href="https://arxiv.org/ps/2112.06078">ps</a>, <a href="https://arxiv.org/format/2112.06078">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> A Note on the Post-Quantum Security of (Ring) Signatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rohit Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Chung%2C+K">Kai-Min Chung</a>, <a href="/search/cs?searchtype=author&query=Liang%2C+X">Xiao Liang</a>, <a href="/search/cs?searchtype=author&query=Malavolta%2C+G">Giulio Malavolta</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="2112.06078v1-abstract-short" style="display: inline;"> This work revisits the security of classical signatures and ring signatures in a quantum world. For (ordinary) signatures, we focus on the arguably preferable security notion of blind-unforgeability recently proposed by Alagic et al. (Eurocrypt'20). We present two short signature schemes achieving this notion: one is in the quantum random oracle model, assuming quantum hardness of SIS; and the oth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.06078v1-abstract-full').style.display = 'inline'; document.getElementById('2112.06078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.06078v1-abstract-full" style="display: none;"> This work revisits the security of classical signatures and ring signatures in a quantum world. For (ordinary) signatures, we focus on the arguably preferable security notion of blind-unforgeability recently proposed by Alagic et al. (Eurocrypt'20). We present two short signature schemes achieving this notion: one is in the quantum random oracle model, assuming quantum hardness of SIS; and the other is in the plain model, assuming quantum hardness of LWE with super-polynomial modulus. Prior to this work, the only known blind-unforgeable schemes are Lamport's one-time signature and the Winternitz one-time signature, and both of them are in the quantum random oracle model. For ring signatures, the recent work by Chatterjee et al. (Crypto'21) proposes a definition trying to capture adversaries with quantum access to the signer. However, it is unclear if their definition, when restricted to the classical world, is as strong as the standard security notion for ring signatures. They also present a construction that only partially achieves (even) this seeming weak definition, in the sense that the adversary can only conduct superposition attacks over the messages, but not the rings. We propose a new definition that does not suffer from the above issue. Our definition is an analog to the blind-unforgeability in the ring signature setting. Moreover, assuming the quantum hardness of LWE, we construct a compiler converting any blind-unforgeable (ordinary) signatures to a ring signature satisfying our definition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.06078v1-abstract-full').style.display = 'none'; document.getElementById('2112.06078v1-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> 11 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.01688">arXiv:2105.01688</a> <span> [<a href="https://arxiv.org/pdf/2105.01688">pdf</a>, <a href="https://arxiv.org/format/2105.01688">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> <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> </div> </div> <p class="title is-5 mathjax"> Height Estimation of Children under Five Years using Depth Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Trivedi%2C+A">Anusua Trivedi</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+M">Mohit Jain</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+N+K">Nikhil Kumar Gupta</a>, <a href="/search/cs?searchtype=author&query=Hinsche%2C+M">Markus Hinsche</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+P">Prashant Singh</a>, <a href="/search/cs?searchtype=author&query=Matiaschek%2C+M">Markus Matiaschek</a>, <a href="/search/cs?searchtype=author&query=Behrens%2C+T">Tristan Behrens</a>, <a href="/search/cs?searchtype=author&query=Militeri%2C+M">Mirco Militeri</a>, <a href="/search/cs?searchtype=author&query=Birge%2C+C">Cameron Birge</a>, <a href="/search/cs?searchtype=author&query=Kaushik%2C+S">Shivangi Kaushik</a>, <a href="/search/cs?searchtype=author&query=Mohapatra%2C+A">Archisman Mohapatra</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rita Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Dodhia%2C+R">Rahul Dodhia</a>, <a href="/search/cs?searchtype=author&query=Ferres%2C+J+L">Juan Lavista Ferres</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="2105.01688v2-abstract-short" style="display: inline;"> Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of stan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.01688v2-abstract-full').style.display = 'inline'; document.getElementById('2105.01688v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.01688v2-abstract-full" style="display: none;"> Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of standing children under five years from depth images collected using a smart-phone. According to the SMART Methodology Manual [5], the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved an average mean absolute error of 1.64% on 57064 test images. For 70.3% test images, we estimated height accurately within the acceptable 1.4 cm range. Thus, our proposed solution can accurately detect stunting (low height-for-age) in standing children below five years of age. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.01688v2-abstract-full').style.display = 'none'; document.getElementById('2105.01688v2-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> 30 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.05749">arXiv:2012.05749</a> <span> [<a href="https://arxiv.org/pdf/2012.05749">pdf</a>, <a href="https://arxiv.org/format/2012.05749">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Data Privacy in Trigger-Action Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yunang Chen</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+A+R">Amrita Roy Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+R">Ruizhe Wang</a>, <a href="/search/cs?searchtype=author&query=Sabelfeld%2C+A">Andrei Sabelfeld</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Fernandes%2C+E">Earlence Fernandes</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="2012.05749v3-abstract-short" style="display: inline;"> Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for mil… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.05749v3-abstract-full').style.display = 'inline'; document.getElementById('2012.05749v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.05749v3-abstract-full" style="display: none;"> Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users' private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.05749v3-abstract-full').style.display = 'none'; document.getElementById('2012.05749v3-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 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.13375">arXiv:2011.13375</a> <span> [<a href="https://arxiv.org/pdf/2011.13375">pdf</a>, <a href="https://arxiv.org/format/2011.13375">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> <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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sayles%2C+A">Athena Sayles</a>, <a href="/search/cs?searchtype=author&query=Hooda%2C+A">Ashish Hooda</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+M">Mohit Gupta</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Fernandes%2C+E">Earlence Fernandes</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="2011.13375v3-abstract-short" style="display: inline;"> Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.13375v3-abstract-full').style.display = 'inline'; document.getElementById('2011.13375v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.13375v3-abstract-full" style="display: none;"> Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes. Rather than modifying the victim object with visible artifacts, we modify light that illuminates the object. We demonstrate how an attacker can craft a modulated light signal that adversarially illuminates a scene and causes targeted misclassifications on a state-of-the-art ImageNet deep learning model. Concretely, we exploit the radiometric rolling shutter effect in commodity cameras to create precise striping patterns that appear on images. To human eyes, it appears like the object is illuminated, but the camera creates an image with stripes that will cause ML models to output the attacker-desired classification. We conduct a range of simulation and physical experiments with LEDs, demonstrating targeted attack rates up to 84%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.13375v3-abstract-full').style.display = 'none'; document.getElementById('2011.13375v3-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> 18 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.06617">arXiv:2007.06617</a> <span> [<a href="https://arxiv.org/pdf/2007.06617">pdf</a>, <a href="https://arxiv.org/format/2007.06617">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Risk Management">q-fin.RM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Golbayani%2C+P">Parisa Golbayani</a>, <a href="/search/cs?searchtype=author&query=Florescu%2C+I">Ionu牛 Florescu</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rupak Chatterjee</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="2007.06617v1-abstract-short" style="display: inline;"> Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods ca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06617v1-abstract-full').style.display = 'inline'; document.getElementById('2007.06617v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.06617v1-abstract-full" style="display: none;"> Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. Related studies have shown that neural networks and support vector machines outperform other techniques by providing better prediction accuracy. The purpose of this paper is two fold. First, we provide a survey and a comparative analysis of results from literature applying machine learning techniques to predict credit rating. Second, we apply ourselves four machine learning techniques deemed useful from previous studies (Bagged Decision Trees, Random Forest, Support Vector Machine and Multilayer Perceptron) to the same datasets. We evaluate the results using a 10-fold cross validation technique. The results of the experiment for the datasets chosen show superior performance for decision tree based models. In addition to the conventional accuracy measure of classifiers, we introduce a measure of accuracy based on notches called "Notch Distance" to analyze the performance of the above classifiers in the specific context of credit rating. This measure tells us how far the predictions are from the true ratings. We further compare the performance of three major rating agencies, Standard $\&$ Poors, Moody's and Fitch where we show that the difference in their ratings is comparable with the decision tree prediction versus the actual rating on the test dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06617v1-abstract-full').style.display = 'none'; document.getElementById('2007.06617v1-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.08592">arXiv:1910.08592</a> <span> [<a href="https://arxiv.org/pdf/1910.08592">pdf</a>, <a href="https://arxiv.org/format/1910.08592">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"> Automatic Post-Editing for Machine Translation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rajen Chatterjee</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="1910.08592v1-abstract-short" style="display: inline;"> Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for improvement, leaving APE as a viable option to improve translation quality as a downstream task - which is the focus of this thesis. This field has received less attention compared to MT due to several reasons, which in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.08592v1-abstract-full').style.display = 'inline'; document.getElementById('1910.08592v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.08592v1-abstract-full" style="display: none;"> Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for improvement, leaving APE as a viable option to improve translation quality as a downstream task - which is the focus of this thesis. This field has received less attention compared to MT due to several reasons, which include: the limited availability of data to perform a sound research, contrasting views reported by different researchers about the effectiveness of APE, and limited attention from the industry to use APE in current production pipelines. In this thesis, we perform a thorough investigation of APE as a downstream task in order to: i) understand its potential to improve translation quality; ii) advance the core technology - starting from classical methods to recent deep-learning based solutions; iii) cope with limited and sparse data; iv) better leverage multiple input sources; v) mitigate the task-specific problem of over-correction; vi) enhance neural decoding to leverage external knowledge; and vii) establish an online learning framework to handle data diversity in real-time. All the above contributions are discussed across several chapters, and most of them are evaluated in the APE shared task organized each year at the Conference on Machine Translation. Our efforts in improving the technology resulted in the best system at the 2017 APE shared task, and our work on online learning received a distinguished paper award at the Italian Conference on Computational Linguistics. Overall, outcomes and findings of our work have boost interest among researchers and attracted industries to examine this technology to solve real-word problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.08592v1-abstract-full').style.display = 'none'; document.getElementById('1910.08592v1-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> 18 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">PhD dissertation on Automatic Post-Editing for Machine Translation (this work has been done between 2014 and 2017)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.04226">arXiv:1909.04226</a> <span> [<a href="https://arxiv.org/pdf/1909.04226">pdf</a>, <a href="https://arxiv.org/format/1909.04226">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.26421/QIC20.7-8-1">10.26421/QIC20.7-8-1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantum Unsupervised and Supervised Learning on Superconducting Processors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarma%2C+A">Abhijat Sarma</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rupak Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Gili%2C+K">Kaitlin Gili</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+T">Ting Yu</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="1909.04226v2-abstract-short" style="display: inline;"> Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the dataset, the computation time for training and using these statistical models grows quickly. Quantum computing offers a new paradigm which may have the ability to overcome these computational difficulties. Here, we propose a quantum analogu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.04226v2-abstract-full').style.display = 'inline'; document.getElementById('1909.04226v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.04226v2-abstract-full" style="display: none;"> Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the dataset, the computation time for training and using these statistical models grows quickly. Quantum computing offers a new paradigm which may have the ability to overcome these computational difficulties. Here, we propose a quantum analogue to K-means clustering, implement it on simulated superconducting qubits, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it has asymptotic complexity $O(N^{3/2}K^{1/2}\log{P})$, where $N$ is the number of data points, $K$ is the number of clusters, and $P$ is the dimension of the data points, giving a significant speedup over the classical analogue. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.04226v2-abstract-full').style.display = 'none'; document.getElementById('1909.04226v2-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> 6 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">Updated to the published version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Quantum Information and Computation 20 (7&8), 541-552 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.00084">arXiv:1909.00084</a> <span> [<a href="https://arxiv.org/pdf/1909.00084">pdf</a>, <a href="https://arxiv.org/format/1909.00084">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Ashvin Agrawal</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rony Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Curino%2C+C">Carlo Curino</a>, <a href="/search/cs?searchtype=author&query=Floratou%2C+A">Avrilia Floratou</a>, <a href="/search/cs?searchtype=author&query=Gowdal%2C+N">Neha Gowdal</a>, <a href="/search/cs?searchtype=author&query=Interlandi%2C+M">Matteo Interlandi</a>, <a href="/search/cs?searchtype=author&query=Jindal%2C+A">Alekh Jindal</a>, <a href="/search/cs?searchtype=author&query=Karanasos%2C+K">Kostantinos Karanasos</a>, <a href="/search/cs?searchtype=author&query=Krishnan%2C+S">Subru Krishnan</a>, <a href="/search/cs?searchtype=author&query=Kroth%2C+B">Brian Kroth</a>, <a href="/search/cs?searchtype=author&query=Leeka%2C+J">Jyoti Leeka</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K">Kwanghyun Park</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+H">Hiren Patel</a>, <a href="/search/cs?searchtype=author&query=Poppe%2C+O">Olga Poppe</a>, <a href="/search/cs?searchtype=author&query=Psallidas%2C+F">Fotis Psallidas</a>, <a href="/search/cs?searchtype=author&query=Ramakrishnan%2C+R">Raghu Ramakrishnan</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Abhishek Roy</a>, <a href="/search/cs?searchtype=author&query=Saur%2C+K">Karla Saur</a>, <a href="/search/cs?searchtype=author&query=Sen%2C+R">Rathijit Sen</a>, <a href="/search/cs?searchtype=author&query=Weimer%2C+M">Markus Weimer</a>, <a href="/search/cs?searchtype=author&query=Wright%2C+T">Travis Wright</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+Y">Yiwen Zhu</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="1909.00084v2-abstract-short" style="display: inline;"> Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, intelligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.00084v2-abstract-full').style.display = 'inline'; document.getElementById('1909.00084v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.00084v2-abstract-full" style="display: none;"> Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, intelligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few. Meanwhile, as the value of data is increasingly recognized and monetized, concerns about securing valuable data and risks to individual privacy have been growing. Consequently, rigorous data management has emerged as a key requirement in enterprise settings. How will these trends (ML growing popularity, and stricter data governance) intersect? What are the unmet requirements for applying ML in enterprise settings? What are the technical challenges for the DB community to solve? In this paper, we present our vision of how ML and database systems are likely to come together, and early steps we take towards making this vision a reality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.00084v2-abstract-full').style.display = 'none'; document.getElementById('1909.00084v2-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 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.13737">arXiv:1905.13737</a> <span> [<a href="https://arxiv.org/pdf/1905.13737">pdf</a>, <a href="https://arxiv.org/format/1905.13737">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Protocols for Checking Compromised Credentials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+L">Lucy Li</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+B">Bijeeta Pal</a>, <a href="/search/cs?searchtype=author&query=Ali%2C+J">Junade Ali</a>, <a href="/search/cs?searchtype=author&query=Sullivan%2C+N">Nick Sullivan</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rahul Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Ristenpart%2C+T">Thomas Ristenpart</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="1905.13737v3-abstract-short" style="display: inline;"> To prevent credential stuffing attacks, industry best practice now proactively checks if user credentials are present in known data breaches. Recently, some web services, such as HaveIBeenPwned (HIBP) and Google Password Checkup (GPC), have started providing APIs to check for breached passwords. We refer to such services as compromised credential checking (C3) services. We give the first formal de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.13737v3-abstract-full').style.display = 'inline'; document.getElementById('1905.13737v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.13737v3-abstract-full" style="display: none;"> To prevent credential stuffing attacks, industry best practice now proactively checks if user credentials are present in known data breaches. Recently, some web services, such as HaveIBeenPwned (HIBP) and Google Password Checkup (GPC), have started providing APIs to check for breached passwords. We refer to such services as compromised credential checking (C3) services. We give the first formal description of C3 services, detailing different settings and operational requirements, and we give relevant threat models. One key security requirement is the secrecy of a user's passwords that are being checked. Current widely deployed C3 services have the user share a small prefix of a hash computed over the user's password. We provide a framework for empirically analyzing the leakage of such protocols, showing that in some contexts knowing the hash prefixes leads to a 12x increase in the efficacy of remote guessing attacks. We propose two new protocols that provide stronger protection for users' passwords, implement them, and show experimentally that they remain practical to deploy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.13737v3-abstract-full').style.display = 'none'; document.getElementById('1905.13737v3-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 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.06428">arXiv:1808.06428</a> <span> [<a href="https://arxiv.org/pdf/1808.06428">pdf</a>, <a href="https://arxiv.org/format/1808.06428">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"> CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pal%2C+A">Anabik Pal</a>, <a href="/search/cs?searchtype=author&query=Chaturvedi%2C+A">Akshay Chaturvedi</a>, <a href="/search/cs?searchtype=author&query=Garain%2C+U">Utpal Garain</a>, <a href="/search/cs?searchtype=author&query=Chandra%2C+A">Aditi Chandra</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Raghunath Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Senapati%2C+S">Swapan Senapati</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="1808.06428v2-abstract-short" style="display: inline;"> This paper presents an approach for automatic detection of Munro's Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils in presence of nucleated cells is solved using the recent advances of deep learning algorithms. Separation of SC layer, extraction of patches from the layer followed by clas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.06428v2-abstract-full').style.display = 'inline'; document.getElementById('1808.06428v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.06428v2-abstract-full" style="display: none;"> This paper presents an approach for automatic detection of Munro's Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils in presence of nucleated cells is solved using the recent advances of deep learning algorithms. Separation of SC layer, extraction of patches from the layer followed by classification of patches with respect to presence or absence of neutrophils form the basis of the overall approach which is effected through an integration of a U-Net based segmentation network and a capsule network for classification. The novel design of the present capsule net leads to a drastic reduction in the number of parameters without any noticeable compromise in the overall performance. The research further addresses the challenge of dealing with Mega-pixel images (in 10X) vis-a-vis Giga-pixel ones (in 40X). The promising result coming out of an experiment on a dataset consisting of 273 real-life images shows that a practical system is possible based on the present research. The implementation of our system is available at https://github.com/Anabik/CapsDeMM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.06428v2-abstract-full').style.display = 'none'; document.getElementById('1808.06428v2-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 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">Accepted at MICCAI 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/1805.09119">arXiv:1805.09119</a> <span> [<a href="https://arxiv.org/pdf/1805.09119">pdf</a>, <a href="https://arxiv.org/ps/1805.09119">ps</a>, <a href="https://arxiv.org/format/1805.09119">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"> Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gaspers%2C+J">Judith Gaspers</a>, <a href="/search/cs?searchtype=author&query=Karanasou%2C+P">Penny Karanasou</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rajen Chatterjee</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="1805.09119v1-abstract-short" style="display: inline;"> This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in or… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09119v1-abstract-full').style.display = 'inline'; document.getElementById('1805.09119v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.09119v1-abstract-full" style="display: none;"> This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09119v1-abstract-full').style.display = 'none'; document.getElementById('1805.09119v1-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> 23 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.07274">arXiv:1803.07274</a> <span> [<a href="https://arxiv.org/pdf/1803.07274">pdf</a>, <a href="https://arxiv.org/ps/1803.07274">ps</a>, <a href="https://arxiv.org/format/1803.07274">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"> eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Negri%2C+M">Matteo Negri</a>, <a href="/search/cs?searchtype=author&query=Turchi%2C+M">Marco Turchi</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+R">Rajen Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Bertoldi%2C+N">Nicola Bertoldi</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="1803.07274v1-abstract-short" style="display: inline;"> Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor. Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effective… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.07274v1-abstract-full').style.display = 'inline'; document.getElementById('1803.07274v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.07274v1-abstract-full" style="display: none;"> Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor. Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effectively apply them at test stage on unseen (source, MT) pairs. In practice, however, their limited availability calls for solutions that also integrate in the training process other sources of knowledge. Along this direction, state-of-the-art results have been recently achieved by systems that, in addition to a limited amount of available training data, exploit artificial corpora that approximate elements of the "gold" training instances with automatic translations. Following this idea, we present eSCAPE, the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit. Translations are obtained both with phrase-based and neural models. For each MT paradigm, eSCAPE contains 7.2 million triplets for English-German and 3.3 millions for English-Italian, resulting in a total of 14,4 and 6,6 million instances respectively. The usefulness of eSCAPE is proved through experiments in a general-domain scenario, the most challenging one for automatic post-editing. For both language directions, the models trained on our artificial data always improve MT quality with statistically significant gains. The current version of eSCAPE can be freely downloaded from: http://hltshare.fbk.eu/QT21/eSCAPE.html. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.07274v1-abstract-full').style.display = 'none'; document.getElementById('1803.07274v1-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 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Accepted at LREC 2018</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 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