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<div class="contents_title"> <div style="float:left">Contents</div> <div class="contentspagination"> <div class="pagination PagedList-pager"><ul><li class="previous PagedList-skipToFirst"><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=1" title="Show first page">«</a></li><li class="PagedList-skipToPrevious"><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=16" title="Show previous page">‹</a></li><li class="disabled PagedList-ellipses"><a>…</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=3" title="Show page 3">3</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=4" title="Show page 4">4</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=5" title="Show page 5">5</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=6" title="Show page 6">6</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=7" title="Show page 7">7</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=8" title="Show page 8">8</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=9" title="Show page 9">9</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=10" title="Show page 10">10</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=11" title="Show page 11">11</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=12" title="Show page 12">12</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=13" title="Show page 13">13</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=14" title="Show page 14">14</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=15" title="Show page 15">15</a></li><li><a data-ajax="true" data-ajax-method="get" data-ajax-mode="replace" data-ajax-update="#unobtrusive" href="/Publication/Descendants/54861?page=16" title="Show page 16">16</a></li><li class="active"><a>17</a></li><li class="disabled PagedList-skipToNext"><a>›</a></li><li class="next disabled PagedList-skipToLast"><a>»</a></li></ul></div> </div> </div> <div class="contents" id="volumearticles" start="401"> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55262">PAIS Papers</a></div> <label>Page</label> <div class="value pages">2947</div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55262" method="post"> <input type="hidden" name="id" value="55262" /> <div id='downloadlink55262' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55262').click(function () { $('form#downloadform55262').submit(); }); }); </script> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55263">Equal Pay for Equal Competences: A Statistical Approach to Address Equal Pay Gap</a></div> <label>Authors</label> <div class="value authors">Andra Anoaica, Ahmed Ben Hassine, Léa A. Deleris</div> <label>Pages</label> <div class="value pages">2949 - 2955</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200468</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Gender wage equality is something that a majority of modern companies work towards. Measuring and addressing gender pay gap in practice can however be challenging due to the variety of roles, educational paths, experiences and responsibilities that compose any given company workforce. In this paper we propose a methodology to (i) determine the level of pay inequality between men and women within a single organisation and to (ii) suggest adjustments to address it. Our approach is novel in the sense that previous studies were done on homogeneous cherry-picked populations from different enterprises at a country level. By contrast, our model aims at decision support for compensation exercises within a single organization. The methodology is applied in two steps. First we evaluate the health of the overall organisation from the gender gap point of view. We combine expert-driven rules with unsupervised learning methods to identify individuals that are comparable, and then provide metrics to assess the level of gender-based pay gap within these groups. For cases where such a gap is ascertained, we build a predictive model for the gender neutral salary of each individual based on comparison with his/her closest peers within the workforce. Once the individuals that have a deficit in terms of compensation are identified, we test how the budgetary constraints reflect on a catch up plan. We have applied this methodology to a European organisation of about 1500 employees to support the HR department in its efforts to assure competitive compensation. The results confirm the existence of differences in compensation between men and women but also that the adjustments proposed by our models do lead to narrowing the pay gap.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55263" method="post"> <input type="hidden" name="id" value="55263" /> <div id='downloadlink55263' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55263').click(function () { $('form#downloadform55263').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55264">Cross-Border Medical Research Using Multi-Layered and Distributed Knowledge</a></div> <label>Authors</label> <div class="value authors">Gábor Bella, Liz Elliot, Subhashis Das, Stephen Pavis, Ettore Turra, David Robertson, Fausto Giunchiglia</div> <label>Pages</label> <div class="value pages">2956 - 2963</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200469</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>As medical research becomes ever finer-grained, experiments require healthcare data in quantities that single countries cannot provide. Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries. Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints. We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology. Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries. We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55264" method="post"> <input type="hidden" name="id" value="55264" /> <div id='downloadlink55264' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55264').click(function () { $('form#downloadform55264').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55265">Entropy-Based Adaptive Exploit-Explore Coefficient for Monte-Carlo Path Planning</a></div> <label>Authors</label> <div class="value authors">Ana Raquel Carmo, Jean-Alexis Delamer, Yoko Watanabe, Rodrigo Ventura, Caroline P.C. Chanel</div> <label>Pages</label> <div class="value pages">2964 - 2971</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200470</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Efficient path planning for autonomous vehicles in cluttered environments is a challenging sequential decision-making problem under uncertainty. In this context, this paper implements a partially observable stochastic shortest path (PO-SSP) planning problem for autonomous urban navigation of Unmanned Aerial Vehicles (UAVs). To solve this planning problem, the POMCP-GO algorithm is used, which is goal oriented variant of POMCP, one of the fastest online state-of-the-art solvers for partially observable environments based on Monte Carlo Planning. This algorithm relies on the Upper Confidence Bounds (UCB1) algorithm as action selection strategy. UCB1 depends on an exploration constant typically adjusted empirically. Its best value varies significantly between planning problems, and hence, an exhaustive search to find the most suitable value is required. This exhaustive search applied to a complex path planning problem may be extremely time consuming. Moreover, considering real applications where online planning is needed, this extensive search is not suitable. Thereby this paper explores the use of an adaptive exploration coefficient for action selection during planning. Monte-Carlo value backup approximation is also applied which empirically demonstrates to accelerate the policy value convergence. Simulation results show that the use of the adaptive exploration coefficient within a user-defined interval achieves better convergence and success rates when compared with most hand-tuned fixed coefficients in said interval, although never achieving the same results as the best fixed coefficient. Therefore, a compromise must be made between the desired quality of the results and the time one is willing to spend on the exhaustive search for the best coefficient value before planning.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55265" method="post"> <input type="hidden" name="id" value="55265" /> <div id='downloadlink55265' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55265').click(function () { $('form#downloadform55265').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55266">Deep Learning Applied to NILM: Is Data Augmentation Worth for Energy Disaggregation?</a></div> <label>Authors</label> <div class="value authors">Aurélien Delfosse, Georges Hebrail, Aimen Zerroug</div> <label>Pages</label> <div class="value pages">2972 - 2977</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200471</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Energy disaggregation (or Non-Intrusive Load Monitoring – NILM) is the task of estimating the electricity consumption of each appliance in a household from the total electricity consumption. Disaggregated consumption gives information on each appliance and helps to find ways to reduce a household’s energy consumption. Recent progress in deep neural networks for computer vision and natural language processing gives inspiration to train general architectures on time series data in order to improve the state of the art on NILM, but lack of supervised data is one of the main problems stalling the improvement of disaggregation algorithms. In this paper, we introduce a new multi-agent based simulator that enables to generate synthetic data according to real time-use surveys. This synthetic dataset is used as a training set in the NILM learning process: we show that this data augmentation improves the accuracy of the disaggregation. In addition, we present four neural network architectures to estimate appliances consumption and establish a baseline architecture on the data coming from the synthetic generator.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55266" method="post"> <input type="hidden" name="id" value="55266" /> <div id='downloadlink55266' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55266').click(function () { $('form#downloadform55266').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55267">Actual Causality Canvas: A General Framework for Explanation-Based Socio-Technical Constructs</a></div> <label>Authors</label> <div class="value authors">Amjad Ibrahim, Tobias Klesel, Ehsan Zibaei, Severin Kacianka, Alexander Pretschner</div> <label>Pages</label> <div class="value pages">2978 - 2985</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200472</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>The rapid deployment of digital systems into all aspects of daily life requires embedding social constructs into the digital world. Because of the complexity of these systems, there is a need for technical support to understand their actions. Social concepts, such as explainability, accountability, and responsibility rely on a notion of actual causality. Encapsulated in the Halpern and Pearl’s (HP) definition, actual causality conveniently integrates into the socio-technical world if operationalized in concrete applications. To the best of our knowledge, theories of actual causality such as the HP definition are either applied in correspondence with domain-specific concepts (e.g., a lineage of a database query) or demonstrated using straightforward philosophical examples. On the other hand, there is a lack of explicit automated actual causality theories and operationalizations for helping understand the actions of systems. Therefore, this paper proposes a unifying framework and an interactive platform (Actual Causality Canvas) to address the problem of operationalizing actual causality for different domains and purposes. We apply this framework in such areas as aircraft accidents, unmanned aerial vehicles, and artificial intelligence (AI) systems for purposes of forensic investigation, fault diagnosis, and explainable AI. We show that with minimal effort, using our general-purpose interactive platform, actual causality reasoning can be integrated into these domains.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55267" method="post"> <input type="hidden" name="id" value="55267" /> <div id='downloadlink55267' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55267').click(function () { $('form#downloadform55267').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55268">Remaining Useful Life Curve Prediction of Rolling Bearings Under Defect Progression Based on Hierarchical Bayesian Regression</a></div> <label>Authors</label> <div class="value authors">Masashi Kitai, Yoshinobu Akamatsu, Ryoji Tani, Hiroki Fujiwara, Masayuki Numao, Ken-ichi Fukui</div> <label>Pages</label> <div class="value pages">2986 - 2992</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200473</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progressing, robustness for individual difference and fluctuation of vibration features are challenging issues. In this research, we propose a novel RUL prediction method that uses a hierarchical Bayesian method to consider the individual difference of RUL, and uses an intermediate variable indicating the defect condition instead of predicting RUL directly from vibration features. The proposed method can perform a monotonous RUL prediction curve and improved prediction accuracy especially for early stage of defect progression.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55268" method="post"> <input type="hidden" name="id" value="55268" /> <div id='downloadlink55268' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55268').click(function () { $('form#downloadform55268').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55269">Point-Of-Interest Semantic Tag Completion in a Global Crowdsourced Search-and-Discovery Database</a></div> <label>Authors</label> <div class="value authors">Nikolaos Lagos, Salah Ait-Mokhtar, Ioan Calapodescu</div> <label>Pages</label> <div class="value pages">2993 - 3000</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200474</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Applications that process Point-of-Interest data are omnipresent nowadays. They range from digital maps to recommender systems for places to visit, and personal assistants. The success of such applications critically depends on the quality of the ingested data. However, corresponding databases, especially when they are crowdsourced, are often incomplete. Existing work on automatic data completion approaches has only partially considered the task for Points-of-Interest (POI). Such entities have a number of distinctive properties – notably multiscript names, geo-spatial identity, and temporally defined context –, which make the task more challenging. Here we present an approach to automatically complete POI semantic tags in a crowdsourced database. We perform experiments on multi-lingual data from Foursquare, a global location-based social network, and observe that (i) POI names are strong predictors of POI semantic tags: a character-based LSTM model trained only on POI names gives 72.5% worst-case micro-precision and 50.38% micro F1 scores, (ii) appropriate use of spatio-temporal data leads to consistent improvements (iii) using a structured representation of time gives higher precision and requires less computation time than string-based LSTM variants, however, the higher precision is achieved at a cost of lower recall and micro-F1, (iv) an LSTM model trained on semi-structured strings representing time, is competitive to fully structured inputs in terms of recall.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55269" method="post"> <input type="hidden" name="id" value="55269" /> <div id='downloadlink55269' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55269').click(function () { $('form#downloadform55269').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55270">FishNet: A Unified Embedding for Salmon Recognition</a></div> <label>Authors</label> <div class="value authors">Bjørn Magnus Mathisen, Kerstin Bach, Espen Meidell, Håkon Måløy, Edvard Schreiner Sjøblom</div> <label>Pages</label> <div class="value pages">3001 - 3008</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200475</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying individual salmon is imperative to their research. The current methods of individual salmon tagging and tracking rely on physical interaction with the fish. This process is inefficient and can cause physical harm and stress for the salmon. In this paper we propose FishNet, based on a deep learning technique that has been successfully used for identifying humans, to identify salmon. We create a dataset of labeled fish images and then test the performance of the FishNet architecture. Our experiments show that this architecture learns a useful representation based on images of salmon heads. Further, we show that good performance can be achieved with relatively small neural network models: FishNet achieves a false positive rate of 1% and a true positive rate of 96%.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55270" method="post"> <input type="hidden" name="id" value="55270" /> <div id='downloadlink55270' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55270').click(function () { $('form#downloadform55270').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55271">Machine Learning for Gas and Oil Exploration</a></div> <label>Authors</label> <div class="value authors">Vito Alexander Nordloh, Anna Roubíčková, Nick Brown</div> <label>Pages</label> <div class="value pages">3009 - 3016</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200476</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Drilling boreholes for gas and oil extraction is an expensive process and profitability strongly depends on characteristics of the subsurface. As profitability is a key success factor, companies in the industry utilise well logs to explore the subsurface beforehand. These well logs contain various characteristics of the rock around the borehole, which allow petrophysicists to determine the expected amount of contained hydrocarbon. However, these logs are often incomplete and, as a consequence, the subsequent analyses cannot exploit the full potential of the well logs.</p> <p>In this paper we demonstrate that Machine Learning can be applied to fill in the gaps and estimate missing values. We investigate how the amount of training data influences the accuracy of prediction and how to best design regression models (Gradient Boosting and neural network) to obtain optimal results. We then explore the models’ predictions both quantitatively, tracking the prediction error, and qualitatively, capturing the evolution of the measured and predicted values for a given property with depth. Combining the findings has enabled us to develop a predictive model that completes the well logs, increasing their quality and potential commercial value.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55271" method="post"> <input type="hidden" name="id" value="55271" /> <div id='downloadlink55271' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55271').click(function () { $('form#downloadform55271').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55272">Intelligent Recommendation & Decision Technologies for Community-Driven Requirements Engineering</a></div> <label>Authors</label> <div class="value authors">Ralph Samer, Martin Stettinger, Alexander Felfernig, Xavier Franch, Andreas Falkner</div> <label>Pages</label> <div class="value pages">3017 - 3025</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200477</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Requirements Engineering (RE) represents a critical phase in the management and planning of software projects. One of the main reasons for project failure is missing or incomplete RE. In order to reduce the risk of project failure, there exists a high and urgent demand for applying intelligent technologies in RE. Since the RE process is mainly decision- and community-driven, Recommender Systems are supposed to be applied in this particular context to support stakeholders in decision-making and, hence, to increase the quality of the decisions taken by the stakeholders. This paper introduces a variety of innovative recommendation tools developed within the scope of the European Horizon 2020 research project OPENREQ. Moreover, we give an overview of user studies conducted to evaluate our approaches and present final results of selected studies. The study results indicate that the developed concepts have the potential to significantly improve the quality of requirements definition and requirements prioritization.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55272" method="post"> <input type="hidden" name="id" value="55272" /> <div id='downloadlink55272' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55272').click(function () { $('form#downloadform55272').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55273">ASP-Based Signal Plan Adjustments for Traffic Flow Optimization</a></div> <label>Authors</label> <div class="value authors">Thomas Eiter, Andreas Falkner, Patrik Schneider, Peter Schüller</div> <label>Pages</label> <div class="value pages">3026 - 3033</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200478</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Worldwide, many cities spend considerable effort to reduce traffic and specifically to avoid traffic congestions. Adaptive traffic control systems serve this purpose by dynamically adjusting traffic signals for optimizing the traffic flow on intersections. Systems such as SCOOT are based on an “intelligent” combination of different traffic optimization strategies. However, they miss the possibility (i) to add and change on-demand rules to implement new optimization strategies, and (ii) to simulate the outcome of new strategies on-the-fly which is similar to the capabilities of microscopic traffic simulation tools such as SUMO. In order to overcome the above limitations, we present a novel approach for calculating signal phase plans (SPPs) used for optimizations in traffic control systems. Our approach is based on Answer Set Programming (ASP) and combines ASP encodings of an abstract mesoscopic flow model and a strategy for generating possible SPPs. Experimental results shows that traffic simulation can be well approximated and that the generated SPPs improve the traffic flow effectively.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55273" method="post"> <input type="hidden" name="id" value="55273" /> <div id='downloadlink55273' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55273').click(function () { $('form#downloadform55273').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55274">KNOWLEDGECHECKR: Intelligent Techniques for Counteracting Forgetting</a></div> <label>Authors</label> <div class="value authors">Martin Stettinger, Trang Tran, Ingo Pribik, Gerhard Leitner, Alexander Felfernig, Ralph Samer, Müslüm Atas, Manfred Wundara</div> <label>Pages</label> <div class="value pages">3034 - 3039</div> <label>DOI</label> <div class="value doi">10.3233/FAIA200479</div> <label>Category</label> <div class="value category">Research Article</div> <div class="abstract"> <b>Abstract</b><br /> <section> <p>Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus of the KNOWLEDGECHECKR environment is to take into account forgetting processes which immediately start after a learning unit has been completed. In this context, techniques are needed that are able to predict which learning units are the most relevant ones to be repeated in future learning sessions. In this paper, we provide an overview of the recommendation approaches integrated in KNOWLEDGECHECKR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering. In order to show the applicability of the presented techniques, we provide an overview of the results of empirical studies that have been conducted in real-world scenarios.</p> </section> </div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55274" method="post"> <input type="hidden" name="id" value="55274" /> <div id='downloadlink55274' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55274').click(function () { $('form#downloadform55274').submit(); }); }); </script> <div class="button openaccesslicense"> <a rel="license" target="_blank" title="This work is licensed under a Creative Commons License" href="https://creativecommons.org/licenses/by-nc/4.0/deed.en_US"> <img alt="Creative Commons License" style="border-width: 0" src="https://i.creativecommons.org/l/by-nc/4.0/80x15.png" /></a></div> </div> </div> </div> <div class="bookseriesvolumearticlelistitem"> <div class="content"> <div class="cover"></div> <div class="metadata"> <div class="expandable"> <div class="value title"><a href="/volumearticle/55275">Author Index</a></div> <label>Pages</label> <div class="value pages">3041 - 3053</div> <label>Category</label> <div class="value category">Author Index</div> </div><div class="expandbuttons"><div class="expand">↓ more</div><div class="collapse">↑ less</div></div> </div> <div class="actions"> <form action="/Download/Pdf" id="downloadform55275" method="post"> <input type="hidden" name="id" value="55275" /> <div id='downloadlink55275' class="button getpdf">Download </div> </form> <script type="text/javascript"> $(function () { $('div#downloadlink55275').click(function () { $('form#downloadform55275').submit(); 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