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Software engineering and computer science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Local and global sensitivity analyses on the overflow probability of Abadia de Goiás radioactive near surface repository

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Nuclear engineering and Design 2025年 439卷

作者： Teixeira, D.G. Frutuoso e Melo, P.F. Alvim, L.G.M. Alvim, A.C.M. Alves, A.S.M. Graduate Program of Nuclear Engineering COPPE Federal University of Rio de Janeiro RJ Rio de Janeiro Brazil Department of Computer Science Multidisciplinary Institute Federal Rural University of Rio de Janeiro UFRRJ RJ Nova Iguaçu Brazil Eletronuclear S. A. RJ Rio de Janeiro Brazil

This work presents a sensitivity analysis of the overflow probability of the near- surface radioactive repository built after the Abadia de Goiás accident, where radioactive 137Cs was released. The purpose of the analysis is to help the repository owner and the regulatory body make decisions on design parameters that affect the overflow probability. The following parameters have been considered: degradation function of the repository ceiling, internal porosity of the repository, internal area of the repository base, repository base and wall thicknesses, repository height, height of the liquid column inside the repository, average rainfall rate, evapotranspiration rate, surface runoff and hydraulic conductivity of the repository concrete. The choice for the range of each parameter is discussed. An investigation of the role of the mentioned parameters is detailed based on results obtained in a previous paper, and the dependencies among the rate parameters on the average rainfall rate is emphasized and modeled. To give a deeper perspective, local and global sensitivity analyses have been performed. In the case of global analysis, two approaches have been followed: in the first, the concept of Normalized Mutual Information and the other one leverages Shapley values derived from Light Gradient Boosting Machine and Shapley values have been used for investigating non-linearities. For some parameters the lowest values are to be considered, e.g., the repository base thickness and the average rainfall rate, while for others the highest value is desirable, like the evapotranspiration rate and the surface runoff in order to obtain lower overflow probabilities. These results should be looked with care because nonlinearities have been identified by the Shapley value analysis. Overall, the degradation function and the evapotranspiration rate emerge as the most influential parameters. The legacy of this work lies in the robust methodological framework it provides, paving the way fo

关键词： Nuclear reactor accidents

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： engineering education

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A Comparative Evaluation of the SIR and SEIZ Epidemiological Models to Describe the Diffusion Characteristics of COVID-19 Polarizing Viewpoints on Online Social Networks 58

A Comparative Evaluation of the SIR and SEIZ Epidemiological...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Maleki, Maryam Agarwal, Nitin Systems Engineering Program California State University Dominguez Hills United States COSMOS Research Center UA - Little Rock AR United States International Computer Science Institute University of California Berkeley United States

ISBN: (纸本)9780998133188

To understand and characterize the diffusion trends of opposing viewpoints on Twitter (X), we applied two epidemiological models to six datasets related to COVID-19. We compared the results of the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models. We collected six datasets indicative of polarizing viewpoints related to contentious subjects surrounding the COVID-19 pandemic. Three of the datasets fall into an anti-subject hashtag group, and three of the datasets fall into a pro-subject hashtag group. The timeframe of each dataset is from January 1, 2020, to the end of 2021. Our findings demonstrate that while both the SIR and the SEIZ models are able to evaluate the propagation trends of the polarizing viewpoints, the SEIZ model is more accurate with relatively less error compared to the SIR model. This work sets the stage for ultimately leading to the ability to develop methods to prevent the propagation of ideas that lack scientific evidence while promoting the spread of scientifically backed ideas. © 2025 IEEE computer Society. All rights reserved.

关键词： Tweets

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The Women in science and engineering (WiSE) program at the University of Southern California: Achievements and challenges of the first five years

The Women in Science and Engineering (WiSE) program at the U...

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114th Annual ASEE Conference and Exposition, 2007

作者： Hawkes, Nicole Morrison, Jean Raghavendra, Cauligi WiSE University of Southern California United States University of Southern California United States Program University of Southern California United States Department of Earth Sciences Program USC Viterbi School of Engineering United States Departments of Electrical Engineering-Systems and Computer Science University of Southern California United States

The establishment of the Women in science and engineering (WiSE) program represents the serious commitment of the University of Southern California to address the under-representation of women in science and engineering. Since the launch of the program in 2000, WiSE has helped to more than double the number of tenured and tenure-track women faculty at USC from 15 to 35, through its recruitment and retention programs. Its complimentary programs to support undergraduates, graduate students, and postdoctoral scholars through a range of financial awards and activities have also grown in scope and impact. This paper describes the programs developed and administered by WiSE, the perceived successes to date, the challenges that remain, and the future directions of the program to ensure continued progress toward gender equity in science and engineering at USC. Through an analysis of the WiSE program at USC, we hope to present a model from which others can draw on to tackle the same issues at their own institutions. © American Society for engineering Education, 2007.

关键词： engineering education

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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Towards supporting the specification of context-aware software system test cases 23

Towards supporting the specification of context-aware softwa...

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23rd Iberoamerican Conference on Software engineering, CIbSE 2020

作者： de Souza Doreste, Andréa Cristina Travassos, Guilherme Horta Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil

In Software engineering, context can be understood as the overall set of information used to characterize the situation of an entity. A software system is context-aware if it uses the context to provide relevant information or services to the user. Nowadays, different types of software systems with a profound impact on the user's life can be considered context-aware (e.g., ubiquitous and Internet of Things software systems). Therefore, it is crucial to guarantee that they behave correctly. Software Testing is the primary process for verifying system behavior. Because of the particularities of Context-Aware Software systems (CASS), conventional testing methods are not enough to test such systems. Based on that, we propose CATS#, a technique to support software engineers with the specification of CASS test cases and capture not only the context itself but its variation as well. CATS# is an evolution of the CATS (Context-Aware Test Suite) Design technique. It introduces two relevant evolutions: test case models and a new test template. The test case models were proposed after we searched for context-aware applications through the literature and realized that the context could affect the test process in different manners. The test template was proposed to capture the variation of context by specifying which variation should occur during the test case and when it should occur. We selected one example application to apply our technique, deriving different types of test cases and showing its relevance in these initial steps. © CIbSE 2020.

关键词： Software testing

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Towards the conception of scientific workflows for in silico experiments in software engineering 10

Towards the conception of scientific workflows for in silico...

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Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software engineering and Measurement

作者： Pereira, Wallace M. Travassos, Guilherme H. Federal University of Rio de Janeiro/COPPE Systems Engineering and Computer Science Program Brazil

ISBN: (纸本)9781450300391

The evolution of science has been supported by complex computerized infrastructures with growing interest in simulation based experiments. This trend can also be observed in Software engineering. Our capacity of acquiring evidences to describe phenomena of interest in the field allowed the building of in silico models that can virtually replicate feasible software behaviors and improve our capacity of observation. In silico experiments demand additional concerns for its planning. One of them is regarding the scientific workflow conception. This task is not easy and to apply ad-hoc approaches can risky the experiment execution, validity and future replications. Regarding this topic, this paper presents some initial results from our research towards an approach to support the conception of scientific workflows for in silico experiments in Software engineering are presented. © 2010 ACM.

关键词： computer software

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