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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Yang, Janghoon Computer Science Program Division of Science and Engineering Penn State Abington AbingtonPA United States

ISBN: (纸本)9798350353464

While deep learning can offer a promising approach to sentiment analysis, it often presents challenges in complexity and explainability. Alternatively, sentiment analysis based on dictionaries has been explored. In this paper, subword-level dictionaries in English are considered to address performance degradation resulting from domain mismatches. To work at subword level, a framework based on naïve bayes machine learning algorithm is exploited. Furthermore, stopwords at the subword level have been proposed to remove additional interference intrinsic to subword tokenization. Numerical experiments demonstrate that the proposed method achieves higher accuracy and F1 scores compared to the conventional dictionary-based method when there is a mismatch between the dictionary and the corpus of documents while performing marginally worse than state-of-the-art deep learning methods when applied to datasets from the same domain. ©2024 IEEE.

关键词： Contrastive Learning

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Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information 21

Protein Structure Prediction Using Dynamic Speciation Evolut...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Parpinelli, Rafael Stubs Wojeicchowski, Nicholas Will, Nilcimar Neitzel State University of Santa Catarina Graduate Program in Applied Computing Joinville Brazil State University of Santa Catarina Computer Science Department Joinville Brazil

ISBN: (纸本)9798350356632

Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time. © 2024 IEEE.

关键词： Prediction models

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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The Influence of Well-Dressed NPCs on Player Perception, Immersion and Decision-Making in Gaming 5

The Influence of Well-Dressed NPCs on Player Perception, Imm...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paradeda, Raul B. Freitas, Georgy Alves Filho, Sebastiao E. Abner Souza, Anderson State University of Rio Grande do Norte Department of Computer Science Natal Brazil State University of Rio Grande do Norte Computer Science Graduate Program UERN-UFERSA Department of Informatics Mossoró Brazil

ISBN: (纸本)9798350322774

This empirical study involved volunteers who played a game featuring NPCs specially developed for the research. The research investigated the influence and behaviour of NPC appearance on some factors regarding the players. Results suggest that NPC appearance can significantly affect the player's emotions, immersion, perception, and decision-making. Poorly dressed NPCs negatively influenced the player's emotional state, while well-dressed NPCs generated higher trustworthiness and positively influenced the player's decision-making. This study may help develop more efficient strategies for creating characters that influence the player's decision-making in video games and encourage the development of new research in the field of human-agent interaction and game development. © 2023 IEEE.

关键词： Decision making

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Conversion of Traditional Face-to-Face MLS Hematology Courses Into Remote Delivery

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Journal of College science Teaching 2023年第5期52卷 6-13页

作者： Jafar, Sanjeeda Budhani, Shruti Wilson, Diane Medical Laboratory Science (MLS) Program School of Computer Mathematical and Natural Sciences United States MLS Program United States Morgan State University Baltimore MD United States

The design of a hybrid Hematology I course (prepandemic) was adopted for Hematology II and facilitated our conversion of Hematology II into a fully remote course by fall 2020, after the university went into remote instruction. When the university suspended face-to-face (F2F) classes in March 2020, only the laboratory final exam was pending for Hematology I, and it was therefore administered remotely. Because students had taken another F2F practical exam before lockdown, we were able to compare the two. There was a significant difference between the practical examination grades for the online versus F2F exams: 94% vs. 77% mean scores, respectively. However, there was no difference between the written portion of the exams. In this article, we report the final lab and lecture grades for both courses. In spring 2021, our students completed their clinical practicums with our hospital affiliates and received scores of ≥ 90%. We also report the performance of students who sat for the American Society of Clinical Pathology Board of Certification (BOC) examination for medical laboratory scientists. All students who appeared in the BOC examination performed satisfactorily in the hematology subdiscipline. These external assessments are positive objective benchmarks for our courses and students. © 2023 National science Teaching Association.

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Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube 16

Robot Finder v2: Search and Ingestion of Educational Robotic...

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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Urbano Lopes, Gabriel Batistuta de Santana Costa, Ryllari Raianne Marques Alves Filho, Sebastião Emidio B.Sc. in Computer Science Rio Grande do Norte State University RN Mossoró Brazil Graduate Program in Computer Science - PPgCC UERN/UFERSA Rio Grande do Norte State University Federal University of the Semi-Arid Region RN Mossoró Brazil

ISBN: (纸本)9798350391084

Educational Robotics offers an interactive learning approach where students can turn abstract theories into concrete projects. With the exponential growth in content creation on internet platforms, finding specific, high-quality learning objects about robotics in education can be challenging. Platforms like YouTube, for example, have a vast collection of educational videos that cover various topics related to educational robotics. However, they are scattered among a massive amount of available data, making it difficult to have more assertive searches and identify the best resources. This work aims to present Robot Finder v2, a software that uses an automated approach to feed RepositORE, a repository of Learning Objects in Educational Robotics. The Robot Finder v2 searches and stores data from YouTube videos to make the search for this content simpler and more effective. The collected data feeds the RepostiORE, where these objects can be adapted and researched by users looking to acquire skills in educational robotics. For the objects to be found accurately, the software uses the YouTube Data API v3, which has a set of metadata in addition to the descriptions of the videos provided by their creators. The processing done in the Robot Finder optimizes the search and registration of information by mapping API endpoints and seeking correlations of metadata between RepositORE objects and YouTube videos. ©2024 IEEE.

关键词： Educational robots

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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments 6

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

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6th International Conference of Women in Data science at Prince Sultan University, WiDS-PSU 2023

作者： Makda, Tahira Jehan Luiza Barros, Ana Dilek, Selma University of Doha for Science and Technology College of Business Management Doha Qatar University of Ceará Computer Science Program State Fortaleza Brazil Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665477239

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology. © 2023 IEEE.

关键词： Actuators

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Ensuring Ethical, Transparent, and Auditable Use of Education Data and Algorithms on AutoML 6

Ensuring Ethical, Transparent, and Auditable Use of Educatio...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Griep, Kylie Stone, Leah Edwards, Stephen Hill, Malik Cui, Jialin Liu, Chengyuan Jia, Qinjin Song, Yang Department of Computer Science University of North Carolina Wilmington United States Master of Data Science Program University of North Carolina Wilmington United States Department of Computer Science NC State University United States

ISBN: (纸本)9798400709241

Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to evaluate those models, and due to the black-box nature of the machine learning models, problems can arise with regard to algorithmic biases and fairness. Such biases can escalate in future applications, necessitating a structured approach for fairness evaluation in AutoML. This involves defining fairness criteria, selecting appropriate metrics, assessing fairness across groups, and addressing biases. In the realm of educational data mining, where AutoML is prevalent, biases related to attributes like gender or race can lead to unethical outcomes. Since fairness metrics vary in definition and strength, and some may even contradict others, making fairness evaluation more complex. In this paper, ten fairness metrics were chosen, explored, and implemented on four AutoML tools, Vertex AI, AutoSklearn, AutoKeras, and PyCaret. We identified two open educational datasets and built both prediction and classification models on those AutoML frameworks. We report our work in evaluating different machine learning models created by AutoML and provide discussions about the challenges in evaluating fairness in those models and our effort to mitigate and resolve the problems of algorithmic bias in educational data mining. © 2023 ACM.

关键词： Data mining

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Intelligence Based Condition Monitoring Model 7th

Intelligence Based Condition Monitoring Model

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Jenab, Kouroush Ward, Tyler Isaza, Cesar Ortega-Moody, Jorge Anaya, Karina School of Engineering & Computer Science Morehead State University MoreheadKY United States Networking and Telecommunication Academic Program Polytechnique University of Queretaro Queretaro Mexico

ISBN: (纸本)9783031396182

Condition-Based Maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on changes in equipment condition (e.g., changes in vibration, changes in power usage, changes in operating performance, changes in temperatures, changes in noise levels, changes in chemical composition, increase in debris content and changes in the volume of material). In this study, we present the newly developed Condition Monitoring Model (CMM) based on an ensemble machine-learning model that utilizes the random forest, support vector machine, and artificial neural network classifiers, to classify data points from the normal state of a rotating machine. The efficacy of the model in adequately detecting and diagnosing faults in the rotating machine for maintenance planning is discussed in this paper. The developed model can efficiently avoid unnecessary maintenance and make timely actions by analyzing the received vibration signals from the rotating machine. An illustrative example is demonstrated to present the application of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset 8

An Approach to Detect Abnormal Submissions for CodeWorkout D...

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8th Educational Data Mining in computer science Education Workshop, CSEDM 2024

作者： Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech Blacksburg VA Dept of Computer Science Utah State University Logan UT Intelligent Systems Program University of Pittsburgh Pittsburgh PA School of Informatics Computing and Cyber Systems North Arizona University Flagstaff AZ Dept. of Computer Science University of Virginia wCharlottesville VA

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024 Copyright for this paper by its authors.

关键词： CS1 Dataset Cleaning Dataset Standards Educational Data Mining Introductory programming

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