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2024 International Conference on Intelligent Education and computer Technology, IECT 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400709920

In response to the shortcomings of the SPOC course "Introduction to Computational Thinking"at Beijing University of Civil engineering and Architecture, the teaching team has transformed and improved the course design and practice based on the existing High quality undergraduate courses in Beijing universities and Beijing Curriculum Ideological and Political Demonstration Course. We aim to maintain consistency and linkage in learning objectives, learning assessments, teaching materials, learning activities, and course technology under the principle of consistent construction. Learning objectives are clearer and more measurable;learning assessment is more scientific, diverse, and reliable;Curriculum resources can meet the needs of students at different levels and majors;learning activities can fully mobilize students' subjective initiative;Curriculum technology helps to achieve learning objectives, and can reflect the characteristics of the discipline, with universality and progressiveness. Through the construction and practice of the FD-QM standard, the course team has established the principles of course standardization, clarified the direction of course material construction, drawn a knowledge graph of course knowledge points, accurately grasped the degree of learning objectives, upgraded the "characteristic"teaching resource library, enhanced student interest, and integrated ideological and political education into the curriculum, ultimately achieving a comprehensive improvement in the quality of course teaching. © 2024 ACM.

关键词： Knowledge graph

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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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computer systems Science & engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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Deep learning-based vehicle event identification

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Multimedia Tools and Applications 2024年第41期83卷 89439-89457页

作者： Chen, Yen-Yu Chen, Jui-Chi Lian, Zhen-You Chiang, Hsin-You Huang, Chung-Lin Chuang, Cheng-Hung Department of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology No.57 Sec. 2 Zhongshan Rd. Taichung411030 Taiwan Department of Computer Science and Information Engineering Asia University Taichung Japan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

In recent years, there has been a rapid development of intelligent driving assistance systems. Although most vehicles nowadays are equipped with driving assistance systems, the number of car accidents continues to rise. The main cause of car accidents is still largely attributed to human factors. Therefore, there has been an increasing focus on research related to vehicle event recognition and traffic video analysis. This study used deep learning methods to automatically recognize vehicle events from recorded driving videos. We expanded Inception-V3 to a 3D structure and replaced the bottom architecture of SlowFastNet with 3D-Inception-V3 to form a 3D-Inception-V3 + SlowFastNet model, making the network more suitable for video data recognition. In the experiment, we cut all videos into one-second clips and label each clip with vehicle event categories, including vehicle stopping, straight driving, turning, and collision. The experiment utilized training and testing procedures with eight distinct models, i.e., R3D, I3D, I3D + Kinetics-400, SlowFastNet(50), SlowFastNet(101), SlowFastNet(152), SlowFastNet(200), and the proposed 3D-Inception-V3 + SlowFastNet models. Comparative analysis through experiments revealed that our proposed network achieved the highest recognition accuracy of 93.3%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning 41

Temporal Logic Specification-Conditioned Decision Transforme...

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41st International Conference on Machine Learning, ICML 2024

作者： Guo, Zijian Zhou, Weichao Li, Wenchao Division of Systems Engineering Boston University United States Department of Electrical and Computer Engineering Boston University United States

Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real-world applications that involve complex tasks with rich temporal and logical structures. In this paper, we propose temporal logic Specification-conditioned Decision Transformer (SDT), a novel framework that harnesses the expressive power of signal temporal logic (STL) to specify complex temporal rules that an agent should follow and the sequential modeling capability of Decision Transformer (DT). Empirical evaluations on the DSRL benchmarks demonstrate the better capacity of SDT in learning safe and high-reward policies compared with existing approaches. In addition, SDT shows good alignment with respect to different desired degrees of satisfaction of the STL specification that it is conditioned on. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Framework for Effective Utilization of Distributed Scrum in Software Projects

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computer systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global Software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics

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IEEE Transactions on Emerging Topics in Computing 2024年 1-15页

作者： Chu, Yun-Wei Hosseinalipour, Seyyedali Tenorio, Elizabeth Cruz, Laura Douglas, Kerrie Lan, Andrew S. Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University IN USA NY USA Department of Engineering Education University of Florida FL USA School of Engineering Education Purdue University IN USA Manning College of Information and Computer Sciences University of Massachusetts Amherst MA USA

Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology that optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta- gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. The evaluation of the proposed methodology considers case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks, as evidenced by an increased average prediction quality and decreased variance across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns clustered into different student subgroups, consistent with the performance enhancements we obtain over the baselines. IEEE

关键词： Students

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Analysis and Practice of Ideological and Political Education in computer Public Basic Courses in Architecture Universities Based on Machine Learning and Deep Learning 24

Analysis and Practice of Ideological and Political Education...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Education Digitalization and computer Science, EDCS 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400710360

In response to the shortcomings of ideological and political education in the computer basic course of our school, the teaching team has conducted in-depth research on the connotation of ideological and political education in the course, accurately grasped the direction of ideological and political education in the course, innovatively proposed the"progressive method of ideological and political education", and constructed an integrated curriculum system of ideological and political education in the course;Using Orange3 software and machine learning algorithms to deeply explore the ideological and political elements of the curriculum, constructing an analytical model, formulating personalized teaching and learning strategies, and constructing a double-layer teaching evaluation system that emphasizes both morality and ability. Through the analysis of course data, it is shown that the effectiveness of ideological and political education in the curriculum has been comprehensively improved, and the quality of teaching has been comprehensively enhanced. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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TraiNDSim: A Simulation Framework for Comprehensive Performance Evaluation of Neuromorphic Devices for On-Chip Training 24

TraiNDSim: A Simulation Framework for Comprehensive Performa...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Heo, Donghyeok Bang, Hyeonsu Ko, Jong Hwan Department of Ai Systems Engineering Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706011

The advancement of neuromorphic devices (NDs) for processing deep neural networks has narrowed the accuracy gap with software-trained models. To accurately assess ND performance, reliable simulation frameworks for on-chip training are crucial. However, existing frameworks encounter difficulties accurately reflecting the characteristics of NDs in training simulations. Consequently, we introduce TraiNDSim, a novel framework that comprehensively evaluates the performance of NDs to address these difficulties. Specifically, we propose an advanced conductance normalization strategy called layer-wise normalization, which limits the weight range by taking the initial weight distribution into account. Additionally, our framework integrates three conductance models, notably refining one of the conventional models to depend solely on nonlinearity. Moreover, it features a bi-directional weight representation method with a unique conductance compensation technique. Our comprehensive analysis using TraiNDSim demonstrates its effectiveness in accurately reflecting the impact of ND parameters on training, promising more precise device performance evaluations. Our framework is available at https://***/donghyeokheo/TraiNDSim. © 2024 Copyright held by the owner/author(s).

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