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13th International Conference on Educational and Information technology, ICEIT 2024

作者： Wu, Mei Wang, Xining Lu, Tao O'Hare, Gregory M.P School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology China School of Education School of Computer Science and Statistics Trinity College Dublin Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9798350372663

As English becomes the mainstream language in communication and education worldwide, more and more Chinese universities emphasize the importance of adding English content to the higher education curriculum. This paper explores the integration of English content into computer science education in Chinese universities, with a focus on bilingual programming courses. It proposes a blended learning strategy that combines face-to-face classroom interaction with platform-based self-directed learning. It designed the experiment and the number of participants is 84. The experimental results show that the adoption of this blended teaching strategy can enhance students' enthusiasm for bilingual programming courses and facilitate students in achieving the desired learning effect. © 2024 IEEE.

关键词： Students

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BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition 17th

BiEfficient: Bidirectionally Prompting Vision-Language Model...

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17th Asian Conference on computer Vision, ACCV 2024

作者： He, Haichen Liu, Weibin Xing, Weiwei School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819609079

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have shown great success in various image tasks. However, how to efficiently transfer such powerful VLMs into video domain is still an open problem. Given that full finetuning VLMs for video tasks could be computationally expensive, recent studies turn their focus on parameter-efficient finetuning (PEFT). The great potential of VLMs lies in leveraging the bidirectional semantic connections between the two modalities of vision and language. Nevertheless, most current PEFT methods use the vision-only framework and usually ignore the semantic connections between vision and language. In this paper, we propose a novel method called BiEfficient, which use bidirectional prompting schemes to efficiently transfer the VLM to video recognition task with a small number of tunable parameters: 1) Vision-to-Language: we propose two prompt mechanisms, Pre-Prompt and Post-Prompt, which act before and after the text encoder respectively to generate discriminative video-level text representation for each input video. 2) Language-to-Vision: we propose Word-Guided Visual-Prompt, which enhances the temporal modeling of videos using textual knowledge in an almost parameter-free manner. Experiments on Kinetics-400, UCF-101, HMDB-51 demonstrate that the proposed method can achieve comparable or even better performance to the full finetuning methods with much fewer tunable parameters across closed-set and zero-shot video recognition benchmarks. Code is available here: https://***/WbLiuBJTULab/BiEfficient. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Predicting the Hardness of Low Alloy Metal using Machine Learning Model 6

Predicting the Hardness of Low Alloy Metal using Machine Lea...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Logeswaran, K. Savitha, S. Suresh, P. Sah, Swati Priyanka, B. Ragav, R. Vishal, R.S. Mohamed Arsath, H. Kongu Engineering College Department of Artificial Intelligence TamilNadu Erode638060 India Vellore Institute of Technology School of Computer Science and Engineering Department of Database Systems TamilNadu Vellore632014 India K.S.R College of Engineering Department of Computer Science and Engineering Tamilnadu Trichengode637215 India Sharda University School of Engineering and Technology Nodia India

ISBN: (纸本)9798331516796

In industries like materials engineering and manufacturing, predicting the hardness of low alloy metals is crucial for ensuring quality and performance. Traditional methods, which often rely on formulas or manual testing, can be slow and inaccurate due to variations in material properties. This paper presents a new approach for predicting metal hardness using machine learning techniques, including Multi-linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. These algorithms are used to improve the accuracy and reliability of hardness predictions compared to older methods. The paper covers how the data was collected, which features were chosen, and how the models were trained. It also compares the performance of these algorithms, looking at how accurate and effective they are in different situations. The results show that the machine learning models offer better predictions than traditional methods, leading to more consistent and reliable outcomes. Finally, the research discusses how these models can be used in real-world applications to make processes more efficient and reduce errors. © 2024 IEEE.

关键词： Prediction models

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Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Book Genre Classification System Through Supervised Learning Technique 1

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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A Review of Driver Fatigue Detection Based on Facial Expression Recognition 20

A Review of Driver Fatigue Detection Based on Facial Express...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Jia, Chen Jianping, Li Changrun, Cui Lixi, Chen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Fatigue driving is one of the main causes of traffic accidents. Under fatigue, the driver's reaction time increases, and they cannot take timely remedial measures in emergencies, which leads to the occurrence of traffic accidents or worsens their severity. Real-time fatigue detection during driving is highly effective in preventing accidents caused by fatigue driving. The current fatigue detection methods can be roughly divided into three categories: (1) detection methods based on driver's physiological characteristics;(2) detection methods based on vehicle driving state;(3) detection methods based on driver's facial features. Because the first two methods have low robustness, the third method holds the most research significance. This paper analyzes several methods for detecting fatigue based on facial features. The text briefly describes the method without facial expression features, the method based on facial expression features, and the process of multi-feature fusion with facial expression features. It also summarizes and analyzes the methods and technologies used in the key steps of the process. The future research is also being anticipated. © 2023 IEEE.

关键词： Feature extraction

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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Research on Community Discovery Techniques Based on Knowledge Distillation 20

Research on Community Discovery Techniques Based on Knowledg...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Hong, Tang Zhen, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Graph Neural Networks (GNN) have found broad applications in diverse domains, including community detection, classification of nodes, and prediction of links. Unfortunately, in the real world, many networks usually have the problem of insufficient connection and label information, and GNN mainly relies on graph-based feature propagation and smoothing operations for graph task analysis. In the absence of connection and label information, the feature propagation operation of GNN will be greatly affected., so these problems pose an obstacle to the performance of GNN. In this context, we introduce an innovative model for community discovery based on knowledge distillation. The objective is to extract information on community structures from intricate networks and apply it to a student model, thus improving the effectiveness of community detection tasks. © 2023 IEEE.

关键词： Distillation

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Exploring Multi-Layer Perceptrons for Time Series Classification in Networks 5th

Exploring Multi-Layer Perceptrons for Time Series Classifica...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Belwal, Ankit Senthilkumar, S. Alam, Intekhab Jaison, Feon Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Modern-day time series classification (TSC) in networks has emerged as famous and is presently an energetic research location. However, traditional methods in TSC in networks, including okay-nearest neighbors and help vector machines, require large training units and an extensive computational attempt as they need more capacity to extract complicated capabilities. This paper explores Multi-Layer Perceptrons (MLPs) as an alternative records-pushed approach to tackle the TSC task. We first design experiments to benchmark the performance of trendy MLPs educated on representative time series datasets from diverse domain names. We then look at the different architectures of state-of-the-art MLPs for the TSC hassle by exploring contemporary activation functions and structure variations. In addition, we speak about how distinct initialization techniques and hyperparameters may impact the schooling state-of-the-art MLP version. The outcomes reveal the higher accuracy of the MLPs in TSC networks. Eventually, we gift viable traces of the latest similar investigation for the proposed method. The technical summary explores using multi-layer perceptrons (MLP) for network time collection classification. MLP is a supervised deep mastering class set of rules used to expect time series lessons from entered data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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