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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

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Source Code Summarization in the Era of Large Language Models

Source Code Summarization in the Era of Large Language Model...

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International Conference on software engineering (ICSE)

作者： Weisong Sun Yun Miao Yuekang Li Hongyu Zhang Chunrong Fang Yi Liu Gelei Deng Yang Liu Zhenyu Chen College of Computing and Data Science Nanyang Technological University Singapore Singapore State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering University of New South Wales Sidney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of coderelated tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLMbased code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs' model settings (including top_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs' abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types (e.g., procedural and object-oriented programming languages). Finally, we unexpectedly find that CodeLlamaInstruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code design rationale and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.

关键词： computer languages Codes Systematics Source coding Large language models Object oriented modeling software Prompt engineering Object oriented programming software engineering

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Hybrid Makes Better: Privacy-Preserving Medical Image Classification Based on Federated Learning

Hybrid Makes Better: Privacy-Preserving Medical Image Classi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhi Wang Haotian Chi Yonggang Li Yuxuan Zhang Shunrong Jiang Xiaojiang Du Mohsen Guizani School of Computer Science China University of Mining and Technology Xuzhou China School of Automation and Software Engineering Shanxi University Taiyuan China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients’ private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients’ privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients’ privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget.

关键词： Privacy Differential privacy Accuracy Federated learning Inference algorithms Classification algorithms Security Optimization Biomedical imaging Image classification

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational Science and technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

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computers, Materials & Continua 2021年第3期66卷 2655-2670页

作者： Jaiteg Singh Tanya Gera Farman Ali Deepak Thakur Karamjeet Singh Kyung-sup Kwak Chitkara University Institute of Engineering&Technology Chitkara UniversityPunjab140401India Department of Software Sejong UniversitySeoul05006South Korea Department of Computer Science and Engineering Thapar UniversityPatiala147001India Department of Information&Communication Engineering Inha UniversityIncheon22212South Korea

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8%of the market *** popularity of Android has drawn the attention of cybercriminals and malware *** malicious applications can steal sensitive information like contacts,read personal messages,record calls,send messages to premium-rate numbers,cause financial loss,gain access to the gallery and can access the user’s geographic *** surveys on Android security have primarily focused on types of malware attack,their propagation,and techniques to mitigate *** the best of our knowledge,Android malware literature has never been explored using information modelling ***,promulgation of contemporary research trends in Android malware research has never been done from semantic point of *** paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis(LSA).An extensive corpus of 843 articles on Android malware and security,published during 2009–2019,were processed using ***,the truncated singular Value Decomposition(SVD)technique was used for dimensionality ***,machine learning methods were deployed to effectively segregate prominent topic solutions with minimal *** to observed term and document loading matrix values,this five core research areas and twenty research trends were ***,potential future research directions have been detailed to offer a quick reference for information *** study concludes to the fact that Android security is crucial for pervasive Android *** analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near *** trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation,financial attacks and stealing user information.

关键词： Android security research trends latent semantic analysis vulnerabilities malware machine learning clustering

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Research and application of facial expression recognition based on attention mechanism 2

Research and application of facial expression recognition ba...

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2nd IEEE Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2021

作者： Zhang, Xin Chen, Zhuang Wei, Qingjie Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400065 China

ISBN: (纸本)9781728190181

The scale of the existing convolutional neural network is getting larger and larger, resulting in too large amounts of parameters, and the structure is not light enough. Moreover, existing convolutional neural networks are difficult to recognize the subtle changes of facial expressions and cannot extract facial expression features accurately. Therefore, the performance of facial expression recognition needs to be improved. To solve the above problems, a new deep learning network model is proposed for facial expression recognition. Based on the deep residual network, the attention mechanism module (Convolutional Block Attention Module, CBAM) is added to the last layer of convolution and the first layer of convolution of the network. The spatial attention mechanism and channel attention mechanism are used to suppress the unimportant feature information and focus on the effective feature information. In the bottom layer, the influence of other factors is eliminated as much as possible, and more attention is paid to the extraction of facial expression features, which enriches the learning of facial expression features and improves the accuracy of facial expression recognition. The method proposed in this paper has been tested and verified on two public data sets FER2013 and CK+, and the results prove that the method has a high accuracy rate. © 2021 IEEE.

关键词： Convolution

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Wireless Traffic Prediction with L2 Paradigm Optimized Federated Learning

Wireless Traffic Prediction with L2 Paradigm Optimized Feder...

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International Conference on Communication technology (ICCT)

作者： Fuwei Gao Chuanting Zhang Jingping Qiao Anming Dong School of Information Science and Engineering Shandong Normal University Jinan China School of Software Shandong University Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Decentralized wireless traffic prediction is crucial for intelligent communication systems. But, in the conventional federated learning framework, ensuring the global model’s performance depends on frequent local-server communications, leading to substantial communication costs. This paper introduces an L2 norm-optimized federated learning framework, namely FedL2. FedL2 develops an L2 norm optimization method and devises an L2 norm-based local model uploading decision mechanism and an adaptive aggregation strategy for wireless traffic prediction tasks. In the local model uploading decision mechanism, the cumulative difference between the local and global models is compared with the communication threshold, selecting the most valuable local models for upload to reduce the local-server communication frequency. Furthermore, in the adaptive aggregation strategy, the performance of the prediction model is enhanced by quantifying the contribution of local models to the global model and capturing the spatial correlations between local base stations. The effectiveness of FedL2 is demonstrated on two real-world datasets, showing that FedL2 outperforms benchmark algorithms in terms of predictive performance and communication efficiency.

关键词： Wireless communication Adaptation models Base stations Costs Correlation Data analysis Federated learning Optimization methods Predictive models Prediction algorithms

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FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework

FPPL: An Efficient and Non-IID Robust Federated Continual Le...

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IEEE International Conference on Big Data

作者： Yuchen He Chuyun Shen Xiangfeng Wang Bo Jin School of Computer Science and Technology East China Normal University Shanghai China Shanghai Formal-Tech Information Technology Co. Lt Shanghai China School of Software Engineering TongJi University Shanghai China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storage and computational burdens. In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed. The FPPL can collaboratively learn lightweight prompts augmented by prototypes without rehearsal. On the client side, a fusion function is employed to fully leverage the knowledge contained in task-specific prompts for alleviating catastrophic forgetting. Additionally, global prototypes aggregated from the server are used to obtain unified representation through contrastive learning, mitigating the impact of non-IID-derived data heterogeneity. On the server side, locally uploaded prototypes are utilized to perform debiasing on the classifier, further alleviating the performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate the effectiveness of FPPL, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. Code is available at: https://***/ycheoo/FPPL.

关键词： Continuing education Degradation Training Privacy Codes Federated learning Prototypes Contrastive learning Robustness Servers

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks Data models

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on computer and Information Processing technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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