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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models

arXiv

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arXiv 2024年

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Overview of Medical Data Privacy Protection based on Blockchain Technology 7

Overview of Medical Data Privacy Protection based on Blockch...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： Chen, Lin Yu, Qingchun Liang, Wei Cai, Jiahong Zhu, Hangyu Xie, Songyou Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Xiangtan China Shenzhen Research Institute Sun Yat-sen University and Novel Software Technology ShenZhen China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchain has the characteristics of decentralization, traceability, and immutability, which can provide new ideas for fine-grained secure access to medical research. This article first introduces blockchain and blockchain-based privacy protection technology;then analyzes the advantages and disadvantages of electronic medical records, and introduces the current development status of electronic medical records based on blockchain technology;then from data encryption, access the three aspects of control and transaction anonymity introduce the medical data privacy protection method based on blockchain technology;finally, the full text is summarized and prospected. © 2022 IEEE.

关键词： Blockchain

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An Ensemble Model for software Defect Prediction 2

An Ensemble Model for Software Defect Prediction

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ali, Amad Rizwan Ur Rehman, Attique Nawaz, Ali Ali, Tahir Muhammad Abbas, Muhammad UserMaven Inc United States University of Sialkot Department of Software Engineering Sialkot Pakistan College of Electrical and Mechanical Engineering National University of Science and Technology Rawapindi Pakistan Gulf University for Science and Technology Department of Computer Science College of Science and Arts Kuwait

ISBN: (纸本)9781665498197

software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resources of software. Therefore, it is important to construct the procedure which is not only able to perform the efficient testing but also minimizes the utilization of project resources. The goal of software testing is to find maximum defects in the software system. As world is continuously moving toward data driven approach for making important decision. Therefore, in this research paper we performed the machine learning analysis on the publicly available datasets and tried to achieve the maximum accuracy. The major focus of the paper is to apply different machine learning techniques on the datasets and find out which technique produce efficient result. Particularly, we proposed an ensemble learning models and perform comparative analysis among KNN, Decision tree, SVM and Naïve Bayes on different datasets and it is demonstrated that performance of Ensemble method is more than other methods in term of accuracy, precision, recall and F1-score. The classification accuracy of ensemble model trained on CM1 is 98.56%, classification accuracy of ensemble model trained on KM2 is 98.18% similarly, the classification accuracy of ensemble learning model trained on PC1 is 99.27%. This reveals that ensemble learning is more efficient method for making the defect prediction as compared other techniques. © 2022 IEEE.

关键词： software testing

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Multiset Transformer: Advancing Representation Learning in Persistence Diagrams

arXiv

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arXiv 2024年

作者： Wang, Minghua Huang, Ziyun Xu, Jinhui Department of Computer Science and Engineering State University of New York at Buffalo United States Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States

To improve persistence diagram representation learning, we propose Multiset Transformer. This is the first neural network that utilizes attention mechanisms specifically designed for multisets as inputs and offers rigorous theoretical guarantees of permutation invariance. The architecture integrates multiset-enhanced attentions with a pool-decomposition scheme, allowing multiplicities to be preserved across equivariant layers. This capability enables full leverage of multiplicities while significantly reducing both computational and spatial complexity compared to the Set Transformer. Additionally, our method can greatly benefit from clustering as a preprocessing step to further minimize complexity, an advantage not possessed by the Set Transformer. Experimental results demonstrate that the Multiset Transformer outperforms existing neural network methods in the realm of persistence diagram representation learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Study of EMG Signals Using QNET Myoelectric Board and LabVIEW software 4

A Study of EMG Signals Using QNET Myoelectric Board and LabV...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Alharbi, Hadeel Fahad Patel, Akshet Dasari, Uday Randhawa, Princy University of ha'Il Hail College of Computer Science and Engineering Department of Computer Science and Software Engineering 1234 Saudi Arabia Manipal University Jaipur Department of Mechatronics Engineering Jaipur India Manipal University Jaipur Department of Information Technology Engineering Jaipur India

ISBN: (纸本)9781728189390

This project's aim is to monitor the electrical activity of the user's muscle cells using the QNET Myoelectric Board and NI ELVIS II+ board. An opto-isolated sensor and a grounding strap are included in this electromyograph. The board includes an on-board signal processing and filtering mechanism, as well as a PCI-based data collection system. The board is connected using the LabVIEW application, which stands for Laboratory Virtual Instrument engineering Workbench (LabVIEW). LabVIEW is a graphical programming language and hence a direct connection between the user and the board is possible. The data is acquired, and five assessment criteria are applied to it. Graphs are shown based on time stamps for two participants (male and female), and the analyzed data is compiled to draw crucial conclusions. © 2022 IEEE.

关键词： Machine learning

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D2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving

arXiv

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arXiv 2025年

作者： Wang, Haodong Zhou, Qihua Hong, Zicong Guo, Song Hong Kong University of Science and Technology Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

The mixture of experts (MoE) model is a sparse variant of large language models (LLMs), designed to hold a better balance between intelligent capability and computational overhead. Despite its benefits, MoE is still too expensive to deploy on resource-constrained edge devices, especially with the demands of on-device inference services. Recent research efforts often apply model compression techniques, such as quantization, pruning and merging, to restrict MoE complexity. Unfortunately, due to their predefined static model optimization strategies, they cannot always achieve the desired quality-overhead trade-off when handling multiple requests, finally degrading the on-device quality of service. These limitations motivate us to propose the D2MoE, an algorithm-system co-design framework that matches diverse task requirements by dynamically allocating the most proper bit-width to each expert. Specifically, inspired by the nested structure of matryoshka dolls, we propose the matryoshka weight quantization (MWQ) to progressively compress expert weights in a bit-nested manner and reduce the required © 2025, CC BY.

关键词： Quality of service

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DeNC: Unleash Neural Codecs in Video Streaming with Diffusion Enhancement 39

DeNC: Unleash Neural Codecs in Video Streaming with Diffusio...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qihua Li, Ruibin Guo, Jingcai Huang, Yaodong Xu, Zhenda Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug- and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder’s compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder’s compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Video streaming

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Spark Spectrum Allocation for D2D Communication in Cellular Networks

引用

computers, Materials & Continua 2022年第3期70卷 6381-6394页

作者： Tanveer Ahmad Imran Khan Azeem Irshad Shafiq Ahmad Ahmed TSoliman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Electrical and Electronic Engineering Auckland University of TechnologyAuckland1010New Zealand Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabad44000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The device-to-device(D2D)technology performs explicit communication between the terminal and the base station(BS)terminal,so there is no need to transmit data through the BS *** establishment of a short-distance D2D communication link can greatly reduce the burden on the BS *** present,D2D is one of the key technologies in 5G technology and has been studied in depth.D2D communication reuses the resources of cellular users to improve system key parameters like utilization and ***,repeated use of the spectrum and coexistence of cellular users can cause co-channel *** at the interference problem under the constraint of fair resource allocation and improving the system throughput,this paper proposes an effective resource optimization scheme based on the firework *** main idea is to expand the weighted sum rate and convert the allocated resource expression into fireworks to determine the correlation *** simulation results show that,compared with the existing scheme,this scheme improves system performance by reducing interference.

关键词： D2D communication 5G technology spectrum allocation

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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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