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IEEE International Conference on data science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, V. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： Value engineering

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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CASSOCK: Viable Backdoor Attacks against DNN in the Wall of Source-Specific Backdoor Defences

arXiv

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arXiv 2022年

作者： Wang, Shang Gao, Yansong Fu, Anmin Zhang, Zhi Zhang, Yuqing Susilo, Willy Liu, Dongxi School of Computer Science and Engineering Nanjing University of Science and Technology China CSIRO’s Data61 Australia University of Western Australia Australia University of Chinese Academy of Sciences China School of Computing and Information Technology University of Wollongong Australia

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SABAs. Nonetheless, existing SSBAs suffer from two major limitations. First, they can hardly achieve a good trade-off between ASR (attack success rate) and FPR (false positive rate). Besides, they can be effectively detected by the state-of-the-art (SOTA) countermeasures (e.g., SCAn [40]). To address the limitations above, we propose a new class of viable source-specific backdoor attacks coined as CASSOCK. Our key insight is that trigger designs when creating poisoned data and cover data in SSBAs play a crucial role in demonstrating a viable source-specific attack, which has not been considered by existing SSBAs. With this insight, we focus on trigger transparency and content when crafting triggers for poisoned dataset where a sample has an attacker-targeted label and cover dataset where a sample has a ground-truth label. Specifically, we implement CASSOCKTrans that designs a trigger with heterogeneous transparency to craft poisoned and cover datasets, presenting better attack performance than existing SSBAs. We also propose CASSOCKCont that extracts salient features of the attacker-targeted label to generate a trigger, entangling the trigger features with normal features of the label, which is stealthier in bypassing the SOTA defenses. While both CASSOCKTrans and CASSOCKCont are orthogonal, they are complementary to each other, generating a more powerful attack, called CASSOCKComp, with further improved attack performance and stealthiness. To demonstrate their viability, we perform a comprehensive evaluation of the three CASSOCK-based attacks on four popular datasets (i.e., MNIST, CIFAR10, GTSRB and LFW) and three

关键词： Deep neural networks

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Analytical study for cyber threat intelligence (CTI)

Analytical study for cyber threat intelligence (CTI)

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7th IET Smart Cities Symposium (SCS 2023)

作者： Ibrahim R. Alkhateeb Qasem Abu Al-Haija Saleh Abu-Soud Department of Cybersecurity/ Data Science King Hussein School of Computing Sciences Prince Sumaya University for Technology P.O. Box 1438 Amman 11941 Jordan Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan

Cyber Threat Intelligence (CTI) involves collecting and analyzing cyber security data using advanced algorithms and rigorous techniques to identify and address potential threats, harmful incidents, and existing vulnerabilities that cybercriminals exploit. Threat intelligence facilitates faster and well-informed security decisions while understanding threat actors' motives, targets, and attack patterns. Defenders employ diverse strategies to prevent, detect, and disrupt incoming attacks. However, traditional security solutions are inadequate for today's complex security landscape, often proving ineffective against the rapid proliferation of cyber threats. Automating information sharing related to cyber threats presents new challenges for researchers and analysts, requiring them to detect deceptive practices and produce accurate, timely, and pertinent intelligence. This research explores the profound impact of Artificial Intelligence (AI) and Machine Learning (ML) in cyber security, including their various types, applications, challenges, and promising future. Leveraging AI knowledge, particularly ML technology, organizations can strengthen their defenses against malicious threats, reduce cyber risks, and establish a dynamic, automated, and up-to-date security system by analyzing security data.

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A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems

arXiv

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arXiv 2025年

作者： Jin, Yichen Lin, Qingfeng Li, Yang Zhu, Hancheng Cheng, Bingyang Wu, Yik-Chung Zhang, Rui Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China School of Computing and Information Technology Great Bay University Dongguan523000 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation in MA-aided systems faces challenges due to the non-convex and coupled constraints on antenna positions. This paper systematically reveals the challenges posed by the minimum antenna separation distance constraints. Furthermore, we propose a penalty optimization framework for resource allocation under such new constraints for MA-aided systems. Specifically, the proposed framework separates the non-convex and coupled antenna distance constraints from the movable region constraints by introducing auxiliary variables. Subsequently, the resulting problem is efficiently solved by alternating optimization, where the optimization of the original variables resembles that in conventional resource allocation problem while the optimization with respect to the auxiliary variables is achieved in closed-form solutions. To illustrate the effectiveness of the proposed framework, we present three case studies: capacity maximization, latency minimization, and regularized zero-forcing precoding. Simulation results demonstrate that the proposed optimization framework consistently outperforms state-of-the-art schemes. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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Efficient and Anonymous Authentication for Healthcare Service with Cloud Based WBANs

Efficient and Anonymous Authentication for Healthcare Servic...

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Yang, Xu Yi, Xun Nepal, Surya Khalil, Ibrahim Huang, Xinyi Shen, Jian Fujian Normal University School Of Mathematics And Statistics Fuzhou China RMIT University School Of Computing Technologies Melbourne Australia CSIRO Data61 Sydney Australia Fujian Normal University College Of Computer And Cyber Security Fuzhou China Nanjing University Of Information Science And Technology School Of Computer And Software Jiangsu Nanjing China

ISBN: (纸本)9781665427197

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and a high level of medical service. Specifically, to make sure those people with a high incidence of chronic diseases, including hypertension, diabetes, and cardiovascular diseases, are taking care of which further reduces social costs. Thus, both industry and academia in recent years pay widely attention to WBANs technology. After years of research, security, privacy, and efficiency of the healthcare services in WBANs have remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. To ensure the security and privacy of patients' sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs. © 2021 IEEE.

关键词： Efficiency

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Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

Evaluating the Use of Large Language Model to Identify Top R...

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Chen, Huiqin Huang, Fangwan Liu, Xuanyun Tan, Yinli Zhang, Dihua Li, Dongqi Borchert, Glen M. Tan, Ming Huang, Jingshan Sun Yatsen University Department of Pediatrics the Third Affiliated Hospital Guangzhou China College of Computer and Data Science Fuzhou University Fuzhou China Sun Yat-sen University Department of Nephrology the First Affiliated Hospital Guangzhou China University of California Irvine Department of Economics and School of Information and Computer Sciences Irvine United States College of Medicine University of South Alabama Department of Pharmacology Mobile United States Institute of Biochemistry and Molecular Biology Institute of Biomedical Sciences China Medical University Taichung Taiwan School of Computing College of Medicine University of South Alabama Mobile United States

ISBN: (纸本)9798350302301

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology. © 2023 IEEE.

关键词： Stem cells

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yu Sun Weisheng Li Lele Xie Xin Lu Frederic Dufaux Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science University of Central Arkansas Computing Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University CNRS CentraleSupélec Laboratoire des Signaux et systèmes Université Paris-Saclay School of Information and Communication Engineering University of Electronic Science and Technology of China

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

关键词： Machine learning algorithms Costs Signal processing algorithms Machine learning Gaussian distribution Prediction algorithms Encoding

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High Performance computing Framework for Variable Selection on Genome-wide Association Studies

High Performance Computing Framework for Variable Selection ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Dehui Wei Qipeng Xie Xia Li Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University IoT Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science ETH Zurich Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers.

关键词： Accuracy High dimensional data Input variables High performance computing Instruments Noise Genomics Feature extraction Bioinformatics Optimization

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