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Disruptive Technologies (ICDT), International Conference on

作者： Mullaikodi C Sharath Kumar Jagannathan Jayanthi. N Rakhi Singh Yesha Tomar B Jaison Department of ECE J. N. N Institute of Engineering Tiruvallur Tamilnadu India Data Science Institute Saint Peter's University New Jersery USA School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Faculty of Commerce and Management SGT University Gurugrm Haryana India School of Management Doon University Dehradun Uttarakhand India Department of Computer Science and Engineering RMK Engineering College Chennai Tamilnadu India

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting.

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Multi-Behavior Recommendation with Cascading Graph Convolution Networks

arXiv

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arXiv 2023年

作者： Cheng, Zhiyong Han, Sai Liu, Fan Zhu, Lei Gao, Zan Peng, Yuxin Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences China School of Computing National University of Singapore Singapore School of Information Science and Engineering Shandong Normal University China Wangxuan Institute of Computer Technology Peking University China Peng Cheng Laboratory China

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior’s embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively. © 2023, CC BY-NC-SA.

关键词： Collaborative filtering

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A Remote Quantum Error-correcting Code Preparation Protocol on Cluster State

arXiv

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arXiv 2023年

作者： Zhao, Qiang Mao, Haokun Qiao, Yucheng Abd El-Latif, Ahmed A. Li, Qiong Information Countermeasure Technique Institute School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin150001 China Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong School of Information Engineering Guilin University of Electronic Science and Technology Guilin541004 China EIAS Data Science Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shibin Al Kawm32511 Egypt

The blind quantum computation (BQC) protocol allows for privacy-preserving remote quantum computations. In this paper, we introduce a remote quantum error correction code preparation protocol for BQC using a cluster state and analyze its blindness in the measurement-based quantum computation model. Our protocol requires fewer quantum resources than previous methods, as it only needs weak coherent pulses, eliminating the need for quantum memory and limited quantum computing. The results of our theoretical analysis and simulations show that our protocol requires fewer quantum resources compared to non-coding methods with the same qubit error rate. © 2023, CC BY.

关键词： Error correction

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A method on Face Recognition of Contaminated Small Sample

A method on Face Recognition of Contaminated Small Sample

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Jie Li, Zhuoyang Xu, Xiu Jing, Guoliang China University of Mining and Technology Engineering Research Center of Mine Digitalization of Ministry of Education School of Computer Science and Technology Xuzhou China Institute of Medical Photonics Beihang University Beijing Advanced Innovation Center for Biomedical Engineering Beijing China China University of Mining and Technology School of Computer Science and Technology Xuzhou China Shandong Polytechnic College Department of Aviation Jining China

ISBN: (纸本)9781665401265

In recent years, face recognition technology has been rapidly promoted and used because of its advantages of convenience, non-contact and good recognition performance, and achieved good results. But for the polluted situation, such as expression change, illumination influence, posture change, occlusion, etc., because the collected image lost the original intrinsic characteristics, seriously affect the effect of face recognition. In recent years, the SRC method and its variants have good robustness to shaded and contaminated samples. However, when certain training samples and test samples are contaminated, the performance will degrade. The method based on low rank matrix recovery (LRMR) can effectively deal with the simultaneous contamination of training samples and test samples. However, they either ignore relationships between similar samples or fail to learn a compact dictionary from contaminated training data. To solve these problems, a locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is developed and applied to robust face recognition. In this paper, the theoretical and experimental research of locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is carried out. A low-rank representation is introduced to deal with possible contamination of training and test data, and local constraints are introduced to identify the inherent manifold structure of training data. Under local constraints, similar samples often have similar representations, and the learned representations can be directly used for classification. At the same time, this method can learn a compact dictionary with better refactoring and recognition ability. The superiority of LCLRRDL algorithm is verified by comparing it with support vector machine (SVM) and Sparse representation (SRC) for contaminated small sample face recognition. By comparing the best recognition effect and classification time of the three algorithms, it is foun

关键词： Face recognition

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

An Overview of Video Tampering Detection Techniques: State-o...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Saroj Kumar Pandey Lalan Kumar Gaurav Kumar Ankit Kumar Kamred Udham Singh Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as “video tampering.” A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh mov

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Swarm-Net: Firmware Attestation in IoT Swarms using Graph Neural Networks and Volatile Memory

arXiv

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arXiv 2024年

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab The Department of Electrical and Computer Engineering National University of Singapore Singapore117417 Singapore The Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India The School of Computing University of Nebraska-Lincoln LincolnNE68588 United States

The Internet of Things (IoT) is a network of billions of interconnected, primarily low-end embedded devices. Despite large-scale deployment, studies have highlighted critical security concerns in IoT networks, many of which stem from firmware-related issues. Furthermore, IoT swarms have become more prevalent in industries, smart homes, and agricultural applications, among others. Malicious activity on one node in a swarm can propagate to larger network sections. Although several Remote Attestation (RA) techniques have been proposed, they are limited by their latency, availability, complexity, hardware assumptions, and uncertain access to firmware copies under Intellectual Property (IP) rights. We present Swarm-Net, a novel swarm attestation technique that exploits the inherent, interconnected, graph-like structure of IoT networks along with the runtime information stored in the Static Random Access Memory (SRAM) using Graph Neural Networks (GNN) to detect malicious firmware and its downstream effects. We also present the first datasets on SRAM-based swarm attestation encompassing different types of firmware and edge relationships. In addition, a secure swarm attestation protocol is presented. Swarm-Net is not only computationally lightweight but also does not require a copy of the firmware. It achieves a 99.96% attestation rate on authentic firmware, 100% detection rate on anomalous firmware, and 99% detection rate on propagated anomalies, at a communication overhead and inference latency of ∼ 1 second and ∼ 10−5 seconds (on a laptop CPU), respectively. In addition to the collected datasets, Swarm-Net's effectiveness is evaluated on simulated trace replay, random trace perturbation, and dropped attestation responses, showing robustness against such threats. Lastly, we compare Swarm-Net with past works and present a security analysis. © 2024, CC BY.

关键词： Firmware

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A Detailed Investigation on Potential Impact of Quantum computing on Improving Artificial Intelligence

A Detailed Investigation on Potential Impact of Quantum Comp...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： G. Nagaraj Niraj Upadhayaya Atheer Matroud N Sabitha Reshma V.K Regonda Nagaraju Department of Mechanical Engineering Sethu Institute of Technology Tamilnadu India Department of CSE J.B. Institute of Engineering & Technology Hyderabad India School of Engineering and Computing De Montfort University Dubai United Arab Emirates Department of Computer Science and Engineering Maturi Venkata Subba Rao (MVSR) Engineering College Telangana India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Secunderabad Telangana India

Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform.

关键词： computers Quantum computing Quantum algorithm Surgery Collaboration Transforms computer industry

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Towards Quantum Federated Learning

arXiv

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arXiv 2023年

作者： Ren, Chao Yan, Rudai Zhu, Huihui Yu, Han Xu, Minrui Shen, Yuan Xu, Yan Xiao, Ming Dong, Zhao Yang Skoglund, Mikael Niyato, Dusit Kwek, Leong Chuan School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Division of Information Science and Engineering KTH Royal Institute of Technology Sweden City University of Hong Kong China Center for Quantum Technologies National University of Singapore Singapore Nanyang Technological University Singapore

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

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A Novel computerized Cognitive Test for the Detection of Mild Cognitive Impairment and Its Association with Neurodegeneration in Alzheimer’s Disease Prone Brain Regions

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Advances in Alzheimer's Disease 2023年第3期12卷 38-54页

作者： Rosie E. Curiel Cid D. Diane Zheng Marcela Kitaigorodsky Malek Adjouadi Elizabeth A. Crocco Mike Georgiou Christian Gonzalez-Jimenez Alexandra Ortega Mohammed Goryawala Natalya Nagornaya Pradip Pattany Efrosyni Sfakianaki Ubbo Visser David A. Loewenstein Center for Cognitive Neuroscience and Aging and Department of Psychiatry and Behavioral Sciences Miller School of Medicine University of Miami Miami Florida USA Center for Advanced Technology and Education Department of Electrical and Computer Engineering College of Engineering and Computing Florida International University Miami Florida USA Department of Radiology and Nuclear Medicine Miller School of Medicine University of Miami Miami Florida USA Department of Computer Science University of Miami Miami Florida USA

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.

关键词： Mild Cognitive Impairment Proactive Semantic Interference MRI Volume Cortical Thickness LASSI-L

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