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arXiv 2024年

作者： Zhang, Ruoyu Cheng, Lei Zhang, Wei Guan, Xinrong Cai, Yueming Wu, Wen Zhang, Rui Ministry of Education School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen518055 China College of Communications Engineering Army Engineering University of PLA Nanjing210007 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 117583 Singapore

In this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead, we propose a tensor decomposition-based method for estimating the parameters of multi-path channel components, including their azimuth and elevation angles, as well as complex gain coefficients, thereby reconstructing the wireless channel between any pair of Tx and Rx MA positions in the Tx and Rx regions. First, we introduce a two-stage Tx-Rx successive antenna movement pattern for pilot training, such that the received pilot signals in both stages can be expressed as a third-order tensor. Then, we obtain the factor matrices of the tensor via the canonical polyadic decomposition, and thereby estimate the angle/gain parameters for enabling the channel reconstruction between arbitrary Tx/Rx MA positions. In addition, we analyze the uniqueness condition of the tensor decomposition, which ensures the complete channel reconstruction between the whole Tx and Rx regions based on the channel measurements at only a finite number of Tx/Rx MA positions. Finally, simulation results are presented to evaluate the proposed tensor decomposition-based method as compared to existing methods, in terms of channel estimation accuracy and pilot overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Fake Profile Identification Using Machine Learning

Fake Profile Identification Using Machine Learning

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： T. Sudhakar Bhuvana Chendrica Gogineni J. Vijaya School of Computer Science and Engineering VIT-AP University Vijayawada Andrapradesh School of Computer Science and Engineering Ira A. Fulton Schools of Engineering Arizona State University United States Department Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh

Online social networks have permeated our social lives in the current generation. These sites have allowed us to see our social lives differently than they did in the past. Nowadays we can connect with new friends and maintain relationships with them via social and personal activities become quite easy. Online Social Networks (OSN) are contributed in all areas such as Research in all domains, Job-related areas, Technology oriented areas, Health care, and business-oriented areas, Information gathering and data collection, and so on. One of the biggest problems on these social media platforms is fake profiles. Impersonating to be someone else and causing harm and defamation to the real person or advertising or popularizing removed propaganda on someone’s name to get more benefit is the motto of such profile creators. There have been many studies regarding these fake accounts and how can they be mitigated. Many approaches such as graph-level activities or feature analysis have been taken into consideration to identify fake profiles. These methods are outdated when compared to arising issues of these days. In this paper, we proposed a technique using machine learning for fake profile detection which is efficient. . The benchmark data set is collected and mixed with manual data first furthermore; a data cleaning technique is used to present the data more feasibly. Then the preprocessed data is used for model building with sufficient information such as profile name, profile ID name, number of followers, and so on. We added Cross validation process where many training algorithms are implemented on the given data and are then tested on the same data. Based on the experiments the RF classifier performed better than the other classification methods. The Random Forest classifier is used to forecast the profile whether is fake or genuine in an efficient way.

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Online Doctor Recommendation Method for Doctor-Patient Interaction data and Doctor Attributes

Online Doctor Recommendation Method for Doctor-Patient Inter...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Chen Yi Li Chusong Tang Minglu Shanghai Institution of Computing Technology Ltd. Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Distributed Computing and Data Security Shanghai China Shanghai Maritime University Shanghai China

At present, LDA topic model, Word2Vec model and knowledge graph have been used to recommend doctors for online consultation, and have achieved certain results. However, the single word vector model will lead to some deficiencies in the recommendation results. In online consultation, patients have short questions and doctors have long answers, which is difficult to deal with uniformly. Therefore, this paper combines Sentence BERT sentence vector model, ELMO word vector model and doctor attribute model to build a set of combined recommendation model for intelligent doctor recommendation. In the empirical study, this paper chooses a large number of test patients as the research object. The results show that compared with the single word vector model, the combined recommendation model proposed in this paper is better in the accuracy rate, mean reciprocal rank and coverage rate of doctor recommendation.

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A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

A Complex Belief Jensen-Shannon Divergence in Complex Eviden...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Linlu Gao Mingrui Yin Fuyuan Xiao Zehong Cao The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China STEM Mawson Lakes Campus University of South Australia Adelaide Australia

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority.

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Research on Task Allocation Model of Takeaway Platform Based on RWS-ACO Optimization Algorithm

Research on Task Allocation Model of Takeaway Platform Based...

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作者： Jianjun, Li Xiaodi, Xu Yu, Yang Fang, Yang School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China Heilongjiang Key Laboratory of E-Commerce and Information Processing Harbin150028 China East University of Heilongjiang Harbin150066 China

This paper research the task distribution of the takeaway platform, builds a task distribution model for the takeaway platform, and proposes the roulette ant colony algorithm (RWS-ACO) that combines the roulette algorithm with the ant colony algorithm, then conducts simulation experiments to solve the simulation data. The distribution plan is obtained, which verifies the effectiveness of the model and algorithm and achieves a win-win situation for multiple parties in the distribution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ant colony optimization

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Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases

arXiv

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arXiv 2023年

作者： Hu, Zheng Cai, Shi-Min Wang, Jun Zhou, Tao Compleχ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users’ preferences but easily ignore the representation of users’ dislikes. Nevertheless, modelling users’ dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users’ dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users’ historical interactions are divided into positive and negative interactions, used to model the user’s preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model’s performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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A Blockchain and IoT-Enabled Secure Health data Handling Framework

A Blockchain and IoT-Enabled Secure Health Data Handling Fra...

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International Conference on Advances in Biomedical engineering (ICABME)

作者： Mahmoud Elkhodr Ergun Gide Farnaz Farid Farhad Ahamed School of Engineering and Technology Central Queensland University Sydney Australia Department of Social Science Western Sydney University Parramatta Australia Department of Computer Mathematics and Data Science Western Sydney University Parramatta Australia

Secure data sharing while preserving user privacy is a critical challenge in Biomedical applications. This paper presents the Secure Health data Handling Framework (SHDHF), a comprehensive and multi-layered solution designed to address this challenge. SHDHF leverages blockchain technology and a hierarchical architecture consisting of four primary layers: User, Gateway, Blockchain, and Hospital Cloud. Each layer incorporates sublayers to provision secure data handling and user-controlled access. The framework enables real-time data collection from wearable IoT devices, secure data transmission, decentralised access control, and robust encryption for storage. SHDHF also provides a comprehensive solution for privacy-preserving biomedical data sharing, supporting personalised healthcare and biomedical research.

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SVM-based Predictive Modeling for Sustainable Solar Solutions in Off-Grid Areas

SVM-based Predictive Modeling for Sustainable Solar Solution...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Giriprasad. S S. Sangeetha G Manikandan G Bhuvaneswari B Meenakshi S. Sujatha Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rising need for sustainable energy solutions in off-grid regions has prompted the investigation of creative predictive modeling methodologies. This research examines Support Vector Machine (SVM) techniques to enhance solar energy production and use. SVM, a powerful machine learning instrument, assesses diverse environmental variables, such as solar radiation, temperature, and humidity, to precisely forecast solar energy production. It presents a SVM model to forecast solar energy production in off-grid areas, optimizing resource distribution and improving sustainability with analysis of environmental variations and energy consumption data. The model improves decision-making for deploying solar systems in distant areas using historical data and real-time observations. The prediction skills of SVM enable the creation of customized solar energy systems that address the specific energy requirements of off-grid settlements. This technique enhances sustainability by optimizing solar installation efficiency, decreasing dependence on fossil fuels, and mitigating environmental consequences. The results indicate that SVM-based predictive modeling substantially enhances the dependability and efficiency of solar energy systems, making them more feasible for off-grid applications. It highlights the capacity of advanced machine learning methodologies to promote sustainable energy solutions, hence aiding the worldwide shift towards renewable energy sources.

关键词： Support vector machines Renewable energy sources Energy consumption Solar energy Production Predictive models Market research Real-time systems data models Sustainable development

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability database.

关键词： Privacy data privacy Distributed ledger databases Smart contracts Fabrics Blockchains Software reliability Security Software development management

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Effects of Adaptive Random Deceleration on Traffic Flow

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Journal of Applied Mathematics and Physics 2023年第3期11卷 618-628页

作者： Qi Wang Shu Zhu Liuhua Zhu School of computer Jiangsu University of Science and Technology Zhenjiang China School of Physics and Telecommunication Engineering Yulin Normal University Yulin China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin China Optoelectronic Information Research Center Yulin Normal University Yulin China

Based on the Nagel-Schreckenberg model, an improved cellular automaton traffic flow model is proposed, in which the random deceleration probability of each vehicle is no longer fixed, but is adaptively adjusted according to the local traffic density in its vision and its current velocity. The numerical simulation results show that the maximum traffic capacity of the improved model under the same parameters is greater than that of the Nagel-Schreckenberg model, and is closer to the measured data. In addition, the traffic flow and vehicle velocity under different meteorological conditions are simulated by using the improved model, and the synchronized flow phenomenon consistent with the actual traffic is reproduced. Meanwhile, the results show that under the same parameters, when the traffic density is equal to 0.3, the traffic flow of the improved model increases by about 11% compared with the original model, and when the traffic density increases to 0.6, the traffic flow increases by about 27%. .

关键词： Traffic Flow Cellular Automaton Adaptive Random Deceleration Synchronized Flow

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