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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

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Computer Modeling in Engineering & sciences 2023年第10期137卷 879-902页

作者： Lemei Da Hai Liang Yong Ding Yujue Wang Changsong Yang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic TechnologyGuilin541004China Cyberspace Security Research Center Pengcheng LaboratoryShenzhen518055China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin541004China

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information *** to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security *** paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition *** UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative *** successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for *** are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,*** security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution *** also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)*** performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

关键词： Unmanned aerial vehicle cluster network certificateless signcryption certificateless signature batch verification source authentication data privacy blockchain

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JobViz:Skill-driven visual exploration of job advertisements

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Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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A Quick and Effective Loan Eligibility Analysis Model Using LightGBM 1st

A Quick and Effective Loan Eligibility Analysis Model Using ...

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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Gowdham, C. Ashwin, R. Raj, T. S. Senthil Department of Computer Science Hindustan Institute of Technology and Science Tamil Nadu Chennai India School Of Computing & Information Technology REVA University Bangalore560064 India

ISBN: (纸本)9783031759567

In recent times, it is extremely common for the people to make use of bank credits to satisfy their day to day needs. This is increasing rapidly. There are lot of schemes are provided by the banks. Lending money is one of the most crucial bank programmes. Banks generally provide loans to customers based on their requirements. But, due to some situation, some clients cannot repay their debts on time or they will make it delay because of lack of finances. There are so many people who take advantage of this and misuse the facilities that are provided by the bank. To overcome this issue, Banks should utilize certain methods to help in predicting the status of loan repayment. The financial system needs an appropriate modelling system to handle a variety of problems. For any bank, one of the most challenging tasks is the task of anticipating loan late payers. However, the banks will undoubtedly be able to minimise their non-profit assets in order to decrease their loss as a result of the expected bankruptcies. As a result, it enables the repayment of approved debts to proceed with no losses and contributes to the credit report. This demonstrates the significance of researching this technique to foresee lending decisions. The proposed method utilizes machine learning methods for predicting default on loans. This is because they provide greater accuracy on prediction challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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A Creative Research in the Field of Urban Fire Detection 5

A Creative Research in the Field of Urban Fire Detection

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Feng, Junming Wang, Ruixiang Xi, Chenyang Peng, Jiayu Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Information Science and Engineering Harbin China Harbin Institute of Technology School of Software Harbin China

ISBN: (纸本)9798350373820

As the most common disaster in human daily production and life, fire is very likely to threaten people's life and property safety. In order to avoid fire, this paper designs and implements a fire detection system based on video and YOLOv5 model, and describes the improvement of YOLOv5 compared with the previous generation YOLO model. Collect fire images through the network, use image processing technology to enhance the data, make fire data set and divide the data set by setting aside method. Then the self-made fire data set is used to train the fire detection model based on YOLOv5 model, and the model is improved by adding transfer learning, multi-scale training and other technologies to improve the fire detection rate, and the corresponding weight files are obtained. Finally, the training method of the model and the ability of the model to detect fire are expounded, and the innovative application of the model in the field of fire detection is realized. © 2024 IEEE.

关键词： Fires

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Boosting Metamorphic Relation Prediction via Code Representation Learning: An Empirical Study

Boosting Metamorphic Relation Prediction via Code Representa...

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作者： Zheng, Xuedan Jiang, Mingyue Quan Zhou, Zhi School of Computer Science and Technology Zhejiang Sci-Tech University Zhejiang Hangzhou China School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective testing technique having a broad range of applications. One key task for MT is the identification of metamorphic relations (MRs), which is a fundamental mechanism in MT and is critical to the automation of MT. Prior studies have proposed approaches for predicting MRs (PMR). One major idea behind these PMR approaches is to represent program source code information via manually designed code features and then to apply machine-learning–based classifiers to automatically predict whether a specific MR can be applied on the target program. Nevertheless, the human-involved procedure of selecting and extracting code features is costly, and it may not be easy to obtain sufficiently comprehensive features for representing source code. To overcome this limitation, in this study, we explore and evaluate the effectiveness of code representation learning techniques for PMR. By applying neural code representation models for automatically mapping program source code to code vectors, the PMR procedure can be boosted with learned code representations. We develop 32 PMR instances by, respectively, combining 8 code representation models with 4 typical classification models and conduct an extensive empirical study to investigate the effectiveness of code representation learning techniques in the context of MR prediction. Our findings reveal that code representation learning can positively contribute to the prediction of MRs and provide insights into the practical usage of code representation models in the context of MR prediction. Our findings could help researchers and practitioners to gain a deeper understanding of the strength of code representation learning for PMR and, hence, pave the way for future research in deriving or extracting MRs from program source code. © 2024 John Wiley & Sons Ltd.

关键词： Forecasting

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Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) for Depression Recognition with Facial Expression and Pupil Diameter

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IEEE Journal of Biomedical and Health Informatics 2023年第6期29卷 1-12页

作者： Yang, Minqiang Wu, Yushan Tao, Yongfeng Hu, Xiping Hu, Bin School of Information Science and Engineering Lanzhou University Lanzhou China Beijing Institute of Technology Beijing China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Gansu China

Facial expressions have been widely used for depression recognition because it is intuitive and convenient to access. Pupil diameter contains rich emotional information that is already reflected in facial video streams. However, the spatiotemporal correlation between pupillary changes and facial behavior changes induced by emotional stimuli has not been explored in existing studies. This paper presents a novel multimodal fusion algorithm - Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) to optimize the feature space and build a more robust depression recognition model, which innovatively combines the spatiotemporal relevance and complementarity between facial expression and pupil diameter features. TSTCCA explores the interaction between trials and obtains an effective fusion representation of two modalities from a trial subset related to depression. The experimental results show that TSTCCA achieves the highest accuracy of 78.81% with the subset of 25 trials. IEEE

关键词： Face recognition

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The Distribution of Zeros of Quasi-Polynomials

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 301-304页

作者： Honghai Wang Qing-Long Han the College of Information Science and Engineering Northeastern UniversityShenyang 110004China IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

PROFESSOR Yitang Zhang, a number theorist at the University of California, Santa Barbara, USA, has posted a paper on arXiv [1] that hints at the possibility that he may have solved the Landau-Siegel zeros conjecture. He has claimed that he has disproved a weaker version of the Landau-Siegel zeroes conjecture, an important problem related to the *** conjecture is that there are solutions to the zeta function that do not assume the form prescribed by the Riemann hypothesis. Inspired by his work, in this Perspective, we would like to discuss about the distribution of zeros of quasi-polynomials for linear time-invariant(LTI) systems with time delays.

关键词： conjecture assume invariant

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Constraint-aware and multi-objective optimization for micro-service composition in mobile edge computing

Constraint-aware and multi-objective optimization for micro-...

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作者： Wu, Jintao Zhang, Jingyi Zhang, Yiwen Wen, Yiping School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Technology Anhui University Anhui China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan China

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro-services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro-services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro-services constructed by ignoring the processing capabilities of different micro-services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro-services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro-service composition. In order to improve edge resource utilization and user experience on micro-service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro-services composition problem in MEC as a constrained multi-objective optimization problem. Then, a micro-service composition optimization method M3C combining graph search and branch-and-bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment. © 2023 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

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Analyze Factors Affecting Thread Consumption in a Garment and Develop a Machine Learning-based Prediction Model 8

Analyze Factors Affecting Thread Consumption in a Garment an...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Ubayawickrema, Vindya Arachchi, Samantha Mathara Pallewatta, Pandula Karunanayaka, Kasun Maithripala, Akila University of Colombo School of Computing Colombo Sri Lanka Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798331509170

The garment manufacturing industry faces challenges related to excess thread stock, leading to increased write-off expenses and environmental impact. This research utilizes Machine Learning (ML) techniques to predict sewing thread consumption in producing full underwear briefs, considering key factors such as garment style, fabric/seam thickness, stitch length, seam type, and estimated wastage. Multiple ML models, including Linear Regression, Ensemble Models (Random Forest, Gradient Boosting), and Artificial Neural Networks (ANN), were employed to predict thread consumption for two thread types (TKT 120 and TKT 160). These models are integrated into a user-friendly Streamlit interface to make real-Time predictions based on user input, improving inventory management decision-making. ANN models outperformed others, with a validation dataset MAPE of \mathbf{1. 2 1 \%} for TKT 120 and 1.99 \% for TKT 160. This study shows how ML optimises thread usage, reduces inventory costs, and supports garment manufacturing sustainability. The ANN model performs well, but refinement and feature exploration could improve prediction accuracy and generalization. The research emphasises ML and usercentric tools for industry resource management efficiency. © 2024 IEEE.

关键词： Garment industry

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