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arXiv 2024年

作者： Ma, Fei Li, Yukan Xie, Yifan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Information Technology Carleton University Canada

Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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CRDB: Commodity Recommendation Model Combining DGNN and BiGRU

CRDB: Commodity Recommendation Model Combining DGNN and BiGR...

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International Conference on Advanced computer Theory and engineering, ICACTE

作者： Jiajing Zhang Yunli Zhang Jinlan Chen Shufeng Chen School of Electronics and Information Anhui Jianzhu University Hefei China School of Mathematics & Physics Anhui Jianzhu University Hefei China School of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei China Basic Software Technology Center Beijing institute of computer technology and application Beijing China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

As potent filters of information, recommendation systems not only streamline the process for users to identify desirable products but also uncover latent interests in potential offerings. Although the current hybrid graph neural network and the recommendation algorithm of review text have achieved good results, the influence of timing information is not fully considered, and the problem of users' long and short-term interest deviation is ignored. At the same time, the contextual emotion features of long review text are not sufficiently extracted. To solve these problems, this paper proposes a product recommendation algorithm based on Dynamic Graph Neural Network(DGNN) and Bidirectional Gated Recurrent Unit(BiGRU). The algorithm uses a DGNN to extract the long and short-term correlation features between users and products. Then BERT-BiGRU is used to mine the sentiment features in the review text fully, and an attention mechanism is introduced to increase the weight of words that contribute a lot to semantic expression, to improve the accuracy of feature extraction. Finally, the user's long-term and short-term interest features and product features extracted by DGNN are combined individually with the user's sentiment and product features mined by BERT-BiGRU for matching user features and product features, to recommend products with a high feature matching degree to users. Experiments on Amazon datasets show that the CRDB model proposed in this paper reduces the mean root mean square error of LGDL prediction by 3.20% compared with the baseline model and 2.57% compared with the newer CRRG model.

关键词： Attention mechanisms Accuracy Reviews Heuristic algorithms Computational modeling Predictive models Feature extraction Data models Graph neural networks Timing

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Survey on Lie Group Machine Learning

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Big Data Mining and Analytics 2020年第4期3卷 235-258页

作者： Mei Lu Fanzhang Li the School of Software Engineering Jinling Institute of TechnologyNanjing 211169China the School of Computer Science and Technology Jiangsu Normal UniversityXuzhou 221000China the School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Lie group machine learning is recognized as the theoretical basis of brain intelligence,brain learning,higher machine learning,and higher artificial *** sets of Lie group matrices are widely available in practical *** group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed *** study aims to provide a comprehensive survey on recent advances in Lie group machine *** introduce Lie group machine learning techniques in three major categories:supervised Lie group machine learning,semisupervised Lie group machine learning,and unsupervised Lie group machine *** addition,we introduce the special application of Lie group machine learning in image *** work covers the following techniques:Lie group machine learning model,Lie group subspace orbit generation learning,symplectic group learning,quantum group learning,Lie group fiber bundle learning,Lie group cover learning,Lie group deep structure learning,Lie group semisupervised learning,Lie group kernel learning,tensor learning,frame bundle connection learning,spectral estimation learning,Finsler geometric learning,homology boundary learning,category representation learning,and neuromorphic synergy ***,this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine *** will enable researchers to comprehensively understand the state of the field,identify the most appropriate tools for particular applications,and identify directions for future research.

关键词： Lie group machine learning Lie group subspace orbit generation learning quantum group learning symplectic group learning Lie group fiber bundle learning

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Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

arXiv

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arXiv 2025年

作者： Hu, Yongting Lin, Yuxin Liu, Chengliang Luo, Xiaoling Dou, Xiaoyan Xu, Qihao Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Ophthalmology Department Shenzhen Second People’s Hospital Shenzhen China

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://***/HuYongting/WGLIN. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

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Improved distributed quantum algorithm for Simon’s problem

arXiv

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arXiv 2025年

作者： Li, Hao Qiu, Daowen Qiu, D. Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China

Simon’s problem is one of the most important problems demonstrating the power of quantum computing. Recently, an interesting distributed quantum algorithm for Simon’s problem was proposed, where a key sorting operator requiring a large number of qubits was employed. In this paper, we design an improved distributed quantum algorithm for Simon’s problem without using sorting operators, and our algorithm has the advantage of reducing half number of qubits required for a single computing node. Moreover, our algorithm does not involve the classical search process. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Exploration on the Construction of "Student-centered" Professional Three-Dimensional Practical Teaching System

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Journal of Contemporary Educational Research 2021年第1期5卷 78-81页

作者： Ji Xi Wei Jiang Xianglian Meng Software Engineering School of Computer Information&EngineeringChangzhou Institute of TechnologyChina

Talent cultivation is the primary task of *** general undergraduate colleges and universities should adhere to the basic guidelines of systematization,practicality and integration,continuously explore the concept of"studentcentered"talent cultivation,and build a threedimensional practical teaching system from three aspects:strengthening the planning and design of the three-dimensional practical teaching system;building an internal and external practical teaching platform;and improving the evaluation and guarantee system of practical teaching *** system of practical teaching quality evaluation and guarantee is *** order to improve the cultivation ability of applied talents in all aspects.

关键词： teaching centered guarantee

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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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Methods for Monitoring the Photovoltaic Panel: A Review

Methods for Monitoring the Photovoltaic Panel: A Review

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International Conference on Agro-Geoinformatics

作者： Yongheng Shang Haiyu He Zilun Zhang Xiaoyu Zhang Zhengwei Shen Jianwei Yin Xiaoye Miao JunHan Institute of Advanced Technology Zhejiang University Hangzhou China School of Software Engineering Zhejiang University Ningbo China School of Computer Science and Technology Zhejiang University Hangzhou China Research Institute of Hainan Zhejiang University Sanya China Polytechnic Institute Zhejiang University Hangzhou China State Grid Jiangsu Economic Research Institute Najing China

ISBN: (数字)9798350380606

ISBN: (纸本)9798350380613

With the rapid development of Photovoltaic (PV) solar energy technology, a vast array of PV systems have been installed globally. According to the latest reports from the International Energy Agency (IEA), an astonishing 420GW of solar power has been installed, representing a doubling of solar energy capacity from 2022 to 2023, equivalent to the entire world’s output in 2022. PV systems primarily fall into two categories: centralized large-scale PV power stations and distributed rooftop PV systems. Centralized PV power stations transmit their generated energy efficiently into the power grid through dedicated transmission lines. Conversely, distributed rooftop PV systems feed energy back into the grid via inverters positioned close to the roof-mounted panels. However, the increasing installation of photovoltaic panels poses significant challenges to the resilience and carrying capacity of the power grid, potentially compromising its safety, especially during peak daylight hours. Therefore, diligent monitoring of photovoltaic panel installations and precise estimation of the peak power fed into the national grid are of utmost importance. Currently, two primary methodologies are utilized for this purpose: hardware-based monitoring and remote sensing techniques. The hardware approach offers unparalleled accuracy by directly reading parameters from photovoltaic systems, but it comes with a substantial cost. In contrast, remote sensing methods, which employ satellites, video, or infrared imagery, provide a cost-effective, convenient, and standardized approach to estimating power generation across vast areas. This comprehensive review examines the various methodologies used for photovoltaic monitoring, aiming to provide a robust foundation for the future development of solar photovoltaic power generation. By doing so, it significantly contributes to the advancement and widespread adoption of clean energy solutions, paving the way for a greener and more sustainable future f

关键词： Photovoltaic systems Green energy Wireless sensor networks Image resolution Solar energy Planning Safety

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Detecting Memory-Related Bugs by Tracking Heap Memory Management of C++ Smart Pointers 36

Detecting Memory-Related Bugs by Tracking Heap Memory Manage...

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36th IEEE/ACM International Conference on Automated software engineering, ASE 2021

作者： Ma, Xutong Yan, Jiwei Wang, Wei Yan, Jun Zhang, Jian Qiu, Zongyan Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Peking University School of Mathematical Sciences China

ISBN: (纸本)9781665403375

The smart pointer mechanism, which is improved in the continuous versions of the C++ standards over the last decade, is designed to prevent memory-leak bugs by automatically deallocating the managed memory blocks. However, not all kinds of memory errors can be immunized by adopting this mechanism. For example, dereferencing a null smart pointer will lead to a software failure. Due to the lack of specialized support for smart pointers, the off-the-shelf C++ static analyzers cannot effectively reveal these *** this paper, we propose a static approach to detecting memory-related bugs by tracking the heap memory management of smart pointers. The behaviors of smart pointers are modeled during their lifetime to trace the state transitions of managed memory blocks. and the specially designed checkers are used to check the state changes according to five collected bug patterns. To evaluate the effectiveness of our approach, we implement it on the top of the Clang Static Analyzer. A set of handmade code snippets, as well as nine popular open-source C++ projects, are used to compare our tool against four other analyzers. The results show that our approach can successfully discover nearly all the built-in bugs. and 442 out of 648 reports generated from the open-source projects are true positives after manual reviewing, where the bugs of dereferencing null smart pointers are most frequently reported. To further confirm our reports, we design patches for Aria2, Restbed, MySQL and LLVM, in which seven pull requests covering 76 bug reports have been merged by the developers up to now. The results indicate that pointers should always be carefully used even after migrated to smart pointers and static analysis upon specialized models can effectively detect such bugs. © 2021 IEEE.

关键词： Static analysis

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