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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

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computers in Biology and Medicine 2024年 174卷 108484-108484页

作者： Ma, Xinqian Li, Zhen Du, Zhenya Xu, Yan Chen, Yifan Zhuo, Linlin Fu, Xiangzheng Liu, Ruijun School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325027 China Guangzhou Xinhua University Guangzhou510520 China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Guizhou Duyun558000 China Institute of Computational Science and Technology Guangzhou University Guangzhou510000 China College of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China School of Software Beihang University Beijing China

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at:

关键词： Genes

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MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

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IEEE Transactions on Image Processing 2025年 34卷 3593-3608页

作者： Liu, Yepeng Sun, Zhichao Yu, Baosheng Zhao, Yitian Du, Bo Xu, Yongchao Cheng, Jun Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan 430070 China Nanyang Technological University Lee Kong Chian School of Medicine 308232 Singapore Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Zhejiang Ningbo 315211 China Institute for Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Fusionpolis Way #21-01 Connexis South Tower 138632 Singapore

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pre-trained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available. © 1992-2012 IEEE.

关键词： feature descriptor optimization graph neural networks Multimodal image matching stable diffusion

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Chinese Text Sentiment Analysis Model Based on BERT and BiTCN

Chinese Text Sentiment Analysis Model Based on BERT and BiTC...

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International Conference on Advanced computer Theory and engineering, ICACTE

作者： Jinlan Chen Jian Zhang Jiajing Zhang Shufeng Chen School of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei China School of Mathematics & Physics Anhui Jianzhu University Hefei China School of Electronics and Information 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 one of the research hotspots in natural language processing, text sentiment analysis has received extensive attention in recent years. Aiming at the problem that the unidirectional TCN in BERT-TCN cannot fully extract the contextual semantic features of the word vectors of text, a text sentiment analysis model based on BERT and Bidirectional Temporal Convolutional Network (BiTCN) is proposed. The model first uses the BERT model to obtain the word vector representations of text, and then the word vectors are input into BiTCN to extract contextual semantic features from the forward and backward directions of the word vectors. Finally, the contextual semantic features are input into a softmax classifier for sentiment classification. Experiments show that compared with the BERT-TCN model, the BERT-BiTCN model has improved accuracy, precision, recall, and F1 value.

关键词： Analytical models Sentiment analysis Computational modeling Semantics Bidirectional control Feature extraction Vectors Encoding Data models Context modeling

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Implementing an NFT by Developing a Smart Contract in OpenSea Marketplace 6th

Implementing an NFT by Developing a Smart Contract in OpenSe...

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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Shanmugam, Udhayakumar Krisha, Kotte Mohan Nancy John Mari Vienni, Silviya Nandhini, D. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Software Analyst Virtusa Corporation Chennai India School of Information and Communications Technology Saskatchewan Polytechnic College SaskatoonSK Canada Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031840586

Blockchain-based security applications have tremendously increased in the recent past with a decentralized network being the best way to implement a secure environment. Blockchain is one such implementation methodology that is considered the biggest disruptive technology. Smart contract-based transactions and assets have a high value and work because of a consensus mechanism without centralized trusted authority. Developing a smart contract for non-fungible tokens has various scopes and our proposed work uses various tools and libraries to achieve it. Brownie is used to implement the smart contract and Infura is used for the Ethereum node. Opensea marketplace is used for rendering the transaction. An Interplanetary distributed file system is used to store the NFTs. The work proposes the coordination of multiple tools to create a blockchain that is reliable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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A Review of Human Emotion Synthesis Based on Generative technology

arXiv

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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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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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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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