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International Conference on Image, Vision and Computing (ICIVC)

作者： Yang Zhou Xingjian Wang Guoqiang Hu Yong Luo Zheng Chai Wei Xu Qiuming Liu Information and Communication Branch State Grid Jiangxi Electric Power Co Nanchang China Computer College Nanjing University of Posts and Telecomunications Nanjing China State Grid Nanchang Power Supply Company Nanchang China School of Software Jiangxi Normal University Nanchang China School of Software Engineering Jiangxi University of Science and Technology Nanchang China

At present, the safety monitoring of electric power sites mainly monitors the monitoring video through personnel, which has problems such as heavy workload, missed detection, and false alarms. In order to realize the intelligent recognition of the behavior of personnel at the electric power operation site, an abnormal behavior recognition method based on the key points of human skeleton is proposed, which can realize the real-time detection of the behavior of personnel at the operation site. First, the backbone network of YOLOv5s is improved for target detection, and the trajectory of the target is obtained by combining the DeepSort target tracking algorithm; then, the AlphaPose attitude estimation method is used to extract the target skeleton sequence from the trajectory; finally, the multi-partition spatiotemporal graph convolutional network is used to track the personnel Abnormal behavior identification. The experimental results show that the recognition accuracy rate of the algorithm reaches 93.5%, and the processing speed reaches 18.32 fps, which can identify abnormal behaviors of electric power workers in real time and accurately.

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An end-to-end graph attention network hashing for cross-modal retrieval 24

An end-to-end graph attention network hashing for cross-moda...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Huilong Jin Yingxue Zhang Lei Shi Shuang Zhang Feifei Kou Jiapeng Yang Chuangying Zhu Jia Luo College of Engineering Hebei Normal University College of Computer and Cyber Security Hebei Normal University State Key Laboratory of Media Convergence and Communication Communication University of China and Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education School of Computer Science Beijing University of Posts and Telecommunications and Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology State Key Laboratory of Media Convergence and Communication Communication University of China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Chongqing Research Institute Beijing University of Technology

ISBN: (纸本)9798331314385

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search. However, most existing hashing methods are often limited by uncomprehensive feature representations and semantic associations, which greatly restricts their performance and applicability in practical applications. To deal with this challenge, in this paper, we propose an end-to-end graph attention network hashing (EGATH) for cross-modal retrieval, which can not only capture direct semantic associations between images and texts but also match semantic content between different modalities. We adopt the contrastive language image pretraining (CLIP) combined with the Transformer to improve understanding and generalization ability in semantic consistency across different data modalities. The classifier based on graph attention network is applied to obtain predicted labels to enhance cross-modal feature representation. We construct hash codes using an optimization strategy and loss function to preserve the semantic information and compactness of the hash code. Comprehensive experiments on the NUS-WIDE, MIRFlickr25K, and MS-COCO benchmark datasets show that our EGATH significantly outperforms against several state-of-the-art methods.

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Multi-modal Protein Knowledge Graph Construction and Applications

arXiv

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arXiv 2022年

作者： Cheng, Siyuan Liang, Xiaozhuan Bi, Zhen Chen, Huajun Zhang, Ningyu School of Software Technology Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China

Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at https://***/proteinkg65. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding 39

HeGTa: Leveraging Heterogeneous Graph-enhanced Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Rihui Li, Yu Qi, Guilin Hu, Nan Li, Yuan-Fang Chen, Jiaoyan Wang, Jianan Chen, Yongrui Min, Dehai Bi, Sheng School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University) Ministry of Education China Monash University Australia The University of Manchester United Kingdom Alibaba Group China Law and Innovation Lab Law School Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Table Understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HeGTa, a heterogeneous graph (HG)-enhanced large language model (LLM) designed for few-shot TU tasks. This framework aligns structural table semantics with the LLM's parametric knowledge through soft prompts and instruction tuning. It also addresses complex tables with a multi-task pre-training scheme by incorporating novel multi-granularity self-supervised HG pretext tasks. We empirically demonstrate the effectiveness of HeGTa, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

AGMI: Attention-Guided Multi-omics Integration for Drug Resp...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Feng, Ruiwei Xie, Yufeng Lai, Minshan Chen, Danny Z. Cao, Ji Wu, Jian Zhejiang University Coll. of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Polytechnic Institute Hangzhou China University of Notre Dame Dept. of Computer Science and Engineering Notre Dame United States Zhejiang University Coll. of Pharmaceutical Sciences Hangzhou China Zhejiang University School of Public Health Hangzhou China

ISBN: (纸本)9781665401265

Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multiedge Graph (MeG) for each cell line, and then aggregates multi-omics features to predict drug response using a novel structure, called Graph edge-aware Network (GeNet). For the first time, our AGMI approach explores gene constraint based multi-omics integration for DRP with the whole-genome using GNNs. Empirical experiments on the CCLE and GDSC datasets show that our AGMI largely outperforms state-of-the-art DRP methods by 8.3%-34.2% on four metrics. Our data and code are available at https://***/yivan-WYYGDSG/AGMI. © 2021 IEEE.

关键词： Graph neural networks

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Mining conversation data for reward estimation in dialog policy learning 24

Mining conversation data for reward estimation in dialog pol...

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24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020

作者： Nguyen, Anh Duy Li, Minyi Vo, Bao Quoc Swinburne University of Technology School of Software and Electrical Engineering Australia RMIT University Australia Computer Science Australia

Reinforcement Learning approaches are commonly used for dialog policy learning. Reward function is an important part of RL algorithms which affects the training and quality of the policy. Handcrafted reward functions have been replaced by machine-learned reward functions in recent approaches with promising results. Such reward models compare agent actions with human actions, more human-like agent actions receive higher rewards. Reward models so far consider only the latest dialog turn when computing reward for agent action. In this paper, we hypothesize that using a sequence of turns to decide next agent action is more beneficial. Towards this claim we mine for common patterns in human-human task-oriented dialog data. The experiment results suggest that there are obvious patterns i.e., human-human communication in task-oriented dialogs follows some common sequences of actions. Such patterns can be potentially incorporated into reward models to train agents that could better imitate human behaviors. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

关键词： Information use

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics Engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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A Novel Throughput Based Temporal Violation Handling Strategy for Instance-Intensive Cloud Business Workflows 6th

A Novel Throughput Based Temporal Violation Handling Strateg...

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6th International Conference on Data science, ICDS 2019

作者： Wang, Futian Liu, Xiao Zhang, Wei Zhang, Cheng School of Computer Science and Technology Anhui University Hefei230601 China Software of Information Technology Deakin University Melbourne3125 Australia School of Software East China Institute of Technology Nanchang330013 China

ISBN: (纸本)9789811528095

Temporal violations take place during the batch-mode execution of instance-intensive business workflows running in the cloud environments which may significantly affect the QoS (Quality of Service) of cloud workflow system. However, currently most research in the area of workflow temporal QoS focuses on single scientific workflow rather than business workflow with a batch of parallel workflow instances. Therefore, how to handle temporal violations of instance-intensive cloud business workflows is a new challenge. To address such a problem, in this paper, we propose a novel throughput based temporal violation handling strategy. Specifically, firstly we present a definition of throughput based temporal violation handling point to determine where temporal violation handling should be conducted, and secondly we design a new method for adding necessary cloud computing resources for recovering detected temporal violations. Experimental results show that our temporal violation handling strategy can effectively handle temporal violations in cloud business workflow and thus guarantee satisfactory on-time completion rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Cloud computing

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RSAFormer: A method of polyp segmentation with region self-attention transformer

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computers in Biology and Medicine 2024年 172卷 108268-108268页

作者： Yin, Xuehui Zeng, Jun Hou, Tianxiao Tang, Chao Gan, Chenquan Jain, Deepak Kumar García, Salvador School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune412115 India Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models. © 2024 Elsevier Ltd

关键词： Decoding

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A cross Transformer for image denoising

arXiv

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arXiv 2023年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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