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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Wu, Bo Mao, Yingchi Chi, Fudong Liu, Yingjie Ding, Silong Qi, Rongzhi Hohai University College of Computer Science and Software Engineering Nanjing China Huaneng Lancang River Hydropower Inc Technology Research and Development Center Kunming China

ISBN: (纸本)9798331527235

Knowledge Graph Reasoning based on Representation Learning maps the entities and relations into a vector space, assessing entity-pair similarities to deduce unknown facts. However, existing methods focus solely on the local importance of entities and ignore isolated entities, leading to the issue of missing feature information. To solve this, we propose Knowledge Representation Learning based on Global Information (KRLGI). KRLGI adopts an attention-based biased random walk algorithm to obtain global information and determine the importance of global entities. The global entity importance goes through a conversion into attention weights, and these weights are integrated with the local entity importance. Subsequently, the local and global entity importance can be used together to represent entity embeddings. Globally integrated representations can reveal richer semantics and enhance reasoning capabilities. Experimental results indicate that KRLGI outperforms other baselines in reasoning accuracy on four public datasets. Notably, on the FB15k-237 dataset, KRLGI shows significant improvements over the best baseline, with increases of 6.37% in MRR, 2.5% in HITS@1, and 5.5% in HITS@10. © 2024 IEEE.

关键词： Knowledge graph

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Deep Structured Clustering of Short Text 9th

Deep Structured Clustering of Short Text

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9th CCF Conference on Big Data, BigData 2021

作者： Wu, Junxian Chen, Xiaojun Cai, Shaotian Li, Yongqi Wu, Huzi College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China

ISBN: (纸本)9789811697081

Short text clustering is beneficial in many applications such as articles recommendations, user clustering and event exploration. Recent works of short text clustering boost the clustering results by improving the representation of short text with deep neural networks, such as CNN and autoencoder. However, existing short text deep clustering methods ignore the structure information of short texts. In this paper, we present a GCN-based clustering method for short text clustering, named as Deep Structured Clustering (DSC) method, to explore the relationships among short texts for representation learning. We first construct a k -nn graph to capture the relationships among the short texts, and then jointly learn the short text representations and perform clustering with a dual self-supervised learning module. The experimental results demonstrate the superiority of our proposed method, and the ablation experimental results verify the effectiveness of the modules in our proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Images Quality Representation Learning

GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cai, Xiangwen Lei, Haijun Xie, Hai Zhang, Guoming Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Shenzhen Eye Hospital Shenzhen China

ISBN: (纸本)9798350386226

Fundus image is vital for the diagnosis and monitoring of various eye diseases, where the accuracy of diagnostic results is largely determined by the quality of the obtained images. However, the performance of existing Fundus Image Quality Assessment(FIQA) methods is primarily constrained by the quantity and quality of retinal datasets, highlighting a significant challenge in the field due to the absence of a large annotated quality dataset. To alleviate this problem, we purpose the Global-Local Denoising Auto-Encoder(GL-DAE), a novel and efficient self-supervised learning pre-training approach in FIQA to overcome the scarcity of annotated fundus images. Specifically, in the pre-training phase, given a batch of unlabeled fundus images, we apply different distortion algorithms to degrade the original fundus images. The degraded fundus images are then subjected to global and local cropping, and subsequently, global and local features are extracted using a joint encoder. By reconstructing global and local fundus images, the model learns to extract features related to fundus quality. Afterwards, to complement the global reconstruction task, we further design a noise identification based contrative learning to guide the models to identify different types of degradation algorithm. Fine-tuning results across two FIQA datasets highlight the superiority of our method compared to other classical image classification methods, demonstrating its potential to advance the reliability of FIQA. © 2024 IEEE.

关键词： Self-supervised learning

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CHAR: Composite Head-body Activities Recognition with A Single Earable Device

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6532-6549页

作者： Zhu, Peizhao Zhu, Yuzheng Li, Wenyuan He, Yanbo Zou, Yongpan Wu, Kaishun Leung, Victor C. M. Shenzhen University College of Computer Science and Software Engineering 3688 Nanhai Ave Shenzhen518060 China Hong Kong University of Science and Technology Information Hub 1 Duxue Road Guangzhou511453 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 89.7% and 85.1% in sufficient data and insufficient data cases, respectively. © 2002-2012 IEEE.

关键词： Gesture recognition

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning 13

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： He, Chengwei Hong, Huaping Ma, Lijia Zhu, Qingling Bai, Yuan College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases. © 2024 IEEE.

关键词： Deep reinforcement learning

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Knowledge-Assisted DRL for Energy Harvesting Based Multi-Access Wireless Communications 23

Knowledge-Assisted DRL for Energy Harvesting Based Multi-Acc...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Luo, Sheng Zhang, Haodi Li, Qifan Wu, Kaishun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665494571

This paper investigates the use of deep reinforcement learning (DRL) to maximize the long-term throughput of multi-access wireless communication systems. Specifically, we consider the scenario in which an access point (AP) without dedicated power supply harvests energy from the ambient environment and uses the harvested energy to deliver data packets to multiple users. We formulate the access control problem as a Markov Decision Process (MDP) with unknown system dynamics and apply the deep reinforcement learning (DRL) approach to solve this problem, i.e., the optimal access strategy is approximated by using a double deep Q-network (DDQN). As the considered system usually has large state and action spaces, some domain knowledge is embedded into the training process to improve the performance of DDQN. It is shown that with the help of domain knowledge, access control strategies which can achieve a higher system throughput can be found by the knowledge-embedded DDQN. Moreover, the performance of the access control policy obtained by using the knowledge-embedded DDQN is compared with that of several conventional transmission schemes. Experiment results show that the transmission policy obtained by using our proposed model can achieve better performance. © 2021 IEEE.

关键词： Access control Wireless communication Training System dynamics Smart cities Power supplies Process control

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software Defect Prediction via Code Grayscale Pixel Visualization with Fusion Attention 36

Software Defect Prediction via Code Grayscale Pixel Visualiz...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Qiu, Shaojian Wang, Shaosheng Rong, Wei Liao, Lili Lin, Yishen College of Software Engineering South China Agricultural University China School of Computer Science and Engineering South China University of Technology China Guangdong Tobacco Meizhou Limited Company China

ISBN: (纸本)1891706594

software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems. In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features. To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP). Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping. Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention. We conduct empirical experiments on ten open-source Java projects from the PROMISE repository. The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： software reliability

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Unsupervised feature selection via multiple graph fusion and feature weight learning

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science China(Information sciences) 2023年第5期66卷 56-72页

作者： Chang TANG Xiao ZHENG Wei ZHANG Xinwang LIU Xinzhong ZHU En ZHU School of Computer Science China University of Geosciences State Key Laboratory for Novel Software Technology Nanjing University School of Computer National University of Defense Technology National Supercomputing Center in Jinan Qilu University of Technology (Shandong Academy of Sciences) College of Mathematics Physics and Information Engineering Zhejiang Normal University

Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised learning task, most previous methods often use a coefficient matrix for feature reconstruction or feature projection, and a certain similarity graph is widely utilized to regularize the intrinsic structure preservation of original data in a new feature space. However, a similarity graph with poor quality could inevitably afect the final results. In addition, designing a rational and efective feature reconstruction/projection model is not easy. In this paper, we introduce a novel and efective unsupervised feature selection method via multiple graph fusion and feature weight learning（MGF2WL） to address these issues. Instead of learning the feature coefficient matrix, we directly learn the weights of diferent feature dimensions by introducing a feature weight matrix, and the weighted features are projected into the label space. Aiming to exploit sufficient relation of data samples, we develop a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph, which is then deployed to regularize the local data structure of original data in a projected label space. Finally, we design a block coordinate descent algorithm with a convergence guarantee to solve the resulting optimization problem. Extensive experiments with sufficient analyses on various datasets are conducted to validate the efficacy of our proposed MGF2WL.

关键词： unsupervised feature selection graph fusion feature weight learning local structure preservation

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