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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance data Generation Gene Annotation Positive-Unlabeled Learning

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A novel conflict measurement method based on cosine similarity and Deng entropy in Dempster-Shafer evidence theory

A novel conflict measurement method based on cosine similari...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Xu Tang, Yongchuan Zhou, Deyun Chongqing University School of Big Data and Software Engineering Chongqing401331 China Northwestern Polytechnical University School of Microelectronics Shaanxi Xi'an710072 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

As a generalization of probability theory, Dempster-Shafer evidence theory is superior in dealing with uncertain information. However, a counter-intuitive result is often obtained when combining highly conflicting evidence. In this paper, a new method based on similarity and Deng entropy of the evidence is proposed to measure the conflict and a new framework of fusing conflicting evidence is built based the proposed method. When most evidence has the same view, this evidence is given the higher weight. Moreover, the lower the entropy of the evidence, the stronger its ability to provide accurate information, and should be paid more attention. Experiments on real data show that this method can effectively solve the combination problem of conflicting evidence and it has a higher accuracy rate in the classification problem compared with other methods. © 2022 IEEE.

关键词： Entropy

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A New Quantum Dempster Rule of Combination

arXiv

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arXiv 2023年

作者： He, Huaping Xiao, Fuyuan School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Dempster rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster Rule of Combination increases exponentially. To address this issue, we propose a novel quantum Dempster rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum theory

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A new correlation belief transfer method in the evidence theory

A new correlation belief transfer method in the evidence the...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Xu Tang, Yongchuan Zhou, Deyun Chongqing University School of Big Data and Software Engineering Chongqing401331 China School of Microelectronics Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Dempster-Shafer evidence theory (D-S evidence theory) is an effective method in dealing with uncertain information. However, it may get counterintuitive results when using traditional Dempste's combination rule directly to fuse highly conflicting data. How to manage conflict in data fusion is still an open issue in D-S evidence theory. In this paper, a new correlation belief function is proposed to modify the basic belief assignment before combination in closed-world. The method transfers the belief from a certain proposition to another related proposition to avoid the loss of information when data are fused, which effectively solves the problem of conflict management in D-S evidence theory. The advantage of the proposed method is that it does not lose belief value in main propositions related to decision-making and also expresses the conflict information effectively. Several numerical examples and experiments with real data sets from the University of California Irvine Machine Learning Repository are adopted to verify the rationality and validity of the proposed method. © 2022 IEEE.

关键词： Decision making

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PC-CSP: An Efficient Partial Convolution in Cross Stage Partial Network for Small Targets Detection 7

PC-CSP: An Efficient Partial Convolution in Cross Stage Part...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Huang, Mei School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Operation Data Center of Postal Savings Bank of China Beijing Beijing610000 China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu610299 China

ISBN: (纸本)9798350350890

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces. © 2024 IEEE.

关键词： Feature extraction

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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua Science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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Ranking Enhanced Fine-Grained Contrastive Learning for Recommendation

Ranking Enhanced Fine-Grained Contrastive Learning for Recom...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunhang Yao Min Gao Hongwei Zhou Zongwei Wang Zehua Zhao Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Faculty of Engineering The University of Sydney Sydney Australia

Contrastive learning (CL) has been widely used to improve recommendation performance, since its self-supervised signals can effectively alleviate the data sparsity issue in recommender systems. Nevertheless, most existing CL-based recommendation models construct negative sample pairs following the common practice, which may include many false negatives (i.e., highly similar nodes). Some studies detect potential false negatives through specific rules and either remove these samples from the negative samples or treat them all as positive samples. However, they lack a finer-grained consideration of the samples. To address this limitation, we propose a Ranking Enhanced Fine-grained Contrastive Learning method (REFCL). Specifically, we devise two sampling strategies to generate two sets of positive samples with different confidence levels. These well-ordered positive sample sets are then integrated into our novel contrastive loss, which allows for a more nuanced consideration of distinctions between samples and thereby enhancing the discriminative qualities of the learned user/item representations. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness and generality of REFCL.

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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