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32nd ACM International Conference on Multimedia, MM 2024

作者： Tian, Long Zhao, Hongyi Lu, Ruiying Wang, Rongrong Wu, Yujie Wang, Liming He, Xiongpeng Liu, Xiyang National Pilot School of Software Engineering Xi'Dian University Xi'an Shi Chang'an Qu China School of Cyber Engineering Xidian University Xi'an Shi Chang'an Qu China National Key Lab. of Radar Signal Processing Xidian University Xi'an Shi Chang'an Qu China National Pilot School of Software Engineering Xi'Dian University Xi'an Chang'an Qu China

ISBN: (纸本)9798400706868

Few-Shot Industrial Anomaly Detection (FS-IAD) has drawn great attention most recently since data efficiency and the ability to design algorithms for fast migration across products have become the main concerns. The difficulty of memory-based IAD in low-data regime primarily lies in inefficient measurement between the memory bank and query images. We address such a pivotal issue from a new perspective of optimal matching between features of image regions. Taking the unbalanced nature of query features into consideration, we adopt Conditional Transport (CT) as a metric to compute the structural distance between representations of the two sets to determine feature relevance. CT distance generates the optimal matching flows between unbalanced structural elements that achieve the minimum matching cost, which can be directly used for IAD since it well reflects the differences of query images compared with the normal memory. Realizing the fact that query images usually come one-by-one or batch-by-batch, we further propose an Online Conditional Transport (OCT) by making full use of the current and historical query images for IAD via simultaneously calibrating the memory bank using the online query images and matching features between the calibrated memory and the current query image. Go one step further, for sparse foreground products, we employ a predominant segment model to implement Foreground-aware OCT (FOCT) for improving the effectiveness and efficiency of OCT by forcing the model to pay more attention to diverse targets rather than redundant backgrounds when calibrating the memory bank. FOCT can improve the diversity of calibrated memory, which is critical for robust FS-IAD in practice. Besides, FOCT is flexible since it can be friendly plugged and played with any pre-trained backbones, such as WRN, and any pre-trained segment models, such as SAM. The effectiveness and efficiency of our model is demonstrated across diverse datasets, including benchmarks of MVTec and

关键词： Anomaly detection

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Disentangled Hypergraph Collab.rative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collab.rative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collab.rative filtering

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Parameters optimization design of high-speed reluctance coil launchers 10

Parameters optimization design of high-speed reluctance coil...

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch Technology, EAPPC-BEAMS-EML 2024

作者： Zhang, Yadong Zhou, Ao Sun, Zhiqiang Wuhan University State Key Lab. of Pwr. Grid Environmental Protection School of Electrical Engineering and Automation Wuhan China Guangzhou GuoYao Technology Co. Ltd Guangzhou China

ISBN: (纸本)9789038661353

Muzzle speed and efficiency are two important factors that constrain the development of reluctance coil launchers. In order to improve the muzzle speed of the reluctance coil launcher, the number of stages of the launcher is bound to increase, which leads to the complication of the simulation model, long simulation time consuming and low optimisation efficiency. Aiming at the above problems, this paper proposes a fast optimisation method for high-speed reluctance coil launchers based on genetic algorithm and machine learning. Firstly, based on a 7-stage reluctance coil launcher, determine that the wire diameter and the number of coil layers are the optimization objects;based on machine learning, the wire diameter and the number of coil layers are the optimization objects. Based on machine learning, the fast calculation model is trained with the wire diameter and the number of coil layers as inputs and the muzzle speed as output;the fast calculation model is invoked with the genetic algorithm, and the maximum muzzle speed of the launcher is taken as the fitness function, so as to achieve the fast optimisation of the parameters of the high-speed reluctance coil launcher stage by stage. The results show that after optimisation, an muzzle speed of 113.59m/s can be achieved under the condition of 32-stage launcher with an efficiency of 9.96%. © 2024 Eindhoven University of Technology.

关键词： Wire

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Reach-avoid Controllers Synthesis for Safety Critical Systems

arXiv

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arXiv 2023年

作者： Xue, Bai State Key Lab. of Computer Science Institute of Software Cas Beijing China

In this paper we propose sufficient conditions to synthesizing reach-avoid controllers for deterministic systems modelled by ordinary differential equations and stochastic systems modeled by stochastic differential equations based on the notion of control guidance-barrier functions. We begin with considering deterministic systems. Given an open safe set, a target set and a nominal controller, we attempt to synthesize a reach-avoid controller, which modifies the nominal controller in a minimal way enforcing the reach-avoid objective, i.e., the system will enter the target set eventually while staying inside the safe set before the first target hitting time. Three control guidance-barrier functions are developed and three corresponding conditions for synthesizing reach-avoid controllers are constructed, which get progressively weaker and thus facilitate the optimal controller design. The first and second ones are termed exponential and asymptotic control guidance-barrier functions, which guarantee that every trajectory starting from the safe set will enter the target set respectively at an exponential rate and in an asymptotic way. However, it is observed that these two control guidance-barrier functions have one condition in common of enforcing invariance of its every positive sublevel set until the system enters the target set, which is strict and thus limits the space of admissible controllers. Consequently, a lax control guidance-barrier function is further developed such that only the safe set set is an invariance before the system enters the target set, expanding the space of admissible control inputs. Then, we extend the methodologies for deterministic systems to stochastic systems, which synthesize reach-avoid controllers in the probabilistic setting. Finally, several numerical examples demonstrate the performance of proposed methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Palmprint Anti-spoofing via Frequency Enhancement and Selection 18th

Palmprint Anti-spoofing via Frequency Enhancement and Select...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ren, Yani Shao, Huikai Zhong, Dexing School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu NanJing210000 China Xi’an Yizhanghui Technology Co. Shannxi Xi’an712000 China Pazhou Lab Guangzhou510335 China Research Institute of Xi’an Jiaotong University Zhejiang311215 China

ISBN: (纸本)9789819610679

Palmprint recognition has attracted considerable interest from researchers as a convenient and secure biometric identification technology. The majority of research on palmprint recognition focuses on enhancing recognition accuracy and speed. However, the security aspects of palmprint recognition are rarely considered. This paper proposed a palmprint anti-spoofing method, called Frequency Enhancement and Selection (FES), based on frequency-domain features. Firstly, the palmprint images are transformed using the discrete cosine transform to obtain frequency-domain features. These features are then enhanced by the frequency-domain enhancement module. Finally, the frequency channels for classification are filtered based on the gate module. In this paper, experiments are conducted on a palmprint anti-spoofing database, and the experimental results demonstrate that our method can obtain the anti-spoofing accuracy of 99.81% and can outperform other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Frequency domain analysis

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A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions 26

A Dynamic Selective Parameter Sharing Mechanism Embedded wit...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Yan He, Ying Ming, Zhong Yu, F. Richard Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Integrated Services Networks Xidian University China Shenzhen China

ISBN: (纸本)9781643684369

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents' abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collab.ration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods. © 2023 The Authors.

关键词： Dynamics

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Fast Point-to-Surface Projection for Large CAD Models and Quality Testing 3

Fast Point-to-Surface Projection for Large CAD Models and Qu...

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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Liu, Wu Bao, Ziheng Yang, Chunhui Liu, Yusheng Wu, Lei Fifth Electronic Research Institute of Ministry of Industry and Information Technology MIIT Key Lab of Industrial Software Engineering Application Technology Guangzhou510610 China State Key Lab of CAD&CG Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781510667426

Point projection for Non-Uniform Rational B-Splines (NURBS) surfaces is a fundamental operator in Computer-Aided modeling (CAD) modeling. This operator takes as input a query point and a NURBS surface in 3, and outputs the UV parameter values whose corresponding 3D point (one on the surface) has the minimum distance between the query point and a variable point on the surface. Existing projection methods consistently employ an iterative searching strategy, which suffers from efficiency issues, especially for tasks involving enormous query points such as 3D CAD model quality testing. This paper proposes a parallel, subdivision-based strategy to increase query speed. It uses geometric subdivision to quickly cull out potential parameter regions where a query point’s corresponding UV parameters reside, then utilizes the Gauss-Newton method to quickly march to the precise UV values. All steps are done completely on GPU: the geometric subdivision is shared across all query points, and the Gauss-Newton marching is done in parallel as well. Experimental results show that a significant increase of at least 11x can be attained using the proposed method. © 2023 SPIE. All rights reserved.

关键词： 3D modeling

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A Sentiment Classification Model Based on Syntactic Graph Attention Networks 24

A Sentiment Classification Model Based on Syntactic Graph At...

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2024 International Conference on Intelligent Education and Computer Technology, IECT 2024

作者： Cheng, Yan Zhou, Ziwei School of Computer Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Prov. Key Lab. of Intelligent Information Processing and Affective Computing of Jiangxi Province 330022 China School of Software Jiangxi Normal University 330022 China

ISBN: (纸本)9798400709920

Text Sentiment Classification, a significant task in Natural Language Processing, aims to comprehend user needs and expectations by categorizing the sentiments of texts posted on platforms. Despite their utility, existing models for this task do not fully account for the influence of contextual and syntactic text features on sentiment classification. To address these issues, a text sentiment classification model based on syntactic graph attention network is proposed in this study. The model begins by extracting the contextual features of a sentence using BiGRU. It then generates a syntactic graph through a syntactic analyzer, using the contextual features as the node features of the syntactic graph. A syntactic graph attention network is constructed to extract the syntactic features of the syntactic graph. These feature vectors are then input into a softmax function to achieve text sentiment classification. Experimental results indicate that the proposed model outperforms similar models on two public datasets in terms of classification accuracy, thus demonstrating its effectiveness. © 2024 ACM.

关键词： Syntactics

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A Comprehensive Study of Present Data Deduplication 23

A Comprehensive Study of Present Data Deduplication

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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： Xue, Zilong Qian, Huixun Shen, Lingling Wu, Xiaotong School of Computer and Electronic Information Nanjing Normal University Nanjing China Nanjing University State Key Lab. for Novel Software Technology China

ISBN: (纸本)9781665494571

With the proliferation of the Internet of Things (IoT), various computing paradigms have been proposed and are developing rapidly these years, which led to the explosive increase of the data amount. However, the significant increase in data imposed a significant burden on contemporary server storage. Many approaches have been designed to tackle the challenge. Among them, deduplication is a quite effective data reduction technique, which received considerable attention from both academia and industry in the large-scale storage systems field. Data deduplication identifies redundant data at chunk level by using secure fingerprints, which not only removes replicated data, decreases the bandwidth, but also minimizes the storage usage and cost. This paper aims at describing the general framework of deduplication compression systems with duplicate and resemblance detection in detail. First, we use a flow chart to present the overall framework and process of the deduplication compression system. Then we summarize the existing algorithm applied to duplicate detection and resemblance detection. Additionally, we make a detailed evaluation of different resemblance detection algorithms. Finally, we make a conclusion of delta compressed prototype system, outline the open problems and shed a light on the future research directions in data deduplication systems. © 2021 IEEE.

关键词： Industries Costs Smart cities Prototypes Bandwidth Fingerprint recognition Explosives

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