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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Song, Wei Chang, Zhen Jacobsen, Hans-Arno Zhang, Pengcheng Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China University of Toronto Middleware Systems Research Group Toronto Canada Hohai University College of Computer and Information Nanjing China

ISBN: (纸本)9798350322279

While process mining has gained much attention in the past decade, surprisingly, discovering structural errors (i.e., deadlock and lack of synchronization) from event logs has seldom been studied. Since event logs may involve erroneous event occurrences caused by unsynchronized activities, discovering deadlocks and lack of synchronization errors may influence each other. To this end, we first extract from the original event log two independent event logs which are employed to discover deadlocks and lack of synchronization errors, respectively. We then discard the erroneous event occurrences in the two event logs, from which our event relation based mining rules can discover the corresponding structural errors. We have implemented our approach, and the experimental results corroborate that our approach can effectively and efficiently discover process structural errors from the event logs involving sufficient event sequences. © 2023 IEEE.

关键词： Synchronization

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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Pedestrian Identification and Tracking within Adaptive Collaboration Edge Computing* 26

Pedestrian Identification and Tracking within Adaptive Colla...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tao, Ming Li, Xueqiang Xie, Renping Ding, Kai Dongguan University of Technology School of Computer Science and Technology Dongguan China

ISBN: (纸本)9798350331684

Nowadays, video surveillance is widespread used to achieve security life in the construction of smart city. As a result, prevalence of video surveillance equipments and technologies enables pedestrian identification and tracking to be research hotspots in the field of computer vision, whose development and application are of great significance to the construction of a good social security environment. However, pedestrian identification and tracking in monitoring scenarios still have problems of low recognition accuracy and high model complexity, and computer vision based adaptive recognition methods still have a large room for improvement and development. To address this issue, real-time pedestrian identification and tracking within adaptive collaboration edge computing environment is investigated in this paper. Within the paradigm of edge computing, the Raspberry Pi 3B+ acting as the edge computing node is adopted to handle the related issues of pedestrian identification and tracking. The combination of HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine) is investigated to achieve pedestrian identification, where, HOG is employed as the feature descriptor and SVM is employed as the classification algorithm. Furthermore, the pixel-based visual tracking algorithm is investigated to achieve effective and uninterrupted pedestrian tracking. By implementing a prototype on Raspberry Pi 3B+ using OpenCV libraries, the experimental results finally have been shown to demonstrate the efficiency of the investigations. © 2023 IEEE.

关键词： Edge computing

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DSP-Based Industrial Defect Detection for Intelligent Manufacturing 26

DSP-Based Industrial Defect Detection for Intelligent Manufa...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yue, Han Wang, Rucen Gao, Yi Xia, Ailing Zhang, Jianhua Tianjin University of Technology School of Computer Science and Engineering Tianjin China Zhejiang University of Technology College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331684

Internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss. © 2023 IEEE.

关键词： Internet of things

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Research on target detection based on spatio-temporal filtering and KCF under low light conditions 4

Research on target detection based on spatio-temporal filter...

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4th International Conference on Laser, Optics, and Optoelectronic technology, LOPET 2024

作者： Bao, Zhichen Xu, Yinghu Qian, Weixian School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu province Nanjing210094 China

ISBN: (纸本)9781510681903

Weak light environment poses challenges for target detection. It is difficult for the existing target detection algorithms to track the target in the noise-flooded image. In order to improve the accuracy of the target detection algorithm, it is necessary to process the low-light image first. In this paper, a time-domain filter is used, which combines the dynamic detection of time-domain filter and the denoising ability of spatio-temporal filter to provide help for target detection under low light conditions. This paper focuses on the motion compensation filtering by calculating the associated parameters of the front and back frames through multi-direction edge filtering. The down-sampling differential pyramid is used to avoid the discontinuity of the partition blocks. A convolutional strong edge detection method based on multi-directional template is proposed to improve the edge detection function of traditional spatial filtering template. Finally, kernel correlation filtering (KCF) is applied to the image to realize target detection. By comparing the target detection rate and accuracy before and after filtering, it is proved that the algorithm can effectively improve the detection ability of low-light level image. By comparing with other algorithms, it is proved that the proposed algorithm has more advantages than the uncombined traditional algorithm. © 2024 SPIE.

关键词： Wiener filtering

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Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks

Resource-Efficient Generative AI Model Deployment in Mobile ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liang, Yuxin Yang, Peng He, Yuanyuan Lyu, Feng Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350351255

The surging development of Artificial Intelligence-Generated Content (AIGC) marks a transformative era of the content creation and production. Edge servers promise attractive benefits, e.g., reduced service delay and backhaul traffic load, for hosting AIGC services compared to cloud-based solutions. However, the scarcity of available resources on the edge pose significant challenges in deploying generative AI models. In this paper, by characterizing the resource and delay demands of typical generative AI models, we find that the consumption of storage and GPU memory, as well as the model switching delay represented by I/O delay during the preloading phase, are significant and vary across models. These multidimensional coupling factors render it difficult to make efficient edge model deployment decisions. Hence, we present a collaborative edge-cloud framework aiming to properly manage generative AI model deployment on the edge. Specifically, we formulate edge model deployment problem considering heterogeneous features of models as an optimization problem, and propose a model-level decision selection algorithm to solve it. It enables pooled resource sharing and optimizes the trade-off between resource consumption and delay in edge generative AI model deployment. Simulation results validate the efficacy of the proposed algorithm compared with baselines, demonstrating its potential to reduce overall costs by providing feature-aware model deployment decisions. © 2024 IEEE.

关键词： Generative adversarial networks

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Constrained Subset Selection from Data Streams for Profit Maximization 23

Constrained Subset Selection from Data Streams for Profit Ma...

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2023 World Wide Web Conference, WWW 2023

作者： Cui, Shuang Han, Kai Tang, Jing Huang, He School of Computer Science and Technology Suzhou Institute for Advanced Research University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Soochow University JiangSu Suzhou China The Hong Kong University of Science and Technology China

ISBN: (纸本)9781450394161

The problem of constrained subset selection from a large data stream for profit maximization has many applications in web data mining and machine learning, such as social advertising, team formation and recommendation systems. Such a problem can be formulated as maximizing a regularized submodular function under certain constraints. In this paper, we consider a generalized k-system constraint, which captures various requirements in real-world applications. For this problem, we propose the first streaming algorithm with provable performance bounds, leveraging a novel multitudinous distorted filter framework. The empirical performance of our algorithm is extensively evaluated in several applications including web data mining and recommendation systems, and the experimental results demonstrate the superiorities of our algorithm in terms of both effectiveness and efficiency. © 2023 ACM.

关键词： Machine learning

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Crowdsourcing Tasks Allocation based on Incentives via Group Multirole Assignment 26

Crowdsourcing Tasks Allocation based on Incentives via Group...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Chenyang Liang, Lu Guangdong University of Technology School of Computer Science and Technology Guangzhou China

ISBN: (纸本)9798350331684

Motivation is a key factor in improving the enthusiasm and work performance of crowdsourcing workers. It is important for crowdsourcing platforms to allocate tasks. In order to increase the enthusiasm of workers and thus improve their work performance, this paper formalizes the Crowdsourcing Tasks Allocation (CTA) based on incentives and proposes an adaptive task allocation method with Role-Based Collaboration (RBC) and its E-CARGO model by considering the dynamic changes of the weights of the evaluation factors and the evaluation values. Large-scale randomized experiments show that this method is effective and reliable, which can maximize the performance of the team. More importantly, it can motivate workers under the influence of multiple incentive factors and the environment of real-time growth of crowdsourcing tasks. © 2023 IEEE.

关键词： Crowdsourcing

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Variation-Guided Condition Generation for Diffusion Inversion in Few-Shot Image Classification

Variation-Guided Condition Generation for Diffusion Inversio...

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2023 International Conference on New Trends in Computational Intelligence, NTCI 2023

作者： Wu, Sining Gao, Xiang Wang, Fan Hu, Xiaopeng School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798350380859

Few-shot learning (FSL) learns to recognize objects based on very limited examples of each category. An intuitive approach for FSL is to generate additional samples for few-shot categories. This is typically achieved by transferring general knowledge from the 'many-shot' (base) categories. However, most existing methods do not fully exploit the intra-class variations within these categories, leading to limited diversity and quality among the generated samples. In this paper, we proposed a variation-guided condition generation network for diffusion Inversion in FSL, which not only learns to decompose embeddings obtained by the inversion into class-related and variation-related information to utilize abundant variations in the base dataset, but also incorporates the knowledge embedded in the general pre-trained Diffusion model into generated images. The proposed method consists of three steps. First, a conditional mapping network is trained by reversing each training image into the output space of the text encoder to obtain a set of embeddings as unique conditional vectors for each image. These vectors are then decomposed into class-related and variation-related conditions. The model learns to generate new conditional vectors by combining class-related and variation-related conditions from different categories. Finally, in the testing phase, the vectors are perturbed and denoised by Stable Diffusion to generate additional images for few-shot classes and help build few-shot classifiers. Experiments conducted on three popular few-shot datasets demonstrate that the proposed method can generate diverse and discriminative examples and significantly improve classification accuracy. © 2023 IEEE.

关键词： Image classification

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A Recommender Algorithm Based on Knowledge Graph Convolutional Network and Knowledge Reasoning Optimization 26

A Recommender Algorithm Based on Knowledge Graph Convolution...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Tiyong Cheng, Shiwei Zhejiang University of Technology School of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331684

The collaborative filtering-based recommender algorithm suffers the interaction sparsity problem and cold start problem, experts have introduced knowledge graphs (KGs) and applied graph neural network models to achieve more personalized recommendations. However, potential links between entities, which can further extend the user interaction data within a KG and thus improve the performance of the recommender algorithm, are yet to be fully explored and mined. A novel knowledge graph convolutional network (KGCN) recommender algorithm based on knowledge reasoning optimization is proposed. Firstly, knowledge graph completion was conducted based on the knowledge reasoning algorithm as to obtain a KG with a better user interaction and richer semantic information. Secondly, the KGCN was applied to capture more higher-order features and enhance the performance of personalized recommendation in embedding and aggregating the completed KG. Recommendation experiments on movie, book and music datasets were conducted and reached the highest AUC, F1-score and recall rates of 0.986, 0.940 and 0.503, respectively. These results verify that our method is effective and indicate that it can facilitate the performance of recommender algorithms. © 2023 IEEE.

关键词： Knowledge graph

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