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28th International Conference on Database Systems for Advanced applications, DASFAA 2023

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

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An Approach for Performance Calculation of AVS/RS in Multi-floor Workshop 3rd

An Approach for Performance Calculation of AVS/RS in Multi-f...

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3rd International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2023 and the 7th Asian Conference of Management Science and applications, ACMSA 2023

作者： Liu, Xiaopeng Wu, Cuiying Zhang, Baolin Xi, An Chen, Xuanrui Ministry of Industry and Information Technology Key Laboratory of Industrial Software Engineering Application Technology China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou510619 China School of Computer and Information Engineering Hanshan Normal University Guangdong Chaozhou521041 China

ISBN: (纸本)9789819701933

Warehouse management is an important part of the production process. As a result of the development of technology, more and more companies tend to use automated logistics technologies such as AVS/RS to handle the internal logistics of the production system. Especially in multi-floor workshops, the production process on each floor are connected with each other through AVS/RS. It is becoming more and more important to study the integration process of AVS/RS and multi-floor workshop production process. In this paper, an integrated queuing network model of multi-floor workshop and AVS/RS was established, and a calculation method of system performance index was proposed. The accuracy and efficiency of the proposed method are demonstrated by comparing the results with simulations from numerical experiments. The work of this article can provide a basis for the design of autonomous vehicle storage and retrieve systems in multi-floor workshop, and the calculation method can be used as the support of WMS technology base. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floors

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Long-range Brain Graph Transformer 38

Long-range Brain Graph Transformer

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. © 2024 Neural information processing systems foundation. All rights reserved.

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Building Edge Detection technology from Remote Sensing Image Based on NSCT and Tensor Voting 8

Building Edge Detection Technology from Remote Sensing Image...

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2024 8th International Conference on Machine Vision and Information technology, CMVIT 2024

作者： Wu, Yingbin Zhao, Peng Zhou, Mingquan Geng, Shengling Zhang, Dan School of Computer Science Qinghai Normal University Qinghai Xining China School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China

An edge detection technology based on the combination of non-downsampling contour wave transform (NSCT) and tensor voting is proposed, which aims to obtain more accurate and detailed edge information of buildings in remote sensing images. Firstly, NSCT is used for image decomposition to obtain the subband frequency information of different scales and angles. Then, position encoding is performed on these subband coefficients to obtain second-order symmetric tensors at the corresponding positions. Tensors of different scales and angles at the same position are weighted and summed to complete feature fusion. Finally, the edge features of the image are obtained based on tensor voting theory. The experimental results show that compared to common edge detection technologies, such as Canny, Fast Edge (Fast edge detection using structured forests) and HED (Holistically-Nested Edge Detection), our method can more accurately and intensively reflect the boundaries of buildings and the edge information of roofs, providing better support for the analysis of building types and architectural styles. Compared to the HED method, which is based on deep learning, our method improves PSNR and SSIM metrics by 0.98 and 0.03, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Edge detection

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Joint Visual Perception and Linguistic Commonsense for Daily Events Causality Reasoning

Joint Visual Perception and Linguistic Commonsense for Daily...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Ma, Bole Tong, Chao School of Computer Science and Engineering Beihang University China Beihang University State Key Laboratory of Software Development Environment China Yunnan Key Laboratory of Blockchain Application Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Multimodal networks that juxtapose visual and linguistic modalities are currently widely adopted for solving vision-and-language tasks. They perform well in simple and intu-itive tasks, but are prone to mistakes in tasks involving latent or implicit details, due to the difficulty of capturing crucial but imperceptible visual signals in the real world. Perception errors lead to nonsensical results, but can be corrected by commonsense knowledge. To this end, we combine visual perception and linguistic commonsense to solve the challenging daily events causality reasoning task. We propose a novel Object-Aware Reasoning Network to focus on object inter-action while ignoring distracting information to refine visual perception. Further, a language branch with an independent prediction head is supervised to learn causality commonsense to help correct obvious perception errors, resulting in more plausible conclusions. Extensive experiments demonstrate that our method achieves new state-of-the-art results on Vis-Causal dataset. © 2022 IEEE.

关键词： Linguistics

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Lightweight 6-bit S-boxes with DPA Resistance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

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Research on Pedestrian Detection Based on Improved YOLOv3 7

Research on Pedestrian Detection Based on Improved YOLOv3

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2022 7th International Conference on Electronic technology and Information Science, ICETIS 2022

作者： Wang, Hansong Liang, Quan Xiong, Neng Hu, Jinjing School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Ming and Application Fuzhou China

ISBN: (纸本)9783800757794

The YOLOv3 algorithm is an anchor-based algorithm, which is difficult to cover all shapes of targets. It is often necessary to perform cluster analysis to determine the optimal anchors before training. These anchors have weak generalization performance and increase the complexity of detection. Cause the detection speed to slow down. Aiming at the different poses of pedestrians in pedestrian detection, the paper proposes an improved YOLOv3 algorithm for pedestrian detection. The anchor free method is used to cover all shapes of pedestrian poses, and the HSV data enhancement method is used to process the training images to improve the accuracy and speed of pedestrian detection. The experimental results show that compared with the original algorithm, in the INRIA pedestrian dataset and the Caltech pedestrian dataset, the improved algorithm in the article has increased accuracy by 9.23% and 5.01%. The speed has increased by 0.021 s and 0.025 s, respectively. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Anchors

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A Novel Approach for Concrete Crack and Spall Detection Based on Improved YOLOv8 5

A Novel Approach for Concrete Crack and Spall Detection Base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Rouf, Md Abdur Iwahori, Yuji Chen, Haisong Wang, Aili Heilongjiang Province Key Laboratory Of Laser Spectroscopy Technology And Application Harbin University Of Science And Technology Harbin150080 China Department Of Computer Science Chubu University Aichi Japan Shenzhen Polytechnic University Shenzhen518115 China

ISBN: (纸本)9798400709272

The detection and prevention of concrete cracks and spalls is crucial to ensure the structural integrity and longevity of civil infrastructure. In this research paper, we propose a novel method for concrete crack and spall detection based on YOLOv8 with ByteTrack and supervision. The proposed method exploits the advantages of the YOLOv8 object detection framework, which provides real-time and accurate detection, tracking and counting of various objects. By integrating ByteTrack, a state-of-the-art tracking algorithm, we increase system performance and robustness in tracking cracks and spalls over time. Furthermore, supervision is employed to improve detection accuracy through iterative training and fine-tuning. To train the model, an extensive dataset of concrete crack and spall images is collected, annotated, and enhanced. The dataset includes a variety of scenarios, lighting conditions, and crack/spill sizes, which ensures the model's ability to generalize to real-world situations. Transfer learning is used to support the YOLOv8 backbone with pre-trained weights, to accelerate the convergence of the training process. Experimental evaluation is conducted on a benchmark dataset, and the proposed method outperforms existing techniques in terms of accuracy, precision, and recall. YOLOv8 with ByteTrack and Supervision achieves an overall accuracy of 94% detection rate for concrete cracks and spalls, even under challenging conditions. Real-time deployment is achieved on a high-performance computing platform, allowing efficient and timely monitoring of concrete structures. The proposed method demonstrates its potential as a valuable tool for infrastructure management and maintenance, enabling early detection of concrete cracks and spalls. By promptly identifying such defects, necessary repair and reinforcement measures can be implemented, preventing further deterioration and ensuring the safety and longevity of civil infrastructure. The high detection performance of the

关键词： Object detection

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of computer Science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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Refined multi-channel inception network for automobile insurance fraud identification based on attention mechanism and dense connection structure

Refined multi-channel inception network for automobile insur...

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2023 International Conference on computer application and Information Security, ICCAIS 2023

作者： Wang, Deguang Zhang, Ting Li, Fengqi Tong, Ning Xu, Fengqiang School of Software Dalian Jiaotong University Liaoning Dalian116028 China School of Computer Communication and Engineering Dalian Jiaotong University Liaoning Dalian116028 China The Dalian Key Laboratory of Blockchain Technology and Application Liaoning Dalian116028 China

ISBN: (纸本)9781510675032

Automobile insurance fraud causes huge economic losses to insurance companies, detection technology has become an urgent research topic. However, feature selection manually is prone to subjective judgment, which results in a decline in recognition accuracy. To settle this problem, a refined multi-channel Inception network for automobile insurance fraud identification based on an attention mechanism and dense connection architecture is proposed. Firstly, we redesign the backbone structure of inception network to strengthen feature extraction ability. Secondly, attention mechanism is fused to inception module, enabling network to pay attention to important features. Furthermore, a dense connection structure is introduced to Inception network, which strengthens the transmission of features. Finally, this paper carries out mass experiments on a real-world "***" dataset. Experimental results reveal that our proposed approach achieves impressive performance on automobile insurance fraud identification tasks and can reach 98.12% accuracy, 98.19% precision and 98.12% recall and 98.12% f1_score, respectively. © 2024 SPIE.

关键词： Feature extraction

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