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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhu, Rongjing Li, Peng Liu, Yu Deng, Miao School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Time series data mining is an important problem in data mining. It discovers the intrinsic correlation rules by analyzing a large amount of time series data. However, high-frequency data may exist in the dataset. High-frequency data is the data that occurs frequently in time series data, which will affect the mining of association rules. Traditional time series association mining tends to classify high-frequency data as correlated data because they co-occur too many times. In this paper, we propose the list mining framework to mine the association data by list co-occurrence to achieve the mining of massive data. We divide it into three phases, list generation phase, matrix generation phase, and rule generation phase. First, we create data lists based on time windows. Second, we create the initial count matrix, update the count matrix based on the list, and propose a GCPM algorithm to generate a conditional probability matrix based on the count matrix. Third, we propose a List Mining (LM) algorithm to generate the initial rules based on the conditional probability matrix and optimize the initial rules to get the final association rules. In addition, we weigh the rules according to the list mining method and propose a Weighted List Mining (WLM) algorithm to filter rules by reducing the weight of frequent item interference. The experimental results show that LM and WLM can effectively improve the accuracy of mining results. © 2022 IEEE.

关键词： Data mining

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Research on low energy task allocation and scheduling algorithm based on imprecise heterogeneous multi-core technology

Research on low energy task allocation and scheduling algori...

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

作者： Gu, Haonan Wu, Jing Hu, Wei Ma, Tianao Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In heterogeneous embedded systems, we try to find a new non real-time scheduling algorithm, which can not only meet the reliability requirements, but also achieve low energy consumption. 'Resource reservation time deterministic cyclic scheduling (RRTDCS)' algorithm is a kind of scheduling for offline cache pre-allocation. This scheduling algorithm combines time reservation and priority strategy. The algorithm can dynamically adjust according to different task sets. It can also adjust its own scheduling process to adapt to different scheduling environments. The periodic cyclic strategy enables low power consumption in task allocation. In the experimental part, we compared the algorithm proposed in this paper with the current mainstream real-time scheduling scheme. Experimental results show that RRTDCS algorithm has better performance. © 2022 IEEE.

关键词： Scheduling

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Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell 3

Cascaded mixed pyramid pooling for surface defect segmentati...

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3rd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2024

作者： Wang, Yaokun Fu, Xiaowei Li, Xi Xu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510680449

To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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Duration-Aware Task Assignment for Heterogeneous Mobility Users in Crowdsensing 25

Duration-Aware Task Assignment for Heterogeneous Mobility Us...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhiyuan Li, Peng Huang, Weiyi Wang, Jiahu Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms. © 2022 IEEE.

关键词： Combinatorial optimization

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An Ensemble Learning Approach for Wind Power Forecasting 3

An Ensemble Learning Approach for Wind Power Forecasting

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3rd International Conference on Electronic Information Engineering and computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 IEEE.

关键词： Decision trees

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FanPad: A Fan Layout Touchpad keyboard for Text Entry in VR

FanPad: A Fan Layout Touchpad Keyboard for Text Entry in VR

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IEEE Annual International Symposium virtual Reality

作者： Jian Wu Ziteng Wang Lili Wang Yuhan Duan Jiaheng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

Text entry poses a significant challenge in the realm of virtual reality (VR). This paper introduces FanPad, a novel solution designed to facilitate dual-hand text input within head-mounted displays (HMDs). FanPad accomplishes this by ingeniously mapping and curving the 26 typing keys (T26) QWERTY keyboard onto the touchpads of both controllers. The curved key layout of FanPad is derived from the natural movement of the thumb when interacting with the touchpad, resembling an arc with a thumb-length fixed *** optimize the experience, we introduce a customization process for the FanPad curve to better cope with individual hand shapes and thumb movements. We also provide a version with more overlap area named FanPad-Ov for different users with different typing *** first user study examined the effects of curving and different overlap areas by comparing four potential layouts. The results clearly favor the FanPad and FanPad-Ov layout compared to the nocurving version, SKPad(-Ov). Subsequently, the second user study was conducted to assess long-term performance and improvement on customized FanPads. Notably, novices achieved a typing speed of 19.73 words per minute (WPM), demonstrating a remarkable increase of 58.47% after a 60-phrase training in six days. The highest typing speed reached an impressive 24.19 WPM.

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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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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SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures

arXiv

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arXiv 2025年

作者： Liu, Hui Jia, Chen Shi, Fan Cheng, Xu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System Ministry of Education

Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU. The code is available at https://***/Karl1109/SCSegamba. © 2025, CC BY.

关键词： State space methods

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： Information centric networking caching strategy two-layer caching model hierarchical division

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