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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Qiao, Baojun He, Siyuan Du, Ying Zuo, Xianyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9789819924424

To reduce the uncertainty factors in the remote sensing data processing process and the complexity of remote sensing data processing software, a remote sensing data processing workflow model is presented based on Petri net and UniNet theory. The model can not only describe the data, tasks and their logical relationships involved in remote sensing data processing workflow formally, but also analyze the logic of the process dynamically. Furthermore, the model can be effectively combined with workflow technology, which provide great help for solving remote sensing application needs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Multimodal Aspect-Based Sentiment Classification with knowledge-Injected Transformer

Multimodal Aspect-Based Sentiment Classification with Knowle...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zenan Xu Qinliang Su Junxi Xiao School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

The multimodal aspect-based sentiment classification task (mABSC) aims to recognize the sentiment polarities of aspect entities according to the associated textual and visual resources. Inspired by the cross-modal alignment ability of Transformers, some recent mABSC methods have also proposed to use Transformers to discover relevance between aspect entity and cross-modal visual regions, based on which relevant visual regions can be identified and leveraged to help recognize the sentiment polarity of aspect entities. However, due to the limited training data in mABSC tasks, it is found that in the Transformers, the aspect entities are often attended to irrelevant visual information, which obviously will not benefit the sentiment polarity recognition. To address the issue, we seek help from external knowledge, including textual syntax and cross-modal relevancy knowledge. The basic idea is to cut off the irrelevant connections among textual or cross-modal modalities in the Transformer layer using a knowledge-induced matrix. To have the matrix going beyond simply capturing direct relations, a mechanism is developed to enable it to reflect multi-hop relations, followed by a discretization operation to filter out extreme relevancy. Extensive experiments on two public multimodal datasets show that our method outperforms all competing baselines. Further studies demonstrate the effectiveness of each component, and suggest that the introduced external knowledge can instruct the model to learn the correct relevance among textual or cross-modal features, thus benefiting the mABSC task.

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Prescribed Performance-Based Finite-Time Neural Adaptive Backstepping Control for the Chaotic PMSM

Prescribed Performance-Based Finite-Time Neural Adaptive Bac...

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International Conference on Mechanical and Electronics engineering (ICMEE)

作者： Fengbin Wu Junxing Zhang Shaobo Li Shaoyang Li Xiao Wu Shuai Wang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data School of Mechanical Engineering Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

This paper presents a prescribed performance-based finite-time neural adaptive backstepping control scheme for the chaotic permanent magnet synchronous motor (PMSM). Specifically, an error transformation coupled with a prescribed performance function is introduced to guarantee that the tracking error keeps within a defined bound. The finite-time stability theory and backstepping framework are further combined to design finite-time adaptive laws and controllers. Then, it is shown that all signals are ultimately bounded in finite time and the tracking error can converge to a defined region in finite time. Finally, simulation results are presented to verify the feasibility of the proposed controller.

关键词： Backstepping Simulation Delay effects Permanent magnet motors Stability analysis Filtering theory

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DGA-Net: Dynamic Gaussian Attention Network for Sentence Semantic Matching 1st

DGA-Net: Dynamic Gaussian Attention Network for Sentence Se...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Zhang, Kun Lv, Guangyi Wang, Meng Chen, Enhong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783030930486

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism. © 2021, Springer Nature Switzerland AG.

关键词： Gaussian distribution

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Intersecting-boundary-sensitive fingerprinting for tampering detection of DNN models 24

Intersecting-boundary-sensitive fingerprinting for tampering...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xiaofan Bai Chaoxiang He Xiaojing Ma Bin Benjamin Zhu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security Microsoft School of Computer Science and Technology Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF.

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data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

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23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

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Blockchain Traceability Platform Based on Green Agricultural Products 15th

Blockchain Traceability Platform Based on Green Agricultural...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Tian, Xiao Yao, Yu-Ge Xu, Hui-Tong Xie, Ying-Jie Wei, Li-Jie Xie, Guo-jie Wu, Ting Gao, Yan-Wei Jining Normal University Inner Mongolia Ulanqab012000 China Baotou Big Data Research Institute National Engineering Laboratory for E-Commerce Transaction Technology Inner Mongolia Baotou014014 China Zhejiang Key Laboratory of Open Data Hangzhou310000 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

ISBN: (纸本)9783031239014

This paper designs and completes a blockchain traceability platform based on green agricultural products. Using item coding technology, cryptography technology, blockchain, big data and other new generation information technologies, green agricultural products (especially Ulanqab potatoes, oats and other geographical indication products) from growth to harvest to circulation throughout the life cycle. The key data can be trusted and stored in the blockchain, providing consumers with real-time agricultural product traceability query services, and providing agricultural and animal husbandry departments with effective agricultural product quality and safety supervision and management mechanisms and means. With the help of QR code as the mobile Internet entrance, link online and offline, design O2O e-commerce transaction system, form a closed loop of planting, traceability and sales, digitally control the quality of authentic agricultural products, and empower the brand value of agricultural products. Realize the whole chain supervision of green products from planting source to circulation through a perfect traceability system, improve product safety and quality, enhance the market's popularity and recognition of agricultural products in the region, form effective protection of agricultural product geographical indication products, and achieve agricultural economic development "Increase quality and efficiency", and then promote the construction of smart agricultural big data projects. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Agricultural products

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A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

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Computers, Materials & Continua 2023年第9期76卷 2879-2900页

作者： Shengyao Sun Ying Du Jiajun Chen Xuan Zhang Jiwei Zhang Yiyi Xu School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475004China School of Geography and Tourism Zhengzhou Normal UniversityZhengzhou450044China Science and Engineering College South China University of TechnologyGuangzhou510641China Department of Electrical and Electronic Engineering Luohe Vocational Technology CollegeLuohe462002China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing100876China Cardiff School of Engineering Cardiff UniversityCardiffCF103XQ 15UK

In Multi-access Edge Computing(MEC),to deal with multiple user equipment(UE)’s task offloading problem of parallel relationships under the multi-constraints,this paper proposes a cooperation partial task offloading method(named CPMM),aiming to reduce UE’s energy and computation consumption,while meeting the task completion delay as much as *** first studies the task offloading of single-UE and then considers the task offloading ofmulti-UE based on single-UE task *** the critical path algorithmto divide the modules into key and non-key *** to some constraints of UE-self when offloading tasks,it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for *** on fully considering the competition between multiple UEs for communication resources and MEC service resources,CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’*** achieves its goal by selecting reasonable modules to offload and using the cooperation ofUE,MEC,andCloudCenter to determine the execution location of *** experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6%on average,task completion delay reducing around 5%on average,and better task execution success rate than other similar methods.

关键词： MEC partial task offloading parallel dependencies completion delay

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Investigation of Vertically Stacked Horizontal Gate-All-Around SI Nanosheet Ion Sensitive Field Effect Transistor For Detection of C-Reactive Protein

Investigation of Vertically Stacked Horizontal Gate-All-Arou...

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2023 China Semiconductor Technology International Conference, CSTIC 2023

作者： Liu, Yang Zhang, Qingzhu Li, Junjie Wu, Cinan Cao, Lei Luo, Yanna Zhang, Zhaohao Wei, Shuhua Wei, Qianhui Yao, Jiaxin Hu, Jiawei Qin, Meiyan Liu, Enxu Han, Yanchu Li, LianLian Li, YingLu Yang, Tao Zhou, Na Gao, Jianfeng Li, Junfeng Institute of Microelectronics Chinese Academy of Sciences Key Laboratory of Microelectronic Devices and Integration Technology Beijing100029 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China North China University of Technology School of Information Science and Technology Beijing100144 China State Key Laboratory of Advanced Materials for Smart Sensing Grinm Group Co. Ltd. Beijing100088 China

ISBN: (纸本)9798350311006

In this work, based on advanced gate-all-around (GAA) technology, the extended sensing gate (ESG) GAA Si nanosheet (SiNS) ion sensitive field effect transistor (ISFET) sensor was fabricated and reported for the first time. Due to the GAA structures and the vertically-stacked SiNS channels, the average sensitivity of the ESG GAA SiNS ISFET sensor can be reached 58.8 mV/pH, which provides good gate control and ultra-sensitive detection capabilities. In addition, the actual minimum detection concentration of C-reactive protein (CRP) by ESG GAA SiNS ISFET sensor in 1× PBS environment is as low as 100pg/mL, which is much lower than the normal concentration of human body. © 2023 IEEE.

关键词： Nanosheets

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