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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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Cross-modality Complementary Interaction Network 8

Cross-modality Complementary Interaction Network

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Cao, Zhengnan Gao, Yue Shanghai Institute of Technology School of Science Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350369564

Salient Object Detection (SOD) aims to identify the most conspicuous objects and regions in the human vision. This paper presents the Cross-Modality Complementary Interaction Network(CCINet), a novel approach for obtaining complementary features between different modalities. We introduce feature complementarity Module (FCM) to capture inter-modality complementary features. Subsequently, the Multi-Scale Aggregation Module (MAM) is utilized to acquire multilevel information across different resolutions. Finally, we employ a decoder to generate saliency maps. Experimental results demonstrate the superiority of our model over previous methods, as it outperforms them across four evaluation indicators on five challenging datasets. © 2023 IEEE.

关键词： Object detection

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Crowd2: Multi-agent Bandit-based Dispatch for Video Analytics upon Crowdsourcing 42

Crowd2: Multi-agent Bandit-based Dispatch for Video Analytic...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Chen, Yu Zhang, Sheng Yan, Yuting Jin, Yibo Chen, Ning Ji, Mingtao Xiao, Mingjun Nanjing University State Key Lab. for Novel Software Technology China University of Science and Technology of China School of Computer Science and Technology China

ISBN: (纸本)9798350334142

Many crowdsourcing platforms are emerging, leveraging the resources of recruited workers to execute various outsourcing tasks, mainly for those computing-intensive video analytics with high quality requirements. Although the profit of each platform is strongly related to the quality of analytics feedback, due to the uncertainty on diverse performance of workers and the conflicts of interest over platforms, it is non-trivial to determine the dispatch of tasks with maximum benefits. In this paper, we design a decentralized mechanism for a Crowd of Crowdsourcing platforms, denoted as Crowd2, optimizing the worker selection to maximize the social welfare of these platforms in a long-term scope, under the consideration of both proportional fairness and dynamic flexibility. Concretely, we propose a video analytics dispatch algorithm based on multi-agent bandit, for which the more accurate profit estimates are attained via the decoupling of multi-knapsack based mapping problem. Via rigorous proofs, a sub-linear regret bound for social welfare of crowdsourcing profits is achieved while both fairness and flexibility are ensured. Extensive trace-driven experiments demonstrate that Crowd2 improves the social welfare by 36.8%, compared with other alternatives. © 2023 IEEE.

关键词： Profitability

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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A Novel Configurable RO-Obfuscated PUF Design with Machine Learning Immunity

A Novel Configurable RO-Obfuscated PUF Design with Machine L...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Fan, Lu Huang, Zhao Wang, Junjun Zhou, Lirong Zhu, Yi'an Wang, Quan School of Software Northwestern Polytechnical University Xi'an China School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350327380

Strong physical unclonable function (PUF) is a low-cost hardware security primitive to protect Internet-of-Things (IoT) devices. However, it may be attacked by machine learning (ML). Various PUF models designed in complex structures, such as nonlinearity or challenge-response pair (CRP) obfuscation, have been presented to combat these risks. However, these methods mainly increase area overhead, and the prediction rate is still very high. Therefore, this paper proposes a structure obfuscated PUF named SO-PUF. Our proposal derives from the configurable ring oscillator (CRO) PUF. The CRO can be transformed into two different structures depending on the challenges by randomly deleting one of the two NOT gates in each stage. Thus, half of the CRPs generated by SO-PUF are invalid, which will confuse the attackers. We have implemented and verified the performance of SO-PUF on the Xilinx-6XC6SLX25 microboard. Experimental results show that compared with the dual-mode PUF, the SO-PUF improves uniqueness by 0.71 %, temperature reliability by 38.4 %, and voltage reliability by 1.92 %. In addition, the SO-PUF also reduces the prediction rates of LR, SVM, and ANN modeling attacks by 0.26 %, 0.38 %, and 5.62 %, respectively. The results prove that SO-PUF has better resistance to ML attacks than dual-mode PUF. © 2023 IEEE.

关键词： Internet of things

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Incentive-Driven Pricing Game for Multi-Edge Service Providers towards Optimal Profits

Incentive-Driven Pricing Game for Multi-Edge Service Provide...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Pang, Shengye Zhao, Xinkui Luo, Jiayin Zheng, Bangpeng Yin, Jianwei Zheng, Xiaolin Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China

ISBN: (纸本)9798350304855

The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further progress. Without proper pricing guidance, service providers tend to formulate pricing strategies solely based on their own interests, which frequently hinders the maximization of overall market benefits. This problem is even more challenging in edge computing scenarios as different Edge Service Providers (ESPs) are located in distributed regions and influenced by multiple factors, making it difficult to formulate a single pricing model. This paper proposes a multi-participant stochastic game model to formalize the multi-edge service pricing problem. An incentive mechanism based on Pareto improvement is then proposed to drive the game to the Pareto optimal direction with optimal profits. Finally, an improved PSO algorithm is proposed to solve the game model and analyze the equilibrium states under different evolutionary mechanisms. Experimental results indicate that the proposed pricing incentive mechanism can promote a more effective and reasonable pricing allocation, avoiding 21.6% anarchism loss of the overall profits, while showcasing the effectiveness of our algorithm in solving the game. © 2023 IEEE.

关键词： Costs

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Fast Super-Resolution Network via Dynamic Path Selection 2

Fast Super-Resolution Network via Dynamic Path Selection

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Jia, Longfei Hu, Yuguo Tian, Xianlong Luo, Wenwei University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350331417

In the context of edge computing environments, where the storage space and computational resources are constrained, complex super-resolution network models face significant challenges during inference. In this paper, we propose a Fast Super-Resolution Network (FSRN) based on the dynamic path selection mechanism. This method employs a policy network to boost the inference process of super-resolution network models, enabling efficient super-resolution tasks under resource limitations. The primary function of the policy network is to intelligently select appropriate inference paths based on input data and available computational resources. Its key objective is to minimize the degradation in super-resolution quality while reducing the computational burden as much as possible. In view of this objective, we designed a reward function to guide the policy network in discovering the optimal strategies. With the guidance of the policy network, we successfully enhanced the inference speed of super-resolution network models on edge devices while efficiently reducing computational overhead. Extensive experiments verify that the proposed method can significantly reduce the inference time at the cost of slight or even no performance degradation. © 2023 IEEE.

关键词： accelerate reasoning edge computing resource constrained Super-resolution

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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