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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Hao, Qi Huang, Jie Wang, Bin Zhou, Feng Cyber Science and Engineering Southeast University Nanjing China Zhejiang Key Laboratory of Artificial Intelligence of Things Network and Data Security Hangzhou China

ISBN: (纸本)9798350352214

Classic image features were once widely used in image classification but have been almost entirely replaced by neural networks today. While the performance of neural networks, especially convolutional neural networks (CNNs), is indisputable, their lack of interpretability has become a significant limitation in recent years. This paper explores the effective combination of classic image features and convolutional image features, investigating their differences and similarities. Through a series of experiments, a method is proposed to effectively integrate these two types of features by employing a multiplicative attention mechanism. This approach combines standardized MPEG-7 descriptors with convolutional features before feeding them into the fully connected layer for classification. The final model demonstrates an improvement in classification accuracy and indicates the potential of learning traditional image features from the integrated features. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

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Computers, Materials & Continua 2024年第2期78卷 1731-1754页

作者： Bin Luo Liangguo Chen Shuhua Ruan Yonggang Luo School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China Cyber Science Research Institute Sichuan UniversityChengdu610065China

Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack *** paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity ***,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these ***,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack *** last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT *** results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack *** to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.

关键词： Advanced persistent threat provenance graph multi-head self-attention graph neural network

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Puwei Sun, Zhouxing Li, Rui Chen, Jinchuan Gong, Ping Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China Renmin University of China School of Mathematics China Fujian Normal University College of Computer and Cyber Security China

ISBN: (纸本)9798350304855

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently. © 2023 IEEE.

关键词： Blockchain

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data-Driven Fault Compensation Tracking Control for Coupled Wastewater Treatment Process

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 294-297页

作者： Peihao Du Weimin Zhong Xin Peng Linlin Li Zhi Li the Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China IEEE the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education the School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China

Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process(WWTP)subject to sensor faults. Invariant set theory is introduced to eliminate the completely bounded and differentiable conditions of coupled non-affine dynamics and to explicitly express the control *** adaptive fault compensation mechanism is constructed to accommodate the effects of sensor faults. By employing a cubic absolutevalue Lyapunov criteria。

关键词： differentiable eliminate absolute

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SlagNet: A Real-Time Slag Segmentation Model Based on Context and Edge Fusion 16th

SlagNet: A Real-Time Slag Segmentation Model Based on Conte...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Liu, Kecheng Li, Jiangyun Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the process of iron and steel smelting, steel slag is inevitably produced as a byproduct. Accurately identifying steel slag is a prerequisite for controlling the content of steel slag. Conventional vision-based steel slag processing solutions are constrained by real-time performance or robustness against disturbances, rendering them directly applied in industrial sites. To address these issues, SlagNet, a steel slag segmentation network with real-time performance and strong disturbance resistance, is designed in this paper. By designing a lightweight branch to extract rich context information, and combining edge branches to supplement the slag edge information, 77.69 mIOU and 13.25 FPS are achieved in the field collection of slagging data set. The results indicate that SlagNet achieves the optimal balance between real-time performance and accuracy in comparison with other segmentation methods, thereby meeting the requirements of industrial sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slags

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年第13期12卷 24441-24456页

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Prediction of Rolling Force in Hot Rolling Process Based on Rollformer Model 16th

Prediction of Rolling Force in Hot Rolling Process Based on ...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Li, Qing Jiang, Pengyuan Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the steel manufacturing industry, the precise forecasting of rolling forces in the hot rolling process is vital for enhancing production efficiency and ensuring product quality. Traditional forecasting methods exhibit limitations when addressing the nonlinearity and variability of industrial data, and current deep learning models are slow to respond to real-time data, with room for improvement in predictive accuracy within dynamic production settings. This paper introduces the Rollformer deep learning model, which adeptly captures intricate nonlinear patterns through the self-attention mechanism's nonlinear transformation capabilities, and boosts sensitivity and adaptability to data variability via adaptive weight distribution. The Rollformer model also pioneers the Roll Self-attention mechanism, leveraging probabilistic sparsity to diminish computational complexity and accelerate the model’s response. Additionally, it employs a generative decoder for swift long-sequence predictions, thereby augmenting the model’s predictive precision. Validated with extensive real-world data from steel mills, the Rollformer model demonstrates superior predictive accuracy and response speed when compared to other Transformer-based deep learning models such as Autoformer and Conformer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing 400065China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China Chongqing Innovation Center of Industrial Big-Data Co.Lt Chongqing400700China

The lack of facial features caused by wearing masks degrades the performance of facial recognition *** occluded face recognition methods cannot integrate the computational resources of the edge layer and the device ***,previous research fails to consider the facial characteristics including occluded and unoccluded *** solve the above problems,we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature ***,the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face ***,a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain ***,a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load,computational power,bandwidth,and delay tolerance constraints of the *** method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition *** experimental results show that the proposed method achieves an average gain of about 21%in recognition latency,while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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Energy-optimal DNN model placement in UAV-enabled edge computing networks

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Digital Communications and Networks 2024年第4期10卷 827-836页

作者： Jianhang Tang Guoquan Wu Mohammad Mussadiq Jalalzai Lin Wang Bing Zhang Yi Zhou State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China School of Engineering The University of British Columbia OkanaganKelownaV1V 1V7Canada College of General Aviation and Flight Nanjing University of Aeronautics and AstronauticsNanjing210016China

Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication *** the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical *** neural networks(DNNs)are becoming a driving force to extract valuable information from sensing ***,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of *** this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks *** is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge *** Lyapunov optimization technique is leveraged for the proposed DNN model placement *** on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues ***,extensive simulations are provided to depict the effectiveness of the AOP *** numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.

关键词： UAV-Enabled edge computing DNN model Placement 6G networks Inference tasks

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