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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

Fast Mapping of Flexibility Regions at TSO-DSO Interfaces un...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Patig, Alice Stanojev, Ognjen Aristidou, Petros Kiprakis, Aristides Hug, Gabriela Eth Eeh - Power Systems Laboratory Zurich Switzerland University of Edinburgh School of Engineering Institute for Energy Systems United Kingdom Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Cyprus

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a PQ-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the PQ-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method. © 2022 IEEE.

关键词： Electric power distribution

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Energy-Efficient Wireless Federated Learning: A Secrecy Oriented Design via Sequential Artificial Jamming

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IEEE Transactions on Vehicular technology 2023年第5期72卷 6412-6427页

作者： Wang, Tianshun Huang, Ning Wu, Yuan Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer and Information Science 999078 China University of Macau State Key Lab of Internet of Things for Smart City The Department of Computer and Information Science 999078 China Zhuhai UM Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Tainan701 Taiwan

Wireless federated learning (FL) is envisioned as a promising paradigm of distributed learning in wireless networks without disclosing users' data privacy. However, radio channel leads to a potential risk of eavesdropping attack when sending the trained model data in wireless networks. To address this eavesdropping attack, in this work, we propose an energy-efficient wireless FL by using artificial jamming. Specifically, the group of wireless devices (WDs) adopt the time division multiple access (TDMA) approach to send their locally trained models to the FL server for model aggregation subject to the eavesdropping attack of a malicious node. When one of the WDs sends its local model, all the other WDs send the artificial jamming signals to interfere with the eavesdropper, which helps increase the secrecy throughput of the targeted WD for uploading its local model. Different from many existing studies using stochastic gradient descent (SGD), we adopt the stochastic average gradient (SAG) method in the local training to improve the convergence of FL and derive the corresponding lower bound of the FL convergence rate via SAG. Furthermore, we formulate an optimization problem that aims at minimizing the energy consumption of the WDs by jointly optimizing different WDs' local training time, their uploading transmission time in TDMA and the transmit-powers for providing artificial jamming, as well as the FL configurations of the local/global iterations. We also propose an efficient algorithm for solving this non-convex optimization problem. Numerical results are illustrated to validate the advantages of our design of wireless FL and the corresponding algorithms. In particular, the results demonstrate that our secrecy oriented energy-efficient FL can significantly outperform the other heuristic FL schemes. © 1967-2012 IEEE.

关键词： Energy efficiency

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Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

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computers, Materials & Continua 2022年第6期71卷 6275-6291页

作者： Riad Alharbey Ameen Banjar Yahia Said Mohamed Atri Abdulrahman Alshdadi Mohamed Abid Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia CES Laboratory ENISUniversity of SfaxTunisia

Hajj and Umrah are two main religious duties for *** help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each *** the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management *** this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence *** detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security *** proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and *** main contribution of this work focuses on the detection and tracking model which is based on an improved object detection *** improved Yolo v4 was used for face detection and *** has been very effective in detecting small objects in highresolution *** novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired *** wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building *** main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive *** demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available *** wider faces dataset was used for the train and the evaluation of the proposed detection and tracking *** proposed model has achieved good results with 91.2%of mAP and a processing speed

关键词： Crowdmanagement Hajj and Umrah face detection object tracking convolutional neural networks(CNN) adaptive attention mechanisms

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Method for Image-Based Preliminary Assessment of Car Park for the Disabled and the Elderly Using Convolutional Neural Networks and Transfer Learning 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hanpinitsak, Panawit Posawang, Pitiphum Phankaweerat, Sumate Pattara-atikom, Wasan Department of Computer Engineering Khon Kaen University Khon Kaen40002 Thailand Intelligent Transportation Systems Laboratory National Electronics and Computer Technology National Science and Technology Development Agency Pathumthani12120 Thailand Department of Computer Science Vongchavalitkul University Nakhon Ratchasima30000 Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

It is critical to assess the standards of disabled facilities in order to ensure the comfort and safety of disabled individuals who use them. In this study, deep convolutional neural networks (CNNs) with multi-label classification capability are employed for a preliminary evaluation of the car park for the disabled and the elderly in accordance with ministerial regulations, reducing the burden of on-site inspection by specialists. Using a transfer learning technique, the weights of an Inception-V3, Xception, and EfficientNet-B2 architectures previously trained on the ImageNet dataset were updated with the disabled car park image dataset. We used 4,812 training images and 355 test images to train, evaluate, and compare the model. The results revealed that the EfficientNet-B2 model yielded the best performance for 5 out of 6 classes, with the F1-score between 79.8% and 95.6%. In contrast, the remaining one class was best predicted by the Xception model, where the F1-score was 83.33%. This implies that it is possible to apply CNNs to aid in the evaluation of handicap facilities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Skeleton-Embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Luo, Li Ren, Ziliang Qin, Yong Zhang, Qieshi Gao, Xiangyang Dongguan University of Technology Department of Computer Science Dongguan52300 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Chinese University of HongKong Hong Kong Hong Kong

ISBN: (纸本)9798350327182

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained. © 2023 IEEE.

关键词： Semantics

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DAANet: Dual Attention Aggregating Network for Salient Object Detection

DAANet: Dual Attention Aggregating Network for Salient Objec...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Yijie Wang, Hewei Li, Zhenqi Wang, Shaofan Dev, Soumyabrata Zuo, Guoyu Beijing University of Technology Beijing-Dublin International College Beijing100124 China Beijing University of Technology Faculty of Information Technology Beijing100124 China University College Dublin School of Computer Science Dublin Ireland Beijing University of Technology Faculty of Information Technology China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350325706

When the Convolutional neural network (CNN) has been introduced for computer vision, many researches use CNN-based model to perform salient object detection (SOD). In recent years, The feature pyramid network (FPN) based structure is more popular for salient object detection tasks. In this paper, to improve the overall performance of salient object detection tasks, we propose a dual attention aggregating network (DAANet), which is an FPN-based deep convolutional neural network with a dual attention aggregation module (DAAM) and dilated refinement block (DRB). The DRB module uses convolutions with different dilation rates to expand the receptive field. The DAAM considers the salient map prediction from the low-level output as pseudo-attention which can efficiently aggregate multi-scale information. The convolution block attention module (CBAM) in DAAM can refine the aggregation of pseudo-attention which enables better performance. We evaluate DAANet on six benchmark datasets that prove the effectiveness of DAANet and its components. Our implementation can be found at: https://***/Att100/DAANet. © 2023 IEEE.

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules

Boosting Document-Level Relation Extraction by Mining and In...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Fan, Shengda Mo, Shasha Niu, Jianwei School of Cyber Science and Technology Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

Document-level relation extraction (DocRE) aims at extracting relations of all entity pairs in a document. A key challenge to DocRE lies in the complex interdependency between the relations of entity pairs. Unlike most prior efforts focusing on implicitly powerful representations, the recently proposed LogiRE (Ru et al., 2021) explicitly captures the interdependency by learning logical rules. However, LogiRE requires extra parameterized modules to reason merely after training backbones, and this disjointed optimization of backbones and extra modules may lead to sub-optimal results. In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. MILR first mines logical rules from annotations based on frequencies. Then in training, consistency regularization is leveraged as an auxiliary loss to penalize instances that violate mined rules. Finally, MILR infers from a global perspective based on integer programming. Compared with LogiRE, MILR does not introduce extra parameters and injects logical rules during both training and inference. Extensive experiments on two benchmarks demonstrate that MILR not only improves the relation extraction performance (1.1%-3.8% F1) but also makes predictions more logically consistent (over 4.5% Logic). More importantly, MILR also consistently outperforms LogiRE on both counts. Code is available at https://***/XingYing-stack/MILR. © 2022 Association for Computational Linguistics.

关键词： Integer programming

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Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow

Studying the Impact of Semi-Cooperative Drivers on Overall H...

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IEEE Symposium on Intelligent Vehicle

作者： Noam Buckman Sertac Karaman Daniela Rus Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA Laboratory of Information and Decision Systems Massachusetts Institute of Technology Cambridge USA

Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. In addition, new autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. Yet the overall impact on traffic flow for this new class of planners remain to be understood. In this work, we present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response assuming knowledge of the SVOs of other agents. We simulate nominal traffic flow and investigate whether the proportion of prosocial agents on the road impact individual or system-wide driving performance. Experiments show that the proportion of prosocial agents has a minor impact on overall traffic flow and that benefits of semi-cooperation disproportionally affect egoistic and high-speed drivers.

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