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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Gao, Yuan Li, Haokang College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang050000 China

ISBN: (纸本)9798350303780

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Sentiment analysis

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Adaptive Structural Hyper-Parameter Configuration by Q-Learning

arXiv

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arXiv 2020年

作者： Zhang, Haotian Sun, Jianyong Xu, Zongben School of Mathematics and Statistics National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China

Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence. Performance of an evolutionary algorithm depends not only on its operation strategy design, but also on its hyper-parameters. Hyper-parameters can be categorized in two dimensions as structural/numerical and time-invariant/time-variant. Particularly, structural hyper-parameters in existing studies are usually tuned in advance for time-invariant parameters, or with handcrafted scheduling for time-invariant parameters. In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the structural hyper-parameter which controls computational resource allocation in the CEC 2018 winner algorithm by Q-learning. Experimental results show favorably against the winner algorithm on the CEC 2018 test functions. Copyright © 2020, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Learning to be Global Optimizer

arXiv

引用

arXiv 2020年

作者： Zhang, Haotian Sun, Jianyong Xu, Zongben School of Mathematics and Statistics National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China

The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization phase and an escaping phase) global optimization algorithm for smooth non-convex functions. For the minimization phase, a model-driven deep learning method is developed to learn the update rule of descent direction, which is formalized as a nonlinear combination of historical information, for convex functions. We prove that the resultant algorithm with the proposed adaptive direction guarantees convergence for convex functions. Empirical study shows that the learned algorithm significantly outperforms some well-known classical optimization algorithms, such as gradient descent, conjugate descent and BFGS, and performs well on ill-posed functions. The escaping phase from local optimum is modeled as a Markov decision process with a fixed escaping policy. We further propose to learn an optimal escaping policy by reinforcement learning. The effectiveness of the escaping policies is verified by optimizing synthesized functions and training a deep neural network for CIFAR image classification. The learned two-phase global optimization algorithm demonstrates a promising global search capability on some benchmark functions and machine learning tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning

SurveilEdge: Real-time Video Query based on Collaborative Cl...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Shibo Wang Shusen Yang Cong Zhao National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Department of Computing Imperial College London

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype 1 with multiple edge devices and a public Cloud, and conduct extensive experiments using real-world surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7× less bandwidth cost and 5.4× faster query response time than the cloud-only solution; and can improve query accuracy by up to 43.9% and achieve 15.8× speedup respectively, in comparison with edge-only approaches.

关键词： Streaming media Cameras Surveillance Cloud computing Training Task analysis Real-time systems

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Research on Construction and Application of Knowledge Mapping of Intelligent Transportation Inspection 7

Research on Construction and Application of Knowledge Mappin...

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7th International Conference on Information Science and Control engineering, ICISCE 2020

作者： Wang, Zhiqiang Hu, Shijun Mei, Feng Jing, Wenfeng State Grid Zhejiang Electric Power Corporation Information Telecommunication Branch Hangzhou China State Grid Anhui Electric Power Research Institute Hefei China Xi'An Jiaotong University National Engineering Laboratory for Big Data Analytics Xi'an China

ISBN: (纸本)9781728164069

The business knowledge of intelligent transportation inspection is scattered in the business system, database and expert experience, and it is impossible to visualize the entire business logic of transportation inspection. Aiming at the characteristics of complex and wide variety of operational inspection business relationships, an intelligent operational inspection knowledge mapping is constructed. Through the steps of operation inspection business ontology construction, knowledge acquisition, knowledge fusion, knowledge mapping display, knowledge mapping application and other steps, a logical knowledge mapping of operation inspection business is formed, which facilitates the recognition of the entire business relationship and assists the decision-making analysis of the operation inspection personnel. © 2020 IEEE.

关键词： Inspection

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Modeling the homophily effect between links and communities for overlapping community detection 25

Modeling the homophily effect between links and communities ...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zhang, Hongyi Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.

关键词： Matrix factorization

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Rethinking Node-wise Propagation for Large-scale Graph Learning

arXiv

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arXiv 2024年

作者： Li, Xunkai Su, Daohan Ma, Jingyuan Zhang, Wentao Wu, Zhengyu Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes’ local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglect local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remain orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

引用

Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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