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Chinese Control and Decision Conference, CCDC

作者： Xintong Wang Yibo Sun Xuan Wang Enliang Wang Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P.R. China Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Nanjing P.R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Information Technology and Electrical Engineering The University of Queensland Brisbane Brisbane Australia

Traffic prediction is the basis for dynamic network services and resource optimization. Software Defined Networking (SDN) provides a global view for network measurement and management, and a reliable way for network information collection and network traffic prediction. Recently, SDN traffic prediction model enabled by deep learning mainly focuses on mining the temporal correlation of traffic data, while the modeling of its spatial correlation remains to be explored. From the perspective of the problem, a novel Spatial-Temporal Residual Graph Convolutional Network (STRGCN) model for SDN Traffic Matrix (TM) prediction is proposed. Combined with SDN network topology, TM prediction is defined as time series data prediction in non-Euclidean space. Temporal Gated-Conv Blocks are based on Gated Convolutional Layer, and Spatial Graph-Conv Blocks are based on Graph Convolutional Network (GCN). Through identity mapping between Spatial-Temporal Conv Blocks, the deep spatial and temporal features extraction is implemented. Tested by TM data from CERNET and GÉANT datasets, the accuracy and the timely performance of our model are evaluated. Experimental results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics

An efficient face recognition attack method based on generat...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Hu Ding Shumeng He Yanwen Wu Yongli Jin Lin Gan Gaodi Xu Houqun Yang School of Computer Science and Technology Hainan University Haikou Hainan School of Big Data and Software Engineering Chongqing University Chongqing School of Computer Science and Technology Anhui University Hefei Anhui Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette USA Hainan Association for Artificial Intelligence Haikou Hainan

ISBN: (纸本)9781665426312

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks.

关键词： Training Measurement Deep learning Resistance Target recognition Face recognition Perturbation methods

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A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

arXiv

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arXiv 2021年

作者： Wu, Le He, Xiangnan Wang, Xiang Zhang, Kun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230029 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China University of Science and Technology of China Hefei230026 China National University of Singapore Singapore

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data;2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph;and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field. © 2021, CC BY.

关键词： Surveys

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Phase transitions in mixed traffic streams comprising HDVsand CAVs through the implementation of delayed control mechanisms to mitigate traffic jerk

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Chaos, Solitons & Fractals 2025年 199卷

作者： Tong Zhou Yudan Tian Meilan Zhu Guanghan Peng Darong Huang School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing 402260 China College of Physical Science and Technology Guangxi Normal University Guilin 541004 China Engineering Research Center of Autonomous Unmanned System Technology of Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology and the School of Artificial Intelligence Anhui University Hefei 230601 China

Due to pedestrian traffic and frequent lane changes, rapid acceleration and deceleration of vehicles often occur, resulting in the traffic jerk phenomenon. This issue is particularly pronounced in mixed fleets of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), posing significant safety hazards. To address this, we propose a heterogeneous lattice model incorporating delayed control to mitigate traffic jerk in environments involving both CAVs and HDVs. Linear stability analysis reveals that traffic jerk adversely affects the stability of heterogeneous vehicles, whereas delayed control mitigates this negative impact. Additionally, nonlinear analysis is conducted to derive the equation related to delayed control aimed at countering traffic jerk. Finally, through simulations examining density, limit cycles, and spectral analysis in scenarios involving CAVs and HDVs, it is demonstrated that delayed control effectively resists the detrimental effects of traffic jerk on heterogeneous vehicles.

关键词： Health hazards

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Developing Global Digital Economy after COVID-19: Preface for ITQM 2020 & 2021

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Procedia Computer Science 2021年 199卷 1-9页

作者： Liu, Yubin Shi, Yong Wang, Yong Ergu, Daji Berg, Daniel Tien, James Li, Jianping Tian, Yingjie Southwest Minzu University Sichuan Chengdu610041 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Engineering University of Miami Coral GablesFL33124 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Welcome to the Eighth International Conference on Information technology and Quantitative Management (ITQM 2020 & 2021), July 9-11, Chengdu, China. The theme of ITQM 2020 & 2021 is “Developing Global Digital Economy after COVID-19”. ITQM 2020 & 2021 is organized by International Academy of Information technology and Quantitative Management (IAITQM), Southwest Minzu University, Chinese Academy of Sciences and University of Nebraska at Omaha. IAITQM was formally inaugurated on June 3, 2012 with more than 50 founding members from China, United States, Australia, Japan, Lithuania, Poland, Romania, Spain, Singapore, South Korea, The Netherlands, Turkey and other countries. The International Conference on Information technology and Quantitative Management (ITQM), established by IAITQM, is a global forum for exchanging research findings and case studies that bridge the latest information technology and quantitative management techniques. It explores how the use of information technology techniques to improve quantitative management and how the development of management tools can reshape the development of information technology. The First International Conference on Information technology and Quantitative Management (ITQM 2013) took place in Suzhou, China. The Second International Conference on Information technology and Quantitative Management (ITQM 2014) was held in Moscow, Russia. The Third International Conference on Information technology and Quantitative Management (ITQM 2015) was held at Rio de Janeiro, Brazil. The Forth International Conference on Information technology and Quantitative Management (ITQM 2016) was held at Asan, Korea. The Fifth International Conference on Information technology and Quantitative Management (ITQM 2017) was held at New Delhi, India. The Sixth International Conference on Information technology and Quantitative Management (ITQM 2018) was hosted at Omaha, USA. The Seventh International Conference on Information technology and Quantitative

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Research on Multiscale Information Storage of MEG of Depression Based on ARFI Model 21

Research on Multiscale Information Storage of MEG of Depress...

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Luo, Dayou Yan, Wei Li, Jin Hou, Fengzhen Wang, Jun Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Psychiatry Affiliated Brain Hospital of Nanjing Medical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9781450389853

As a noninvasive brain function detection technique, Magnetoencephalography (MEG) has been widely used in the research of depression. By analyzing the amount of information storage, the difference of MEG information storage between patients with depression and healthy people was studied. Our analysis was carried out in the popular multiscale entropy framework, in which the time series were first "coarse-grained" on the selected time scale by low-pass filtering and down-sampling, and then its complexity was evaluated ac-cording to conditional entropy. Within this framework, we used the linear fractional integral autoregressive (ARFI) model to derive the analytical expression of information storage calculated at multiple time scales. We used the information storage expression derived from the ARFI model and then collected the information storage of MEG through positive, negative and neutral stimuli and finally calculate it. The experimental results showed that it was best to distinguish between patients with depression and healthy people through the information storage of MEG through positive stimuli, and it was best to distinguish healthy people from patients with depression at a higher frequency if it was negative or neutral stimuli.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Magnetoencephalography

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An improved bat algorithm based on hybrid with ant lion optimizer 13th

An improved bat algorithm based on hybrid with ant lion opti...

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13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Dao, Thi-Kien Chu, Shu-Chuan Pan, Jeng-Shyang Nguyen, Trong-The Ngo, Truong-Giang Nguyen, Trinh-Dong Tran, Huu-Trung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

ISBN: (纸本)9789811533075

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

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Maize Crop Row Recognition Algorithm Based on Improved Unet Network

SSRN

引用

SSRN 2022年

作者： Diao, Zhihua Guo, Peiliang Zhang, Baohua Zhang, Dongyan Yan, Jiaonan He, Zhendong Zhao, Suna Zhao, Chunjiang School of Electrical Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China College of Artificial Intelligence Nanjing Agricultural University Nanjing211800 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China

The accurate identification of the centerlines of crop rows represents the core principle of agricultural robot visual navigation technology. However, few existing deep-learning-based maize crop row centerline recognition algorithms generally have limitations such as poor robustness and weak adaptability. To mitigate these issues, this study proposes a centerline detection algorithm based on a convolutional neural network. The U-shaped Network (UNet) – a traditional semantic segmentation network – was enhanced to create the Atrous Spatial Pyramid Pooling UNet (ASPP-UNet) network for maize crop row and background segmentation, and the improved vertical projection method was subsequently employed to measure the crop rows’ feature points. Finally, the least squares method was used to fit the centerlines. Experimental results yielded the Mean Intersection Over Union (MIoU), Mean Pixel Accuracy (MPA), Mean Precision (MP), and Mean Recall (MR) metrics of ASPP-UNet network to be 83.23%, 90.18%, 91.79%, and 90.18% respectively. These figures represent respective increases of 10.03%, 11.86%, 9.43% and 11.24% compared to the Fully Convolutional Network (FCN), and 7.80%, 5.52%, 2.71%, and 5.52% compared to the UNet. Furthermore, the average fitting time and angle error of the improved vertical projection method were reduced to 65 ms and 4.57°, compared to 80 ms and 6.12°in the traditional vertical projection method, and 86 ms and 5.67°in the left and right edge centerline method. Likewise, the accuracy of proposed method increased to 91.79%, compared to 87.21% and 90.16% of the two aforementioned methods, respectively. Therefore, the proposed method successfully meets the accuracy and real-time demands of agricultural robot vision navigation, and can function effectively under varying environmental pressures. © 2022, The Authors. All rights reserved.

关键词： Agricultural robots

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