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2022 International Conference on Energy Internet and Power Systems, ICEIPS 2022

作者： She, Wei Zhang, Renzhong Liu, Wei Zhong, Lihong Chen, Bin Tian, Zhao School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450000 China Henan Collaborative Innovation Center for Internet Medical and Health Services Zhengzhou University Zhengzhou450000 China

Due to the excellent wind power probabilistic prediction performance, Mixture Density Network (MDN) is used in short-term wind power forecasting, but the density leakage problem the Not a Number (NaN) loss problem and the choice of hyperparameters in the MDN seriously affect the model performance. GA-TDMDN is proposed in this paper for wind power probabilistic forecasting. GA-TDMDN uses truncated distribution as kernel function to solve density leakage. For the NaN loss problem that occurs during model training, different output layer activation methods and improved loss function are used for different mixture component parameters, so that the shape of the truncated normal distribution can be better controlled. Genetic Algorithms (GA) is used to optimize key hyperparameters in the MDN structure. The experimental results show that it is feasible to use truncated distribution to solve the density leakage problem, and using the GA algorithm to optimize the model structure can improve the model performance © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Semantic-Based Heterogeneous Controller Collaboration for Space-Terrestrial Integrated Networks 23

Semantic-Based Heterogeneous Controller Collaboration for Sp...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Bao, Yaru Tang, Feilong Li, Xu Chen, Long Liu, Jiacheng Yang, Yanqin Xu, Wenchao Cao, Lijun Han, Ping Shanghai Jiao Tong University Department of Computer Science & Engineering China School of Data Science & Engineering East China Normal University China School of Communication & Electronic Engineering East China Normal University China Innovation Resource Center Kedacom Co. Ltd China The Sci-Tech Information Office of Xuzhou Public Security Bureau China

ISBN: (纸本)9781665494571

In heterogeneous SDN networks, controllers deploying different protocols need to cooperate with each other and establish a global network view that can be passed to the network applications for unified management and end-to-end decision-making. Unfortunately, there is no standard northbound interface protocol to integrate heterogeneous SDN controllers. To address this issue in this paper, we propose an approach to find the mapping among controller ontologies in the SDN network with heterogeneous controllers in this paper. We regard these controller ontologies as a solution for discovering similar elements between local ontologies and global ontology. By mapping controller ontologies in the heterogeneous network together, we can form the complete network view to achieve centralized management. The idea behind the approach is to use ontology to represent the conceptual data of various heterogeneous controllers and then map similar semantic data together to form a consistent, global network knowledge. Evaluation results show that our approach has better accuracy than other algorithms and has an acceptable execution time. © 2021 IEEE.

关键词： Knowledge engineering Satellites Protocols Semantics Decision making Collaboration Ontologies

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SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation

arXiv

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arXiv 2024年

作者： Hong, Ming-Yi Huang, Yi-Hsiang Lin, Shao-En Teng, You-Chen Wang, Chih-Yu Lin, Che Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for robust baselines and expansive graph datasets is accentuated, particularly in the context of Heterogeneous Information Networks (HIN). Addressing this, we introduce SynHING, a novel framework for Synthetic Heterogeneous Information Network Generation aimed at enhancing graph learning and explanation. SynHING systematically identifies major motifs in a target HIN and employs a bottom-up generation process with intra-cluster and inter-cluster merge modules. This process, supplemented by post-pruning techniques, ensures the synthetic HIN closely mirrors the original graph’s structural and statistical properties. Crucially, SynHING provides ground-truth motifs for evaluating GNN explainer models, setting a new standard for explainable, synthetic HIN generation and contributing to the advancement of interpretable machine learning in complex networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Security Analysis of Audit Tools

Security Analysis of Audit Tools

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Armita Widyasuri Dimas Febriyan Priambodo Abdul Azzam Ajhari Septia Ulfa Sunaringtyas Research Center for Data and Information Science National Research and Innovation Agency Jakarta Indonesia Cybersecurity Engineering National Cyber and Crypto Polytechnic Bogor Indonesia Departement of Informatics Universitas Siber Asia Jakarta Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

BRIN has built an Electronic-Based Government System (EBS) application and infrastructure audit tool called Application Audit Tools by the mandate of Presidential Regulation No. 95/2018. However, it is necessary to ensure that the web-based audit tool that has been built is guaranteed security. This research was conducted to measure the vulnerability of the audit tool with a blackbox testing method that utilizes the Burpsuite and The Open Worldwide Application Security Project Zed Attack Proxy (OWASP ZAP) applications. The results obtained state that the audit tool has vulnerabilities. Although the results are still at a medium level such as Content Security Policy, Cross Domain Misconfiguration, and Vulnerability JS library by OWASP ZAP and TLS cookies without secure flag set, all of them need attention to improve system security. Recommendations are also given focusing on improving Content Security Policy, Cross Domain Misconfiguration, patches on javascript components such as jQuery, and handling Cookies and Cache.

关键词： Digital control Government Focusing Closed box Regulation Libraries Application security Security Informatics Testing

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Validity Matters: Uncertainty-Guided Testing of Deep Neural Networks

Validity Matters: Uncertainty-Guided Testing of Deep Neural ...

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作者： Jiang, Zhouxian Li, Honghui Wang, Rui Tian, Xuetao Liang, Ci Yan, Fei Zhang, Junwen Liu, Zhen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Faculty of Psychology Beijing Normal University Beijing China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China School of Automation and Intelligence Beijing Jiaotong University Beijing China

Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out-of-distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large-scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy-based and ensemble-based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty-guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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An Image Comment Method Based on Emotion Capture Module 3

An Image Comment Method Based on Emotion Capture Module

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3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

作者： Li, Qin Yin, Jiankai Wang, Yan Beihang University School of Automation Science and Electrical Engineering Beijing China Beijing Advanced Innovation Center for Big Data Based Precision Medicine Beijing China

ISBN: (纸本)9781665406055

The task of generating comments for images has been studied by many scholars. Existing methods, such as sentence generation based on recurrent neural network (RNN), have poor variability due to their structural limitations. In addition, the generated comment is so stiff that lacks the emotional color and value orientation of human language. In order to make the image comment more open and meet the needs of emotion control, we designed a comment generation and style transfer module for pictures. This paper mainly completes the following work: (1) Imitate human emotions by giving positive and negative colors to the resulting sentences. Using the characteristics of generative adversarial network, comments can not only meet the requirement of verisimilitude, but also encourage naturalness and diversity. (2) Design an emotion capture module (ECM) and a more compound loss function. It can complete the emotional reversal of the caption content that needs to be controlled, making it more colloquial and life-like. Experimental results on public datasets and generated dataset show that the proposed model has good effect on style transfer accuracy and content preservation. © 2021 IEEE.

关键词： Generative adversarial networks

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Model learning:a survey of foundations,tools and applications

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Frontiers of Computer science 2021年第5期15卷 71-92页

作者： Shahbaz ALI Hailong SUN Yongwang ZHAO Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191China SKLSDE School of Computer Science and EngineeringBeihang UniversityBeijing 100191China School of Software Beihang UniversityBeijing 100191China School of Cyber Science and Technology College of Computer ScienceZhejiang UniversityHangzhou 310058China

Software systems are present all around us and playing their vital roles in our daily *** correct functioning of these systems is of prime *** addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software ***,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging *** mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be *** complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully *** paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and ***,we review the background and foundations of model learning,which form the basis of subsequent ***,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison ***,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.

关键词： model learning active automata learning automata learning libraries/tools inferring behavior models testing and formal verification

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Face recognition

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Lower Limb Muscle State Transition Control Based on Complex Network 42

Lower Limb Muscle State Transition Control Based on Complex ...

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42nd Chinese Control Conference, CCC 2023

作者： Geng, Junjie Chen, Lingling Guo, Xin Chen, Rui Wang, Mengge School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China Intelligent Rehab. Device and Detection Technol. Eng. Research Centre of the Ministry of Education Tianjin300401 China School of Electrical Engineering Hebei University of Technology Tianjin300130 China Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Tianjin300401 China

ISBN: (纸本)9789887581543

Muscle activation cannot achieve the activation state experienced during normal activity throughout the rehabilitation process, and effectiveness of rehabilitation will be enhanced if the muscle state can be controlled from the 'zero EMG' state to the normal motion state. It is challenging to precisely regulate the muscle state during the model establishment process because the lower limb muscle group is a highly interconnected complicated system. Therefore, this study provides a muscle state transfer control method based on complex network control theory. A linear time-invariant functional myoelectric network model is modeled, and the structural controllability of the network is demonstrated using the PBH rank criterion to determine the optimal set of control nodes. Functional Electrical Stimulation is used as an exogenous input to drive state transfer between muscles. The results suggest that the method we propose is effective in controlling muscle state transitions. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Muscle

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