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2023 3rd International Conference on Measurement Control and Instrumentation, MCAI 2023

作者： Yang, Yang Ai, Hao Li, Shibo Xiao, Guokai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Given that airborne electronic equipment may have a variety of wiring methods and may encounter a set of systems using multiple power supplies, it is necessary to take some measures on the circuit to ensure the normal work of the power supply and the post-circuit. A portable aviation detection equipment power supply system is designed in this paper. The system supports simultaneous access of two power supplies to identify and judge the input power supply and switch between the battery and the external power supply, effectively avoiding the security risks caused by battery backflow current. The battery power is monitored in real-time through the battery power detection circuit and will be reminded when the battery power is too low. Since the circuit uses a MOS tube to control the switching of the power supply, there is almost no voltage drop loss. In addition, the EMC protection circuit is set up at the power supply side to further improve the safety and reliability of the system. © Published under licence by IOP Publishing Ltd.

关键词： Oscillators (electronic)

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Fairness of Large Music Models: From a Culturally Diverse Perspective 9

Fairness of Large Music Models: From a Culturally Diverse Pe...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Qinyuan Gu, Bruce Zhang, He Li, Yunfeng University of Sydney School of Music NSW2008 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China CNPIEC KEXIN Ltd. Beijing100020 China

ISBN: (纸本)9798350391367

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Competitive Swarm Optimizer Based on Individual Learning Mechanism

Competitive Swarm Optimizer Based on Individual Learning Mec...

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： JingQi Tang Wei Li Lei Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

In view of the disadvantages of the traditional competitive swarm optimizer (CSO), such as falling into local minimization or poor convergence accuracy, this paper proposed an enhanced CSO algorithm called competitive swarm optimizer based on individual learning mechanism (ILCSO). Firstly, the individual selection rate and the learning rate are designed to dynamically select the winner and loser. The losers are updated by the precise competitive learning strategy to improve the algorithm exploitation ability. Secondly, the mutation strategy based on performance improvement is introduced, which improves the search ability of algorithm. The proposed algorithm effectively balances the local search and global exploration with the individual learning mechanism, and improves the probability of finding the global optimal solution. Finally, ILCSO is compared with six classical meta-heuristic algorithms on CEC2014 benchmark functions. The Wilcoxon rank-sum test is used to demonstrate that the ILCSO is effective. Experimental results and statistical analysis show that ILCSO has higher convergence speed and convergence accuracy.

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Large-Scale network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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Prediction of miRNA family based on class-incremental learning

Prediction of miRNA family based on class-incremental learni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Lulu Qiu Aimin Li Rong Fei Junhuai Li Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Department of Mechanical Engineering Department of Computer Science Division of Biomedical Engineering Saskatoon Canada

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA’s function.

关键词： Sequential analysis Incremental learning Annotations Biological system modeling Benchmark testing Bioinformatics

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Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space

arXiv

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arXiv 2024年

作者： Zhao, Minghua Qin, Xiangdong Du, Shuangli Bai, Xuefei Lyu, Jiahao Liu, Yiguang Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'An China College of Computer Science Sichuan University Chengdu China

Unlike single image task, stereo image enhancement can use another view information, and its key stage is how to perform cross-view feature interaction to extract useful information from another view. However, complex noise in low-light image and its impact on subsequent feature encoding and interaction are ignored by the existing methods. In this paper, a method is proposed to perform enhancement and de-noising simultaneously. First, to reduce unwanted noise interference, a low-frequency information enhanced module (IEM) is proposed to suppress noise and produce a new image space. Additionally, a cross-channel and spatial context information mining module (CSM) is proposed to encode long-range spatial dependencies and to enhance inter-channel feature interaction. Relying on CSM, an encoder-decoder structure is constructed, incorporating cross-view and cross-scale feature interactions to perform enhancement in the new image space. Finally, the network is trained with the constraints of both spatial and frequency domain losses. Extensive experiments on both synthesized and real datasets show that our method obtains better detail recovery and noise removal compared with state-of-the-art methods. In addition, a real stereo image enhancement dataset is captured with stereo camera ZED2. The code and dataset are publicly available at: https://***/noportraits/LFENet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Petri net modeling and vulnerability analysis of the Heartbleed

Petri net modeling and vulnerability analysis of the Heartbl...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Wang, Yichuan Wang, He Hei, Xinhong Ji, Wenjiang Zhu, Lei Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9781665441582

In recent years, a variety of network attacks emerge in an endless stream, and network attacks gradually show the characteristics of higher secrecy and greater harm. At present, the analysis of system vulnerabilities is generally focused on the characteristics analysis and impact hazard level, and lack of formal modeling and vulnerability analysis methods. In this paper, we model the OpenSSL service's Heartbleed vulnerability based on Petri net. We build a formal model combined with the source code and system state, analyze the vulnerability of the system running state, and propose an automatic vulnerability repair scheme. Experiments show that this model can carry out fine-grained formal analysis and simulation of the Heartbleed, which is of great significance to explore the system vulnerability modeling method. © 2021 IEEE.

关键词： Petri nets

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing 9

Continuous Verification of Catastrophic Recalling in Machine...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Y. Neil Ji, Zonghao Cui, Lei Zhang, Chong Liu, Lei Tian, Zhiyi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Deakin University School of IT VIC3125 Australia Xidian University Guangzhou Institute of Technology Guangzhou510555 China University of Technology School of Computer Science SydneyNSW2007 Australia

ISBN: (纸本)9798350391367

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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OpenViewer: Openness-Aware Multi-View Learning 39

OpenViewer: Openness-Aware Multi-View Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)157735897X

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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