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IEEE International Conference on Systems, Man and Cybernetics

作者： Juanjuan Li Xiaolong Liang Rui Qin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Decentralized Autonomous Organizations (DAOs) have been gaining popularity in recent years due to their promise of realizing the decentralized Web 3.0. However, most DAOs rely heavily on token-centric value systems as well as allocate decision-making authority and yield-sharing rights according to the held tokens, which often lead to monopolization of power and rights. To address this issue, this paper contributes to propose a truly democratic organization model, named True Autonomous Organizations and Operations (TAOs), that does not count upon tokens and is guided by principles of contribution-based and on-demand allocation. We first discuss the design of TAOs, including their infrastructures, power structures, and value systems, and then provide a technical roadmap for implementing TAOs in the DeSci context. This research can provide a valuable guidance for the construction and application of TAOs.

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Cortical reorganization pattern of stroke patients under upper limb multi-joint linkage task 12

Cortical reorganization pattern of stroke patients under upp...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Chu, Qitong Huo, Congcong Zhang, Tengyu Zhang, Xuemin Xu, Gongcheng Cheng, Shengcui Li, Huiyuan Xie, Ping Guo, Xin School of Artificial Intelligence and Data Science Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology The Ministry of Education Hebei University of Technology Tianjin300130 China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Key Laboratory of Neuro-functional Information and Rehabilitation Engineering The Ministry of Civil Affairs Beijing100176 China Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China School of Electrical Engineering Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Hebei Qinhuangdao066004 China Qinhuangdao Research Institute of National Research Center for Rehabilitation Technical Aids Qinhuangdao066004 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Motor dysfunction in stroke patients is caused by the brain damage, which greatly affects the life quality of the survivors. It is essential to explore the cortical reorganization pattern of stroke patients for revealing the mechanism of neural remodeling and guiding rehabilitation training. A total of 9 stroke patients and 12 age matched healthy subjects were recruited. Changes of oxyhemoglobin concentration (ΔHbO2) of bilateral prefrontal and motor cortex were measured in resting state and upper limb multi-j oint linkage motor state using functional near-infrared spectroscopy (fNIRS) neuroimaging technique. Cortical activation and functional connectivity were analyzed to describe the brain functional status associated with specific motor task. Results showed that compared with healthy subjects, significantly greater cortical activation was observed in stroke patients in responding to the multi-joint motor of the affected limb. Besides, inter-hemisphere connectivity of bilateral homotopic MC and PFC were significantly lower than healthy controls, however, the connectivity between PFC and MC were significantly higher than healthy controls. The results indicate the brain functional reorganization of stroke patients and offer new insights into the neural basis for cortical plasticity for stroke rehabilitation. To perform complex motor tasks, more brain resources was expended and more brain regions were involved in stroke patients. © 2022 IEEE.

关键词： Brain

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Imbalanced Least Squares Regression with Adaptive Weight Learning

SSRN

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SSRN 2022年

作者： Li, Yanting Jin, Junwei Ma, Jiangtao Zhu, Fubao Jin, Baohua Liang, Jing Chen, C.L. Philip College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology China

Least squares regression (LSR) has demonstrated promising performance in various classification tasks owing to the effectiveness and efficiency. However, there are some deficiencies that seriously plague its application in imbalanced data scenarios. The first is the success of LSR heavily relies on the balanced class distribution, more fundamentally, a severely imbalanced class distribution may damage the effectiveness of the algorithm seriously. Secondly, the utilized binary label matrix in conventional LSR model may be too strict to learn a discriminative transform matrix for imbalanced learning. To address the above issues, in this paper an adaptive weight learning mechanism and a novel label relaxation constraint are proposed and incorporated into the framework of LSR to tackle the imbalanced classification problem. Specifically, the weight of each sample can be adaptively obtained according to the original distribution information of the imbalanced data, in which the importance of the minority class samples can be better reflected with greater weights. Then a new label relaxation matrix is constructed to widen the margins between different classes. Further, we provide an iterative algorithm to solve the resulting optimization problem. Extensive experimental results on diverse imbalanced datasets show that the proposed method outperforms the majority of imbalanced learning approaches. © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging 22

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Fu, Lidan Lu, Binchun Li, Lingbing Shi, Xiaojing Tian, Jie Hu, Zhenhua Cas Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese Pla General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9798331520526

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000-1300 nm). NIR-IIb (1500-1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice. © 2025 IEEE.

关键词： Molecular imaging

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Empirical Evaluation of Phishing Attack Identification Over Websites by Using Intelligent Deep Learning Principle

Empirical Evaluation of Phishing Attack Identification Over ...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Anitha G Mouleeswaran S K Dickson Irudayaraj V G B Santhi Rajkumar Chadge Hani Attar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE Dayananda Sagar University Bangalore India Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of CSE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Phishing schemes keep on dominating the cyber threat landscape hence requiring enhancement of detection methods. This research sets out a new and novel idea for the detection of phishing website approach incorporating the XGBoost with ANN to boost the identification performance. The proposed model was compared with nine machine learning and deep learning models which includes Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), SVM, KNN, Naïve Bayes, XG Boost, artificial Neural Networks (ANN), CNN. It is apparent that the Ensembled XGBoost-ANN model was able to attain a higher accuracy rate of 9,463% and ranks on top of all the other models. It also showed great accuracy to prove precision, recall and F1-score metrics of the model. The findings reported herein support the suitability of the ensemble technique in modeling the intricate relationships characteristic of phishing websites, thereby holding the promise of being a practical solution in real-world environments. The reason why this methodology is promising for deployment in cybersecurity frameworks targeting the mitigation of phishing attacks is its capability of achieving high detection rates without making many false-positive detections.

关键词： Deep learning Support vector machines Radio frequency Measurement Accuracy Phishing Prevention and mitigation artificial neural networks Nearest neighbor methods Random forests

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LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

LHQ-SVC: Lightweight and High Quality Singing Voice Conversi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yubo Huang Xin Lai Muyang Ye Anran Zhu Zixi Wang Jingzehua Xu Shuai Zhang Zhiyuan Zhou Weijie Niu School of Civil Engineering Southwest Jiaotong University China Zhenguan AI Lab ZhenGuan Innovation (Shenzhen) Technology Co. Ltd China SWJTU-Leeds Joint School Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Shenzhen International Graduate School Tsinghua University China Department of Data Science New Jersey Institute of Technology USA School of Economics and Management Southwest Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer’s voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet real-time performance requirements, even in resource-constrained environments.

关键词： Performance evaluation Computational modeling Static VAr compensators Parallel processing Signal processing Rhythm Real-time systems Timbre Speech processing Tuning

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Chain-Based Outlier Detection for Complex data Scenarios

Chain-Based Outlier Detection for Complex Data Scenarios

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IEEE International Conference on Big data

作者： Huiwen Dong Qing-Guo Wang Wei Ding Institute of Artificial Intelligence and Future Networks Beijing Normal University Beijing China Guangdong Key Lab of AI and MM Data Processing Guangdong Provincial Key Laboratory IRADS IAS DST BNU-HKBU United International College Institute for Advanced Study Zhuhai China Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu China

Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.

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Privacy Preservation in Gen AI Applications

arXiv

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arXiv 2025年

作者： Swetha, S. Shaju, Ram Sundhar K. Rakshana, M. Ganesh, R. Balavedhaa, S. Thiruvaazhi, U. Information Science and Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Computer Science and Engineering Kumaraguru College Of Technology Tamil Nadu Coimbatore India Artificial Intelligence and Data Science Kumaraguru College Of Technology Tamil Nadu Coimbatore India

The ability of machines to comprehend and produce language that is similar to that of humans has revolutionized sectors like customer service, healthcare, and finance thanks to the quick advances in Natural Language Processing (NLP), which are fueled by Generative artificial intelligence (AI) and Large Language Models (LLMs). However, because LLMs trained on large datasets may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions, these capabilities also raise serious privacy concerns. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information, which raises serious concerns about the privacy and security of AI-driven data. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. A privacy-preserving Generative AI application that is resistant to these assaults is then developed. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs. In order to determine how well cloud platforms like Microsoft Azure, Google Cloud, and AWS provide privacy tools for protecting AI applications, the study also examines these technologies. In the end, this study offers a fundamental privacy paradigm for generative AI systems, focusing on data security and moral AI implementation, and opening the door to a more secure and conscientious use of these tools. © 2025, CC BY.

关键词： Privacy by design

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Effect of spatial heterogeneity on level of rejuvenation in Ni80P20 metallic glass

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Chinese Physics B 2022年第9期31卷 435-443页

作者： Tzu-Chia Chen Mahyuddin KM Nasution Abdullah Hasan Jabbar Sarah Jawad Shoja Waluyo Adi Siswanto Sigiet Haryo Pranoto Dmitry Bokov Rustem Magizov Yasser Fakri Mustafa A.Surendar Rustem Zalilov Alexandr Sviderskiy Alla Vorobeva Dmitry Vorobyev Ahmed Alkhayyat Dhurakij Pundit University Bangkok 10210Thailand Data Science and Computational Intelligence Research Group Universitas Sumatera UtaraMedanIndonesia Optical Department College of Health and Medical TechnologySawa UniversityMinistry of Higher Education and Scientific ResearchAl-MuthanaaSamawahIraq College of Health&Medical Technology Al-Ayen UniversityIraq Faculty of Engineering Universitas Muhammadiyah SurakartaJawa Tengah 57102Indonesia Department of Mechanical Engineering Faculty of Science and TechnologyUniversitas Muhammadiyah Kalimantan TimurSamarinda 75124Indonesia Institute of Pharmacy Sechenov First Moscow State Medical University8 Trubetskaya St.Bldg.2Moscow 119991Russia Kazan Federal University KazanRussia Department of Pharmaceutical Chemistry College of PharmacyUniversity of MosulMosul-41001Iraq Department of Pharmacology Saveetha Dental College and HospitalSaveetha Institute of Medical and Technical SciencesChennaiIndia Nosov Magnitogorsk State Technical University MagnitogorskRussia Innovative University of Eurasia PavlodarRepublic of Kazakhstan K.G.Razumovsky Moscow State University of Technologies and Management(The First Cossack University) Moscow 109004Russia College of Technical Engineering The Islamic UniversityNajafIraq

Understanding the relation between spatial heterogeneity and structural rejuvenation is one of the hottest topics in the field of metallic glasses(MGs).In this work,molecular dynamics(MD)simulation is implemented to discover the effects of initial spatial heterogeneity on the level of rejuvenation in the Ni_(80)P_(20)*** this purpose,the samples are prepared with cooling rates of 10^(10) K/s-10^(12) K/s to make glassy alloys with different atomic ***,it is found that the increase in the cooling rate leads the Gaussian-type shear modulus distribution to widen,indicating the aggregations in both elastically soft and hard *** the primary evaluations,the elastostatic loading is also used to transform structural rejuvenation into the atomic *** results indicate that the sample with intermediate structural heterogeneity prepared with 10^(11) K/s exhibits the maximum structural rejuvenation which is due to the fact that the atomic configuration in an intermediate structure contains more potential sites for generating the maximum atomic rearrangement and loosely packed regions under an external *** features of atomic rearrangement and structural changes under the rejuvenation process are discussed in detail.

关键词： metalic glasses mechanical properties molecular dynamics simulation disordered solids

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Raman response, piezoelectricity, and transport properties of the two-dimensional Janus HfSiX3H (X=N/P/As) semiconductors: A first-principles study

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Physical Review B 2024年第23期110卷 235403-235403页

作者： Tuan V. Vu Nguyen T. Hiep Huynh V. Phuc Bui D. Hoi A. I. Kartamyshev Nguyen N. Hieu Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 70000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 70000 Vietnam Institute of Research and Development Duy Tan University Da Nang 550000 Vietnam Faculty of Natural Sciences Duy Tan University Da Nang 550000 Vietnam Division of Physics School of Education Dong Thap University Cao Lanh 870000 Vietnam Faculty of Physics University of Education Hue University Hue 530000 Vietnam

Exploring advanced materials with extraordinary properties is necessary for developing high-performance devices. In this work we construct two-dimensional Janus HfSiX3H (X=N/P/As) monolayers and systematically examine their physical properties by utilizing first-principles calculations. Specifically, we focus on the stabilities, Raman spectra, electronic structures, piezoelectricity, and transport properties of the monolayers. As a result, the studied HfSiN3H, HfSiP3H, and HfSiAs3H monolayers are found as semiconductors with good structural, mechanical, dynamic, and thermodynamic stabilities. The piezoelectric coefficients (e11 and e31) are calculated to examine the piezoelectricity of the HfSiX3H monolayers. It is noted that besides the in-plane piezoelectricity, the three monolayers show additional out-of-plane piezoelectric effects because of their noncentrosymmetric structures. We also include fully anisotropic acoustic deformation potential, ionized impurity, piezoelectric, and polar electron–phonon scattering to calculate the total carrier mobilities of Janus HfSiX3H. It is demonstrated that Janus HfSiX3H monolayers exhibit low carrier mobilities at room temperature (<100cm2V−1s−1). Scattering from polar optical phonons is found to dominate the total mobilities of both electrons and holes.

关键词： Electronic structure Piezoelectricity Transport phenomena Vibrational states Layered semiconductors First-principles calculations

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