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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Samantha Levinson, Lila H. Paschall, Courtnie J. Herron, Jeffrey Weaver, Kurt Hauptman, Jason Ko, Andrew Ojemann, Jeffrey Rao, Rajesh P. N. University of Washington Paul G. Allen School of Computer Science & Engineering Department of Bioengineering SeattleWA98195 United States University of Washington Graduate Program in Neuroscience Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Neurological Surgery SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Radiology SeattleWA98195 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes and reducing stimulation-induced side effects. Mapping the relationship between electrical stimulation input and neural activity response can help reveal their interactions and can give us tools to iterate and improve on our stimulation protocols. Here, we introduce methods for identifying low-dimensional subspaces of human intracortical responses to electrical stimulation in invasive electroencephalography. In epilepsy patients (n=4) undergoing clinical monitoring, we applied a stimulation protocol of varying amplitude and frequency in 5-second intervals to capture a range of responses to different stimulation conditions. We characterized these responses using time-frequency spectral power, applied baseline subtraction and outlier removal procedures, and performed principal component analysis across frequencies. We identified that intracortical responses to different stimulation conditions can be represented in a 3-dimensional subspace, accounting for more than 95% of the variance. Using support vector machine classification, we demonstrated separability of intracortical responses in different stimulation conditions across subjects, where this separability was contingent on applying baseline subtraction and outlier removal. Our results represent a first step towards building a predictive model of neural response from stimulation input, an important prerequisite for adaptive closed-loop stimulation for targeted neurorehabilitation. © 2022 IEEE.

关键词： Electroencephalography

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GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation

arXiv

引用

arXiv 2023年

作者： Gu, Zhouhong Jiang, Sihang Liu, Jingping Xiao, Yanghua Feng, Hongwei Li, Zhixu Liang, Jiaqing Zhong, Jian Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China School of Data Science Fudan University China HUAWEI CBG Edu AI Lab. China

Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, pre-trained language models are used to retrieve the representation of the concepts quickly. The experiments on three real-world large-scale datasets with two different languages show that GANTEE improves the performance of the existing taxonomy expansion methods in both effectiveness and efficiency. © 2023, CC BY.

关键词： Taxonomies

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Towards an Attention Maps Guided Adversarial Attack Approach for Object Detection 5

Towards an Attention Maps Guided Adversarial Attack Approach...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Ma, Zhicheng Zhao, Jinxiong Zhao, Hong Yin, Bangguo Yu, Jun Geng, Jiangyi Digital and Communication Technology Research Institute State Grid Gansu Electric Power Company Electric Power Science Research Institute Lanzhou China Wuhan University School of Computer Science Wuhan China Co. Ltd Baoding China Hubei Intelligent Linked Open Data Technology Co. Ltd Wuhan China

ISBN: (纸本)9798350309034

The deep learning based object detection has been widely used. The adversarial attack algorithms reduce the accuracy of the recognition model by adding perturbation noise to the target image to achieve the purpose of hiding sensitive objects. Existing adversarial attack algorithms cannot balance the aggression and imperceptibility issues of adversarial perturbations. To address this problem, an Attention maps guided Adversarial Attack Method for object detection (AAAM) is proposed. This method first designs an initial perturbation region positioning strategy based on the Class Activation Mapping (CAM) algorithm and then uses the RPAttack's gradient based contribution judgment perturbation region refinement strategy during the adversarial perturbation iteration generation phase, thereby achieving stronger attacks with a smaller perturbation region. The AAAM improves adversarial attack effectiveness through more efficient initial perturbation region selection. Compared to RPAttack, DPAttack and PGD, when attacking Faster RCNN AAAM and YOLOv4 AAAM achieves an improvement rate of over 7.2% and 16.2% in terms of the attack success rate (ASR) as an attack effectiveness metric. © 2023 IEEE.

关键词： Adversarial Attack Attack Effectiveness Attention Maps Object Detection

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Exfiltration-resistant Proxy Re-Encryption for IoT data Sharing in Unreliable Clouds

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Yuyang Zhao, Liang Li, Fagen Takagi, Tsuyoshi School of Cyberspace Security Hainan University Haikou570000 China Department of Mathematical Informatics Graduate School of Information Science and Technology The university of Tokyo Tokyo113-8656 Japan School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management [Sichuan University Ministry of Education China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which we implement using the JPBC library. The security analysis reveals that the PRE-RF scheme offers effective resistance against ASA, while also providing strong collision security, chosen plaintext attack security, and key-private security. Compared to similar PRE schemes with more security features, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system;also, our scheme’s computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, as security levels rise, the portion of time dedicated to reverse firewall operations within our scheme diminishes. These advantages collectively render it a secure and efficient choice for sharing IoT data in unreliable cloud environments. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

引用

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

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers

An Advanced Intrusion Detection Solution for Networks based ...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： K. Veena K. Meena Mercy Theresa M Hemalatha C D. Rajalakshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM- Bangalore India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Sri Sairam Institute of Technology Chennai

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.

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Test-Driven Feature Extraction of Web Components

引用

Journal of computer science & Technology 2022年第2期37卷 389-404页

作者： Yong-Hao Long Yan-Cheng Chen Xiang-Ping Chen Xiao-Hong Shi Fan Zhou School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen UniversityGuangzhou 510006China School of Design The Hong Kong Polytechnic UniversityHong KongChina Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for *** some cases,the developer may want to reuse some features which cannot be customized by the component’s ***/she has to extract the implementation by *** is labor-intensive and *** the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web *** satisfaction of the user^requirements is transformed into the passing rate of user-specified test *** this way,the quality of the extraction result can be evaluated ***,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI *** extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest *** compare our method with two existing feature extraction *** result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

关键词： feature extraction genetic algorithm graphical user interface software reuse

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Intelligent Decision Support System for Higher Education Institutions

Intelligent Decision Support System for Higher Education Ins...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Nunik Afriliana Meyliana Ford Lumban Gaol Haryono Soeparno Computer Science Department Binus Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited.

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 IEEE.

关键词： Intensive care units

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Optimizing for the Shortest Path in Denoising Diffusion Model

arXiv

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arXiv 2025年

作者： Chen, Ping Zhang, Xingpeng Liu, Zhaoxiang Hu, Huan Liu, Xiang Wang, Kai Wang, Min Qian, Yanlin Lian, Shiguo Data Science & Artificial Intelligence Research Institute China Unicom China Unicom Data Intelligence China Unicom China DJI Technology Co. Ltd. China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior arts. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://***/UnicomAI/ShortDF. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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