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arXiv 2024年

作者： Zhu, Peican Pan, Zechen Liu, Yang Tian, Jiwei Tang, Keke Wang, Zhen School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China School of Computer Science Northwestern Polytechnical University China Air Traffic Control and Navigation College Air Force Engineering University China Cyberspace Institute of Advanced Technology Guangzhou University China

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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E2: Entropy Discrimination and Energy Optimization for Source-free Universal Domain Adaptation

E2: Entropy Discrimination and Energy Optimization for Sourc...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Meng Shen Andy J. Ma Pong C. Yuen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Universal domain adaptation (UniDA) transfers knowledge under both distribution and category shifts. Most UniDA methods accessible to source-domain data during model adaptation may result in privacy policy violation and source-data transfer inefficiency. To address this issue, we propose a novel source-free UniDA method coupling confidence-guided entropy discrimination and likelihood-induced energy optimization. The entropy-based separation of target-known and unknown classes is too conservative for known-class prediction. Thus, we derive the confidence-guided entropy by scaling the normalized prediction score with the known-class confidence, that more known-class samples are correctly predicted. Due to difficult estimation of the marginal distribution without source-domain data, we constrain the target-domain marginal distribution by maximizing (minimizing) the known (unknown)-class likelihood, which equals free energy optimization. Theoretically, the overall optimization amounts to decreasing and increasing internal energy of known and unknown classes in physics, respectively. Extensive experiments demonstrate the superiority of the proposed method.

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iGateLink: A Gateway Library for Linking IoT, Edge, Fog, and Cloud computing Environments 1st

iGateLink: A Gateway Library for Linking IoT, Edge, Fog, and...

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1st International Conference on Intelligent and Cloud computing, ICICC 2019

作者： Mancini, Riccardo Tuli, Shreshth Cucinotta, Tommaso Buyya, Rajkumar Lab School of Computing and Information System The University of Melbourne Melbourne Australia Scuola Superiore Sant’Anna Pisa Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9789811559709

In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting as a gateway between IoT devices and edge/fog/cloud computing environments. Thanks to its pluggable design, modules providing connectivity with a number of devices acting as data sources or fog/cloud frameworks can be easily reused for different applications. Using iGateLink in two case studies replicating previous works in the healthcare and image processing domains, the library proved to be effective in adapting to different scenarios and speeding up development of gateway applications, as compared to the use of conventional methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces

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IEEE Internet of Things Journal 2025年

作者： Mujtaba, Ghulam Khowaja, Sunder Ali Dev, Kapal Dublin City University School of Computing Faculty of Engineering and Computing Ireland University of Sindh Faculty of Engineering and Technology Pakistan Munster Technological University Department of Computer Science Cork Ireland University of Johannesburg Department of Institute of Intelligent Systems Auckland Park 2006 South Africa Chitkara University Chitkara University Institute of Engineering and Technology Rajpura 140401 India Anderson College of Business and Computing Regis University Denver CO United States

Social media has become integral to minors’ daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional content moderation techniques have proven ineffective against exploiters’ evolving tactics. To address these growing challenges, we propose the EdgeAIGuard content moderation approach that is designed to protect minors from online grooming and various forms of digital exploitation. The proposed method comprises a multi-agent architecture deployed strategically at the network edge to enable rapid detection with low latency and prevent harmful content targeting minors. The experimental results show the proposed method is significantly more effective than the existing approaches. © 2014 IEEE.

关键词： Agentic AI Agentic LLMs Minor Protection Multi-agent architecture Online Grooming

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Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

arXiv

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arXiv 2024年

作者： Zeng, Hongye Zou, Ke Chen, Zhihao Gao, Yuchong Chen, Hongbo Zhang, Haibin Zhou, Kang Wang, Meng Goh, Rick Siow Mong Liu, Yong Jiang, Chang Zheng, Rui Fu, Huazhu School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Department of Orthopedics Zhongshan Hospital Fudan University Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. © 2024, CC BY-NC-SA.

关键词： Deep learning

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A Multi-Model Framework to Explore ADHD Diagnosis from Neuroimaging Data

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Journal of Data science 2024年第2期22卷 191-207页

作者： Ozdemir, Yagmur Yavuz Nukala, Naga Chandra Padmini Molinari, Roberto Deshpande, Gopikrishna Department of Mathematics and Statistics Auburn University Auburn AL United States AU MRI Research Center Department of Electrical and Computer Engineering Auburn University Auburn AL United States Department of Psychological Sciences Auburn University Auburn AL United States Alabama Advanced Imaging Consortium Auburn AL United States Center for Neuroscience Auburn University Auburn AL United States School of Psychology Capital Normal University Beijing China Key Laboratory for Learning and Cognition Capital Normal University Beijing China Department of Psychiatry National Institute of Mental Health and Neurosciences Karnataka India Centre for Brain Research Indian Institute of Science Bangalore India Department of Heritage Science and Technology Indian Institute of Technology Hyderabad India

Attention Deficit Hyperactivity Disorder (ADHD) is a frequent neurodevelopmental disorder in children that is commonly diagnosed subjectively. The objective detection of ADHD based on neuroimaging data has been a complex problem with low ranges of accuracy, possibly due to (among others) complex diagnostic processes, the high number of features considered and imperfect measurements in data collection. Hence, reliable neuroimaging biomarkers for detecting ADHD have been elusive. To address this problem we consider a recently proposed multi-model selection method called Sparse Wrapper AlGorithm (SWAG), which is a greedy algorithm that combines screening and wrapper approaches to create a set of low-dimensional models with good predictive power. While preserving the previous levels of accuracy, SWAG provides a measure of importance of brain regions for identifying ADHD. Our approach also provides a set of equally-performing and simple models which highlight the main feature combinations to be analyzed and the interactions between them. Taking advantage of the network of models resulting from this approach, we confirm the relevance of the frontal and temporal lobes as well as highlight how the different regions interact to detect the presence of ADHD. In particular, these results are fairly consistent across different learning mechanisms employed within the SWAG (i.e. logistic regression, linear and radial-kernel support vector machines) thereby providing population-level insights, as well as delivering feature combinations that are smaller and often perform better than those that would be used if employing their original versions directly. © 2024 The Author(s).

关键词： automated detection functional magnetic resonance imaging interpretability prediction accuracy SWAG

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Unlocking the Potential of Competition-Based Learning: A Case Study of Kaggle in Big Data Analytics Education

Unlocking the Potential of Competition-Based Learning: A Cas...

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International Conference on Smart computing & Communications (ICSCC)

作者： Pengfei Fan Erick Purwanto Na Li Jia Wang Ting Ting Tay Ling Wang Qiufeng Wang Department of Intelligent Science School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China School of Electronic Engineering and Computer Science School of Physical and Chemical Sciences Queen Mary University of London London UK Department of Computing School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Department of Educational Studies Academy of Future Education Xi'an Jiaotong-Liverpool University Suzhou China Educational Development Unit Academy of Future Education Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big Data Analytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data was collected from students who completed the Big Data Analytics module. Results indicate high satisfaction levels among participants, with the majority expressing willingness to recommend CBL using Kaggle to future students. Moreover, the CBL approach significantly enhanced student engagement and improved their understanding of Big Data Analytics concepts and techniques. The matic analysis revealed positive aspects such as real-time scoring and leaderboards, but also highlighted challenges including the lack of structured learning and guidance. Solutions proposed by students include additional support mechanisms, a balance between competition and learning, and diverse competition themes. Overall, this study underscores the potential of CBL using Kaggle to transform Big Data Analytics education, while also emphasizing the need for tailored support and refinement of course design.

关键词： Ethics Education Collaboration Transforms Big Data Real-time systems

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MDNN: Predictive Modeling of Breast Cancer Subtypes Using Modified Deep Neural Networks

MDNN: Predictive Modeling of Breast Cancer Subtypes Using Mo...

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International Conference on Smart Structures and Systems (ICSSS)

作者： Kiran Chand Ravi Sanjay Akaram Jadhav R. Krishnamoorthy K. Rajkumar B. Swapna Smita Sharma MLR Institute of Technology Hyderabad Institute of Management Bharati Vidyapeeth (Deemed to be University) Kolhapur Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of ECE School of Engineering SR University Telangana Dr MGR Educational and Research Institute Chennai Department of Computer Science and Engineering IES University Bhopal

Breast cancer remains a complex and heterogeneous disease, encompassing various molecular subtypes with distinct clinical implications. Accurate subtype classification is pivotal for tailoring treatment strategies and improving patient outcomes. In this research, we introduce a novel approach for predictive modeling of breast cancer subtypes, leveraging a Modified Deep Neural Network (MDNN) that combines Convolutional Neural Networks (CNNs) and GoogleNet architecture. Our study draws upon a comprehensive dataset that includes gene expression profiles and histopathological images from a large cohort of breast cancer patients. The MDNN architecture is meticulously crafted to harness the strengths of both CNNs and GoogleNet, allowing for the extraction of intricate features from histopathological images while effectively integrating gene expression data. The CNN component of MDNN excels at automatically capturing fine-grained patterns within histopathological images, enabling the model to discern subtle tissue nuances characteristic of different breast cancer subtypes. Simultaneously, the GoogleNet architecture enhances the model's ability to process and analyze high-dimensional gene expression data, facilitating a more holistic understanding of the underlying molecular characteristics. We employ rigorous data preprocessing, cross-validation, and hyperparameter tuning to train and optimize our MDNN model and obtained an accuracy of 98%. Comprehensive evaluation on an independent validation dataset showcases the superior performance of our approach in comparison to traditional methods.

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Python And Opencv For Sign Language Recognition

Python And Opencv For Sign Language Recognition

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： R Srinivasan R Kavita M Kavitha Basetty Mallikarjuna Sandeep Bhatia Bhawna Agarwal Vinay Ahlawat Amit Goel Dept of Computer Science and Engineering Veltech University Chennai School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Birla Institute of Technology Mesra (Jaipur Center) Department of CSE KIET NCR Delhi Uttar Pradesh India

Hearing-impaired people cannot communicate with normal people easily. Most people are not aware of sign language recognition. To support this, machine learning and CV can be used to create an impact on the impaired. This can be improved into automatic editors, in which the person can easily understand the sign language of the impaired people by just using hand sign recognition. In non-verbal communication, hand gesture always being an important mode of communication and it plays a vital role to bridge gap between deaf and dumb people. Several sign language recognition systems have been developed but the systems are not flexible and cost-effective. Physically challenged people can express their emotions and feelings through sign language. In this paper, we develop a sign detector that can detect signs and numbers and also other signs that are used in sign language with the help of OpenCV and Keras modules in python. By using this technology, we can understand what they want to convey through sign language which is not a common language to communicate with people. OpenCV and Keras of python are the modules used to achieve our work and the proposed work proved to be a user-friendly approach to communication by using Python language to recognize sign languages for hearing-impaired persons.

关键词： Bridges Gesture recognition Machine learning Detectors Assistive technologies Feature extraction Communications technology

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