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International Conference on Advanced Robotics and Intelligent systems (ARIS)

作者： Chih-Hsuan Shih Chih-Yi Wu Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Mechanical and Mechatronics System Research Laboratories Industrial Technology Research Institute Hsinchu 31040 Taiwan

Robotic arms play a crucial role in factory automation, offering immense versatility, precision, and the capability for autonomous operation, thereby eliminating the need for manual labor. In this study, we have developed a robotic slag removal system with automatic position compensation and defect detection capabilities, which is capable of locating the features of workpieces, perform deslagging, and detect defects using the YOLO (You Only Look Once) algorithm. The experimental workpiece in this study is a knee femoral implant. Firstly, a position compensation method is employed to precisely locate the clamping hole of the workpiece. The robot trajectory is then automatically generated by the software for the deburring of the clamping hole slag. Subsequently, the system utilizes the detected defect locations to generate a re-deslagging path, ensuring that all remnants are thoroughly cleaned.

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A Method for Discovering Hidden Patterns of Cybersecurity Knowledge Based on Hierarchical Clustering∗ 6

A Method for Discovering Hidden Patterns of Cybersecurity Kn...

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Ding, Zhaoyun Cao, Deqi Liu, Lina Yu, Donghua Ma, Haoyang Wang, Fei National University Of Defense Technology Key Laboratory Of Information System Engineering Changsha China School Of Electronic And Information Engineering Soochow University Suzhou China Shaoxing University Department Of Computer Science And Engineering Shaoxing China

ISBN: (纸本)9781665418157

The construction of cybersecurity knowledge graphs improves the full-dimensional cognitive ability of cyber threats. However, most of the current research on the cybersecurity knowledge graphs focuses on the one-hop or multi-hop correlation queries of knowledge, while ignoring the hidden patterns in the knowledge graphs. Based on the knowledge graph stated in this paper[1], we propose a method for discovering hidden patterns of cybersecurity knowledge based on hierarchical clustering, using the hacker group features of security technologies in the knowledge graph, calculating the distance between the security technology knowledge in the knowledge graph based on the Phicoefficient, and mining cybersecurity technologies that are often used by hacker groups by employing the ward linkage method of hierarchical clustering. The experimental results show that three similar cybersecurity technologies, namely Peripheral Device Discovery, Data from Removable Media and Junk Data, all of which are aimed at stealing or attacking the externally-connected devices of computers, are used by most hacker groups. © 2021 IEEE.

关键词： Knowledge graph

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PointGait: Boosting End-to-End 3D Gait Recognition with Point Clouds via Spatiotemporal Modeling

PointGait: Boosting End-to-End 3D Gait Recognition with Poin...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rui Wang Chuanfu Shen Chao Fan George Q. Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology WeBank Institute of Financial Technology Shenzhen Audencia Financial Technology Institute Shenzhen University Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Systems Engineering The Hong Kong Polytechnic University

LiDAR is a new type of sensor used for gait recognition. Previous LiDAR-based state-of-the-art methods mostly exploit gait features from the depth maps generated by projecting point clouds in a 3D-to-2D manner, rather than directly using the raw 3D point data. However, these projection-based methods require an additional preprocessing step, which obstructs the universality of the method among different types of LiDARs. On the other hand, while existing point-based methods have achieved promising results in 3D object recognition, they have underperformed in 3D gait recognition, indicating the presence of a domain gap between coarse-grained 3D object classification and fine-grained 3D pedestrians recognition. By analyzing the success achieved by camera-based methods, we perceive that point-based gait recognition fails mainly because of neglecting to capture local representation. To address this issue, we propose an end-to-end 3D gait recognition framework named PointGait, which can directly capture informative gait features from point cloud data. Specifically, PointGait is a multi-stream model consisting of a Global and Local Gait Feature Extractor to extract holistic and fine-grained spatial features. Besides, a Personalized Motion Extractor is introduced to capture inter-frame motion features. Our experimental results on a LiDAR gait dataset, SUSTech1K, outperform all popular point-based methods, demonstrating the effectiveness and potential of our approach. In conclusion, the proposed PointGait promotes the development of point-based gait recognition by highlighting the importance of incorporating fine-grained spatiotemporal information.

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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IEEE International Conference on systems, Man and Cybernetics

作者： Hong Chen Tao Zhou Bo Wu Graduate School of Human Sciences Waseda University Saitama Japan Department of Information and Electronics System Engineering Daiichi Institute of Technology Taito Tokyo Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare.

关键词： Data privacy Cloud computing Hidden Markov models Prototypes Aging Data models Internet of Things Security Older adults Medical diagnostic imaging

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Rainfall-Induced Landslide Prediction using Graph-Based Convolutional Network Models

Rainfall-Induced Landslide Prediction using Graph-Based Conv...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： P. Rajasekar K. M. Devendraiah A Tamizhselvi Droupti Yadav A. Priyadharshini D. Suganthi Department of Data Science and Business System SRM Institute of Science and Technology India V. V. Sangha's Independent P U College Ballari India Department of Information Technology St. Joseph's College of Engineering Chennai India Department of Environmental Science Technology School of Basic Sciences CSJM University Kanpur India Department of Computer Science and Engineering (Cyber Security) Nandha Engineering College Erode India Saveetha College of Liberal Arts and Sciences SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Landslides are natural disasters that are devastating and unpredictable. Community notification and casualty reduction require early warning systems. Using data analytics and machine learning technology (MLT), IoT-enabled early warning models can monitor environmental variables and predict landslides. However, current models struggle to anticipate *** innovative strategy suggested by this work involves feature extraction, preprocessing, and model training. The first step to high-quality data is preparing the landslip grid points with Multicollinearity Analysis. Simulating salp chains improves feature selection during feature extraction with the Salp Swarm Algorithm (SSA). During training, a Graph-Based Convolutional Neural Network (G-CNN) is used for optimum predictions. The suggested strategy was more predictive than before, averaging 92.76% Due to its accuracy, the G-CNN outperforms graph-based methods and standard CNNs in handling complex environmental *** SSA-based feature extraction with G-CNN training improves landslip prediction accuracy, a promising early warning system development. This technique could improve natural catastrophe prediction and response by using IoT and ML advances.

关键词： Training Landslides Analytical models Accuracy Biological system modeling Disasters Predictive models Feature extraction Prediction algorithms Convolutional neural networks

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and Control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Study on the Use of Virtual Synchronous Generators to Improve Large Scale system's Stability 3

Study on the Use of Virtual Synchronous Generators to Improv...

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3rd International Conference on High Voltage engineering and Power systems, ICHVEPS 2021

作者： Li, Diya Sitompul, Sandro Fujita, Goro Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Regional Environment System Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9781665443548

influence of environmental problems and resource exhaustion problems, the method of applying distributed generation in power systems is gradually being widely used. However, in large-scale interconnected power systems, low-frequency vibrations caused by mechanical vibrations of power devices and disturbances in a certain system will affect other modules connected in the same system, thereby affecting the stability of the entire power system. In order to eliminate this kind of inter-regional influence, maintain the stability of the power system. This paper attempts to design a control method based on VSG. VSG is often used in power systems where there are distributed power sources such as solar power generation for renewable energy generation. Because this kind of distributed power source does not have the same inertia as the synchronous generator, so when the system is disturbed, the overall stability of the system will deteriorate, and the VSG has the ability to track the maximum power point, which can make this type of distributed generation have the same inertia as the synchronous generator. When the stability of the small system changes, the overall system can still remain stable. This article will focus on the effect of the VSG on the transient stability of the large-scale system. Through the establishment of a simulation model, and verify that the whole system can keep stable due to the function of VSG when fault or disturbances occur in the system. © 2021 IEEE.

关键词： Distributed power generation

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Transparency Paths - Documenting the Diversity of User Perceptions 21

Transparency Paths - Documenting the Diversity of User Perce...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Giunchiglia, Fausto Kleanthous, Styliani Otterbacher, Jahna Draws, Tim Department of Information Engineering and Computer Science University of Trento Italy Open University of Cyprus and Cyens Centre of Excellence Cyprus Web Information System and Department of Software Technology Tu Delft Netherlands

ISBN: (纸本)9781450383677

We are living in an era of global digital platforms, eco-systems of algorithmic processes that serve users worldwide. However, the increasing exposure to diversity online - of information and users - has led to important considerations of bias. A given platform, such as the Google search engine, may demonstrate behaviors that deviate from what users expect, or what they consider fair, relative to their own context and experiences. In this exploratory work, we put forward the notion of transparency paths, a process by which we document our position, choices, and perceptions when developing and/or using algorithmic platforms. We conducted a self-reflection exercise with seven researchers, who collected and analyzed two sets of images;one depicting an everyday activity, "washing hands,"and a second depicting the concept of "home."Participants had to document their process and choices, and in the end, compare their work to others. Finally, participants were asked to reflect on the definitions of bias and diversity. The exercise revealed the range of perspectives and approaches taken, underscoring the need for future work that will refine the transparency paths methodology. © 2021 ACM.

关键词： Transparency

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Multi-label advertising image classification using traditional deep neural networks and vision language models: dataset and annotation agreement method

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Multimedia Tools and Applications 2025年 1-30页

作者： Mir, Tatheer Hussain Wei, Jui-Cheng Simanjuntak, Mutiara Cheng, Wan-Shu Lee, Yi-Hsun Zhang, You-Chen Chen, Jia-Bin Dai, Hong-Jie Intelligent System Laboratory Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Management Providence University Taichung Taiwan Research and Development Department Linker AI Co. Ltd Taipei Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan

Effectively classifying advertising images is crucial in targeting the right audience and maximizing marketing performance. To address this problem, this paper presents a multi-label advertising image classification study using popular deep-learning architectures. First, we compile a dedicated dataset for this task and evaluate the performance of traditional deep learning-based models based on the convolutional neural network (CNN) and vision transformer architectures. To ensure the quality of dataset annotations, we introduce an extended Krippendorf’s Alpha (α) method based on the Jaccard index to provide a reliable measure of inter-annotation agreement which can address the missing annotations and multiple labels to establish the dataset’s annotation consistency. Our results demonstrate that transformer-based architectures like ViT and Swin outperform the CNN-based model’s baseline and differential learning rate settings. Through the visualization analysis of saliency maps, we gain insights into the model’s decision-making processes and identify the factors influencing their predictions. Furthermore, we assess the impact of annotation quality on model performance, comparing models trained on different annotation reliability levels. Our results indicate that higher annotation consistency, as quantified by α-Jaccard, leads to improved model performance, emphasizing the importance of high-quality datasets in advertising image classification. Beyond traditional deep learning models, we explore the effectiveness of vision language models (VLMs) in this task by employing prompt engineering and comparing their performance with fine-tuned deep learning models. Our findings indicate that while VLMs provide richer contextual annotations, they suffer from over-classification tendencies, subjective biases, and significantly higher computational costs. In contrast, deep learning models remain a more efficient and scalable solution for structured, large-scale advertising classi

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