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34th Medical Informatics Europe Conference, MIE 2024

作者： Vasili, Aliki Schiza, Eirini Schizas, Christos N. Pattichis, Constantinos S. Department of Computer Science and Biomedical Engineering Research Center University of Cyprus Cyprus CYENS Centre of Excellence Cyprus National eHealth Authority Cyprus

ISBN: (纸本)9781643685335

The integration of chatbots in healthcare has gained attention due to their potential to enhance patient engagement and satisfaction. This paper presents a healthcare chatbot providing comprehensive access to patient summaries, aligned with the European Patient Summary. Leveraging Natural Language Processing (NLP) capabilities, our chatbot employs intent classification using the fine-tuned bioBERT model to categorize user queries effectively and extract relevant information from the patient summary stored in a database. We detail our methodology, which involves dataset creation, hyperparameter tuning, and model evaluation. Results demonstrate the effectiveness of our approach, with the trained model achieving high precision, recall, and F1 score across intent classes. Our study underscores the potential of emerging NLP techniques in patient interaction and healthcare delivery, covering the way for smarter, user-friendly companions. © 2024 The Authors.

关键词： Electronic health record

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The Role of AI in Modern Classroom Management 2

The Role of AI in Modern Classroom Management

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gayaki, Unnati Daronde, Subodh Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Biomedical Engineering Faculty of Engineering and Technology Maharashtra Wardha India

ISBN: (纸本)9798331505745

Artificial Intelligence is changing the way classrooms are managed, encouraging new ideas, making things easier, and helping students learn in their own way. Adaptive platforms, smart tutoring systems, and tools like I-MINDS customize learning experiences to fit different needs while making it easier for teachers. It takes care of jobs like grading, tracking attendance, and analyzing behavior, allowing teachers to concentrate on important thinking skills and emotional growth. AI also enhances teamwork through group tools, promotes fairness, and assists in real-time decision-making. Technologies like VR, AR, and MR allow people to learn through experience, especially with hard subjects. Incorporating AI learning into school curricula readies students for a future driven by AI. Despite issues with data privacy, biased algorithms, and ethical questions, AI is making learning environments inclusive, dynamic, and just. New technology, combined with ways that are focused on people, is how AI seeks to transform education into a holistic and flexible system for learners and teachers alike. © 2025 IEEE.

关键词： Decision making

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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A flexible,multifunctional,optoelectronic anticounterfeiting device from high-performance organic light-emitting paper

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Light(science & Applications) 2022年第3期11卷 142-152页

作者： Teng Pan Shihao Liu Letian Zhang Wenfa Xie Cunjiang Yu State Key Laboratory of Integrated Optoelectronics College of Electronics Science and EngineeringJilin University130012 ChangchunChina Department of Engineering Science and Mechanics Department of Biomedical EngineeringMaterials Research InstitutePennsylvania State University16802 University ParkUSA

As a primary anticounterfeiting technology,most paper anticounterfeiting devices take advantage of photoresponsive behaviors of certain security materials or structures,thus featuring low-security threshold,which has been a critical global *** incorporate optoelectronic devices into existing anticounterfeiting technology suggests a feasible avenue to address this *** we report a high-performance organic light-emitting paper-based flexible anticounterfeiting(FAC)device with multiple stimuli-responsiveness,including light,electricity,and their *** sacrificing the preexisted security information on the paper,we fabricate FAC device in a facile,low-cost yet high-fidelity fashion by integrating patterned electro-responsive and photo-responsive organic emitters onto paper *** introducing optical microcavities,the FAC device shows considerable color shift upon different viewing angle and applied voltage,which is easily discernible by naked ***,the FAC device is bendable,unclonable,and durable(a half-lifetime over 4000 hours at 100 cd m^(−2)).

关键词： optoelectronic light performance

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A combined protocol for isolation, culture, and patch-clamp recording of dorsal root ganglion neurons

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Biophysics Reports 2025年第2期11卷 87-95页

作者： Ruolin Wang Yu Lu Jianbo Zhao Xueting Duan Yang Chen Zhuoyu Zhang Rong Huang Neuroscience Research Center Key Laboratory of Biomedical Information Engineering of Ministry of EducationSchool of Life Science and Technology Xi'an Jiaotong University Neurological Department of Tongji Hospital School of Medicine Tongji University

The dorsal root ganglion(DRG) neurons are crucial in transmitting sensory information from the peripheral nervous system to the central nervous system, including touch, pain, temperature, and proprioception. Understanding the functions and mechanisms of DRG neurons is essential for studying sensory processing and developing efficient treatments for sensory disorders. In addition, electrophysiological patch-clamp recording is a powerful and classical tool to study the functions and mechanisms of the nervous system. Building upon the strategies outlined in published works and our group's abundant research experience in DRG neurons' functions by patch-clamp, we have summarized and put forward a comprehensive step-by-step protocol combining juvenile rat DRG neuron isolation and culture,and patch-clamp recording. This protocol would be a powerful guidance document for neuroscience researchers to study sensory DRG neurons' physiological and pathological functions using electrophysiological tools.

关键词： Sensory dorsal root ganglion neurons Isolation Culture Patch-clamp recording

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ILRM: Imitation Learning Based Resource Management for Integrated CPU-GPU Edge Systems with Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This paper focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability *** propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: offline Oracle generation and online imitation learning. In the offline phase, we design a fast-converging heuristic to generate near optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize imitation learning using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++net

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SN computer science 2024年第6期5卷 705页

作者： Murthy, S.V.N. Bhargavi, Kovvuri N. Isaac, Samson N.Ganesh, E. Department of Computer Science and Engineering SJC Institute of Technology Information Technology Aditya College of Engineering and Technology Biomedical Engineering Karunya Institute of Technology and Sciences Department of ECE St. Peter’s Institute of Higher Education and Research

A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and machine learning have emerged as promising tools for the automated detection and analysis of DFUs. We gathered a sizable foot imaging dataset, including DFU from multiple patients. This paper proposes a novel preprocessing technique based on the Shades of Gray color constancy algorithm to cope with noise and lighting variations in diabetic foot ulcer (DFU) images captured from different devices. The algorithm aims to enhance image quality, improve illumination normalization, and mitigate the impact of noise, thus providing more reliable and accurate DFU analysis and detection. Using the Diabetic Foot Infection Network with the Adam Optimizer (DFINET-AO), features were retrieved after the dataset had been preprocessed and divided. In order to comprehend the normal and pathological spectrum of diabetes, image data and numerical/text data are separated independently. Foot images of patients with aberrant diabetes coverage are separated from each other and classified using Pre-trained Fast Convolutional Neural Network (PFCNN), which has been trained on the U++network. Classification techniques, like foot ulcer analysis, forecast a etiology. This study's primary goal was to establish a novel method for evaluating the likelihood that diabetes individuals may acquire foot ulcers by imaging analysis of existing foot ulcers. The data was preprocessed and segmented after the researchers gathered a collection of foot photographs and medical information from historical records of diabetes patients. The amount of normal and pathological diabetes was then determined from numerical and textual data by extracting characteristics from the segmented data using DFINET-AO. To detect foot ulc

关键词： Diabetic foot infection network with the adam optimizer Diabetic foot ulcer Pre-trained fast convolutional neural network

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