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7th Workshop on Natural Language for Artificial Intelligence, NL4AI 2023

作者： Bacco, Luca Dell'Orletta, Felice Merone, Mario Department of Engineering Unit of Computer Systems and Bioinformatics Campus Bio-Medico University of Rome Via Alvaro del Portillo 21 Rome00128 Italy ItaliaNLP Lab National Research Council Istituto di Linguistica Computazionale "antonio Zampolli" Via Giuseppe Moruzzi 1 Pisa56124 Italy

The healthcare industry is experiencing an unprecedented era of transformation, driven by the proliferation of Electronic Health Records (EHRs) and the emergence of vast amounts of natural language data from sources like social media. This dynamic landscape presents novel opportunities for companies, healthcare practitioners, and researchers, underpinned by the transformative potential of Natural Language Processing (NLP) technologies. This paper offers a brief overview of the rationale for studying NLP in healthcare, highlighting its foundational importance. It explores the current trends and invaluable resources available in the field, while also delving into the multifaceted challenges that must be addressed to harness the full potential of NLP in healthcare. By consolidating essential insights and bridging the gap between opportunities and challenges, this manuscript serves as a valuable resource for both established researchers and newcomers seeking to navigate the complex terrain of NLP in healthcare, ultimately contributing to the advancement of this critical domain. © 2023 Copyright for this paper by its authors.

关键词： Metadata

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Two Novel Methods for Multiple Kinect v2 Sensor Calibration 6th

Two Novel Methods for Multiple Kinect v2 Sensor Calibration

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6th International Conference on computer Vision and Image Processing, CVIP 2021

作者： Hazra, Sumit Pisipati, Manasa Puhan, Amrit Nandy, Anup Scherer, Rafal Machine Intelligence and Bio-Motion Research Lab Department of Computer Science and Engineering NIT Rourkela Odisha Rourkela769008 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India Department of Intelligent Computer Systems Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

ISBN: (纸本)9783031113482

Camera calibration is an essential step for measuring an instrument’s accuracy by using its parameters. In this paper, we propose two methods for calibrating eight Kinect v2.0 sensors, namely, pairwise and simultaneous single-camera calibration. In the first method, an experimental setup is managed so that the eight Kinect cameras can view a 3D object on a treadmill in consecutive pairs. The novelty of this method infers pairwise calibration of eight Kinects using six steps, specifically: precalibration process, acquiring images from Kinect, exporting the parameters, generation of independent live point clouds, feeding exported parameters for point cloud matching and ensuring successful calibration through merged point clouds. In the second method, a novel octagonal model is developed to calibrate each of the eight cameras on the same experimental setup. We obtain an overall mean reprojection error of 1.37 pixels and 0.42 pixels for the first method and second method, respectively. The smallest reprojection error for the first method is reported to be 0.63 pixels and 0.27 pixels for the second method. The efficiency of the proposed methods is compared with the state-of-art techniques using the root mean square and the reprojection error metrics. We observed that the proposed methods outperform the existing techniques. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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Improving the Derivation of Sound Security Metrics

Improving the Derivation of Sound Security Metrics

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： George O. M. Yee Dept. of Systems and Computer Engineering Computer Research Lab Aptusinnova Inc. Carleton University Ottawa Canada

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

We continue to tackle the problem of poorly defined security metrics by building on and improving our previous work on designing sound security metrics. We reformulate the previous method into a set of conditions that are clearer and more widely applicable for deriving sound security metrics. We also modify and enhance some concepts that led to an unforeseen weakness in the previous method that was subsequently found by users, thereby eliminating this weakness from the conditions. We present examples showing how the conditions can be used to obtain sound security metrics. To demonstrate the conditions' versatility, we apply them to show that an aggregate security metric made up of sound security metrics is also sound. This is useful where the use of an aggregate measure may be preferred, to more easily understand the security of a system.

关键词： Measurement Aggregates Conferences Buildings Software Security

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A Comprehensive Threat Model for Autonomous Vehicle Communication Security

A Comprehensive Threat Model for Autonomous Vehicle Communic...

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Advancements in Computing (ICAC), International Conference on

作者： Niranjan W. Meegammana Harinda Fernando Cyber Security Research Lab Shilpa Sayura Foundation Kandy Sri Lanka Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry.

关键词： Threat modeling Visualization Transportation Sensor fusion Vectors Safety Security Stakeholders Reliability Autonomous vehicles

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A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

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IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Optimizing Transistor Selection Using AH-TOR for Low Noise Amplifier Design in Sensing Applications

Optimizing Transistor Selection Using AH-TOR for Low Noise A...

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International Conference on Sensing Technology, ICST

作者： Dayarnab Baidya Karthick Thiyagarajan Mitradip Bhattacharjee Yang An Department of Electrical Engineering and Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications.

关键词： Low-noise amplifiers Wireless sensor networks PHEMTs Decision making Receivers Electron mobility Robustness Sensors Transistors Integrated circuit reliability

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Language Fusion for Parameter-Efficient Cross-lingual Transfer

arXiv

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arXiv 2025年

作者： Borchert, Philipp Vulić, Ivan Moens, Marie-Francine De Weerdt, Jochen Research Centre for Information Systems Engineering KU Leuven Belgium IESEG School of Management France Language Technology Lab University of Cambridge United Kingdom Department of Computer Science KU Leuven Belgium

Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for languages other than English. This ‘under-representation’ has motivated recent cross-lingual transfer methods to leverage the English representation space by e.g. mixing English and ‘non-English’ tokens at the input level or extending model parameters to accommodate new languages. However, these approaches often come at the cost of increased computational complexity. We propose Fusion for Language Representations (FLARE) in adapters, a novel method that enhances representation quality and downstream performance for languages other than English while maintaining parameter efficiency. FLARE integrates source and target language representations within low-rank (LoRA) adapters using lightweight linear transformations, maintaining parameter efficiency while improving transfer performance. A series of experiments across representative crosslingual natural language understanding tasks, including natural language inference, question-answering and sentiment analysis, demonstrate FLARE’s effectiveness. FLARE achieves performance improvements of 4.9% for Llama 3.1 and 2.2% for Gemma 2 compared to standard LoRA fine-tuning on question-answering tasks, as measured by the exact match metric.1 © 2025, CC BY.

关键词： Linear transformations

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Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue Detection

Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue D...

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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Shuaiyue Xie Joong Huang Chuah Grace Mei Ting Chai Electronics and Computer Science University of Southampton Southampton United Kingdom Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Smart Manufacturing Systems Research Group University of Southampton Malaysia Iskandar Puteri Johor Malaysia

Driver fatigue is a significant cause of road accidents. Effective real-time fatigue detection systems are necessary to improve road safety. Utilizing a lightweight and fast model and creating an effective fatigue judgment rule is important in detecting fatigue in real-time. In this research, we propose a fatigue detection model based on You Look Only Once version 8 (YOLOv8) model to detect driver's facial features and use percentage of eyelid closure over the pupil over time (PERCLOS) and Percentage of Open Mouth (POM) indicators to identify fatigue. This research is divided into two parts. The first part involves creating, labeling, and preprocessing the dataset. We use the YawDD dataset as the original dataset and manually label a new dataset using the Roboflow platform. We then preprocessed the new dataset to train the YOLOv8 model for face features detection. In the second part, we established a multi-indicator fatigue determination rule that uses PERCLOS and POM indicators and set a threshold to determine whether the driver is fatigue. By combining the YOLOv8 model with the fatigue determination rules based on PERCLOS and POM, we successfully built a model to identify a driver's fatigue. Finally, the fatigue detection model based on YOLOv8 achieves a high accuracy rate of 96.9% and a high speed of 11.4 ms/frame in face features detection, surpassing other traditional models and previous generations of YOLO models. This model can effectively detect fatigue in both driver-driving pictures and videos.

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on smart Glasses

arXiv

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arXiv 2025年

作者： Cai, Runze Janaka, Nuwan Kim, Hyeongcheol Chen, Yang Zhao, Shengdong Huang, Yun Hsu, David Synteraction Lab School of Computing National University of Singapore Singapore Synteraction Lab Smart Systems Institute National University of Singapore Singapore Synteraction Lab National University of Singapore Singapore College of Design and Engineering National University of Singapore Singapore Synteraction Lab School of Creative Media Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States School of Computing Smart Systems Institute National University of Singapore Singapore

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet’s effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025, CC BY.

关键词： Contrastive Learning

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