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检索条件"机构=Informatics and Computer Engineering"

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

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GestureSet: Public Domain Dataset for Human Gesture Recognition using Wrist-worn Devices: A Preliminary Version 19

GestureSet: Public Domain Dataset for Human Gesture Recognit...

引用

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Tzamalis, Pantelis Nikoletseas, Sotiris Spirakis, Paul G. University of Patras Computer Engineering and Informatics Department Patras Greece Patras Greece University of Liverpool Department of Computer Science United Kingdom

ISBN: (纸本)9798350346497

Human Gesture Recognition (HGR) has become an increasingly important research area in recent years, driven by the need for more natural and intuitive ways of interacting with machines. Wearable smart devices, such as smartwatches, smart wristbands, and other wrist-worn devices have the potential to enable accurate and efficient gesture recognition in real-world settings. However, equivalent problems, such as object detection in computer vision and audio identification in audio analysis, have obtained enormous advantages from comprehensive datasets over the last years, for instance, ImageNet and AudioSet, accordingly. This paper proposes the structure and the creation of a preliminary version of GestureSet, an HGR large-scale multitask dataset, which includes manually annotated data of gesture events that were collected from wrist-worn devices. In particular, it proposes a hierarchy abstraction schema for the proper dataset structure and instantiates the basis of the dataset through the first HGR task that is related to the collection and annotation of gestures related to allergic rhinitis identification. Thus, through this preliminary version, we describe how the proper dataset structure is divided into different groups, each one including sessions, individual gestures, and pilot data that could be used for evaluating the learning algorithms' performance in real-world scenarios. To the best of our knowledge, this is the first attempt that envisions a unified dataset for various HGR tasks that can find applications in different domains. Our vision, specifically, is for this work to set the basis for the dataset enrichment in a wide range of various tasks, and its data to be used in multiple learning and identification settings, providing circularity in different domains, and enabling reproducibility of the dataset's gestures events. © 2023 IEEE.

关键词： Object detection

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Accurate Deep Learning-Based Sleep Apnea Detection from Cardiac Physiological Signals 8

Accurate Deep Learning-Based Sleep Apnea Detection from Card...

引用

8th IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2023

作者： Delimayanti, Mera Kartika Muharram, Asep T. Pradipta, Ayres Ismail, Muhammad K. Ryanari, Rinaldito A. Prasetyo, Raditya A. Faisal, Mohammad Reza Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Lambung Mangkurat University Department of Computer Science Banjarbaru Indonesia

ISBN: (纸本)9798350327861

Sleep apnea is a severe sleep disorder characterized by interrupted breathing during sleep. Early detection and accurate diagnosis of sleep apnea are essential to avoiding potentially dangerous health complications. This study proposes a sleep apnea detection model using cardiac physiological signals and a deep learning approach. This model uses Convolutional Neural Networks (CNN) to learn patterns and features in cardiac signal data. Heart signal data is collected from individuals with sleep apnea and normal sleeping individuals as datasets. Datasets consisting of heart signal data were gathered from patients diagnosed with sleep apnea and people with normal sleeping patterns. The public dataset was enhanced and utilized for training, employing a Convolutional Neural Network (CNN) architecture of six layers. The improved data is derived from the process of segmenting the data. The trained model demonstrated a high accuracy of 98.4% in effectively classifying physiological heart signal samples into two categories: 'normal' and 'sleep apnea'. This project aims to develop a model that can assist medical professionals in diagnosing sleep apnea and providing early warning to patients. © 2023 IEEE.

关键词： Diagnosis

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment 19

Realizing Predictive Maintenance in Production Machinery Thr...

引用

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Filios, Gabriel Karatzas, Stylianos Krousarlis, Marios Nikoletseas, Sotiris Panagiotou, Stefanos H. Spirakis, Paul University of Patras Computer Engineering and Informatics Department Greece Greece University of Patras Civil Engineering Department Greece University of Liverpool Department of Computer Science United Kingdom

ISBN: (纸本)9798350346497

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed. © 2023 IEEE.

关键词： Internet of things

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Jointly Preserving Subject and Object Anonymity in Nearby Friends Querying for Curious Service Providers

Jointly Preserving Subject and Object Anonymity in Nearby Fr...

引用

2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Abul, Osman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering TOBB University of Economics and Technology Ankara Ankara Turkey

ISBN: (纸本)9798400716928

This work studies the urban area location privacy preserving location updates and nearby friends (NF) querying for the centralized proximity based services (PBSs). The urban area constraint forces the user mobility to be on the city road network, modeled as a weighted directed graph. The location privacy model exploits location anonymity through subgraph cloaking. Two kinds of location privacy issues have been identified and handled in a unified framework, (i) Subject Anonymity on location updates, i.e., providing location anonymity of the location updater (against the curious PBS provider), and (ii) Object Anonymity on NF querying, i.e., providing location anonymity of friends in NF query responses (against the curious query issuer). In the former, the user simulates the PBS provider's belief (a subgraph) on his location whereabouts, and locally blocks anonymity violating location update requests which is understood by a simulated attack. In the latter, as the NF query processor, the PBS provider simulates the user's mutual beliefs (subgraphs) on friends' location whereabouts obtained through previous NF queries. By a simulated attack, some friends' existence are suppressed from the NF query responses. An experimental evaluation on a real dataset has been conducted. © 2024 ACM.

关键词： Location

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Fog Computing-based Real-Time Emotion Recognition using Physiological Signals 27

Fog Computing-based Real-Time Emotion Recognition using Phys...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Erzurumluoglu, Omur Fatmanur Ozlem, Kadir Atalay, Asli Tuncay Atalay, Ozgur Ince, Gokhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9791188428137

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system's ability to support multiple users simultaneously. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fog computing

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Sampling-Based Motion Planning for Guide Robots Considering User Pose Uncertainty 25th

Sampling-Based Motion Planning for Guide Robots Considering...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Mosquera-Maturana, Juan Sebastian Hernández Vega, Juan David Romero Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031720581

In this paper, we propose a framework to address the problem of guiding a person within a semi-structured environment in a socially acceptable manner that prioritises safety and comfort. We propose an algorithm based on the optimal Rapidly exploring Random Tree (RRT*) algorithm for path planning. Our proposal utilises Dubins curves and takes into account the user during path planning to generate a navigation path that allows the robot to follow a feasible path that can also be navigated by the user. A comparative analysis against standard path planning based on the RRT* algorithm and the Social Force Model validates the efficacy of our proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Microrobots

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training 5

Adaptive Learning and Virtual Reality based Conceptual Frame...

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Sherif, Meriam Barakat, Nahla Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Sceince Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Sceince Department of Artifical Intellegence Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Sceince Department of Software Enginerring Cairo Egypt

ISBN: (纸本)9798350303254

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students' education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability. © 2023 IEEE.

关键词： Virtual reality

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Direction-of-Arrival Estimation with Arbitrary Linear Array: A Turbo-HS Approach 5

Direction-of-Arrival Estimation with Arbitrary Linear Array:...

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Chen, Zhendong Huang, Yongfeng Li, Dingzhao Lu, Xicheng Sun, Haixin School of Informatics Xiamen University Xiamen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9798331533694

Conventional subspace-based direction-of-arrival (DOA) estimation algorithms require optimal environments to achieve satisfactory estimation accuracy. With the advancement of sparse signal recovery theory, sparse optimization-based DOA estimation algorithms have exhibited commendable performance. One of the popular sparse optimization method is sparse Bayesian learning (SBL), due to the fact that it does not require regularization parameters. However, existing SBL algorithms do not adequately address the issue of noise modeling. In order to more accurately model the received signal covariance matrix under finite snapshots, we transform the signal covariance matrix into the form of sparse representation, normalize the variance of the observation matrix and vectorize it. Based on this, we introduce the Turbo-CS framework, which divides the DOA estimation problem into two subproblems: LMMSE estimation for external noise separation and a novel prior for internal SBL DOA estimation. As demonstrated by the experimental results, the proposed algorithm performs optimally in a variety of scenarios. © 2025 IEEE.

关键词： Direction of arrival

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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration 3

A Dynamic Security Assessment Tool for Modern Power Systems ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Therapontos, Phivos Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources' (IBRs') protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded Cyprus Power System. © 2024 IEEE.

关键词： Dynamic security assessment frequency security IBR modeling low-inertia grids system strength

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