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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Yan Watequlis Syaifudin Dewi Yanti Liliana Andi Baso Kaswar Triana Fatmawati Nobuo Funabiki Indrazno Siradjuddin Dept. of Information Technology State Polytechnic of Malang Malang Indonesia Dept. of Informatics Engineering State Polytechnic of Jakarta Jakarta Indonesia Dept. of Computer Engineering State University of Makassar Makassar Indonesia Dept. of Elec. and Comm. Engineering Okayama University Okayama Japan Dept. of Electrical Engineering State Polytechnic of Malang Malang Indonesia

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment’s specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components.

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A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives

arXiv

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arXiv 2025年

作者： Hossain, Delower Chen, Jake Y. Dept. of Computer Science The University of Alabama Birmingham United States Dept. of Genetic Biomedical Engineering Informatics and Data Science Heersink School of Medicine The University of Alabama Birmingham United States Systems Pharmacology AI Research Center The University of Alabama Birmingham United States

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments. Moreover, it identifies critical challenges in applying Neuro-Symbolic AI to medicine and represents a comprehensive exploration of its transformative potential in the biomedical field. © 2025, CC BY-NC

关键词： Knowledge representation

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Development of Visual Dashboard for River Monitoring System 8

Development of Visual Dashboard for River Monitoring System

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8th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Ahmad, Yasser Asrul Gunawan, Teddy Surya Munir, Muhd Hisyamuddin Mohd Rasidi Al Yusoff, Nelidya Md. Kartiwi, Mira Mansor, Hasmah International Islamic University Malaysia Electrical and Computer Engineering Dept Kuala Lumpur53100 Malaysia Razak Faculty of Tech. and Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur53100 Malaysia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

Monitoring a tributary's water depth and velocity can provide a wealth of information for ecological systems. Typically, tributaries are located deep within the jungle, and manual measurement is the norm. The research aims to develop a real-time remote monitoring system for measuring water depth and velocity. The system uses ultrasonic and water flow sensors to measure velocity and river depth. The data will be transmitted to the back-end software, including Arduino IDE and Node-RED, for data collection. The data is then stored in the cloud database InfluxDB for future use. The data can be set to be automatically deleted every 1 hour, 1 day, 1 month, or at other time intervals. The Grafana platform can produce an interactive Graphical User Interface (GUI), and the administrator can modify the visual dashboard using either their program code or existing features. It can then be made available to the general public as an observer for the online visual dashboard via the Uniform Resource Locator (URL). © 2022 IEEE.

关键词： Remote control

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Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Technique to Determine Weights of Uncertainty Issues in School Reopening Factors During COVID-19

Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Tech...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Feby Artwodini Muqtadiroh Tsuyoshi Usagawa Diana Purwitasari Riris Diana Rachmayanti Eko Mulyanto Yuniarno Supeno Mardi Susiki Nugroho Mauridhi Hery Purnomo Dept. of Electrical Engineering Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Dept. of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Health Promotion and Behavioral Sciences Faculty of Public Health Universitas Airlangga Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The decision to open schools during a pandemic is considerable controversy because it increases the risk of a spike in COVID-19 cases more quickly. However, the decision to open schools is currently based solely on national policies, which generalize opening schools in every region, potentially leading to unintended consequences during the COVID-19 crisis. This study aims to present new decision-making to rank regions determining which schools are safe to open. By focusing on problem prioritization, we integrate the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) based on a fuzzy approach and the Urgency-Seriousness-Growth (USG) scoring technique, which we call FT-USG. First, we determine the weight of the expert's argument while putting special attention on the level of urgency, seriousness, and growth of COVID-19 to the school opening issue. The fuzzy number is then calculated based on the average weight generated from the USG result. Thus, the weighted normalization computations can be executed on each factor (criteria). Then, we calculate the rankings for each alternative using the Fuzzy TOPSIS method to determine which region and school can safely reopen. To provide a comprehensive ranking that serves as a basis for decision-making, we conduct a case study to show how the suggested approach is used to address the problems.

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Alternating optimization for multimodal collaborating odometry estimation in CAVs

Alternating optimization for multimodal collaborating odomet...

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Mediterranean Conference on Control and Automation (MED)

作者： Nikos Piperigkos Aris S. Lalos Kostas Berberidis Computer Engineering and Informatics Dept University of Patras Greece Industrial Systems Institute Athena Research Center Patras Science Park Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Cooperative, Connected and Automated Mobility will enable the close coordination of actions between vehicles, road users and traffic infrastructures, resulting in profound socioeconomic impacts. In this context, location and yaw angle of vehicles is considered vital for safe, secured and efficient driving. Motivated by this fact, we formulated a multimodal sensor fusion problem which provides more accurate localization and yaw information than the original sources. Simultaneously estimating location and yaw parameters of vehicles can be treated as the task of cooperative odometry or awareness. To do so, V2V communication as well as multimodal self and inter-vehicular measurements from various sensors are considered for the problem formulation. The solution strategy is based on the maximum likelihood criterion as well as a novel alternating gradient descent approach. To simulate realistic traffic conditions, CARLA autonomous driving simulator has been used. The detailed evaluation study has shown that each vehicle, relying only on its neighborhood, is able to accurately re-estimate both its own and neighboring states (comprised of locations and yaws), effectively realising the vision of 360 ◦ awareness.

关键词： Location awareness Maximum likelihood estimation Visualization Laser radar Multimodal sensors Roads Vehicular ad hoc networks

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Design and Experiment of a Four-Fingered Robotic Hand for T-FLoW 4.0 humanoid robot

Design and Experiment of a Four-Fingered Robotic Hand for T-...

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International Electronics Symposium (IES)

作者： Kevin Ilham Apriandy Bima Sena Bayu Dewantara Raden Sanggar Dewanto Dadet Pramadihanto Catoer Ryando Dept. of Informatics and Computer Engineering Robotics and Intelligent Systems Center (RoISC) Politeknik Elektronika Negeri Surabaya (PENS) Dept. of Mechanical and Energy Engineering Robotics and Intelligent Systems Center (RoISC) Politeknik Elektronika Negeri Surabaya (PENS) Dept. of Electrical Engineering Politeknik Elektronika Negeri Surabaya (PENS)

In this research study, we present a novel design of a four-fingered robotic hand for T-FLoW 4.0 humanoid robot along with its experiments to validate its grasping performance. Our proposed robotic hand designed with four fingers and five Degree-of-Freedom (DoF), with a configuration of a thumb, index, middle, and ring fingers to remain within the criteria of an anthropomorphic type robot hand. Our hand is built with a modular design on each finger and its mechanism, allowing for easy assembly and achieving a compact hand result. Main controller Arduino Mega implement the system for grasping movement, and then PCA9685 PWM controller get the hand movement instructions from it and generates the required PWM signal for the servo motors. The process of manufacturing the hand involves 3D printing technology to produce hands with a realistic appearance, and use PLA filament material achieve lightweight, affordable, and easy-to-maintain outcomes. The grasping experimental results show that our proposed robotic hand could perform basic grasping movements on household objects. With the use of Cini’s taxonomy of grasp as the hand functionality standard, we achieved a success rate of 8 out of 14 tests conducted.

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Channel Estimation for UAV-based mmWave Massive MIMO Communications with Beam Squint

Channel Estimation for UAV-based mmWave Massive MIMO Communi...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

The incorporation of UAVs in 5G and envisioned 6G wireless communication systems is considered for many applications and use-cases, either as part of the infrastructure, providing coverage and connectivity (e.g., during unforeseen and rare events) or as an end-user, e.g., in remote sensing, real-time monitoring and surveillance, to name a few. From the perspective of the physical layer and the involved signal processing algorithms, the transmission environment between the UAVs and the ground communication devices, along with the utilisation of massive MIMO in the mmWave spectrum, require new channel estimation algorithms to support the required physical layer functionality. In this paper, the problem of channel estimation in a multi-user, UAV-based mmWave massive MIMO system is considered in view of the so-called beam squint effect as well as the time-varying nature of the involved channels due to mobility. The proposed approach takes advantage of the low-rank channel matrix and solves a minimisation problem via ADMM, leading to a low complexity, iterative algorithm. The performance of the proposed algorithm is evaluated via simulations and its efficacy is demonstrated over other algorithms from the relevant literature.

关键词： Wireless communication Upper bound Surveillance Channel estimation Signal processing algorithms Massive MIMO Physical layer

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State of play and future directions in industrial computer vision AI standards

arXiv

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arXiv 2025年

作者： Stefanidou, Artemis Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Sarigiannidis, Panagiotis Varlamis, Iraklis Papadopoulos, Georgios Th. Dept. of Informatics and Telematics Harokopio University of Athens Athens Greece K3Y Ltd Sofia Bulgaria Dept. of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor. © 2025, CC BY.

关键词： Deep learning

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Predicting the Temperature Decrease of Metal between the Furnace-Bucket Machine and the SCCP (steel continuous casting plant) 2

Predicting the Temperature Decrease of Metal between the Fur...

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2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2020

作者： Tyurin, A.S. Lipetsk State Technical University Dept. of Informatics and Computer Engineering Lipetsk Russia

ISBN: (纸本)9781728188409

The process of constructing mathematical models of the temperature decrease of metal between the furnace-bucket machine and the SCCP is described in this work. As a part of the study, a number of computational experiments were conducted on the steel melting data from 2018 to 2019. As a result, regression models were obtained and their comparative analysis was performed. © 2020 IEEE.

关键词： Decision trees

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Machine Learning and Neuromorphic Computing Approaches for the mitigation of transmission impairments in high baud rate transmission systems

Machine Learning and Neuromorphic Computing Approaches for t...

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European Conference on Optical Communication (ECOC)

作者： Adonis Bogris Kostas Sozos Stavros Deligiannidis George Sarantoglou Charis Mesaritakis Dept. of Informatics and Computer Engineering. Aghiou Spiridonos University of West Attica Athens Egaleo Greece Dept. of Information and Communication Systems Engineering University of the Aegean Karlovassi

ISBN: (数字)9781957171159

ISBN: (纸本)9781665475570

We review our recent work in machine learning and neuromorphic processing for the mitigation of transmission impairments at very high baud rates. Bidirectional recurrent neural networks and neuromorphic recurrent spectral slicers emerge as promising solutions for mid-term deployment in long-haul and short-reach communication systems respectively.

关键词： Recurrent neural networks Neuromorphic engineering Equalizers Europe Machine learning Optical fiber communication Complexity theory

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