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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Insights Into the Future: XAI Integration in O-RAN and Space Communication systems

Insights Into the Future: XAI Integration in O-RAN and Space...

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Multimedia University engineering Conference (MECON)

作者： H. Ahmed Tahir Fahad Nabi Muhammad Ziad Tariq Ahmad Fasih Khan Azwan Mahmud Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Electrical Engineering & Information Technology University of Kiel Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as “black boxes,” making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Surveys Space vehicles Explainable AI Space communications Decision making Open RAN Reliability Telemetry Artificial intelligence Virtualization

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

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Information sciences 2024年 674卷

作者： Zou, Juan Tang, Li Liu, Yuan Yang, Shengxiang Wang, Shiting Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources. © 2024 Elsevier Inc.

关键词： Multiobjective optimization

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Optimizing Wireless Sensor Network Topology with Deep Reinforcement Learning for Multi-Source/Destination Scenarios

Optimizing Wireless Sensor Network Topology with Deep Reinfo...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer Programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

关键词： Measurement Integer programming Wireless sensor networks Network topology Deep reinforcement learning Real-time systems Sensors Planning Topology Optimization

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator

Analyzing Code Injection Attacks on Applications of Android ...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Adhitia Erfina Rahmat Hidayat Rafli Radya Rizaldi Puput Riko Maulana Falentino Sembiring Departement of Infomation System Faculty of Engineering Computing and Design Nusa Putra University Sukabumi Indonesia Master of Computer Science Faculty of Information Technology Budi Luhur University Jakarta Indonesia

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application’s source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion.

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Driver Fatigue Detection Using OpenCV and Dlib Library

Driver Fatigue Detection Using OpenCV and Dlib Library

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Shen, Kuang Ramli, Rusyaizila Chuah, Joong Huang Chai, Grace Mei Ting School of Electronics and Computer Science University of Southampton Southampton United Kingdom University of Southampton Malaysia Carbon Neutrality Research Group Johor Iskandar Puteri79100 Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia University of Southampton Malaysia Smart Manufacturing System Reasearch Group Johor Iskandar Puteri79100 Malaysia

ISBN: (纸本)9798350341072

Driver fatigue and distraction are major contributors to fatal road accidents worldwide. To enhance vehicle safety, this article proposes a fatigue detection system using EAR and MAR algorithms to identify signs of fatigue, such as eye closure and yawning. When prolonged eye closure or yawning is detected, the system issues an alert to the driver. It leverages OpenCV and Dlib libraries for implementation. Additionally, the system detects driver distraction by monitoring head swivels, counting left and right movements. It's worth noting that Dlib may yield facial landmark deviations for non-frontal faces. To mitigate this, the study selected 69 videos from the YawDD database, focusing on near-frontal face views, achieving a 91.3% accuracy rate. © 2023 IEEE.

关键词： Vehicle safety

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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Congress on Evolutionary Computation

作者： Yuto Inokuchi Pedro Henrique González Israel Mendonça Masayoshi Aritsugi Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy.

关键词： Training Accuracy Graph convolutional networks Roads Noise Layout Evolutionary computation

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