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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Baziana, Peristera Drainakis, Georgios Georgantas, David Bogris, Adonis University of Thessaly Department of Informatics and Telecommunications Greece National Technical University of Athens School of Electrical and Computer Engineering Greece University of West Department of Informatics and Computer Engineering Attica Greece

ISBN: (纸本)9789532901382

Artificial intelligence (AI) and machine learning (ML) are vastly becoming key parts of today's applications, supporting diverse human activities and decision making. New training models are used requiring an explosive traffic exchange among data centers (DCs) servers, putting tremendous pressure on DC network (DCN). Therefore, new DCN design challenges are required to efficiently handle the emerging AI/ML applications traffic. Contrary to existing works that do not account for these challenges, in this paper we introduce an optical intra-rack DCN architecture, as an optical local area network (LAN) paradigm, and an efficient wavelength division multiplexing access (WDMA) algorithm tailored to serve the AI/ML traffic. For the proposed WDMA scheme, we assume diverse AI/ML traffic classes according to the required service time and quality of service (QoS) delivery and we adopt appropriate access rules for each of them to achieve high DCN performance and access fairness. Simulation results show that our proposal reaches high bandwidth utilization (90 % under nominal load 400 Gbps) and low end-to-end latency ( © 2024 University of Split, FESB.

关键词： Quality of service

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CNN Architectures for Vehicle Detection in V2X Environments: A Comparative Study

CNN Architectures for Vehicle Detection in V2X Environments:...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Efthymiou, Vasileios Basagiannis, Stylianos Petridou, Sophia University of Macedonia Department of Applied Informatics Greece International Hellenic University Department of Computer Informatics & Telecommunications Engineering Greece

ISBN: (纸本)9798331507428

Leveraging convolutional neural networks (CNNs) to Vehicle-to-Everything (V2X) communication systems offers a promising approach to vehicle detection, which is essential for improving road safety and traffic efficiency. At the same time, parameterization of CNNs' architectures in order to achieve high accuracy in moving objects' detection becomes an engineering challenge, especially when data pre- and post-processing solutions need to respect timing requirements. Our study evaluates the performance of five CNN architectures, namely YOLO in v5, v8, and v9, Faster R-CNN, and VGG16, for car detection. We utilize an open dataset including images derived from road-traffic environments and assess the models' performance using benchmark evaluation metrics, i.e., precision, recall, mean Average Precision (mAP50, mAP50 - 95), and F1-score. Furthermore, we analyze their computational requirements, in terms of execution time and resource demands, which are critical factors when considering real-time applications in V2X systems, such as autonomous driving and traffic management. Our evaluation results indicate that, while the YOLO architecture exhibits the best balance of speed and accuracy, Faster R-CNN has higher detection performance at the cost of greater computational demand. This paper provides insights into each model's strengths and limitations offering guidance for selecting optimal architectures tailored to V2X-related applications. © 2024 IEEE.

关键词： Convolutional neural networks

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Restoring Mycenaean Linear B’A&B’ series tablets using supervised and transfer learning 1

Restoring Mycenaean Linear B’A&B’ series tablets using sup...

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1st Workshop on Machine Learning for Ancient Languages, ML4AL 2024

作者： Papavassileiou, Katerina Kosmopoulos, Dimitrios I. Department of History and Archaeology Computer Engineering Informatics Department University of Patras Patra Greece

ISBN: (纸本)9798891761445

We investigate the problem of restoring Mycenaean linear B clay tablets, dating from about 1400 B.C. to roughly 1200 B.C., by using text infilling methods based on machine learning models. Our goals here are: first to try to improve the results of the methods used in the related literature by focusing on the characteristics of the Mycenaean Linear B writing system (series D), second to examine the same problem for the first time on series A&B and finally to investigate transfer learning using series D as source and the smaller series A&B as target. Our results show promising results in the supervised learning tasks, while further investigation is needed to better exploit the merits of transfer learning. © 2024 Association for Computational Linguistics.

关键词： Self-supervised learning

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural Computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion 21st

Predicting from a Different Perspective: A Re-ranking Mode...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Iwamoto, Yuki Kaneiwa, Ken Department of Computer Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9789819601240

Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model’s predictions. In this paper, we focus on the behavior of such models. We propose a re-ranking-based model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction). This model enhances the effectiveness of re-ranking by leveraging the difference in the predictions between the initial retriever and the re-ranker. ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Decoding Gender on Social Networks: An In-depth Analysis of Language in Online Discussions Using Natural Language Processing and Machine Learning

Decoding Gender on Social Networks: An In-depth Analysis of ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Vonitsanos, Gerasimos Kanavos, Andreas Mylonas, Phivos University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350324457

In today's digital era, the internet is an indispensable platform for self-expression, facilitating communication, idea sharing, and community formation. Language, a pivotal tool in these online interactive spaces, is vital in reflecting personal identities, notably gender identification. This paper investigates gender identification on online discussion platforms, recognizing the crucial role of language in reflecting personal identities. The study employs Natural Language Processing techniques and machine learning algorithms to analyze data from a public discussion website. Beginning with a comprehensive literature review, the research explores the nexus between gender and language in online and offline contexts. The methodology involves data gathering, extensive preprocessing, and in-depth exploratory analysis, employing statistical methods and graphical representations. The study then rigorously evaluates their accuracy and effectiveness by applying diverse algorithms and models for gender-based text categorization. Results indicate the superior performance of transformer models, particularly distilBERT, in categorizing gender accurately. Additionally, the research underscores the challenges of gender-neutral analysis, emphasizing the need for inclusive methodologies in non-binary gender classification. The study contributes to the broader field of gender studies, providing valuable insights for future research and discussions on the interplay of gender and language in online spaces. © 2023 IEEE.

关键词： Gender Identification Machine Learning Natural Language Processing Non-binary Gender Classification Text Categorization

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Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach 12

Stress Detection Using EEG Signals: A SVM-Based Classificati...

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12th Electrical Power, Electronics, Communications, Controls and informatics Seminar, EECCIS 2024

作者： Sifaunnufus, Fi Imanur Bachtiar, Fitra Abdurrachman Prasetio, Barlian Henryranu Brawijaya University Faculty of Computer Science Department of Computer Engineering Malang Indonesia Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350379914

Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power Spectral Density (PSD) into power value for each signal frequency. The preprocessed input signals are then classified into indications of stress levels. This study uses Random Forest, SVM, and Decision Tree. The model is stored on a portable system with Raspberry Pi as the main component. EEG signals are taken using electrodes on Muse 2 and sent to the system. The classification model generates predicted stress levels and is displayed on a 16×2 LCD. SVM as the best model test result achieved a testing accuracy of 1, and an average computation time of 0.92. © 2024 IEEE.

关键词： Electroencephalography

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A transformer model for boundary detection in continuous sign language

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Multimedia Tools and Applications 2024年第42期83卷 89931-89948页

作者： Rastgoo, Razieh Kiani, Kourosh Escalera, Sergio Electrical and Computer Engineering Faculty Semnan University Semnan*** Iran Sergio Escalera Department of Mathematics and Informatics Universität de Barcelona and Computer Vision Center Barcelona Spain

Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gesture recognition

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