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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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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research 47

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Brdnik, Saša Šumak, Boštjan University Of Maribor Institute Of Informatics Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and Science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field. © 2024 IEEE.

关键词： Artificial intelligence

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Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge 12

Comparison of Q&A Classification Using BiLSTM Based on Close...

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

作者： Gunawan, Alifi Lazuardi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma University Malang Faculty of Computer Science Brawijaya Informatics Engineering Department Indonesia

ISBN: (纸本)9798350379914

The Faculty of computer Science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Teaching

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Flexible Real-Time Emulation of Fading Channels on SoC-FPGA Devices

Flexible Real-Time Emulation of Fading Channels on SoC-FPGA ...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Papalamprou, Ilias Armeniakos, Giorgos Stratakos, Ioannis Lentaris, George Soudris, Dimitrios National Technical University of Athens School of Electrical and Computer Engineering Greece University of West Attica Department of Informatics and Computer Engineering Greece

ISBN: (纸本)9798350318845

Modeling and replicating the channel effects between a transmitter and receiver, is crucial in any telecommunication system, in order to evaluate the impact on the transmitted data. Therefore the necessity of having access to a real time channel emulator is vital. In order to meet these constraints, in this work we present a flexible, high performance and resource efficient FPGA-based channel emulator, focusing on fading models. Additionally, we perform a trade-off analysis between bit accuracy, resources utilization and system's accuracy in terms of Mean Relative Error and the Probability Density Function. Our study shows that by using a circuit with reduced bit-widths, we can achieve 24%-46% reduction in various resources, while preserving the algorithmic accuracy of the channel emulator and operating at a throughput of 500Msps. © 2024 IEEE.

关键词： Fading channels

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Lightweight Privacy Architecture for Homomorphic Operations in e-Health Systems

Lightweight Privacy Architecture for Homomorphic Operations ...

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2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： Tsantikidou, K. Sklavos, N. SCYTALE Group Computer Engineering and Informatics Department University of Patras Hellas Greece

ISBN: (纸本)9798331521219

In recent years, traditional hospital facilities are adopting new technologies that provide novel services. Nevertheless, these services may be performed by third parties, that pose a threat to data privacy. As health data is sensitive, it must be constantly encrypted from the moment it is collected. Thus, an appropriate privacy scheme that is suitable for the resource constrained e-Health environment is required. In this paper, a lightweight privacy architecture, applied for homomorphic operations in IoT systems for e-Health is proposed. The focus is its easy implementation in devices with resource limitations, while also enhancing privacy. Two security versions of the same architecture are presented, with different parameters. Each proposed version achieves a minimum of 70.27% and 49.2% resource reduction compared to published related works. The proposed compact version achieves a minimum of 19.7% area-delay reduction compared to published designs. The main reasons for these results are the employment of synthesizable operations, the fusion of functions and the pipeline architecture of operations. ©2024 IEEE.

关键词： Electronic health record

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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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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Slovakia Information Systems and Software Engineering Slovak University of Technology Institute of Informatics Slovakia

ISBN: (纸本)9798350318845

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead. © 2024 IEEE.

关键词： Error correction

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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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Intelligent Agents with Graph Mining for Link Prediction over Neo4j 19

Intelligent Agents with Graph Mining for Link Prediction ove...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Nikolaou, Michalis Drakopoulos, Georgios Mylonas, Phivos Sioutas, Spyros Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian Univerity Kerkyra Greece Informatics and Computer Engineering Department University of West Attica Athens Greece

ISBN: (纸本)9789897586729

Intelligent agents (IAs) are highly autonomous software applications designed for performing tasks in a broad spectrum of virtual environments by circulating freely around them, possibly in numerous copies, and taking actions as needed, therefore increasing human digital awareness. Consequently, IAs are indispensable for large scale digital infrastructure across fields so diverse as logistics and long supply chains, smart cities, enterprise and Industry 4.0 settings, and Web services. In order to achieve their objectives, frequently IAs rely on machine learning algorithms. One such prime example, which lies in the general direction of evaluating the network structure integrity, is link prediction, which depending on the context may denote growth potential or a malfunction. IAs employing machine learning algorithms and local structural graph attributes pertaining to connectivity patterns are presented. Their performance is evaluated with metrics including the F1 score and the ROC curve on a benchmark dataset of scientific citations provided by Neo4j containing ground truth. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Intelligent agents

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Credit card default prediction using ML and DL techniques

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

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