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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Ensuring Model Fairness via Stratified Training: TP53 Mutation Prediction with Estrogen Receptor Stratification in Breast Histopathology 46

Ensuring Model Fairness via Stratified Training: TP53 Mutati...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Tsiknakis, Nikos Wang, Kang Salgkamis, Dimitrios Tzoras, Evangelos Manikis, Georgios C. Sifakis, Emmanouil Bergh, Jonas Zerdes, Ioannis Marias, Kostas Matikas, Alexios Foukakis, Theodoros Karolinska Institutet Stockholm17177 Sweden Breast Cancer Center Karolinska University Hospital Stockholm17177 Sweden Institute of Computer Science Foundation for Research and Technology Hellas Heraklion70013 Greece Hellenic Mediterranean University Dept. of Electrical and Computer Engineering Heraklion71410 Greece

ISBN: (纸本)9798350371499

Developing AI models on medical images as decision support systems has seen a huge increase in interest during the last few years. However, most published studies have neglected testing the model's robustness against certain dataset-related biases and unbalanced variables. For example, although the prevalence of TP53 mutations is higher in Estrogen Receptor (ER)-negative breast cancer, while most ER-positive tumors are not mutated, published models have been developed on the entirety of the available data without testing for such intrinsic biases that can lead to overfitting. In this study we show that models trained for TP53 mutation prediction overfit on ER status and that stratification of training on the basis of ER is beneficial for all subgroups while it reduces bias and increases generalizability and fairness. (Implementation: https://***/tsikup/er-stratified-training-tp53-prediction) © 2024 IEEE.

关键词： Prediction models

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RAN Explainable Anomaly Prediction for 6G Networks 29

RAN Explainable Anomaly Prediction for 6G Networks

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona Spain Iquadrat Informatica S.L. Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350377644

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly. © 2024 IEEE.

关键词： Radio access networks

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantinos Kyritsis Charalampos M. Liapis Nikolaos Spatiotis Isidoros Perikos Michael Paraskevas Electrical & Computer Engineering Dept. University of Peloponnese Patras Greece Mathematics Dept. University of Patras Patras Greece Computer Technology Institute & Press “Diophantus” Patras Greece Computer Engineering & Informatics Dept. University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification.

关键词： Training Analytical models Text categorization Transformers

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN) 9

Effect of Interest Variation and Prefix Length on Pending In...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Acoustic measurement framework for audio systems based on structured periodic test signals 12

Acoustic measurement framework for audio systems based on st...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Kawahara, Hideki Yatabe, Kohei Sakakibara, Ken-Ichi Mizumachi, Mitsunori Kitamura, Tatsuya Wakayama University Cntr. Innovative Research and Liaison Wakayama Japan Tokyo University of Agriculture and Technology Dept. Electrical Engineering and Computer Science Tokyo Japan Health Sciences University of Hokkaido Dept. Speech-Language-Hearing Therapy Hokkaido Japan Kyushu Institute of Technology Dept. Electrical and Electronic Eng. Kitakyushu Japan Konan University Dept. Intelligence and Informatics Kitakyushu Japan

ISBN: (纸本)9798350340181

We introduce a framework for acoustic measurement that allows everyday sounds into acoustic measurement test signals and standard test signals such as swept-sine and MLS (Maximum Length Sequence). The framework extends our extended Time-Stretched-Pulse generation method and safeguarding method for any signals to make them appropriate for acoustic measurement. The extension uses the periodic nature of discrete Fourier transform combined with orthogonalization based on the Walsh-Hadamerd matrix. This framework enables simultaneous measurement of acoustic attributes such as linear time-invariant response, signal-dependent non-linear and time-invariant components, and random time-varying components. We also discuss and demonstrate practical scenarios in classes and laboratory lessons using tools developed based on the proposed framework. © 2023 IEEE.

关键词： Audio systems

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements 36

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Somogyi, Árpád Tettamanti, Tamás Varga, Pál Szalay, Zsolt Baranyai, Dániel Lovas, Tamás Budapest University of Technology and Economics Fac. of Civil Engineering Dept. of Photogrammetry and Geoinformatics Budapest Hungary Institute for Computer Science and Control The Eötvös Loránd Research Network Systems and Control Laboratory Budapest Hungary Budapest University of Technology and Economics Fac. of Electrical Engineering and Informatics Dept. of Telecommunications and Media Informatics Budapest Hungary Budapest University of Technology and Economics Fac. of Transportation Eng. and Vehicle Eng. Dept. of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665477161

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Resource Efficient Federated Learning for Deep Anomaly Detec...

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International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Aris Lalos Athena Research Center Industrial Systems Institute Greece Dept. of Computer Engineering & Informatics University of Patras

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.

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