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A Mobility-Aware Reinforcement Learning Proactive Solution for State Data Migration in Edge Computing 29

A Mobility-Aware Reinforcement Learning Proactive Solution f...

引用

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

作者： Dritsas, Elias Ramantas, Kostas Verikoukis, Christos Iquadrat Barcelona Spain University of Patras Department of Computer Engineering and Informatics Patras Greece Industrial Systems Institute Athena Research and Innovation Center Patras Greece

ISBN: (纸本)9798350377644

To handle the demands of modern applications for storage, computing, low latency, and bandwidth, various services are offloaded from the cloud to edge servers, bringing them closer to end-users. This shift in computing resources closer to the users is known as Edge Computing whose services are highly affected by users' mobility. As users move and change geographical locations, the services provided by an edge server may be compromised or become distant from the user, leading to a decrease in the quality of service (QoS) for applications. Service migration is a common approach to ensure that essential services remain close to users, thereby improving the application QoS. The synergy of Edge Computing with Artificial Intelligence (AI) can open up new capabilities for the Lifecycle Management (LCM) of applications, besides the adoption of the innovative microservice-based architecture for building them and, especially, stateful to ensure seamless service continuity and minimal latency during user's mobility. The leverage of AI-based LCM of such applications can significantly benefit edge computing by automating and optimising, in terms of resource allocation, the process of decision-making during deployment and run-time operation of microservices running on edge devices. In this direction, we elaborate a mobility-induced proactive migration mechanism empowered by efficient Deep Learning (DL) and Reinforcement Learning (RL) algorithms to identify the optimal edge node for application and state data migration without violating Service Level Agreement - SLA (e.g., latency) and ensure efficient resource usage at the edge. © 2024 IEEE.

关键词： Edge Computing Life-Cycle-Management Migration Reinforcement Learning Trajectory Prediction

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Performance and Security Analysis of the Diffie-Hellman Key Exchange Protocol 19

Performance and Security Analysis of the Diffie-Hellman Key ...

引用

19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Athena Research and Innovation Center Industrial Systems Institute Patras26504 Greece University of West Attica Department of Informatics and Computer Engineering Egaleo12243 Greece

ISBN: (纸本)9798331504502

The Diffie-Hellman Key Exchange Protocol (DHKE) is a fundamental element of modern cryptographic systems, enabling secure key exchange over unsecured channels. The present research work aims to provide a comprehensive analysis of DHKE, with a dual focus on performance and security. We examine the protocol's robustness against classical and emerging threats, particularly in the context of the Discrete Logarithm Problem (DLP) and the advent of quantum computing. Performance is evaluated by measuring execution times for key lengths of 512 and 1024 bits, each recorded over 100 iterations. The results are analyzed to provide insights into both computational efficiency and variability, with graphical representations to illustrate the findings. The study underscores the balance between security and performance in DHKE and offers recommendations for future enhancements to maintain cryptographic resilience in the face of advancing computational capabilities. © 2024 IEEE.

关键词： Authentication Protocol

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

引用

3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Analysis of ECG Image File Encryption using ECDH and AES-GCM Algorithm 7

Analysis of ECG Image File Encryption using ECDH and AES-GCM...

引用

7th International Workshop on Big Data and Information Security, IWBIS 2022

作者： Oktivasari, Prihatin Agustin, Maria Akbar, Rahmat Esa Mohammad Kurniawan, Asep Zain, Ayu Rosyida Murad, Fachroni Arbi Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (数字)9781665489508

ISBN: (纸本)9781665489508

Image data is used in various fields, including the health sector. One of the image data in the health sector is an electrocardiogram (ECG). The ECG contains the identifying information of a person which must be guarded and secured. ECG image data security can be done by encrypting image data using encryption algorithms. The encryption algorithms used in this thesis are ECDH and AES-GCM. ECDH is used to generate key pairs which are then used as keys for AES-GCM encryption and decryption. The encryption and decryption process are carried out in the python programming language. The results of the encryption time and decryption time increase because of the dimensions and size of the ECG image file. The nonce value and authentication tag are checked to be able to perform the decryption process. The histogram test results prove the uniformity of pixels in the encrypted ECG image file. PSNR and SSIM test results prove the difference between the initial and encrypted ECG image files. The results of the NIST Statistical Test Suite test prove that the algorithm used produces random output so that it can be used to secure ECG image files. © 2022 IEEE.

关键词： Electrocardiograms

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CNN-based Segmentation and Classification of Sound Streams under realistic conditions 26

CNN-based Segmentation and Classification of Sound Streams u...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Tsalera, Eleni Papadakis, Andreas Samarakou, Maria Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Greece Athens Greece

ISBN: (纸本)9781450398541

Audio datasets support the training and validation of Machine Learning algorithms in audio classification problems. Such datasets include different, arbitrarily chosen audio classes. We initially investigate a unifying approach, based on the mapping of audio classes according to the Audioset ontology. Using the ESC-10 audio dataset, a tree-like representation of its classes is created. In addition, we employ an audio similarity calculation tool based on the values of extracted features (spectrum centroid, the spectrum flux and the spectral roll-off). This way the audio classes are connected both semantically and in feature-based manner. Employing the same dataset, ESC-10, we perform sound classification using CNN-based algorithms, after transforming the sound excerpts into images (based on their Mel spectrograms). The YAMNet and VGGish networks are used for audio classification and the accuracy reaches 90%. We extend the classification algorithm with segmentation logic, so that it can be applied into more complex sound excerpts, where multiple sound types are included in a sequential and/or overlapping manner. Quantitative metrics are defined on the behavior of the combined segmentation and segmentation functionality, including two key parameters for the merging operation, the minimum duration of the identified sounds and the intervals. The qualitative metrics are related to the number of sound identification events for a concatenated sound excerpt of the dataset and per each sound class. This way the segmentation logic can operate in a fine- and coarse-grained manner while the dataset and the individual sound classes are characterized in terms of clearness and distinguishability. © 2022 ACM.

关键词： Classification (of information)

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Support Vector Machine: Melanoma Skin Cancer Diagnosis based on Dermoscopy Image 24

Support Vector Machine: Melanoma Skin Cancer Diagnosis based...

引用

24th International Electronics Symposium, IES 2022

作者： Ibrahim, Tirta Farisaldi Setiawardhana Sigit, Riyanto Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Department Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Melanoma is one of the most deadly types of skin cancer. Until now, the diagnosis of melanoma skin cancer is still using the biopsy method, which is the procedure of taking a small portion of tissue from the patient's body to be examined using a microscope. This method is considered less effective because finding the type of melanoma skin cancer requires a long preparation and a slightly longer wound healing time. Suppose this melanoma can be diagnosed from the start for patients suffering from malignant melanoma tumors. In that case, it can be treated immediately so that the percentage of patient recovery will be higher. Therefore, in this study, a rapid dermoscopy image computerized analysis system was designed so that it can be used to classify the type of melanoma image or not (nevus) with Gray Level Co-occurrence Matric (GLCM). For the classification method used, the support vector machine (SVM) is one of the supervised learning that is usually used for classifications such as (Support Vector Classification) and regression (Support Vector Regression). This method is believed to be quite good and accurate in performing dermoscopy image recognition which is used as input and for image processing. The system for diagnosing melanoma will be implemented in a graphic user interface that will facilitate the user interface experience. The results of this study are information about skin cancer from the image, whether it is melanoma or not (nevus) which can help dermatologists diagnose melanoma quickly. In this study, 2589 data training was conducted, and testing was carried out on 192 testing data. The test results obtained an accuracy rate of 83% and an error percentage of 17%. © 2022 IEEE.

关键词： Support vector machines

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Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization 1

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Nüßlein, Jonas Zielinski, Sebastian Gabor, Thomas Linnhoff-Popien, Claudia Feld, Sebastian Institute for Informatics LMU Munich Munich Germany Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

We introduce a novel approach to translate arbitrary 3-sat instances to Quadratic Unconstrained Binary Optimization (qubo) as they are used by quantum annealing (QA) or the quantum approximate optimization algorithm (QAOA). Our approach requires fewer couplings and fewer physical qubits than the current state-of-the-art, which results in higher solution quality. We verified the practical applicability of the approach by testing it on a D-Wave quantum annealer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Qubits

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Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs

Exploring the Impact of Synthetic Data on Human Gesture Reco...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： George Kontogiannis Pantelis Tzamalis Sotiris Nikoletseas Computer Engineering & Informatics University of Patras Patras Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep Generative Models (DGMs) to address data scarcity, enhance data quality, and improve classification metrics scores. Among these types of models, Generative Adversarial Networks (GANs) have arisen as a powerful tool for generating synthetic data that mimic real-world scenarios with high fidelity. However, Human Gesture Recognition (HGR), a subset of HAR, particularly in healthcare applications, using time series data such as allergic gestures, remains highly *** this paper, we examine and evaluate the performance of two GANs in the generation of synthetic gesture motion data that compose a part of an open-source benchmark dataset. The data is related to the disease identification domain and healthcare, specifically to allergic rhinitis. We also focus on these AI models’ performance in terms of fidelity, diversity, and privacy. Furthermore, we examine the scenario if the synthetic data can substitute real data, in training scenarios and how well models trained on synthetic data can be generalized for the allergic rhinitis gestures. In our work, these gestures are related to 6-axes accelerometer and gyroscope data, serving as multi-variate time series instances, and retrieved from smart wearable devices. To the best of our knowledge, this study is the first to explore the feasibility of synthesizing motion gestures for allergic rhinitis from wearable IoT device data using Generative Adversarial Networks (GANs) and testing their impact on the generalization of gesture recognition systems. It is worth noting that, even if our method has been applied to a specific category of gestures, it is designed to be generalized and can be deployed also to other motion data in the HGR domain.

关键词： Training Gesture recognition Benchmark testing Generative adversarial networks Data models Internet of Things Human activity recognition

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Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs

Late Breaking Results: Energy-Efficient Printed Machine Lear...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Spyridon Besias Ilias Sertaridis Florentia Afentaki Konstantinos Balaskas Georgios Zervakis Department of Computer Engineering & Informatics University of Patras Patras Greece

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art.

关键词： Support vector machines Energy consumption Accuracy Machine learning algorithms Limiting Circuits Europe Energy efficiency Hardware Batteries

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Estimation of Ball Position Using Depth Camera for Middle Size Goalkeeper Robot 24

Estimation of Ball Position Using Depth Camera for Middle Si...

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24th International Electronics Symposium, IES 2022

作者： Suwandi, Dekasari Putri Wibowo, Iwan Kurnianto Bachtiar, Mochamad Mobed Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

EEPIS Robot Soccer On Wheeled (ERSOW) has a goalkeeper robot used as a defense to prevent the opposing team from scoring goals. The ability to detect the ball is one of the main abilities that a goalkeeper robot must-have. The goalkeeper robot needs the ability to able to detect the ball when it bounces in the air. In this research, a ball detection system will be designed for the goalkeeper robot using a Depth Camera which is placed above the robot's head and facing perpendicular to the front from the robot's point of view. The depth camera was chosen because it has a 3D-based object recognition feature so that it can be used to calculate the distance between the robot's position and the ball even though the ball is soaring in the air. Data from the depth camera will be presented in Red, Green, Blue, and Depth (RGB-D) formats. The image from the camera reading with the Depth format will be processed for estimation of the ball's position. The results of the position estimation test get an average position error on the X, Y, and Z axes of 69.53 mm, 46.11 mm, and 65.77 mm. © 2022 IEEE.

关键词： Robots

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