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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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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziru Chen Zhou Ni Peiyuan Guan Lu Wang Lin X. Cai Morteza Hashemi Zongzhi Li Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA Department of Electrical Engineering and Computer Science University of Kansas Lawrence USA Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms—Federated Vehicular Network, NOMA

关键词： Wireless communication NOMA Data privacy Federated learning Simulation Power control Road safety Real-time systems Resource management Vehicles

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Computational Approaches for Integrating out Subjectivity in Cognate Synonym Selection

arXiv

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arXiv 2024年

作者： Häuser, Luise Jäger, Gerhard Stamatakis, Alexandros Computational Molecular Evolution group Heidelberg Institute for Theoretical Studies Heidelberg Germany Institute for Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany University of Tübingen Germany Biodiversity Computing Group Institute of Computer Science Foundation for Research and Technology - Hellas Computational Molecular Evolution group Heidelberg Institute for Theoretical Studies Heidelberg Germany Institute for Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany

Working with cognate data involves handling synonyms, that is, multiple words that describe the same concept in a language. In the early days of language phylogenetics it was recommended to select one synonym only. However, as we show here, binary character matrices, which are used as input for computational methods, do allow for representing the entire dataset including all synonyms. Here we address the question how one can and if one should include all synonyms or whether it is preferable to select synonyms a priori. To this end, we perform maximum likelihood tree inferences with the widely used RAxML-NG tool and show that it yields plausible trees when all synonyms are used as input. Furthermore, we show that a priori synonym selection can yield topologically substantially different trees and we therefore advise against doing so. To represent cognate data including all synonyms, we introduce two types of character matrices beyond the standard binary ones: probabilistic binary and probabilistic multi-valued character matrices. We further show that it is dataset-dependent for which character matrix type the inferred RAxML-NG tree is topologically closest to the gold standard. We also make available a Python interface for generating all of the above character matrix types for cognate data provided in CLDF format. © 2024, CC BY.

关键词： Phylogenetic

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Walking the Future: Bridging Foot Augmentation into Next Steps of Human Augmentation

Walking the Future: Bridging Foot Augmentation into Next Ste...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Sabnis, Nihar Wittchen, Dennis Otaran, Ata Badmos, Fatima Liu, Siyi Saini, Aryan Regal, Georg van Rheden, Vincent Mueller, Florian Nanayakkara, Suranga Matthies, Denys J.C. Nachtigall, Troy Strohmeier, Paul Vidal, Laia Turmo Elvitigala, Don Samitha Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Faculty of Informatics / Mathematics Dresden University of Applied Sciences Saxony Dresden Germany Saarland University Saarland Informatics Campus Saarland Saarbrücken Germany Technological University Dublin Dublin Ireland Faculty of Information Technology Monash University Melbourne Australia Exertion Games Lab. Department of Human-Centred Computing Monash University MelbourneVIC Australia Center for Technology Experience AIT Austrian Institute of Technology GmbH Vienna Austria Human Computer Interaction Division University of Salzburg Salzburg Austria Augmented Human Lab. School of Computing National University of Singapore Singapore Singapore Augmented Human Lab. Auckland Bioengineering Institute Auckland New Zealand Eindhoven University of Technology Eindhoven Netherlands KTH Royal Institute of Technology Stockholm Sweden Department of Human Centred Computing Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9798400713958

Over the past decade, a noticeable increase in literature can be seen in wearable foot interfaces, which have evolved from activity tracking to enhancing human capabilities. Our legs, being the largest body limbs, play an essential role in various functions such as locomotion, maintaining balance, supporting proper posture and providing ground-contact using our feet. Hence, foot augmentations offer the opportunity to augment our entire body. However, most prior research focuses on specific application areas, thus affording a research agenda to further understand the full potential of feet in designing augmentations and to contextualize it in the broader human augmentation space. To achieve this, in this workshop, we invite researchers, designers, and practitioners, novice and expert, interested in designing human and foot augmentations. We will discuss how early foot interfaces helped in augmenting humans, and based on current work and trends in foot augmentation, we will formulate strategies for the next steps and discuss the applicability of such strategies in the broader space of human augmentation. © 2025 Copyright held by the owner/author(s).

关键词： Human engineering

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Joint System Latency and Data Freshness Optimization for Cache-enabled Mobile Crowdsensing Networks

arXiv

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arXiv 2025年

作者： Shi, Kexin Fu, Yaru Guo, Yongna Wang, Fu Lee Zhang, Yan School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Informatics University of Oslo Norway

Mobile crowdsensing (MCS) networks enable large-scale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cache-enabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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Machine Speech Chain with Emotion Recognition

Machine Speech Chain with Emotion Recognition

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Akeyla Pradia Naufal Dessi Puji Lestari Ayu Purwarianti Kurniawati Azizah Dipta Tanaya Sakriani Sakti School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics.

关键词： Training Emotion recognition Accuracy Error analysis Natural languages Training data Speech recognition Speech enhancement Data models Speech synthesis

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Improving the CNN Model for Arabic Crime Tweet Detection Based on an Intelligent Dictionary

Improving the CNN Model for Arabic Crime Tweet Detection Bas...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Zainab Khyioon Abdalrdha Abbas Mohsin Al-Bakry Alaa K. Farhan Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq University of Information Technology and Communication (UoITC) Baghdad Iraq Department of Computer Sciences University of Technology Baghdad Iraq

Rising crime rates in Iraq and the Arab World pose significant challenges for law enforcement, exacerbated by vast data on criminal activity, technological advances, and high population density. This research aims to extract credible information from this data to understand crime better and assist in future prevention efforts. One challenge is the complexity of the Arabic language in crime detection. The study introduces an innovative approach using deep learning techniques, including creating an intelligent dictionary for detecting crime-related content in Arabic tweets. This method employs the Aho-Corasick algorithm to address the dynamic nature of language and evolving criminal terminology. The methodology applies deep learning and machine learning techniques, emphasizing natural language processing (NLP) for preprocessing and extracting relevant tweet characteristics. Created a labeled tweet dataset and classified it into various crime-related categories. Deep learning models, such as an improved CNN model based on chi-square testing and Grid search, were trained and evaluated using precision, recall, F1-score, accuracy, and MAP accuracy metrics. The analysis of an Arabic tweet dataset, comprising 18,493 tweets with ten features, demonstrated that the improved CNN model achieved a high accuracy of 99.25% and a macro F1-score of 99.9%. These results indicate that the proposed deep learning model outperforms others regarding accuracy.

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ROAR: A Benchmark for NFT Rarity Meters

ROAR: A Benchmark for NFT Rarity Meters

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Dmitry Belousov Maksim Shuklin Alexander Stepin Yury Yanovich Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Faculty of Computer Science HSE University Moscow Russia Wireless Center Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350316742

ISBN: (纸本)9798350316759

Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rarity meters, it requires access to NFT collection data, which can be challenging for researchers without a background in blockchain technology. This has created a demand for an easily accessible rarity meter benchmark. In this paper, we introduce the Rating over all Rarities (ROAR) benchmark, which includes data from one hundred popular NFT collections from the Ethereum blockchain, implemented a weighted correlation-based performance measurement function, as well as four state-of-the-art rarity meters (***, Kramer, OpenRarity, and NFTGo), along with a new rarity meter called ROAR. Our experiments show that the ROAR rarity meter, an ensemble of the other four meters, outperforms its competitors, with *** and Kramer as runner-ups. The ROAR benchmark is a tool for examination and testing of rarity meter ideas, and we challenge readers to develop models that can outperform the ROAR rarity meter.

关键词： Meters Measurement Industries Smart contracts Benchmark testing Blockchains Cryptocurrency

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Saturated Drawings of Geometric Thickness k

arXiv

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arXiv 2025年

作者： Bachmann, Patricia Brötzner, Anna Goetze, Miriam Kindermann, Philipp Pfretzschner, Matthias Terziadis, Soeren University of Passau Germany Department of Computer Science and Media Technology Malmö University Sweden Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany Algorithms Group Trier University Germany Algorithms Group TU Eindhoven Netherlands

We investigate saturated geometric drawings of graphs with geometric thickness k, where no edge can be added without increasing k. We establish lower and upper bounds on the number of edges in such drawings if the vertices lie in convex position. We also study the more restricted version where edges are precolored, and for k = 2 the case for vertices in non-convex position. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning computer architecture Generators Data models Standards Interoperability Load flow

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