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arXiv 2025年

作者： Erdogan, Enes Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Information Technology Halmstad University Halmstad Sweden

Recognition of human manipulation actions in real-time is essential for safe and effective human-robot interaction and collaboration. The challenge lies in developing a model that is both lightweight enough for real-time execution and capable of generalization. While some existing methods in the literature can run in real-time, they struggle with temporal scalability, i.e., they fail to adapt to long-duration manipulations effectively. To address this, leveraging the generalizable scene graph representations, we propose a new Factorized Graph Sequence Encoder network that not only runs in real-time but also scales effectively in the temporal dimension, thanks to its factorized encoder architecture. Additionally, we introduce Hand Pooling operation, a simple pooling operation for more focused extraction of the graph-level embeddings. Our model outperforms the previous state-of-the-art real-time approach, achieving a 14.3% and 5.6% improvement in F1-macro score on the KIT Bimanual Action (Bimacs) Dataset and Collaborative Action (CoAx) Dataset, respectively. Moreover, we conduct an extensive ablation study to validate our network design choices. Finally, we compare our model with its architecturally similar RGB-based model on the Bimacs dataset and show the limitations of this model in contrast to ours on such an object-centric manipulation dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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LOGICAL ANALYSIS AND CONTRADICTION DETECTION IN HIGH-LEVEL REQUIREMENTS DURING THE REVİEW PROCESS USING SAT-SOLVER

arXiv

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arXiv 2024年

作者： Yatkın, Simge Ovatman, Tolga Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey MGEO Test and Verification Directorate ASELSAN Inc. Ankara Turkey

DO-178C stands out as a guiding standard for aviation system development processes. This standard not only mandates ensuring the consistency of requirements in the software verification process but also recognizes it as a mandatory element. The main objective of this study is to introduce a method for analyzing and identifying inconsistencies between high-level requirements using information obtained from a data dictionary. This method aims to transform high-level requirements into logical expressions and then thoroughly examine them using a SAT Solver to detect inconsistencies. While methods focused on identifying inconsistencies among requirements often appear in the literature, this study presents a novel approach to detect contradictions between non-natural language, systematically structured, and language-independent requirements. The goal of this approach is to significantly reduce the review time of high-level requirements in the software verification process. Evaluations indicate that the use of this method results in substantial time savings in the inconsistency detection process. © 2024, CC BY-SA.

关键词： Model checking

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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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Exploring the Semantic Complexity of Adjective-Noun Collocations between Uzbek and English for Improved Machine Translation

Exploring the Semantic Complexity of Adjective-Noun Collocat...

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International Conference on computer Science and engineering (UBMK)

作者： Nilufar Abdurakhmonova Nargiza Shamieva Eşref Adali Dept. of Computational and applied linguistics National University of Uzbekistan Tashkent Uzbekistan Dept. of Foreign language and Literature National Univ. of Uzbekistan Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The bilingual thesaurus of Uzbek-English adjectives is a field that has not been studied well explored yet for machine translation. Several websites and electronic dictionaries created nearby show that not enough semantically close adjectives are included in them. Despite extensive research on the meaning the adjectives in both languages, there remains a lack of understanding about the relationship between the adjective itself and the noun, which has an absolutely new connotation in collaboration. Our result shows that a recent bilingual thesaurus demonstrates how one adjective could acquire numerous new synonyms while combining with different nouns. The meanings of words, in both languages have been extensively studied, Little is understood about the dynamic connection between these words and the nouns they describe. The complexity of how adjectives pair with nouns is evident in how this relationship leads to meanings and connotations. Our study aims to bridge this gap by demonstrating how a bilingual thesaurus can capture the meanings and variations of adjectives when combined with nouns. The findings indicate that depending on the noun it accompanies a contemporary bilingual thesaurus can generate synonyms, for a single adjective.

关键词： Vocabulary Dictionaries Sensitivity Semantics Linguistics Thesauri Complexity theory Machine translation Cultural differences Probes

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Switching Activity Reduction in Graphene PN Junction Circuits using Circuit Re-structuring

Switching Activity Reduction in Graphene PN Junction Circuit...

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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Das, Subrata Deb, Arighna Fiser, Petr Sister Nivedita University Dept. of Computer Science & Engineering Kolkata India School of Electronics Engineering Kiit Bhubaneswar India Czech Technical University Faculty of Information Technology Prague Czech Republic

ISBN: (纸本)9798350315042

Graphene PN Junction (GPNJ) logic circuits received significant attention from the researchers thanks to the availability of electrostatically doped graphene PN Junction (GPNJ) device- a promising one for designing low-power, high-speed circuits. Several design approaches for GPNJ logic circuits realizing important arithmetic and arbitrary Boolean functions exist in literature. However, no detailed evaluations of the resulting circuit complexities with respect to relevant cost metrics are reported. It remains open, how do we synthesize GPNJ logic circuits with minimal switching activity, an essential cost metric influencing the dynamic power dissipation in the resulting circuits. In this paper, we introduce a synthesis approach for GPNJ logic circuits that interconnects a set of GPNJ devices in parallel. Such parallel circuit structures ensure the reduction of switching activity in the resulting GPNJ logic circuits. To the best of our knowledge, so far only one research work discussed the computation of switching activity of GPNJ logic circuits. Experimental evaluations confirm that an average 67.35% reduction in switching activity can be achieved using the proposed approach over the existing one. © 2023 IEEE.

关键词： Graphene

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Deep Learning Based Topic Classification for Sensitivity Assignment to Personal Data 6

Deep Learning Based Topic Classification for Sensitivity Ass...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Yayık, Apdullah Apik, Hasan Tosun, Ayşe Mobildev R&D Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781665429085

This study aims to understand non-linear relations in Turkish textual contents to predict their topic with the help of machine learning models and to discuss contributions of the models to compliance with Personal Data Protection Rule (PDPR). Since the exponential growth of concerns in personal data processing, it has been a necessity to know the topic of the textual contents to interpret their usage in which environment they are located. The topic of the document is a piece of inclusive information presented together with all the data in its content, by this reason the categories of personal data defined in the PDPR may vary due to semantic impacts from its environment in which it is being processed. Many experiments are conducted with the fasttext model employing logistic regression and deep bidirectional transformers (BERT) models having attention layers. Model performances and statistical post-hoc test results on the model predictions are analyzed, then model deployment together with the industrial usage are discussed. As a result, a fasttext model having a macro-averaged F1-measure of 94.73 ± 0.67% is created and integrated into production efficiently. © 2021 IEEE

关键词： Forecasting

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Ensemble and Cross-Project Software Reliability Growth Models for Safety-Critical Systems 6

Ensemble and Cross-Project Software Reliability Growth Model...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Azturk, Elifnur Tosun, Ayse Aselsan A.S. Ankara Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781665429085

Software Reliability Growth Models (SRGMs) have been widely used for software reliability estimation, measurement of the current state, and prediction of the future state, based on results of software testing activities. Although there are various models that have been introduced to predict software reliability, none of them provides a common or general solution. In this study, we propose a new ensemble model (EM), which helps to prevent any extra work while finding out an SRGM, where the project data is fitting and suitable mostly. This proposed model tries to approach the SRGM with the lowest training error and also handles models with slightly worse training but good test results. The EM we created with this motivation has achieved its purpose. It has substantially saved us from the uncertainty of the correct SRGM selection process and helps us to get satisfying results in many cases. The other approach is the ensemble of cross projects (ECP), with this method, much better results are obtained by combining the results of a single cross-project prediction on ongoing projects. Good results are obtained through similarity analysis and proportioning according to the number of errors per unit time before combining. With the help of this study, we believe that any practitioner or researcher, who is interested in SRGMs will benefit from our results. © 2021 IEEE

关键词： Software reliability

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Machine Learning Algorithms for Predicting Chronic Diseases

Machine Learning Algorithms for Predicting Chronic Diseases

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Furkan Buluş Bilal Saoud Ibraheem Shayea Zuleikha Syzdykova Dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey Dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria Dept. of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management.

关键词： Machine learning algorithms Data analysis Prevention and mitigation Medical services Predictive models Prediction algorithms Robustness Bayes methods Decision trees Diseases

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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

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Neural Computing and Applications 2024年第24期36卷 14727-14756页

作者： Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Dobrojevic, Milos Simic, Vladimir Sadasivuni, Kishor Kumar Tirkolaee, Erfan Babaee Faculty of Technical Sciences Singidunum University Danijelova 32 Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Tamilnadu Thandalam Chennai602105 India MEU Research Unit Middle East University Amman Jordan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Yuandong Rd. Zhongli Dist. Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Center for Advanced Materials Qatar University Doha Qatar Department of Industrial Engineering Istinye University Istanbul34396 Turkey Department of Industrial and Mechanical Engineering Lebanese American University Byblos Lebanon

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.

关键词： Crops

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Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?

Does Twinning Vehicular Networks Enhance Their Performance i...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Sarah Al-Shareeda Sema F. Oktug Yusuf Yaslan Gokhan Yurdakul Berk Canberk Faculty of Computer Engineering and Informatics Istanbul Technical University Turkey BTS Group Turkey School of Computing Engineering and The Built Environment Edinburgh Napier University UK

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions.

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