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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiawei Li Dawei Wang Jiankang Zhang Osama Alfarraj Yixin He Saba Al-Rubaye Keping Yu Shahid Mumtaz School of Electronics and Information Northwestern Polytechnical University Xi’an China Department of Computing and Informatics Bournemouth University UK Department of Computer Science Community College King Saud University Riyadh Saudi Arabia College of Information Science and Engineering Jiaxing University Jiaxing China School of Aerospace Transport and Manufacturing Cranfield University UK School of Science and Engineering Hosei University Tokyo Japan Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Computer Sciences Nottingham Trent University UK

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

关键词： Simulation Transforms Receivers Reconfigurable intelligent surfaces Programming Autonomous aerial vehicles Taylor series Communication networks Relays Trajectory optimization

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Task-Aware Waypoint Sampling for Robotic Planning 31

Task-Aware Waypoint Sampling for Robotic Planning

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31st International Conference on Automated Planning and Scheduling, ICAPS 2021

作者： Keren, Sarah Canal, Gerard Cashmore, Michael Harvard University School of Engineering and Applied Sciences Cambridge United States The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Department of Informatics King’s College London United Kingdom University of Strathclyde Computer and Information Sciences Glasgow United Kingdom

ISBN: (纸本)9781713832317

To achieve a complex task, a robot often needs to navigate in a physical space in order to complete activities in different locations. For example, it may need to inspect several structures, making multiple observations of each structure from different perspectives. Typically, the positions from which these activities can be performed are represented as waypoints – discrete positions that are sampled from the continuous physical space and used to find a task plan. Existing approaches to waypoint selection either iteratively consider the entire space or use domain knowledge to consider each activity separately. This can lead to task planning problems that are more complex than is necessary or to plans of compromised quality. Moreover, all previous approaches only consider geometric constraints that can be imposed on the waypoint selection process. We present Task-Aware Waypoint Sampling (TAWS), which offers two key novelties. First, it is an anytime approach that combines the benefits of random sampling with the use of domain knowledge in waypoint selection by performing a onetime computation of the connectivity graph from which waypoints are sampled. In addition, TAWS is the first approach that accounts for performance preferences, which are preferences a system operator may have about the generated task plan. These can account, for example, for areas near doorways where it is preferable that the robot does not stop to perform activities. We demonstrate the performance benefits of our approach on simulated automated manufacturing tasks. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Domain Knowledge

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A $(k, n)$-Secret Image Sharing With Steganography Using Generalized Tent Map

A $(k, n)$-Secret Image Sharing With Steganography Using Gen...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Bishoy K. Sharobim Salwa K. Abd-El-Hafiz Ahmed G. Radwan Heba K. Aslan Nanoelectronics Integrated Systems Center (NISC) Nile University Giza Egypt Centre of Informatics Science Faculty of Information Technology and Computer Science Nile University Giza Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza Egypt School of Engineering and Applied Sciences Nile University Giza Egypt

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

Secret Image Sharing (SIS) transfers an image to mutually suspicious receivers as $n$ meaningless shares, where $k$ or more shares must be present to recover the secret. This paper proposes a $(k, n)$ -SIS system for any image type using polynomial interpolation based on Lagrange polynomials, where the generated shares are of size $1/k$ of the secret image size. A full encryption system, consisting of substitution and permutation stages, is employed by using the generalized Tent map as a source of randomness. In addition to using a long and sensitive system key, steganography using the Least Significant Bits (LSBs) embedding technique is utilized to improve security. Detailed experimental analysis of the security, robustness and performance of the proposed system is provided, which is more comprehensive than the analyses given in other related works. Security is demonstrated using statistical tests, and robustness against noise and cron attacks is validated.

关键词： Steganography Interpolation Circuits and systems Noise Receivers Robustness Polynomials

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Hybrid Approach to Identify Druglikeness Leading Compounds against COVID-19 3CL Protease

arXiv

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arXiv 2022年

作者： Aqeel, Imra Majid, Abdul Biomedical Informatics Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages. © 2022, CC BY.

关键词： SARS

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A structure-activity relationship modelling of opioid compounds by using machine learning 15

A structure-activity relationship modelling of opioid compou...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Ivanova, Antonina Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria Department of Computer Science Faculty of International Economics and Administration Varna Free University Chernorizets Hrabar 84 Yanko Slavchev Str. Chaika Resort Varna9007 Bulgaria

Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands. © Published under licence by IOP Publishing Ltd.

关键词： Medical problems

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs 7

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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7th World Symposium on Communication engineering, WSCE 2024

作者： Michoulis, George Tsourdinis, Kyros Niotis, George Kyranou, Konstantinos Fountoukidis, Eleutherios Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis Psannis, Konstantinos E. Goudos, Sotirios K. Sidroco Holdings Ltd R&D Dpt. Thessaloniki Greece Sidroco Holdings Ltd R&D Dpt. Nicosia Cyprus Democritus University of Thrace Dpt. of Computer Science Kavala Greece University of Western Macedonia Dpt. of Electrical & Computer Engineering Kozani Greece Kingston University London Kingston Upon Thames Dpt. of Networks and Digital Media Surrey United Kingdom University of Macedonia Department of Applied Informatics Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798331542825

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework's claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management. © 2024 IEEE.

关键词： 5G mobile communication systems

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Markov Chain-Based Model of Blockchain Radio Access Networks

Markov Chain-Based Model of Blockchain Radio Access Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Vasileios Kouvakis Stylianos E. Trevlakis Alexandros-Apostolos A. Boulogeorgos Hongwu Liu Theodoros A. Tsiftsis Octavia A. Dobre Department of Research and Development InnoCube P.C. Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China Department of Informatics & Telecommunications University of Thessaly Lamia Greece Faculty of Engineering and Applied Science Memorial University St. John's NL Canada

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Security has always been a priority, for re- searchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security as- pects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.

关键词： Wireless communication Technological innovation Privacy Computational modeling Blockchains Timing History Usability Queueing analysis Radio access networks

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Programmable large-scale simulation of lattices with photonic synthetic frequency dimensions

Programmable large-scale simulation of lattices with photoni...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Senanian, Alen Wright, Logan G. Wade, Peter F. Doyle, Hannah K. McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

We present a programmable analog simulator with up to 105 lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary lattic...

ISBN: (纸本)9781957171258

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Influence of structural defects on charge density waves in 1T-TaS_(2)

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Nano Research 2023年第8期16卷 11528-11539页

作者： Iaroslav Lutsyk Karol Szalowski Pawel Krukowski Pawel Dabrowski Maciej Rogala Witold Kozlowski Maxime Le Ster Michal Piskorski Dorota A.Kowalczyk Wojciech Rys Rafal Dunal Aleksandra Nadolska Klaudia Toczek Przemyslaw Przybysz Ewa Lacinska Johannes Binder Andrzej Wysmolek Natalia Olszowska Jacek J.Kolodziej Martin Gmitra Takuma Hattori Yuji Kuwahara Guang Bian Tai-Chang Chiang Pawel J.Kowalczyk Faculty of Physics and Applied Informatics University of LodzPomorska 149/15390-236 LodzPoland Faculty of Physics University of WarsawPasteura 502-093 WarsawPoland Faculty of Physics Astronomyand Applied Computer ScienceJagiellonian UniversityLojasiewicza 1130-348 KrakowPoland National Synchrotron Radiation Centre SOLARIS Jagiellonian UniversityCzerwone Maki 9830-392 KrakówPoland Institute of Physics Faculty of SciencePavol JozefŠafárik University in KošicePark Angelinum 904001 KošiceSlovakia Institute of Experimental Physics Slovak Academy of SciencesWatsonova 4704001 KošiceSlovakia Department of Precision Engineering Graduate School of EngineeringOsaka University2-1 Yamada-okaSuita 565-0871Japan Department of Physics and Astronomy University of MissouriColumbiaMissouri 65211USA Department of Physics and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois 61801-3080USA

The influence of intrinsic defects of 1T-TaS_(2)on charge density waves(CDWs)is studied using scanning tunneling microscopy and spectroscopy(STM,STS),angle-resolved photoelectron spectroscopy(ARPES),and density functional theory(DFT).We identify several types of structural defects and find that most have a local character limited to a single CDW site,with a single exception which effectively behaves as a dopant,leading to band-bending and affecting multiple neighboring *** only one type of defect can be observed by STM topographic imaging,all defects are easily resolved in STS *** results indicate modulation of the Mott band gap commensurate with the CDW and breaking of the three-fold symmetry of electronic *** calculations(with included Coulomb interactions)are used to investigate the electronic structure,focusing on both sulfur vacancy and oxygen-sulfur *** sulfur vacancy system,characterized with a metallic behavior,is identified as the origin of one of the experimentally observed ***,the effect of oxidation of 1T-TaS_(2)depends on the substitution site,leading to the heterogeneity of electronic properties.

关键词： structural charge sulfur

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