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9th computer Science On-line Conference, CSOC 2020

作者： Pokorný, Pavel Ševčík, Daniel Department of Computer and Communication Systems Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 Zlín760 05 Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 Zlín760 05 Czech Republic

ISBN: (纸本)9783030519643

Nowadays, many technical devices are controlled by logic circuits. These circuits evaluate the situation based on the proposed truth functions, which they then use to perform one of the defined actions. In line with the increasing complexity of individual technical devices, the logic circuits and truth functions are also increasingly complex. For this reason, it makes sense to minimise these functions since they can thereby achieve a simpler technological process and the higher reliability of whole logic systems. This paper describes the Karnaugh Studio application, which resolves the minimisation of these truth functions - and was developed at our faculty (FAI, TBU in Zlín). The minimisation is performed using the Karnaugh Map Method - with support for up to eight variables on input. The application is based on the C++ programming language and the Dear ImGui and Magick++ libraries. Its functionality has been verified on a number of examples. This proved its applicability and ability to be used in the solution of logic circuits in industrial practice. © 2020, Springer Nature Switzerland AG.

关键词： Boolean functions

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On the undecidability of the panopticon detection problem

arXiv

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arXiv 2021年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Koatakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece

The Panopticon (which means "watcher of everything") is a well-known structure of continuous surveillance and discipline proposed by Bentham in 1785. This device was, later, used by Foucault and other philosophers as a paradigm and metaphor for the study of constitutional power and knowledge as well as a model of individuals’ deprivation of freedom. Nowadays, technological achievements have given rise to new, non-physical (unlike prisons), means of constant surveillance that transcend physical boundaries. This, combined with the confession of some governmental institutions that they actually collaborate with these Internet giants to collect or deduce information about people, creates a worrisome situation of several co-existing Panopticons that can act separately or in close collaboration. Thus, they can only be detected and identified through the expense of (perhaps considerable) effort. In this paper we provide a theoretical framework for studying the detectability status of Panopticons that fall under two theoretical, but not unrealistic, definitions. We show, using Oracle Turing Machines, that detecting modern day, ICT-based, Panopticons is an undecidable problem. Furthermore, we show that for each sufficiently expressive formal system, we can effectively construct a Turing Machine for which it is impossible to prove, within the formal system, either that it is a Panopticon or it is not a Panopticon. © 2021, CC BY.

关键词： Turing machines

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Wafiq Zariful Najeebah Az-Zahra Tashim Tiong Hoo Lim Aida Maryam Basri Suriayati Chuprat Seno Adi Putra Pengcheng Liu Hanif Fakhrurroja Electrical and Electronic Engineering Universiti Teknologi Brunei Bandar Seri Begawan Brunei School of Applied Sciences and Mathematics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Johor Bahru Malaysia School of Industrial and System Engineering Telkom Universiti Bandung Indonesia Department of Computer Science University of York York United Kingdom Research Center for Smart Mechatronics National Research and Innovation Agency Telkom University Bandung Indonesia

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications.

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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

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

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IEEE World Symposium on Communication engineering (WSCE)

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

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

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.

关键词： 5G mobile communication Distributed ledger Throughput Dynamic scheduling Fabrics Resource management Nonfungible tokens Low latency communication

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Low c-differential uniformity for functions modified on subfields

arXiv

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arXiv 2021年

作者： Bartoli, Daniele Calderini, Marco Riera, Constanza Stanica, Pantelimon Department of Mathematics and Informatics University of Perugia Via Vanvitelli 1 Perugia06123 Italy Department of Informatics University of Bergen Postboks 7803 BergenN-5020 Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Applied Mathematics Department Naval Postgraduate School MontereyCA93943 United States

In this paper, we construct some piecewise defined functions, and study their c-differential uniformity. As a by-product, we improve upon several prior results. Further, we look at concatenations of functions with low differential uniformity and show several results. For example, we prove that given βi (a basis of Fqn over Fq), some functions fi of c-differential uniformities δi, and Li (specific linearized polynomials defined in terms of βi), 1 ≤ i ≤ n, then (Equation presented) has c-differential uniformity equal to (Equation presented).MSC Codes 06E30, 11T06, 94A60, 94D10 Copyright © 2021, The Authors. All rights reserved.

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Improved Method of Segmentation of Images From Space-Based Optoelectronic Observation Systems Based on Otsu's Algorithm with Global and Adaptive Thresholds

Improved Method of Segmentation of Images From Space-Based O...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Hennadii Khudov Temir Kalimulin Rostyslav Khudov Ihor Butko Viktoriia Burtseva Valeriy Burtsev Department of Radar Troops Tactic Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Theoretical and Applied Informatics V.N.Karazin Kharkiv National University Kharkiv Ukraine Department of Computer Sciences and Software Engineering Academician Y. Bugay International Scientific and Technical University Kyiv Ukraine Scientific Research Department of Military Standards Metrology Center of Military Standards Kharkiv Ukraine Department of Control Systems of Antiaircraft Missile Troops Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

An analysis of optoelectronic image, received from space-based observation system has been carried out. It has been found out, that this image has a complex structure and a unimodal intensity histogram. Otsu method is chosen as to segment the image. This method is easy and relatively quick, when talking of its realization. Methods of global and adaptive thresholding have been researched to perform image segmentation. Results of experimental research of segmentation of optoelectronic image from space-based observation system with use of Otsu method are developed. Ways for future research in this area are defined.

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Exploring Koopman Operator Based Surrogate Models—Accelerating the Analysis of Critical Pedestrian Densities 13th

Exploring Koopman Operator Based Surrogate Models—Accelerat...

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13th Conference on Traffic and Granular Flow, TGF 2019

作者： Lehmberg, Daniel Dietrich, Felix Kevrekidis, Ioannis G. Bungartz, Hans-Joachim Köster, Gerta Department of Computer Science and Mathematics Munich University of Applied Sciences Munich Germany Department of Informatics Technical University of Munich Garching Germany Whiting School of Engineering Johns Hopkins University Baltimore United States Princeton University Princeton United States

ISBN: (纸本)9783030559724

We apply the Koopman operator framework to pedestrian dynamics. In an example scenario, we generate crowd density time series data with a microscopic pedestrian simulator. We then approximate the Koopman operator in matrix form through Extended Dynamic Mode Decomposition, using Geometric Harmonics on the data as a dictionary. The Koopman matrix is integrated into a surrogate model, which allows to approximate crowd density time series data to be generated, independently from the original microscopic simulator. The evaluation of the constructed surrogate model is orders of magnitude faster, and enables us to use methods that require many model evaluations. © 2020, Springer Nature Switzerland AG.

关键词： Dynamic mode decomposition

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MRP 2019: Cross-framework meaning representation parsing 23

MRP 2019: Cross-framework meaning representation parsing

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2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd Conference for Computational Language Learning, CoNLL 2019

作者： Oepen, Stephan Abend, Omri Hajič, Jan Hershcovich, Daniel Kuhlmann, Marco O'Gorman, Tim Xue, Nianwen Chun, Jayeol Straka, Milan Urešová, Zdeňka University of Oslo Department of Informatics Norway Hebrew University of Jerusalem School of Computer Science and Engineering Israel Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Linköping University Department of Computer and Information Science Sweden University of Colorado at Boulder Department of Linguistics United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781950737604

The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the training and evaluation data for the task, packaged in a uniform graph abstraction and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of extra training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2019 Association for Computational Linguistics

关键词： Directed graphs

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