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9th International Conference on computer Science and engineering, UBMK 2024

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

ISBN: (纸本)9798350365887

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. © 2024 IEEE.

关键词： Machine translation

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Semi-automated Video Archiving Using Object Detection, Tracking and Ontological Annotation

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SSRN 2023年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Performance Impact of Immersion and Collaboration in Visual Data Analysis

Performance Impact of Immersion and Collaboration in Visual ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Daniel Garrido João Jacob Daniel Castro Silva Department of Informatics Engineering Faculty of Engineering University of Porto and Artificial Intelligence and Computer Science Laboratory

Immersive Analytics is a recent field of study that focuses on utilizing emerging extended reality technologies to bring visual data analysis from the 2D screen to the real/virtual world. The effectiveness of Immersive Analytics, when compared to traditional systems, has been widely studied in this field’s corpus, usually concluding that the immersive solution is superior. However, when it comes to comparing collaborative to single-user immersive analytics, the literature is lacking in user studies. As such, we developed a comprehensive experimental study with the objective of quantifying and analysing the impact that both immersion and collaboration have on the visual data analysis process. A two-variable (immersion: desktop/virtual reality; number of users: solo/pair) full factorial study was conceived with a mixed design (within-subject for immersion and between subject for number of users). Each of the 24 solo and 24 pairs of participants solved five visual data analysis tasks in both a head-mounted display-based virtual world and a desktop computer environment. The results show that, in terms of task time to completion, there were no significant differences between desktop and virtual reality, or between the solo and pair conditions. However, it was possible to conclude that collaboration is more beneficial the more complex the task is in both desktop and virtual reality, and that for less complex tasks, collaboration can be a hindrance. System Usability Scale scores were significantly better in the virtual reality condition than the desktop one, especially when working in pairs. As for user preference, the virtual reality system was significantly more favoured both as a visual data analysis platform and a collaborative data analysis platform over the desktop system. All supplemental materials are available at https://***/k94u5/.

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Systematic Literature Review Langchain Proposed 25

Systematic Literature Review Langchain Proposed

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25th International Electronics Symposium, IES 2023

作者： Asyrofi, Rakha Dewi, Mutia Rahmi Lutfhi, Muhammad Irfan Wibowo, Prasetyo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350314731

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review. © 2023 IEEE.

关键词： Software testing

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Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

Real time detection of acoustic anomalies in industrial proc...

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作者： Bayram, Barış Duman, Taha Berkay Ince, Gökhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking. © 2020 John Wiley & Sons, Ltd

关键词： Anomaly detection

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation

Semi-Automated Video Archiving Using Object Detection, Track...

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Artificial Intelligence, Robotics and Control (AIRC), International Conference on

作者： Shehab Eldeen Ayman Walid Hussein Omar H. Karam Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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Advanced Design Pattern Selection Methodology through Weighted Semantic Ontologies for Microservices

Advanced Design Pattern Selection Methodology through Weight...

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International Conference on computer and Applications (ICCA)

作者： Rabab Atef Attia Samy Ghoniemy Abeer Hamdy Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Computer Science and Engineering Program Galala University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity, which allows connections at any time and from any location. By structuring applications as separate services that can communicate both internally inside a platform and externally across platforms, microservices (MS) architecture satisfies these requirements. One of the most difficult parts, though, is still controlling communication between MS services, which calls for knowledge of the best design patterns. In order to solve this, we suggest a context-aware recommender system that is adapted to MS design patterns. This system not only makes recommendations for appropriate patterns, but it also gives statistical scores that show how relevant each solution is to the particular issue at hand. Other patterns with ratings indicating their possible feasibility are also displayed by our algorithm. On the other hand, sentence transformers handle intricate, human-like problem descriptions, while our method uses MS Domain Ontologies to distinguish pattern applications, constraints, and best practices. Our methodology provides a refined examination of solution suitability and recommendations through the use of weighing and correlation algorithms. Extensively tested with more than 237 developer-style scenarios, our framework offers qualitative and quantitative insights into its performance and efficacy, showing notable gains over current solutions in terms of offering pattern-specific, contextually accurate guidance for MS communication challenges.

关键词： Hands Adaptation models Accuracy Correlation Knowledge based systems Semantics Microservice architectures Ontologies Reliability Testing

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Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

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computer Modeling in engineering & Sciences 2024年第2期138卷 1385-1403页

作者： Muhammad Farman Ali Akgül Mir Sajjad Hashemi Liliana Guran Amelia Bucur Institue of Mathematics Khwaja Fareed University of Enginnering and Information TechnologyRaheem Yar KhanPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Mathematics Art and Science FacultySiirt UniversitySiirt56100Turkey Department of Mathematics Mathematics Research CenterNear East UniversityNicosiaTurkey Department of Computer Engineering Biruni UniversityIstanbul34010Turkey Department of Hospitality Services Babes-Bolyai UniversityCluj-Napoca400174Romania Department of Pharmaceutical Sciences Vasile Goldi竤Western University of AradArad310025Romania Department of Mathematics and Informatics Faculty of SciencesLucian Blaga University of SibiuSibiu550012Romania Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennaiTamilnaduIndia

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal *** applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

关键词： COVID-19 model fractal-fractional operator Ulam-Hyers stability existence and uniqueness numerical simulation

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Detecting Retinal Nerve Fiber Layer Using Gray Level Co-occurrence Matrix and Machine Learning Approach

Detecting Retinal Nerve Fiber Layer Using Gray Level Co-occu...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Septiarini, Anindita Hamdani, Hamdani Setyaningsih, Emy Maharani, Septya Munir, Aam Shodiqul Winarno, Edy Mulawarman University Faculty of Engineering Department of Informatics Samarinda Indonesia Institut Sains Teknologi AKPRIND Department of Computer System Engineering Yogyakarta Indonesia Universitas Stikubank Faculty of Information Technology Semarang Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

The retinal nerve fiber layer (RNFL) is the white scratch part in the fundus image. It could be an indicator of several diseases characterized by depletion of RNFL. This study intends to construct an RNFL method using Gray Level Cooccurrence Matrix (GLCM) as texture features and implementing random forest as the classifier. The approach has four primary steps: defining the region of interest (ROI), forming the area of features extraction, pre-processing, feature extraction, and classification. Forming the ROI was applied through two main steps: The segmentation with K-means clustering and dividing the fundus image into 12 sub-sectors. The image was then turned into a grayscale as part of the pre-processing. Afterward, the features were extracted using GLCM, followed by features reduction with Correlation-based Feature Selection (CFS). The last step was to use the artificial neural network (ANN) method to classify the data. In this study, the RNFL detection method achieved the accuracy value of 96.7% against the dataset consisting of 40 images. © 2022 IEEE.

关键词： Neural networks

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