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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Bahroun, Sahbi Ghayoula, Ridha Manai, Elyes Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Institut Supérieur d'Informatique Université de Tunis El-Manar Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Reliability and Timeliness of Servicing Requests in Infocommunication Systems, Taking into Account the Physical and Information Recovery of Redundant Storage Devices 6

Reliability and Timeliness of Servicing Requests in Infocomm...

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6th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

Markov models of reliability of fault-tolerant computer systems are proposed, taking into account two stages of recovery of redundant memory devices. At the first stage, the physical recovery of memory devices is implemented, and at the second, the informational one consists in entering the data necessary to perform the required functions. Memory redundancy is carried out to increase the stability of the system to the loss of unique data generated during the operation of the system. Data replication is implemented in all functional memory devices. Information recovery is carried out using replicas of data stored in working memory devices. The model takes into account the criticality of the system to the timeliness of calculations in real time and to the impossibility of restoring information after multiple memory failures, leading to the loss of all stored replicas of unique data. The system readiness coefficient and the probability of its transition to a non-recoverable state are determined. The readiness of the system for the timely execution of requests is evaluated, taking into account the influence of the shares of the distribution of the performance of the computer allocated for the maintenance of requests and for the entry of information into memory after its physical recovery. © 2022 IEEE.

关键词： Recovery

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Constrained Intelligent Frequency Control in an AC Microgrid: An Online Reinforcement Learning Based PID Tuning Approach

Constrained Intelligent Frequency Control in an AC Microgrid...

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IEEE General Meeting Power& Energy Society

作者： K. Nosrati A. Tepljakov E. Petlenkov V. Skiparev J. Belikov Y. Levron Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation control systems with excellent results. Nevertheless, a potential problem is that the application of such advanced techniques with a large search space is not enough to deal with highly dynamic environment and real-time operations of MGs. In this light, the present study introduces a flexible artificial neural network (ANN)-based frequency deviation control solution in a constrained structure that operates as follows. First, the stable controller parameter space of the PID-based AC microgrid is derived by using the stability boundary locus method. Then, the controller parameters are tuned and updated online by searching for an optimal combination of the coefficients with consideration of output variations sensed by a constrained ANN in the derived reduced parameter space. To accomplish this step, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed technique has been verified under a given scenario to demonstrate how the reduced parameter space should facilitate the optimization procedure.

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CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation

arXiv

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arXiv 2023年

作者： Truong, Thanh-Dat Duong, Chi Nhan Dowling, Ashley Phung, Son Lam Cothren, Jackson Luu, Khoa Department of Computer Science and Computer Engineering University of Arkansas United States Department of Computer Science and Software Engineering Concordia University Canada Department of Entomology and Plant Pathology University of Arkansas United States Faculty of Engineering and Information Sciences University of Wollongong Australia Department of Geosciences University of Arkansas United States

Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. With the limitations of large-scale densely labeled data, semantic scene segmentation for UAV views requires a broad understanding of an object from both its top and side views. Adapting from well-annotated autonomous driving data to unlabeled UAV data is challenging due to the cross-view differences between the two data types. Our work proposes a novel Cross-View Adaptation (CROVIA) approach to effectively adapt the knowledge learned from on-road vehicle views to UAV views. First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views. Second, cross-view correlations from image space are effectively transferred to segmentation space without any requirement of paired on-road and UAV view data via a new Geometry-Constraint Cross-View (GeiCo) loss. Third, the multi-modal bijective networks are introduced to enforce the global structural modeling across views. Experimental results on new cross-view adaptation benchmarks introduced in this work, i.e., SYNTHIA → UAVID and GTA5 → UAVID, show the State-of-the-Art (SOTA) performance of our approach over prior adaptation methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An Image Classification Approach to Pre-Determine Extent of Development of Post-Operative Necrosis in Skin Flaps

An Image Classification Approach to Pre-Determine Extent of ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： G. Spacagna T. Fevens J. Barralet Department of Surgery McGill University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada Faculty of Dentistry McGill University Montreal Canada

Purpose - Clinicians and care workers currently cannot determine the final extent of necrosis once it begins. If the area is relatively small, it may heal like a normal wound, whereas re-operation may be the only option if it spreads. This paper represents work toward a predictive algorithm using full-thickness random rat skin flap photographs to determine whether the tissue will develop irretrievable *** - Using post-surgery images taken over a series of days of ischemic flaps, features were extracted, selected, and input into a classification algorithm to see if it could provide information on the future condition of the flaps. We split our data into two groups: flaps that underwent normal healing and slow healing. When consulting with a specialist, it was observed that the resulting dermal damage was not severe when a flap had ≤ 40% necrosis over its length on the final day. Three classifiers were implemented: K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM).Results - The trained KNN was able to correctly determine whether a flap developed a necrotic area larger or less than 40% of its length with an accuracy of 91% using only images one day post-surgery. Under leave-one-out cross-validation, the Random Forest and SVM achieved accuracies of 82% and 79.5%, respectively, using images spanning ten days *** - We have shown that a classifier can accurately determine whether ischemic skin flaps will develop severe necrotic *** Relevance— An algorithm that assesses early timepoint images and predicts necrosis spread is not only a vital tool for patients and clinicians but is also an extremely important tool to accelerate research into necrosis reduction strategies that ultimately may find application for more life-threatening necrosis-related conditions.

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques

Early Fault Detection in Rolling Element Bearings Using Sign...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg DE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-to-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method.

关键词： Vibrations Fault diagnosis Low voltage Smart cities Rolling bearings Signal processing Motors Robots Monitoring Investment

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Algorithmic Cluster Expansions for Quantum Problems

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PRX Quantum 2024年第1期5卷 010305-010305页

作者： Ryan L. Mann Romy M. Minko Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW 2007 Australia School of Mathematics University of Bristol Bristol BS8 1UG United Kingdom

We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of the abstract polymer model formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness.

关键词： Phase transitions Quantum computation Quantum-to-classical transition Approximation methods for many-body systems Combinatorics Computational complexity Ising model Lee-Yang & Fisher zeroes Series expansions & exact enumeration

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A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition

arXiv

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arXiv 2024年

作者： Khurma, Ruba Abu Alhenawi, Esraa Braik, Malik Hashim, Fatma A. Chhabra, Amit Castillo, Pedro A. MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Computer Science Department Al- Ahliyya Amman University Amman Jordan Software Engineering Department Al- Ahliyya Amman University Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Faculty of Engineering Helwan University Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, that is, they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms (MAs) as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, S

关键词： Evolutionary algorithms

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Analysis of Web Monitoring Servers and Tools for Cloud Computing Services 1st

Analysis of Web Monitoring Servers and Tools for Cloud Compu...

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1st International Conference on engineering software for Modern Challenges, ESMoC 2021

作者： Sarwar, Nadeem Qureshi, Junaid Nasir Iqbal, Tahir Waheed, Haroon Abdul Yasin, Muhammad Hammad Irshad, Asma Computer Science Department Bahria University Lahore Campus Lahore Pakistan Department of Software Engineering University of Central Punjab Lahore Pakistan Faculty of Computing Riphah International University Islamabad Pakistan School of Biochemistry & Biotechnology University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031199677

This research aims to find best cloud service monitoring tools and techniques according to trending parameters. The services of cloud computing are most popular services now a days. Many organizations are migrating to cloud technology in order to reduce the management’s complications of resources. The effective monitoring methodologies should be implemented, to certify the Quality of Experience (QoE) and Quality of Services (QoS) of cloud computing services for end users. For this purpose, cloud services should be monitor and control using different tools and techniques. There are so many server-based or web-based monitoring tools are available. In this paper, analysis of different tools is based on their pricing, working efficiency, key features, platforms and environments in which they could be used. This paper also analyzes the cloud computing structure and investigates that which type of monitoring services are required at which level. This research paper shows that no monitoring tool is mature enough to fulfil all requirements and desires of clients. This research also interprets that which tool is recommended for which kind of platform and environment. © 2022, Springer Nature Switzerland AG.

关键词： Quality of service

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