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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nina Kuchuk Igor Ruban Oleksandr Zakovorotnyi Andriy Kovalenko Andrii Shyshatskyi Iurii Sheviakov Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computerized Management Systems National Aviation University Kyiv Ukraine Civil Aviation Institute Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

Industrial Internet of NanoThings (IIoNT) traffic model proposed. The model is based on the developed algorithm for Dynamic Data Composition Control. The application of the algorithm made it possible to reduce the total amount of transmitted information from the end nodes of IIoNT. The model made it possible to determine the parameters of aggregation traffic of IIoNT cluster gateway. With the calculated parameters, the value of the aggregated package is maximized. The main limitation is not exceeding the value of the boundary delay for the transmission of sensor readings. The model also allows you to dynamically determine the required amount of the gateway buffer memory.

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Real-Time Gait Reconstruction For Virtual Reality Using a Single Sensor

Real-Time Gait Reconstruction For Virtual Reality Using a Si...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Tobias Feigl Lisa Gruner Christopher Mutschler Daniel Roth Fraunhofer Institute of Integrated Circuits (IIS) Nürnberg Programming Systems Group Friedrich-Alexander University (FAU) Erlangen-Nürnberg Ludwig-MaximiliansUniversity (LMU) Technical University Munich (TUM) Munich Chair for Computer Aided Medical Procedures and Augmented Reality Technical University Munich (TUM)

ISBN: (数字)9781728176758

ISBN: (纸本)9781728176765

Embodying users through avatars based on motion tracking and reconstruction is an ongoing challenge for VR application developers. High quality VR systems use full-body tracking or inverse kinematics to reconstruct the motion of the lower extremities and control the avatar animation. Mobile systems are limited to the motion sensing of head-mounted displays (HMDs) and typically cannot offer *** propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations. We designed four models: a threshold-based, a correlation-based, a Support Vector Machine (SVM) -based and a bidirectional long-term short-term memory (BLSTM) -based model. Our experiments show that, while the BLSTM approach is the most accurate, only the correlation approach runs on a mobile VR system in real time with sufficient accuracy. Our user study with 21 test subjects examined the effects of our approach on simulator sickness and showed significantly less negative effects on disorientation.

关键词： Support vector machines Solid modeling Tracking Avatars Animation Real-time systems Delays

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Forecasting Nonlinear Nonstationary Processes in Machine Learning Task

Forecasting Nonlinear Nonstationary Processes in Machine Lea...

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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Peter Bidyuk Aleksandr Gozhyj Irina Kalinina Victoria Vysotska Mikhail Vasilev Romanna Malets Department of Mathematical Methods of System Analysis National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Intelligent Information Systems Petro Mohyla Black Sea National University Nikolaev Ukraine Department of Information Systems And Networks National University "Lvivska politechnika" Lviv Ukraine Programming Department Ivan Franko Lviv National University Lviv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.

关键词： Forecasting Time series analysis Machine learning Load modeling Currencies Predictive models Classification algorithms

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of Engineering & Technology 2023.

关键词： Machine learning

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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Computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Revealing academic web interlinking motives through webometrics and statistical techniques: Nigerian university websites as a case study

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International Journal of Web Engineering and Technology 2016年第4期11卷 335-350页

作者： Nwohiri, Anthony M. Pechnikov, Andrey A. Department of Programming Technologies Faculty of Applied Mathematics and Control Processes St. Petersburg State University 7-9 Universitetskaya Naberezhnaya St. Petersburg199034 Russia Laboratory for Telecommunications Systems Institute of Applied Mathematical Research Karelian Research Centre Russian Academy of Sciences Russia 11 Pushkinskaya Street Petrozavodsk Karelia Rep.185910 Russia

The websites of the universities of Africa's most populous country Nigeria were examined. The goal was to unearth the reasons why external hyperlinks are placed on these sites and identify any peculiarities by geographical location. Academic website interlinking motivations were formulated. Over 6,000 hyperlinks placed on the websites of 86 Nigerian universities were categorised under these interlinking motivations. Categorising the motivations by examining the source and target pages of each hyperlink was an arduous task. Statistical analysis offered a clearer picture of the interlinking motives. A chi-squared test revealed distinctive features, which are characteristic of specific geographical regions of Nigeria. The study serves as a route to further research on feasibility of findings in other developing countries. Results obtained could be of great importance for academic webpage developers and authors. The results could modify their work towards improving the use of hyperlinks as a major communication tool on the web. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Data mining

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Property-Directed verification of recurrent neural networks

arXiv

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arXiv 2020年

作者： Khmelnitsky, Igor Neider, Daniel Roy, Rajarshi Barbot, Benoît Bollig, Benedikt Finkel, Alain Haddad, Serge Leucker, Martin Ye, Lina LSV CNRS ENS Paris-Saclay Université Paris-Saclay France Inria France Max Planck Institute for Software Systems Kaiserslautern Germany Université Paris-Est Créteil France Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany CentraleSupélec Université Paris-Saclay France Institut Universitaire de France France

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping towards faulty flows hinting at the underlying error in the *** Codes 68Q60, 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Changes in EEG spectrograms, event-related potentials and event-related desynchronization induced by relative beta training in ADHD children

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Journal of Neurotherapy 2007年第2期11卷 3-11页

作者： Kropotov, Jury D. Grin-Yatsenko, Vera A. Ponomarev, Valery A. Chutko, Leonid S. Yakovenko, Elena A. Nikishena, Inna S. Institute of Psychology Norwegian University of Science and Technology (NTNU) Trondheim Norway Laboratory for Neurobiology of Action Programming Institute of the Human Brain of Russian Academy of Sciences Trondheim Norway Laboratory for Rehabilitation of Sensory Systems Human Brain of Russian Academy of Sciences Trondheim Norway Laboratory for Neurophysiology of Electromagnetic Therapy Institute of the Human Brain of Russian Academy of Sciences Trondheim Norway

Background. During the last three decades EEG-based biofeedback (neurofeedback) was used as an alternative treatment for reducing symptoms of ADHD. The goal of this study was to objectively assess the efficacy of biofeedback training by comparing spectrograms, ERPs and ERDs, measured before and after 20 sessions of neurotherapy in a group of ADHD children. Method. Electroencephalogram (EEG), Event related potentials (ERPs) and event related synchronisation/desynchronisation (ERD/ERS) were recorded and computed in auditory GO/NOGO task before and after 15-22 sessions of EEG biofeedback. Eighty-six ADHD children participated in the study. Each session consisted of 30 min of relative beta training. The patients were divided into two groups (good performers and poor performers) depending on their ability to elevate beta activity during sessions. Results. Amplitude of late positive components of evoked potentials in response to NOGOstimuli increased, and event-related synchronisation in alpha frequency band measured at central areas decreased after the whole set of sessions of neurofeedback training in the group of good performers but did not change for the poor performers group. Evoked potential differences between post- and pre-treatment conditions for good performers were distributed over frontal-central areas, reflecting activation of frontal cortical areas associated with beta training. Conclusion. Relative beta training with electrodes located above the frontal areas was associated with an increase of the late positive NOGO component. This activation likely indicates recovery of normal functioning of the executive system. © 2007 by The Haworth Press, Inc. All rights reserved.

关键词： Attention Deficit Hyperactivity Disorder Beta training EEG biofeedback (neurofeedback) Event-related potentials Event-related synchronisation Executive functions GO/NOGO paradigm

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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