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4th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Solodukha, Roman A. Perminov, Gennadiy V. Atlasov, Igor V. Voronezh State University of Engineering Technologies Control and Informatics in Technological Systems Dept. Voronezh Russia Legal Informatics Information Law and Natural Sciences Dept. Russian State Voronezh Russia Moscow University of the Ministry of Internal Affairs of Russia Mathematics and Systems Modeling Dept. Moscow Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The technology that allows to decreasing the set of steganalytical methods that determine the payload value in the image spatial domain using quantitative detectors of Least Significant Bits steganography. It consists of mathematical model, an algorithm for implementing the model and computer program. The mathematical apparatus of regression analysis is used. The results of the development and testing of the technique for reducing the set of steganalytical methods taking into account the accuracy and reliability to the diminution of the computational complexity of steganalytical expertise are presented. © 2022 IEEE.

关键词： Reliability

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Misogynistic Content Detection in Roman Urdu Tweets Based on Transformer Models

Misogynistic Content Detection in Roman Urdu Tweets Based on...

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IEEE International Conference on Multi Topic

作者： Waseem Ullah Khan Salman Ahmed Safdar Nawaz Khan Marwat Yousaf Khan Aftab Khan Shafi Ullah Kamran Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Dept. of Electrical Engg University of Engineering & Technology Peshawar Pakistan Dept. of Computer Science Allama Iqbal Open University Islamabad Pakistan Zong CMPak Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Facebook, Instagram and Twitter serve as influential social media platforms for individuals where they express and share their thoughts, skills, knowledge and talents with a broad audience. However, these platforms are also used to disseminate offensive content including trolling and content that targets a person’s gender, religion, or race. When women become the targets of such content, it is often manifests as misogyny. In recent years, the growing prevalence of racial and verbal abuse directed at women on social media has drawn considerable attention to the issue of online misogyny and women based offending, by making the automatic detection of such offensive content an urgent priority. Moreover, many researchers have addressed misogyny detection in high resource languages i.e., English, Italian, Arabic, Hindi, and more, tackling this issue in low resource language like Roman Urdu presents a significant challenge. In this paper, a framework is proposed for detection of misogynistic content from Roman Urdu tweets. This paper leverages well known transformer models such as BERT, RoBERTa, GPT-2, and XLNet to train and evaluate misogyny detection performance. The research findings reveal that RoBERTa surpasses the other models in misogyny detection, by achieving the highest accuracy, precision, F1-score and recall i.e., 90.87%, 90%, 89%, and 91%, respectively.

关键词： Accuracy Social networking (online) Blogs Linguistics Transformers Web sites Reliability Multimedia communication

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Faster Response Chemical Reaction Using Machine Learning

Faster Response Chemical Reaction Using Machine Learning

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Srivastava, Bharvee Sivakumar, C. Kumar, Prashant Maguluri, Lakshmana Phaneendra Naini Prayagraj India Dept. of Computer Science and Systems Engineering Andhra Pradesh Tirupati India Lovely Professional University Department of Chemical Engineering Punjab India Koneru Lakshmaiah Education Foundation Department of Cse Guntur AP India

ISBN: (纸本)9798350323887

The majority of advancements are still generally attributed to trial-and-error experimentation, which takes a long time and a lot of money and can be a significant portion of research in physics, chemistry, and materials science. This type of experimentation can be a significant portion of the process. Despite the fact that a large percentage of research is devoted to the identification and characterization of novel chemicals that have the potential to be of use to society. In this paper, the chemical reactions are quickened using machine learning modelling. The paper uses supervised machine learning approach to fasten the process of chemical reaction by learning it in a supervised manner. The research provides details of how the process of chemical reactions taking place in conventional and using machine learning models. The simulation is conducted in python to test if the chemical reactions are produced with faster convergence time than the existing methods. © 2023 IEEE.

关键词： Chemical reactions

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning

Resource Allocation as a Market: A Case Study on Multi-Serve...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Maria Diamanti Aisha B Rahman Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model’s accuracy, while incorporating clients’ total computing resource constraints and servers’ budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature.

关键词： Federated learning Computational modeling Biological system modeling Ecosystems Distributed databases Pricing Data models Resource management Servers Artificial intelligence

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A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

A Tensor Low-Rank Approximation for Value Functions in Multi...

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Asilomar Conference on Signals, systems & computers

作者： Sergio Rozada Santiago Paternain Juan Andrés Bazerque Antonio G. Marques Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices.

关键词： Wireless communication Training Performance evaluation Tensors Stochastic processes Reinforcement learning Multitasking Throughput Parametric statistics Optimization

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IoT Based Framework for Monitoring and controlling Movable Harbor Cranes

IoT Based Framework for Monitoring and Controlling Movable H...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Awad, A.H. Saraya, M.S. Elksasy, M.S.M. Ali-Eldin, Amr M. T. Mansoura University Faculty of Engineering Computer Engineering and Control System Dept. Mansoura Egypt

ISBN: (纸本)9781665412438

The Internet of Things refers to the type of network that connects anything to the Internet on the basis of the protocols stipulated by the information sensing equipment for the exchange of information and communication in order to achieve smart recognition, positioning, tracing, monitoring and administration. Movable harbor crane (MHC) is a major part of logistics services. Logistics is the management of the flow of goods between the point of origin and the point of consumption in order to meet the requirements of customers or corporations. MHC is mainly used for the loading and unloading of ships. This paper presents recommendations and practices to be used in the future of the smart industry internet (IoSI) and explores the different applications of smart sensor networks in the movable harbor crane. The main objective of the current paper is to develop a conceptual framework for monitoring and controlling harbor movable crane using a cloud on the IoT-PLC control system. Experimental results show that the proposed system is applicable and effective. © 2021 IEEE.

关键词： Internet of things

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Computationally efficient predictive control based on ANN state-space models

arXiv

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arXiv 2023年

作者： Hoekstra, Jan H. Cseppento, Bence Beintema, Gerben I. Schoukens, Maarten Kollár, Zsolt Tóth, Roland Dept. of Measurement and Information Systems The Budapest University of Technology and Economics Hungary The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system. © 2023, CC BY-SA.

关键词： Neural networks

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Efficient Hardware Implementation of Seizure Detector Using Machine Learning

Efficient Hardware Implementation of Seizure Detector Using ...

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International Conference on Microelectronics, ICM

作者： Mariam K. El-Tanbadawy Mohammed A. Megeed Salem Mohamed A. Abd ElGhany Electronics Engineering Dept. German University in Cairo Egypt Computer Engineering Dept. German University in Cairo Egypt Integrated Electronic Systems Lab TU Darmstadt Germany

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

In the past few decades, Seizure Detection has been a vital cornerstone in biomedical research. Both hardware and software implementations have been explored to improve algorithms, detection performance and resources. In this paper, three handcrafted features composed of Coastline Function, Hurst Exponent and Fractal Dimension are used as training data for supervised learning models to build an efficient and sensitive seizure detector. Random Forest classifier exhibits the best accuracy at 98%. Hardware efficiency, too, is exploited through a modified Feature Extractor that combines the aforementioned features. The features are adjusted to reduce their hardware resources on Virtex-7 FPGA. The area utilization is improved by 10%, power utilization is improved by 20% and latency decreased by 32%.

关键词： Training Coastlines Accuracy Training data Detectors Feature extraction Hardware Software Fractals Random forests

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Blockchain Based Digital Forensic Data for Unbreakable Trust and Security

Blockchain Based Digital Forensic Data for Unbreakable Trust...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： M. Imrankhan M. Viji R. Sathishkumar T. Gnanaprakash R. Karthik Roshan G. Saravanan Dept. of CSE Manakula Vinayagar Institute of Technology Puducherry Dept. of Computer Science and Business Systems Sri Manakula Vinayagar Engineering College Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This can be one of the most magnificent advantages realized from applying blockchain technology in a forensic investigation context, namely, a transformative approach to address challenges embedded within legal and criminal justice systems. For example, smart contracts, being self-executing agreements with the automated inclusion of predefined rules, may even automate and secure the process of tracking evidence, managing chain of custody, and access control. This smart contract will ensure that forensic operations are transparent and of sound integrity. Every transaction and action performed on the blockchain is recorded there, instantaneously bringing down tampering to the lowest possibility and any chance for human mistakes. Regarding decentralized nature and cryptography-based security combined with immutability, blockchain is a technology known to have safeguards over sensitive data. Once recorded on the blockchain, the information thereupon becomes completely un-changeable, assuring the reliability and authenticity of evidence. This will be especially important in the management of an unforgeable chain of custody and in ensuring that forensic evidence remains unchanged and can be relied on with integrity throughout the court process.[6] Critical data can then be maintained tamper-proof for management and storage by developing Ethereum-based smart contracts on Remix IDE using Solidity.[15]Each transaction will be time-stamped and safely logged on the Ethereum blockchain, thus developing an unchallenged ledger that makes any investigation that much more trustworthy. In a nutshell, the application of blockchain in forensic activities builds greater control and efficiencies, reduces possible errors and conflicts, and provides transparency, integrity, and an unchangeable, permanent record for forensic processes. The invention thereby significantly upgrades the general integrity and confidence in legal and criminal justice inquiries.

关键词： Technological innovation Forensics Smart contracts Digital forensics Process control Market research Blockchains Reliability Protection Standards

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Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using Machine Learning

Enhancing Cybersecurity: A Multilayered Approach to Phishing...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Lakshmi Narayanaa T Skandarsini R R S. Jhansi Ida Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms—Decision Tree, Random Forest, XGBoost, Deep Learning, Autoencoder Neural Network, and Support Vector Machines—are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape.

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