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arXiv 2024年

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Bois, Naomi Du Smith, Alice E. Coyle, Damien Intelligent Systems Research Center School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Industrial and Systems Engineering Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent aimed at addressing these issues. The agent employs a double deep q-network to choose a specific evolutionary operator based on feedback it receives from the environment during optimization. The algorithm’s structure contains five single-objective evolutionary algorithm operators. This single-objective structure is transformed into a multi-objective one using the R2 indicator. This indicator serves two purposes within our structure: first, it renders the algorithm multi-objective, and second, provides a means to evaluate each algorithm’s performance in each generation to facilitate constructing the reinforcement learning-based reward function. The proposed R2-reinforcement learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with six other multiobjective algorithms that are based on R2 indicators. These six algorithms include the operators used in R2-RLMOEA as well as an R2 indicator-based algorithm that randomly selects operators during optimization. We benchmark performance using the CEC09 functions, with performance measured by inverted generational distance and spacing. The R2-RLMOEA algorithm outperforms all other algorithms with strong statistical significance (p © 2024, CC BY.

关键词： Reinforcement learning

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic Control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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ARITHMETICAL INDEPENDENCE OF CERTAIN UNIFORM SETS OF ALGEBRAIC INTEGERS

arXiv

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arXiv 2023年

作者： Saito, Asaki Tamura, Jun-Ichi Yasutomi, Shin-Ichi Department of Complex and Intelligent Systems Future University Hakodate 116-2 Kamedanakanocho Hokkaido Hakodate041-8655 Japan Institute for Mathematics and Computer Science Tsuda College 2-1-1 Tsuda-machi Kodaira Tokyo187-8577 Japan Faculty of Science Toho University 2-2-1 Miyama Funabashi Chiba274-8510 Japan

We study four (families of) sets of algebraic integers of degree less than or equal to three. Apart from being simply defined, we show that they share two distinctive characteristics: almost uniformity and arithmetical independence. Here, "almost uniformity" means that the elements of a finite set are distributed almost equidistantly in the unit interval, while "arithmetical independence" means that the number fields generated by the elements of a set do not have a mutual inclusion relation each other. Furthermore, we reveal to what extent the algebraic number fields generated by the elements of the four sets can cover quadratic or cubic *** Codes 11J71, 11R11, 11R16 Copyright © 2023, The Authors. All rights reserved.

关键词： Algebra

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Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets

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International Journal of Information Management Data Insights 2025年第1期5卷

作者： Aliyu, Yusuf Sarlan, Aliza Danyaro, Kamaluddeen Usman Sani abd Rahman, Abdullahi Muazu, Aminu Aminu Abubakar, Mustapha Yusuf Department of Computer and Information Science Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Center for Foundation Studies Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Centre for Cyber-Physical Systems (C2PS) Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Computer Science Department Faculty of Natural and Applied Science Umaru Musa Yar'adua University Katsina Nigeria Computer Science Department School of Technology Kano State Polytechnic Kano 700231 Nigeria

Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities. © 2025 The Author(s)

关键词： Code-switched Hausa language Low-resource Sentiment analysis Transformer Word-embedding

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Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

arXiv

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Towards Holistic Analysis of Wireless Capsule Endoscopic Videos: A Taxonomy-Driven Machine Learning Framework for Clinically Comprehensive WCE Frame Level Analysis

Towards Holistic Analysis of Wireless Capsule Endoscopic Vid...

引用

Electrics and computer (INTCEC), Interdisciplinary Conference on

作者： Haroon Wahab Rita Goel Maida Alamgir Irfan Mehmood Khan Muhammad Hassan Ugail Centre for Visual Computing & Intelligent Systems School of Computer Science AI and Electronics University of Bradford Bradford England UK Department of Applied Artificial Intelligence Visual Analytics Knowledge Laboratory (VIS2KNOW) Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Manual perusal of capsule endoscopic videos is laborious due to their length, redundancy, and poor quality frames, risking misdetection. Limited representation of diverse anomalies and subtle lesions led to less generalized approaches targeting only individual or few anomalous findings. To deploy automated analysis in clinical settings effectively, a holistic, end-to-end approach is crucial, distinguishing between frame-level analysis and disease-level diagnosis. This study proposes a holistic framework for frame-level analysis based on a taxonomy of findings, leveraging clinical consensus. Clinical knowledge implicit in the taxonomy can enhance computer Aided Diagnostics (CAD) systems. Convolutional Neural Networks (CNNs) trained at appropriate granularity levels in the taxonomy show improved performance metrics. Validation on the Kvasir-capsule dataset demonstrates a 2-4% performance boost. Taxonomy-based inference scores effectively resolve mimicked anomalies without additional learning.

关键词： Training Wireless communication Measurement Taxonomy Redundancy Merging Manuals

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Location Optimization and Resource Allocation of IRS in a Multi-User Indoor mmWave VR Network

Location Optimization and Resource Allocation of IRS in a Mu...

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IEEE Conference on Wireless Communications and Networking

作者： Jalal Jalali Maria Bustamante Madrid Filip Lemic Hina Tabassum Jakob Struye Jeroen Famaey Xavier Costa Perez Internet and Data Lab University of Antwerp - imec Belgium AI-Driven Systems Lab i2Cat Foundation Spain Electrical Engineering and Computer Science Department York University Canada NEC Labs Europe GmbH Germany ICREA Spain

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

关键词： Wireless communication Surface waves Array signal processing Virtual reality Throughput Trajectory Resource management

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Detection of Intrusion Attacks Using Neural Networks 3

Detection of Intrusion Attacks Using Neural Networks

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3rd International Scientific and Practical Conference on Information Security and Information Technologies, ISecIT 2021

作者： Karpinski, Mikolaj Shmatko, Alexander Yevseiev, Serhii Jancarczyk, Daniel Milevskyi, Stanislav University of Bielsko-Biala Department of Computer Science and Automatics Willowa Str. 2 Bielsko-Biala43-309 Poland Simon Kuznets Kharkiv National University of Economics Cybersecurity and Information Systems Department ave. Science 9-A Kharkiv61166 Ukraine

The rapid expansion of computer networks makes security issues among computer systems one of the most important. Intrusion detection systems are using artificial intelligence more and more. This article discusses intrusion detection. Multi-layer perceptron (MLP) is used to detect offline intrusion attacks. The work uses the issues of determining the type of attack. Various neural network structures are considered to detect the optimal neural network by the number of input neurons and the number of hidden layers. It has also been investigated that activation functions and their influence on increasing the ability to generalize a neural network. The results show that the neural network is a 15x31x1 way to classify records with an accuracy of about 99% for known types of attacks, with an accuracy of 97% for normal vectors and 34% for unknown types of attacks. ©? 2021 Copyright for this paper by its authors.

关键词： Expert systems

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Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping

arXiv

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arXiv 2023年

作者： Kok, Manon Solin, Arno Schön, Thomas B. Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Department of Computer Science Aalto University Konemiehentie 2 AaltoFI-00076 Finland Department of Information Technology Uppsala University Box 337 UppsalaSE-751 05 Sweden

Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic interpretation of the SLAM task allows for incorporating prior knowledge and for operation under uncertainty. Contrary to the common practice of computing point estimates of the system states, we capture the full posterior density through approximate Bayesian inference. This dynamic learning task falls under state estimation, where the state-of-the-art is in sequential Monte Carlo methods that tackle the forward filtering problem. In this paper, we introduce a framework for probabilistic SLAM using particle smoothing that does not only incorporate observed data in current state estimates, but it also back-tracks the updated knowledge to correct for past drift and ambiguities in both the map and in the states. Our solution can efficiently handle both dense and sparse map representations by Rao-Blackwellization of conditionally linear and conditionally linearized models. We show through simulations and real-world experiments how the principles apply to radio (BLE/Wi-Fi), magnetic field, and visual SLAM. The proposed solution is general, efficient, and works well under confounding noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic fields

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Towards a Theoretical Framework of Autonomous systems Underpinned by Intelligence and systems sciences

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

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