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Weighting Attributes Based on the Greedy Algorithm Properties

Procedia computer science 2024年 246卷 4883-4892页

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Bȩdzińska 39 41-200 Sosnowiec Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

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Integrating Artificial Intelligence into Electric Vehicle Energy systems: A Survey 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Hua, Weiqi Mullen, Daniel Wahid, Abdul Sitabkhan, Khadija Mason, Karl Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Engineering University of Edinburgh EdinburghEH9 3DW United Kingdom School of Computer Science University of Galway GalwayH91 FYH2 Ireland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

Electric vehicles play an important role in the global transition to "Net Zero" and the decarbonisation of point source emissions from road transport, as their market share continues to grow each year. However, integrating these electric vehicles into existing electricity grids, refuelling stations and supply chains presents significant challenges. But most challenges can be effectively addressed through the implementation of artificial intelligence solutions. This paper starts with an introduction to artificial intelligence and an investigation of the challenges associated with the widespread adoption of electric vehicles in society. Then, the paper analyses in detail how artificial intelligence can facilitate the smooth integration of electric vehicles. In addition, the paper provides a detailed investigation of the life-cycle emissions of electric vehicles and examines the limitations of existing research in this area and suggests potential avenues for future research. © IFIP International Federation for Information Processing 2024.

关键词： Renewable energy

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering systems (KES 2022)

关键词： Knowledge representation

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Toward Energy Efficient Multiuser IRS-Assisted URLLC systems: A Novel Rank Relaxation Method

Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Jalali, Jalal Lemic, Filip Tabassum, Hina Berkvens, Rafael Famaey, Jeroen University of Antwerp - Imec IDLab Sint-Pietersvliet 7 Antwerp2000 Belgium i2Cat Foundation AI-Driven Systems Lab Barcelona Spain York University Department of Electrical Engineering and Computer Science TorontoON Canada

ISBN: (纸本)9798350370218

This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features a multi-antenna base station (BS) transmitting data traffic to a group of URLLC users with short packet lengths. We maximize the total network's energy efficiency (EE) through the optimization of active beamformers at the BS and passive beamformers (a.k.a, phase shifts) at the IRS. The main non-convex problem is divided into two sub-problems. An alternating optimization (AO) approach is then used to solve the problem. Through the use of the successive convex approximation (SCA) with a novel iterative rank relaxation method, we construct a concave-convex objective function for each sub-problem. The first sub-problem is a fractional program that is solved using the Dinkelbach method and a penalty-based approach. The second sub-problem is then solved based on semi-definite programming (SDP) and the penalty-based approach. The iterative solution gradually approaches the rank-one for both the active beamforming and unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy of the proposed solution compared to existing benchmarks. © 2023 IEEE.

关键词： Alternating optimization (AO) intelligent re-flecting surface (IRS) semi-definite programming (SDP) ultra-reliable low-latency communication (URLLC)

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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

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Paladyn 2021年第1期12卷 402-422页

作者： Koay, Kheng Lee Webster, Matt Dixon, Clare Gainer, Paul Syrdal, Dag Fisher, Michael Dautenhahn, Kerstin Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom School of Computer Science and Mathematics Liverpool John Moores University LiverpoolL2 2QP United Kingdom Department of Computer Science University of Manchester ManchesterM13 9PL United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom Departments of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human-robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human-robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants' views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants' technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation. © 2021 Kheng Lee Koay et al., published by De Gruyter.

关键词： Human robot interaction

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On the potential for human-centered, cognitively inspired AI to bridge the gap between optimism and reality for autonomous robotics in healthcare: a respectful critique

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2024年第1期13卷 106 - 112页

作者： Stephanie Tulk Jesso Christopher Greene Shiqi Zhang Amy Booth Michael DiFabio Grace Babalola Adeola Adegbemijo Sumantra Sarkar 1Department of Systems Science and Industrial Engineering SUNY Binghamton2Human-Centered Mindful Technologies Lab SUNY Binghamton 1Department of Systems Science and Industrial Engineering SUNY Binghamton 3Department of Computer Science SUNY Binghamton 4New York United Health Services 5School of Management SUNY Binghamton

Historically, the fields of computer science, cognitive science, and neuroscience have been tightly linked. To date, this collaboration has yielded major advances in how the brain and mind are understood, as well as the ways in which artificial minds can be constructed to serve as new collaborators to humans. Yet there are still significant gaps between the capabilities of state-of-the-art autonomous robots and the expectations developed by real users who are now encountering autonomous robots on the job. We present our views as well as a case study of our evaluation of two autonomous robots intended to aid nurses within hospital settings: Moxi and TUG. Both cobots were originally considered for procurement by our collaborating healthcare system,UHS, at which time our team began the process of trying to systematically vet each option to aid in the decision making process. What we found was a lack of evidence on either platform in academic literature, which led us to analyze user comments on social media. In order to improve the fit of autonomous robots into human environments, research must be conducted and evidence must be shared, and the Human Factors community can aid in this effort

关键词： Cognitively Plausible AI Autonomous Robots Cobots Healthcare Nursing 4E Cognition

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

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Numerical evaluation on sub-Nyquist spectrum reconstruction methods

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Frontiers of computer science 2023年第6期17卷 199-201页

作者： Zihang SONG Han ZHANG Sean FULLER Andrew LAMBERT Zhinong YING Petri MAHONEN Yonina ELDAR Shuguang CUI Mark DPLUMBLEY Clive PARINI Arumugam NALLANATHAN Yue GAO Department of Engineering King’s College LondonStrandLondon WC2R 2LSUK School of Computer Science and Electronic Engineering University of SurreyGuildfordSurrey GU27XHUK National Instruments Corporation(UK)Ltd. NewburyBerkshire RG142PZUK Electronic Media Services Ltd. BordonHampshire GU350FJUK Sony Research Center Sony CorporationLund 22188Sweden Institute for Networked Systems RWTH Aachen UniversityKackertstrasseAachen 52072Germany Faculty of Math&CS Weizmann Institute of ScienceRehovot 7610001Israel School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK School of Computer Science Fudan UniversityShanghai 200433China

1 Introduction As wireless technology continues to expand,there is a growing concern about the efficient use of spectrum *** though a significant portion of the spectrum is allocated to licensed primary users(PUs),studies indicate that their actual utilization is often limited to between 5%to 10%[1].The underutilization of spectrum has given rise to cognitive radio(CR)technology,which allows secondary users(SUs)to opportunistically access these underused resources[2].However,wideband spectrum sensing,the key of CR,is limited by the need for high-speed analog-to-digital converters(ADCs),which are costly and *** spectrum sensing(CSS)addresses this challenge by employing sub-Nyquist rate *** efficiency of active transmission detection heavily depends on the quality of spectrum reconstruction.

关键词： spectrum continue Nyquist

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