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Exact cluster dynamics of indirect reciprocity in complete graphs

Physical Review E 2024年第5期110卷 L052301页

作者： Minwoo Bae Takashi Shimada Seung Ki Baek Department of Physics Department of Systems Innovation Graduate School of Engineering Department of Scientific Computing

Heider's balance theory emphasizes cognitive consistency in assessing others, as expressed by the phrase “The enemy of my enemy is my friend.” At the same time, the theory of indirect reciprocity provides us with a dynamical framework to study how to assess others based on their actions as well as how to act toward them based on the assessments. Well known are the “leading eight” from L1 to L8, the eight norms for assessment and action to foster cooperation in social dilemmas while resisting the invasion of mutant norms prescribing alternative actions. In this work, we begin by showing that balance is equivalent to stationarity of dynamics only for L4 and L6 (stern judging) among the leading eight. Stern judging reflects an intuitive idea that good merits reward, whereas evil warrants punishment. By analyzing the dynamics of stern judging in complete graphs, we prove that this norm almost always segregates the graph into two mutually hostile groups as the graph size grows. We then compare L4 with stern judging: The only difference of L4 is that a good player's cooperative action toward a bad one is regarded as good. This subtle difference transforms large populations governed by L4 to a “paradise” where cooperation prevails and positive assessments abound. Our study thus helps us understand the relationship between individual norms and their emergent consequences at a population level, shedding light on the nuanced interplay between cognitive consistency and segregation dynamics.

关键词： Patterns in complex systems Social systems

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Towards an ontology on project portfolio management 16

Towards an ontology on project portfolio management

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16th International Conference on ENTERprise Information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Melo, Héctor Avila, Oscar Villamil, María Del Pilar Department of Systems and Computing Engineering School of Engineering Universidad de Los Andes Bogotá Colombia

Project Portfolio Management (PPM) is essential for organizations aiming to align projects with strategic goals. Different organizations adopt diverse PPM frameworks and standards to manage project portfolios each employing its own terminology. This semantic heterogeneity leads to communication barriers, knowledge silos, and difficulty in integrating information across various platforms in an interorganizational context. This article proposes a PPM ontology to establish a common language encapsulating key concept, addressing this challenge. The methodology involves systematic revision of three prominent PPM standards-ISO 21504, PMI Standard for Portfolio Management, and AXELOS Management of Portfolios, and employs a novel algorithm to identify equivalences and containment relationships between terms across all three standards, resolving semantic ambiguities and enriching the ontology's expressiveness. This contribution benefits researchers, academics, portfolio managers, project managers, and PPM practitioners by providing a common vocabulary and framework for understanding and improving PPM practices, facilitates knowledge representation, improves communication and collaboration among stakeholders, and lays the groundwork for developing intelligent PPM systems capable of leveraging shared semantic understanding. © 2025 The Authors.

关键词： Project management

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Predicting Football Match Outcomes Using Event Data and Machine Learning Algorithms 35

Predicting Football Match Outcomes Using Event Data and Mach...

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35th Irish systems and Signals Conference, ISSC 2024

作者： Hassard, Peter Kerr, Dermot School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

ISBN: (纸本)9798350352986

This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data;once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted. © 2024 IEEE.

关键词： Forecasting

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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A Dual-Task Deep Reinforcement Learning and Domain Transfer Architecture for Bootstrapping Swarming Collective Motion Skills

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IEEE systems Journal 2025年第1期19卷 327-338页

作者： Abpeikar, Shadi Garratt, Matt Anavatti, Sreenatha Ghanem, Reda Kasmarik, Kathryn University of New South Wales School of Engineering Technology CanberraACT2600 Australia University of New South Wales School of Systems Computing CanberraACT2600 Australia

Recent research has shown it is possible for groups of robots to automatically "bootstrap" their own collective motion behaviors, particularly movement in a group. However, existing work has primarily provided proof of concept in regular, open arenas without obstacles. For practical applications on real robots, multiple collective motion skills are required. This article proposes a novel, multitask deep reinforcement learning algorithm and domain transfer architecture permitting multiple collective motion skills to be bootstrapped automatically and applied to real robots. The proposed approach is tested for tuning two collective motion skills for grouped movement and obstacle avoidance, without requiring a map of the environment. We show that our approach can tune obstacle avoidance parameters while maintaining high-quality swarming collective behavior when an obstacle is detected. Furthermore, learned collective motion skills can be transferred from a point mass simulation onto real mobile robots using our domain transfer architecture, without loss of quality. Transferability is comparable to that of an evolutionary algorithm run in a high-fidelity simulator. © 2007-2012 IEEE.

关键词： Deep reinforcement learning

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction 4

Artificial Intelligence Models in Power Generation for Energ...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Tan, Lit Yang Chen Kwan, Charis Shwu Ajibade, Samuel-Soma M. Ramly, Athirah Mohd School of Engineering and Technology Sunway University Department of Computing and Information Systems Selangor Malaysia School of Architecture Computing and Engineering University of East London Department of Computing London United Kingdom

ISBN: (纸本)9798350353266

The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models, often used in different fields, to predict energy-related patterns, providing a unique and specialized viewpoint. The synergy of academicians, AI experts, and industry professionals in the energy sector has resulted in the creation of customized AI models to optimize operational efficiency. By customizing various AI models to suit the distinct attributes of energy scenarios and datasets, these models are positioned to transform energy management methods. This study examines the utilization of AI models to enhance energy efficiency in power generation in Malaysia. The project seeks to predict future power consumption in various sectors, analyze growth rates, and identify sectors with investment potential by developing a Linear Regression model. In addition, a thorough power plan is developed using the estimated energy usage. A comparative analysis is performed to determine the most appropriate model for this particular scenario, which will improve decision-making in the energy sector. The results of this study present promising opportunities for further investigation. By broadening the study's focus to encompass a broader array of AI models and their assessment of performance, it is possible to gain useful insights for predicting power generation. Furthermore, the integration of real-time data streams and the inclusion of feedback loops in the AI models could improve their ability to adapt and increase their accuracy as time progresses. © 2024 IEEE.

关键词： Prediction models

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Web Application Vulnerabilities 8

Web Application Vulnerabilities

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8th International Symposium on Computer Science and Intelligent Control, ISCSIC 2024

作者： McNally, Stephen Curran, Kevin Ulster University School of Computing Engineering & Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350380286

In the digital age web applications have become a staple of our daily lives due to their convenience and efficiency. It is our main form of communication, transactions and how we access information. With the abilities of web applications, we have become reliant on them for our day-to-day tasks. Although this is not inherently bad, it has become clear that web application security is a paramount tool for online use, particularly among businesses and organisations. Web application security is the practice of protecting websites, applications, and APIs from attacks. This form of security aims to ensure that web applications function smoothly and remain unhindered by cyber vandalism, data theft, and other malicious attacks. This report will explore the top three vulnerabilities highlighted by the most recent OWASP top ten vulnerabilities report, last updated in 2021. These vulnerabilities include broken access control, cryptography failures and injection attacks. Moreover, each section will highlight how these security techniques defend, can be exploited and what measures developers can take to better defend web applications. The paper addresses the future of web application security in the context of emerging technologies such as artificial intelligence (AI) and machine learning. It speculates on how these technologies might be integrated into security strategies to enhance threat detection and response capabilities. The concept of DevSecOps is introduced as an evolutionary approach to integrate security into the development process of web applications, emphasizing the shift towards proactive security practices. In conclusion, the paper highlights the critical nature of web application security in the digital age. It provides a detailed examination of the most pressing vulnerabilities and offers guidance on implementing effective security measures. The aim is to equip developers, security professionals, and organizations with the knowledge and tools necessary to build

关键词： Cyber attacks

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Real-Time Human Pose Estimation as a Cost-Effective Solution for the Teleoporation of a 6-Axis Cobot Arm 22

Real-Time Human Pose Estimation as a Cost-Effective Solution...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Henderson, Benn Coleman, Sonya Kerr, Dermot Quinn, Justin Harrigan, Shane School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798331527471

This paper explores the application of BlazePose, a monocular human pose estimation (HPE) model, within a teleoperation framework for a UR5 six-axis robot. Achieving teleoperation with only a single RGB camera and a device without a powerful GPU will improve accessibility and cost effectiveness of teleoperation solutions. This study evaluates the 2D pose estimation capabilities of BlazePose for robotic teleoperation tasks. Given the necessity of manipulating the UR5 in three-dimensional space, we implement a 2D-based controller that translates the teleoperator’s 2D right hand position within a configurable hand workspace to the corresponding position of the robot’s Tool Centre Point (TCP) within the robot’s available workspace along two dimensions. The left hand is then utilised for controlling the robot’s motion along the third dimension and operating the attached OnRobot RG2 gripper during the pick- and-place task. Additionally, we explore an alternative control paradigm utilising the 3D pose estimation of BlazePose for a more intuitive controller. Two experiments are conducted: the pick-and-place task to assess the 2D-based controller in common robotic tasks, and a hold position task. The hold position task aims to assess the amount of excess movement attributable to the HPE model when utilising the 3D-based controller. The results reveal that while the 2D pose estimation capabilities enable effective teleoperation, the utilisation of 3D estimation results in poor translation to robot control and significant excess motion. These findings underscore the importance of accurate depth estimation in 3D HPE models for precise and reliable teleoperation. © 2024 IEEE.

关键词： Remote control

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