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IEEE Education engineering (EDUCON)

作者： Li Hong Idris Lim Andi Sudjana Putra Jennifer Rudolph Elliot Law Akshay Narayan Engineering Design and Innovation Centre National University of Singapore Singapore Department of Mechanical Engineering University of Melbourne Melbourne Australia Humanities & Arts Worcester Polytechnic Institute Massachusetts United States School of Computing National University of Singapore Singapore

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

Developing self-directed learners through experiential learning is a transformative approach in education that requires the thoughtful curation of strategies to encourage active engagement, reflection, and application of knowledge. This involves creating real-world learning opportunities and fostering autonomy. We embarked on a study across two groups of students at the National University of Singapore. The first group consists of students from a residence house who participated in an overseas immersion programme in Indonesia. The second group of students from an interest group (NUS Calibur Robotics) participated in an overseas competition, RoboMaster University League in Colorado, Boulder, United States. Our research question in this study was whether experiential learning results in the development of self-directed learning. Several instruments have been adapted, namely the Self-Directed Learning Readiness Scale (SDLRS) originally developed by Murray J. Fisher [1], and revised by Kumar, A.P. [2], as well as the measures of experiential learning and measures of students' approaches to learning and perceived learning generated by Young, M.R. ***. [3]. Significant improvements were shown across the two groups of students in various dimensions of self-directed learning, like selfmanagement, perceived need for learning, critical thinking and perceived standard of learning. The measurement of all four stages of experiential learning was above 72 %. In summary, this study has demonstrated positive attainment of self-directed learning through experiential learning experiences.

关键词： Knowledge engineering Instruments Reflection Teamwork engineering education Standards Robots

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An End-to-End Bi-Objective Approach to Deep Graph Partitioning

SSRN

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SSRN 2024年

作者： Wei, Pengcheng Fang, Yuan Wen, Zhihao Xiao, Zheng Chen, Binbin Information Systems Technology and Design Pillar Singapore University of Technology and Design 485998 Singapore School of Computing and Information Systems Singapore Management University 178902 Singapore College of Computer Science and Electronic Engineering Hunan University Hunan410082 China

desirable to partition a large graph into smaller partitions. The bi-objective graph partitioning problem aims to minimize both the "cut" across partitions and the imbalance among the sizes of partitions. Existing heuristic methods either suffer scalability issues or ignore partition-wise balance, resulting in inferior partitioning quality. On the other hand, recent deep learning-based methods only utilize node-level features without paying attention to the broader features on the graph, and none of them is trained in a truly end-to-end framework, which leads poor performance on partitioning. In this paper, we propose a novel method based on graph neural networks (GNN) which employs multilevel graph features, and solves the problem end-to- end through a bi-objective formulation. Specifically, we explore node-, local-, and global-level graph features. Then, we propose a well-bounded bi-objective objective function which not only minimizes the cut, but focuses on partition-wise balance across all partitions. Finally, we propose a GNN-based deep model with a Hardmax operator, which enables the model to optimize partitions in a truly end-to-end manner. Experimental results on 11 datasets in various applications and scales show that our method can achieve notable improvements in partitioning quality and scalability than existing bi-objective baselines and deep graph partitioning baselines. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Using Artificial Intelligence Methods to Predict the Compressive Strength of Concrete Containing Sugarcane Bagasse Ash

SSRN

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SSRN 2023年

作者： Pazouki, Gholamreza Kang, Won-Hee Tao, Zhong Saeed, Nariman Centre for Infrastructure Engineering School of Engineering Design & Built Environment Western Sydney University PenrithNSW Australia School of Computing Mathematics and Engineering Faculty of Business Justice and Behavioural Sciences Charles Sturt University Bathurst Australia

Sugarcane bagasse ash is an agricultural and industrial waste material produced in millions of tonnes annually. While traditionally used as a fertilizer or buried underground, incorporating this material into concrete production not only reduces cement usage and addresses environmental concerns but also enhances the mechanical properties of concrete. This research aims to investigate the predictive capabilities of various Artificial Intelligence (AI) models, including radial basis function neural network (RBFNN), artificial neural network (ANN), and support vector regression (SVR), for estimating the compressive strength of concrete containing sugarcane bagasse ash (SCBA). A dataset comprising 1819 data points from previous studies was collected, consisting of three main groups: 340 data of concrete with SCBA, 459 data of concrete without SCMs, and 1020 concrete data incorporating other supplementary cementitious materials (SCMs). The dataset was utilised in three different ways to evaluate the influence of data on model performance. Firstly, models were trained solely on SCBA data (Group A);subsequently, SCBA data was combined with concrete data without any SCMs (Group B);finally, the entire dataset was utilised (Group C). Statistical analysis revealed that the SVR model trained on SCBA data and the RBFNN model trained on group B data demonstrated the best results and accuracy. However, further refinement of the concrete without SCMs data in group B led to improved model performance, surpassing even that of the SVR model trained on group A data. © 2023, The Authors. All rights reserved.

关键词： Compressive strength

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Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study

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IEEE Network 2025年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Wen, Yonggang Kim, Dong In Nanyang Technological University College of Computing and Data Science Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

Generative AI (GenAI), exemplified by Large Language Models (LLMs), such as OpenAI’s ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the infrastructure support for GenAI operation, but also provisions GenAI services to users. Hence, this article explores the interplay between GenAI and DCNs, analyzing their symbiotic relationship and mutual advances. We begin by reviewing the current challenges of DCNs and GenAI-based solutions, such as data augmentation, process automation, and domain transfer. We then discuss the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on GenAI-empowered DCN digital twins. Specifically, we employ an LLM equipped with retrieval augmented generation to formulate optimization problems for DCNs (e.g., resource allocation and routing) and adopt diffusion-deep reinforcement learning to solve optimization. The experimental results on a representative DCN optimization problem, i.e., knowledge placement, demonstrate the validity and efficiency of our proposals. We anticipate that this article can promote further research to enhance the virtuous interaction between GenAI and DCNs. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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The dynamic nature of trust: Trust in Human-Robot Interaction revisited

arXiv

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arXiv 2023年

作者： Rhim, Jimin Kwak, Sonya S. Lim, Angelica Millar, Jason University of Ottawa School of Engineering Design and Teaching Innovation Canada KIST Center for Intelligent and Interactive Robotics Korea Republic of Simon Fraser University School of Computing Science Canada

The role of robots is expanding from tool to collaborator. Socially assistive robots (SARs) are an example of collaborative robots that assist humans in the real world. As robots enter our social sphere, unforeseen risks occur during human-robot interaction (HRI), as everyday human space is full of uncertainties. Risk introduces an element of trust, so understanding human trust in the robot is imperative to initiate and maintain interactions with robots over time. While many scholars have investigated the issue of human-robot trust, a significant portion of that discussion is rooted in the human-automation interaction literature. As robots are no longer mere instruments, but social agents that co-exist with humans, we need a new lens to investigate the longitudinal dynamic nature of trust in HRI. In this position paper, we contend that focusing on the dynamic nature of trust as a new inquiry will help us better design trustworthy robots. © 2023, CC BY.

关键词： Machine design

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Game System Models: Toward Semantic Foundations for Technical Game Analysis, Generation, and design 18

Game System Models: Toward Semantic Foundations for Technica...

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18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2022

作者： Cardona-Rivera, Rogelio E. Zagal, José P. Debus, Michael S. School of Computing University of Utah Salt Lake CityUT United States Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT United States School of Architecture Design and Conservation Royal Danish Academy of Fine Arts Copenhagen Denmark

ISBN: (纸本)1577358775

Game system models introduce abstractions over games in order to support their analysis, generation, and design. While excellent, models to date leave tacit what they abstract over, why they are ontologically adequate, and how they would be realized in the engine underlying the game. In this paper we model these abstraction gaps via the first-order modal µcalculus. We use it to reify the link between engines to our game interaction model, a player-computer interaction framework grounded in the Game Ontology Project. Through formal derivation and justification, we contend our work is a useful code studies perspective that affords better understanding the semantics underlying game system models in general. Copyright © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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Preface: 4^th International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2023)

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AIP Conference Proceedings 2024年第1期3216卷

作者： Doss, Arockia Selvakumar Arockia Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Department of Mechatronics and Mechanical Engineering University of Applied Sciences School of Computing Engineering and Digital Technologies Teesside University Department of Mechatronics and Mechanical Engineering University of Applied Sciences

Preface: 4th International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2023), AIP Conference Proceedings, Volume 3216, Issue

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Deep Reinforcement Learning with Human Guidance for Autonomous Driving Car Following

Deep Reinforcement Learning with Human Guidance for Autonomo...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wenbo Wang Qingchao Xu Xingfu Yang Yandong Liu School of Software Engineering Dalian University Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

To solve the complex modeling and difficult parameter adjustment of traditional car-following control methods, we propose the car-following framework KgPPO-CF. The KgPPOCF brings expert experience into the interactive process of Deep Reinforcement Learning, which improves the speed and robustness of training policies. We use four different reward functions and compare the effects of policies trained by different designs on car-following control. In the Carla simulator, KgPPO-CF achieves car-following control in a continuous action space. The experimental results show that, compared with the traditional control methods, the control strategy obtained by KgPPO-CF training has better adaptability.

关键词： Training Accuracy Mechatronics Aerospace electronics Deep reinforcement learning Robustness Automobiles Robotics and automation Standards Autonomous vehicles

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Empirical Investigation of Discipline-Specific Skills Required for the Employability of Built Environment Graduates

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International Journal of Construction Education and Research 2023年第4期19卷 460-479页

作者： Aliu, John Aghimien, Douglas Aigbavboa, Clinton Oke, Ayodeji Ebekozien, Andrew Temidayo, Osunsanmi Institute for Resilient Infrastructure Systems College of Engineering University of Georgia Athens GA United States School of Art Design and Architecture Faculty of Arts Design and Humanities De Montfort University Leicester United Kingdom CIDB Centre of Excellence Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Quantity Surveying Federal University of Technology Akure Akure Nigeria CIDB centre of Excellence University of Johannesburg South Africa Department of Construction Management and Quantity Surveying University of Johannesburg Johannesburg South Africa School of Social Sciences Universiti Sains Malaysia Gelugor Malaysia Department of Quantity Surveying Auchi Polytechnic Auchi Nigeria School of Computing Engineering and Built Environment Edinburgh Napier University United Kingdom

As the world continues to experience significant and dynamic changes, the concept of graduate employability remains a well-discussed subject in the body of knowledge. Consequently, the concept has attracted the interest of educators, policymakers, researchers and graduates themselves. As a vital cog in the employability conversation, the quality of present-day graduates is highly dependent on the effectiveness of training received from higher education institutions. This formal training provides learners with discipline-specific skills (academic skills) and knowledge which helps them obtain a firm foundation in their chosen discipline or profession. This study seeks to unearth the various discipline-specific skills (DSS) that built-environment graduates need to possess to thrive in the labor market after graduation. A quantitative research approach was adopted to achieve this study’s objective with close-ended questionnaires developed and administered to built environment professionals based in the Gauteng province of South Africa. Retrieved data were analyzed using several statistical tools such as percentage, frequency, Mean Item Score, One-Sample T-test and Exploratory Factor Analysis. Findings revealed four clusters highlighting the key DSS required by built environment graduates. These include lifelong learning, hands-on experience, digital literacy and knowledge of the subject area. The outcomes of this study will be beneficial to several stakeholders involved in construction education and employability skills discussion. © 2022 Associated schools of Construction.

关键词： Built environment construction education discipline-specific skills employability employability skills engineering education graduateness pedagogy

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A Sustainable Circular Framework for Financing Infrastructure Climate Adaptation: Integrated Carbon Markets

arXiv

引用

arXiv 2025年

作者： Li, Chao Su, Xing Fan, Chao Wang, Jun Wang, Xiangyu College of Civil Engineering and Architecture Zhejiang University Zhejiang Hangzhou310000 China College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29631 United States School of Engineering Design and Built Environment Western Sydney University PenrithNSW2751 Australia School of Civil Engineering and Architecture East China Jiaotong University Jiangxi Nanchang330000 China School of Design and the Built Environment Curtin University PerthWA6845 Australia

Climate physical risks pose an increasing threat to urban infrastructure, necessitating urgent climate adaptation measures to protect lives and assets. Implementing such measures, including the development of resilient infrastructure and retrofitting existing systems, demands substantial financial investment. Unfortunately, due to the unprofitability stemming from the long-term returns, uncertainty, and complexity of infrastructure adaptation projects and the short-term profit-seeking objectives of private capital, a massive financial gap remains. This study suggests incentivizing private capital to bridge financial gaps through integrated carbon markets. Specifically, the framework combines carbon taxes and carbon markets to involve infrastructure and individuals in the climate mitigation phase, using the funds collected for climate adaptation. Moreover, it integrates lifestyle reformation, environmental mitigation, and infrastructure adaptation to establish harmonized standards and provide circular positive feedback to sustain the markets. We further explore how integrated carbon markets can facilitate fund collection and discuss the challenges of incorporating them into infrastructure climate adaptation. This study aims to foster collaboration between private and public capital to enable a more scientific, rational, and actionable implementation of integrated carbon markets, thus supporting sustainable financial backing for infrastructure climate adaptation. Copyright © 2025, The Authors. All rights reserved.

关键词： Decentralized finance

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