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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Efficient integration of deep neural networks in sequential multiscale simulations

Efficient integration of deep neural networks in sequential ...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Tröger, Jendrik-Alexander Eivazi, Hamidreza Hartmann, Stefan Wittek, Stefan Rausch, Andreas Institute of Applied Mechanics Clausthal University of Technology Clausthal-Zellerfeld Germany Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

Multiscale computations involving finite elements are often unfeasible due to their substantial computational costs arising from numerous microstructure evaluations. This necessitates the utilization of suitable surrogate models, which can be rapidly evaluated. In this work, we apply a purely data-based deep neural network as a surrogate model for the microstructure evaluation. More precisely, the surrogate model predicts the homogenized stresses, which are typically obtained by the solution of (initial) boundary-value problems of a microstructure and subsequent homogenization. Furthermore, the required consistent tangent matrix is computed by leveraging reverse mode automatic differentiation. To improve data efficiency and ensure high prediction quality, the well-known Sobolev training is chosen for creating the surrogate model. This surrogate model is seamlessly integrated into a Fortran-based finite element code using an open-source library. As a result, this integration, combined with just-in-time compilation, leads to a speed-up of more than 6000×, as demonstrated in the example of a plate with a hole examined in this work. Furthermore, the surrogate model proves to be applicable even in load-step size controlled simulations, where it overcomes certain load-step size limitations associated with the microstructure computations. © 2023 The Authors. Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Deep neural networks

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A Sequential-based Deep Learning Model for Dry Beans Classification

A Sequential-based Deep Learning Model for Dry Beans Classif...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Sujatha, R. Chatterjee, Jyotir Moy Rohith, A. Ramadan, Rabie A. Vellore Institute of Technology Software and Systems Engineering Vellore India Information Technology Lord Buddha Education Foundation Kathmandu Nepal Cairo University Computer Engineering Giza Egypt College of Computer Science and Engineering Hail University Hail Saudi Arabia

ISBN: (纸本)9798350347050

People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI Machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%. © 2023 IEEE.

关键词： Deep learning

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Transformer-Based Scenario Generation of Multi-segment Monotonic Increasing Bidding Curves of Power Plants

Transformer-Based Scenario Generation of Multi-segment Monot...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Xie, Haochuan Lv, Jianhu Wang, Ying Yuan, Kun College of Software Southeast University Suzhou China China Electric Power Research Institute Beijing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789819618637

With the relaxation of electricity market regulation, power plants are permitted to engage in bidding. The bidding outcomes in the competitive market differ significantly from those in the previously regulated market. These differences in bidding outcomes will further impact power flow and system security. To provide a reference for practical electricity market operations, make market simulation more realistic, it is crucial to analyze and establish a model to simulate the bidding behavior of power generators. This paper applies Transformer networks to predict multi-segment bidding behaviors of power plants, we have improved the classic Transformer network by adopting a one encoder-two decoder format, considering six key factors influencing bidding behavior: unit ID, fuel type, capacity, location, system load, and load ratio. Additionally, monotonicity of the multi-segment bidding curves of power plants is taken into account, and monotonicity loss functions are introduced to achieve this goal. We apply this method to predict bidding behaviors in power plants in the Australian electricity market, validating the accuracy of the model. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： bidding curves generation monotonicity constraint power market Transformer-based

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On the road towards a smart Urban district supported by a mock platform 15

On the road towards a smart Urban district supported by a mo...

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International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information systems, MCCSIS 2021

作者： Elberzhager, Frank Mennig, Patrick Stüpfert, Phil Fraunhofer Institute for Experimental Software Engineering IESE Fraunhofer Platz 1 Kaiserslautern Germany

ISBN: (纸本)9789898704320

The world is becoming more and more connected, which is also true for smart cities. The digital transformation is making rapid progress, and part of this are digital ecosystems, consisting of a platform and digital services that support several stakeholders with digital services. In the context of smart cities, we are currently developing a climate-neutral smart urban district and use digital solutions to address topics such as mobility, energy, smart home, and communication among the citizens involved. However, new ideas for services are challenging to invent, develop, and test in our situation, as this particular urban district is currently under construction. This is why we have developed a mock platform, which makes it much easier to evaluate new ideas fast, as qualities of a productive environment such as security or performance are not as highly relevant as an easy-to-use environment. We present details of our mock platform and our experiences in applying it to date. Such a solution is also beneficial for other practitioners and researchers in other smart cities and smart urban districts who are developing a digital ecosystem with new digital services. © 2021 International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information systems, MCCSIS 2021. All rights reserved.

关键词： Automation

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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Virtual Trajectories for I–24 MOTION: Data and Tools

Virtual Trajectories for I–24 MOTION: Data and Tools

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IEEE Forum on Integrated and Sustainable Transportation System (FISTS)

作者： Junyi Ji Yanbing Wang Derek Gloudemans Gergely Zachár William Barbour Daniel B. Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University

This paper introduces a toolbox for generating virtual trajectories, which we call VT-tools v1.0, to address challenges in analyzing large but imperfect trajectory datasets. VT-tools v1.0 is able to generate virtual trajectories from large raw datasets that are typically challenging to process due to their size. We also provide a set of these virtual trajectories resulting from I–24 MOTION INCEPTION v1.0 data and demonstrate the practical utility of these trajectories in as-sessing speed variability and travel times across different lanes within the INCEPTION dataset. The virtual trajectory toolbox opens future research on traffic waves and their impact on energy. The VT-tools v1.0 Python implementation and sample data are publicly available at https://***.

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RealCaPP: Real-Time Capable Plug & Produce Service Architecture for Distributed Robot Control

RealCaPP: Real-Time Capable Plug & Produce Service Architect...

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IEEE International Conference on Robotic Computing (IRC)

作者： Christian Eymuller Julian Hanke Alexander Poeppel Constantin Wanninger Wolfgang Reif Institute for Software and Systems Engineering University of Augsburg Germany

The industry is transforming from mass production to individual production in which each product can be unique. This change requires the ability to easily add, replace, or remove plant components at runtime to make plants flexible and adaptable. Another important point for flexible production is that not only hardware components can be added, changed, or removed, but also software components can be distributed to different system components at runtime. All of this can be summarized under the term Plug & Produce. Therefore, we propose a service architecture that can distribute modular software components across multiple control devices at runtime.

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Runtime Safety Assurance of Autonomous Vehicles used for Last-mile Delivery in Urban Environments

arXiv

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arXiv 2023年

作者： Aslam, Iqra Aniculaesei, Adina Buragohain, Abhishek Bamal, Daniel Rausch, Andreas Institute for Software and Systems Engineering TU Clausthal Germany

Last-mile delivery of goods has gained a lot of attraction during the COVID-19 pandemic. However, current package delivery processes often lead to parking in the second lane, which in turn has negative effects on the urban environment in which the deliveries take place, i.e., traffic congestion and safety issues for other road users. To tackle these challenges, an effective autonomous delivery system is required that guarantees efficient, flexible and safe delivery of goods. The project LogiSmile, co-funded by EIT Urban Mobility, pilots an autonomous delivery vehicle dubbed the Autonomous Hub Vehicle (AHV) that works in cooperation with a small autonomous robot called the Autonomous Delivery Device (ADD). With the two cooperating robots, the project LogiSmile aims to find a possible solution to the challenges of urban goods distribution in congested areas and to demonstrate the future of urban mobility. As a member of Niedersächsische Forschungszentrum für Fahrzeugtechnik (NFF), the institute for software and systems engineering (ISSE) developed an integrated software safety architecture for runtime monitoring of the AHV, with (1) a dependability cage (DC) used for the on-board monitoring of the AHV, and (2) a remote command control center (CCC) which enables the remote off-board supervision of a fleet of AHVs. The DC supervises the vehicle continuously and in case of any safety violation, it switches the nominal driving mode to degraded driving mode or fail-safe mode. Additionally, the CCC also manages the communication of the AHV with the ADD and provides fail-operational solutions for the AHV when it cannot handle complex situations autonomously. The runtime monitoring concept developed for the AHV has been demonstrated in 2022 in Hamburg. We report on the obtained results and on the lessons learned. © 2023, CC BY.

关键词： Autonomous vehicles

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