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2019 Workshop on Cultural Informatics, CI 2019

作者： Kaldeli, Eirini Tsakou, Gianna Giglitto, Danilo Cesaroni, Francesca Tzouvaras, Vassilis Stamou, Giorgos Intelligent Systems Content and Interaction Laboratory National Technical University of Athens Athens Greece European Programmes Department SingularLogic S.A. Athens Greece Cultural Communication and Computing Research Institute Sheffield Hallam University Sheffield United Kingdom Cooperativa Sociale COOSS Marche Onlus Italy

Studies and practice in the cultural field have long acknowledged the importance of participatory approaches for engaging visitors of cultural institutions, however, it is only recently that we are talking about steps to connecting institutional heritage with civic initiatives that can aid social cohesion and community empowerment. In dialogue with ongoing practices in this context, CultureLabs aims to develop novel methodologies and digital tools that can facilitate the organisation and wider deployment of participatory projects around cultural her - itage, focusing on the social inclusion of disadvantaged groups, and particularly of migrant communities. As a first step in this process, the CultureLabs team has conducted a series of interviews and surveys with the aim to identify and analyse the organisational needs and lessons learnt by different actors from the cultural, social, educational and public administration fields as well as the needs and viewpoints of different migrant communities. These needs have guided the design of an innovative online platform which seeks to offer a number of services for supporting more efficient and participatory governance of cultural heritage on one hand and for enabling inclusive and creative interactions with digital cultural heritage on the other. The CultureLabs platform will allow multiple and diverse stakeholders to discover and combine differ - ent resources and elements of best practices, the "ingredients", in order to form new "recipes" for social innovation according to their own needs and objectives. © 2019 CEUR-WS. All rights reserved.

关键词： Public administration

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Emotion Regulation in intelligent Tutoring systems: A Systematic Literature Review

Emotion Regulation in Intelligent Tutoring Systems: A System...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Rihab Khadimallah Mouna Abdelkefi Ilhem Kallel REGIM-Lab: Research Groups in Intelligent Machines ISIMS: Higher Institute of Computer Science and Multimedia University of Sfax Tunisia MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory ENIS: National School of Engineers Route Soukra Km 3.5 BP 1173 3038 University of Sfax Tunisia

ISBN: (数字)9781728169422

ISBN: (纸本)9781728169439

Emotion regulation (ER) is a process responsible for altering emotions' reactions and expressions as well as situations that simulate such feelings to better respond to environmental ***, ER is actually a mere mental and behavioral process that many of us already perform consciously and unconsciously. In this issue, with the imperious role of emotions in learning process which they crucially influence student's cognitive performances and academic outcomes, several researchers proposed different methods and tools for regulating learners' negative emotions while learning in online environment. In the present paper, we discuss the current and original research works that apply different strategies and methods for emotion regulation in different smart environments (i.e., intelligent Tutoring systems) by adopting a Systematic Literature Review (SLR) approach.

关键词： Systematics Bibliographies Education Machine learning Tools Regulation Erbium

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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From Mind to Products:Towards Social Manufacturing and Service

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 47-57页

作者： Gang Xiong Fei-Yue Wang Timo R. Nyberg Xiuqin Shang Mengchu Zhou Zhen Shen Shuangshuang Li Chao Guo Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing 100190 China School of Science at Aalto University Aalto FI-00076 Finland Laboratory for Discrete Event Systems New Jersey Institute of Technology NJ-07102 USA Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing 100190 China Qingdao Academy of Intelligent Industries Qingdao 266109 China Cloud Computing Center Dongguan 523808 China IEEE

After reviewing the development of industrial manufacturing, a novel concept called social manufacturing(SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of "from mind to products", and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend,and is expected to become popular in more and more ***, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies,like Internet of Things(Io T), social networks, cloud computing,3 D printing, and intelligent systems, are introduced and analyzed,and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3 D printing are suggested for different user cases.

关键词： Big data cloud computing intelligent system social manufacturing social networks 3D printing

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Multi-robot Path Planning and Trajectory Smoothing

Multi-robot Path Planning and Trajectory Smoothing

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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ACG-Engine: An Inference Accelerator for Content Generative Neural Networks

ACG-Engine: An Inference Accelerator for Content Generative ...

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IEEE International Conference on Computer-Aided Design

作者： Haobo Xu Ying Wang Yujie Wang Jiajun Li Bosheng Liu Yinhe Han University of Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences

The technological breakthrough in Generative Adversarial Networks (GAN) has propelled the advancement of content generative applications such as AI-based paintings, style transfer, and music composition. However, in contrast to previous deep learning models for prediction and categorization, generative networks generally rely on instance normalization (IN) layer for better feature distribution, which performs significantly better than batch normalization(BN) in image style-transfer, image to image translation, etc. Unlike batch or group normalization that can be fused into convolutional layers and ignored during the network inference stage, an instance normalization layer induces intensive computation and memory access. However, prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations, which could become a performance bottleneck on edge devices with insufficient computational power. To address this problem, we propose an inference accelerator for content generation (ACG-Engine) aimed to support the fundamental operations of generative networks, including convolution layers, deconvolution layers, specifically instance normalization layer. We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness, so that it can be efficiently mapped to the classic 2D processing element array. Owing to the proposed optimization techniques, ACG-Engine achieves 4.56X speedup and improve power efficiency up to 29X compared to prior baseline acceleration scheme in generative network acceleration. In addition, ACG-Engine can achieve performance comparable to the classic CNN-specific accelerators with negligible power consumption and area overhead.

关键词： Performance evaluation Deep learning Deconvolution Convolution Neural networks Generative adversarial networks Acceleration

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QoS based Safety Message Dissemination Protocol For VSN in Smart City

QoS based Safety Message Dissemination Protocol For VSN in S...

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International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)

作者： Soumia Bellaouar Mohamed Guerroumi Samira Moussaoui Vehicular Networks for intelligent transport systems (VNets) Group Laboratory for Research in Intelligent Computing Mathematics and Applications (RIIMA) University of Science and Technology Houari Boumediene Bab-Ezzouar Algiers Algeria

ISBN: (纸本)9781728166346

In Vehicular Sensor Networks (VSNs), road safety applications are important to ensure the safety of motorists by broadcasting alert messages in this network. The dissemination process is the use of adaptive broadcasting algorithms that make efficient use of vehicular communications avoiding the problem of broadcast storms. Therefore, an effective warning message broadcast protocol should represent the appropriate transmission node for each message, maximizing the number of vehicles informed by the hazard of the situation, while simultaneously reducing the time needed to inform them and the amount of traffic generated in the wireless channel. In this work, we propose safety messages dissemination protocol based on QoS metrics as delay, link quality and flow for the choice of a reliable, fast and efficient broadcaster. Our experiments proved the efficiency of our proposal compared to other existing solutions in the literature and using the OMNETT ++ simulator.

关键词： broadcast communication protocols quality of service road safety smart cities telecommunication traffic vehicular ad hoc networks wireless channels wireless sensor networks Quality of service wireless channels Road safety Smart cities telecommunication traffic protocols VSN Wireless sensor networks V2V Broadcast Network Dissemination

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Ordinal Optimization for Optimal Orientation Problems in 3D Printing

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IFAC-PapersOnLine 2020年第5期53卷 97-102页

作者： Can Luo Gang Xiong Zhishuai Li Zhen Shen Li Wan MengChu Zhou Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan China Macau University of Science and Technology Taipa Macau

Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.

关键词： Digital Manufacturing Genetic Algorithm intelligent Optimization Machine Learning Ordinal Optimization Orientation Optimization 3D Printing

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An IoT Edge computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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IEEE International Conference on Networking, Sensing and Control

作者： Shichao Chen Qijie Li Hua Zhang Fenghua Zhu Gang Xiong Ying Tang Macau University of Science and Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau China Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Engineering Research Center of Intelligent Systems and Technology Institute of Automation and Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Glassboro NJ USA Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

关键词： Energy consumption Processor scheduling Computer architecture Internet of Things Optimization Monitoring Edge computing

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