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Digital learning model based on intelligent tutoring system in 3DMUVLE 6

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Digital learning model based on intelligent tutoring system ...

6th International Seminar on Mathematics, Science, and computer Science Education, MSCEIS 2018

作者： Rasim Munir Rosmansyah, Y. Langi, A.Z.R. School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Computer Science Education Universitas Pendidikan Indonesia Bandung Indonesia

Information and Communication technology (ICT) has been used in broad education both for pre-learning, in-learning, and post-learning. But its use often generalizes learning material for all students even though each student has different characters and abilities. Intelligent Tutoring System (ITS) is able to provide learning materials according to students' models. This study aims to model ITS-based digital learning. This model consists of four parts, namely: domain knowledge, pedagogy, students' model, and interface. Domain knowledge produces learning materials that are suitable for each student' model and pedagogy while virtual learning environment is a learning interface for students. This interface integrates MOODLE, SLOODLE and OPENSIM. The student's model used is Myer Briggs Type Indicator (MBTI). The results of this study are 16 types of students and 16 types of learning material. The implication of this research is a comprehensive model that integrates ITS and VLE. © Published under licence by IOP Publishing Ltd.

关键词： Students

Sustainability through cognition aware safety systems - Next level human-machine-interaction

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arXiv 2021年

作者： Mangler, Juergen Diwold, Konrad Etz, Dieter Rinderle-Ma, Stefanie Ferscha, Alois Reiner, Gerald Kastner, Wolfgang Bougain, Sebastien Pollak, Christoph Haslgrübler, Michael Technical University of Munich Faculty of Computer Science Munich Germany Johannes Kepler University Linz Faculty of Computer Science Linz Austria Technical University of Vienna Faculty of Computer Science Vienna Austria Vienna University of Economics and Business Department of Information Systems and Operations Management Institute for Production Management Center for Digital Production GmbH Vienna Austria Pro2Future GmbH Linz Austria Graz University of Technology Department of Electrical Engineering Institute of technical Informatics Graz Austria

Industrial Safety deals with the physical integrity of humans, machines and the environment when they interact during production scenarios. Industrial Safety is subject to a rigorous certification process that leads to inflexible settings, in which all changes are forbidden. With the progressing introduction of smart robotics and smart machinery to the factory floor, combined with an increasing shortage of skilled workers, it becomes imperative that safety scenarios incorporate a flexible handling of the boundary between humans, machines and the environment. In order to increase the well-being of workers, reduce accidents, and compensate for different skill sets, the configuration of machines and the factory floor should be dynamically adapted, while still enforcing functional safety requirements. The contribution of this paper is as follows: (1) We present a set of three scenarios, and discuss how industrial safety mechanisms could be augmented through dynamic changes to the work environment in order to decrease potential accidents, and thus increase productivity. (2) We introduce the concept of a Cognition Aware Safety System (CASS) and its architecture. The idea behind CASS is to integrate AI based reasoning about human load, stress, and attention with AI based selection of actions to avoid the triggering of safety stops. (3) And finally, we will describe the required performance measurement dimensions for a quantitative performance measurement model to enable a comprehensive (triple bottom line) impact assessment of CASS. Additionally we introduce a detailed guideline for expert interviews to explore the feasibility of the approach for given scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Accident prevention

Correction to: A novel driver emotion recognition system based on deep ensemble classification

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Complex & Intelligent Systems 2023年第1期10卷 1663-1663页

作者： Zaman, Khalid Zhaoyun, Sun Shah, Babar Hussain, Tariq Shah, Sayyed Mudassar Ali, Farman Khan, Umer Sadiq Information Engineering School Chang’an University Xi’an China College of Technological Innovation Zayed University Dubai UAE School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China Department of Computer Science and Engineering School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea School of Computer and Information Science Hubei Engineering University Xiaogan China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan China

AI-Based Channel Prediction in D2D Links: An Empirical Validation

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arXiv 2022年

作者： Simmons, Nidhi Ferreira Gomes, Samuel B. Yacoub, Michel Daoud Simeone, Osvaldo Cotton, Simon L. Simmons, David E. Institute of Electronics Communications and Information Technology Queen’s University BelfastBT3 9DT United Kingdom Wireless Technology Laboratory School of Electrical and Computer Engineering University of Campinas Campinas 13083-970 Brazil Centre for Telecommunications Research Department of Informatics King’s College LondonWC2R 2LS United Kingdom Area of Machine Learning Belfast United Kingdom

Device-to-Device (D2D) communication propelled by artificial intelligence (AI) will be an allied technology that will improve system performance and support new services in advanced wireless networks (5G, 6G and beyond). In this paper, AI-based deep learning techniques are applied to D2D links operating at 5.8 GHz with the aim at providing potential answers to the following questions concerning the prediction of the received signal strength variations: i) how effective is the prediction as a function of the coherence time of the channel? and ii) what is the minimum number of input samples required for a target prediction performance? To this end, a variety of measurement environments and scenarios are considered, including an indoor open-office area, an outdoor open-space, line of sight (LOS), non-LOS (NLOS), and mobile scenarios. Four deep learning models are explored, namely long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), and dense or feedforward networks (FFNs). Linear regression is used as a baseline model. It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression. This indicates that GRUs and LSTMs are able to better account for temporal dependencies in the D2D data sets. We also provide recommendations on the minimum input lengths that yield the required performance given the channel coherence time. For instance, to predict 17 and 23 ms into the future, in indoor and outdoor LOS environments, respectively, an input length of 25 ms is recommended. This indicates that the bulk of the learning is done within the coherence time of the channel, and that large input lengths may not always be beneficial. © 2022, CC BY-SA.

关键词： Forecasting

On the variational perspectives to the graph isomorphism problem

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arXiv 2021年

作者： Chatterjee, Turbasu Mohtashim, Shah Ishmam Kundu, Akash Department of Computer Science and Engineering Calcutta Institute of Engineering and Management Maulana Abul Kalam Azad University of Technology Kolkata India Department of Chemistry University of Dhaka Dhaka Bangladesh Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Joint Doctoral School Silesian University of Technology Akademicka 2a Gliwice44-100 Poland

This paper studies the Graph Isomorphism Problem from a variational algorithmic perspective, specifically studying the Quadratic Unconstrained Binary Optimization (QUBO) formulation of the Graph Isomorphism Problem and subsequent execution using the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). This study presents the results of these algorithms and the variations that occur therein for graphs of four and five nodes. The main findings of this paper include the clustering in the energy landscape for the QAOA in isomorphic graphs having an equal number of nodes and edges. This trend found in the QAOA study was further reinforced by studying the ground state energy reduction using VQEs. Furthermore, this paper examines the trend under which isomorphic pairs of graphs vary in the ground state energies, with varying edges and nodes. © 2021, CC BY.

关键词： Ground state

Generalization error bound for hyperbolic ordinal embedding

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arXiv 2021年

作者： Suzuki, Atsushi Nitanda, Atsushi Wang, Jing Xu, Linchuan Cavazza, Marc Yamanishi, Kenji School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Department of Computing Hong Kong Polytechnic University Hong Kong Department of Mathematical Informatics Graduate School of Information Science and Technology University of Tokyo Japan

Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the formof entity i ismore similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property. However, its theoretical analysis has been limited to ideal noiseless settings, and its generalization error in compensation for hyperbolic space's exponential representation ability has not been guaranteed. The difficulty is that existing generalization error bound derivations for ordinal embedding based on the Gramian matrix do not work in HOE, since hyperbolic space is not inner-product space. In this paper, through our novel characterization of HOE with decomposed Lorentz Gramian matrices, we provide a generalization error bound of HOE for the first time, which is at most exponential with respect to the embedding space's radius. Our comparison between the bounds of HOE and Euclidean ordinal embedding shows that HOE's generalization error is reasonable as a cost for its exponential representation ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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Control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback

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IEEJ Transactions on Electronics, Information and Systems

IEEJ Transactions on Electronics, Information and Systems 2019年第10期139卷 1159-1166页

作者： Urasaki, Shinpachiro Kobayashi, Yoshimitsu Kami, Yasushi Mitani, Yuichiroh Nobuyama, Eitaku Department of Electronic Control Engineering National Institute of Technology Gifu College 2236-2. Kamimakuwa. Motosu. Gifu 501-0495 Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3. Nishioka. Uozumi Akashi. Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600. Ooka. Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4.Kawazu. Iizuka Fukuoka820-8502 Japan

This paper describes control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback. The position of the levitated object is calculated from the hall voltage and current of the electromagnet. We show the uncertainty for magnetic flux characteristic and design the stabilizing controller by proportional feedback with the hall voltage and current. Moreover, we tried to design servo controller by two-degree-of-freedom control by using a finite number of frequency response (FNFR) model including the uncertainty and confirmed the validity of the proposed method by experiment results. © 2019 The institute of Eleclrical Engineers of Japan.

关键词： Frequency response

CoMSeC++: PUF-based secured light-weight mutual authentication protocol for Drone-enabled WSN

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computer Networks 2021年 199卷

作者： Mall, Priyanka Amin, Ruhul Obaidat, Mohammad S. Hsiao, Kuei-Fang Department of Computer Science and Engineering Dr. Shyama Prasad Mukherjee International Institute of Information Technology Naya Raipur India College of Computing and Informatics University of Sharjah United Arab Emirates KAIST University of Jordan Amman Jordan University of Science and Tech Beijing China Department of Information System Unversity of Sharjah United Arab Emirates

An unattended environment like a disputed area, a battlefield, or a dense forest area is an environment that is completely inaccessible. Hence, certain kinds of activities that may occur in such an environment would be difficult to notice, which actually is required to be known. Therefore, sensors could be mounted in those places so that the essential data could be gathered. In this paper, we are considering an unattended environment with sensors mounted at certain spots from which the data could be collected through mobile device which actually is an Unmanned Aerial Vehicle (UAV) or say, Drone. We have first designed a suitable architecture for the same and designed a light-weight protocol to establish a secure communication among devices and the cloud via the relocatable drone. Our protocol also utilizes the benefits of Physically Unclonable Function (PUF) to generate, which is used to encrypt the message in communication. The well-popular Scyther simulator has been used to simulate the protocol and the results shows that the protocol is completely protected against leakage of private information. Considering the hardness problem of hash function and PUF, the informal analysis also confirms the high-level security protection against known attacks. The protocol is not only better in terms of security strength, but it also achieves better performance when compared to existing competitive works. © 2021 Elsevier B.V.

关键词： Drones