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2nd international conference on intelligent computing and Optimization, ICO 2019

作者： Thomas, J. Joshua Ali, Abdalla M. Department of Computing School of Engineering Computing and Built Environment KDU Penang University College George TownPenang10400 Malaysia

ISBN: (纸本)9783030335847

The ability to predict a student’s performance can be beneficial for actions in modern educational systems. The manipulation of big volume learning data is a critical challenge for the design of personalized curricula and learning experiences. The purpose of this research article is twofold: (i) an integrated framework for Dispositional Learning analytics (DLA) to trace student disposition data from the learning management system (LMS), directing to spread learning theory by methodically collecting data from digital platforms. (ii) Analyse the student academic data together with disposition data to perform deep learning model such as recurrent neural networks, bidirectional recurrent neural networks, long-term short memory (LSTM) algorithms to the sustenance of the student behaviour prevention at the same time predict academic performance. To associate the (i) and (ii), we have experimented with a particular course on grade prediction. We have applied feature selection algorithm, machine learning, and deep learning models to identify the prediction. The results show positive and move towards the direction into dispositional learning analytics trace data with academic data to predict student performance. © 2020, Springer Nature Switzerland AG.

关键词： Students

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Situation-aware Building Information Models for Next Generation Building Management Systems 22

Situation-aware Building Information Models for Next Generat...

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22nd international conference on Enterprise Information Systems (ICEIS)

作者： Noran, Ovidiu Bernus, Peter Caluianu, Sorin Griffith Univ IIIS Ctr Enterprise Architecture Res & Management Brisbane Qld Australia Tech Univ Construct Bucharest Fac Installat Engn Bucharest Romania

ISBN: (纸本)9789897584237

Technical advances in Information and Communication Technology have enabled the collection and storage of large amounts of data, rising hopes of improving asset decision-making and related building management support systems. It appears however that the gap between the required decision-making knowledge and the actual useful information provided by current technologies appears to increase, rather than contract. Thus, often the multitude of patterns afforded by current data analytics techniques does not deliver a set of scenarios prone to effective decision making. This paper advocates a decision analytics solution featuring the use of Situated Logic to create 'narratives' providing adequate meaning to data analytics results, and the use of Channel Theory so as to support adequate situational awareness. This approach is also analysed in the context of a Building Management System-of-Systems paradigm, highly relevant to the emerging complex Clusters of intelligent Buildings within smart Cities, featuring collaborative decision-making centres and their associated decision support systems.

关键词： Building Information Model Building Management System of Systems Decision Support Systems Situational Awareness Big data data Warehousing Decision Model Situated Reasoning Channel Theory

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Hybrid Workflow Provisioning and Scheduling on Edge Cloud computing Using a Gradient Descent Search Approach 19

Hybrid Workflow Provisioning and Scheduling on Edge Cloud Co...

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19th international Symposium on Parallel and Distributed computing (ISPDC)

作者： Alsurdeh, Raed Calheiros, Rodrigo N. Matawie, Kenan M. Javadi, Bahman Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW Australia

ISBN: (纸本)9781728189468

The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively.

关键词： Multiobjective optimization

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intelligent security and optimization in Edge/Fog computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2020年 107卷 1140-1142页

作者： Qiu, Meikang Kung, Sun-Yuan Gai, Keke Columbia Univ Dept Elect Engn New Year NY 10007 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Edge/Fog computing enables mobility support, location awareness and low latency by offloading cloud resources and services to the edge. Researchers and practitioners have embraced Edge/Fog computing as a new approach that has the potential for a profound impact in our daily life and world economy. Particularly, security and privacy protection is a critical concern in the development and adoption of Edge/Fog computing. To avoid system fragility and defend against vulnerabilities exploration from cyber attacker, various cyber security techniques and tools have been developed for Edge/Fog systems. This special issue focuses on the challenging topic of intelligent Security and Optimization in Edge/Fog computing and invites the state-of-the-art research results. (c) 2020 Published by Elsevier B.V.

关键词： Edge/Fog computing Cybersecurity Decentralized cloud Artificial intelligence Editorial

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Effective Approach to Cultivating Physical Education Teaching Skills of Preschool Majors in the Context of Big data

Effective Approach to Cultivating Physical Education Teachin...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Wang, Baolong Qing Dao BinHai University Qingdao266555 China

ISBN: (纸本)9783030627454

Under the guidance of the major national education strategy of building healthy China and realizing the modernization of education, strengthening P.E. teaching in school has become the main theme of current school physical education. However, children’s physical education is still a weak link in school physical education. With the incomplete awareness of the importance of children’s physical education and the shortages of professional teachers, the development of children’s physical education lacks sufficient scientific guidelines which deviating from the requirements of the Guidelines for Kindergarten Education. By literature review and qualitative research, this paper takes the curriculum system of preschool education as the research object to find an effective approach to cultivating physical education teaching skills of preschool majors. Moreover, through standardized physical education skills training and with the help of the Big data, the study noted that the curriculum training system and the skills of students of this major can be enhanced from 5 dimensions: teaching design, sports technique, teaching practice, teaching evaluation and research. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Internet of Things (IoT) Enhanced Educational Toolkit for Teaching Learning of Science, Technology, Engineering and Mathematics (STEM)

Internet of Things (IoT) Enhanced Educational Toolkit for Te...

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11th IEEE international conference on System Engineering and Technology, ICSET 2021

作者： Habib, Kaiser Kai, Elson Ee Teng Saad, Mohamad Hanif Md Hussain, Aini Ayob, Afida Ahmad, Ammar Syafiq Sabaahul Department of Electrical Electronic and Systems Engineering Bangi Selangor Malaysia Institute of IR4.0 Universiti Kebangsaan Malaysia Department of Mechanical and Materials Engineering Bangi Selangor Malaysia Department. of Mechanical and Materials Engineering Bangi Selangor Malaysia

ISBN: (纸本)9781665437660

The advancement of the Internet of Things (IoT) fortifies intelligent system development for diverse information management in various domains. Information produced from Science, Technology, Engineering, and Mathematics (STEM) fields across the diverse networks, also can be successfully integrated to analyze virtually with the help of IoT. Thus, learning relative IoT technology has to be carried out to achieve the initiative's agenda. However, the current Teaching and Learning (TL) systems are lacking in smart learning toolkit to equip the students with IoT-based STEM knowledge and skillsets relative to the IoT. The purpose of this study is to develop a smart toolkit in association with a TL module to expose STEM knowledge to the students. The developed toolkit comprises an Input/output (I/O) test board to carry out both analog and digital data acquisition. Raspberry Pi is the heart of this toolkit that processes and transfers the acquired data to a cloud platform named ThingsSentral™ to structurally store, analyze and represent data graphically. Besides, a TL module was designed based on this developed toolkit functionalities to expose STEM discipline to the younger generation. Based on the performance observations, the proposed system offers a cost-effective cloud-facilitated solution for the Project-Based Learning (PBL) approach, also exhibits salient suitability to effectively introduce IoT-based STEM knowledge to the students, non-Technical personnel, even domain experts at the shortest possible time. © 2021 IEEE.

关键词： Cloud computing Visualization Education data acquisition Systems engineering and theory Internet of Things Servers

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Strategies on traditional Chinese medicine translation teaching in the age of big data

Strategies on traditional Chinese medicine translation teach...

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Gong, Yan Yu, Yawei School of Humanities Jiangxi University of Traditional Chinese Medicine Nanchang China

ISBN: (纸本)9789811525674

The era of big data brings both challenges and opportunities for Traditional Chinese Medicine translation teaching. Firstly, it analyzes the characteristics of the era of big data and the cognitive features and learning habits of students. Then the major problems in current TCM Translation teaching are analyzed such as insufficient practice of TCM translation, too much emphasis on the translation knowledge and skills while less on the cultivation of thinking;less class hours, and insufficient feedback from teachers. Then combined with the characteristics of the era of big data, the strategies of TCM translation teaching reform are put forward: make full use of resources on network and technology, use TCM translation corpus, use network platform and fully explore student big data in order to provide references for Traditional Chinese medicine translation teaching. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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A survey of head pose estimation methods 13

A survey of head pose estimation methods

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2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on smart data, smartdata 2020

作者： Shao, Xiaofeng Qiang, Zhenping Lin, Hong Dong, Yueyu Wang, Xiaorui Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9781728176475

For computer systems head pose estimating is still a challenge. In this paper, we discuss the inherent difficulties of head pose estimation and describe the evolution of the field in recent years. We simply classify these methods and focus on the advantages and disadvantages of each method, including related articles in the past 10 years. Our comparison of these systems focuses on their capabilities, estimates of rough and fine head pose, and the prominent method is very suitable for unconstrained environments. © 2020 IEEE.

关键词： Convolutional neural networks

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Load forecasting in different scale and horizon - A review 3

Load forecasting in different scale and horizon - A review

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3rd smart Cities Symposium, SCS 2020

作者： Iskandarnia, Elham Al-Amal, Hesham Al-Madany, Wael College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9781839535635

A review of the latest load forecasting techniques will be presented in this paper, in the context of different scale and horizons. Today, the competitive energy market demands more accurate forecasting at different scales, ranging from a single smart-meter (end-user) up to a whole power grid system. As a result of this market need, load forecasting is considered today as an essential part of the electricity industry's planning process. In other hand the large volumes of data generated by the vast amount of smart meters calls for intelligent methods and solutions for the real-time data analytics. Although some past review papers investigate load forecasting, but a very few reviews are done by considering all different scales and horizons. An up to date survey of load forecasting techniques will also be presented by considering new trends such as distributed load forecasting, cloud computing techniques, real-time, and stream processing algorithms. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： smart meters

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Early Warning Management of Enterprise Human Resource Crisis Based on Fuzzy data Mining Algorithm of Computer

Early Warning Management of Enterprise Human Resource Crisis...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Wu, Hua Business School Macau University of Science and Technology Macau610041 China

ISBN: (纸本)9783030627454

Since the 21st century, human resources, as an important force to promote the healthy and rapid development of enterprises, has been attached great importance to by all walks of life, which has effectively promoted the rapid development of human resources in China’s enterprises in recent decades. However, with the increasingly fierce market competition and the penetration of highly developed information internet, the current enterprise human resource management has gradually produced many crises, which constitutes a serious constraint on the development of enterprises. A data mining method is designed, which can cluster and analyze the existing talents in the organization, so as to discover the types of talents in the organization and judge which type an employee belongs to. How to deal with the human resource crisis effectively and change the crisis into a turning point has become the most urgent problem for many enterprises. Combined with practice, taking effective early warning management is an important way to solve this problem. Based on the author’s many years of enterprise human resource management practice, this paper will first summarize the human resource crisis of the enterprise, and then discuss the current difficulties faced by the crisis early warning management, and on this basis explore some corresponding countermeasures. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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