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Day 1 11/12/2020
Room #1

Welcome message by the General Chair Deze Zhang 08:45 - 08:50

Starts at 8:45 AM China standard time

Welcome message by the EAI Conference Manager 08:50 - 08:55

Aleksandra Sledziejowska

Welcome message by the EAI Community Manager 08:55 - 09:00

Michal Dudic

Keynote speaker Prof. Changqin Huang 09:00 - 09:30

Title: Educational Intelligence Research and Practice Driven by Big data

Session #1 09:30 - 10:55

Session Chair : Jing Zhao
09:30 - 09:50
Constructing Knowledge Graph for Prognostics and Health Management of On-board Train Control System based on Big Data and XGBoost

Train control system plays a significant role in safe and efficient operation of the railway transport system. In order to enhance the system capability and cost efficiency from a full life cycle perspective, the establishment of a Con-dition-based Maintenance (CBM) scheme will be beneficial to both the cur-rently in use and next generation train control systems. Due to the complex-ity of the fault mechanism of on-board train control system, a data-driven method is of great necessity to enable the Prognostics and Health Manage-ment (PHM) for the equipments in field operation. In this paper, we propose a big data platform to realize the storage, management and processing of his-torical field data from on-board train control equipments. Specifically, we fo-cus on constructing the Knowledge Graph (KG) of typical faults. The Ex-treme Gradient Boosting (XGBoost) method is adopted to build big-data-enabled training models, which reveal the distribution of the feature im-portance and quantitatively evaluate the fault correlation of all related fea-tures. The presented scheme is demonstrated by a big data platform with in-cremental field data sets from railway operation process. Case study results show that this scheme can derive knowledge graph of specific system fault and reveal the relevance of features effectively.
Authors: jiang liu, bai-gen cai (beijing jiaotong university), zhong-bin guo (beijing jiaotong university), xiao-lin zhao (beijing jiaotong university),
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09:50 - 10:10
A big data intelligence marketplace and secure analytics experimentation platform for the aviation industry

The unprecedented volume, diversity and richness of aviation data that can be acquired, generated, stored, and managed provides unique capabilities for the aviation-related industries and pertains value that remains to be unlocked with the adoption of the innovative Big Data Analytics technologies. Despite the large efforts and investments on research and innovation, the Big Data technologies introduce a number of challenges to its adopters. Besides the effective storage and access to the underlying big data, efficient data integration and data interoperability should be considered, while at the same time multiple data sources should be effectively combined by performing data exchange and data sharing between the different stakeholders. However, this reveals additional challenges for the crucial preservation of the information security of the collected data, the trusted and secure data exchange and data sharing, as well as the robust data access control. The current paper aims to introduce the ICARUS big data-enabled platform that aims provide a multi-sided platform that offers a novel aviation data and intelligence marketplace accompanied by a trusted and secure analytics workspace. It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis of data originating from heterogeneous data sources with different velocity, variety and volume in a trusted and secure manner.
Authors: Dimitrios Miltiadou (UBITECH), Stamatios Pitsios (UBITECH), Dimitrios Spyropoulos (UBITECH), Dimitrios Alexandrou (UBITECH), Fenareti Lampathaki (SUITE5), Domenico Messina (Engineering Ingegneria Informatica S.p.A), Konstantinos Perakis (UBITECH),
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10:10 - 10:40
Early Detecting the At-risk Students in Online Courses based on Their Behavior Sequences

Online learning has developed rapidly, but the participation of learners is very low. So it is of great significance to construct a prediction model of learning results, to identify students at risk in time and accurately. We select nine online learning behaviors from one course in Moodle, take one week as the basic unit and 5 weeks as the time node of learning behavior, and the aggregate data and sequence data of the first 5 weeks, the first 10 weeks, the first 15 weeks, the first 20 weeks, the first 25 weeks, the first 30 weeks, the first 35 weeks and the first 39 weeks are formed. Eight classic machine learning methods, i.e. Logistic Regression(LR), Navie Bayes(NB), Radom Forest(RF), K-Nearest Neighbors(KNN), Support Vector Machine(SVM), Iterative Dichotomiser3(ID3), Classification and Regression Tress (CART), and Neural Network (NN), are used to predict the learning results in different time nodes based on aggregate data and sequence data. The experimental results show that sequence data is more effective than aggregate data to predict learning results. The experimental result shows that RF model and CART model are the best models of the eight classic prediction models. Then RF model, CART model, RNN model and LSTM model are used to predict learning results on sequence data, the experimental results show that LSTM model is a model with the highest value of AUC and stable growth based on sequence data, and it is the best model of all models for predicting learning results.
Authors: Shuai Yuan (Central China Normal University), Huan Huang (South-Central University for Nationalities), Tingting He (Central China Normal University), Rui Hou (South-Central University for Nationalities),
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10:40 - 10:55
Do College Students Adapt to Their Personal Learning Environment(PLE)? A Single-Group Study

Home-based online learning is a typical application of personal learning environ-ment. Understanding the adaptability and characteristics of college students in the personal learning environment (PLE) can effectively tap the potential of online courses and provide valuable references for learners' online and lifelong learning. In this single-group study, 80 college students received a 90-minute self-regulated learning training. In pre- and post-class evaluations, media multi-tasking self-efficacy, perceived attention problems, self-regulation strategies and learning satisfaction are used as key variables in online learning to assess their personal learning environment adaptability and characteristics. Using descriptive statistics and one-dimensional intra-group variance to analyze the data, it was found that: Learners have a moderate degree of attention deficit in their personal learning envi-ronment, which is manifested in three aspects: perceived attention discontinuity, lingering thought, social media notification.; Under simple training or natural conditions, students have poor adaptability in the personal learning en-vironment, and their behavior perception and behavior adjustment levels have im-proved, but they have not yet reached expectations; Participation in online learn-ing has significantly increased the application of learners' self-regulation strate-gies, especially the application of behavior strategies.
Authors: Changsheng Chen (Shandong Youth University of Political Science), Xiangzeng Meng (Shandong Normal University), Junxiao Liu (Shandong Normal University), Zhi Liu (Central China Normal Uuniversity),
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Lunch Break 10:55 - 12:55

Session #2 12:55 - 14:35

Session Chair : Huan Huang
12:55 - 13:10
A Multi-Valued Logic Assessment of Organizational Performance via Workforce Social Networking

Social Media have changed the conditions and rules of Social Networking (SNet) where it comes from people intermingling with each other, i.e., SNet is to be un-derstood as a process that works on the principle of many-to-many; any individu-al can create and share content. One’s aim is to look at and explore the complex dynamics between SNet, Logic Programming (LP), and the Laws of Thermody-namic (LoT) in terms of entropy by drawing attention to how Multi-Value Logic (MVL) intertwines with SNet, LP and LoT, i.e., its norms, strategies, mechanisms and methods for problem solving that underpin its dynamics in terms of pro-grammability, connectivity and organizational performance. Indeed, one’s focus is on the tactics and strategies of MVL in order to evaluate the issues under which social practices unfold and to assess their impact on organizational performance.
Authors: José Neves (University of Minho), Florentino Fdez-Riverola (University of Vigo), Victor Alves (University of Vigo), Filipa Ferraz (University of Minho), Lia Sousa (Instituto Politécnico de Saúde do Norte), António Costa (University of Minho), Jorge Ribeiro (Instituto Politécnico de Viana do Castelo), Henrique Vicente (University of Évora),
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13:10 - 13:30
Statistical Research on Macroeconomic Big Data:Using a Bayesian Stochastic Volatility Model

The alternative variation of variance in Stochastic Volatility (SV) models provides a big data modelling solution that is more suitable for the fluctuation process in macroeconomics for de-scribing unobservable fluctuation features. The estimation method based on Monte Carlo simula-tion shows unique advantages in dealing with high-dimensional integration problems. The statis-tical research on macroeconomic big data based on Bayesian stochastic volatility model builds on the Markov Chain Monte Carlo estimation. The critical values of the statistics can be defined exactly, which is one of the drawbacks of traditional statistics. Most importantly, the model pro-vides an effective analysis tool for the expected variable generation behaviour caused by macroe-conomic big data statistics.
Authors: Minglei Shan (Shandong Youth University Of Political Science),
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13:30 - 13:55
NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems

Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have become a promising tool to protect networks against cyberattacks. A wide range of datasets are publicly available and have been used for the development and evaluation of a large number of ML-based NIDS in the research community. However, since these NIDS datasets have very different feature sets, it is currently very difficult to reliably compare ML models across different datasets, and hence if they generalise to different network environments and attack scenarios. This paper addresses this limitation, by providing five NIDS datasets with a common, practically relevant feature set, based on NetFlow. These datasets are generated from the following four existing benchmark NIDS datasets: UNSW-NB15, BoT-IoT, ToN-IoT, and CSE-CIC-IDS2018. We have used the raw packet capture files of these datasets, and converted them to the NetFlow format, with a common feature set. The benefits of using NetFlow as a common format include its practical relevance, its wide deployment in production networks, and its scaling properties. The generated NetFlow datasets presented in this paper have been labelled for both binary- and multi-class traffic and attack classification experiments, and we have made them available for to the research community~\cite{netflow_datasets_2020}. As a use-case and application scenario, the paper presents an evaluation of an Extra Trees ensemble classifier across these datasets.
Authors: Mohanad Sarhan (University of Queensland), Siamak Layeghy (University of Queensland), Nour Moustafa (University of New South Wales), Marius Portmann (University of Queensland),
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13:55 - 14:15
Introducing and benchmarking a one-shot learning gesture recognition dataset

Deep learning techniques have been widely and successfully applied, over the last five years, to recognize the gestures and activities performed by users wearing electronic devices. However, the collected datasets are built in an old fashioned way, mostly comprised by subjects that perform many times few different gestures/activities. This paper addresses the lack of a wearable gesture recognition dataset for exploring one-shot learning techniques. The current dataset consists of 46 gestures performed only one time by 35 subjects, wearing a smartwatch equipped with 3 motion sensors and is publicly available. Moreover, 3 one-shot learning classification approaches are benchmarked on the dataset, exploiting two different deep learning classifiers. The results of the benchmark depict the difficulty of the one-shot learning task, exposing new challenges for wearable gesture/activity recognition.
Authors: Panagiotis Kasnesis (University of West Attica), Christos Chatzigeorgiou (University of West Attica), Charalampos Patrikakis (University of West Attica), Maria Rangoussi (University of West Attica),
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14:15 - 14:35
Research on the Sharing and Application of TCM Digital Resources

With the vigorous development of online teaching and online learning, it has further increased the demand for digital resources, and further enhanced the feasibility of digital education, the necessity of digital resource construction and the importance of digital resource sharing in the information age. In this study, the status quo of TCM digital resources was studied from the aspects of literature research and resource construction, and a questionnaire survey was conducted among teachers and students in the major of TCM acupuncture in a TCM university. On this basis, suggestions on the application of digital resources in TCM acupuncture courses were proposed.
Authors: Min Hu (Hubei University of Chinese Medicine), Hao Li (Central China Normal University),
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Clossing message and Best Paper Award by the Program Chair Rui Hou 14:35 - 14:40