Day 1 07/12/2019
Room #1

Opening Ceremony 09:30 - 10:00

Keynote 10:00 - 10:50

Prof. Shaohua Wan

Coffee Break 10:50 - 11:00

Session 1 11:00 - 12:00

11:00 - 11:20
EuWireless RAN Architecture and Slicing Framework for Virtual Testbeds

The most recent evolutionary steps in the development of mobile communication network architectures have introduced the concepts of virtualisation and slicing also into the Radio Access Network (RAN) part of the overall infrastructure. This trend has made RANs more flexible than ever before, facilitating resource sharing concepts which go far beyond the traditional infrastructure and RAN sharing schemes between commercial Mobile Network Operators (MNO). This paper introduces the EuWireless concept for a pan-European mobile network operator for research and presents its vision for RAN slicing and network resource sharing between the infrastructures of the EuWireless operator, commercial MNOs and local small-scale research testbeds around Europe. The EuWireless approach is to offer virtual large-scale testbeds, i.e. experiment slices, to European mobile network researchers by combining the experimental technologies from the local small-scale testbeds with the commercial MNO resources, such as licensed spectrum, through the distributed EuWireless architecture based on inter-connected local installations, so-called Points of Presences (PoP).
Authors: Jarno Pinola (VTT Technical Research Centre of Finland), Ilkka Harjula (VTT Technical Research Centre of Finland), Adam Flizikowski (IS-Wireless), Maria Safianowska (IS-Wireless), Arslan Ahmad (IS-Wireless), Suvidha Mhatre (IS-Wireless),
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11:20 - 11:40
Research Progress in the Processing of Crowdsourced Test Reports

In recent years, crowdsourced testing, which using group intelligence to solve complex testing tasks has gained widespread attention in academia and industry. However, due to the large number of workers participating in crowdsourcing testing tasks, the submitted test report base is too large, making it difficult for developers to review test reports. Therefore, how to effectively process and integrate crowdsourcing test reports is always an important challenge in the public measurement process. This paper deals with the crowdsourcing test report processing, sorts out some achievements in this field in recent years, and classifies existing research results from four directions: repeated report detection, test report aggregation and classification, priority ranking, and report summary. Inductive and contrast, finally explored the possible research directions, opportunities and challenges of the crowdsourcing test report.
Authors: Naiqi Wang (1.School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China 2.Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, China), Lizhi Cai (Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, China), Mingang Chen (Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, China), Chuwei Zhang (Shanghai Foreign Affairs Service Center, Shanghai, China),
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11:40 - 12:00
Evaluating the Effectiveness of Wrapper Feature Selection Methods with Artificial Neural Network Classifier for Diabetes Prediction

Feature selection is an important preprocessing technique used to determine the most important features that contributes to the classification of a dataset, typically performed on high dimension datasets. Various feature selection algorithms have been proposed for diabetes prediction. However, the effectiveness of these proposed algorithms have not been thoroughly evaluated statistically. In this paper, three types of feature selection methods (Sequential Forward Selection, Sequential Backward Selection and Recursive Feature Elimination) classified under the wrapper method are used in identifying the optimal subset of features needed for classification of the Pima Indians Diabetes dataset with an Artificial Neural Network (ANN) as the classifying algorithm. All three methods manage to identify the important features of the dataset (Plasma Glucose Concentration and BMI reading), indicating their effectiveness for feature selection, with Sequential Forward Selection obtaining the feature subset that most improves the ANN. However, there are little to no improvements in terms of classifier evaluation metrics (accuracy and precision) when trained using the optimal subsets from each method as compared to using the original dataset, showing the ineffectiveness of feature selection on the low-dimensional Pima Indians Diabetes dataset.
Authors: M. A. Fahmiin (Universiti Teknologi Brunei), Tiong Hoo Lim (Universiti Teknologi Brunei),
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Lunch 12:00 - 13:30

Session 2 13:30 - 14:30

13:30 - 13:50
Formal Modeling and Verification of Software-Defined Networking with Multiple Controllers

Traditional SDN has one controller, but more recent SDN approaches use multi-ple controllers on one network. However, the multiple controllers need to be syn-chronized with each other in order to guarantee a consistent network view, and complicated control management and additional control overhead are required. To overcome these limitations, Kandoo[5] has been proposed in which a root con-troller manages multiple unsynchronized local controllers. However, in this ap-proach, loops can form between the local controllers because they manage differ-ent topologies. We propose a method for modeling a hierarchical design to detect loops in the topology and prevent them from occurring using UPPAAL model checker. In addition, the properties of multiple controllers are defined and verified based UPPAAL framework. In particular, we verify the following properties in a multiple controller: (1) elephant flows go through the root controller, (2) all flows go through the switch that is required to maintain security, and (3) they avoid un-necessary switches for energy efficiency.
Authors: Miyoung Kang (Korea University), Jin-Young Choi (Korea University),
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13:50 - 14:10
Energy Management of Multiple Microgrids in Active Distribution Networks

This paper presented an energy management model for managing an active distribution network (ADN) consisting of multiple microgrids. The distribu-tion system operator (DSO) of the ADN needs to coordinate the microgrids to achieve optimal energy management. This paper formulated the energy management of ADN with multiple microgrids as a mixed integer second-order cone programming (MISOCP), which considered network reconfigura-tion, on-load tap changer (OLTC) and static Var compensators (SVC). A case study on a modified IEEE 33-bus distribution network demonstrates the ef-fectiveness of the proposed method.
Authors: Yi Zhao (Harbin Institute of Technology, Shenyang Institute of Engineering), Jilai Yu (School of Electrical Engineering and Automation, Harbin Institute of Technology),
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14:10 - 14:30
Bivariate Fisher–Snedecor F Distribution with arbitrary fading parameters

In this paper, we present a bivariate Fisher–Snedecor F composite distribution with arbitrary fading parameters. Novel closed-form expressions of the statistical characteristics for the correlated F composite fading model are derived, which include the joint probability density function (PDF), the joint cumulative distribution function (CDF), the joint moments and the power correlation coefficient. Capitalizing on the joint CDF, the outage probability and the bit error rate (BER) of binary digital modulation systems of a correlated dual-branch selection diversity system, and the level crossing rate (LCR) and the average fade duration (AFD) of a sampled Fisher-Snedecor F composited fading envelope are obtained, respectively. Finally, numerical and simulation results are shown to verify the accuracy of the theoretical analysis under various correlated fading and shadowing scenarios.
Authors: Weijun Cheng, Xianmeng Xu (School of Information Engineering, Minzu University of China, Beijing, P.R. China), Xiaoting Wang (School of Information Engineering, Minzu University of China, Beijing, P.R. China), Xiaohan Liu (School of Information Engineering, Minzu University of China, Beijing, P.R. China),
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Coffee Break 14:30 - 15:00

Session 3 15:00 - 16:00

15:00 - 15:20
Enabling Heterogeneous 5G Simulations with SDN Adapters

5G networks are expected to consist of multiple radio access technologies with an SDN core and so simulating these networks will require connecting multiple subnetworks with different technologies. Despite the availability of simulators for various technologies, there is currently no tool that can simulate a complete heterogeneous 5G network. To enable rapid prototyping of heterogeneous next generation networks, we develop a novel SDN adapter to enable seamless inter-working of multiple flavours of simulation/emulation tools such as NS-3, Mininet-WiFi, Omnet++, and OpenAirInterface5G. Using the adapter, we have built a large scale 5G simulator with multiple networking technologies by connecting existing simulators. We show that our adapter solution is easy-to-use, scalable, and can be used to connect arbitrary simulation tools. Using our solution, we show that Mininet-WiFi exhibits unreliable behaviour when connected to other networks. We compare our solution against other alternatives and show that our solution is superior both in terms of performance and cost. Finally, we simulate for the first time a large heterogeneous 5G network with all the latest technologies using only a standard commodity personal computer.
Authors: Thien Pham (The University of Adelaide), Jeremy McMahon (The University of Adelaide), Hung Nguyen (The University of Adelaide),
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15:20 - 15:40
Text Classification Based on Improved Information Gain Algorithm and Convolutional Neural Network

Feature selection is an important step in the text classification process, which can filter some irrelevant features, improve the classifier speed and reduce the interference to the classification effect. The traditional IG feature selection algorithm only considers the number of documents that feature items appear in different categories, but does not consider the influence of word frequency information within the class on classification. First, an improved information gain algorithm is proposed by introducing three parameters: intra-class word frequency, inter-class separation degree and intra-class dispersion degree. Second, the improved IG algorithm is used for feature selection, and important feature words with high IG value are selected according to the threshold value. Third, the important feature words in the text are expressed as two-dimensional word vectors and input into Convolutional Neural Network (CNN) to train the classification. Then a text classification model based on improved information gain and convolutional neural network is proposed, I - CNN for short. In the end, experimental results show that the improved IG algorithm is better than the traditional feature selection algorithm, and the classification effect of the model is better than the traditional classification model.
Authors: Mengjie Dong (School of Computer Engineering and Science, Shanghai University, Shanghai), Huahu Xu (School of Computer Engineering and Science, Shanghai University, Shanghai), Qingguo Xu (School of Computer Engineering and Science, Shanghai University, Shanghai),
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15:40 - 16:00
End-to-end Based Tibetan Multi-dialect Speech Recognition

Tibetan has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicon and word inventories for some dialects. In this paper, we present an end-to-end model for Tibetan multi-dialect speech recognition. It avoids processing the pronunciation dictionary and word segmentation for new dialects. We build the multi-dialect speech recogni-tion based on WaveNet-CTC. The dialect information is used in output for train-ing to improve the accuracy. The experimental results show our method has better performance compared with dialect-specific model.
Authors: Yue Zhao (School of information and Engineering, Minzu University of China), Fei Gao (School of information and Engineering, Minzu University of China), Xuefei Yi (Liaoning Equipment Manufacture College of Technology), Jianjian Yue (School of information and Engineering, Minzu University of China), Zeng Lai (School of information and Engineering, Minzu University of China), Xiaona Xu (School of information and Engineering, Minzu University of China), Licheng Wu (Minzu University of China),
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Session 4 16:00 - 17:00

16:00 - 16:20
Correlation study of emotional brain area induced by video

Emotions are physiological phenomena caused by complex cognitive activities. With the in-depth study of artificial intelligence and brain mechanism of emotion, affective computing has become a hot topic in computer science. In this paper, we used the existed emotional classification model based on electroencephalograph (EEG) to calculate the accuracy of emotion classification in 4 brain areas roughly sorted into frontal, parietal, occipital, and temporal lobes in terms of brain functional division, to infer the correlation between the emotion and 4 brain areas based on the accuracy rate of the emotion recognition. The result shows that the brain areas most related to emotions are located in the frontal and temporal lobes, which is consistent with the brain mechanism of emotional processing. This research work will provide a good guideline for selecting most relevant electrodes with emotions to enhance the accuracy of emotion recognition based on EEG.
Authors: Huiping Jiang (Brain Cognitive Computing Lab, School of Information Engineering, Minzu University of China), Zequn Wang (Brain Cognitive Computing Lab, School of Information Engineering, Minzu University of China), Xinkai Gui (Brain Cognitive Computing Lab, School of Information Engineering, Minzu University of China), Guosheng Yang (College of Information Engineering, Minzu University of China),
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16:20 - 16:40
Activity Recognition and Classification via Deep Neural Networks

Based on the Wi-Fi widely separated in the world, Wi-Fi-based wireless activity recognition has attracted more and more research efforts. Now, device-based activity awareness is being used for commercial purpose as the most important solution. Such devices based on various acceleration sensors and direction sensor are very mature at present. With more and more profound understanding of wireless signals, commercial wireless routers are used to obtain signal information of the physical layer: channel state information (CSI) more granular than the RSSI signal information provides a theoretical basis for wireless signal perception. Through research on activity recognition techniques based on CSI of wireless signal and deep learning, the authors proposed a system for learning classification using deep learning, mainly including a data preprocessing stage, an activity detection stage, a learning stage and a classification stage. During the activity detection model stage, a correlation-based model was used to detect the time of the activity occurrence and the activity time interval, thus solving the problem that the waveform changes due to variable environment at stable time. During the activity recognition stage, the network was studied by innovative deep learning to conduct training for activity learning. By replacing the fingerprint way, which is used broadly today, with learning the CSI signal information of activities, we classified the activities through trained network.
Authors: Zhi Wang (Xi’an Jiaotong University), Liangliang Lin (Xi’an Jiaotong University), Ruimeng Wang (The University of New South Wales), Boyang Wei (Georgetown University), Yueshen Xu (Xidian University), Zhiping Jiang (Xidian University), Rui Li (Xidian University),
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16:40 - 17:00
A Link Analysis Based Approach to Predict Character Death in Game of Thrones

Mysterious and uncertain deaths in the "Game of Thrones" novel-series have been stupefying to the vast pool of readers and hence interested researchers to come up with various models to predict the deaths. In this paper, we propose a Death-Prone Score model to predict if the candidate character is going to die or stay alive in the upcoming book in the series. We address the challenge of high-dimensional data and train our model on the most significant attributes by computing feature importance in the vector space. Further, we address the challenge of multiple interactions between characters and create a social network representing the weighted similarity between each character pair in the book. The proposed model takes similarity and proximity in a social network into account and generates a death-prone score for each character. To evaluate our model, we divide the characters data into training (characters died before year 300) and testing (characters died in the year 300 and characters alive till year 300). Our results show that the proposed Death-Prone Score model achieves an f-score of 86.2%.
Authors: Swati Agarwal (BITS Pilani, Goa, India), Rahul Thakur (IIT Roorkee, India), Sudeepta Mishra (BITS Pilani, Hyderabad, India),
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Welcome Banquet 17:30 - 21:00

Day 2 08/12/2019
Room #1

Session 5 09:00 - 10:00

09:00 - 09:20
Genetic Algorithm based Solution for Large-Scale Topology Mapping

Simulating large-scale network experiments requires powerful physical resources. However, partitioning could be used to reduce the required power of the resources and to reduce the simulation time. Topology mapping is a partitioning technique that maps the simulated nodes to different physical nodes based on a set of conditions. In this paper, genetic algorithm-based mapping is proposed to solve the topology mapping problem. The obtained results prove a high reduction in simulation time, in addition to high utilization of the used resources (The number of used resources is minimum).
Authors: Nada Osman (Alexandria University, Alexandria, Egypt), Mustafa Elnainay (Alexandria University, Alexandria, Egypt), Moustafa Youssef (Alexandria University, Alexandria, Egypt),
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09:20 - 09:40
PLDetect: A Testbed for Middlebox Detection using PlanetLab

Designing, coordinating and deploying repeatable experiments outside of a fully controlled environment pose a serious challenge when conducting network research. In particular, it can be difficult to correctly schedule experiments that collect bulk data using a shared re- source. To address this problem, we introduce PLDetect, a simple testbed built on top of PlanetLab which simplifies configuring, scheduling, and deploying large scale Internet experiments for evaluating middlebox detection methods.
Authors: Paul Kirth, Vahab Pournaghshband (University of San Francisco),
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09:40 - 10:00
Ransomware Detection Based on an Improved Double-Layer Negative Selection Algorithm

Ransomware is a type of malware that prevents users from accessing their system or personal files until the ransom is paid. The encrypting ransomware using public key cryptography is almost impossible to decrypt, so early detection and prevention is more important. Signature matching technology used by anti-virus software has low detection rate for unknown or polymorphic ransomware, some intelligent algorithms have been proposed for solving this problem. Inspired by the Artificial Immune System (AIS), an improved double-layer negative selection algorithm (DL-NSA) was proposed which can reduce the amount of holes and increase the detection rate. In order to obtain the behavior characteristics (e.g., files read or write, cryptography APIs call and network connection) of ransomware, a Cuckoo sandbox to simulate the malicious code running environment was build. After dynamic analysis, the behavior characteristics of ransomware were encoded to antigens. The improved double-layer negative selection algorithm has two immune detectors sets. The first layer detectors set was generated by the original negative selection algorithm using r-contiguous bits matching rule, the second layer detectors set was directional generated holes’ detectors using r-chunk matching rule with variable matching threshold based on hole set and self set. Simulation result shows that, comparing with NSA, this algorithm achieves wider space coverage of non-self, and can increasing the detection rate.
Authors: Tianliang Lu, Yanhui Du (People’s Public Security University of China), Jing Wu (People’s Public Security University of China), Yuxuan Bao (People’s Public Security University of China),
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Coffee Break 10:00 - 10:30

Session 6 10:30 - 11:30

10:30 - 10:50
Power Micro-blog Text Classification Based on Domain Dictionary and LSTM-RNN

The micro-blog texts of the national grid provinces and cities will be ana-lyzed as the main data, including the micro-blogs and corresponding com-ments, which will help us understand the events of power industry and peo-ple's attitudes towards these events. In this work, the data set is composed of 420,000 micro-blog texts. Firstly, the professional vocabulary of electric power is extracted, and these vocabulary are manually labeled, thus propos-ing a new field dictionary closely related to the power industry. Secondly, using the new power domain dictionary to classify the 2018 electric micro-blogs, and we can find that classification accuracy increased from 88.7% to 95.2%. Finally, a classification model based on LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) is used to deal with the comments under the micro-blog. The experimental result shows that the classification of the LSTM-RNN is more accurate. The rate was 83.1%, which was significantly better than the traditional LSTM and RNN text clas-sification models of 78.4% and 73.1%.
Authors: mengyao shen, jingsheng lei (Shanghai University of Electric Power,SUEP), feiye du (Shanghai University of Electric Power,SUEP), Zhongqin Bi (Shanghai University of Electric Power),
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10:50 - 11:10
Food Recognition and Dietary Assessment for Healthcare System at Mobile Device End using Mask R-CNN

Monitoring and estimation of food intake is of great significance to health-related research, such as obesity management. Traditional dietary records are performed in manual way. These methods are of low efficiency and a waste of labor, which are highly dependent on human interaction. In recent years, some researches have made progress in the estimation of food intake by using the computer vision technology. However, the recognition results of these researches are usually for the whole food object in the image, and the accuracy is not high. In terms of this problem, we provide a method to the food smart recognition and automatic dietary assessment on the mobile device. First, the food image is processed by MASK R-CNN which is more efficient than traditional methods. And more accurate recognition, classification and segmentation results of the multiple food items are output. Second, the OpenCV is used to display the food category and the corresponding food information of unit volume on the recognition page. Finally, in order to facilitate daily use, TensorFlow Lite is used to process the model to transplant to the mobile device, which can help to monitor people's dietary intake.
Authors: Hui Ye (Shanghai University), qiming zou (shanghai university), Jiangang Shi (Shanghai Shang Da Hai Run Information System Co),
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