BEST PAPER AWARDS

Date: May 21 (Tuesday)
Time: 9:00-10:30
Session: E4
Room: Pine Hall
Candidate List:

Time	Title/Author/Abstract
09:00-09:15	Achieving 3GPP Fairness for LTE-U and WiFi Coexisting Networks in Unlicensed Spectrum Shuangfeng Fang, Yayu Gao, Chengwei Zhang, Xiaojun Hei (Huazhong University of Science and Technology, P.R. China) One of the major challenges for the development of next generation mobile communications is how to meet the growing demand of high data rate with the increasingly scarce spectrum resources. In the past a few years, introducing Long Term Evolution (LTE) into the unlicensed spectrum (LTE-U) has attracted extensive attention, where a key question is how to achieve fair coexistence between LTE-U and WiFi under the unlicensed spectrum. In this paper, we study how to adaptively tune the duty cycle to maintain 3GPP fairness between LTE-U and WiFi with the LTE-U duty-cycling mechanism. Based on a unified analytical model, explicit expression of the optimal duty cycle is derived for achieving 3GPP fairness. The analysis is well verified by simulation results, which provides practical insights on LTE/WiFi coexistence issue.
09:15-09:30	Optimization on Unified Theory of Acceptance and Use of Technology for Driverless Car Test Behavior Chih-Cheng Tsai (National Kaohsiung Normal University, Taiwan), Li-Chen Lom (National Pingtung University, Taiwan), Yuh-Ming Cheng (Shu-Te University, Taiwan), Shi-Jer Lou (National Pingtung University of Science and Technology, Taiwan) To demonstrate that the integrated technology acceptance model based on the keras multilayer perceptrons (MLP) has good optimization effects, and predicts the ability of the public to test the behavior of unmanned vehicles more accurately than traditional statistical methods, The training and testing of MLP and SPSS linear regression were compared under 96 valid questionnaires. The results show that when MLP modeling is completed, its MSE=1.208 is significantly better than SPSS MSE=1.316, which shows that the model has good prediction effect after optimization training through deep learning.
09:30-09:45	Modeling and Evaluation of IoT Worm with Lifespan and Secondary Infectivity by Agent-Oriented Petri Net PN2 Shingo Yamaguchi (Yamaguchi University, Japan) In this paper, we proposed to extend IoT worm called Hajime that fights against IoT malware called Mirai by introducing lifespan and secondary infectivity (the ability to infect a device infected by Mirai). We first proposed a method for modeling the extended Hajime by agent-oriented Petri nets called PN2. This method enables us to represent a battle of Mirai and the extended Hajime as a PN2 model. Then, using the PN2 model, we evaluated the effect of the extended Hajime against Mirai. We found that (i) the higher the secondary infectivity became, the smaller Mirai's infection rate became; (ii) without the secondary infectivity, Mirai's infection rate could not be almost reduced; and (iii) when the lifespan was long, Hajime's infection rate could not be sufficiently reduced.
09:45-10:00	Energy Efficiency of Broadcast Wireless Channels Under Non-Gaussian Aggregate Interference Mohammad Ranjbar (University of Akron, USA), Hung Nguyen-Le (The University of Danang, Vietnam), Nghi H Tran (University of Akron, USA) In this paper, we propose an effective method to calculate the energy efficiency (EE) of a downlink broadcast wireless channel from an access point to multiple users under Gaussian-mixture aggregate interference. The considered downlink model is realistic for future wireless networks, such as cellular and Wi-Fi networks, having multi-tier heterogeneous architectures. In particular, we exploit Kullback-Leibler divergence to evaluate the minimum energy per bit E_b/N_0_{\min} for reliable communication and the wideband slope of the spectral efficiency as a function of energy per bit E_b/N_0_{\min} for different input signaling schemes. The proposed method provides a more accurate baseline for the analysis and design of next generation mobile wireless networks.
10:00-10:15	Conversation Partner Grouping Based on Speech Contents Li-Hsien Lin, Jian-Tao Huang, Yi-Ching Lyu, Po-Chuan Huang, Cheng-Wei Wu (Ilan University, Taiwan) Conversation analysis plays an important role in social psychology, interpersonal relationship management, and human-care computing. However, few of existing studies considers speech contents for effective conversation partner grouping (abbr. CPG). In this paper, we propose a new framework for conversation partner grouping based on speech contents. Under the proposed framework, we propose two novel algorithms for effective CPG, called CPG-LDA and CPG-LSI, respectively. Both of them use voice recognition tools to convert audio-based speech data into text-based speech contents, and then apply topic modeling and k-means algorithms for CPG. However, the former is based on LDA topic modeling, while the latter is LSI. The experiments show that both CPG-LDA and CPG-LSI have good performance for GPC. More impressively, the proposed CPG-LSI algorithm archives up to 95.83% recognition rate in the experiments.
10:15-10:30	Classification of Medical Sensitive Data based on Text Classification Huimin Jiang, Chunling Chen, Shengchen Wu, Yongan Guo (Nanjing University of Posts and Telecommunications, P.R. China) With the medical informatization, the privacy data of patients in medical big data is gradually increasing, and the sensitivity of data is increasing. Different from traditional medical sensitive data processing methods, text classification technology is proposed for medical information privacy protection, and sensitive data is classified. Experiments have shown the feasibility of using text classification techniques to classify medically sensitive data.