Day 1 19/08/2019
Room #1

Conference registration & Orientation 11:00 - 18:00

(Faraday Wing Building Reception)
Day 2 20/08/2019
Room #1

Registration 09:00 - 10:00

(Nelson Haden Lecture Theatre, Faraday Wing Building)

Opening Ceremony 10:00 - 10:20

(Nelson Haden Lecture Theatre, Faraday Wing Building)

Keynote Speech I: Tasos Dagiuklas 10:20 - 11:10

Title: Challenges and Opportunities in 5G and Beyond

Keynote Speech II: Jun Xu 11:10 - 12:00

Title: The Past and Future Impacts of Cloud Computing

Coffee break 12:00 - 12:30

Panel Discussion: RESCUE-2019: Civil Protection Volunteers Training (CiProVoT) 12:30 - 13:30

Tasos Dagiuklas, Muddesar Iqbal, Tom Hales, Richard Abbot, Ian Greatbatch, Raheela Saad

Lunch 13:30 - 14:30

Tutorial I: AI as a Service 14:30 - 15:20

Muddesar Iqbal

Tutorial II: IoT Forensics: Potential Evidences in the Connected Life 15:20 - 16:10

Shancang Li

Coffee break 16:10 - 16:40

Tutorial III: Protection of Critical National Infrastructures 16:40 - 17:30

Leandros Maglaras
Day 3 21/08/2019
Room #1

Registration 08:30 - 09:00

(Nelson Haden Lecture Theatre, Faraday Wing Building)

Keynote Speech III: Professor Niki Trigoni 11:00 - 12:00

(Nelson Haden Lecture Theatre, Faraday Wing Building)

The Cruise Gala Dinner 19:15 - 22:15

Aboard the CityCruises boat Westminster
Room #2

SESSION 1A - Cloud, IoT & Edge Computing (Nelson Haden Lecture Theatre, Faraday Wing Building) 09:00 - 10:30

Session Chair: Tong Liu
09:00 - 10:30
AccuracyGuaranteed Event Detection via Collaborative Mobile Crowdsensing with Unreliable Users

Recently mobile crowdsensing has become a promising paradigm to collect rich spatial sensing data by taking advantage of widely distributed sensing devices like smartphones Based on sensing data event detection can be conducted in urban areas to monitor abnormal incidents like traffic jam However how to guarantee the detection accuracy is still an open issue especially when unreliable users who may report wrong observations are considered In this work we focus on the problem of user recruitment in collaborative mobile crowdsensing aiming to optimize the finegrained detection accuracy in a large urban area Unfortunately the problem is proved to be NPhard which means there is no polynomialtime algorithm to achieve the optimal solution unless P NP To meet the challenge we first employ a probabilistic model to characterize the unreliability of users and measure the uncertainty of inferring event occurrences given collected observations by Shannon entropy Then by leveraging the properties of adaptive monotonicity and adaptive submodularity we propose an adaptive greedy algorithm for user recruitment which is theoretically proved to achieve a constant approximation ratio guarantee Extensive simulations are conducted which show our proposed algorithm outperforms baselines under different settings
Authors: Tong Liu (Shanghai University), Wenbin Wu (Shanghai University), Yanmin Zhu (Shanghai Jiao Tong University), Weiqin Tong (Shanghai University),
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09:00 - 10:30
A Dynamic DifficultySensitive Worker Distribution Model for Crowdsourcing Quality Management

Crowdsourcing utilizes the intelligence of people to solve problems that are difficult for machines such as entity resolution sentiment analysis and image recognition In crowdsourcing systems requesters publish tasks that are answered by workers However the responses collected from the crowd are ambiguous as the workers on internet with unknown and very diverse abilities skills interests and knowledge background In order to ensure the quality of crowdsourcing results it is important to characterize worker quality accurately Many previous works model the worker quality by a fixed value such as probability value or confusion matrix But even when workers complete the same type of tasks the quality is affected by some factors task difficulty to varying degrees Here we propose a dynamic difficultysensitive worker quality distribution model In our model the workers ability is affected by task difficulty and fits a functional distribution This model reflects the relationship between worker reliability and task difficulty In addition we utilize ExpectationMaximization approach EM to obtain maximum likelihood estimates of the parameters of worker quality distribution model and the true answers to the tasks We conduct extensive experiments with synthetic data and realworld data The experimental results show that our method significantly outperforms other stateoftheart approaches
Authors: Miao Zheng (), Lizhen Cui (School of Software,Shandong University,Jinan,China), Wei He (School of Software,Shandong University,Jinan,China), Wei Guo (School of Software,Shandong University,Jinan,China), Xudong Lu (School of Software,Shandong University,Jinan,China),
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09:00 - 10:30
Prioritybased Optimization of IO Isolation for Hybrid Deployed Services

With the increasing of software complexity and user demands collaborative service is becoming more and more popular Each service focuses on its own specialty their cooperation can support complicated task with high efficiency To improve the resources utilization virtualization technology like container is used and it enables multiple services running in the same physical machine However since the host physical machine is shared by several services the resource competition is inevitable Isolation is an effective solution but the weak isolation mechanisms of container cannot handle such complicated scenarios In the worst situation the performance of services cannot meet the requirements and the system may crash In order to solve this problem we propose a prioritybased optimization mechanism for IO isolation after analyzing the characteristics of typical service workloads Based on the realtime performance data priority is automatically assigned to each service and corresponding optimization methods are applied We evaluate the optimization effects of the prioritybased mechanism in both static and dynamic workload cases besides the influence of different priority order is also analyzed The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied
Authors: Jiancheng Zhang (Hangzhou Dianzi University), Youhuizi Li (Hangzhou dianzi University), Li Zhou (Hangzhou Dianzi University), Zujie Ren (Zhejiang Lab), Jian Wan (Zhejiang LabZhejiang University of Science and Technology), Yuan Wang (NetEase Hangzhou),
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09:00 - 10:30
Optimal Device Management Service Selection in InternetofThings

In InternetofThingsIoT IoT device management is a challenge for device owners considering the huge amount of devices and their heterogeneous quality of serviceQoS requirements Recently IoT device management serviceMS providers are arising to serve device owners Device owners can now easily manage their devices by using IoT device MSs It is critical to select suitable MSs from numerous candidates for devices An optimal service selection must maximize the number of MS managed devices and minimize the total cost while ensuring the QoS requirements of IoT system To optimize the IoT Device Management Service Selection problem we propose IDMSS a Lexicographic Goal ProgrammingLGP based approach However due to the high computational complexity of the IoT Device Management Service Selection problem an alternative heuristicbased approach called GA4MSS is proposed Two series of experiments have been conducted and the experimental results show the performance of our approaches
Authors: Weiling Li (Chongqing University), Yunni Xia (Chongqing University), Wanbo Zheng (Kunming University of Science and Technology), Peng Chen (Xihua University), Jia Lee (Chongqing University), Yawen Li (Chongqing University),
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09:00 - 10:30
A Dynamic Planning Framework for QoSbased Mobile Service Composition under CloudEdge Hybrid Environments

In cloudedge hybrid environments when QoS constraints of the SOAbased mobile service composition change a dynamic reconfiguration needs to be performed Different from traditional cloud service the cloudedge hybrid environment has the characteristics of limited resource storage limited energy at the edge and uncertain users who move frequently Dynamic reconfiguration in this mode is challenging QoS is an important indicator of service evaluation Most studies focus on only the static QoS attributes of the service However the QoS of a service is not statically constant it changes dynamically over time Therefore to avoid the immediate failure of the service and ensure the stability of the mobile service composition after dynamic reconfiguration an LSTM neural network is used to predict the future value of QoS for candidate service which is used as an evaluation indicator for the service during dynamic reconfiguration Then attributes such as energy consumption traffic and moving track are considered A costreward mechanism is constructed to calculate the cost and reward of the service when it is invoked The reasonable restriction conditions are added for controlling dynamic reconfiguration Finally the dynamic reconfiguration problemsolving process and framework for mobile service composition based on QoS in a cloudedge hybrid environment is introduced guiding the mobile service composition dynamic reconfiguration task in cloudedge hybrid environments
Authors: honghao gao (School of Computer Engineering and Science, Shanghai University, Shanghai, China), wanqiu huang (School of Computer Engineering and Science, Shanghai University, Shanghai, China), qiming zou (Computing Center, Shanghai University, Shanghai, China), xiaoxian yang (School of Computer and Information Engineering, Shanghai Polytechnic University),
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09:00 - 10:30
A Security Framework to Protect Edge Supported Software Defined Internet of Things Infrastructure

Managing the huge IoT infrastructure poses a vital challenge to the network community Software Defined Networking SDN due to its characteristics of centralized network management has been considered as an optimal choice to manage IoT Edge computing brings cloud recourses near the IoT to localize the cloud demands Hence SDN IoT and edge computing create a resourceful SDIoTEdge architecture to efficiently orchestrate cloud services and utilize resourcelimited IoT devices in a flexible way Besides a wide adoption of IoT the vulnerabilities present in this less secure infrastructure can be exploited by the adversaries to attack the OpenFlow channel using Distributed Denial of Service DDoS attacks DDoS on OpenFlow channel have the ability to disrupt the whole network hence providing security for the OpenFlow channel is a key challenge in SDIoTEdge We propose a security framework called SDIoTEdge Security SIESec against the security vulnerabilities present in this architecture SIESec prototype employs machine learningbased classification strategy blacklist integration and contextual network flow filtering to efficiently defend against the DDoS attacks We perform extensive simulations using Floodlight controller and Mininet network emulator Our results proclaim that SIESec provides extensive security against OpenFlow channel DDoS attacks and pose a very less overhead on the network
Authors: Wajid Rafique (Nanjing University, Nanjing, P. R. China), Wanchun Dou (Nanjing University, Nanjing, P. R. China), Nadeem Sarwar (Bahria University, Lahore, Pakistan), Maqbool Khan (Nanjing University, P. R. China),
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Coffee break (Nelson Haden Lecture Theatre, Faraday Wing Building) 10:30 - 11:00

Lunch (Nelson Haden Lecture Theatre, Faraday Wing Building) 12:00 - 13:00

SESSION 2A - Collaborative IoT and Services Applications (Nelson Haden Lecture Theatre, Faraday Wing Building) 13:00 - 15:00

Session Chair: Yueshen Xu
13:00 - 15:00
An Integrated and Intelligent Dental Healthcare System with Mobile Services

Medical informatization takes a key role in medical and healthcare industries which is a necessary way of improving service quality and treatment experience in a hospital In this paper we design and implement an integrated and intelligent healthcare system with mobile services specific to dental healthcare The developed system contains four components including WeChat official account intelligent question and answering QA system mobile followup care mini program and AI speech assistant The developed WeChat official account and intelligent QA system provide a large amount of professional knowledge on dental health which can help narrow the knowledge gap between doctors and patients The two components facilitate patients to follow up our applications without downloading any extra softwares as the developed functions eg voice service are provided through mobile services These two components also facilitate followup management decreasing manpower and resource usage in hospital Our system is developed to serve patients as well as dentists and provides a group of interactive healthcare services in a low cost In this paper we elaborate the whole system architecture and implementation detail of each component We also report the performance test result and training process of the classifier
Authors: Yueshen Xu (Xidian University), Yi Lu (Stomatological Hospital, Xi'an Jiaotong University), Rui Li (Xidian University), Lin Niu (Stomatological Hospital, Xi'an Jiaotong University), Wenzhi Du (Stomatological Hospital, Xi'an Jiaotong University), Ni An (Stomatological Hospital, Xi'an Jiaotong University), Yaning Liu (Xidian University), Xinyi Liu (Xidian University), Yan Jiang (Xidian University), Zhenhua Li (Xidian University), Jin Guo (Xidian University), Xiangdong Wang (Shaanxi Lishengruihui Technology Co., Ltd),
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13:00 - 15:00
An Edge Computingbased Framework for Marine Fishery Vessels Monitoring Systems

Vessel Monitoring Systems VMS have been adopted by many countries which provide information on the spatial and temporal distribution of fishing activity Realtime communication and interaction between fishing vessels and shorebased systems is a weakness of traditional vessel monitoring systems This paper proposes a novel framework of edge computingbased VMS ECVMS The framework of ECVMS mainly consists of four layers An edge computing terminal is used on each vessel and the BeiDou navigation satellite system BDS is adopted for communication Meanwhile edge computing servers interact with corresponding management vessels and the cloud In order to decrease the communication cost a data transmission policy called Adaptable Trajectory Transmission Model ATTM is presented in this paper The experimental results illustrate the efficiency of the proposed ECVMS with the average communication time greatly decreased in a typical scenario Moreover ECVMS improves the realtime performance of the system
Authors: Fengwei Zhu (School of Computer Science and Technology, Hangzhou Dianzi University), Yongjian Ren (School of Computer Science and Technology, Hangzhou Dianzi University), Jie Huang (School of Computer Science and Technology, Hangzhou Dianzi University), Jian Wan (School of Computer Science and Technology, Hangzhou Dianzi University), Hong Zhang (Institute of Science and Technology Information Research of Zhejiang Province),
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13:00 - 15:00
A Mobile and Webbased Approach for Targeted and Proactive Participatory Sensing

Participatory sensing applications have gained popularity due to the increased use of mobile phones with embedded sensors One of the main issues in participatory sensing applications is the uneven coverage of areas ie some areas might be covered by multiple participants while there is no data for other areas In this paper we design mobile and webbased infrastructure to enable domain scientists to effectively acquire crowdsensed data from specific areas of interest AOIs to support the goal of even coverage for data collection Scientists can mark the AOIs on a webportal then volunteers will be proactively informed about the participatory sensing opportunities near their current location We present a caching algorithm to increase the performance of our proposed system and studied the performance of the caching algorithm for different realworld scenarios on different mobile phones We observed that prefetching data improves the performance to some extent however it starts to degrade after a certain point depending upon the number of nearby AOIs
Authors: Navid Hashemi Tonekaboni (University of Georgia), Lakshmish Ramaswamy (University of Georgia), Sakshi Sachdev (University of Georgia),
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13:00 - 15:00
Forecasting Longterm Call Traffic based on Seasonal Dependencies

How to use future call traffic for scheduling different staffs to work in a month or a week is an important task for call center In this problem setting the call traffic should be predicted in a longterm way where the forecasting results for different periods are required However it is very challenging to solve this problem due to the randomness nature of the call traffic and the multiple forecasting in long term Current methods cannot solve this problem since they either merely focus on shortterm forecasting for the next hour or next day or ignore callholding time for call traffic prediction In this paper we propose an effective method for predicting longterm call traffic with multiple forecasting results for different future periods eg every 15 minutes and take both call arrival rate and callholding time into consideration through the Erlang In our method the seasonal dependencies are summarized by performing data analysis then different features based on these dependencies are extracted for training the prediction modelIn order to forecast call traffic of multiple time buckets we propose two strategies based on different features The evaluation results show that the features the prediction models and the strategies are feasible
Authors: Cao Longchun (Zhejiang University of Technology), Ma Kui (Zhejiang University of Technology), Cao Bin (Zhejiang University of Technology), Fan Jing (Zhejiang University of Technology),
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13:00 - 15:00
Attentionbased Bilinear Joint Learning Framework for Entity Linking

Entity Linking EL is a task that links entity mentions in the text to corresponding entities in a knowledge base The key to building a highquality EL system involves accurate representations of word and entity In this paper we propose an attentionbased bilinear joint learning framework for entity linking First a novel encoding method is employed for coding EL This method jointly learns words and entities using an attention mechanism Next for ranking features a weighted summation model is introduced to model the textual context and coherence Then we employ a pairwise boosting regression tree PBRT to rank candidate entities As input PBRT takes both features constructed with a weighted summation model and conventional EL features Finally through the experiment we demonstrate that the proposed model learns embedding efficiently and improves the EL performance compared with other stateoftheart methods Our approach achieves superior result on two standard EL datasets CoNLL and TAC 2010
Authors: Min Cao (Shanghai University), Penglong Wang (Shanghai University), Honghao Gao (Shanghai University), Jiangang Shi (Shanghai Shang Da Hai Run Information System Co), Yuan Tao (Shanghai University), Weilin Zhang (Shanghai University),
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13:00 - 15:00
A Collaborative Anomaly Detection Approach of Marine Vessel Trajectory

Trajectory anomaly detection plays a very important role in safety management of navigation Most trajectory anomaly detection methods mainly detect the spatial information of vessel39s trajectory These methods neglect vessel39s dynamic behavior characteristics such as course speed and acceleration In this paper a Vessel Trajectory Multifactor Collaborative Anomaly Detection VTMCAD approach is proposed to realize anomaly detection of vessels at sea by studying trajectory characteristics Firstly the trajectory behavior of historical vessels is identified and the trajectory characteristics such as heading speed and acceleration are extracted for different trajectory behaviors Then the current trajectory behavior is identified when the trajectory anomaly is detected On the basis of TRAOD method the corresponding trajectory feature model components such as heading speed and acceleration are used to detect the trajectory anomaly and the trend points of the trajectory anomaly are obtained Finally the outlier trend score of each component is combined to calculate the final outlier trend score VTMCAD can change the weight of components according to the detection effectiveness of different components and avoid excessive dependence on one component so it has better robustness and reliability The experimental results on realworld vessel data show that VTMCAD can effectively capture abnormal trajectories
Authors: Zejun Huang (Hangzhou Dianzi University), Jian Wan (Hangzhou Dianzi University), Jie Huang (Hangzhou Dianzi University), Gangyong Jia (Hangzhou Dianzi University), Wei Zhang (Hangzhou Dianzi University),
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13:00 - 15:00
Classification of skin lesions based on data collaboration under imbalance dataset

Imbalance data is a common problem in machine learning task which often impacts the accuracy of models An effective way to solve this is to increase the number of minority class samples in the dataset There are many methods to solve the problem of imbalance data in the field of machine learning But these are all for lowdimensional data For highdimensional data such as images these methods are not well applicable In this paper an image generation method based on generative adversarial network is introduced to carry out pattern learning for samples of minority class in the dataset so as to realize the expansion of data for minority class And finally the classification network for skin lesions is trained by data collaboration which consist of real images and generated images The experimental results indicate that the addition of generated images further improves the accuracy of the network while alleviating the imbalance problem to some extent
Authors: Weijia Ji (School of Information Science and Engineer, East China University of Science and Technology, Shanghai, China), Lizhi Cai (Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Com-puter Software Technology, Shanghai, China), Mingang Chen (Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Com-puter Software Technology, Shanghai, China), Naiqi Wang (School of Information Science and Engineer, East China University of Science and Technology, Shanghai, China),
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13:00 - 15:00
WiFi Imaging Based Segmentation and Recognition of Continuous Activity

Automatic segmentation and action recognition have been a longstanding problem in sensorless sensing In this paper we propose CHAR a continuous human activity recognition system to solve these problems in a different way Weve noticed that these challenges have been solved in image processing field so CHAR could effectively perform action segmentation and recognition after WiFi imaging The key idea behind WiFi imaging is that different body part reflects transmitted signal the receiver receives the combination of them and then we separate the received signals from different directions and get the signal intensity in each direction to get the heat map showing the shape of the object The imaging sequence contains multiple pictures records a continuous action at different time and we can easily separate and recognize the action based on IC2image classification a classification framework we proposed We implement CHAR using commodity WiFi devices to evaluate its performance under different environment The results show that the imaging result is better than prior works facilitating CHAR to achieving an average recognition accuracy ie 95
Authors: Yang Zi (Xi'an Jiaotong University), Wei Xi (Xi'an Jiaotong University), Li Zhu (Xi'an Jiaotong University), Fan Yu (Xi'an Jiaotong University), Kun Zhao (Xi'an Jiaotong University), Zhi Wang (Xi'an Jiaotong University),
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Coffee break (Nelson Haden Lecture Theatre, Faraday Wing Building) 15:00 - 15:30

SESSION 3A - Security and Trustworthy (Nelson Haden Lecture Theatre, Faraday Wing Building) 15:30 - 16:30

Session Chair: Youhuizi Li
15:30 - 16:30
Application and Implementation of Multivariate Public Key Cryptosystem in Blockchain

Blockchain is one of the most revolutionary and innovativetechnologies in recent years The traditional asymmetric encryption algorithms guarantee the security of data on blockchain However with the rapid development of quantum computing technologies as long as largescale quantum computers appear these kind of encryption systems can be deciphered by shor algorithm in polynomial time Therefore blockchain technologies are going to face potential security threats Tosolve this problem the best solution at present is to replace the asymmetric encryption algorithms in the blockchain with postquantum cryptosystems In this paper we apply the Rainbow algorithm with highsignature efficiency to the existing Ethereum platform and test the feasibility of the scheme by building a private chain In addition we compare the signature efficiency of Rainbow algorithm with ECDSA which is expected to provide direction and inspiration for future research on blockchain resistance to quantum computing
Authors: Ruping Shen (Chongqing University), Hong Xiang (Chongqing University), Xin Zhang (Chongqing University), Bin Cai (Chongqing University), Tao Xiang (Chongqing University),
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15:30 - 16:30
Secure Sharing Model Based On Block Chain in Medical Cloud

The cloud storage and sharing system are widely used in medical systems The unique characteristics of cloud storage enable healthy data being efficiently delivered and retrieved Nevertheless traditional medical cloud system suffers two flaws For one thing centralized cloud servers are vulnerable to malicious attack and single point of failure For another these systems cannot offer a powerful capability to protect medical health data Blockchain technology is considered to be one of vital technologies of Bitcoin In this paper we propose a security model which combines the cloud storage technology with blockchain technology Our model adopts Delegate Proof of Stake DPOS consensus mechanism to ensure that all nodes have unified state in the network Additionally the CPABE scheme is introduced into the Proxy Reencryption to store and share medical data which supports the keywords searching Moreover we rank medical institutions that different ranks have different duties In our secure sharing models there are no central nodes and it is a distributed environment It not only can reduce the access overhead of the blockchain but also better resist the collusion attack Furthermore our security analysis indicates that the proposed scheme achieves provable security under the qDBDHE assumption in the random oracle model Then the comparisons show that our model is more efficient and practical than previous ones
Authors: Tao Feng (Lanzhou university of technology), ying jiao (Lanzhou university of technology),
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15:30 - 16:30
A Smart Topology Construction Method for Antitracking Network based on the Neural Network

Antitracking network is the effective method to protect the network users39 privacy confronted with the increasingly rampant network monitoring and network tracing But the architecture of the current antitracking network is easy to be attacked traced and undermined In this paper We propose smart topology construction method STon to provide the selfmanagement and selfoptimization of topology for antitracking network We firstly deploy the neural network on each node of the antitracking network Each node can collect its local network state and calculate the network state parameters with the neural network to decide the link state with other nodes At last each node optimizes its local topology according to the link state With the collaboration of all nodes in the network the network can achieve the selfmanagement and selfoptimization of its own topology The experimental results showes that STon has a better robustness communication efficiency and antitracking performance than the current popular P2P structures
Authors: Changbo Tian (School of Cyber Security, University of Chinese Academy of Sciences), YongZheng Zhang (Institute of Information Engineering, Chinese Academy of Sciences), Tao Yin (Institute of Information Engineering, Chinese Academy of Sciences), Yupeng Tuo (Institute of Information Engineering, Chinese Academy of Sciences), Ruihai Ge (Institute of Information Engineering, Chinese Academy of Sciences),
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15:30 - 16:30
ContextAware PointofInterest Recommendation Algorithm with Interpretability

With the rapid development of mobile Internet smart devices and positioning technologies locationbased social networks LBSNs are growing rapidly In LBSNs pointofinterest POI recommendation is a crucial personalized location service that has become a research hotspot To address extreme sparsity of user checkin data a growing line of research exploits spatialtemporal information social relationship content information popularity and other factors to improve recommendation performance However the temporal and spatial transfers of user preferences are seldom mentioned in existing works and interpretability which is an important factor to enhance credibility of recommendation result is overlooked To cope with these issues we propose a contextaware POI recommendation framework which integrates users longterm static and timevarying preferences to improve recommendation performance and provide explanations Experimental results over two realworld LBSN datasets demonstrate that the proposed solution has better performance than other advanced POI recommendation approaches
Authors: Guoming Zhang (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China), Lianyong Qi (School of Information Science and Engineering, Qufu Normal University, Qufu, China), Xuyun Zhang (Department of Electrical Computer Engineering, The University of Auckland, Auckland, New Zealand), Xiaolong Xu (School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China), Wanchun Dou (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China),
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SESSION 4A - Algorithm, Network and Testbed (Nelson Haden Lecture Theatre, Faraday Wing Building) 16:30 - 17:30

Session Chair: Rafique Wajid
16:30 - 17:30
An Influence Maximization Algorithm Based on Realtime and Desuperimposed Diffusibility

With the great development of the social network exploring an influence maximization algorithm with strong adaptability and superior performance will undoubtedly produce great market value Existing algorithms select a seed node with the greatest influence This will inevitably have an influence in mutual coverage which will have a more or less negative impact on the final results and reduce the performance of the algorithm In this paper Node Diffusibility is proposed and it is updated it in real time and eliminated the deviation caused by its overlay On the basis of traditional calculation of node influence more attention was paid to the influence of a node39s neighboring nodes rather than to the characteristics of the nodes themselves The proposed algorithm was evaluated by experiments conducted on selected real data sets Compared with the classical rankingbased algorithms MaxDegree and PageRank the proposed algorithm achieved better results in terms of efficiency and time complexity
Authors: Yue Ren (Shanghai University), Xinyuan Zhang (Shanghai University), Liting Xia (Shanghai University), Yongze Lin (Shanghai University), Yue Zhao (Shanghai University), Weimin li (Shanghai University),
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16:30 - 17:30
Detecting Overlapping Communities of Nodes with Multiple Attributes from Heterogeneous Networks

Many methods have been proposed for detecting communities from heterogeneous information networks with general topologies However most of these methods can detect communities with homogeneous structures containing nodes with only a single attribute Investigating methods for detecting communities containing nodes with multiple attributes from heterogeneous information networks with general topologies has been understudied Such communities are realistic in realworld social structures and exhibits many interesting properties Towards this we propose a system called DOMAIN that can detect overlapping communities of nodes with multiple attributes from heterogeneous information networks with general topologies The framework of DOMAIN focuses on domains ie attributes that describe human characteristics such as ethnicity culture religion demographic age or the like The ultimate objective of the framework is to detect the smallest subcommunities with the largest possible number of domains to which an active user belongs The smaller a subcommunity is the more specific and granular its interests are The interests and characteristics of such a subcommunity is the union of the interests and characteristics of the single domain communities from which it is constructed We evaluated DOMAIN by comparing it experimentally with three methods Results revealed marked improvement
Authors: Kamal Taha (Khalifa University), Paul Yoo (University of London),
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16:30 - 17:30
Dynamical rating prediction with topic words of reviews a hierarchical analysis approach

Social commerce is an important part of the social network which contains a large number of user behaviors and user relationships Users generate reviews social relations userproduct or productproduct mapping information that can reflect an evolution of product characteristics and user preferences in using social commerce It is a popular topic by using these information to conduct rating prediction in the field of intelligent recommendation In this paper optimizing the rating prediction based on topic analysis in two aspectsOn the one hand in the process of data preprocessingconstructing a dynamic hierarchical tree of topic wordsDHTTW which can not only capture the change of users39 preferences for product property but also reflect the impact of different product property on users39 preferences at the same time Based on DHTTWdesigning the mapping rules from user reviews to DHTTW to generate user preference vectors On the other hand in the process of predictionproposing a prediction method named combination of gradient boosting decision tree and multiclass linear regressionGBDTMCLR which further improves the accuracy of rating prediction
Authors: Huibing Zhang (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology, Guilin 541004, China.), Hao Zhong (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology, Guilin 541004, China.), Qing Yang (Guangxi Key Laboratory of Automatic Measurement Technology and Instrument, Guilin University of Electronic Technology, Guilin 541004, China.), Fei Jia (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology, Guilin 541004, China.), Ya Zhou (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology, Guilin 541004, China.), Fang Pan (Teaching Affairs Office, Guangxi Normal University, Guilin 541004, China.),
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16:30 - 17:30
NTS A Scalable Virtual Testbed Architecture with Dynamic Scheduling and Backpressure

Experimental platforms perform a key role in evaluating the proofofconcept and innovations Nowadays researchers from academia and industriesrely on expensive physical testbeds to evaluate their experimentswhile there arevery limited software testbeds in market which usually not available or costly Inaddition the applications of existing traffic generators are restricted to their single function and performance in network area It has come to a point that lack ofvalidation and testing tools has tremendously jeopardized the innovation in thisfield In this paper we propose NTS which is a scalable softwarebased virtualtestbed architecture The scheduling and management framework can dynamically schedule resource of services The scheduling algorithm adopts the concept ofcost proportional fairness scheduling which takes the evaluated traffic proportion and packet arrival rate into account By leveraging container technology theresources of services are restrictedly managed and fully isolated without tampering the OS kernels scheduling mechanisms Another advantage of the proposedtestbed architecture is that the software can generate most kinds of backbone network traffic and can also be extended easily for customized protocol or trafficpatterns Our experiments show that the virtual testbed is generic scalable andcostefficient which is suitable and affordable for researchers in the field of network
Authors: Youbing Zhong (Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences), Zhou Zhou (Institute of Information Engineering), Da Li (Dept. of Electrical and Computer Engineering, University of Missouri-Columbia), Wenliang He (Institute of Information Engineering, Chinese Academy of Sciences), Chao Zheng (Institute of Information Engineering, Chinese Academy of Sciences), Qingyun Liu (Institute of Information Engineering, Chinese Academy of Sciences), Li Guo (Institute of Information Engineering, Chinese Academy of Sciences),
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Room #3

SESSION 1B - Data Analysis & Recommendation (FW-114., Faraday Wing Building) 09:00 - 10:30

Session Chair: Weilong Ding
09:00 - 10:30
SMART A Serviceoriented Statistical Analysis Framework on Spatiotemporal Big Data

Spatiotemporal data is one of the most important assets in the context of smart cities Spatiotemporal big data comes from a variety of sensor equipment implies the state of urban operation insight into the development trend Due to the multidimensional characteristics and diverse analysis needs of large spatialtemporal data data analysis based on large spatialtemporal data must take into account the large capacity diversity and frequent changes of data as well as the query integration and visualization of data This makes spatial and temporal data analysis more difficult In order to simplify the analysis of spatiotemporal data a serviceoriented intelligent framework is proposed Firstly the concept of spatiotemporal data service is introduced into the framework and several common spatiotemporal data service models are defined Then a spatiotemporal data service composition language based on BPEL is proposed to define analysis applications We also developed a prototype tool to implement spatiotemporal data services on Hadoop In order to prove the applicability of our method we demonstrate the effectiveness of our work through a practical applicationbased study
Authors: Jie Zhou (1.Data Engineering Institute, North China University of Technology Beijing 100144 2.Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing), Weilong Ding (1.Data Engineering Institute, North China University of Technology Beijing 100144 2.Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing), Zhuofeng Zhao (1.Data Engineering Institute, North China University of Technology Beijing 100144 2.Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing), Han Li (1.Data Engineering Institute, North China University of Technology Beijing 100144 2.Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing),
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09:00 - 10:30
Collaborative Contextual Combinatorial Cascading Thompson Sampling

We design and analyze collaborative contextual combinatorial cascading Thompson sampling C4TS C4TS is a Bayesian heuristic to address the cascading bandit problem in the collaborative environment C4TS utilizes posterior sampling strategy to balance the explorationexploitation tradeoff and it also incorporates the collaborative effect to share information across similar users Utilizing these two novel features we prove that the regret upper bound for C4 TS is tildeO du pmKT where d is the dimension of the feature space u is the number of users m is the number of clusters K is the length of the recommended list and T is the time horizon This regret upper bound matches the theoretical guarantee for UCBlike algorithm in the same settings We also conduct a set of simulations comparing C4TS with the stateoftheart algorithms The empirical results demonstrate the advantage of our algorithm over existing works
Authors: Zhenyu Zhu (USTC), Liusheng Huang (USTC), Hongli Xu (USTC),
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09:00 - 10:30
MultiLabel Recommendation of Web Services with the Combination of Deep Neural Networks

With the increasing number of web services on the Internet how to effectively classify and recommend service labels has become a research issue It plays an important role in web service organization and management However the deficiency of current approaches is that they either recommend only a single label for a web service or a set of independent labels without order ranking that is still difficult for service providers to publish their web services In this paper together with label embedding techniques we propose a novel approach for service multilabel recommendation using deep neural networks Unlike traditional approaches the predicted service labels of our approach not only satisfy the demands of service multilabel recommendation but also provide the importance with an ordered label ranking The experiments are conducted to validate the effectiveness on a largescale dataset from ProgrammableWeb involving 13869 realworld Web services The experimental results demonstrate that our approach for multilabel recommendation of web services outperforms the competing approaches in terms of multiple evaluation metrics
Authors: Yanglan Gan (School of Computer Science and Technology, Donghua University), Yang Xiang (School of Computer Engineering and Science, Shanghai University; Shanghai Institute for Advanced Communication and Data Science, Shanghai University), Guobing Zou (School of Computer Engineering and Science, Shanghai University; Shanghai Institute for Advanced Communication and Data Science, Shanghai University), Huaikou Miao (School of Computer Engineering and Science, Shanghai University; Shanghai Key Laboratory of Computer Software Evaluating and Testing), Bofeng Zhang (School of Computer Engineering and Science, Shanghai University),
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09:00 - 10:30
An Approach for Item Recommendation Using Deep Neural Network combined with the Bayesian Personalized Ranking

This paper proposes a deep neural network model SDAEBPR based on Stack Denoising AutoEncoder and Bayesian Personalized Ranking for the problem of accurate product recommendation First we use the Stack Denoising AutoEncoder SDAE as the input of the items rating data and obtain the hidden features after encoding Second the Bayesian personalized Ranking BPR method is used to learn the hidden feature vector of the corresponding item This model can avoid the influence of the sparseness of the matrix Therefore this model achieves the effect of more accurate recommendations of items Third to reduce the cost of model training a unique pretraining and finetuning strategy is proposed in the deep neural network Finally based on the Movielens 20M dataset the results of the SDAEBPR a traditional itembased collaborative filtering model and a userbased collaborative filtering model are compared It is shown that the SDAEBPR has higher accuracy This method improves the accuracy of parameter estimation and the efficiency of model training
Authors: Zhongqin Bi (College of Computer Science and Technology, ShangHai University of Electric Power, Shanghai, China), Siming Zhou (College of Computer Science and Technology,ShangHai University of Electric Power), Xiaoxian Yang (School of Computer and Information Engineering, Shanghai Polytechnic University), Ping Zhou (College of Computer Science and Technology, ShangHai University of Electric Power, Shanghai, China), Jiale Wu (College of Computer Science and Technology, ShangHai University of Electric Power, Shanghai, China),
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09:00 - 10:30
Itinerary Recommendation for User Groups in Temporary Social Network

Temporary social networks have been used at hotels concerts theme parks and sports arenas where people with a common interest or activity form a social group for a short time People conned to such specic places or activities are allowed to join the temporary social networks using their main social network accounts eg Foursquare Facebook Users registered for the same businessresearch conferencemay have common connections and thus may be willing to travel together in the conference city Currently renting cars to travel has become very common since it helps improve users experience as well as save travel costs eg renting and oil costs Thus we propose a groupwise itinerary planning framework to minimize the travel costs for each user in temporary social network Experimental results obtained by using real data sets illustrate the eectiveness of our proposed framework
Authors: Jing Xia (Hangzhou Dianzi University), Yu Li (Hangzhou Dianzi Universtiy), Yuyu Yin (Hangzhou Dianzi University),
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09:30 - 10:30
Extracting Topics from Semistructured Data for Enhancing Enterprise Knowledge Graphs

Unifying information across the organizational data silos that lack documentation structure and automated semantic discovery has been of an intense interest in the recent years Enterprise knowledge graph is a common tool of data integration and knowledge discovery and it has become a backbone to APIs that demand access to structured knowledge A piece which was previously unnoticed in building enterprise knowledge graph is adding an abstract layer of themes and concepts which is mapped to various documents stored as semistructured files in databases Augmenting enterprise knowledge graphs by concepts will help companies to find the trends in their data and get a holistic view over their entire data stores Extracting topics from semistructured data suffers from lack of corpus or description as its major challenge In this research we investigate the impact of selfsupplementation of words and documents on probabilistic topic modeling upon semistructured data Another contribution of this paper is finding the best tuning of probabilistic topic modeling that fits semistructured data We consider 2 inferencing techniques and demonstrate the results on real data pools from Open City data and Kaggle data containing 75 GB and 115 GB of data stored in MongoDB collections respectively We also propose a selection heuristic for effective identification of topics hidden in various data sources
Authors: Neda Abolhassani (University of Georgia), Lakshmish Ramaswamy (University of Georgia),
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Coffee break (Nelson Haden Lecture Theatre, Faraday Wing Building) 10:30 - 11:00

Lunch (Nelson Haden Lecture Theatre, Faraday Wing Building) 12:00 - 13:00

SESSION 2B - Artificial Intelligence (FW-114., Faraday Wing Building) 13:00 - 15:00

Session Chair: Yu Weng
13:00 - 15:00
A next location predicting approach based on a recurrent neural network and selfattention

On most locationbased social applications today users are strongly encouraged to share activities by checkingin In this way vast amounts of usergenerated data can be accumulated which include spatial and temporal information Much research has been conducted on these data which enables heightening the understanding of human mobility Therefore the next location problem has attracted significant attention and has been extensively studied In this paper we propose a next location prediction approach based on a recurrent neural network and selfattention mechanism Our model can explore sequence regularity and extract temporal feature according to historical trajectories information We conduct our experiments on the locationbased social network LBSN dataset and the results indicate the effectiveness of our model when compared with the other three frequentlyused methods
Authors: Jun Zeng (School of Big Data & Software Engineering, Chongqing University, Chongqing, China), Xin He (School of Big Data & Software Engineering, Chongqing University, Chongqing, China), Ran Tang (School of Big Data & Software Engineering, Chongqing University, Chongqing, China), Hao Wen (School of Big Data & Software Engineering, Chongqing University, Chongqing, China),
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13:00 - 15:00
A Food Dish Image Generation Framework Based on Progressive Growing GANs

The generative adversarial networks GANs have demonstrated the ability to synthesize realistic images However there are few researches applying GANs into the field of food image synthesis In this paper we propose an extension to GANs for generating more realistic food dish images with rich detail which adds a food condition that contains taste and other information That makes the model generate images with rich details To improve the quality of the generated image the taste information condition is added to each stage of the generator and discriminator First the model learns embedding conditions of food information including ingredients cooking methods tastes and cuisines Secondly the training model grows progressively and the model learns details increasingly during the training process which allows the model to generate images with rich details To demonstrate the effectiveness of our proposed model we collect a dataset called Food121 which includes the names of the food ingredients cooking methods tastes and cuisines The results of experiment show that our model can produce complex details of food dish image and obtain high inception score on the Food121 dataset compared with other models
Authors: Su Wang (Shanghai University), Honghao Gao (Shanghai University), Yonghua Zhu (Shanghai University), Weilin Zhang (Shanghai University), Yihai Chen (Shanghai University),
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13:00 - 15:00
Integration of Machine Learning Techniques as Auxiliary Diagnosis of Inherited Metabolic Disorders Promising Experience with Newborn Screening Data

Tandem mass spectrometry is an advanced biochemical analysis method and has been widely used in screening of inherited metabolic disorders IMDs Obtained examination results are filtered by cutoff value and then interpreted based on doctor39s knowledge to get diagnoses However cutoffbased approaches have difficulties with the correlations of multiple metabolites Doctor39s experiences affect the diagnostic decisionmaking as well The rapidly increasing availability of newborn screening data 15M cases in this study enables the application of machine learning ML techniques to provide more accurate diagnoses of IMDs compared to simple cutoff values We investigated two tasks in this study ie complicated patterns between metabolites and better auxiliary diagnostic means Experimental results show that novel metabolic patterns found in the study are effective and meaningful Integrating ML techniques with these patterns improved predictive performance compared to existing diagnostic methods suggesting ML techniques are becoming valuable as auxiliary diagnostic tools
Authors: Bo Lin (College of Computer Science and Technology, Zhejiang University), Jianwei Yin (College of Computer Science and Technology, Zhejiang University), Qiang Shu (The Children's Hospital, Zhejiang University School of Medicine), Shuiguang Deng (College of Computer Science and Technology, Zhejiang University), Ying Li (College of Computer Science and Technology, Zhejiang University), Pingping Jiang (The Children's Hospital, Zhejiang University School of Medicine), Rulai Yang (The Children's Hospital, Zhejiang University School of Medicine), Calton Pu (Department of Electrical and Computer Engineering, Georgia Institute of Technology),
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13:00 - 15:00
Nemesis Detecting Algorithmically Generated Domains with An LSTM Language Model

Various malware families frequently apply Domain Generation Algorithms DGAs to generate numerous pseudorandom domain names to communicate with their Command and Control CC servers Security researchers make a lot of efforts to detect Algorithmically Generated Domains AGDs for fighting Botnets and relevant malicious network behaviors In this paper we propose a new AGD detection approach Nemesis based on a Long ShortTerm Memory LSTM language model Nemesis can identify whether given domain names are AGDs according to their string compositions and without additional information Nemesis first leverages an ngram dictionary which is built on real domain names to tokenize domain names into ngrams Then a pretrained detector is used to classify domain names as real ones or AGDs according to the tokenized results We evaluate Nemesis abilities to detect domain names generated by known DGAs and to discover new DGA families It turns out that Nemesis can accurately detect AGDs with the precision of 986 and the recall of 967 Besides we verify that Nemesis largely outperforms several existing effective approaches
Authors: Dunsheng Yuan (Institute of Information Engineering, Chinese Academy of Sciences), Ying Xiong (National Computer Network Emergency Response Technical Team/Coordination Center of China), Tianning Zang (Institute of Information Engineering, Chinese Academy of Sciences), Ji Huang (Institute of Information Engineering, Chinese Academy of Sciences),
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13:00 - 15:00
PositiveUnlabeled Learning for Sentiment Analysis with Adversarial Training

Sentiment classification is a critical task in sentiment analysis and other text mining applications As a subproblem of sentiment classification positive and unlabeled learning or positiveunlabeled learning PU learning problem widely exists in realworld cases but it has not been given enough attention In this paper we aim to solve PU learning problem under the framework of adversarial training and neural network We propose a novel model for PU learning problem which is based on adversarial training and attentionbased long shortterm memory LSTM network In our model we design a new adversarial training technique We conducted extensive experiments on two realworld datasets The experimental results demonstrate that our proposed model outperforms the compared methods including the wellknown traditional methods and stateoftheart methods We also report the training time and discuss the sensitivity of our model to parameters
Authors: Yueshen Xu (Xidian University), Lei Li (Xidian University), Jianbin Huang (Xidian University), Yuyu Yin (Hangzhou Dianzi University), Wei Shao (RMIT University), Zhida Mai (Xanten Guangdong Development Co., Ltd), Lei Hei (Xidian University),
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13:00 - 15:00
CNASV A Convolutional Neural Architecture SearchTrain Prototype for Computer Vision Task

Neural Architecture Search NAS has become more and more prevalent in the field of deep learning in the past two years Existing work often focus on image classification and few works recently extend NAS to another computer vision task such as semantic image segmentation The semantic image segmentation is essentially a dense prediction for each pixel on whole image Therefore we choose the same basic primitive operations to build the search space for the two computer vision task respectively Searching good neural network architectures and then training them from scratch is a regular procedure for NAS In this paper we design a prototype system that deploy search module and train module to collaborate with each other Follow the former work we initialize overparameterized cells architecture and then transform to the continuous relaxation of the architecture to derive the good subnetwork by gradient descent Our system can support any differential search algorithm such as oneshot DARTS or ProxylessNAS We illustrate the effectiveness of our chosen primitive operations in the image classification and ability to transfer these operations to build search space for semantic image segmentation
Authors: Tianbao Zhou (College of Information Engineering, Minzu University of China), Yu Weng (College of Information Engineering, Minzu University of China), Guosheng Yang (College of Information Engineering, Minzu University of China),
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13:00 - 15:00
Web Services Classification with topical attention based BiLSTM

With the rapid growth of the number of Web services on the Internet how to classify web services correctly and efficiently become more and more important in the development and application of Web services Existing functionbased service clustering techniques have some problems such as the sparse document semantics unconsidered word order and the context information so the accuracy of service classification needs to be further improved To address this problem this paper exploits the attention mechanism to combine the local implicit state vector of BiLSTM and the global LDA topic vector and proposes a method of Web services classification with topical attention based BiLSTM Specifically it uses BiLSTM to automatically learn the feature representation of Web service Then it utilizes the offline training to obtain the topic vector of Web service document and performs the topic attention strengthening processing for Web service feature representation and obtains the importance or weight of the different words in Web service document Finally the enhanced Web service feature representation is used as the input of the softmax neural network layer to perform the classification prediction of Web service The experimental results validate the efficiency and effectiveness of the proposed method
Authors: Yingcheng Cao (Hunan University of Science and Technology), Jianxun Liu (Hunan Univeristy of Science and Technology), Buqing Cao (Hunan Univeristy of Science and Technology), Min Shi (Hunan Univeristy of Science and Technology), Yiping Wen (Hunan Univeristy of Science and Technology), Zhenlian Peng (Hunan Univeristy of Science and Technology),
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13:00 - 15:00
Relation Extraction toward Patent Domain based on Keyword Strategy and AttentionBi_LSTM Model

Patent terminology relation extraction is of great significance to the construction of patent Knowledge graph In order to solve the problem of longdistance dependency in traditional depth learning a new method of patent terminology relation extraction is proposed which combines attention mechanism and bidirectional LSTM model and with keyword strategy Category keyword features in each sentence obtained by the improved TextRank with the patent text information vectorization added BiLSTM neural work and attention mechanism are employed to extract the temporal information and sentencelevel global feature information Moreover pooling layer is added to obtain the local features of the text Finally we fuse the global features and local features and output the final classification results through the softmax classifier The addition of category keywords improves the distinction of categories Substantial experimental results demonstrate that the proposed model outperform the stateofart neural model in patent terminology relation extraction
Authors: Xueqiang Lv (Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University), Xiangru Lv (Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University), Xindong You (Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University), Zhian Dong (Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University),
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Coffee break (Nelson Haden Lecture Theatre, Faraday Wing Building) 15:00 - 15:30

SESSION 3B - Software Development (FW-114., Faraday Wing Building) 15:30 - 16:30

Session Chair: Kuang Li
15:30 - 16:30
Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network Two Case Studies

Bug triaging is an essential activity of defect repair which is closely related to the cost of software maintenance Researchers have proposed automatic bug triaging approaches to recommend bug fixers more efficiently and accurately In addition to text features most of the previous studies focused on singlelayer bug tossing or reassignment graphs but they ignored the multiplex or multilayer network characteristics of human cooperative behavior In this study we build a collaborative multiplex network composed of a tossing graph and an email communication graph in the bug triaging process By integrating the idea of network embedding and multiplex network measures we propose a new strategy of random walks Moreover we present a bug fixer prediction model that takes structure and text features as inputs Experimental results on two largescale opensource projects show that the proposed method outperforms the selected baseline approaches in terms of commonlyused evaluation metrics
Authors: Yutao Ma (School of Computer Science, Wuhan University), Jinxiao Huang (School of Computer Science, Wuhan University),
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15:30 - 16:30
Maintainable Software Solution Development using Collaboration between Architecture and Requirements in Heterogeneous IoT Paradigm

Internet of Things IoT has been tremendously involved in the development of smart infrastructure Software solutions in IoT have to consider lack of abstractions heterogeneity multiple stakeholders scalability and interoperability among the devices The developers need to implement application logic on multiple hardware platforms to satisfy the fundamental business goals Moreover longterm maintenance issues due to the frequent introduction of new requirements and hardware platforms pose a vital challenge in IoT solution development Numerous techniques have been devised to satisfy the issues mentioned above for ubiquitous and smart infrastructure However these techniques lack in providing a comprehensive approach in dealing with the above challenges In this paper we argue that fundamentally there is no difference between the architecturally significant requirements and the architectural design decisions in IoT solution development The architecture revolves around the requirements gathered by the analyst at the requirements gathering phase We stress that the requirements elicitation process must consider the software architectural assessment for maintainable software development By adopting this perspective we identify areas where both requirements and architecture communities collaborate to effectively increase the user acceptability maintainability and fulfill the heterogeneous needs of IoT solutions
Authors: Wajid Rafique (Nanjing University), Wanchun Dou (Nanjing University), Maqbool Khan (Nanjing University),
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15:30 - 16:30
Typebased Modelling and Collaborative Programming for ControlOriented Systems

Domainspecific languages are more expressive and therefore tackle complexity better making software development easier and more efficient They raise the level of abstraction and together with domainspecific generators can automate the creation of quality code This paper proposes a typebase approach to requirement modelling called CosRDL to design high quality realtime embedded systems A set of rules and formal methods are defined to build CosRDL models for embedded systems from which the model may be verified apart the specification CosRDL can specify the features of eventdriven behaviors that support communication between active objects processes to support concurrency and collaborative computing The control processing and properties can be described by CosRDL syntax as an model extensionand to make system implementation model Meanwhile a case study is presented to illustrate our approach to requirement modelling of control systems
Authors: weidong ma (Institute of Electronic Engineering, China Academy of Engineering Physics), Zhaohui Luo (Royal Holloway, University of London, UK),
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15:30 - 16:30
Predicting Traffic Flow Based on EncoderDecoder Framework

Predicting traffic flow is of great importance to traffic management and public safety and it has high requirements on accuracy and efficiency However the problem is very challenging because of highdimensional features spatial levels and sequence dependencies On the one hand we propose an effective endtoend model called FedNet to predict traffic flow of each region in a city First for the temporal trend period closeness properties we obtain lowdimensional features by downsampling highdimensional input features Then we perform temporal fusion to get temporal aggregations of different spatial levels Next we generate traffic flow by upsampling the fused features which are obtained by combining the corresponding temporal aggregation and the output of the previous upsample block Finally the traffic flow is adjusted by external factors like weather and date On the other hand we transfer the original task into a sequence task and then use teacher forcing to train our model which make it learn the sequence dependencies We conduct extensive experiments on two types of traffic flow newflowendflow and inflowoutflow in New York City and Beijing to demonstrate that the FedNet outperforms five wellknown methods
Authors: Xiaosen Zheng (Central South University), Zikun Yang (Central South University), Liwen Liu (Central South University), Li Kuang (Central South University),
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SESSION 4B - Collaborative Applications for Recognition and Classification (FW-114., Faraday Wing Building) 16:30 - 17:30

Session Chair: Bin Cao
16:30 - 17:30
GeoCET Accurate IP Geolocation via Constraintbased Elliptical Trajectories

The geographical location of the IP device is crucial for many network security applications such as locationaware authentication fraud prevention and securitysensitive forensics Since most data miningbased methods are subject to the privacy protection policies the delaybased measurement methods have broader application prospects However these methodologies are relying on heavyweight traffic on networks and high deployment costs Besides the worst case errors in estimation made by delaybased measurement methods render them ineffective In this paper we propose an accurate IP geolocation approach called GeoCET This methodology only requires a small number of oneway delays OWDs to locate the targets combining with elliptical trajectory constraints and maximum loglikelihood estimation technique We introduce polynomial regression to fit the delaydistance model and enhance the accuracy of the localization To evaluate GeoCET we leverage realworld data which come from China India Western United States and Central Europe Experimental results demonstrate that GeoCET performs better for all existing measurementbased IP geolocation methodologies
Authors: Fei Du (School of Cyber Security, University of Chinese Academy of Sciences), Xiuguo Bao (National Internet Emergency Center, CNCERT/CC), Yongzheng Zhang (Institute of Information Engineering, Chinese Academy of Sciences), Huanhuan Yang (National Internet Emergency Center, CNCERT/CC),
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16:30 - 17:30
Covering Diversification and Fairness for Better Recommendation

Smart applications are appealing an accurate matching between users and items in which recommendation technologies are applied widely Since recommendation serve for two roles namely users and items accuracy is not the only focus the diversification and fairness should also be paid more attention for improving recommendation performance The tradeoff among the accuracy diversification and fairness on recommendation is bringing a big challenge This paper proposed a novelty recommendation model to ensure the recommendation performance which introduces a multivariate linear regression model to cooperate with the collaborative filtering method This study utilizes an improved similarity metrics to discover the closeness between users and item categories under the help of the collaborative filtering methods and exploits the micro attribute information of items by a multivariate linear regression model to decide the final recommended items The experimental results show that our proposed method can provide better recommendation accuracy diversification and fairness than the recommendation based on pure collaborative filtering method
Authors: Qing Yang (Guilin University of Electronic Technology), Li Han (Guilin University of Electronic Technology), Ya Zhou (Guilin University of Electronic Technology), Shaobing Liu (Guilin University of Electronic Technology), Jingwei Zhang (Guilin University of Electronic Technology), Zhongqin Bi (Shanghai University of Electronic Power), Fang Pan (Guangxi Normal University),
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16:30 - 17:30
Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification

With the growing popularity of services which meet the divergent requirements from users service selection and recommendation have drawn significant attention in services computing community Service ranking is the most important part in service selection and recommendation Although there have been several existing approaches of service ranking which is basically ratingbased suffering from the heterogeneity of ranking criteria from users Moreover the efficiency of such comparisonbased approaches is the bottleneck in reality To attack these challenges an efficient pairwise ranking scheme with multidimensional classification is proposed in this paper which also fully considers the context information of service and users Furthermore the scheme is able to mitigate data sparsity of users similarity matrix and improve accuracy Next we introduce a random walk model for ranking formulation and propose a Markov chain based approach to obtain the global ranking Finally the efficacy of our approach is validated by experiments adopting the realworld YELP dataset
Authors: Yingying Yuan (Beijing University of Posts and Telecommunications), Jiwei Huang (China University of Petroleum - Beijing), Yeping Zhu (Chinese Academy of Agricultural Sciences), Yufei Hu (Beijing Boyu Kaixin Machinery Equipment Co., Ltd.),
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16:30 - 17:30
Collaborative Computing of Urban Builtup Area Identification from Remote Sensing Image

Urban builtup area is one of the important criterions of urbanization Remote sensing can quickly acquire dynamic temporal and spatial variation of urban builtup area but how to identify and extract urban builtup area information from massive remote sensing data has become a bottleneck arousing widespread concerns in the field of the data mining and application for remote sensing Based on the traditional urban builtup area identification and data mining of remote sensing this paper proposed a new collaborative computing method for urban builtup area identification from remote sensing image In the method the normalized difference builtup index NDBI and the normalized differential vegetation index NDVI feature images were constructed firstly from the spectrum clustering map and then the urban builtup area was identified and extracted by the mapspectrum synergy and mathematical morphology methods Finally a case of collaborative computing of urban builtup areas in Chongqing city China is presented And the experimental results show that the total accuracy of urban builtup area identification in 1988 and 2007 reached 9258 and 9141 the Kappa coefficient reached 08933 and 08722 respectively and the good results in the temporal and spatial variation monitoring of urban builtup area are achieved
Authors: chengfan li (Shanghai University), lan liu (Shanghai University of Engineering Science), yongmei lei (Shanghai University), xiankun sun (Shanghai University of Engineering Science), junjuan zhao (Shanghai University),
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Day 4 22/08/2019
Room #1

Registration 08:30 - 09:00

(Nelson Haden Lecture Theatre, Faraday Wing Building)

SESSION 5 - Smart Transportation (Nelson Haden Lecture Theatre, Faraday Wing Building)) 09:00 - 10:00

Session Chair: Liu Jing
09:00 - 10:00
Intelligentprediction Model of Safetyrisk for CBTC System by Deep Neural Network

Safetyrisk estimation aims to provide guidance of the train39s safe operation for communicationbased train control system CBTC system which is vital for hazards avoiding In this paper we present a novel intelligentprediction model of safetyrisk for CBTC system to predict which kind of risk state will happen under a certain operation condition This model takes advantages of popular deep learning models which is Deep Belief Networks DBN Some risk prediction factors is selected at first and a critical function factor in CBTC system is generated by statistical model checking Afterwards for each input of samples the model utilizes DBN to extract more condensed features followed by a softmax layer to decouple the features further into different risk state Through experiments on realworld dataset we prove that our new proposed intelligentprediction model outperforms traditional methods and demonstrate the effectiveness of the model in the safetyrisk estimation for CBTC system
Authors: Yan Zhang (Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China), Jing Liu (Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China), Junfeng Sun (R&D Institute, CASCO Signal Ltd., Shanghai, China), Xiang Chen (R&D Institute, CASCO Signal Ltd., Shanghai, China), Tingliang Zhou (R&D Institute, CASCO Signal Ltd., Shanghai, China),
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09:00 - 10:00
A Platform Service for Passenger Volume Analysis on Massive Smart Card Data in Public Transportation Domain

In current public transportation of modern cities the passenger volume analysis counts the bus passengers in multiple perspectives and it is significant to optimize the bus scheduling and evaluate transportation capacity On the smart card data of passengers taking buses traditional solutions have inherent limitations about long processing delay inaccuracy result and poor scalability In this paper the spatiotemporal correlation with business restrictions is considered and an effective platform service for passenger volumes analyses are proposed on massive smart card Our service has been applied in practical usage for three types of passenger volume and holds minutelevel latencies on weekly data with nearly linear scalability in extensive conditions
Authors: Weilong Ding (North China University of Technology), Zhe Wang (North China University of Technology), Zhuofeng Zhao (North China University of Technology),
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09:00 - 10:00
Reliable Collaborative SemiInfrastructure VehicletoVehicle Communication for Local File Sharing

Recently Vehicular Cloud Communication VCC has been gaining momentum targeting intelligent and efficient data transmission VCC is a type of mobile adhoc network comprising heterogeneous vehicles sharing their resources to perform collaborative activities In this paper we propose a new semiinfrastructure filebrowsing in order to provide Network as a service NaaS enabling internetindependent browsing In our scenario a central management platform plays the role of controlling and managing the selection of relaying vehicles supporting the source to destination file transmission procedure NagelSchreckenberg rules for traffic cellular automata CA are used as the basis for our scenario simulation NagelSchreckenberg rules simulate the behavior of a group of hypothetical vehicles moving across a highway We study the reliability and efficiency of file transfer in such settings Simulation results show that the number of selected relays required to establish the network highly impacts the probability of successfully sending the requested files In addition the distances between the selected relays influence the network throughput and the probability of network failure Moreover the density of relays strongly affects the overall delay that occurs due to the continuous retransmission of the selected files among different hops
Authors: Bassem Mokhtar (Electrical Engineering Department, Alexandria University, Alexandria, Egypt.), Mohamed Azab (Department of Computer and Information Sciences, Virginia Military Institute, USA.), Efat Fathalla (Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA), Esraa M. Ghourab (Electrical Engineering Department, Alexandria University, Alexandria, Egypt.), Mohamed Magdy (College of computing and information technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt), Mohamed Eltoweissy (Department of Computer and Information Sciences, Virginia Military Institute, USA.),
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09:00 - 10:00
An Efficient Mutual Authentication Framework with Conditional Privacy Protection in VANET

Vehicular Ad Hoc Network VANET is a special application of traditional Mobile Ad Hoc Network MANET in traffic roads which has attracted extensive attention due to its important role in intelligent traffic and road services In order to ensure the safety of road traffic and protect the privacy of users it is of vital importance to provide effective anonymous authentication in VANET In this paper we propose an efficient mutual authentication framework with conditional privacy protectionEMAPP which can achieve the security authentication from vehicles to infrastructure and vehicles to vehicles In the proposed framework we are combined with pseudo ID and temporary pseudonym to protect the privacy of vehicles and use the identitybased signature scheme to achieve authentication between vehicles and infrastructure At the same time with the assistance of the roadside unitRSU we utilize an onlineoffline signature scheme to achieve authentication between vehicles in the same RSU area and different RSU area Our scheme has reusability and we have conducted a performance evaluation Without expensive and timeconsuming operations such as bilinear pairing and mapping to pointMTP functions our framework can produce better performance and is appropriate for practical application In addition we also use the Internet Security Protocol and Application Automatic Authentication AVISPA tools to provide formal security analysis
Authors: Ying Wang (Tianjin University), Jing Hu (Tianjin University), Xiaohong Li (Tianjin University), Zhiyong Feng (Tianjin University),
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Coffee break (Nelson Haden Lecture Theatre, Faraday Wing Building) 10:00 - 10:30

SESSION 6 - Workshops SITN, WCCA (Nelson Haden Lecture Theatre, Faraday Wing Building) 10:30 - 12:00

Session Chair: Thomas Tan & Ying Zhang
10:30 - 12:00
Lightweight Computation to Robust Cloud Infrastructure for Future Technologies

Recently lightweight technologies has gain a lot of attentions for future emerging technologies and play a vital role in smart cities As it is providing more efficient and fastest solution than traditional virtual machines and capable to serves in heterogeneous and dynamic environment across multiple domains including IoT Fog computing and multiaccess edge computing In this paper we proposed a lightweight solution for LCC Live Container Cloud that permits the user to access liveremote cloud resources within container LCC can be embed as a fog edge node to permit the users to access allocate and deallocate cloud resources Moreover the effectiveness of container technology is presented in terms of its performance
Authors: Muddesar Iqbal (London South Bank University, London, United Kingdom,), Sonia Shahzadi (Swan Mesh Networks Ltd, Research and Development, London, UK.), George Ubakanma (London South Bank University, London, United Kingdom), Tasos Dagiuklas (London South Bank University, London, United Kingdom), Andrei Andrei Tchernykh (CICESE Research Center, Ensenada, Baja California, Mexico),
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10:30 - 12:00
Evaluation of Underlying Switching Mechanism for Future Networks with P4 and SDN

Future networks and applications requires highest possible bandwidth and lowest congestion in order to maximize the customer satisfaction and achieve requirements for heterogeneous applications With this motivation in mind Software Defined Networking SDN was introduced which was first released targeting data centers and local area networks At present SDN covers all aspects of the network covering multiple scenarios and use cases Programming Protocol independent Packet Processing P4 is a high level language capable of forwarding packets via a programmable parser followed by multiple stages of match action which is considered the most effective mechanism for routing in IP networks This paper takes into the account the latest platforms developed for service providers Open Networking Operating System ONOS to deploy two environments configured in the aforementioned technologies in order to test their performance Two case studies were drawn with four experiments simulating in Mininet for each to further understand and evaluate the performance of aforementioned technologies for switching By incorporating SDN P4 environment a significant reduction of 92 and 70 was achieved in destination host discovery and UDP packet transmission delay respectively E2E was reduced by 7 by SDN P4 case study compared to the SDN only environment
Authors: Omesh Fernando (University of Hertfordshire), Hannan Xiao (University of Hertfordshire), Xianhui Che (University of Hertfordshire),
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10:30 - 12:00
A Novel Featureselection Approach Based on Particle Swarm Optimization Algorithm for Intrusion Detection Systems

Aiming at the KDDCUP99 data set studying the selection of intrusion detection features An intrusion detection feature selection method based on discrete particle swarm optimization PSO is proposed Based on the correlation evaluation method according to the correlation of the particles 14 main feature attributes were selected from all 41 features of the KDDCUP99 data set as intrusion detection features 57141 data were extracted from the 10 KDDCUP99 data set to construct the experimental sample space and the classification model was built using the classic classifier built into the Weka platform of the intelligent analysis environment The tenfold crossvalidation method was used for training and verification The results show that the proposed feature selection algorithm is applicable to a variety of classifiers which not only improves the correct classification ratio but also shortens the model time of the classifier
Authors: Jianzhen Wang (Business College of Shanxi University), Yan Jin (Business College of Shanxi University),
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10:30 - 12:00
Multiuser Detection using Hybrid ARQ with Incremental Redundancy in Overloaded MIMO Systems

Multiple Input and Multiple Output MIMO systemsuse multiple antennas at both transmitter and receiver endfor increasing link capacity and spectral efficiency Howevercombining schemes used for such systems face critical issues such as presence of interference existence of multiple signals to interference and noise power ratios SINRs and complexityTo overcome the asserted issues in this paper linear multiuser detection techniques are employed in over loaded MIMO systems where the number of transmit antennas Nt is greater than number of receiver antennas Nr using Hybrid Automatic Repeat request with Incremental Redundancy HARQIR The primary aim of this research is to enhance bit error rate BER and throughput by transforming an overloaded MIMO systems Nt Nr into critically loaded system Nt Nr or under loaded MIMO systems Nt
Authors: Muhammad Kashif (Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering (CASE) Islamabad, Pakistan), Zakir Ullah (Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering (CASE) Islamabad, Pakistan), Muddesar Iqbal (London South Bank University, London, United Kingdom,), Leila Musavian (School of Computer Science and Electronic Engineering University of Essex UK), Xinheng Wang (School of Computing and Engineering University of West London UK), Shahid Mumtaz (Instituto de Telecomunicaes Portugal), Sohail Sarwar (National University of Sciences and Technology (NUST) Rawalpindi, Pakistan), Zia ul Qayyum (Department of Computer Science Allama Iqbal Open University Islamabad, Pakistan), M Safyan (Department of Computer Science GC University Lahore Lahore, Pakistan),
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10:30 - 12:00
Quantum Based Networks Analysis of Quantum Teleportation Protocol and Entanglement Swapping

In this paper we consider the quantum teleportation and entanglement swapping protocols used in quantum based networks for passing information between a sender and receiver For the teleportation protocol we observe and identify relationships that exist between EinsteinPodolskyRosen EPR Bell states employed as quantum resources measured sender values and the gates employed at the receiver side For the entanglement swapping protocol we consider input and output EPR states and the relationship between the two We include a review of the concepts and our findings from the analysis carried out
Authors: Preeti Kandwal (University of Hertfordshire, Hatfield, United Kingdom), William Spring (University of Hertfordshire, Hatfield, United Kingdom), Hannan Xiao (University of Hertfordshire, Hatfield, United Kingdom),
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10:30 - 12:00
A Decentralized and Anonymous Data Transaction Scheme Based on Blockchain and Zeroknowledge Proof in Vehicle Networking

Data transaction in internet of vehicles is a transaction occurs between vehicle owner and data buyer Blockchain is a new technology that brings decentralized ledger system for user which means users could make payment without the third party There are several projects combined internet of vehicles and Blockchain however none of them realize a trustworthy anonymous data transaction In this paper we first propose the concept of Super Nodes to guarantee data authenticity then we construct the anonymity for the transaction base on zeroknowledge Succinct Noninteractive Argument of knowledge zkSNARKs and DAP from Zerocash Moreover a smart contract is deployed for mutual benefits Simulation experiment shows this scheme is practical
Authors: Wei Ou (Hunan University of Science and Engineering, Yongzhou, China),
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Lunch & Closing Ceremony (Nelson Haden Lecture Theatre, Faraday Wing Building) 12:00 - 13:00