2017 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第8页

MetroTime: Travel Time Decomposition under Stochastic Time Table for Metro Networks 地铁时间:随机时刻表下地铁网络出行时间分解

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947021

Haengju Lee, Desheng Zhang, Tian He, S. Son

{"title":"MetroTime: Travel Time Decomposition under Stochastic Time Table for Metro Networks","authors":"Haengju Lee, Desheng Zhang, Tian He, S. Son","doi":"10.1109/SMARTCOMP.2017.7947021","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947021","url":null,"abstract":"One of essential components of public transport systems is to provide travel time estimates for a better travel experience. Based on these estimates, travelers can plan their departure time to meet their target time of arrival. Most of existing work has been focused on estimation on passenger riding time, which is relatively stable. However, a significant portion of time for a subway trip is spent on unstable walking and waiting. As a result, the work solely based on riding times underestimates the actual travel times. To fill the gap, we analyze travel data from automated ticketing systems, which are collected from a large group of passengers in a cost-effective way. We estimate each component (i.e., walking, waiting, and riding) of the travel time using tap-in and tap-out records of these passengers, by a novel travel time decomposition. We evaluate the performance of our travel time decomposition method based on large-scale real-world smart card data from more than 2 million users from Chinese city Shenzhen with 15 million smart card records. The results show that our estimation has an average estimation error of 8% on average and outperforms a baseline approach by 38%. Based on our travel time estimates, we further propose a practical application: digital advertising based on up-to-date travel demand.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130543456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Differentially Private Semi-Supervised Classification 差分私有半监督分类

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947001

Xu Long, Jun Sakuma

引用次数: 2

Is This Side Up? Detecting Upside-Down Exception with Passive RFID 这是面朝上吗?用无源RFID检测倒立异常

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947018

Jia Liu, Haipeng Dai, Yingli Yan, Xiaocong Zhang, Xingyu Chen, Lijun Chen

引用次数: 3

Multi-Client Searchable Encryption over Distributed Key-Value Stores 分布式键值存储上的多客户端可搜索加密

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947024

Wanyu Lin, Xu Yuan, Baochun Li, Cong Wang

{"title":"Multi-Client Searchable Encryption over Distributed Key-Value Stores","authors":"Wanyu Lin, Xu Yuan, Baochun Li, Cong Wang","doi":"10.1109/SMARTCOMP.2017.7947024","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947024","url":null,"abstract":"Distributed key-value stores are rapidly evolving to serve the needs of high-performance web services and large-scale cloud computing applications. It is desirable to search directly over an encrypted key value (KV) store, as data is increasingly stored in the cloud. Encrypted, distributed and searchable key- value stores have been the focus of research, where a data owner outsources his key-value store to a remote server in the cloud in the encrypted form, yet still keeping it searchable. In this paper, we explore the encrypted KV store with the secure multi-client query support. In particular, the data owner can authorize multiple trustable clients (third parties) and allow them to search its encrypted database over KV store. The design goal is to ensure the data confidentiality and query privacy. From the data owner's perspective, the authorized query should not leak too much information thus causing threats to its private database. From clients' perspective, they have the explicit requirement that the query values should not be exposed to the data owner. We design two encryption schemes and token generation methods to satisfy different requirements. To validate the effciency of our protocols, we implement the system prototype to evaluate their performance.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133427753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Context-Aware Wireless Sensors for IoT-Centeric Energy-Efficient Campuses 面向物联网节能校园的环境感知无线传感器

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7946995

Naji Najem, D. Benhaddou, M. Abid, H. Darhmaoui, N. Krami, O. Zytoune

引用次数: 16

Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles 固定行程运输车辆正点率优化的无监督机制

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947057

Fangzhou Sun, Chinmaya Samal, Jules White, A. Dubey

{"title":"Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles","authors":"Fangzhou Sun, Chinmaya Samal, Jules White, A. Dubey","doi":"10.1109/SMARTCOMP.2017.7947057","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947057","url":null,"abstract":"The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at timepoints that improve the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131210621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Service Compositions in Challenged Mobile Environments under Spatiotemporal Constraints 时空约束下挑战移动环境下的服务组合

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947044

D. Kasamatsu, Mohan J. Kumar, Peizhao Hu

引用次数: 4

DeepDefense: Identifying DDoS Attack via Deep Learning 深度防御:通过深度学习识别DDoS攻击

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7946998

Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li

{"title":"DeepDefense: Identifying DDoS Attack via Deep Learning","authors":"Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li","doi":"10.1109/SMARTCOMP.2017.7946998","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7946998","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 225

Analyzing Social Media Texts and Images to Assess the Impact of Flash Floods in Cities 分析社交媒体文本和图像以评估城市山洪暴发的影响

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7946987

Bipendra Basnyat, A. Anam, Neha Singh, A. Gangopadhyay, Nirmalya Roy

{"title":"Analyzing Social Media Texts and Images to Assess the Impact of Flash Floods in Cities","authors":"Bipendra Basnyat, A. Anam, Neha Singh, A. Gangopadhyay, Nirmalya Roy","doi":"10.1109/SMARTCOMP.2017.7946987","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7946987","url":null,"abstract":"Computer Vision and Image Processing are emerging research paradigms. The increasing popularity of social media, micro- blogging services and ubiquitous availability of high-resolution smartphone cameras with pervasive connectivity are propelling our digital footprints and cyber activities. Such online human footprints related with an event-of-interest, if mined appropriately, can provide meaningful information to analyze the current course and pre- and post- impact leading to the organizational planning of various real-time smart city applications. In this paper, we investigate the narrative (texts) and visual (images) components of Twitter feeds to improve the results of queries by exploiting the deep contexts of each data modality. We employ Latent Semantic Analysis (LSA)-based techniques to analyze the texts and Discrete Cosine Transformation (DCT) to analyze the images which help establish the cross-correlations between the textual and image dimensions of a query. While each of the data dimensions helps improve the results of a specific query on its own, the contributions from the dual modalities can potentially provide insights that are greater than what can be obtained from the individual modalities. We validate our proposed approach using real Twitter feeds from a recent devastating flash flood in Ellicott City near the University of Maryland campus. Our results show that the images and texts can be classified with 67% and 94% accuracies respectively","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"56 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127921583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

FishBuddy: Promoting Student Engagement in Self-Paced Learning through Wearable Sensing FishBuddy:通过可穿戴传感技术促进学生自主学习

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-05-01 DOI: 10.1109/SMARTCOMP.2017.7947008

Jingjing Chen, Bin Zhu, Olle Bälter, Jianliang Xu, Weiwen Zou, A. Hedman, Rongchao Chen, Mengdie Sang

{"title":"FishBuddy: Promoting Student Engagement in Self-Paced Learning through Wearable Sensing","authors":"Jingjing Chen, Bin Zhu, Olle Bälter, Jianliang Xu, Weiwen Zou, A. Hedman, Rongchao Chen, Mengdie Sang","doi":"10.1109/SMARTCOMP.2017.7947008","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947008","url":null,"abstract":"Student engagement is crucial for successful self-paced learning. Feeling isolated during self-paced learning with neither adequate supervision nor intervention by teachers may cause negative emotions such as anxiety. Such emotions may in turn significantly weaken students' motivation to engage in learning activities. In this paper, we develop a self-pacedlearning environment (FishBuddy) that aims to reduce anxiety and promote student engagement. We construct and implement a physiologically-state-aware performance-evaluation model for identifying potentially fruitful moments of intervention when students show frustration during learning activities using an Apple Watch application that measures heart rate and alerts the student to watch a visualization of his or her own physiological state. We have conducted an experiment with 20 first-year undergraduate students, randomly separated into an experimental group and a control group, who carry out online, self-paced English grammar exercises. The students in the experimental group used FishBuddy and those in the control group did not. The self-reports from both groups show that FishBuddy significantly reduced reported experiences of anxiety and isolation in the experiment. Further to this, students who used FishBuddy were engaged longer with the exercises. The average scores on the exercises between the two groups, however, were not significantly different.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"316 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114058831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11