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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Associative Classification for Human Activity Inference on Smart Phones
(2014-01-01)
Peng, Yu-Hsiang
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Njoo, Gunarto Sindoro
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Li, Shou-Chun
;
Peng, Wen-Chih
;
資訊工程學系
With the population of smart phones, the general trend of human activity inference is prospering under a powerful computation capabilities on modern phones. Such an assistant make users life more convenient and help them prevent from unnecessary interferences. In conventional research, the activity inference problem is considered a classification instance, so in this paper we propose an association-based classifier framework (ACF) that aims at exploring the correlation among collected sensor data. Each data consists of multiple sensor readings with a label, e.g., dining, shopping, working, driving, sporting, and entertaining. Note that ACF caters to the discrete data; as a consequence, the continuous sensor readings are needed to be transformed to some discrete groups. Therefore, we propose an Interval Length-Gini Discretization (LGD) method which considers the groups and misclassified cases to obtain the best hypothesis for a given set of data. After an appropriate discretization, we propose one-cut and memory-iteration-based approach to select a set of useful sensor-value pairs for reducing the model size by removing redundant features and guaranteeing an acceptable accuracy. In the experiments our framework has a good performance on real data set collected from 50 participants in eight months, and a smaller size than the existing classifications.
Indexing spatial data in cloud data managements
(2014-12-01)
Wei, Ling-Yin
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Hsu, Ya-Ting
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Peng, Wen-Chih
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Lee, Wang-Chien
;
資訊工程學系
With the proliferation of smart phones and location-based services, the amount of data with spatial information, referred to as spatial data, is dramatically increasing. Cloud computing plays an important role handling large-scale data analysis, and several cloud data managements (CDMs) have been developed for processing data. CDMs usually provide key-value storage, where each key is used to access its corresponding value. However, user-generated spatial data are usually distributed non-uniformly. In this paper, we present a novel key design based on an R+-tree (KR+-index) for retrieving skewed spatial data efficiently. In the experiments, we implement the KR+-index on Cassandra, and study its performance using spatial data. Experiments show that the KR+-index outperforms the-state-of-the-art methods. (C) 2013 Elsevier B.V. All rights reserved.
Energy-Balanced Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Network
(2010-12-01)
Wang, You-Chiun
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Peng, Wen-Chih
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
We consider a hybrid wireless sensor network with static and mobile nodes. Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. A big challenge is how to schedule these mobile sensors' traveling paths in an energy-balanced way so that their overall lifetime is maximized. We formulate this problem as a multiround sensor dispatch problem and show it to be NP-complete. Then, we propose a centralized and a distributed heuristics to schedule mobile sensors' traveling paths. Our heuristics allow arbitrary numbers of mobile sensors and event locations in each round and have an energy-balanced concept in mind. The centralized heuristic tries to minimize mobile sensors' moving energy while keeping their energy consumption balanced. The distributed heuristic utilizes a grid structure for event locations to bid for mobile sensors. Through simulations, we show the effectiveness of our schemes. This paper contributes in defining a more general multiround sensor dispatch problem and proposing energy-efficient solutions to it.
A regression-based approach for mining user movement patterns from random sample data
(2011-01-01)
Hung, Chih-Chieh
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users. (C) 2010 Elsevier B.V. All rights reserved.
CIM: Community-Based Influence Maximization in Social Networks
(2014-04-01)
Chen, Yi-Cheng
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Zhu, Wen-Yuan
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Peng, Wen-Chih
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Lee, Wang-Chien
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Lee, Suh-Yin
Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
A Temporal Probabilistic Model for Dynamic Circle Recommendation in Mobile Applications
(2013)
Chou, Fan-Kai
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Chiang, Meng-Fen
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
This paper presents a novel framework for dynamic circle recommendation for a query user at a given time point from historical communication logs. We identify the fundamental factors that govern interactions and aim to automatically form friend circles for scenarios, such as, who should I share the photo with in the early morning? Whose post should be listed on top of my Facebook Wall feed at night? We develop a temporal probabilistic model that not only captures temporal tendencies between the query user and each friend candidate but also blends frequency and recency into circle formation. Experimental results on Enron dataset and Call Detail Records prove the effectiveness of dynamic circle formation with proposed temporal probabilistic model.
Distant-Time Location Prediction in Low-Sampling-Rate Trajectories
(2013)
Chiang, Meng-Fen
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Zhu, Wen-Yuan
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Peng, Wen-Chih
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Yu, Philip S.
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資訊工程學系
With the growth of location-based services and social services, low-sampling-rate trajectories from check-in data or photos with geo-tag information becomes ubiquitous. In general, most detailed moving information in low-sampling-rate trajectories are lost. Prior works have elaborated on distant-time location prediction in high-sampling-rate trajectories. However, existing prediction models are pattern-based and thus not applicable due to the sparsity of data points in low-sampling-rate trajectories. For example, it becomes difficult to derive trajectory patterns, let alone utilizing trajectory patterns for distant-time location prediction. In this paper, given a query time, the current location and time, we aim to predict the location of an object at the query time. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (abbreviated as RTMG) to predict locations for distant-time queries. Specifically, we design an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. These data points are then represented as a Time-constrained user mobility Graph (refers to as TG). In light of TG, we further derive the reachability probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possible locations in supporting trajectories is considered as the prediction result. To efficiently process queries, we proposed an index structure SOIT to organize location records for on-line query processing. We conduct extensive experiments on real low-sampling-rate datasets and demonstrate the effectiveness and efficiency of RTMG.
On the Feature Discovery for App Usage Prediction in Smartphones
(2013)
Liao, Zhung-Xun
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Li, Shou-Chung
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Peng, Wen-Chih
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Yu, Philip S.
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Liu, Te-Chuan
With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. The results based on a real dataset demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.
A Framework of Traveling Companion Discovery on Trajectory Data Streams
(2013-12-01)
Tang, Lu-An
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Zheng, Yu
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Yuan, Jing
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Han, Jiawei
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Leung, Alice
The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.
Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communication?
(2014-02-10)
Xiong, Wei
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Hu, Hanping
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Xiong, Naixue
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Yang, Laurence T.
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Peng, Wen-Chih
Cloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been presented, which analyze dynamic characteristics of the network traffic based on the synergetic neural networks and the catastrophe theory. In the former approach, a synergetic dynamic equation with a group of the order parameters is used to describe the complex behaviors of the network traffic system in cloud communications. When this equation is evolved, only the order parameter determined by the primary factors can converge to 1. Then, the anomaly can be detected. In the latter approach; a catastrophe potential function is introduced to describe the catastrophe dynamic process of the network traffic in cloud communications. When anomalies occur, the state of the network traffic will deviate from the normal one. To assess the deviation, an index named as catastrophe distance is defined. The network traffic anomaly can be detected by the value of this index. We evaluate the performance of these two approaches using the standard Defense Advanced Research Projects Agency data sets. Experimental results show that our approaches can effectively detect the network traffic anomaly and achieve the high detection probability and the low false alarms rate. (C) 2013 Published by Elsevier Inc.

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