Computer Sciences Commons^™

Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

To enhance fleet operation and management, logistics companies instrument their vehicles with GPS receivers and network connectivity to servers. Mobility traces from such large fleets provide significant information on commuter travel patterns, traffic congestion and road anomalies, and hence several researchers have mined such datasets to gain useful urban insights. These logistics companies, however, incur significant cost in deploying and maintaining their vast network of instrumented vehicles. Thus research problems, that are not only of interest to urban planners, but to the logistics companies themselves are important to attract and engage these companies for collaborative data analysis. In this paper, …

Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin

Research Collection School Of Computing and Information Systems

The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state e D that is similar to D but s …

A Link-Bridged Topic Model For Cross-Domain Document Classification, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the …

A Scalable Approach For Malware Detection Through Bounded Feature Space Behavior Modeling, Mahinthan Chandramohan, Hee Beng Kuan Tan, Lionel C Briand, Lwin Khin Shar, Bindu Madhavi Padmanabhuni

Research Collection School Of Computing and Information Systems

In recent years, malware (malicious software) has greatly evolved and has become very sophisticated. The evolution of malware makes it difficult to detect using traditional signature-based malware detectors. Thus, researchers have proposed various behavior-based malware detection techniques to mitigate this problem. However, there are still serious shortcomings, related to scalability and computational complexity, in existing malware behavior modeling techniques. This raises questions about the practical applicability of these techniques. This paper proposes and evaluates a bounded feature space behavior modeling (BOFM) framework for scalable malware detection. BOFM models the interactions between software (which can be malware or benign) and security-critical …

Automatically Partition Software Into Least Privilege Components Using Dynamic Data Dependency Analysis, Yongzheng Wu, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

The principle of least privilege requires that software components should be granted only necessary privileges, so that compromising one component does not lead to compromising others. However, writing privilege separated software is difficult and as a result, a large number of software is monolithic, i.e., it runs as a whole without separation. Manually rewriting monolithic software into privilege separated software requires significant effort and can be error prone. We propose ProgramCutter, a novel approach to automatically partitioning monolithic software using dynamic data dependency analysis. ProgramCutter works by constructing a data dependency graph whose nodes are functions and edges are data …

Social-Loc: Improving Indoor Localization With Social Sensing, Jung-Hyun Jun, Yu Gu, Long Cheng, Banghui Lu, Jun Sun, Ting Zhu, Jianwei Niu

Research Collection School Of Computing and Information Systems

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the …

Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Social network analysis has received much attention from corporations recently. Corporations are trying to utilize social media platforms such as Twitter, Facebook and Sina Weibo to expand their own markets. Our system is an online tool to assist these corporations to 1) find potential customers, and 2) track a list of users by specific events from social networks. We employ both textual and network information, and thus produce a keyword-based relevance score for each user in pre-defined dimensions, which indicates the probability of the adoption of a product. Based on the score and its trend, out tool is able to …

Detecting Stable Communities In Large Scale Networks, Sriram Srinivasan

Student Work

A network is said to exhibit community structure if the nodes of the network can be easily grouped into groups of nodes, such that each group is densely connected internally but sparsely connected with other groups. Most real world networks exhibit community structure.

A popular technique for detecting communities is based on computing the modularity of the network. Modularity reflects how well the vertices in a group are connected as opposed to being randomly connected. We propose a parallel algorithm for detecting modularity in large networks.

However, all modularity based algorithms for detecting community structure are affected by the order …

Search Of Small Objects By Topology Matching, Context Modeling, And Pattern Mining, Wei Zhang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

No abstract provided.

Tzuyu: Learning Stateful Typestates, Hao Xiao, Jun Sun, Yang Liu, Shang-Wei Lin, Chengnian Sun

Research Collection School Of Computing and Information Systems

Behavioral models are useful for various software engineering tasks. They are, however, often missing in practice. Thus, specification mining was proposed to tackle this problem. Existing work either focuses on learning simple behavioral models such as finite-state automata, or relies on techniques (e.g., symbolic execution) to infer finite-state machines equipped with data states, referred to as stateful typestates. The former is often inadequate as finite-state automata lack expressiveness in capturing behaviors of data-rich programs, whereas the latter is often not scalable. In this work, we propose a fully automated approach to learn stateful typestates by extending the classic active learning …

Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle …

Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Advances in web technologies have rendered onlineadvertising as an effective means for small and large businesses to target different market segments on the fly. Online advertising is a huge industry. According to Gartner Inc., worldwide online advertising revenue is projected tohit $11.4 billion in 2013, up from $9.6 billion in 2012. Global revenue will also reach $24.5 billion in 2016, with online advertising creating opportunities for app developers, advertising networks, and service providersin various regions. An online advertising ecosystem is typically coordinated by an advertising commissioner, acting as a broker between advertisers and content publishers. An advertiser plans a budget, …

Electroweak Measurements In Electron-Positron Collisions At W-Boson-Pair Energies At Lep, S. Schael, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

Electroweak measurements performed with data taken at the electron–positron collider LEP at CERN from 1995 to 2000 are reported. The combined data set considered in this report corresponds to a total luminosity of about 3 fb −1 collected by the four LEP experiments ALEPH, DELPHI, L3 and OPAL, at centre-of-mass energies ranging from 130 GeV to 209 GeV. Combining the published results of the four LEP experiments, the measurements include total and differential cross-sections in photon-pair, fermion-pair and four-fermion production, the latter resulting from both double-resonant WW and ZZ production as well as singly resonant production. Total and differential cross-sections …

Access Control In And For The Real World, Sara Sinclair

Dartmouth College Ph.D Dissertations

Access control is a core component of any information-security strategy. Researchers have spent tremendous energy over the past forty years defining abstract access-control models and proving various properties about them. However, surprisingly little attention has been paid to how well these models work in real socio-technical systems (i.e., real human organizations). This dissertation describes the results of two qualitative studies (involving 52 participants from four companies, drawn from the financial, software, and healthcare sectors) and observes that the current practice of access control is dysfunctional at best. It diagnoses the broken assumptions that are at the heart of this dysfunction, …

Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao

Research Collection School Of Computing and Information Systems

Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative …

Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai

Research Collection School Of Computing and Information Systems

This book constitutes the proceedings of the 5th International Conference on Social Informatics, SocInfo 2013, held in Kyoto, Japan, in November 2013. The 23 full papers, 15 short papers, and three poster papers included in this volume were carefully reviewed and selected from 103 submissions. The papers present original research work on studying the interplay between socially-centric platforms and social phenomena.

Assessing The Moderating Effect Of Consumer Product Knowledge And Online Shopping Experience On Using Recommedation Agents For Customer Loyalty, Victoria Y. Yoon, R. Eric Hostler, Zhiling Guo, Tor Guimaraes

Research Collection School Of Computing and Information Systems

Social media technologies have greatly facilitated the creation of many types of user-generated information, e.g., product rating information can be used to generate preference-based recommendation. As a decision support tool, a Recommendation Agent (RA) has been widely adopted by many e-commerce websites. The impact of RAs on online shopping has been extensively examined in the IS literature. However, from Marketing and Social Media perspectives, the widely adopted cognitive–affect–conative–action framework of customer loyalty has not been tested in the presence of RAs. Moreover, there has been little research assessing the impact of increasing consumer knowledge about specific product domains on customer …

Budgeted Personalized Incentive Approaches For Smoothing Congestion In Resource Networks, Pradeep Varakantham, Na Fu, William Yeoh, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Congestion occurs when there is competition for resources by sel sh agents. In this paper, we are concerned with smoothing out congestion in a network of resources by using personalized well-timed in- centives that are subject to budget constraints. To that end, we provide: (i) a mathematical formulation that computes equilibrium for the re- source sharing congestion game with incentives and budget constraints; (ii) an integrated approach that scales to larger problems by exploiting the factored network structure and approximating the attained equilib- rium; (iii) an iterative best response algorithm for solving the uncon- strained version (no budget) of the …

Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas

Research Collection School Of Computing and Information Systems

Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose …

Optimization Approaches For Solving Chance Constrained Stochastic Orienteering Problems, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Orienteering problems (OPs) are typically used to model routing and trip planning problems. OP is a variant of the well known traveling salesman problem where the goal is to compute the highest reward path that includes a subset of nodes and has an overall travel time less than the specified deadline. Stochastic orienteering problems (SOPs) extend OPs to account for uncertain travel times and are significantly harder to solve than deterministic OPs. In this paper, we contribute a scalable mixed integer LP formulation for solving risk aware SOPs, which is a principled approximation of the underlying stochastic optimization problem. Empirically, …

Achieving Revocable Fine-Grained Cryptographic Access Control Over Cloud Data, Yanjiang Yang, Xuhua Ding, Haibing Lu, Zhiguo Wan, Jianying Zhou

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) is well suited for finegrained access control for data residing on a cloud server. However, existing approaches for user revocation are not satisfactory. In this work, we propose a new approach which works by splitting an authorized user’s decryption capability between the cloud and the user herself. User revocation is attained by simply nullifying the decryption ability at the cloud, requiring neither key update nor re-generation of cloud data. We propose a concrete scheme instantiating the approach, which features lightweight computation at the user side. This makes it possible for users to use resource-constrained devices such as …

Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow

Research Collection School Of Computing and Information Systems

Enriching many location-based applications, various new skyline queries are proposed and formulated based on the notion of locational dominance, which extends conventional one by taking objects' nearness to query positions into account additional to objects' nonspatial attributes. To answer a representative class of skyline queries for location-based applications efficiently, this paper presents two index-based approaches, namely, augmented R-tree and dominance diagram. Augmented R-tree extends R-tree by including aggregated nonspatial attributes in index nodes to enable dominance checks during index traversal. Dominance diagram is a solution-based approach, by which each object is associated with a precomputed nondominance scope wherein query points …

Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz

Research Collection School Of Computing and Information Systems

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows …

Automatic Recommendation Of Api Methods From Feature Requests, Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall

Research Collection School Of Computing and Information Systems

Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, …

Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall

Research Collection School Of Computing and Information Systems

No abstract provided.

Got Issues? Who Cares About It? A Large Scale Investigation Of Issue Trackers From Github, Tegawende F. Bissyande, David Lo, Lingxiao Jiang, Laurent Reveillere, Jacques Klein, Yves Le Traon

Research Collection School Of Computing and Information Systems

Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering …

Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng

Research Collection School Of Computing and Information Systems

Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the …

Constraint-Based Automatic Symmetry Detection, Shao Jie Zhang, Jun Sun, Chengnian Sun, Yang Liu, Junwei Ma, Jin Song Dong

Research Collection School Of Computing and Information Systems

We present an automatic approach to detecting symmetry relations for general concurrent models. Despite the success of symmetry reduction in mitigating state explosion problem, one essential step towards its soundness and effectiveness, i.e., how to discover sufficient symmetries with least human efforts, is often either overlooked or oversimplified. In this work, we show how a concurrent model can be viewed as a constraint satisfaction problem (CSP), and present an algorithm capable of detecting symmetries arising from the CSP which induce automorphisms of the model. To the best of our knowledge, our method is the first approach that can automatically detect …

Multimedia Modeling, Chong-Wah Ngo, Klaus Schoeffmann, Yiannis Andreopoulos, Christian Breiteneder

Research Collection School Of Computing and Information Systems

Multimedia modeling aims to study computational models for addressing real-world multimedia problems from various perspectives, including information fusion, perceptual understanding, performance evaluation and social media. The topic becomes increasingly important with the massive amount of data available over the Internet, representing different pieces of information in heterogeneous forms that need to be consolidated before being used for multimedia problems. On the other hand, the advancement in technologies such as mobile and sensing devices drive the needs for revisiting the existing models for not only dealing with audio-visual cues but also incorporating various sensory modalities that have potential in providing cheaper …

Why Do I Retweet It? An Information Propagation Model For Microblogs, Fabio Pezzoni, Jisun An, Andrea Passarella, Jon Crowcroft, Marco Conti

Research Collection School Of Computing and Information Systems

Microblogging platforms are Web 2.0 services that represent a suitable environment for studying how information is propagated in social networks and how users can become influential. In this work we analyse the impact of the network features and of the users' behaviour on the information diffusion. Our analysis highlights a strong relation between the level of visibility of a message in the flow of information seen by a user and the probability that the user further disseminates the message. In addition, we also highlight the existence of other latent factors that impact on the dissemination probability, correlated with the properties …