An Empirical Analysis Of A Network Of Expertise,
2013
Singapore Management University
An Empirical Analysis Of A Network Of Expertise, Le Truc Viet, Minh Thap Nguyen
Research Collection School Of Computing and Information Systems
In this paper, we analyze the network of expertise constructed from the interactions of users on the online questionanswering (QA) community of Stack Overflow. This community was built with the intention of helping users with their programming tasks and, thus, questions are expected to be highly factual. This also indicates that the answers one provides may be highly indicative of one's level of expertise on the subject matter. Therefore, our main concern is how to model and characterize the user's expertise based on the constructed network and its centrality measures. We used the user's reputation established on Stack Overflow as …
Learning To Name Faces: A Multimodal Learning Scheme For Search-Based Face Annotation,
2013
Nanyang Technological University
Learning To Name Faces: A Multimodal Learning Scheme For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, Chunyan Miao
Research Collection School Of Computing and Information Systems
Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, …
Best Upgrade Plans For Large Road Networks,
2013
Singapore Management University
Best Upgrade Plans For Large Road Networks, Yimin Lin, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
In this paper, we consider a new problem in the context of road network databases, named Resource Constrained Best Upgrade Plan computation (BUP, for short). Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. Given a source and a destination in G, and a budget (resource constraint) B, the BUP problem is to identify which upgradable edges should be upgraded so that the shortest path distance between source and …
On Fuzzy Soft Matrix Based On Reference Function,
2013
University of New Mexico
On Fuzzy Soft Matrix Based On Reference Function, Florentin Smarandache, Said Broumi, Mamoni Dhar
Branch Mathematics and Statistics Faculty and Staff Publications
In this paper we study fuzzy soft matrix based on reference function. Firstly, we define some new operations such as fuzzy soft complement matrix and trace of fuzzy soft matrix based on reference function. Then, we introduced some related properties, and some examples are given. Lastly, we define a new fuzzy soft matrix decision method based on reference function.
Delayflow Centrality For Identifying Critical Nodes In Transportation Networks,
2013
Singapore Management University
Delayflow Centrality For Identifying Critical Nodes In Transportation Networks, Yew-Yih Cheng, Roy Ka Wei Lee, Ee-Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
In an urban city, its transportation network supports efficient flow of people between different parts of the city. Failures in the network can cause major disruptions to commuter and business activities which can result in both significant economic and time losses. In this paper, we investigate the use of centrality measures to determine critical nodes in a transportation network so as to improve the design of the network as well as to devise plans for coping with network failures. Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of transportation …
Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation,
2013
Singapore Management University
Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying
Research Collection School Of Computing and Information Systems
As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the …
Vigilance Adaptation In Adaptive Resonance Theory,
2013
Singapore Management University
Vigilance Adaptation In Adaptive Resonance Theory, Lei Meng, Ah-Hwee Tan, Donald C. Winsch
Research Collection School Of Computing and Information Systems
Despite the advantages of fast and stable learning, Adaptive Resonance Theory (ART) still relies on an empirically fixed vigilance parameter value to determine the vigilance regions of all of the clusters in the category field (F 2 ), causing its performance to depend on the vigilance value. It would be desirable to use different values of vigilance for different category field nodes, in order to fit the data with a smaller number of categories. We therefore introduce two methods, the Activation Maximization Rule (AMR) and the Confliction Minimization Rule (CMR). Despite their differences, both ART with AMR (AM-ART) and with …
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection,
2013
Nanyang Technological University
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Malicious Uniform Resource Locator (URL) detection is an important problem in web search and mining, which plays a critical role in internet security. In literature, many existing studies have attempted to formulate the problem as a regular supervised binary classification task, which typically aims to optimize the prediction accuracy. However, in a real-world malicious URL detection task, the ratio between the number of malicious URLs and legitimate URLs is highly imbalanced, making it very inappropriate for simply optimizing the prediction accuracy. Besides, another key limitation of the existing work is to assume a large amount of training data is available, …
Large Scale Online Kernel Classification,
2013
Nanyang Technological University
Large Scale Online Kernel Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
In this work, we present a new framework for large scale online kernel classification, making kernel methods efficient and scalable for large-scale online learning tasks. Unlike the regular budget kernel online learning scheme that usually uses different strategies to bound the number of support vectors, our framework explores a functional approximation approach to approximating a kernel function/matrix in order to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) the Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) the …
Understanding User Resistance To Information Technology: Toward A Comprehensive Model In Health Information Technology,
2013
University of Texas-Pan American
Understanding User Resistance To Information Technology: Toward A Comprehensive Model In Health Information Technology, Madison N. Ngafeeson
Theses and Dissertations - UTB/UTPA
The successful implementation of health information systems is expected to increase legibility, reduce medical errors, boost the quality of healthcare and shrink costs. Yet, evidence points to the fact that healthcare professionals resist the full use of these systems. Physicians and nurses have been reported to resist the system. Even though resistance to technology has always been identified as key issue in the successful implementation of information technology, the subject remains largely under-theorized and deficient of empirical testing. Only two proposed model have been tested so far. Hence, though user resistance is clearly identified and defined in literature, not very …
Segmenting Tables Via Indexing Of Value Cells By Table Headers,
2013
University of Nebraska-Lincoln
Segmenting Tables Via Indexing Of Value Cells By Table Headers, Sharad C. Seth, George Nagy
School of Computing: Conference and Workshop Papers
Correct segmentation of a web table into its component regions is the essential first step to understanding tabular data. Our algorithmic solution to the segmentation problem relies on the property that strings defining row and column header paths uniquely index each data cell in the table. We segment the table using only “logical layout analysis” without resorting to any appearance features or natural language understanding. We start with a CSV table that preserves the 2- dimensional structure and contents of the original source table (e.g., an HTML table) but not font size, font weight, and color. The indexing property of …
Computing Immutable Regions For Subspace Top-K Queries,
2013
Singapore Management University
Computing Immutable Regions For Subspace Top-K Queries, Kyriakos Mouratidis, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Given a high-dimensional dataset, a top-k query can be used to shortlist the k tuples that best match the user’s preferences. Typically, these preferences regard a subset of the available dimensions (i.e., attributes) whose relative significance is expressed by user-specified weights. Along with the query result, we propose to compute for each involved dimension the maximal deviation to the corresponding weight for which the query result remains valid. The derived weight ranges, called immutable regions, are useful for performing sensitivity analysis, for finetuning the query weights, etc. In this paper, we focus on top-k queries with linear preference functions over …
Open Source Software Development Process Model – A Grounded Theory Approach,
2013
Singapore Management University
Open Source Software Development Process Model – A Grounded Theory Approach, Keng Siau, Y. Tian
Research Collection School Of Computing and Information Systems
The open source movement has provided software users with more choices, lower software acquisition cost, more flexible software customization, and possibly higher quality software product. Although the development of open source software is dynamic and it encourages innovations, the process can be chaotic. An Open Source Software Development (OSSD) process model to enhance the survivability of OSSD projects is needed. This research uses the grounded theory approach to derive a Phase-Role-Skill-Responsibility (PRSR) OSSD process model. The three OSSD process phases -- Launch Stage, Before the First Release, and Between Releases -- address the characteristics of the OSSD process as well …
Self-Organizing Cognitive Models For Virtual Agents,
2013
Singapore Management University
Self-Organizing Cognitive Models For Virtual Agents, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Three key requirements of realistic characters or agents in virtual world can be identified as autonomy, interactivity, and personification. Working towards these challenges, this paper proposes a brain inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personification. Based on self-organizing neural models, the agent architecture maintains explicit mental representation of desires, intention, personalities, self-awareness, situation awareness and user awareness. Autonomous behaviors are generated via evaluating the current situation with active goals and learning the most appropriate social or goal-directed rule from the available knowledge, in accordance with the personality of each individual agent. We have built …
Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models,
2013
Carnegie Mellon University
Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models, Siyuan Liu, Yisong Yue, Ramayya Krishnan
Research Collection School Of Computing and Information Systems
We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian Process Dynamic Congestion Model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop an efficient algorithm for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability …
Politics, Sharing And Emotion In Microblogs,
2013
Singapore Management University
Politics, Sharing And Emotion In Microblogs, Tuan-Anh Hoang, William Cohen, Ee Peng Lim, Doug Pierce, David Redlawsk
Research Collection School Of Computing and Information Systems
In political contexts, it is known that people act as "motivated reasoners", i.e., information is evaluated first for emotional affect, and this emotional reaction influences later deliberative reasoning steps. As social media becomes a more and more prevalent way of receiving political information, it becomes important to understand more completely the interaction between information, emotion, social community, and information-sharing behavior. In this paper, we describe a high-precision classifier for politically-oriented tweets, and an accurate classifier of a Twitter user's political affiliation. Coupled with existing sentiment-analysis tools for microblogs, these methods enable us to systematically study the interaction of emotion and …
Robust Median Reversion Strategy For On-Line Portfolio Selection,
2013
East China University of Science and Technology
Robust Median Reversion Strategy For On-Line Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Steven Hoi, Shuigeng Zhou
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal …
Student Interaction With Content In Online And Hybrid Courses: Leading Horses To The Proverbial Water,
2013
Kennsaw State University
Student Interaction With Content In Online And Hybrid Courses: Leading Horses To The Proverbial Water, Meg Murray, Jorge Perez, Debra Geist, Alison Hedrick
Faculty Articles
Permutations of traditional and online learning are rapidly advancing along a blended continuum, prompting conjecture that learning and e-learning will soon be indistinguishable. As variations of blended learning evolve, educators worldwide must develop better understanding of how effective interaction with course content impacts engagement and learning. This study compares patterns of access to instructional content in online and hybrid courses offered at a regional university in the United States. Frequency counts and access rates were examined for course content in four categories: core materials, direct support, indirect support, and ancillary materials. Observed results were echoed in responses to a survey …
Video Concept Detection By Learning From Web Images: A Case Study On Cross Domain Learning,
2013
Singapore Management University
Video Concept Detection By Learning From Web Images: A Case Study On Cross Domain Learning, Shiai Zhu, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Concept detection is probably the most important research problem in the area of multimedia. The need to model with sufficient and diverse training instances, however, makes the task computationally and resourcefully expensive. Meanwhile, the popularity of social media has generated massive amount of weakly tagged images which could be leveraged for concept model learning. Therefore, in this paper, we consider exploring weakly taggedWeb images to shed some light on video concept detection. Particularly, two sets of Web images downloaded from Flickr are utilized as training data for concept detection on two real-world large-scale video datasets released by TRECVID. Our experiments …
Mining Direct Antagonistic Communities In Signed Social Networks,
2013
Singapore Management University
Mining Direct Antagonistic Communities In Signed Social Networks, David Lo, Didi Surian, Philips Kokoh Prasetyo, Zhang Kuan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities …
