Physical Sciences and Mathematics Commons^™

Toward A New Understanding Of Virtual Research Collaborations: Complex Adaptive Systems Framework, Arsev U. Aydinoglu

DataONE Sociocultural and Usability & Assessment Working Groups

Virtual research collaborations (VRCs) have become an important method of conducting scientific activity; however, they are often regarded and treated as traditional scientific collaborations. Their success is measured by scholarly productivity and adherence to budget by funding agencies, participating scientists, and scholars. VRCs operate in complex environments interacting with other complex systems. A holistic (or organicist) approach is needed to make sense of this complexity. For that purpose, this study proposes using a new perspective, namely, the complex adaptive systems theory that can provide a better understanding of a VRC’s potential creativity, adaptability, resilience, and probable success. The key concepts …

Improving Reuse In Software Development For The Life Sciences, Nicholas Vincent Iannotti

Open Access Dissertations

The last several years have seen unprecedented advancements in the application of technology to the life sciences, particularly in the area of data generation. Novel scientific insights are now often driven primarily by software development supporting new multidisciplinary and increasingly multifaceted data analysis. However, despite the availability of tools such as best practice frameworks, the current rate of software development is not able to keep up with the needs of scientists. This bottleneck in software development is largely due to code reuse generally not being applied in practice.

This dissertation presents Legwork, a class library of reuse-optimized design pattern implementations …

On Effects Of Visual Query Complexity, Jialie Shen, Cheng Zhiyong

Research Collection School Of Computing and Information Systems

As an effective technique to manage large scale image collections, content-based image retrieval (CBIR) has been received great attentions and became a very active research domain in recent years. While assessing system performance is one of the key factors for the related technological advancement, relatively little attention has been paid to model and analyze test queries. This paper documents a study on the problem of determining visual query complexity as a measure for predicting image retrieval performance. We propose a quantitative metric for measuring complexity of image queries for content-based image search engine. A set of experiments are carried out …

Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we …

Skyhunter: A Multi-Surface Environment For Supporting Oil And Gas Exploration, Teddy Seyed, Mario Costa Sousa, Frank Maurer, Anthony Tang

Research Collection School Of Computing and Information Systems

The process of oil and gas exploration and its result, the decision to drill for oil in a specific location, relies on a number of distinct but related domains. These domains require effective collaboration to come to a decision that is both cost effective and maintains the integrity of the environment. As we show in this paper, many of the existing technologies and practices that support the oil and gas exploration process overlook fundamental user issues such as collaboration, interaction and visualization. The work presented in this paper is based upon a design process that involved expert users from an …

Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao

Research Collection School Of Computing and Information Systems

Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …

Learning Topics And Positions From Debatepedia, Swapna Gottopati, Minghui Qiu, Yanchuan Sim, Jing Jiang, Noah Smith

Research Collection School Of Computing and Information Systems

We explore Debatepedia, a communityauthored encyclopedia of sociopolitical debates, as evidence for inferring a lowdimensional, human-interpretable representation in the domain of issues and positions. We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opinion words. We evaluate the resulting representation’s usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions.

Consistent Stereo Image Editing, Tao Yan, Shengfeng He, Rynson W.H. Lau, Yun Xu

Research Collection School Of Computing and Information Systems

Stereo images and videos are very popular in recent years, and techniques for processing this media are attracting a lot of attention. In this paper, we extend the shift-map method for stereo image editing. Our method simultaneously processes the left and right images on pixel level using a global optimization algorithm. It enforces photo consistence between the two images and preserves 3D scene structures. It also addresses the occlusion and disocclusion problem, which may enable many stereo image editing functions, such as depth mapping, object depth adjustment and non-homogeneous image resizing. Our experiments show that the proposed method produces high …

Designing Mobile Educational Games On Voter‟S Education: A Tale Of Three Engines, Ma. Regina Justina E. Estuar, Nadia Rowena C. Leetian, Michael B. Syson

Department of Information Systems & Computer Science Faculty Publications

The rapid growth of mobile learning is influenced by the ability to access learning content anytime and anywhere. The on demand capability is available because mobile devices allow for convergence of internet and communications technologies. At the same time, the availability of engines makes development of mobile applications faster and seamless. However, not all mobile development engines are alike. This paper discusses on the development of mobile learning applications using mobile development engines in teaching Filipinos on responsible voting. Specifically, this paper discusses how AndEngine, Ren’Py, and homegrown Usbong were used to develop a mobile board game and a mobile …

Search Tool That Utilizes Scientific Metadata Matched Against User-Entered Parameters, Veronika Margaret Megler, David Maier

Computer Science Faculty Publications and Presentations

A method for providing proximate dataset recommendations can begin with the creation of metadata records corresponding to datasets that represent scientific data by a scientific dataset search tool. The metadata records can conform to a standardized structural definition, and may be hierarchical. Values for the data elements of the metadata records can be contained within the datasets. Metadata records with a value that is proximate to a user-entered search parameter can be identified. A proximity score can be calculated for each identified metadata record. The proximity score can express a relevance of the corresponding dataset to the user-entered search parameters. …

Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu

Research Collection School Of Computing and Information Systems

Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted …

The Vireo Team At Mediaeval 2013: Violent Scenes Detection By Mid-Level Concepts Learnt From Youtube, Chun Chet Tan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The Violent Scenes Detection task continues to pose challenge in detecting violent scenes in Hollywood movies. In this working notes paper, we present the framework of our system and briefly discuss the performance results obtained in both objective and subjective subtasks. Besides using the low-level features for training the SVM classifiers for violent scenes detection, we show the feasibility in using the concept detectors to infer the occurrence of violent scenes. External Youtube data is exploited in our implementation to provide more diverse definition to violent scene concepts. Furthermore, we explore the feasibility of using Conditional Random Fields (CRF) to …

Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei

Research Collection School Of Computing and Information Systems

Our objective is to estimate the relevance of an image to a query for image search purposes. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of bridging the gap between semantic textual queries as well as users’ search intents and image visual content. Image search engines therefore primarily rely on static and textual features. Visual features are mainly used to identify potentially useful recurrent patterns or relevant training examples for complementing search by image reranking. Second, image rankers are trained on query-image pairs labeled by human experts, making the …

A Unified Model For Topics, Events And Users On Twitter, Qiming Diao, Jing Jiang

Research Collection School Of Computing and Information Systems

With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture …

Modeling Interaction Features For Debate Side Clustering, Minghui Qiu, Liu Yang, Jing Jiang

Research Collection School Of Computing and Information Systems

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an …

Predictive Handling Of Asynchronous Concept Drifts In Distributed Environments, Hock Hee Ang, Vivek Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In a distributed computing environment, peers collaboratively learn to classify concepts of interest from each other. When external changes happen and their concepts drift, the peers should adapt to avoid increase in misclassification errors. The problem of adaptation becomes more difficult when the changes are asynchronous, i.e., when peers experience drifts at different times. We address this problem by developing an ensemble approach, PINE, that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers. With empirical study on simulated and real-world data sets, we show that PINE handles asynchronous …

Riga: A Rich Internet Geospatial Analytics Application For Area-Based Data, Tin Seong Kam

Research Collection School Of Computing and Information Systems

In this information age, more and more public statistical data such as population census, household living, local economy and business establishment are distributed over the internet within the framework of spatial data infrastructure. By and large, these data are organized geographically such as region, province as well as district. Usually, they are published in the form of digital maps over the internet as simple points, lines and polygons markers limited or no analytical function available to transform these data into useful information. To meet the analytical needs of casual public data users, we contribute RIGA, a rich internet geospatial analytics …

The Myths Of G-Tech For Business Decision Making, Tin Seong Kam

Research Collection School Of Computing and Information Systems

More than 80% of organisation data are location related - the locations where transactions are done, where retailers are found, and of customers who buy their products. Since the early 2005, there has been an increasing interest among the business community to use geospatial technology to enhance decision making process at both strategic and operational levels. Millions of dollars and man-hours have been invested into driving their geo-technology development and implementation. The use of geospatial technology in business, however, tends to confine to simple mapping. Many of these failures are the victims of misperception. Some of the perpetrators are practitioners. …

Rapport: Semantic-Sensitive Namespace Management In Large-Scale File Systems, Yu Hua, Hong Jiang, Yifeng Zhu, Dan Feng

Yifeng Zhu

Explosive growth in volume and complexity of data exacerbates the key challenge to effectively and efficiently manage data in a way that fundamentally improves the ease and efficacy of their use. Existing large-scale file systems rely on hierarchically structured namespace that leads to severe performance bottlenecks and renders it impossible to support real-time queries on multi-dimensional attributes. This paper proposes a novel semantic-sensitive scheme, called Rapport, to provide dynamic and adaptive namespace management and support complex queries. The basic idea is to build files’ namespace by utilizing their semantic correlation and exploiting dynamic evolution of attributes to support namespace management. …

It Is Not Just What We Say, But How We Say Them: Lda-Based Behavior-Topic Model, Minghui Qiu, Feida Zhu, Jing Jiang

Minghui QIU

Textual information exchanged among users on online social network platforms provides deep understanding into users' interest and behavioral patterns. However, unlike traditional text-dominant settings such as o ine publishing, one distinct feature for online social network is users' rich interactions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively …

Query-Oriented Keyphrase Extraction, Minghui Qiu, Yaliang Li, Jing Jiang

Minghui QIU

People often issue informational queries to search engines to find out more about some entities or events. While a Wikipedia-like summary would be an ideal answer to such queries, not all queries have a corresponding Wikipedia entry. In this work we propose to study query-oriented keyphrase extraction, which can be used to assist search results summarization. We propose a general method for keyphrase extraction for our task, where we consider both phraseness and informativeness. We discuss three criteria for phraseness and four ways to compute informativeness scores. Using a large Wikipedia corpus and 40 queries, our empirical evaluation shows that …

Designing An Mis Database For Selection And Recruitment, Umakant Mishra

Umakant Mishra

Selection and recruitment is a key HR function and it is often necessary to maintain at least a small database of all the candidates who have been invited in past interviews. This information may be considered as a part of HR MIS and may be maintained by HR department along with other HR MIS such as employee information, transfers, performance appraisals, trainings etc.

This recruitment MIS database includes all the candidates after the preliminary scrutiny is over. When the candidates go through different stages of interviews, the MIS also captures all their interview results. Maintaining this information not only helps …

Making Sense Of Trends And Data, Singapore Management University

Perspectives@SMU

How do you make sense of data when it is all unpredictable?

A Robust Rgbd Slam System For 3d Environment With Planar Surfaces, Po-Chang Su, Ju Shen, Sen-Ching S. Cheung

Computer Science Faculty Publications

With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth …

Mining Effective Multi-Segment Sliding Window For Pathogen Incidence Rate Prediction, Lei Duan, Changjie Tang, Xiasong Li, Guozhu Dong, Xianming Wang, Jie Zuo, Min Jiang, Zhongqi Li, Yongqing Zhang

Kno.e.sis Publications

Pathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates the application of a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP-based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and …

A Statistical And Schema Independent Approach To Identify Equivalent Properties On Linked Data, Kalpa Gunaratna, Krishnaprasad Thirunarayan, Prateek Jain, Amit P. Sheth, Sanjaya Wijeratne

Kno.e.sis Publications

Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community recently. Currently it consists of approximately 295 interlinked datasets with over 50 billion triples including 500 million links, and continues to expand in size. This vast source of structured information has the potential to have a significant impact on knowledge-based applications. However, a key impediment to the use of LOD cloud is limited support for data integration tasks over concepts, instances, and properties. Efforts to address this limitation over properties have focused on matching data-type properties across datasets; however, matching of object-type properties has not received …

Types Of Property Pairs And Alignment On Linked Datasets - A Preliminary Analysis, Kalpa Gunaratna, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Dataset publication on the Web has been greatly influenced by the Linked Open Data (LOD) project. Many interlinked datasets have become freely available on the Web creating a structured and distributed knowledge representation. Analysis and aligning of concepts and instances in these interconnected datasets have received a lot of attention in the recent past compared to properties. We identify three different categories of property pairs found in the alignment process and study their relative distribution among well known LOD datasets. We also provide comparative analysis of state-of-the-art techniques with regard to different categories, highlighting their capabilities. This could lead to …

Web-Scale Near-Duplicate Search: Techniques And Applications, Chong-Wah Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik

Research Collection School Of Computing and Information Systems

This paper presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Web-scale near-duplicate search problem in a number of relevant domains. The topics range from feature representation, matching, and indexing from different novel aspects to the adaptation of current technologies for mobile media search and photo archaeology mining.

The Impact Of Ineffective Internal Control On The Value Relevance Of Accounting Information, Nan Hu, Baolei Qi, Gaoliang Tian, Lee Yao, Zhen Zeng

Research Collection School Of Computing and Information Systems

This paper investigates the value relevance of accounting information in the presence of ineffective internal control (IIC). Based on Ohlson's valuation model, this paper first documents that IIC can directly affect a firm's market value after control cost of capital, corporate governance, and other, value-relevant variables. Second, this paper finds that the value relevance of earnings and book value in determining a firm's market value are significantly reduced. Collectively, the results of this paper indicate that the effectiveness of internal controls can directly affect a firm's market value and the value relevance of accounting information.

Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and …