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Articles 4891 - 4920 of 6716

Full-Text Articles in Physical Sciences and Mathematics

Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava Aug 2010

Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of teams. This study uses performance data of game players and teams in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for task performing teams. The prediction models provide a projection of task performing team's future performance based on the past performance patterns of participating players on the team as well as team characteristics. While the existing game system lacks the ability to predict team-level performance, the prediction models proposed in this study are expected to be …

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Cross-Market Model Adaptation With Pairwise Preference Data For Web Search Ranking, Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen Aug 2010

Cross-Market Model Adaptation With Pairwise Preference Data For Web Search Ranking, Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen

Kno.e.sis Publications

Machine-learned ranking techniques automatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibility required of a commercial web search. However, manually labeled training data (with multiple absolute grades) has become the bottleneck for training a quality ranking function, particularly for a new domain. In this paper, we explore the adaptation of machine-learned ranking models across a set of geographically diverse markets with the market-specific pairwise preference data, which can be easily obtained from clickthrough logs. We propose a novel adaptation algorithm, Pairwise-Trada, which is able to adapt ranking models that are trained …

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Pattern Space Maintenance For Data Updates And Interactive Mining, Mengling Feng, Guozhu Dong, Jinyan Li, Yap-Peng Tan, Limsoon Wong Aug 2010

Pattern Space Maintenance For Data Updates And Interactive Mining, Mengling Feng, Guozhu Dong, Jinyan Li, Yap-Peng Tan, Limsoon Wong

Kno.e.sis Publications

This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer− (PSM−), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art …

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A Design Science Based Evaluation Framework For Patterns, Stacie Clarke Petter, Deepak Khazanchi, John D. Murphy Aug 2010

A Design Science Based Evaluation Framework For Patterns, Stacie Clarke Petter, Deepak Khazanchi, John D. Murphy

Information Systems and Quantitative Analysis Faculty Publications

Patterns were originally developed in the field of architecture as a mechanism for communicating good solutions to recurring classes of problems. Since then, many researchers and practitioners have created patterns to describe effective solutions to problems associated with disparate areas such as virtual project management, human-computer interaction, software development and engineering, and design science research. We believe that the development of patterns is a design science activity in which an artifact (i.e., a pattern) is created to communicate about and improve upon the current state-of-practice. Design science research has two critical components, creation and evaluation of an artifact. While many …

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Investigating Capabilities Associated With Ict Access And Use In Latino Micro-Enterprises, Travis Good, Luis Flores Morales, Sajda Qureshi Aug 2010

Investigating Capabilities Associated With Ict Access And Use In Latino Micro-Enterprises, Travis Good, Luis Flores Morales, Sajda Qureshi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

While the process by which Information Technology enables growth in medium and large enterprises has been wellresearched, the corresponding processes in micro-enterprises are poorly understood. In fact, such micro-enterprises lie at the heart of many economies. This insight is important as information technology enables businesses to connect with each other through knowledge networking to carry out their basic business operations. There is thus a need to build our understanding of how micro-enterprises access and use technology in order to be able to assess the benefits they derive from ICT adoption. Following an analysis of two case studies of Latino micro-enterprises …

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Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow Aug 2010

Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow

Research Collection School Of Computing and Information Systems

Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message and responding to an incoming message. We propose various models for the two behaviors also known as engagingness and responsiveness. Our experiments show that …

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Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim Aug 2010

Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We introduce MobiTOP, a Web-based system for organizing and retrieving hierarchical location-based annotations. Each annotation contains multimedia content (such as text, images, video) associated with a location, and users are able to annotate existing annotations to an arbitrary depth, in effect creating a hierarchy. An evaluation was conducted on a group of potential users to ascertain their perceptions of the usability of the application. The results were generally positive and the majority of the participants saw MobiTOP as a useful platform to share location-based information. We conclude with implications of our work and opportunities for future research.

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A Hubel Wiesel Model For Hierarchical Representation Of Concepts In Textual Documents, Kiruthika Ramanathan, Luping Shi, Chong Chong Tow Aug 2010

A Hubel Wiesel Model For Hierarchical Representation Of Concepts In Textual Documents, Kiruthika Ramanathan, Luping Shi, Chong Chong Tow

Research Collection School Of Computing and Information Systems

Hubel Weisel models of the cortex describe visual processing as a hierarchy of increasingly sophisticated representations. While several models exist for image processing, little work has been done with Hubel Weisel models out of the domain of object recognition. In this paper, we describe how such models can be extended to the representation of concepts, resulting in a model that shares several properties with the PDP model of semantic cognition. The model that we propose is also capable of incremental learning, in which the knowledge is stored in the strength of the neuron connections. Degradation of old knowledge occurs as …

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Cloud Storage And Online Bin Packing, Swathi Venigella Aug 2010

Cloud Storage And Online Bin Packing, Swathi Venigella

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cloud storage is the service provided by some corporations (such as Mozy and Carbonite) to store and backup computer files. We study the problem of allocating memory of servers in a data center based on online requests for storage. Over-the-net data backup has become increasingly easy and cheap due to cloud storage. Given an online sequence of storage requests and a cost associated with serving the request by allocating space on a certain server one seeks to select the minimum number of servers as to minimize total cost. We use two different algorithms and propose a third algorithm; we show …

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Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang Aug 2010

Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang

Research Collection School Of Computing and Information Systems

The overwhelming amounts of multimedia contents have triggered the need for automatically detecting the semantic concepts within the media contents. With the development of photo sharing websites such as Flickr, we are able to obtain millions of images with usersupplied tags. However, user tags tend to be noisy, ambiguous and incomplete. In order to improve the quality of tags to annotate web images, we propose an approach to build Semantic Fields for annotating the web images. The main idea is that the images are more likely to be relevant to a given concept, if several tags to the image belong …

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Learning Personal Agents With Adaptive Player Modeling In Virtual Worlds, Yilin Kang, Ah-Hwee Tan Aug 2010

Learning Personal Agents With Adaptive Player Modeling In Virtual Worlds, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have attempted to model the environment and the user’s context directly. However, a critical component for enabling personalized virtual world experience is missing, namely the capability to adapt over time to the habits and eccentricity of a particular player. To address the above issue, this paper presents a cognitive agent with learning player model capability for personalized …

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Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow Aug 2010

Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow

Research Collection School Of Computing and Information Systems

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive …

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A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang Aug 2010

A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by similar users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based …

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Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang Aug 2010

Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are …

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A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao Aug 2010

A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao

Computer Science Faculty Publications

One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can …

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A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse Aug 2010

A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse

Computer Science Faculty Publications

Abstract Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The …

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Merging Schemas In A Collaborative Faceted Classification System, Jianxiang Li Aug 2010

Merging Schemas In A Collaborative Faceted Classification System, Jianxiang Li

Computer Science Theses & Dissertations

We have developed a system that improves access to a large, growing image collection by allowing users to collaboratively build a global faceted (multi-perspective) classification schema. We are extending our system to support both global and local schemas, where global schema provides a complete and uniform view of the collection whereas local schema provides a personal, possibly incomplete and idiosyncratic view of the collection. We argue that although users usually focus on their personal schemas, it is still desirable to have a global schema for the entire collection even if such local schemas are available. In order to keep the …

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Trust Perceptions Of Online Travel Information By Different Content Creators: Some Social And Legal Implications., Stephen Burgess, Carmine Sellitto, Carmen Cox, Jeremy Buultjens Jul 2010

Trust Perceptions Of Online Travel Information By Different Content Creators: Some Social And Legal Implications., Stephen Burgess, Carmine Sellitto, Carmen Cox, Jeremy Buultjens

Carmen Cox

Consumers are increasingly turning to the online environment to provide information to assist them in making purchase decisions related to travel products. They often rely on travel recommendations from different sources, such as sellers, independent experts and, increasingly, other consumers. A new type of online content, user-generated content (UGC), provides a number of legal and social challenges to providers and users of that content, especially in relation to areas such as defamation, misrepresentation and social embarrassment. This paper reports research that examined the level of trustworthiness of online travel information from these different sources. The study used a survey of …

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10302 Summary - Learning Paradigms In Dynamic Environments, Barbara Hammer, Pascal Hitzler Jul 2010

10302 Summary - Learning Paradigms In Dynamic Environments, Barbara Hammer, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data …

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Cloud Based Scientific Workflow For Nmr Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith Harshana Ranabahu, Michael L. Raymer, Amit P. Sheth Jul 2010

Cloud Based Scientific Workflow For Nmr Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith Harshana Ranabahu, Michael L. Raymer, Amit P. Sheth

Kno.e.sis Publications

This work presents a service oriented scientific workflow approach to NMR-based metabolomics data analysis. We demonstrate the effectiveness of this approach by implementing several common spectral processing techniques in the cloud using a parallel map-reduce framework, Hadoop.

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Biomedical Ontologies For Parasite Research, Vinh Nguyen, Satya S. Sahoo, Priti Parikh, Todd Minning, Brent Weatherly, Flora Logan, Amit P. Sheth, Rick Tarleton Jul 2010

Biomedical Ontologies For Parasite Research, Vinh Nguyen, Satya S. Sahoo, Priti Parikh, Todd Minning, Brent Weatherly, Flora Logan, Amit P. Sheth, Rick Tarleton

Kno.e.sis Publications

Trypanosoma cruzi is a protozoan parasite that causes Chagas disease or American trypanosomiasis, which is the leading cause of death in Latin America. The primary objective of this study is to create an ontology-driven information infrastructure to support parasite researchers in identifying gene knockout, vaccination, or drug targets for T. cruzi. This involves querying across multiple datasets from diverse sources, such as proteome, pathway, internal lab data, etc. that are often represented in heterogeneous formats. To address this, a multi-ontology parasite knowledge repository (PKR) is being created with an intuitive graphical query interface called Cuebee. The PKR is underpinned by …

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Trust Model For Semantic Sensor And Social Networks: A Preliminary Report, Pramod Anantharam, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth Jul 2010

Trust Model For Semantic Sensor And Social Networks: A Preliminary Report, Pramod Anantharam, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a …

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A Call To Is Educators To Respond To The Voices Of Women In Information Security, Amy B. Woszczynski, Sherri Shade Jul 2010

A Call To Is Educators To Respond To The Voices Of Women In Information Security, Amy B. Woszczynski, Sherri Shade

Faculty Articles

Much prior research has examined the dearth of women in the IT industry. The purpose of this study is to examine the perceptions of women in IT within the context of information security and assurance. This paper describes results from a study of a relatively new career path to see if there are female-friendly opportunities that have not existed in previous IT career paths. Research methodology focuses on a qualitative analysis of in-depth interviews with women who are self-described information security professionals. A primary goal of the study is to understand the perceptions of women in information security and determine …

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Sit-To-Stand Detection Using Fuzzy Clustering Techniques, Tanvi Banerjee, James M. Keller, Marjorie Skubic, Carmen Abbott Jul 2010

Sit-To-Stand Detection Using Fuzzy Clustering Techniques, Tanvi Banerjee, James M. Keller, Marjorie Skubic, Carmen Abbott

Kno.e.sis Publications

The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding …

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Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou Jul 2010

Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise …

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A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan Jul 2010

A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Comparing with prior systems, the proposed episodic memory model presents a robust approach to encoding key events and episodes and recalling them using partial and erroneous cues. We present experimental studies, …

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Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan Jul 2010

Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum …

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Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan Jul 2010

Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not found in typical machine learning benchmarks. In this paper, we highlight the unique issues and challenges of building learning agents in virtual world using reinforcement learning. Specifically, a self-organizing neural model, named TD-FALCON (Temporal Difference - Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function in …

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Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan Jul 2010

Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. …

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Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan Jul 2010

Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism to control the degree of global exploration against local exploitation while the search progresses. Experimental study on benchmark instances of CARP showed that the proposed probabilistic scheme led to improved search performances when introduced into a recently proposed state-of-the-art …

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