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Articles 5191 - 5220 of 7258
Full-Text Articles in Computer Sciences
Cloud Storage And Online Bin Packing, Swathi Venigella
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 …
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang
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 …
Merging Schemas In A Collaborative Faceted Classification System, Jianxiang Li
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 …
10302 Summary - Learning Paradigms In Dynamic Environments, Barbara Hammer, Pascal Hitzler
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 …
Trust Model For Semantic Sensor And Social Networks: A Preliminary Report, Pramod Anantharam, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth
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 …
Sit-To-Stand Detection Using Fuzzy Clustering Techniques, Tanvi Banerjee, James M. Keller, Marjorie Skubic, Carmen Abbott
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 …
Biomedical Ontologies For Parasite Research, Vinh Nguyen, Satya S. Sahoo, Priti Parikh, Todd Minning, Brent Weatherly, Flora Logan, Amit P. Sheth, Rick Tarleton
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 …
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
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.
Can The Presence Of Online Word Of Mouth Increase Product Sales?, Alanah Mitchell, Deepak Khazanchi
Can The Presence Of Online Word Of Mouth Increase Product Sales?, Alanah Mitchell, Deepak Khazanchi
Information Systems and Quantitative Analysis Faculty Publications
The power and potential impact of online word of mouth has increased substantially. Consumers have come to accept and rely upon online word of mouth, so it is important to understand how it works and what kind of impact it has on online product sales. This article provides an assessment of this question through an analysis of sales and online word of mouth data from a multi-product e-commerce retail firm.
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
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 …
Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
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 …
Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan
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. …
Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan
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 …
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Research Collection School Of Computing and Information Systems
Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) …
Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou
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 …
Evaluation Of Protein Backbone Alphabets: Using Predicted Local Structure For Fold Recognition, Kyong Jin Shim
Evaluation Of Protein Backbone Alphabets: Using Predicted Local Structure For Fold Recognition, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone.
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Research Collection School Of Computing and Information Systems
In this highly volatile and fast-paced financial market, traders and managers working in banking and financial organizations must struggle to cope with large and complex data from multi-sources, that move throughout the market at increasingly high speed. The cost of making poor business and investment decisions is very high. This places great demands on data analysts, who are responsible for providing process information, to support the activities of traders and managers. Static reports and traditional business intelligence tools simply cannot keep up with a market that is changing on a second-to-second basis. By the time the traders and bankers have …
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Research Collection School Of Computing and Information Systems
This research is a partial test of Park et al.’s (2008) model to assess the impact of flow and brand equity in 3D virtual worlds. It draws on flow theory as its main theoretical foundation to understand and empirically assess the impact of flow on brand equity and behavioral intention in 3D virtual worlds. The findings suggest that the balance of skills and challenges in 3D virtual worlds influences users’ flow experience, which in turn influences brand equity. Brand equity then increases behavioral intention. The authors also found that the impact of flow on behavioral intention in 3D virtual worlds …
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Research Collection School Of Computing and Information Systems
Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this …
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We …
A Heuristic Algorithm For Trust-Oriented Service Provider Selection In Complex Social Networks, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee Peng Lim
A Heuristic Algorithm For Trust-Oriented Service Provider Selection In Complex Social Networks, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In a service-oriented online social network consisting of service providers and consumers, a service consumer can search trustworthy service providers via the social network. This requires the evaluation of the trustworthiness of a service provider along a certain social trust path from the service consumer to the service provider. However, there are usually many social trust paths between participants in social networks. Thus, a challenging problem is which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a novel complex social network structure and a new concept, Quality …
Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Research Collection School Of Computing and Information Systems
The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that much semantic information is lost during the codebook generation process, an important step of BoW. This is because the codebook generated by BoW is often obtained via building the codebook simply by clustering visual features in Euclidian space. However, visual features related to the same semantics may not distribute in clusters in the Euclidian space, which is primarily due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a …
Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi
Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Social image retrieval has become an emerging research challenge in web rich media search. In this paper, we address the research problem of text-based social image retrieval, which aims to identify and return a set of relevant social images that are related to a text-based query from a corpus of social images. Regular approaches for social image retrieval simply adopt typical text-based image retrieval techniques to search for the relevant social images based on the associated tags, which may suffer from noisy tags. In this paper, we present a novel framework for social image re-ranking based on a non-parametric kernel …
A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan
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, …
A Call To Is Educators To Respond To The Voices Of Women In Information Security, Amy B. Woszczynski, Sherri Shade
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 …
How To Make Linked Data More Than Data, Prateek Jain, Amit P. Sheth, Kunal Verma, Pascal Hitzler, Peter Z. Yeh
How To Make Linked Data More Than Data, Prateek Jain, Amit P. Sheth, Kunal Verma, Pascal Hitzler, Peter Z. Yeh
Kno.e.sis Publications
The LOD cloud has a potential for applicability in many AI-related tasks, such as open domain question answering, knowledge discovery, and the Semantic Web. An important prerequisite before the LOD cloud can enable these goals is allowing its users (and applications) to effectively pose queries to and retrieve answers from it. However, this prerequisite is still an open problem for the LOD cloud and has restricted it to 'merely more data.' To transform the LOD cloud from 'merely more data' to 'semantically linked data' there are plenty of open issues which should be addressed. We believe this transformation of the …
Semantically Annotated Restful Services For Large-Scale Metabolomics Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith H. Ranabahu, Michael L. Raymer, Amit P. Sheth
Semantically Annotated Restful Services For Large-Scale Metabolomics Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith H. Ranabahu, Michael L. Raymer, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Measurement And Interpolation Of Sea Surface Temperature And Salinity In The Tropical Pacific: A 9,000 Nautical Mile Research Odyssey, Amber Brooks
Earth and Soil Sciences
The purpose of this project was to compare spline and inverse distance weighting interpolation tools on data collected in the tropical Pacific Ocean by ship and data from a global network of CTD floats, known as Argo floats (fig.1), to provide evidence that technological advancement and integration is aiding our understanding of the ocean-atmosphere system of planet Earth. Thirty-one sea surface temperature and salinity samples were manually taken across a 9,000 nautical mile trek of the Pacific Ocean for the months of April, May and June 2008. Argo ASCII globally gridded monthly averaged sea surface temperature and salinity data, from …
Customer Communicator, Eddie Tavarez
Customer Communicator, Eddie Tavarez
Computer Science and Software Engineering
No abstract provided.