Physical Sciences and Mathematics Commons^™

Monetizing User Activity On Social Networks, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang

Kno.e.sis Publications

In this work, we investigate techniques to monitize user activity on public forums, marketplaces and groups on social network sites. Our approach involves (a) identifying the monetization potential of user posts and (b) eliminating o- topic content in monetizable posts to use the most relevant keywords for advertising. Our first user study involving 30 users and data from MySpace and Facebook, shows that 52% of ad impressions shown after using our system were more targeted compared to the 30% relevant impressions generated without using our system. A second smaller study suggests that profile ads that are based on user activity …

Joint Extraction Of Compound Entities And Relationships From Biomedical Literature, Cartic Ramakrishnan, Pablo N. Mendes, Rodrigo A.T.S. De Gama, Guilherme C.N. Ferreira, Amit P. Sheth

Kno.e.sis Publications

In this paper we identify some limitations of contemporary information extraction mechanisms in the context of biomedical literature. We present an extraction mechanism that generates structured representations of textual content. Our extraction mechanism achieves this by extracting compound entities, and relationships between them, occuring in text. A detailed evaluation of the relationship and compound entities extracted is presented. Our results show over 62% average precision across 8 relationship types tested with over 82% average precision for compound entity identification1.

Relative Searching Using An Ordered Token List, Anthony Rosequist

Inquiry: The University of Arkansas Undergraduate Research Journal

Many organizations have large amounts of information, such as consumer data, that need to be processed. Traditional searching algorithms only attempt to find exact matches to particular queries. This is undesirable when data are missing, outdated, or inaccurate. Therefore, a new type of search must be developed to locate records that are considered "interesting" to the user. This research paper examines past attempts to solve this problem and explores a new method involving ordered token lists to achieve this goal. The algorithm was developed, implemented, tested, and optimized.

The Cleveland-Akron-Elyria Region Doing Well: More Persons Attending College And Getting Degrees, 2000 To 2007, Mark Salling

All Maxine Goodman Levin School of Urban Affairs Publications

Discussions of economic development and job availability in northeast Ohio often lament the unavailability of a qualified workforce in some sectors. Workforce training and attracting more educated population to the region are sited as important, even critical, objectives for the region. While a more detailed study of the regions’ workforce by The Center for Community Solutions is nearing completion, the release of new data by the Census Bureau provides some enlightening observations about college enrollments and educational attainment in the region.

Hispanics And Asians Increase In Numbers In Cuyahoga County An Analysis Of 2007 County Population Estimates, Mark Salling

All Maxine Goodman Levin School of Urban Affairs Publications

No abstract provided.

The State Of Public Access To Federal Government Databases Detailed In Recommended New Book, Jennifer L. Behrens

Faculty Scholarship

No abstract provided.

Building Web Applications Using Web Services, Keerthi Nannapaneni

Theses Digitization Project

This project was performed to get hands on experience on the implementation of web services. During this project execution, significant time was spent researching about existing web services and various programming environments that can be used for building the application. The following application - AJAX, JavaScript, Java, Java server pages, HTML, and CSS. Existing data content from the following web service resources were used in this application - Google API, ArcWebServices, Weather XML feed. The purpose of this application is to show the map location along with the weather of the place selected by the user. This application can be …

College Of Extended Learning Online Registration System, Chai-Ching Tsai

Theses Digitization Project

The purpose of this project is to provide staff and future students at College of Extended Learning a web based interface to process course registration online. This system is called College of Extended Learning Online Registration System C♯ (CELORS-C♯). This project is a revision of a previous version of College of Extended Learning Online Registration System. The previous version of CELORS has been used by College of Extended Learning as of March 2007. The difference between the previous CELORS and CELORS-C♯ is that the previous CELORS is coded in JAVA but CELORS-C♯ is coded in C. Also, CELORS-C♯ has a …

Design And Implementation Of A Multi-Player Role Playing Game, Giang Tuan Trang

Theses Digitization Project

The purpose of this project was to solve a multiplayer computer game problem. A team of 15 students were formed to solve a game programming problem in the summer of 2008. The team consisted of students from various programming backgrounds and interests. The team's goal was to finish the first working artifact minus the art work by the end of summer 2008. This first artifact with a concrete, solid architecture will serve as a foundation for future students to learn game programming.

Extending The Solicitation Management System: Pdf Reporting And Database Support, Jiaming Zhang

Theses Digitization Project

The main purpose of this project is to develop new functionalities for the exisiting Solicitation Management System (SMS) to support Office of Technology Transfer and Commercialization (OTTC), California State University San Bernardino (CSUSB) and Center for the Commercialization of Advanced Technology (CCAT), San Diego State University (SDSU) for the 2008 solicitation on 28 January 2008 to improve its reporting capabilities and support the new functional requirements. The new functions include uploading announcements, instructions and proposal templates. The scope of this project includes creation of new PDF reports and functions to support the management of topics and classes.

Networks And Network Security, Michael Kuralt

Theses and Dissertations

The purpose of this thesis is to increase awareness of network security in the office and at home by educating the public, reinforcing business decisions, and providing guidelines for network security.

Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan

Research Collection School Of Computing and Information Systems

The use of a team of weak learners to learn a dataset has been shown better than the use of one single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be one of the best off-the-shelf classifiers. However, some problems still remain, including determining the optimal number of weak learners and the overfitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of genetic algorithm, weak learner and pattern distributor. In this paper, …

Probabilistic Sales Forecasting For Small And Medium-Size Business Operations, Randall E. Duran

Research Collection School Of Computing and Information Systems

One of the most important aspects of operating a business is the forecasting of sales and allocation of resources to fulfill sales. Sales assessments are usually based on mental models that are not well defined, may be biased, and are difficult to refine and improve over time. Defining sales forecasting models for small- and medium-size business operations is especially difficult when the number of sales events is small but the revenue per sales event is large. This chapter reviews the challenges of sales forecasting in this environment and describes how incomplete and potentially suspect information can be used to produce …

Medical Language Processing For Patient Diagnosis Using Text Classification And Negation Labelling, Brian Mac Namee, John D. Kelleher, Sarah Jane Delany

Conference papers

This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.

Document Selection For Extracting Entity And Relationship Instances Of Terrorist Events, Zhen Sun, Ee Peng Lim, Kuiyu Chang, Maggy Anastasia Suryanto, Rohan Kumar Gunaratna

Research Collection School Of Computing and Information Systems

In this chapter, we study the problem of selecting documents so as to extract terrorist event information from a collection of documents. We represent an event by its entity and relation instances. Very often, these entity and relation instances have to be extracted from multiple documents. We therefore define an information extraction (IE) task as selecting documents and extracting from which entity and relation instances relevant to a user-specified event (aka domain specific event entity and relation extraction). We adopt domain specific IE patterns to extract potentially relevant entity and relation instances from documents, and develop a number of document …

Face Annotation Using Transductive Kernel Fisher Discriminant, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of …

The Plant Ontology Database: A Community Resource For Plant Structure And Developmental Stages Controlled Vocabulary And Annotations, Shulamit Avraham, Chih-Wei Tung, Katica Ilic, Pankaj Jaiswal, Elizabeth A. Kellogg, Susan Mccouch, Anuradha Pujar, Leonore Reiser, Seung Yon Rhee, Martin M. Sachs, Mary L. Schaeffer, Lincoln Stein, Peter Stevens, Leszek Vincent, Felipe Zapata, Doreen Ware

Peter Stevens

Review: Steve: The Art Museum Social Tagging Project., Mark Mcbride

Mark F McBride

The Steve Museum is a social tagging project created by volunteers from art museums and galleries. The main goal is to create user generated descriptions for works of art, because we all view, experience, and describe art differently.

Towards Tractable Local Closed World Reasoning For The Semantic Web, Matthias Knorr, Jose Julio Alferes, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Recently, the logics of minimal knowledge and negation as failure MKNF [12] was used to introduce hybrid MKNF knowledge bases [14], a powerful formalism for combining open and closed world reasoning for the Semantic Web. We present an extension based on a new three-valued framework including an alternating fixpoint, the well-founded MKNF model. This approach, the well-founded MKNF semantics, derives its name from the very close relation to the corresponding semantics known from logic programming. We show that the well-founded MKNF model is the least model among all (three-valued) MKNF models, thus soundly approximating also the two-valued MKNF models from …

Video On The Semantic Sensor Web, Cory Andrew Henson, Amit P. Sheth, Prateek Jain, Josh Pschorr, Terry Rapoch

Kno.e.sis Publications

Millions of sensors around the globe currently collect avalanches of data about our world. The rapid development and deployment of sensor technology is intensifying the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data, especially video sensor data, can be annotated with semantic metadata to provide contextual information about videos on the Web. In particular, we present an approach to annotating video sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and …

Semantic Web For Health Care And Biomedical Informatics, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

An Integrated Social Actor And Service Oriented Architecture (Soa) Approach For Improved Electronic Health Record (Ehr) Privacy And Confidentiality In The Us National Healthcare Information Network (Nhin), Gondy Leroy, Elliot Sloane, Steven Sheetz

CGU Faculty Publications and Research

The emerging US National Healthcare Information Network (NHIN) will improve healthcare’s efficacy, efficiency, and safety. The first-generation NHIN being developed has numerous advantages and limitations. One of the most difficult aspects of today’s NHIN is ensuring privacy and confidentiality for personal health data, because family and caregivers have multiple complex legal relationships to a patient. A Social Actor framework is suggested to organize and manage these legal roles, but the Social Actor framework would be very difficult to implement in today’s NHIN. Social Actor Security Management could, however, be effectively implemented using Service Oriented Architectures (SOAs), which are rapidly becoming …

A General Boosting Method And Its Application To Learning Ranking Functions For Web Search, Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun

Kno.e.sis Publications

We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach is based on optimization of quadratic upper bounds of the loss functions which allows us to present a rigorous convergence analysis of the algorithm. More importantly, this general framework enables us to use a standard regression base learner such as decision trees for fitting any loss function. We illustrate an application of the proposed method in learning ranking functions for Web search by combining both preference data and labeled data for training. We present experimental …

Combining Geospatial And Temporal Ontologies, Kripa Joshi

Electronic Theses and Dissertations

Publicly available ontologies are growing in number at present. These ontologies describe entities in a domain and the relations among these entities. This thesis describes a method to automatically combine a pair of orthogonal ontologies using cross products. A geospatial ontology and a temporal ontology are combined in this work. Computing the cross product of the geospatial and the temporal ontologies gives a complete set of pairwise combination of terms from the two ontologies. This method offers researchers the benefit of using ontologies that are already existing and available rather than building new ontologies for areas outside their scope of …

Linking Moving Object Databases With Ontologies, Kraig King

Electronic Theses and Dissertations

This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies …

Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan

Research Collection School Of Computing and Information Systems

This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the …

I’M A Virus Harming The Earth, M. Thulasidas

Research Collection School Of Computing and Information Systems

We humans plunder the raw material from our host planet with such an abandon that is only seen in viruses.

Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias

Research Collection School Of Computing and Information Systems

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without …

Self-Organizing Neural Architectures And Cooperative Learning In A Multiagent Environment, Dan Xiao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Temporal-Difference–Fusion Architecture for Learning, Cognition, and Navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the …

Leveraging Semantic Web Techniques To Gain Situational Awareness, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.