Physical Sciences and Mathematics Commons^™

Developing A Virtual City For Emergency Preparedness Planning And Training, Jon K. Morgan

Theses

Existing techniques for emergency preparedness planning and training fail or lack the ability to convey training on a broad scale and timely fashion. Skill sets that are required for planning, mitigation, response and recovery issues are lost through information overload or failure to identify other channels in which to convey the information. In order to resolve some of the issues with currently existing methods such as tabletop training exercises (TTX), instructional video learning and full-scale exercises we can turn to virtual environments.

In a virtual environment teams can interact with their surroundings from the comfort of the office without having …

Challenges Of Creating A Knowledge-Based Society: Education & Research For India & Gujarat, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

A Generic Approach And Framework For Managing Complex Information, Essam Mansour

Doctoral

Several application domains, such as healthcare, incorporate domain knowledge into their day-to-day activities to standardise and enhance their performance. Such incorporation produces complex information, which contains two main clusters (active and passive) of information that have internal connections between them. The active cluster determines the recommended procedure that should be taken as a reaction to specific situations. The passive cluster determines the information that describes these situations and other descriptive information plus the execution history of the complex information. In the healthcare domain, a medical patient plan is an example for complex information produced during the disease management activity from …

An Infrastructure For Performance Measurement And Comparison Of Information Retrieval Solutions, Gary Saunders

Theses and Dissertations

The amount of information available on both public and private networks continues to grow at a phenomenal rate. This information is contained within a wide variety of objects, including documents, e-mail archives, medical records, manuals, pictures and music. To be of any value, this data must be easily searchable and accessible. Information Retrieval (IR) is concerned with the ability to find and gain access to relevant information. As electronic data repositories continue to proliferate, so too, grows the variety of methods used to locate and access the information contained therein. Similarly, the introduction of innovative retrieval strategies—and the optimization of …

Connectionist Model Generation: A First-Order Approach, Sebastian Bader, Pascal Hitzler, Steffen Holldobler

Computer Science and Engineering Faculty Publications

Knowledge-based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural-symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. We show in this …

Applications Of Voting Theory To Information Mashups, Alfredo Alba, Varun Bhagwan, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Nachiketa Sahoo

Kno.e.sis Publications

Blogs, discussion forums and social networking sites are an excellent source for people's opinions on a wide range of topics. We examine the application of voting theory to "information mashups" - the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments and listening behavior from several Web communities. We consider different voting systems as algorithms to combine opinions from multiple sources and evaluate their effectiveness using social welfare functions. Different voting schemes are found to work better in some …

Text Analytics For Semantic Computing - The Good, The Bad And The Ugly, Meenakshi Nagarajan, Cartic Ramakrishnan, Amit P. Sheth

Kno.e.sis Publications

This tutorial was give at the Second IEEE International Conference on Semantic Computing Santa Clara, CA, USA - August 4-7, 2008.

Tcruzikb: Enabling Complex Queries For Genomic Data Exploration, Pablo N. Mendes, Bobby Mcknight, Amit P. Sheth, Jessica C. Kissinger

Kno.e.sis Publications

We developed a novel analytical environment to aid in the examination of the extensive amount of interconnected data available for genome projects. Our focus is to enable flexibility and abstraction from implementation details, while retaining the expressivity required for post-genomic research. To achieve this goal, we associated genomics data to ontologies and implemented a query formulation and execution environment with added visualization capabilities. We use ontology schemas to guide the user through the process of building complex queries in a flexible Web interface. Queries are serialized in SPARQL and sent to servers via Ajax. A component for visualization of the …

Mediatability: Estimating The Degree Of Human Involvement In Xml Schema Mediation, Karthik Gomadam, Ajith Harshana Ranabahu, Lakshmish Ramaswamy, Amit P. Sheth, Kunal Verma

Kno.e.sis Publications

Mediation and integration of data are significant challenges because the number of services on the Web, and heterogeneities in their data representation, continue to increase rapidly. To address these challenges we introduce a new measure, mediatability, which is a quantifiable and computable metric for the degree of human involvement in XML schema mediation. We present an efficient algorithm to compute mediatability and an experimental study to analyze how semantic annotations affect the ease of mediating between two schemas. We validate our approach by comparing mediatability scores generated by our system with user-perceived difficulty. We also evaluate the scalability of our …

E-Transcript Web Services System Supporting Dynamic Conversion Between Xml And Edi, Myungjae Kwak '11, Woohyun Kang '14, Gondy Leroy, Samir Chatterjee

CGU Faculty Publications and Research

As XML becomes a standard for communications between distributed heterogeneous machines, many schools plan to implement Web Services systems using the XML e-transcript (electronic transcript) standard. We propose a framework that supports both XML e-transcript Web Services and existing EDI e-transcript systems. The framework uses the workflow engine to exploit the benefits of workflow management mechanisms. The workflow engine manages the e-transcript business process by enacting and completing the tasks and sub-processes within the main business process. We implemented the proposed framework by using various open source projects including Java, Eclipse, and Apache Software Foundation’s Web Services projects. Compared with …

An Xml-Based Approach To Handling Tables In Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni

Kno.e.sis Publications

We explore application of XML technology for handling tables in legacy semi-structured documents. Specifically, we analyze annotating heterogeneous documents containing tables to obtain a formalized XML Master document that improves traceability (hence easing verification and update) and enables manipulation using XSLT stylesheets. This approach is useful when table instances far outnumber distinct table types because the effort required to annotate a table instance is relatively less compared to formalizing table processing that respects table’s semantics. This work is also relevant for authoring new documents with tables that should be accessible to both humans and machines.

Classification In P2p Networks By Bagging Cascade Rsvms, Hock Hee Ang, Vikvekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng, Anwitaman Datta

Research Collection School Of Computing and Information Systems

Data mining tasks in P2P are bound by issues like scalability, peer dynamism, asynchronism, and data privacy preservation. These challenges pose difficulties for deploying conventional machine learning techniques in P2P networks, which may be hard to achieve classification accuracies comparable to regular centralized solutions. We recently investigated the classification problem in P2P networks and proposed a novel P2P classification approach by cascading Reduced Support Vector Machines (RSVM). Although promising results were obtained, the existing solution has some drawback of redundancy in both communication and computation. In this paper, we present a new approach to over the limitation of the previous …

Knowledge Transfer Via Multiple Model Local Structure Mapping, Jing Gao, Wei Fan, Jing Jiang, Jiawei Han

Research Collection School Of Computing and Information Systems

The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple …

Authenticating The Query Results Of Text Search Engines, Hwee Hwa Pang, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

The number of successful attacks on the Internet shows that it is very difficult to guarantee the security of online search engines. A breached server that is not detected in time may return incorrect results to the users. To prevent that, we introduce a methodology for generating an integrity proof for each search result. Our solution is targeted at search engines that perform similarity-based document retrieval, and utilize an inverted list implementation (as most search engines do). We formulate the properties that define a correct result, map the task of processing a text search query to adaptations of existing threshold-based …

A Lightweight Buyer-Seller Watermarking Protocol, Yongdong Wu, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

The buyer-seller watermarking protocol enables a seller to successfully identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Based on finite field theory and the homomorphic property of public key cryptosystems such as RSA, several buyer-seller watermarking protocols (N. Memon and P. W. Wong (2001) and C.-L. Lei et al. (2004)) have been proposed previously. However, those protocols require not only large computational power but also substantial network bandwidth. In this paper, we introduce a new buyer-seller protocol that overcomes those weaknesses by managing the watermarks. Compared with the earlier protocols, ours is …

Description Logic Rules, Markus Krotzsch, Sebastian Rudolph, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We introduce description logic (DL) rules as a new rule-based formalism for knowledge representation in DLs. As a fragment of the Semantic Web Rule Language SWRL, DL rules allow for a tight integration with DL knowledge bases. In contrast to SWRL, however, the combination of DL rules with expressive description logics remains decidable, and we show that the DL SROIQ – the basis for the ongoing standardisation of OWL 2 – can completely internalise DL rules. On the other hand, DL rules capture many expressive features of SROIQ that are not available in simpler DLs yet. While reasoning in SROIQ …

Boosting With Incomplete Information, Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao

Kno.e.sis Publications

In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. …

Semantic Web: Promising Technologies, Current Applications & Future Directions, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Dependence Of Binary Associations On Co-Occurrence Granularity In News Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni, Mastan Vali Shaik

Kno.e.sis Publications

We describe and formalize an approach to correlate binary associations (such as between entities and events, between persons and events, etc.) implied by News documents on the co-occurrence granularity (such as document-level, paragraph-level, sentence-level, etc.) of the corresponding text phrases in the documents. Specifically, we present both qualitative and quantitative characterization of searching News documents: former in terms of the nature of the content and the queries, and latter in terms of a metric obtained by adapting the notions of precision and recall. Specifically, the approach tries to reduce the manual effort required to analyze the News documents to compare …

Information Technology’S Influence On Productivity, Jason Smith

Student Work

Previous research has had mixed results correlating information technology investments to increases in productivity. This research surveyed the perceptions of information technology managers to determine the impact that information technology, decentralized decision making, and improved business processes have on productivity. It concluded that information technology’s influence on productivity is to magnify the effect of decentralized decision making and improved business processes.

Active Kernel Learning, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints). However, most previous studies are limited to “passive” kernel learning in which side information is provided beforehand. In this paper we present a framework of Active Kernel Learning (AKL) that actively identifies the most informative pairwise constraints for kernel learning. The key challenge of active kernel learning is how to measure the informativeness of an example pair given its …

Comments-Oriented Document Summarization: Understanding Documents With Readers' Feedback, Meishan Hu, Aixin Sun, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the …

Tree-Based Partition Querying: A Methodology For Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou

Research Collection School Of Computing and Information Systems

Besides traditional domains (e.g., resource allocation, data mining applications), algorithms for medoid computation and related problems will play an important role in numerous emerging fields, such as location based services and sensor networks. Since the k-medoid problem is NP hard, all existing work deals with approximate solutions on relatively small datasets. This paper aims at efficient methods for very large spatial databases, motivated by: (i) the high and ever increasing availability of spatial data, and (ii) the need for novel query types and improved services. The proposed solutions exploit the intrinsic grouping properties of a data partition index in order …

Estimating Local Optimums In Em Algorithm Over Gaussian Mixture Model, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung

Research Collection School Of Computing and Information Systems

EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum.

Ranked Reverse Nearest Neighbor Search, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee

Research Collection School Of Computing and Information Systems

Given a set of data points P and a query point q in a multidimensional space, Reverse Nearest Neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-Nearest Neighbor (RkNN) query (where k ≥ 1) generalizes RNN query to find data points whose kNNs include q. For RkNN query semantics, q is said to have influence to all those answer data points. The degree of q's influence on a data point p (∈ P) is denoted by κp where q is the κp-th NN of p. We introduce a new variant of RNN query, namely, …

User Guidance Of Resource-Adaptive Systems, João Pedro Sousa, Rajesh Krishna Balan, Vahe Poladian, David Garlan, Mahadev Satyanarayanan

Research Collection School Of Computing and Information Systems

This paper presents a framework for engineering resource-adaptive software systems targeted at small mobile devices. The proposed framework empowers users to control tradeoffs among a rich set of ervicespecific aspects of quality of service. After motivating the problem, the paper proposes a model for capturing user preferences with respect to quality of service, and illustrates prototype user interfaces to elicit such models. The paper then describes the extensions and integration work made to accommodate the proposed framework on top of an existing software infrastructure for ubiquitous computing. The research question addressed here is the feasibility of coordinating resource allocation and …

Semi-Supervised Ensemble Ranking, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining …

A Self-Organizing Neural Model For Multimedia Information Fusion, Luong-Dong Nguyen, Kia-Yan Woon, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents a self-organizing network model for the fusion of multimedia information. By synchronizing the encoding of information across multiple media channels, the neural model known as fusion Adaptive Resonance Theory (fusion ART) generates clusters that encode the associative mappings across multimedia information in a real-time and continuous manner. In addition, by incorporating a semantic category channel, fusion ART further enables multimedia information to be fused into predefined themes or semantic categories. We illustrate the fusion ART’s functionalities through experiments on two multimedia data sets in the terrorist domain and show the viability of the proposed approach.

A Framework For Trust And Distrust Networks, Krishnaprasad Thirunarayan

Kno.e.sis Publications

In this age of internet and electronic commerce it is becoming increasingly important to have and to manipulate information about the trustworthiness of the content or service providers in order to make informed decisions. This paper explores realistic models of trust and distrust based on partially ordered discrete values and proposes a framework, which is sensitive to local, relative ordering of values rather than their magnitudes. The framework distinguishes between direct and inferred trust, preferring direct information over possibly conflicting inferred information. It also represents ambiguity or inconsistency explicitly. The framework is capable of handling general trust and belief networks …

A Forgetting-Based Approach For Reasoning With Inconsistent Distributed Ontologies, Guilin Qi, Yimin Wang, Peter Haase, Pascal Hitzler

Computer Science and Engineering Faculty Publications

In the context of multiple distributed ontologies, we are often confronted with the problem of dealing with inconsistency. In this paper, we propose an approach for reasoning with inconsistent distributed ontologies based on concept forgetting. We firstly define concept forgetting in description logics. We then adapt the notions of recoveries and preferred recoveries in propositional logic to description logics. Two consequence relations are then defined based on the preferred recoveries.