Physical Sciences and Mathematics Commons^™

An Event-Driven Approach To Computerizing Clinical Guidelines Using Xml, Bing Wu, Essam Mansour, Kudakwashe Dube, Jianxin Li

Conference Papers

Clinical events form the basis of patient care practice. Their computerization is an important aid to the work of clinicians. Clinical guidelines or protocols direct clinicians and patients on when and how to handle clinical problems. Thus, clinical guidelines are an encapsulation of clinical events. Hence, an event-driven approach to computerizing the management of clinical guidelines is worthy of investigation. In our framework, called SpEM, the main clinical guideline management dimensions are specification, execution, and manipulation. This paper presents an event-driven approach, within the context of the SpEM framework, to manage clinical guidelines. The event-driven approach is based on the …

Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as °uctuations of edge weights. The ¯rst one maintains the query results by processing only updates that may invalidate …

Three Architectures For Trusted Data Dissemination In Edge Computing, Shen-Tat Goh, Hwee Hwa Pang, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Edge computing pushes application logic and the underlying data to the edge of the network, with the aim of improving availability and scalability. As the edge servers are not necessarily secure, there must be provisions for users to validate the results—that values in the result tuples are not tampered with, that no qualifying data are left out, that no spurious tuples are introduced, and that a query result is not actually the output from a different query. This paper aims to address the challenges of ensuring data integrity in edge computing. We study three schemes that enable users to check …

Wireless Indoor Positioning System With Enhanced Nearest Neighbors In Signal Space Algorithm, Quang Tran, Juki Wirawan Tantra, Ah-Hwee Tan, Ah-Hwee Tan, Kin-Choong Yow, Dongyu Qiu

Research Collection School Of Computing and Information Systems

With the rapid development and wide deployment of wireless Local Area Networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common Nearest Neighbor in Signal Space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some …

Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan

Research Collection School Of Computing and Information Systems

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …

Masking Page Reference Patterns In Encryption Databases On Untrusted Storage, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

To support ubiquitous computing, the underlying data have to be persistent and available anywhere-anytime. The data thus have to migrate from devices that are local to individual computers, to shared storage volumes that are accessible over open network. This potentially exposes the data to heightened security risks. In particular, the activity on a database exhibits regular page reference patterns that could help attackers learn logical links among physical pages and then launch additional attacks. We propose two countermeasures to mitigate the risk of attacks initiated through analyzing the shared storage server’s activity for those page patterns. The first countermeasure relocates …

The Viability Of Is Enhanced Knowledge Sharing In Mission-Critical Command And Control Centers, Sameh A. Sabet

Dissertations

Engineering processes such as the maintenance of mission-critical infrastructures are highly unpredictable processes that are vital for everyday life, as well as for national security goals. These processes are categorized as Emergent Knowledge Processes (EKP), organizational processes that are characterized by a changing set of actors, distributed knowledge bases, and emergent knowledge sharing activities where the process itself has no predetermined structure. The research described here utilizes the telecommunications network fault diagnosis process as a specific example of an EKP. The field site chosen for this research is a global undersea telecommunication network where nodes are staffed by trained personnel …

Information Availability And Security Policy, Andrew P. Martin, Deepak Khazanchi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

Information availability is a key element of information security. However, information availability has not been addressed with the same enthusiasm as confidentiality and integrity because availability is impacted by many variables which cannot easily be controlled. The principal goal of this research is to characterize information availability in detail and investigate how effective enterprise security policy can ensure availability.

Geoexpert - An Expert System Based Framework For Data Quality In Spatial Databases, Aditya Kumar

Masters Theses & Specialist Projects

Usage of very large sets of historical spatial data in knowledge discovery process became a common trend, and in order to obtain better results from this knowledge discovery process the data should be of high quality. In this thesis we proposed a framework 'GeoExpert' for data quality assessment and cleansing tool for spatial data that integrates the spatial data visualization and analysis capabilities of the ARCGIS, the reason and inference capability of an expert system. In this thesis we implemented the proposed framework both stand-alone and web versions using ArcGIS Engine and ArcGIS Server, respectively. We used JESS expert system …

A Hybrid Architecture Combining Reactive Plan Execution And Reactive Learning, Samin Karim, Liz Sonenberg, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Developing software agents has been complicated by the problem of how knowledge should be represented and used. Many researchers have identified that agents need not require the use of complex representations, but in many cases suffice to use “the world” as their representation. However, the problem of introspection, both by the agents themselves and by (human) domain experts, requires a knowledge representation with a higher level of abstraction that is more ‘understandable’. Learning and adaptation in agents has traditionally required knowledge to be represented at an arbitrary, low-level of abstraction. We seek to create an agent that has the capability …

Collaborative Image Retrieval Via Regularized Metric Learning, Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring …

Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang

Research Collection School Of Computing and Information Systems

In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a 'data-centric approach' where the evaluation data is assumed to represent the ground truth'. The standard statistical approaches take evaluation and deviation at face value. We argue that attention should be paid to the subjectivity of evaluation, judging the evaluation score not just on 'what is being said' (deviation), but also on 'who says it' (reviewer) as well as on 'whom it is said about' (object). Furthermore, we observe that bias …

Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang

Research Collection School Of Computing and Information Systems

Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semi-supervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently …

An Energy-Efficient And Access Latency Optimized Indexing Scheme For Wireless Data Broadcast, Yuxia Yao, Xueyan Tang, Ee Peng Lim, Aixin Sun

Research Collection School Of Computing and Information Systems

Data broadcast is an attractive data dissemination method in mobile environments. To improve energy efficiency, existing air indexing schemes for data broadcast have focused on reducing tuning time only, i.e., the duration that a mobile client stays active in data accesses. On the other hand, existing broadcast scheduling schemes have aimed at reducing access latency through nonflat data broadcast to improve responsiveness only. Not much work has addressed the energy efficiency and responsiveness issues concurrently. This paper proposes an energy-efficient indexing scheme called MHash that optimizes tuning time and access latency in an integrated fashion. MHash reduces tuning time by …

Personal Privacy Protection Within Pervasive Rfid Environments, Eeva Kaarina Hedefine

Electronic Theses and Dissertations

Recent advancements in location tracking technologies have increased the threat to an individual's personal privacy. Radio frequency identification (RFID) technology allows for the identification and potentially continuous tracking of an object or individual, without obtaining the individual's consent or even awareness that the tracking is taking place. Although many positive applications for RFID technology exist, for example in the commercial sector and law enforcement, the potential for abuse in the collection and use of personal information through this technology also exists. Location data linked to other types of personal information allows not only the detection of past spatial travel and …

Semantic Similarity Of Spatial Scenes, Konstantinos A. Nedas

Electronic Theses and Dissertations

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their …

Querying Formal Contexts With Answer Set Programs, Pascal Hitzler, Markus Krotzsch

Computer Science and Engineering Faculty Publications

Recent studies showed how a seamless integration of formal concept analysis (FCA), logic of domains, and answer set programming (ASP) can be achieved. Based on these results for combining hierarchical knowledge with classical rule-based formalisms, we introduce an expressive common-sense query language for formal contexts. Although this approach is conceptually based on order-theoretic paradigms, we show how it can be implemented on top of standard ASP systems. Advanced features, such as default negation and disjunctive rules, thus become practically available for processing contextual data.

Geospatial Ontology Development And Semantic Analytics, I. Budak Arpinar, Cartic Ramakrishnan, Molly Azami, Amit P. Sheth, E. Lynn Usery, Mei-Po Kwan

Kno.e.sis Publications

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we …

Optimal Adaptation Of Web Processes With Inter-Service Dependencies, Kunal Verma, Prashant Doshi, Karthik Gomadam, John A. Miller, Amit P. Sheth

Kno.e.sis Publications

We present methods for optimally adapting Web processes to exogenous events while preserving inter-service dependencies. For example, in a supply chain process, orders placed by the manufacturer may get delayed in arriving. In response to this event, the manufacturer has the choice of either waiting out the delay or changing the supplier. Additionally, there may be compatibility constraints between the different orders, thereby introducing the problem of coordination between them if the manufacturer chooses to change the suppliers. We present our methods within the framework of autonomic Web processes. This framework seeks to add properties of self-configuration, adaptation, and self-optimization …

Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the domain ontology. To provide personalized information access, we further develop algorithms to learn and exploit user ontology model based on a customized view of the domain ontology. The proposed system has been applied …

Keyframe Retrieval By Keypoints: Can Point-To-Point Matching Help?, Wanlei Zhao, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Bag-of-words representation with visual keypoints has recently emerged as an attractive approach for video search. In this paper, we study the degree of improvement when point-to-point (P2P) constraint is imposed on the bag-of-words. We conduct investigation on two tasks: near-duplicate keyframe (NDK) retrieval, and high-level concept classification, covering parts of TRECVID 2003 and 2005 datasets. In P2P matching, we propose a one-to-one symmetric keypoint matching strategy to diminish the noise effect during keyframe comparison. In addition, a new multi-dimensional index structure is proposed to speed up the matching process with keypoint filtering. Through experiments, we demonstrate that P2P constraint can …

Authenticating Multi-Dimensional Query Results In Data Publishing, Weiwei Cheng, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

In data publishing, the owner delegates the role of satisfying user queries to a third-party publisher. As the publisher may be untrusted or susceptible to attacks, it could produce incorrect query results. This paper introduces a mechanism for users to verify that their query answers on a multi-dimensional dataset are correct, in the sense of being complete (i.e., no qualifying data points are omitted) and authentic (i.e., all the result values originated from the owner). Our approach is to add authentication information into a spatial data structure, by constructing certified chains on the points within each partition, as well as …

Hierarchical Hidden Markov Model For Rushes Structuring And Indexing, Chong-Wah Ngo, Zailiang Pan, Xiaoyong Wei

Research Collection School Of Computing and Information Systems

Rushes footage are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, it is difficult to mine the "gold" from the rushes since usually only minimum metadata is available. This paper focuses on the structuring and indexing of the rushes to facilitate mining and retrieval of "gold". We present a new approach for rushes structuring and indexing based on motion feature. We model the problem by a two-level Hierarchical Hidden Markov Model (HHMM). The HHMM, on one hand, represents the semantic concepts in its higher level to provide simultaneous structuring and indexing, on …

Gaussian Mixture Models And Neural Networks For Automatic Speaker Identification, Usha Gayatri Chalkapally

Electrical & Computer Engineering Theses & Dissertations

Automatic Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information contained in speech signals. This technique of Automatic Speaker Recognition makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

In this thesis, the techniques of Gaussian Mixture Models and Neural Networks for Automatic Speaker Identification are presented. Algorithms for Speaker Identification using Gaussian Mixture Models were developed, …

Prediction-Based Gesture Detection In Lecture Videos By Combining Visual, Speech And Electronic Slides, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong

Research Collection School Of Computing and Information Systems

This paper presents an efficient algorithm for gesture detection in lecture videos by combining visual, speech and electronic slides. Besides accuracy, response time is also considered to cope with the efficiency requirements of real-time applications. Candidate gestures are first detected by visual cue. Then we modifity HMM models for complete gestures to predict and recognize incomplete gestures before the whole gestures paths are observed. Gesture recognition is used to verify the results of gesture detection. The relations between visual, speech and slides are analyzed. The correspondence between speech and gesture is employed to improve the accuracy and the responsiveness of …

Extraction Of Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage …

Student Interactive Campus Map At Marshall University, Edward Aractingi, Jamie Wolfe

IT Research

Marshall University is a state-funded university in Huntington, West Virginia. Like many universities, it is a large organization with multiple and diverse units (colleges, departments, centers, etc.) and depends on data to run efficiently. Much of this data is used by multiple entities. To better manage the needed data collected by the university, the Marshall University Geographic Information System (MUGIS) has been developed. MUGIS will address several needs of Marshall University’s principal stakeholders. Stakeholders include the university administration, faculty, and students. One of the first applications developed for MUGIS was an interactive campus map. This Web-based application is intended to …

A Metamodel And Uml Profile For Rule-Extended Owl Dl Ontologies, Saartje Brockmans, Peter Haase, Pascal Hitzler, Rudi Studer

Computer Science and Engineering Faculty Publications

In this paper we present a MOF compliant metamodel and UML profile for the Semantic Web Rule Language (SWRL) that integrates with our previous work on a metamodel and UML profile for OWL DL. Based on this metamodel and profile, UML tools can be used for visual modeling of rule-extended ontologies.

Masquerader Detection Using Oclep: One-Class Classification Using Length Statistics Of Emerging Patterns, Lijun Chen, Guozhu Dong

Kno.e.sis Publications

We introduce a new method for masquerader detection that only uses a user’s own data for training, called Oneclass Classification using Length statistics of Emerging Patterns (OCLEP). Emerging patterns (EPs) are patterns whose support increases from one dataset/class to another with a big ratio, and have been very useful in earlier studies. OCLEP classifies a case T as self or masquerader by using the average length of EPs obtained by contrasting T against sets of samples of a user’s normal data. It is based on the observation that one needs long EPs to differentiate instances from a common class, but …

Interacting With Web Hierarchies, Saverio Perugini, Naren Ramakrishnan

Computer Science Faculty Publications

Web site interfaces are a particularly good fit for hierarchies in the broadest sense of that idea, i.e. a classification with multiple attributes, not necessarily a tree structure. Several adaptive interface designs are emerging that support flexible navigation orders, exposing and exploring dependencies, and procedural information-seeking tasks. This paper provides a context and vocabulary for thinking about hierarchical Web sites and their design. The paper identifies three features that interface to information hierarchies. These are flexible navigation orders, the ability to expose and explore dependencies, and support for procedural tasks. A few examples of these features are also provided