Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (178)
- University of Dayton (54)
- Old Dominion University (14)
- University of Arkansas, Fayetteville (6)
- University of Nebraska - Lincoln (6)
-
- Technological University Dublin (5)
- Dakota State University (4)
- Institute of Business Administration (4)
- LSU New Orleans (4)
- San Jose State University (4)
- University of Nevada, Las Vegas (4)
- City University of New York (CUNY) (3)
- East Tennessee State University (3)
- Montclair State University (3)
- Portland State University (3)
- The University of San Francisco (3)
- University of Kentucky (3)
- University of Louisville (3)
- California Polytechnic State University, San Luis Obispo (2)
- Claremont Colleges (2)
- Georgia Southern University (2)
- Kennesaw State University (2)
- New Jersey Institute of Technology (2)
- Southern Methodist University (2)
- The University of Akron (2)
- University of Malaya (2)
- University of Missouri, St. Louis (2)
- University of North Florida (2)
- Ursinus College (2)
- Association of Arab Universities (1)
- Keyword
-
- Online learning (15)
- Algorithms (12)
- Algorithm (11)
- Machine Learning (10)
- Data mining (9)
-
- Machine learning (8)
- Artificial intelligence (7)
- Clustering (7)
- Big data (6)
- Classification (6)
- Peer-to-peer computing (6)
- Algorithm design and analysis (5)
- Computer science (5)
- Fairness (5)
- Query processing (5)
- Deep Learning (4)
- Delay (4)
- Graph theory (4)
- Optimization (4)
- Peer to peer computing (4)
- Privacy (4)
- Probability (4)
- Real-world datasets (4)
- Resilience (4)
- Support vector machines (4)
- Active learning (3)
- Adaptive (3)
- Analytical models (3)
- Approximation algorithm (3)
- Approximation algorithms (3)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (175)
- Computer Science Faculty Publications (44)
- MAICS: The Modern Artificial Intelligence and Cognitive Science Conference (12)
- Electronic Theses and Dissertations (5)
- Graduate Theses and Dissertations (5)
-
- International Conference on Information and Communication Technologies (4)
- LSU New Orleans Theses and Dissertations (4)
- Complex Systems Faculty Publications and Presentations (3)
- Conference papers (3)
- Department of Computer Science Faculty Scholarship and Creative Works (3)
- Dissertations (3)
- Media Studies (3)
- School of Computing: Dissertations, Theses, and Student Research (3)
- CGU Faculty Publications and Research (2)
- Computer Science Summer Fellows (2)
- Computer Science Working Papers (2)
- Dissertations and Theses Collection (Open Access) (2)
- Electrical & Computer Engineering Faculty Research (2)
- Electrical & Computer Engineering Theses & Dissertations (2)
- Engineering Management & Systems Engineering Faculty Publications (2)
- Library Philosophy and Practice (e-journal) (2)
- Masters Theses & Doctoral Dissertations (2)
- SMU Data Science Review (2)
- SWITCH (2)
- Theses (2)
- Theses and Dissertations--Computer Science (2)
- UNF Graduate Theses and Dissertations (2)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (2)
- Williams Honors College, Honors Research Projects (2)
- Al Jinan الجنان (1)
- Publication Type
- File Type
Articles 301 - 330 of 353
Full-Text Articles in Theory and Algorithms
Link Lifetimes And Randomized Neighbor Selection In Dhts, Zhongmei Yao, Dmitri Loguinov
Link Lifetimes And Randomized Neighbor Selection In Dhts, Zhongmei Yao, Dmitri Loguinov
Computer Science Faculty Publications
Several models of user churn, resilience, and link lifetime have recently appeared in the literature [12], [13], [34], [35]; however, these results do not directly apply to classical Distributed Hash Tables (DHTs) in which neighbor replacement occurs not only when current users die, but also when new user arrive into the system, and where replacement choices are often restricted to the successor of the failed zone in the DHT space. To understand neighbor churn in such networks, this paper proposes a simple, yet accurate, model for capturing link dynamics in structured P2P systems and obtains the distribution of link lifetimes …
Symbolic Links In The Open Directory Project, Saverio Perugini
Symbolic Links In The Open Directory Project, Saverio Perugini
Computer Science Faculty Publications
We present a study to develop an improved understanding of symbolic links in web directories. A symbolic link is a hyperlink that makes a directed connection from a web page along one path through a directory to a page along another path. While symbolic links are ubiquitous in web directories such as Yahoo!, they are under-studied, and as a result, their uses are poorly understood. A cursory analysis of symbolic links reveals multiple uses: to provide navigational shortcuts deeper into a directory, backlinks to more general categories, and multiclassification. We investigated these uses in the Open Directory Project (ODP), the …
Enabling Synergy Between Psychology And Natural Language Processing For E-Government: Crime Reporting And Investigative Interview System, Alicia Iriberri '06, Chih Hao Ku '12, Gondy Leroy
Enabling Synergy Between Psychology And Natural Language Processing For E-Government: Crime Reporting And Investigative Interview System, Alicia Iriberri '06, Chih Hao Ku '12, Gondy Leroy
CGU Faculty Publications and Research
We are developing an automated crime reporting and investigative interview system. The system incorporates cognitive interview techniques to maximize witness memory recall, and information extraction technology to extract and annotate crime entities from witness narratives and interview responses. Evaluations of the IE components of the system show that it captures 70 to 77% of information from witness narratives with 93 to 100% precision. Our development goal is for the system to approximate progressively the performance effectiveness of a human investigative interviewer and to generate graphical visualizations of crime report information.
Natural Language Processing And E-Government: Crime Information Extraction From Heterogeneous Data Sources, Chih Hao Ku '12, Alicia Iriberri '06, Gondy Leroy
Natural Language Processing And E-Government: Crime Information Extraction From Heterogeneous Data Sources, Chih Hao Ku '12, Alicia Iriberri '06, Gondy Leroy
CGU Faculty Publications and Research
Much information that could help solve and prevent crimes is never gathered because the reporting methods available to citizens and law enforcement personnel are not optimal. Detectives do not have sufficient time to interview crime victims and witnesses. Moreover, many victims and witnesses are too scared or embarrassed to report incidents. We are developing an interviewing system that will help collect such information. We report here on one component, the crime information extraction module, which uses natural language processing to extract crime information from police reports, newspaper articles, and victims’ and witnesses’ crime narratives. We tested our approach with two …
Neighborhood Defined Adaboost Based Mixture Of Color Components For Efficient Skin Segmentation, Ramya Reddy Maaram
Neighborhood Defined Adaboost Based Mixture Of Color Components For Efficient Skin Segmentation, Ramya Reddy Maaram
Electrical & Computer Engineering Theses & Dissertations
A skin segmentation algorithm robust to illumination changes and skin-like backgrounds is developed in this thesis. So far skin pixel classification has been limited to only individual color spaces and there has not been a comprehensive evaluation of which color components or combination of color components would provide the best classification accuracy, Color components in a given color space form the feature set for the classification of skin pixels. The combination of the color components or the features present within a single color space may not be the best when it comes to skin pixel classification as the discriminatory power …
A Lateral Symmetry Approach To Percentage-Based Hybrid Pattern (Php) Training, Sheng-Uei Guan, Kiruthika Ramanathan
A Lateral Symmetry Approach To Percentage-Based Hybrid Pattern (Php) Training, Sheng-Uei Guan, Kiruthika Ramanathan
Research Collection School Of Computing and Information Systems
In this paper, we investigate the application of lateral symmetry to supervised learning using genetic algorithms. The hypothesis is motivated by the presence of symmetry in the animal brain and by research results showing approximately equal task division between the two hemispheres of the brain. In this paper, each training pattern is considered a task. By applying the concept of lateral symmetry, we use global training (a typically right brained activity) to learn half the tasks and local training (a left brained activity) to learn the rest of the tasks. We verified the use of this Percentage-based Pattern (PHP) training …
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Near-duplicate keyframes (NDK) play a unique role in large-scale video search, news topic detection and tracking. In this paper, we propose a novel NDK retrieval approach by exploring both visual and textual cues from the visual vocabulary and semantic context respectively. The vocabulary, which provides entries for visual keywords, is formed by the clustering of local keypoints. The semantic context is inferred from the speech transcript surrounding a keyframe. We experiment the usefulness of visual keywords and semantic context, separately and jointly, using cosine similarity and language models. By linearly fusing both modalities, performance improvement is reported compared with the …
A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Kernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape models from large-scale sets of point cloud samples efficiently. In this paper, we propose a fast solution for approximating implicit surfaces based on a multi-scale Tikhonov regularization scheme. The optimization of our scheme is formulated into a sparse linear equation system, which can be efficiently solved by factorization methods. Different from traditional approaches, our scheme does not employ auxiliary off-surface points, which not only saves the computational cost but also avoids the problem …
Residual-Based Measurement Of Peer And Link Lifetimes In Gnutella Networks, Xiaoming Wang, Zhongmei Yao, Dmitri Loguinov
Residual-Based Measurement Of Peer And Link Lifetimes In Gnutella Networks, Xiaoming Wang, Zhongmei Yao, Dmitri Loguinov
Computer Science Faculty Publications
Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users "created" in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent …
On Node Isolation Under Churn In Unstructured P2p Networks With Heavy-Tailed Lifetimes, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
On Node Isolation Under Churn In Unstructured P2p Networks With Heavy-Tailed Lifetimes, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
Previous analytical studies [12], [18] of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared to uniform selection of neighbors. In fact, the second strategy based on random walks on age-weighted graphs demonstrates that for lifetimes with infinite variance, the system …
Modeling Heterogeneous User Churn And Local Resilience Of Unstructured P2p Networks, Zhongmei Yao, Derek Leonard, Dmitri Loguinov, Xiaoming Wang
Modeling Heterogeneous User Churn And Local Resilience Of Unstructured P2p Networks, Zhongmei Yao, Derek Leonard, Dmitri Loguinov, Xiaoming Wang
Computer Science Faculty Publications
Previous analytical results on the resilience of unstructured P2P systems have not explicitly modeled heterogeneity of user churn (i.e., difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a generic model of heterogeneous user churn, derive the distribution of the various metrics observed in prior experimental studies (e.g., lifetime distribution of joining users, joint distribution of session time of alive peers, and residual lifetime of a randomly selected user), derive several closed-form results on the transient behavior of in-degree, and eventually obtain the joint in/out degree isolation probability as a simple …
Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang
Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective …
Audio Similarity Measure By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo, Cuihua Fang, Xiaoou Chen, Jianguo Xiao
Audio Similarity Measure By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo, Cuihua Fang, Xiaoou Chen, Jianguo Xiao
Research Collection School Of Computing and Information Systems
This paper proposes a new approach for the similarity measure and ranking of audio clips by graph modeling and matching. Instead of using frame-based or salient-based features to measure the acoustical similarity of audio clips, segment-based similarity is proposed. The novelty of our approach lies in two aspects: segment-based representation, and the similarity measure and ranking based on four kinds of similarity factors. In segmentbased representation, segments not only capture the change property of audio clip, but also keep and present the change relation and temporal order of audio features. In the similarity measure and ranking, four kinds of similarity …
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
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 …
Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
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 …
Interacting With Web Hierarchies, Saverio Perugini, Naren Ramakrishnan
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
Adaptive Interpolation Algorithms For Temporal-Oriented Datasets, Jun Gao
Adaptive Interpolation Algorithms For Temporal-Oriented Datasets, Jun Gao
School of Computing: Dissertations, Theses, and Student Research
Spatiotemporal datasets can be classified into two categories: temporal-oriented and spatial-oriented datasets depending on whether missing spatiotemporal values are closer to the values of its temporal or spatial neighbors. We present an adaptive spatiotemporal interpolation model that can estimate the missing values in both categories of spatiotemporal datasets. The key parameters of the adaptive spatiotemporal interpolation model can be adjusted based on experience.
A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin
A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin
Research Collection School Of Computing and Information Systems
Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback …
Realtime Query Expansion And Procedural Interfaces For Information Hierarchies, Saverio Perugini
Realtime Query Expansion And Procedural Interfaces For Information Hierarchies, Saverio Perugini
Computer Science Faculty Publications
We demonstrate the use of two user interfaces for interacting with web hierarchies. One uses the dependencies underlying a hierarchy to perform real-time query expansion and, in this way, acts as an in situ feedback mechanism. The other enables the user to cascade the output from one interaction to the input of another, and so on, and, in this way, supports procedural information-seeking tasks without disrupting the flow of interaction.
Information Assurance Through Binary Vulnerability Auditing, William B. Kimball, Saverio Perugini
Information Assurance Through Binary Vulnerability Auditing, William B. Kimball, Saverio Perugini
Computer Science Faculty Publications
The goal of this research is to develop improved methods of discovering vulnerabilities in software. A large volume of software, from the most frequently used programs on a desktop computer, such as web browsers, e-mail programs, and word processing applications, to mission-critical services for the space shuttle, is unintentionally vulnerable to attacks and thus insecure. By seeking to improve the identification of vulnerabilities in software, the security community can save the time and money necessary to restore compromised computer systems. In addition, this research is imperative to activities of national security such as counterterrorism. The current approach involves a systematic …
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, …
A Performance Comparison Of Data Encryption Algorithms, Aamer Nadeem, Dr. M. Younus Javed
A Performance Comparison Of Data Encryption Algorithms, Aamer Nadeem, Dr. M. Younus Javed
International Conference on Information and Communication Technologies
The principal goal guiding the design of any encryption algorithm must be security against unauthorized attacks. However, for all practical applications, performance and the cost of implementation are also important concerns. A data encryption algorithm would not be of much use if it is secure enough but slow in performance because it is a common practice to embed encryption algorithms in other applications such as e-commerce, banking, and online transaction processing applications. Embedding of encryption algorithms in other applications also precludes a hardware implementation, and is thus a major cause of degraded overall performance of the system. In this paper, …
Visualization Of Retrieved Positive Data Using Blending Function, Muhammad Shoaib, Habib -Ur- Rehman, Dr. Abad Ali Shah
Visualization Of Retrieved Positive Data Using Blending Function, Muhammad Shoaib, Habib -Ur- Rehman, Dr. Abad Ali Shah
International Conference on Information and Communication Technologies
Data visualization is an important technique used in data mining. We present the retrieved data into visual format to discover features and trends inherent to the data. Some features of the data to be retrieved are already known to us. Visualization should preserve these known features inherent to the data. Positivity is one such known feature that is inherent to most of the scientific and business data sets. For example, mass, volume and percentage concentration are meaningful only when they are positive values. However certain visualization techniques do not guarantee to preserve this feature while constructing visualization of retrieved data …
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Computer Science Faculty Publications
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
Aggregate Nearest Neighbor Queries In Spatial Databases, Dimitris Papadias, Yufei Tao, Kyriakos Mouratidis, Chun Kit Hui
Aggregate Nearest Neighbor Queries In Spatial Databases, Dimitris Papadias, Yufei Tao, Kyriakos Mouratidis, Chun Kit Hui
Research Collection School Of Computing and Information Systems
Given two spatial datasets P (e.g., facilities) and Q (queries), an aggregate nearest neighbor (ANN) query retrieves the point(s) of P with the smallest aggregate distance(s) to points in Q. Assuming, for example, n users at locations q1,...qn, an ANN query outputs the facility p belongs to P that minimizes the sum of distances |pqi| for 1 is less than or equal to i is less than or equal to n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p belongs to P that minimizes the maximum distance that …
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Computer Science Faculty Publications
(First paragraph) We are very proud and honored to have been entrusted to be Guest Editors for this special issue. Papers were sought to comprehensively cover the algorithmic issues in the “hot” area of sensor networking. The concentration was on network layer problems, which can be divided into two groups: data communication problems and topology control problems. We wish to briefly introduce the five papers appearing in this special issue. They cover specific problems such as time division for reduced collision, fault tolerant clustering, self-stabilizing graph optimization algorithms, key pre-distribution for secure communication, and distributed storage based on spanning trees …
Recommender Systems Research: A Connection-Centric Survey, Saverio Perugini, Marcos André Gonçalves, Edward A. Fox
Recommender Systems Research: A Connection-Centric Survey, Saverio Perugini, Marcos André Gonçalves, Edward A. Fox
Computer Science Faculty Publications
Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from …
Reconstructability Analysis With Fourier Transforms, Martin Zwick
Reconstructability Analysis With Fourier Transforms, Martin Zwick
Complex Systems Faculty Publications and Presentations
Fourier methods used in two‐ and three‐dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance‐type measure instead of information‐theoretic uncertainty, but the two measures are roughly collinear and the Fourier approach yields results close to that of standard RA. The Fourier method, however, does not require iterative calculations for models with loops. Moreover, the error in Fourier RA models can be assessed without actually generating the full probability distributions of the models; calculations scale with the size of the data rather than the state space. State‐based modeling using the Fourier approach is also …
Staging Transformations For Multimodal Web Interaction Management, Michael Narayan, Christopher Williams, Saverio Perugini, Naren Ramakrishnan
Staging Transformations For Multimodal Web Interaction Management, Michael Narayan, Christopher Williams, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Multimodal interfaces are becoming increasingly ubiquitous with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. In addition to improving access and delivery capabilities, such interfaces enable flexible and personalized dialogs with websites, much like a conversation between humans. In this paper, we present a software framework for multimodal web interaction management that supports mixed-initiative dialogs between users and websites. A mixed-initiative dialog is one where the user and the website take turns changing the flow of interaction. The framework supports the functional specification and realization of such dialogs using staging transformations – …
Program Transformations For Information Personalization, Saverio Perugini
Program Transformations For Information Personalization, Saverio Perugini
Computer Science Faculty Publications
Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation. The central thesis of this dissertation is that modeling interaction explicitly in a representation, and studying how partial information can be harnessed in it by program transformations to direct the flow of the interaction, can provide insight into, reveal opportunities for, and define a model for personalized interaction. To evaluate this thesis, a formal modeling methodology is developed for personalizing interactions with information systems, especially hierarchical hypermedia, based on program transformations. The …