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Prediction-Based Prefetching for Remote Rendering Streaming in Mobile Virtual Environments
Get PDFRemote Image-based rendering (IBR) is the most suitable solution for rendering complex 3D scenes on mobile devices, where the server renders the 3D scene and streams the rendered images to the client. However, sending a large number of images is inefficient due to the possible limitations of wireless connections. In this paper, we propose a prefetching scheme at the server side that predicts client movements and hence prefetches the corresponding images. In addition, an event-driven simulator was designed and implemented to evaluate the performance of the proposed scheme. The simulator was used to compare between prediction-based prefetching and prefetching images based on spatial locality. Several experiments were conducted to study the performance with different movement patterns as well as with different virtual environments (VEs). The results have shown that the hit ratio of the prediction-based scheme is greater than the localization scheme in the case of random and circular walk movement patterns by approximately 35% and 17%, respectively. In addition, for a VE with high level of details, the proposed scheme outperforms the localization scheme by approximately 13%. However, for a VE with low level of details the localization based scheme outperforms the proposed scheme by only 5%
Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems.
Get PDFResearch in personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users - where it is possible to classify items involved and to make personalization based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this report, we present techniques for collecting, storing, processing, and utilizing implicit rating data of Digital Libraries for analysis and decision support. We present our pilot study to find virtual user groups using implicit rating data. We demonstrate the effectiveness of implicit rating data for characterizing users and finding virtual user communities, through statistical hypothesis testing. Further, we describe a visual data mining tool named VUDM (Visual User model Data Mining tool) that utilizes implicit rating data. We provide the results of formative evaluation of VUDM and discuss the problems raised and plans for further studies
Recent Developments in Document Clustering
Get PDFThis report aims to give a brief overview of the current state of document clustering research and present recent developments in a well-organized manner. Clustering algorithms are considered with two hypothetical scenarios in mind: online query clustering with tight efficiency constraints, and offline clustering with an emphasis on accuracy. A comparative analysis of the algorithms is performed along with a table summarizing important properties, and open problems as well as directions for future research are discussed
Trusting Remote Users… Can They Identify Problems Without Involving Usability Experts?
Get PDFBased on our belief that critical incident data, observed during usage and associated closely with specific task performance are the most useful kind of formative evaluation data for finding and fixing usability problems, we developed a Remote Usability Evaluation Method (RUEM) that involves real users self-reporting critical incidents encountered in real tasks performed in their normal working environments without the intervention of evaluators. In our exploratory study we observed that users were able to identify, report, and rate the severity level of their own critical incidents with only brief training
Email-Set Visualization: Facilitating Re-Finding in Email Archives
Get PDFIn this paper we describe ESVT – EmailSet Visualization Tool, an email archive tool that provides users a visualization to re-find and discover information in their email archive. ESVT is an end-to-end email archive tool that can be used from archiving a user’s email messages to visualizing queries on the email archive. We address email archiving by allowing import of email messages from an email server or from a standard existing email client. The central idea in ESVT’s visualization, an “email-setâ€, is the set of emails that are the result of a query on a user’s email archive. ESVT provides a multiple email-set view - visualization of multiple email-sets on a time axis. In addition, each email set can be individually visualized based on person and time axis, using the single email-set view. Query logs, individual email visualization, multiple email set visualization provide rich contextual cues, thus enabling end users to deal with email overload and re-find past email which otherwise wouldn’t be discovered easily
A Mathematical Programming Formulation for the Budding Yeast Cell Cycle
Get PDFThe budding yeast cell cycle can be modeled by a set of ordinary differential
equations with 143 rate constant parameters. The quality of the model (and an associated vector of
parameter settings) is measured by comparing simulation results to the experimental data derived
from observing the cell cycles of over 100 selected mutated forms. Unfortunately, determining
whether the simulated phenotype matches experimental data is difficult since the experimental
data tend to be qualitative in nature (i.e., whether the mutation is viable, or which development
phase it died in). Because of this, previous methods for automatically comparing simulation results
to experimental data used a discontinuous penalty function, which limits the range of techniques
available for automated estimation of the differential equation parameters. This paper presents a
system of smooth inequality constraints that will be satisfied if and only if the model matches the
experimental data. Results are presented for evaluating the mutants with the two most frequent
phenotypes. This nonlinear inequality formulation is the first step toward solving a large-scale
feasibility problem to determine the ordinary differential equation model parameters
Algorithm XXX: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT
Get PDFVTDIRECT95 is a Fortran 95 implementation of D.R. Jones' deterministic global optimization algorithm called DIRECT, which is widely used in multidisciplinary engineering design, biological science, and physical science applications. The package includes both a serial code and a data-distributed massively parallel code for different problem scales and optimization (exploration vs. exploitation) goals. Dynamic data structures are used to organize local data, handle unpredictable memory requirements, reduce the memory usage, and share the data across multiple processors. The parallel code employs a multilevel functional and data parallelism to boost concurrency and mitigate the data dependency, thus improving the load balancing and scalability. In addition, checkpointing features are integrated into both versions to provide fault tolerance and hot restarts. Important alogrithm modifications and design considerations are discussed regarding data structures, parallel schemes, error handling, and portability. Using several benchmark functions and real-world applications, the software is evaluated on different systems in terms of optimization effectiveness, data structure efficency, parallel performance, and checkpointing overhead. The package organization and usage are also described in detail
Clustering for Data Reduction: A Divide and Conquer Approach
Get PDFWe consider the problem of reducing a potentially very large dataset to a subset of representative prototypes. Rather than searching over the entire space of prototypes, we first roughly divide the data into balanced clusters using bisecting k-means and spectral cuts, and then find the prototypes for each cluster by affinity propagation. We apply our algorithm to text data, where we perform an order of magnitude faster than simply looking for prototypes on the entire dataset. Furthermore, our "divide and conquer" approach actually performs more accurately on datasets which are well bisected, as the greedy decisions of affinity propagation are confined to classes of already similar items
Re-engineering with reuse: a case study
Get PDFThis paper describes a case study in reuse and reengineering. A C based metrics system was re-engineered to C++ using standard reusable components and a design pattern
Capturing Truthiness: Mining Truth Tables in Binary Datasets
Get PDFWe introduce a new data mining problem: mining truth tables in binary datasets. Given a matrix of objects and the properties they satisfy, a truth table identifies a subset of properties that exhibit maximal variability (and hence, complete independence) in occurrence patterns over the underlying objects. This problem is relevant in many domains, e.g., bioinformatics where we seek to identify and model independent components of combinatorial regulatory pathways, and in social/economic demographics where we desire to determine independent behavioral attributes of populations. Besides intrinsic interest in such patterns, we show how the problem of mining truth tables is dual to the problem of mining redescriptions, in that a set of properties involved in a truth table cannot participate in any possible redescription. This allows us to adapt our algorithm to the problem of mining redescriptions as well, by first identifying regions where redescriptions cannot happen, and then pursuing a divide and conquer strategy around these regions. Furthermore, our work suggests dual mining strategies where both classes of algorithms can be brought to bear upon either data mining task. We outline a family of levelwise approaches adapted to mining truth tables, algorithmic optimizations, and applications to bioinformatics and political datasets