UNLABELLED Sliding-window analysis procedure to detect selective constraints (SWAPSC) is a software system to dissect the constraints on the evolution of protein-coding genes. The program estimates rates of nucleotide substitutions at specific codon regions in each branch of a phylogenetic tree. The program uses several sets of simulated sequence alignments to estimate the probability of synony...

This study presents the “overview of TCP performance on satellite communication networks”, aimed at satellite characteristics, their effects on throughput selected link control protocols and various method proposed for enhancing TCP throughput on satellite networks. Literature reviews on satellite link characteristics and their effects on TCP operation in satellite communication networks. Diffe...

This thesis applies Latent Dirichlet Allocation (LDA) to the problem of topic and topic change in conversational threads using e-mail. We demonstrate that LDA can be used to successfully classify raw e-mail messages with threads to which they belong, and compare the results with those for processed threads, where quoted and reply text have been removed. Raw thread classification performs better...

Testing rare variants directly is possible with next-generation sequencing technology. In this article, we propose a sliding-window-based optimal-weighted approach to test for the effects of both rare and common variants across the whole genome. We measured the genetic association between a disease and a combination of variants of a single-nucleotide polymorphism window using the newly develope...

This work proposes an alternative to ordered subsets to improve the convergence speed of list-mode expectationmaximization image reconstruction algorithms. Instead of subdividing the input data into non-overlapping subsets, the stream of measured coincidence events is immediately processed online. The reconstruction algorithm maintains a sliding window covering the events that contribute to the...

Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data element...

We propose a false-negative approach to approximate the set of frequent itemsets (FIs) over a sliding window. Existing approximate algorithms use an error parameter, ǫ, to control the accuracy of the mining result. However, the use of ǫ leads to a dilemma. A smaller ǫ gives a more accurate mining result but higher computational complexity, while increasing ǫ degrades the mining accuracy. We add...

Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Most approaches use a fixed size sliding window over consecutive samples to extract features— either handcrafted or learned features—and predict a single label for all samples in the window. Two key problems emanate from this approach: i) the samples in one window may not always share the same ...