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Extracting structured information from unstructured text is a critical problem. Over the past few years, various clustering algorithms have been proposed to solve this problem. In addition, various algorithms based on probabilistic topic models have been developed to find the hidden thematic structure from various corpora (i.e publications, blogs etc). Both types of algorithms have been transferred...
The propose of this research was to classify the English as a foreign language (EFL) learners based on their performance on the reading test. Three levels of reading comprehension are customarily defined: (1) Factual level or Reading the lines, (2) Interpretive level or Reading between the lines, and (3) Evaluative level or Reading beyond the lines. Further analyzing and synthesizing factors underlying...
The past few years have seen an exponential growth in data collection capabilities. Unfortunately, the ability to process this vast amount of data has not kept pace with this growth. Taking full advantage of these increased capabilities requires scalable, computationally efficient algorithms to timely and robustly extract actionable information from the very large data sets generated by the sensors...
Expertise retrieval has already gained significant interest in the area of information retrieval due to multitude of concrete application contexts where search for specific experts is required. In this paper, we introduce a formal concept analysis approach for clustering of a group of experts with respect to given subject areas. Initially, the domain of interest is presented at some level of abstraction...
We propose several new concepts for providing enhanced explanations of classifier decisions in linguistic (human readable) form. These are intended to help operators to better understand the decision process and support them during sample annotation to improve their certainty and consistency in successive labeling cycles. This is expected to lead to better, more consistent data sets (streams) for...
a rule based system is a special type of expert system which consists of a set of rules. In practice, rule based systems can be built by using expert knowledge or learning from real data. Due to the vast and increasing size of data, the latter approach has become quite popular for building rule based systems. In particular, rule based systems can be built through use of rule learning algorithms, which...
Outlier temporal pattern mining problem is the study and discovery of abnormal, invalid, anomalous temporal patterns in a given temporal database. In this paper, we address the approach for mining of outlier temporal patterns with respect to a given threshold and reference. To verify if the given pattern is an outlier pattern, we compute the true support of temporal pattern and then obtain the distance...
Twitter is a source of sharing and communicate recent information, ensuing into huge size of records produces every day. Even though, a various applications of Natural Language Processing and Information Retrieval go through rigorously from an erroneous and tiny nature of tweets. We thought to implement a framework in support of segmentation of tweet by collection form, called as HybridSeg. During...
Semantic computing is one of the important and indispensable approaches to analyze various kinds of environmental phenomena and its changes in the real world. In this paper, we present “A Seawater-Quality Analysis Semantic-Space in Hawaii-Islands with Multi-Dimensional World Map System” to realize a global and environmental analysis for ocean environment with the multi-dimensional world map system...
Open Source Software (OSS) hosted in Repositories such as GitHub can be valuable as a source of information for requirements engineers, especially in the apprentice phase of a new application. In this context, we propose a strategy to speed up the discovery of valuable information, since manual search may be time consuming in the vast dataset of GitHub projects. Our strategy is based on the identification...
Detection of hotspots (also known as dense subgraphs) in network data is an important data analysis problem due to it's significance in many contemporary applications. Clique-based formulation of this problem employing maximum flow implementation turns out to be an optimization task limiting the solution to be an approximate one. On the other hand, an iterative method building the hotspots (dense...
A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only...
Along the history, many researchers provided remarkable contributions to science, not only advancing knowledge but also in terms of mentoring new scientists. Currently, identifying and studying the formation of researchers over the years is a challenging task as current repositories of theses and dissertations are cataloged in a decentralized way through many local digital libraries. In this paper,...
Keyword-based search engines are becoming increasingly sophisticated, and yet navigating the ever-increasing collection of academic knowledge remains an arduous task. Keeping abreast of relevant scientific literature is often a fragmented process that breaks the workflow of academic writing.
In recent years, an extensive integration of cyber, physical and social spaces has been occurring. Cyber-Physical-Social Systems (CPSSs) have become the basic paradigm of evolution in the information industry, through which traditional computer science will evolve into cyber-physical-social computational science. Intelligent recommender systems, which are an important fundamental research topic in...
Stored data in database can hide some knowledge, which is interesting, useful to hidden knowledge discover. In this context, an algorithms number a frequent itemsets and association rules extraction were presented. Special feature of these algorithms is to generation a large number of rules, making their exploitation a difficult task. In this paper we will introduce a new algorithm for association...
We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model mining techniques: We target subsets of sequential...
Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect...
There are three main classes of modifiers that can affect the polarity of the sentiments described in natural language texts: negations, intensifiers and diminishers. In this paper, we concentrate on the study of these particular words which have a very important semantic role in any natural language description. Our study is applied on a real data set extracted from the popular Romanian Web site...
Existing methods for Blog keyword extraction usually exploit the context in the specified blog. In this paper, we propose to provide a knowledge context by using small number of nearest neighbor blogs to improve keyword extraction performance. Specifically, knowledge context is build by adding several topic related blogs closed to the specified blog, and then the manifold ranking model is used on...
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