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Software development teams apply security practices to prevent vulnerabilities in the software they ship. However, vulnerabilities can be difficult to find, and security practices take time and effort. Stakeholders can better guide software development if they have empirical data on how security practices are applied by development teams. The goal of this paper is to inform managers and developers...
Feature selection is essential to rule learning in the context of functional verification. In practice today, features are selected manually and the selection requires domain knowledge. In contrast, this work proposes using automatic feature extraction from design documents as a viable approach to support rule learning. To demonstrate its effectiveness, document-extracted features are employed to...
In this paper, we present a heuristic for labeling a given term a taxonomy label. Specifically, for a given term, our goal is to construct a model for determining an "is-a" relationship between the given term and an inferred concept. Such term-labelling problem is not new, but the existing solutions require semi-supervised training processing, e.g., supervised LDA, or rely on lexicographers,...
In past, even though there is a lot of research work is done in the field of recommendation systems, the researchers did not target user contexts while recommending the content to the end users. Traditional recommendation systems while dealing with applications considers only users and items, and do not incorporate user context when delivering recommendations to querying end users. Contextual information...
Pattern mining is a fundamental topic in data mining area. Many pattern mining techniques, such as closed and maximal pattern mining have been proposed for different applications. However, when calculating the frequency of a pattern, the existing techniques treat each word equally. For example, although the word ‘pie’ in ‘I love eating pie.’ is quite different from ‘pie’ in ‘american pie’, ‘pie’ in...
Several studies have been developed over the years in the areas of Text Mining, Social Network Analysis and Detection of Communities. In this paper, we present a new technique for community detection in social networking using the conversation of users in a social network. We showed that the proposal performed very well for a specific theme for defining a community and performed well for joint themes...
Three-dimensional virtual worlds have been studied by many researchers around the world, including in educational contexts. A range of possibilities have emerged from this type of environment, such as improvements in distance education and other educational technologies. But there are some problems related to the use of this environment, such as complex authorship tools, which need to be discussed...
Synonym-based searching is considered to be a complicated problem, as text mining from unstructured data of web is challenging. Finding useful information which matches user need from the bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration...
The development of a topic in a set of topic documents is constituted by a series of person interactions at a specific time and place. Knowing the interactions of the persons mentioned in these documents is helpful for readers to better comprehend the documents. In this paper, we propose a topic person interaction detection method called SPIRIT, which classifies the text segments in a set of topic...
When multiple terms in the query point to a single concept, the solution is easy to map. But, when many morphologically similar terms refer to separate concepts (showing fuzzy behavior), then arriving at a solution becomes difficult. Before applying any knowledge generation or representation techniques to such polysemic words, word sense disambiguation becomes imperative. Unfortunately, with an exponential...
Today, E-Commerce has become the largest revenue generation industry, letting seller sell everything from a pen to plane to the customers across the globe. Over an E-commerce platform where user and vendor merely interact with each other, the trust is undeniably the most important factor for users to perform transactions online. But at the same time it can't be assessed directly using some pre-defined...
Growing volumes of text and increasing expectations on the complexity of analysis entail advanced approaches to text mining. Unsupervised text clustering is an efficient approach to determine structural groupings in a text corpus without the impact of external bias. The information content of such structural groupings needs to be enhanced by integrating semantics into the cluster outcomes. This integration...
Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this...
Statistical topic models represented by Latent Dirichlet Allocation (LDA) and its variants are ubiquitously applied to understanding large corpora. Meanwhile, topic models based on bag-of-words (Bow) rarely adopt contextual information, which encompasses enormous amount of serviceable knowledge in a document, into the probabilistic framework. This shortcoming of LDA leads to its failing to learn contextual...
The acquisition and understanding of data is of paramount importance in any scientific context. However, the complexity of data due to its exponentially increasing size, its dynamical properties, and its internal contradictory information, raises huge challenges, which are at the core of Big Data science. In this paper, we discuss an automatic method to identify, rank and discover knowledge specifically...
The aim of machine learning is to solve a given problem using past experience or example data. Many machine learning applications are using now-a-days already. More aspiring problems can be handled as more data become accessible. Here. in this context we learn in detail about text mining as a multi-dimensional field which involves the closely linked areas or sections like 1. Retrieving information,...
The Multiple Listing Service, commonly known as the MLS, is the singularly most important database where real estate agents and brokers list real estate properties for sale. It is common that agents include textual comments pertinent to the property. Although the information content of comments varies, it is usually expressed in good faith and in many cases is helpful in shedding light on the overall...
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers "interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation...
In the context of Business-to-Business (B2B), an understanding of inter-organizational success factors and their impacts is crucial for effective strategic management. Several studies regarding those success factors and their influences have been conducted and published as articles. We aim at applying existing techniques, especially data mining, to automatically classify relevant sentences describing...
The Health Behavior Change Support Systems minitrack discusses how systems and services aimed at influencing health and/or wellbeing behavior can be designed, developed and implemented. Behavior Change Support Systems (BCSS), in general, are defined as "socio-technical information systems with psychological and behavioral outcomes designed to form, alter or reinforce attitudes, behaviors or an...
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