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With the Internet applications become more complex and diverse, simple network traffic matrix estimation or approximation methods such as gravity model are no longer adequate. In this paper, we advocate a novel approach of approximating traffic matrices with multiple low-rank matrices. We build the theory behind the MULTI-LOW-RANK approximation and discuss the conditions under which it is better than...
A main advantage of app stores is that they aggregate important information created by both developers and users. In the app store product pages, developers usually describe and maintain the features of their apps. In the app reviews, users comment these features. Recent studies focused on mining app features either as described by developers or as reviewed by users. However, extracting and matching...
Usability and user experience (UUX) strongly affect software quality and success. User reviews allow software users to report UUX issues. However, this information can be difficult to access due to the varying quality of the reviews, its large numbers and unstructured nature. In this work we propose an approach to automatically detect the UUX strengths and issues of software features according to...
This paper contains analysis and extension of exploiters-based knowledge extraction methods, which allow generation of new knowledge, based on the basic ones. The main achievement of the paper is useful features of some universal exploiters proof, which allow extending set of basic classes and set of basic relations by finite set of new classes of objects and relations among them, which allow creating...
With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next...
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. In this framework, an image is described with...
Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results...
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
In recent years, ontologies as a semantic knowledge representation become widely used in many information systems. Manual creation of ontologies by domain experts and ontology developers is also a costly task, time consuming and needs extra efforts. Learning Non-Taxonomic Relationships is a subfield of ontology learning which targets automatic extraction of non-taxonomic relationships from input,...
Medical data contain very valuable information which can save many lives if it is analyzed and utilized efficiently. Efficient analysis of this large volume of data demands the right choice of predictors and this in turn can impact the accuracy of the decision support system. Dimensionality reduction and feature subset selection are two techniques to reduce the number of features used in classification...
Feature location is a maintenance task to identify the implementation of a feature within the source code. To automate or support the task, extensive studies have been conducted on feature location techniques. In this paper, we focus on certain static and dynamic constraints regarding feature additions to object-oriented programs, and construct an interactive feature location procedure based on the...
Human motion detection based on CSI (Channel State Information) is a hot research topic. Many researchers started to focus on it. In this paper, we employ the CSI to detect fall behavior. Firstly, a data weighting operation to smooth the noise will be implemented. Secondly, we extract behaviors data by effect size. Thirdly, several features will be extracted to represent the original data. Finally,...
As the malware threat landscape is constantly evolving and over one million new malware strains are being generated every day [1], early automatic detection of threats constitutes a top priority of cybersecurity research, and amplifies the need for more advanced detection and classification methods that are effective and efficient. In this paper, we present the application of machine learning algorithms...
In this paper, we propose the new iris feature extraction method that uses local thresholding with block size fitting to achieve a reliable iris authentication technique. The dispersion index is used to analyze the degree of variation in the pixel intensities. By calculating the pixel intensity variance for the block size, it is possible to quantify the degree of contrast and brightness in the block...
İn this study, we aim at gathering more scientific information about the musical data by using computer techology, and we are willing, to some extent, figure out the maqam structure of Traditional Turkish Art Music interpreted in the compositions. For this reason, 120 compositions from among Muhayyer Kurdi, Acem Kurdi and Kurdi makams in Traditional Turkish Art Music have been analysed and these compositions...
Nowadays, expansion of social media and internet are driving to a whole another level. Most of the users critically review anything on the internet specially foods and services in restaurants to showcase their humble opinion. These opinions are very valuable in decision making process. Analyzing and extracting the actual opinion throughout these reviews manually is practically difficult since there...
The rise of social media has brought about a new realm for people all over the globe to interact and share information and opinions. Twitter, Facebook, Blogs, Google+, etc. have become the most sought after platforms for people to share their opinions, feelings and feedback about the latest trends and products. Unlike formal documentation, people are free to express themselves in multiple languages,...
Technological advancement enables the need of internet everywhere. The power industry is not an exception in the technological advancement which makes everything smarter. Smart grid is the advanced version of the traditional grid, which makes the system more efficient and self-healing. Synchrophasor is a device used in smart grids to measure the values of electric waves, voltages and current. The...
Since malware has caused serious damages and evolving threats to computer and Internet users, its detection is of great interest to both anti-malware industry and researchers. In recent years, machine learning-based systems have been successfully deployed in malware detection, in which different kinds of classifiers are built based on the training samples using different feature representations. Unfortunately,...
Aiming at the problem that the semantic explanation of the existing topic model is poor and the accuracy is not high, a semi-supervised topic learning and representation method based on association rules and metadata is proposed. First, we used the metadata as a priori knowledge to guide the topic learning, and got the probability distribution of the term in the document. Then, we got the frequent...
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