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The software defect can damage the reliability and the quality of the software. The static code software metrics have been widely used and played an important role in software defect prediction. Instead of using whole features, it is quite necessary to remove the redundant features and select some meaningful features to improve the prediction performance. This study focuses on the effective attribute...
Active learning (AL) has shown a great potential in the field of remote sensing to improve the efficiency of the classification process while keeping a limited training dataset. Active learning uses heuristics to select the most informative pixels in each iteration. In literature, there are several metrics and selection criteria. In this paper, we focus on the uncertainty heuristics for large margin...
Nowadays, there are a lot of online repositories containing thousands of very useful educational resources for the educational community. To take full advantage of these resources requires a simple, direct and effective access to those resources that are of interest, therefore, it is necessary that those resources are ordered or ranked based on some criteria? -- that is to say, they have to be classified...
Leader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde's work on crowd...
This article addresses the issue of social image search result diversification. We propose a novel perspective for the diversification problem via Relevance Feedback (RF). Traditional RF introduces the user in the processing loop by harvesting feedback about the relevance of the search results. This information is used for recomputing a better representation of the data needed. The novelty of our...
This paper focuses research focuses on automatic provisioning of cloud resources performed by an intermediary enterprise that provides a virtual private cloud for a single client enterprise by using resources from a public cloud. This paper concerns auto-scaling techniques for dynamically controlling the number of resources used by the client enterprise. We focus on proactive auto-scaling that is...
Different ranking algorithms have been proposed to fulfil the need of ranking. The problem is that most of the existing algorithms and models are just applicable on a specific data. When the data is imbalanced and heterogeneous, finding the records belonging to the minority class is significant especially in failure cases. So considering ranking as a classification problem of predicting the specific...
In this paper, a novel classification paradigm, termed Spectral-Spatial One Dimensional Manifold Embedding (SS1DME), is proposed for classification of hyperspectral imagery (HSI). The proposed paradigm integrates the spectral affinity and spatial information into a uniform metric framework. In SS1DME, a spectral-spatial affinity metric is utilized to learn the similarity of HSI pixels. Moreover, a...
In many new interactions with machines, such as dialogue or output using voice, there is the need to convert information internal to a system into sentences, using Data2Text systems. Trying to avoid the limitations of template-based and classical NLG methods, systems based on automatic translation have been proposed in recent years. Despite providing sentences with the important variability needed...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
Intensive Care Unit (ICU) patients have significant morbidity and mortality, often from complications that arise during the hospital stay. Severe sepsis is one of the leading causes of death among these patients. Predictive models have the potential to allow for earlier detection of severe sepsis and ultimately earlier intervention. However, current methods for identifying and predicting severe sepsis...
An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity,...
Visual inference over a transmission channel is increasingly becoming an important problem in a variety of applications. In such applications, low latency and bit-rate consumption are often critical performance metrics, making data compression necessary. In this paper, we examine feature compression for support vector machine (SVM)-based inference using quantized randomized embeddings. We demonstrate...
There has been an increasing attention on Electroencephalograph (EEG) based personal identification over the last decade. Most existing methods address this problem by Euclidean metric based Nearest Neighbor (NN) search. However, under various recording conditions, simple Euclidean distance cannot model the similarity relations between EEG signals precisely. To overcome this drawback, a local metric...
In this paper, an automated model selection approach guided by Cuckoo search is proposed for k-nearest neighbor (KNN) learning algorithm. The performance of KNN mostly depends on the value of k and the distance metric used. The values of these parameters are computed by optimizing an objective function designed for measuring the classification accuracy of KNN. Cuckoo search being an efficient optimization...
The TRECVID report of 2010 [14] evaluated video shot boundary detectors as achieving "excellent performance on [hard] cuts and gradual transitions." Unfortunately, while re-evaluating the state of the art of the shot boundary detection, we found that they need to be improved because the characteristics of consumer-produced videos have changed significantly since the introduction of mobile...
Mobile ad-hoc networks have to suffer with different types of packet dropping attacks. Therefore, we need strong mechanism to detect these malevolent nodes and to classify normal and abnormal nodes as per the behavior of nodes. Machine learning techniques distinguish outlier nodes quickly and accurately provide classification by observing behavior of those nodes in the network. In this paper, we study...
Text categorization plays an important role in applications where information is filtered, monitored, personalized, categorized, organized or searched. Feature selection remains as an effective and efficient technique in text categorization. Traditional feature selections ignored the effects of unbalanced categories and the distribution of a term in different categories. On this basis, we improved...
Microblogs such as Twitter are characterized by the richness and recency of information shared by their users during major events. However, it is very challenging to automatically mine for information or for users sharing certain information due to the huge variety of unstructured stream of data shared in such microblogs. This work proposes a ranking and classification model for identifying users...
Online Social Networks (OSNs) are facing an increasing threat of sybil attacks. Sybil detection is regarded as one of major challenges for OSN security. The existing sybil detection proposals that leverage graph theory or exploit the unique clickstream patterns are either based on unrealistic assumptions or limited to the service providers. In this study, we introduce a novel sybil detection approach...
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