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In this paper we apply self-labeling algorithms as Semi-Supervised Classification (SSC) techniques in order to automate the classification of functional and non-functional requirements contained in reviews in the App Store. In this domain, where it is easy collect a large number of reviews but difficult to manually annotate then, we found that SSC techniques can successfully perform this task and...
Existing work on identifying security requirements relies on training binary classification models using domain-specific data sets to achieve a high accuracy. Considering that domain-specific data sets are often not readily available, we propose a domain-independent model for classifying security requirements based on two key ideas. First, we train our model on the description of weaknesses from the...
Approximations and redundancies allow mobile and distributed applications to produce answers or outcomes of lesser quality at lower costs. This paper introduces RAPID, a new programming framework and methodology for service-based applications with approximations and redundancies. Finding the best service configuration under a given resource budget becomes a constrained, dual-weight graph optimization...
Previous research has shown how developers "selfadmit" technical debt introduced in the source code, commenting why such code represents a workaround or a temporary, incomplete solution. This paper investigates the extent to which previously self-admitted technical debt can be used to provide recommendations to developers when they write new source code, suggesting them when to "self-admit"...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
Paraphrase Detection is the task of examining if two sentences convey the same meaning or not. Here, in this paper, we have chosen a sentence embedding by unsupervised RAE vectors for capturing syntactic as well as semantic information. The RAEs learn features from the nodes of the parse tree and chunk information along with unsupervised word embedding. These learnt features are used for measuring...
In the Linked Data context, identity link is one of the most important semantic links that can be established between the datasets. It specifies that different identifiers refer to the same real world object and therefore must be linked. The process of detecting these identical instances across different data repositories is referred as instance matching. This is used to connect existing data sources...
This article presents a survey of 278 intelligence analysts' views of fully operational analytic technologies and their newly developed replacements. It was found that usability was an important concept in analysts' reasons for and against using analytic tools. The perceived usability of a tool was not necessarily indicative of its perceived usefulness. Analysts' decisions to recommend an analytic...
Incremental learning allows incorporating new data in a classifier model without full retraining for computational efficiency. In this paper, we present two ways of performing incremental learning on Grassmann manifolds. In a Grassmann kernel learning framework, data are embedded on subspaces and kernels are constructed to map data subspaces to a projection space for classification. As new data samples...
Previous models based on Deep Convolutional Neural Networks (DCNN) for face verification focused on learning face representations. The face features extracted from the models are applied to additional metric learning to improve a verification accuracy. The models extract high-dimensional face features to solve a multi-class classification. This results in a dependency of a model on specific training...
Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma,...
Re-identification refers to the task of finding the same subject across a network of surveillance cameras. This task must deal with appearance changes caused by variations in illumination, a person's pose, camera viewing angle and background clutter. State-of-the-art approaches usually focus either on feature modeling — designing image descriptors that are robust to changes in imaging conditions,...
Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem,...
Neighbors embedding is a promising method for single image super-resolution (SR). However, the fixed number of neighbors for different kind of input low resolution (LR) patches may be improper. In addition, the assumption that low resolution space and high resolution (HR) space has similar local geometry leads to improper HR patches are used for reconstruction. In this paper, we propose a novel single...
Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that would be associated to new faults. This is achieved through a Hybrid Heuristic Algorithm for Evolving Models in scenarios of Classification and Clustering (HHA-EMCC), which is a machine learning algorithm that can...
Network infrastructures are in jeopardy of suffering nowadays since a number of attacks have been developed and grown up enormously. In order to get rid of such security threats, a defense mechanism is much sought-after. This paper proposes an improved model of intrusion detection by using two-level classifier ensemble. The proposed model is made up of a PSO-based feature selection technique and a...
Manifold causes of image blurring make the no-reference evaluation of realistic blurred images very challenging. Previous studies indicate that handcrafted features suffer from poor representation of the intrinsic characteristics of image blurring and thus blind image sharpness assessment (BISA) is unsatisfactory. This paper explores a shallow convolutional neural network (CNN) to address this problem...
Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these scores to compare different results. Observing that it is challenging even for human...
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different...
We consider learning a distance metric in a weakly supervised setting where bags (or sets) of instances are labeled with bags of labels. A general approach is to formulate the problem as a Multiple Instance Learning (MIL) problem where the metric is learned so that the distances between instances inferred to be similar are smaller than the distances between instances inferred to be dissimilar. Classic...
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