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Person re-identification is known as matching an individual captured in one or more cameras using a gallery of provided candidates from a different camera view. It is a hard task owing to variations in illumination, viewpoints, poses and small number of annotated training individuals. For obtaining the proper distance metrics, we propose a novel approach based on dictionary learning. Our method decomposes...
Multi-label text classification plays a significant role in information retrieval area. The effectiveness of the techniques is especially important in the case of medical documents. In the paper, application of feature selection methods for improving multi-label medical text classification is discussed. We examine combining problem transformation methods with different approaches to feature selection...
Today, software maintenance is more expensive than development costs. As class complexity increases, it is increasingly difficult for new programmers to adapt to software projects, causing the cost of the software to go up. Therefore, it's important to produce faultless and understandable code. Moreover, software projects are not developed by one person alone; even a small-scale project needs 3 or...
Hyper-heuristics have emerged as an important strategy for combining the strengths of different heuristics into a single method. Although hyper-heuristics have been found to be successful in many scenarios, little attention has been paid to the subsets of heuristics that these methods manage and apply. In several cases, heuristics can interfere with each other and can be harmful for the search. Thus,...
Increasing demands in endovascular intervention have motivated technical skill training and competency-based measures of performance. However, there are no well-established online metrics for technical skill assessment; few studies have explored operator behavioral patterns from catheter motion and operator hand motions. This paper proposes a platform for active online training and objective assessment...
Proactive anomaly detection refers to anticipating anomalies or abnormal patterns within a dataset in a timely manner. Discovering anomalies such as failures or degradations before their occurrence can lead to great benefits such as the ability to avoid the anomaly happening by applying some corrective measures in advance (e.g., allocating more resources for a nearly saturated system in a data centre)...
Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification...
As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between...
Cloud computing enables end users to execute high-performance computing applications by renting the required computing power. This pay-for-use approach enables small enterprises and startups to run HPC-related businesses with a significant saving in capital investment and a short time to market. When deploying an application in the cloud, the users may a) fail to understand the interactions of the...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
The academic mobility is one of key factors that enable the globalization of research and education. In this paper we study the network of ERASMUS staff and student exchange agreements between academic institutions involved in FETCH - a big European project oriented towards future education and training in computer science. The structure of the network was investigated relying on standard metrics...
The Internet of things (IoT) has emerged in numerous domains for collecting and exchanging large datasets in order to ensure a continuous monitoring and realtime decision-making. IoT incorporates sensors for carrying out raw data acquisition, while data processing and analysis tasks are addressed by high performance computational facilities, such as cloud-based infrastructures (remote processing approach)...
Tendencies in the information personnel analytics, relevance of measurement of personnel indicators, the most often measured indicators, for example, cost of recruitment; complexity of measurement of the more difficult, soft indicators, such as level of engagement of personnel, and other indicators, relevance of measurement of them, approaches to the solution of this task are presented in the.
Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such...
In practice, there are a variety of real-world datasets that have an imbalanced nature where one of two classes dominates the data. These datasets are generally difficult to classify using machine learning algorithms as the skewed nature of the data has a significant impact on the training process. In order to combat this difficulty, many methods of under sampling and over sampling have been proposed...
Most of recent successful researches on action recognition are based on deep learning structures. Nonetheless, training deep neural networks is notorious for requiring huge amount of data. On the other hand, not enough data can lead to an overfitted model. In this work, we propose a novel model, matching video net (MVN), which can be trained with a small amount of data. In order to avoid the problem...
As a fundamental task in automated video surveillance, person re-identification, which has received increasing attention in recent years, aims to match people across non-overlapping camera views in a multi-camera surveillance system. It has been reported that KISS metric learning has been followed by most of the previous supervised work because of its state of the art performance for person re-identification...
Credit scoring plays an important role in financial institutions and debt based crowdfunding platforms as well as peer to peer lending platforms. In the last few years, adopting ensemble methods for credit scoring has become much more popular. However, the performance of ensemble methods is easily affected by the parameter settings and the number of base classifiers. Ensemble classification based...
In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth...
In order to be considered as Linked Data, the datasets on the web must be linked to other datasets. Current studies on dataset interlinking prediction researches do not distinguish the type of links, which are of less help for real application scenarios, as dataset publishers still do not know what kinds of RDF links can be established and furthermore how to configure the data linking algorithms....
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