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Non-cosmic, non-Gaussian disturbances known as “glitches”, show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to classify glitches automatically. The primary purpose of classifying glitches is to understand their characteristics and origin, which facilitates their...
Functional magnetic resonance imaging (fMRI) is a powerful tool to analyze brain development and neuronal activity. Identifying discriminative brain regions between various groups within a population has generated great interest in recent years. In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from fMRI observations belonging to different classes. More...
Forecasting of the time series data is a challenge as its details are more complex in nature. Climate change is a global issue as it influences the environment and it impacts directly affecting the human and the marine species. Global warming is a big threat and it reflects in sea temperature. Due to the rising sea temperature, the fishes like sardine and pelagic are not getting the living sea environment...
Software project artifacts such as source code, requirements, and change logs represent a gold-mine of actionable information. As a result, software analytic solutions have been developed to mine repositories and answer questions such as "who is the expert?,'' "which classes are fault prone?,'' or even "who are the domain experts for these fault-prone classes?'' Analytics often require...
This paper provides information on methods of measuring pilots' reactions to a changed flight parameter while being tested on simulators. The calculations are made on the grounds of mathematical algorithms modeling human behavior. The prepared mathematical models of behavior are based on transfer functions. The subsequent analysis aimed at acquiring time constants that shall appropriately characterize...
The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. This paper presents a novel non-linear discriminant error criterion which can be used in effective feature learning from raw pixels. Unlike many existing methods which assume the problem to be linear in nature, the proposed method...
In scene analysis, the availability of an initial background model that describes the scene without foreground objects is at the basis of many computer vision applications. Multi-modal models of the scene background are frequently adopted in the applications, where each mode tries to keep track of the multiple background modes observed along the sequence. In this work we specifically address the problem...
Object detection and localization in images involve a multi-scale reasoning process. First, responses of object detectors are known to vary with image scale. Second, contextual relationships on a part-level, object-level, and scene-level appear at different scales of the image. This paper studies efficient modeling of these two components by training multi-scale template models. The input to the proposed...
Pattern classification in domains that follow dissimilar distribution and where target domain has insufficient labelled samples, requires transfer of knowledge across domains through a process called domain adaption. Deep learning research demonstrates the transferability of deep convolutional features that are activations of intermediate layers of convolutional neural networks for domain adaption...
In recent years Unmanned Aerial Vehicles (UAVs) have become a very popular topic in many different research fields and industrial applications. These technologies, and the related industries, are expected to grow dramatically by 2020. Although the systems designed to control UAVs are increasingly autonomous, the role of UAV operators is still a critical aspect that guarantee the mission success, specially...
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster?...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. Sentiment classification is one of the research hot spots of Natural Language Processing. Compared with English and Chinese, it is hard for Tibetan to do some research of sentiment analysis because of the situation that we are lack of related sentiment corpus...
Decision making is an important component in a speaker verification system. For the conventional GMM-UBM architecture, the decision is usually conducted based on the log likelihood ratio of the test utterance against the GMM of the claimed speaker and the UBM. This single-score decision is simple but tends to be sensitive to the complex variations in speech signals (e.g. text content, channel, speaking...
Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series...
The integration of neural networks into agent based models can provide a better understanding of dynamic agent responses when modelling complex systems. Additionally, due to the nature of agent based models and the networks that exist in them, individual neural networks can be trained in a supervised learning environment and assigned to individual agents. The advantage of using this approach is that...
Current teaching methods in business schools such as case studies, instructor-led teaching, and corporate internships have come under increasing scrutiny and questioning in the recent years. In response to the sense that these pedagogical approaches may no longer be working, newer student-centric approaches such as using serious games as a pedagogical tool have been gaining prominence. However, there...
In this paper, the problem of missing diacritic marks in most of dialectal Arabic written resources is addressed. Our aim is to implement a scalable and extensible platform for automatically retrieving the diacritic marks for undiacritized dialectal Arabic texts. Different rule-based and statistical techniques are proposed. These include: morphological analyzer-based, maximum likelihood estimate,...
As a fundamental and effective method, sparse representation based classification (SRC) has been applied to computer vision field for many years. However, SRC assumes that the training samples in each class contribute equally to the dictionary which will cause high residual errors and instability. In order to solve the problem and improve classification performance further, class specific centralized...
This study aimed to support the effectiveness and efficiency of employee performance in the Provincial Education Office (Dinas Pendidikan) regarding teacher placements in elementary school, middle school, and high school. Three provinces are chosen as samples: Bali, Yogyakarta, and DKI Jakarta. The employee performance assessment was conducted using criteria and weight calculation regarding teacher's...
In this paper we present a novel writer-independent on-line signature verification system. The proposed system obtains a universal signature representation in dissimilarity space, but remains able to compensate for the personal variability of an individual's handwriting by subsequently employing a writer-specific dissimilarity normalisation strategy. Signature modelling is achieved by utilising either...
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