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The maintenance of complex systems is the most expensive and difficult phase of the software development process life cycle due to the significant amount of time spent by developers trying to comprehend the structure and mainly the system behavior. Therefore, this paper aims to present an approach to support the understanding of the dynamic behavior of complex software systems through Virtual Reality...
Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on...
The software composition using high-granularity entities nowadays is a common practice. The process of software composition is supported by various CASE tools. First tools were made on the basis of very simple formalisms (e.g. intuitionistic propositional logic). During the years the tools evolved to more efficient ones, which are able to deal with concurrency, multiparty sessions and other advanced...
[Background]: Developing conceptual models is an integral part of the requirements engineering (RE) process. Goal models are requirements engineering conceptual models that allow diagrammatic representation of stakeholder intentions and how they affect each other. A specific goal modeling language construct, the contribution of goal satisfaction of one goal to another, plays a central role in supporting...
The human visual system employs an information selection mechanism, visual attention, so that higher-level cognitive processes can be restricted to a potentially important subset of the incoming information. This mechanism is amenable to efficient computational implementation and, consequently, it has been incorporated into many technological applications. Among these applications is autonomous mobile...
[Background]: Conceptual modeling languages have been widely studied in requirements engineering as tools for capturing, representing and reasoning about domain problems. One of these languages, goal models, has been proposed for representing the structure of stakeholder intentions. Like most other conceptual modeling languages, goal models are visualized using box-and-line diagrammatic notations...
Vision is a potent source of information, not just for humans, but for robots as well. Processing visual information is a computationally expensive task, one that is often difficult to accomplish in real-time on embedded hardware. In the broad field of visual research exists egomotion estimation, the process of determining self-motion from optical flow. Here we show a technological adaptation and...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Since the beginning of early civilizations, social relationships derived from each individual fundamentally form the basis of social structure in our daily life. In the computer vision literature, much progress has been made in scene understanding, such as object detection and scene parsing. Recent research focuses on the relationship between objects based on its functionality and geometrical relations...
Referring expression is a kind of language expression that used for referring to particular objects. To make the expression without ambiguation, people often use attributes to describe the particular object. In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension. We first train an attribute learning model from visual objects...
We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively...
We propose “Areas of Attention”, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption...
Solving the global method of Weighted Least Squares (WLS) model in image filtering is both time- and memory-consuming. In this paper, we present an alternative approximation in a time- and memory- efficient manner which is denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a large linear system, we propose to iteratively solve a sequence of subsystems which are one-dimensional...
A cloud platform records a wealth of information regarding program execution. Most cloud service providers offer dashboard monitoring tools that visualize resource usage and billing information, and support debugging. In this paper, we present a tool that visualizes cloud execution logs for a different goal — to facilitate program understanding and generate documentations for an application using...
In this paper, we propose a patched-based deep Boltzmann shape priors for visual tracking. The shape priors are generated from deep Boltzmann machine network. The network consists of three layers of hidden and visible units. The generated shapes not only maintain general shapes from a variety of poses, but also entail local modifications with high probability.
In this study, we investigated the effects of mastering multiple scripts in handwritten character recognition by means of computational simulations. In particular, we trained a set of deep neural networks on two different datasets of handwritten characters: the HODA dataset, which is a collection of images of handwritten Persian digits, and the MNIST dataset, which contains Latin handwritten digits...
Most present methods of saliency detection emphasize too much on the local contrast while ignore the global feature of image. The detailed characteristics of the image can be reflected based on the local comparison of image. However, the overall saliency of the image cannot be reflected. In this paper, a saliency detection model combined local and global features was proposed. Firstly, a local feature...
Traditional textual programming languages are poorly readable and difficult to learn, creating significant hurdles for practitioners of industry (such as electromechanical engineers). However, Graphical Programming Language (GPL), using graphical symbols to construct programs, is becoming increasingly popular as it is intuitive and easy to learn. They have been implemented in many specific areas (such...
This article intends to provide an overview of the state of art in developmental models of cognitive robots. With the development of artificial intelligence, robots have been able to perform a variety of complex tasks controlled by human. However, it is still a challenge for robots that they can explore and develop their cognitive ability in the specific environment like human beings. The current...
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