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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...
What defines a visual style? Fashion styles emerge organically from how people assemble outfits of clothing, making them difficult to pin down with a computational model. Low-level visual similarity can be too specific to detect stylistically similar images, while manually crafted style categories can be too abstract to capture subtle style differences. We propose an unsupervised approach to learn...
The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen...
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importance to the application of such methods. Despite the fact that tensor representations are widely used in Computer Vision to capture multi-linear relationships that affect the data, most existing DA methods are applicable...
Humans infer rich knowledge of objects from both auditory and visual cues. Building a machine of such competency, however, is very challenging, due to the great difficulty in capturing large-scale, clean data of objects with both their appearance and the sound they make. In this paper, we present a novel, open-source pipeline that generates audiovisual data, purely from 3D object shapes and their...
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a...
Omnidirectional images describe the color information at a given position from all directions. Affordable 360° cameras have recently been developed leading to an explosion of the 360° data shared on social networks. However, an omnidirectional image does not contain interesting content everywhere. Some part of the images are indeed more likely to be looked at by some users than others. Knowing these...
One of the greatest challenges for computer science in education is the capacity to provide environments that are intelligent and adaptable to the real needs of students. In order to create efficient adaptive mechanisms for educational content, student models are proposed to identify and to predict the real knowledge level of students. Such models are useful not only for computer systems but also...
Decision support systems for network security represent a critical element in the safe operation of computer networks. Unfortunately, due to their complexity, it can be difficult to implement and empirically assess novel techniques for displaying networks. This paper details an open source adaptive user interface that hopes to fill this gap. This system supports agile development and offers a wide...
Learning styles have been used to explain students' differences in approaching their learning, but there are still deficiencies in interpreting the results of their application, and there are authors that indicate that there are no elements that support their credibility in achievement-based education. For this reason, we proposed a methodology to analyze the results obtained after the application...
Previous works have suggested the role of scene information in directing gaze. The structure of a scene provides global contextual information that complements local object information in saliency prediction. In this study, we explore how scene envelopes such as openness, depth, and perspective affect visual attention in natural outdoor images. To facilitate this study, an eye tracking dataset is...
In this paper, we propose a computational modeling method to investigate head-eye coordination in face-to-face behavior. The method looks into probability density of individuals' head orientation during looking at others' face. We conducted experiment under two different scenarios in human-human interaction. Under each scenario, individuals' head orientation could be fitted with one Gaussian distribution...
While formal mathematical reasoning is the cornerstone of computer science, undergraduates often fail to appreciate the value of mathematical proof in their studies. To alleviate this problem, we propose a novel pedagogy uniting logical reasoning with proofs of program correctness along with a proof assistant, ORC2A, that helps students author proofs in this domain. One of the defining features of...
In this paper we present some preliminary results and conclusions about the experience of using a tool designed and developed as a support for the teaching of relational algebra. Through the tool you can design queries in relational algebra, check their operation and performance, step by step, visually. The experiences were applied to an introductory course of databases in the Engineering in Software...
Tangible User Interfaces (TUI) have garnered significant interest in the past years as a potential solution to embed smarter technologies for education. The intrinsic ability of this technology to engage and intrigue students in active learning pedagogies has recently been successfully proven across all ages using various techniques. Predominantly amongst the effective technologies, has been the development...
We introduce a general framework for visual forecasting, which directly imitates visual sequences without additional supervision. As a result, our model can be applied at several semantic levels and does not require any domain knowledge or handcrafted features. We achieve this by formulating visual forecasting as an inverse reinforcement learning (IRL) problem, and directly imitate the dynamics in...
This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems. Specifically, we tackle the task of categorization of visual input from different domains by learning projections from each domain to a latent (shared) space jointly with the classifier in the latent space, which simultaneously minimizes the domain disparity while maximizing the classifier's...
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