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Ubiquitous availability of human mobility data has opened up new possibilities to address a multitude of application domains. However, so far, the visual analysis of this data has been hindered by the limited ability to explore and query complex movement sequences and to create models that allow meaningful aggregation. To address this problem, this paper presents a novel analytical approach that allows...
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...
This paper aims to present a visual servoing control strategy that uses visual information, acquired by a fiducial system, to solve the regulation to an arbitrary pose problem in differential driven mobile robots. In addition, in order to make this system more robust and immune to occlusion, we will use an Extended Kalman Filter to fuse odometric data and the data acquired from several landmarks....
Personal networks formed within scientific communities and the collaborations they yield are one of the driving forces behind innovation and new discoveries. Luckily, successful collaboration produces analyzable data points in the form of publications that allow us to learn and understand some of the connections and collaborative structures in a scientific community. Co-author information is one important...
Routine can be defined as the frequent and regular activity patterns over a specified timescale (e.g. daily/weekly routine). In this work, we capture routine patterns for a single person from long- term visual data using a Dynamic Bayesian Network (DBN). Assuming a person always performs purposeful activities at corresponding locations; spatial, pose and time-of-day information are used as sources...
Augmented Reality (AR) based applications have existed for some time; however, their true potential in digital marketing remains unexploited. To bridge this gap we create a novel consumer targeting system. First, we analyze consumer interactions on AR-based retail apps to identify her preferred purchase viewpoint during the session. We then target the consumer through a personalized catalog, created...
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...
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...
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...
In the last 20 years the term of Big Data took strength which refers to datasets that in size exceed the ability of typical database tools to capture store manage and analyze. Big Data visual analysis is a new field that is emerging as a powerful tool for extracting useful information. This paper discusses the revision of 83 articles on visualization techniques for Big Data of the last six years for...
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...
This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few...
It is said that a picture is worth a thousand words. Thus, there are various ways to describe an image, especially in aesthetic quality analysis. Although aesthetic quality assessment has generated a great deal of interest in the last decade, most studies focus on providing a quality rating of good or bad for an image. In this work, we extend the task to produce captions related to photo aesthetics...
A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially...
Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands of images per class for training is infeasible in such a scenario, prompting the use of webly supervised data. This paper explores the training of image-recognition systems on large numbers of images and associated user...
Semi-supervised learning (SSL) is an import paradigm to make full use of a large amount of unlabeled data in machine learning. A bottleneck of SSL is the overfitting problem when training over the limited labeled data, especially on a complex model like a deep neural network. To get around this bottleneck, we propose a bio-inspired SSL framework on deep neural network, namely Deep Growing Learning...
High-radix, low-diameter, hierarchical networks based on the Dragonfly topology are common picks for building next generation HPC systems. However, effective tools are lacking for analyzing the network performance and exploring the design choices for such emerging networks at scale. In this paper, we present visual analytics methods that couple data aggregation techniques with interactive visualizations...
Because data collection in HPC systems happens on the nodes and is easily related to the job running on the node, tools presenting the data and subsequent analyses to the user generally present them at the job level. Our position is that this is the wrong level of abstraction and thus limits the value of the analyses, often dissuading users from using any of the offered tools. In this paper we present...
Nowadays environmental science experiences tremendous growth of raster data: N-dimensional (N-d) arrays coming mainly from numeric simulation and Earth remote sensing. An array DBMS is a tool to streamline raster data processing. However, raster data are usually stored in files, not in databases. Moreover, numerous command line tools exist for processing raster files. This paper describes a distributed...
A problem in managing the ever growing computer networks nowadays is the analysis of events detected by intrusion detection systems and the classification whether an event was correctly detected or not. When a false positive is detected by the user, changes to the configuration must be made and evaluated before they can be adopted to productive use. This paper describes an approach for a visual analysis...
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