Visual Mining of Epidemic Networks

We show how an interactive graph visualization method based on maximal modularity clustering can be used to explore a large epidemic network. The visual representation is used to display statistical tests results that expose the relations between the propagation of HIV in a sexual contact network and the sexual orientation of the patients.Comment: 8 page

Mid-level Deep Pattern Mining

Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activations extracted from the first fully-connected layer of CNNs have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern, surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.Comment: Published in Proc. IEEE Conf. Computer Vision and Pattern Recognition 201

Case Study Sanwich Terns - a probabilistic analysis of the ecological effects of dreding

Every year, large amounts of sand are extracted from the North Sea to meet the demands for construction activities. Potential ecological effects of these sand mining activities have to be examined and reported in so called Environmental Impact Assessments (EIA’s). In the Netherlands, the potential impacts of sand mining activities on tern populations often form an important topic in these EIA’s. Sand mining causes an increase in silt concentrations. This increase will influence the turbidity of the water, which may affect populations of visual hunting birds, such as terns

Visual Data Mining

Occlusion is one of the major problems for interactive visual knowledge discovery and data mining in the process of finding patterns in multidimensional data.This project proposes a hybrid method that combines visual and analytical means to deal with occlusion in visual knowledge discovery called as GLC-S which uses visualization of n-D data in 2D in a set of Shifted Paired Coordinates (SPC). A set of Shifted Paired Coordinates for n-D data consists of n/2 pairs of common Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point A is represented as a directed graph A* in SPC, where each node is the 2D projection of A in a respective pair of the Cartesian coordinates. The proposed GLC-S method significantly decrease cognitive load for analysis of n-D data and simplify pattern discovery in n-D data. The GLC-S method iteratively splits n-D data into non-overlapping clusters (hyper-rectangles) around local centers and visualizes only data within these clusters at each iteration. The requirements for these clusters are to contain cases of only one class and be the largest cluster with this property in SPC visualization. Such sequential splitting allows: (1) avoiding occlusion, (2) finding visually local classification patterns, rules, and (3) combine local sub-rules to a global rule that classifies all given data of two or more classes. The computational experiment with Wisconsin Breast Cancer data(9-D), User Knowledge Modeling data(6-D), and Letter Recognition data(17-D) from UCI Machine Learning Repository confirm this capability. At each iteration, these data have been split into training (70%) and validation (30%) data. It required 3 iterations in Wisconsin Breast Cancer data, 4 iterations in User Knowledge Modeling and 5 iterations in Letter Recognition data and respectively 3, 4, 5 local sub-rules that covered over 95% of all n-D data points with 100% accuracy at both training and validation experiments. After each iteration, the data that were used in this iteration are removed and remaining data are used in the next iteration. This removal process helps to decrease occlusion too. The GLC-S algorithm refuses to classify remaining cases that are not covered by these rules, i.e.,., do not belong to found hyper-rectangles. The interactive visualization process in SPC allows adjusting the sides of the hyper-rectangles to maximize the size of the hyper-rectangle without its overlap with the hyper-rectangles of the opposing classes. The GLC-S method splits data using the fixed split of n coordinates to pairs. This hybrid visual and analytical approach avoids throwing all data of several classes into a visualization plot that typically ends up in a messy highly occluded picture that hides useful patterns. This approach allows revealing these hidden patterns. The visualization process in SPC is reversible (lossless). i.e.,., all n-D information is visualized in 2D and can be restored from 2D visualization for each n-D case. This hybrid visual analytics method allowed classifying n-D data in a way that can be communicated to the user’s in the understandable and visual form

Effectiveness of Glass Fiber Mesh Reinforcement Applied to Road Construction Located in a Mining Subsidence Area

Local district roads represent low technological regime when it comes to rehabilitation treatments. Analysed road is located in a mining subsidence area in Upper Silesia in Poland. Glass fibre mesh interlayer reinforcement had been tested numerous times at different roads. The main objective of this paper is to investigate the effectiveness of applied reinforcement for road located in a mining subsidence area. Evaluation was performed by determining a state of cracking indicator in accordance with visual-method of pavement surface evaluation. Results shows high effectiveness of applied reinforcement. Further researches are recommended

Maleku: an evolutionary visual software analytics tool for providing insights into software evolution

Software maintenance is a complex process that requires the understanding and comprehension of software project details. It involves the understanding of the evolution of the software project, hundreds of software components and the relationships among software items in the form of inheritance, interface implementation, coupling and cohesion. Consequently, the aim of evolutionary visual software analytics is to support software project managers and developers during software maintenance. It takes into account the mining of evolutionary data, the subsequent analysis of the results produced by the mining process for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper proposes an evolutionary visual software analytics tool for the exploration and comparison of project structural, interface implementation and class hierarchy data, and the correlation of structural data with metrics, as well as socio-technical relationships. Its main contribution is a tool that automatically retrieves evolutionary software facts and represent them using a scalable visualization design