Accessible and Interactive: New Methods of Data Visualization as Tools for Data Analysis and Information Sharing in Transitional Justice Research

The production and use of datasets is a growing area in transitional justice research. One constant limitation, however, is the way this data is visualized. Relying only on static graphics and tables, many of these datasets are insufficiently explored and analyzed, and remain inaccessible for other researchers. Interactive data visualization tools are an ideal method for overcoming this gap. They are able to adequately present a wide range of quantitative and qualitative data- types, such as geographic, temporal, network, and text data, and their interactive functions allow for a better exploration and understanding of the data. This article examines the visualization needs of transitional justice research, and demonstrates how interactive visualization can facilitate data analysis as well as information sharing. Presenting selected tools for different data types, the article provides hands-on methodological examples for effective handling of transitional justice data using, for example, GIS mapping, Google Motion Charts, and Word Trees

The early history and emergence of molecular functions and modular scale-free network behavior

The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop (EFL) sequences and domain structures defined at the fold superfamily (FSF) level of Structural Classification of Proteins (SCOP). The resulting ‘elementary functionome’ network and its EFL and FSF graph projections unfold evolutionary ‘waterfalls’ describing emergence of primordial functions. Waterfalls reveal how ancient EFLs are shared by FSF structures in two initial waves of functional innovation that involve founder ‘p-loop’ and ‘winged helix’ domain structures. They also uncover a dynamics of modular motif embedding in domain structures that is ongoing, which transfers ‘preferential’ cooption properties of ancient EFLs to emerging FSFs. Remarkably, we find that the emergence of molecular functions induces hierarchical modularity and power law behavior in network evolution as the networks of motifs and structures expand metabolic pathways and translation

A characteristic-based visual analytics approach to detect subtle attacks from NetFlow records

Security is essentially important for any enterprise networks. Denial of service, port scanning, and data exfiltration are among of the most common network intrusions. It\u27s urgent for network administrators to detect such attacks effectively and efficiently from network traffic. Though there are many intrusion detection systems (IDSs) and approaches, Visual Analytics (VA) provides a human-friendly approach to detect network intrusions with situational awareness functionality. Overview visualization is the first and most important step in a VA approach. However, many VA systems cannot effectively identify subtle attacks from massive traffic data because of the incapability of overview visualizations. In this work, we developed two overviews and tried to identify subtle attacks directly from these two overviews. Moreover, zoomed-in visualizations were also provided for further investigation. The primary data source was NetFlow and we evaluated the VA system with datasets from Mini Challenge 3 of VAST challenge 2013. Evaluation results indicated that the VA system can detect all the labeled intrusions (denial of service, port scanning and data exfiltration) with very few false alerts

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research

Massively Parallel Video Networks

We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles. Leveraging operation pipelining and multi-rate clocks, these models perform a minimal amount of computation (e.g. as few as four convolutional layers) for each frame per timestep to produce an output. The models are still very deep, with dozens of such operations being performed but in a pipelined fashion that enables depth-parallel computation. We illustrate the proposed principles by applying them to existing image architectures and analyse their behaviour on two video tasks: action recognition and human keypoint localisation. The results show that a significant degree of parallelism, and implicitly speedup, can be achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Residual acceleration data on IML-1: Development of a data reduction and dissemination plan

The research performed consisted of three stages: (1) identification of sensitive IML-1 experiments and sensitivity ranges by order of magnitude estimates, numerical modeling, and investigator input; (2) research and development towards reduction, supplementation, and dissemination of residual acceleration data; and (3) implementation of the plan on existing acceleration databases

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work