Human-centered machine learning through interactive visualization

The goal of visual analytics (VA) systems is to solve complex problems by integrating automated data analysis methods, such as machine learning (ML) algorithms, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and makes the crucial interplay between automated algorithms and interactive visualizations more concrete. The framework is illustrated through several examples. We derive three open research challenges at the intersection of ML and visualization research that will lead to more effective data analysis

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Multifunctional supramolecular polymer networks as next-generation consolidants for archaeological wood conservation.

The preservation of our cultural heritage is of great importance to future generations. Despite this, significant problems have arisen with the conservation of waterlogged wooden artifacts. Three major issues facing conservators are structural instability on drying, biological degradation, and chemical degradation on account of Fe(3+)-catalyzed production of sulfuric and oxalic acid in the waterlogged timbers. Currently, no conservation treatment exists that effectively addresses all three issues simultaneously. A new conservation treatment is reported here based on a supramolecular polymer network constructed from natural polymers with dynamic cross-linking formed by a combination of both host-guest complexation and a strong siderophore pendant from a polymer backbone. Consequently, the proposed consolidant has the ability to chelate and trap iron while enhancing structural stability. The incorporation of antibacterial moieties through a dynamic covalent linkage into the network provides the material with improved biological resistance. Exploiting an environmentally compatible natural material with completely reversible chemistries is a safer, greener alternative to current strategies and may extend the lifetime of many culturally relevant waterlogged artifacts around the world.This is the author's accepted manuscript. The final version is available from PNAS at http://www.pnas.org/content/111/50/17743.long

Information Visualisation for Project Management: Case Study of Bath Formula Student Project

This paper contributes to a better understanding and design of dashboards for monitoring of engineering projects based on the projects’ digital footprint and user-centered design approach. The paper presents an explicit insight-based framework for the evaluation of dashboard visualisations and compares the performance of two groups of student engineering project managers against the framework: a group with the dashboard visualisations and a group without the dashboard. The results of our exploratory study demonstrate that student project managers who used the dashboard generated more useful information and exhibited more complex reasoning on the project progress, thus informing knowledge of the provision of information to engineers in support of their project understanding

X-ray stereo microscopy for investigation of dynamics in soils

The presented combination of stereo imaging and elemental mapping with soft X-ray microscopy reveals the spatial arrangement of naturally aqueous colloidal systems, e.g. iron oxides in soil colloid clusters. Changes in the spatial arrangement can be induced by manipulating the sample mounted to the X-ray microscope and thus be investigated directly

Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

A fundamental task in Criminal Intelligence Analysis is to analyze the similarity of crime cases, called CCA, to identify common crime patterns and to reason about unsolved crimes. Typically, the data is complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users' trust in the results and hence a reluctance to use the tool}. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centred design decisions made this computational complexity less scary to criminal analysts