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We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

Deep learning plays an important role in the field of artificial intelligence, but in most situation, deep learning requires a larger number of training samples to complete high-precision learning. Few-shot learning, which only uses a small dataset to learn different tasks, has been extensively researched. It can be summarized into three methods: metric learning, optimization-based and model-based. In this paper, we develop a novel method based on Matching Networks: focusing on the relation between samples. We have proposed a simple method based on the idea that we can aggregate intra-class samples and separate inter-class samples. Our experiments on the Omniglot dataset and miniImageNet dataset show that our method outperforms the baseline networks.

Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains., Comment: To appear in 2019 IEEE Transactions on Visualization and Computer Graphics

Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling techniques, which offer new insights that enable the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We conducted a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool., Comment: IEEE VIS (VAST) 2019

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance., Comment: To appear in IEEE VIS 2019 Short Papers

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user's knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.