We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting.

Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training (AT), has been shown to mitigate the negative impact of adversarial attacks by virtue of a min-max robust training method. While effective, it remains unclear whether it can successfully be adapted to the distributed learning context. The power of distributed optimization over multiple machines enables us to scale up robust training over large models and datasets. Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines. We show that DAT is general, which supports training over labeled and unlabeled data, multiple types of attack generation methods, and gradient compression operations favored for distributed optimization. Theoretically, we provide, under standard conditions in the optimization theory, the convergence rate of DAT to the first-order stationary points in general non-convex settings. Empirically, we demonstrate that DAT either matches or outperforms state-of-the-art robust accuracies and achieves a graceful training speedup (e.g., on ResNet-50 under ImageNet). Codes are available at https://github.com/dat-2022/dat.

Sometimes, when programmers use a search engine they know more or less what they need. Other times, programmers use the search engine to look around and generate possible ideas for the programming problem they are working on. The key insight we explore in this paper is that the results found in the latter case tend to serve as inspiration or triggers for the next queries issued. We introduce two search engines, CodeExchange and CodeLikeThis, both of which are specifically designed to enable the user to directly leverage the results in formulating the next query. CodeExchange does this with a set of four features supporting the programmer to use characteristics of the results to find other code with or without those characteristics. CodeLikeThis supports simply selecting an entire result to find code that is analogous, to some degree, to that result. We evaluated how these approaches were used along with two approaches not explicitly supporting iteration, a baseline and Google, in a user study among 24 developers. We find that search engines that support using results to form the next query can improve the programmers’ search experience and different approaches to iteration can provide better experiences depending on the task.

Programming today regularly involves searching for source code online, whether through a general search engine such as Google or a specialized code search engine such as SearchCode, Ohloh, or GitHub. Searching typically is an iterative process, with developers adjusting the keywords they use based on the results of the previous query. However, searching in this manner is not ideal, because just using keywords places limits on what developers can express as well as the overall interaction that is required. Based on the observation that the results from one query create a con-text in which a next is formulated, we present CodeExchange, a new code search engine that we developed to explicitly leverage this context to support fluid, expressive reformulation of queries. We motivate the need for CodeExchange, highlight its key design decisions and overall architecture, and evaluate its use in both a field deployment and a laboratory study.

To date, most dedicated code search engines use ranking algorithms that focus only on the relevancy between the query and the results. In practice, this means that a developer may receive search results that are all drawn from the same project, all implement the same algorithm using the same external library, or all exhibit the same complexity or size, among other possibilities that are less than ideal. In this paper, we propose that code search engines should also locate both diverse and concise (brief but complete) sets of code results. We present four novel algorithms that use relevance, diversity, and conciseness in ranking code search results. To evaluate these algorithms and the value of diversity and conciseness in code search, twenty-one professional programmers were asked to compare pairs of top ten results produced by competing algorithms. We found that two of our new algorithms produce top ten results that are strongly preferred by the programmers.

Studying vast volumes of bug and issue discussions can give an understanding of what the community has been most concerned about, however the magnitude of documents can overload the analyst. We present an approach to analyze the development of the Android open source project by observing trends in the bug discussions in the Android open source project public issue tracker. This informs us of the features or parts of the project that are more problematic at any given point of time. In turn, this can be used to aid resource allocation (such as time and man power) to parts or features. We support these ideas by presenting the results of issue topic distributions over time using statistical analysis of the bug descriptions and comments for the Android open source project. Furthermore, we show relationships between those time distributions and major development releases of the Android OS.