Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online.

Methods for image data stream classification need to update their model constantly and many of these perform this in a supervised way. However, these studies evaluate the performance of their methods assuming that all labels will be available immediately after classification, which is not consistent with various real-world application scenarios. This article proposes a new evaluation method for image data stream classifiers that allows for the exploration of different issues present in real-world applications, such as the emergence of new classes, the evolution of existing classes, and delayed image labels after classification. Through an analysis of the experimental results, we verified that the proposed evaluation method allowed the identification of the issues that most impact the accuracy of the image classifier, indicating a need to direct efforts in carrying out future works to develop strategies to mitigate these issues.

In this paper we describe a general framework for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set, where elements are ranked with respect to relevance and diversity features, i.e., the retrieved elements should be as relevant as possible to the query, and, at the same time, the result set should be as diverse as possible. While addressing relevance is relatively simple and has been heavily studied, diversity is a harder problem to solve. One major contribution of this paper is that, using the above framework, we adapt, implement and evaluate several existing methods for diversifying query results. We also propose two new approaches, namely the Greedy with Marginal Contribution (GMC) and the Greedy Randomized with Neighborhood Expansion (GNE) methods. Another major contribution of this paper is that we present the first thorough experimental evaluation of the various diversification techniques implemented in a common framework. We examine the methods' performance with respect to precision, running time and quality of the result. Our experimental results show that while the proposed methods have higher running times, they achieve precision very close to the optimal, while also providing the best result quality. While GMC is deterministic, the randomized approach (GNE) can achieve better result quality if the user is willing to tradeoff running time.

Complex data is usually represented through signatures, which are sets of features describing the data content. Several kinds of complex data allow extracting different signatures from an object, representing complementary data characteristics. However, there is no ground truth of how balancing these signatures to reach an ideal similarity distribution. It depends on the analyst intent, that is, according to the job he/she is performing, a few signatures should have more impact in the data distribution than others. This work presents a new technique, called Visual Signature Weighting (ViSW), which allows interactively analyzing the impact of each signature in the similarity of complex data represented through multiple signatures. Our method provides means to explore the tradeoff of prioritizing signatures over the others, by dynamically changing their weight relation. We also present case studies showing that the technique is useful for global dataset analysis as well as for inspecting subspaces of interest.

The human vision can naturally interpret data in spaces of 2 or 3 dimensions. When data is in higher dimensional spaces, in most cases the visualization is not intuitive. Regarding metric spaces, the interpretation is even harder, since they often do not have a direct spatial representation. However, the need to analyze how metric-represented data evolve over time is pretty common when one needs to understand several phenomena and in decision making processes, as it occurs in medical and agrometeorological applications. This paper presents three interactive techniques to visualize metric data that vary over time. Each one focus on a different way to interpret the temporal information. The first technique shows data evolving in a timeline axis. The second overlaps evolving snapshots of the space showing how the space varies regarding time. The last one does not treat temporal data as a dimension, it is used instead to define the similarity among complex data, employing the new concept of metric-temporal spaces, which seamlessly integrate time and metric data into a single similarity space. Visualization examples with real datasets are presented to show the usefulness of the proposed techniques.

Abstract Modern database applications are increasingly employing database management systems (DBMS) to store multimedia and other complex data. To adequately support the queries required to retrieve these kinds of data, the DBMS need to answer similarity queries. However, the standard structured query language (SQL) does not provide effective support for such queries. This paper proposes an extension to SQL that seamlessly integrates syntactical constructions to express similarity predicates to the existing SQL syntax and describes the implementation of a similarity retrieval engine that allows posing similarity queries using the language extension in a relational DBMS. The engine allows the evaluation of every aspect of the proposed extension, including the data definition language and data manipulation language statements, and employs metric access methods to accelerate the queries. Copyright © 2008 John Wiley & Sons, Ltd.

A similarity query considers an element as the query center and searches a dataset to find either the elements far up to a bounding radius or the k nearest ones from the query center. Several algorithms have been developed to efficiently execute similarity queries. However, there are queries that require more than one center, which we call Aggregate Similarity Queries. Such queries appear when the user gives multiple desirable examples, and requests data elements that are similar to all of the examples, as in the case of applying relevance feedback. Here we give the first algorithms that can handle aggregate similarity queries on Metric Access Methods (MAM) such as the M-tree and Slim-tree. Our method, which we call Metric Aggregate Similarity Search (MASS) has the following properties: (a) it requires only the triangle inequality property; (b) it guarantees no false-dismissals, as we prove that it lower-bounds the aggregate distance scores; (c) it can work with any MAM; (d) it can handle any number of query centers, which are either scattered all over the space or concentrated on a restricted region. Experiments on both real and synthetic data show that our method scales on both the number of elements and, if the dataset is in a spatial domain, also on its dimensionality. Moreover, it achieves better results than previous related methods.