This paper results from a project for promoting students’ interest in the field of computing through teaching computer science, robotics, and programming in Brazilian public schools. Here, we present a mixed-methods study conducted with elementary and secondary school teachers involved with such a project. The study analyzes teachers’ perceptions regarding the project’s impact on their professional trajectories, on the students, and on the school and community contexts in which they operate. The findings highlight the importance of early exposure to computing as a strategy to bridge the gap between educational levels and as a tool for inclusion and social transformation. The results indicate that participation in the project has generated positive outcomes. Teachers report the development of new skills—particularly technical skills in computing—increased motivation for teaching, and the opportunity to make a meaningful impact on students’ lives. Furthermore, they observe positive changes among the girls, especially in terms of learning and engagement, as well as improvements in the school and community environments, including greater parental involvement in the students’ educational journey.

The intersection of natural language processing (NLP) and literary analysis has yielded valuable insights and applications across various languages. However, the scarcity of labeled data tailored for Portuguese literary texts poses a notable challenge. To address this gap, we present the PPORTAL_ner corpus, an annotated dataset that simplifies the development of Named Entity Recognition (NER) models specifically adapted for Portuguese literary works. Our corpus includes annotations of PER, LOC, GPE, ORG, and DATE entities within a diverse set of 25 literary texts. Annotation of the corpus involved a two-step process: initial pre-annotation using a pre-trained spaCy model followed by correction and refinement using the Prodigy annotation tool. With a total of 125,059 tokens and 5,266 annotated entities, PPORTAL_ner corpus significantly enriches the landscape of resources available for computational literary analysis in Portuguese. This paper details the annotation methodology, guidelines, and dataset statistics while also evaluating four NER models over the PPORTAL_ner corpus. Our evaluation analysis reveals that fine-tuning on domain-specific data significantly improves NER model performance, demonstrating the value of the PPORTAL_ner corpus for developing domain-specific language models.

Increasingly, decisions are based on insights and conclusions derived from the results of data analysis. Thus, determining the validity of these results is of paramount importance. In this paper, we take a step towards helping users identify potential issues in spatio-temporal data and thus gain trust in the results they derived from these data. We focus on processes that are captured by relationships among datasets that serve as the data exhaust for different components of urban environments. In this scenario, debugging data involves two important challenges: the inherent complexity of spatio-temporal data, and the number of possible relationships. We propose a framework for profiling spatio-temporal relationships that automatically identifies data slices that present a significant deviation from what is expected, and thus, helps focus a user's attention on slices of the data that may have quality issues and/or that may affect the conclusions derived from the analysis' results. We describe the profiling methodology and how it derives relationships, identifies candidate deviations, assesses their statistical significance, and measures their magnitude. We also present a series of cases studies using real datasets from New York City which demonstrate the usefulness of spatio-temporal profiling to build trust on data analysis' results.

SIGCSE members are part of a worldwide community of computing educators and professionals. This discussion provides participants an opportunity to share global differences in vocabulary and interpretation of idioms and assumptions that pervade teachers and researchers. Lack of cultural knowledge can hinder understanding meaning of terms; communication among colleagues can be confusing. Lack of understanding also inhibits the leveraging of scholarly work and cooperation among colleagues from different cultures. E.g., information technology in Australia means the field of computing, and the baccalaureate degree "Computer Science and Technology" in China is equivalent to a degree in computer engineering. Terminology that Americans use such as AP for Advance Placement or K-12 for pre-undergraduate education is meaningless in many parts of the world. This discussion is an outgrowth of an ad hoc subcommittee formed by the ACM Education Council on global issues. Serving as a reference for the discussion is the seminal article by Clear, A. et al. "What's in a Name?: International Interpretations of Computing Education Terminology," in The Proceedings of the 2015 ITiCSE on Working Group Reports. Attending this BOF should heighten awareness of what changes might lead to a truly global computing education community. In addition to Owens, Alison Clear from New Zealand, Ming Zhang from China, and John Impagliazzo from the USA have agreed to help lead the discussion.

We perform social analyses over an important community: the open code collaboration network. Specifically, we study the correlation among features that measure the strength of social coding collaboration on GitHub - a Web-based source code repository that can be modeled as a social coding network. We also make publicly available a curated dataset called GitSED, GitHub Socially Enhanced Dataset. Our results have many practical applications such as to improve the recommendation of developers, the evaluation of team formation and existing analysis algorithms.

In co-authorship social networks, nodes are authors linked by co-authorship interactions. As time is a relevant aspect of such interactions, concepts and metrics designed to static networks have to be adapted to temporal networks. Tie strength is one of those concepts. Here, we verify if current tie strength definitions are valid for temporal networks by analyzing the strength of ties dynamism over temporal co-authorship networks. Surprisingly, our results show that most ties, even the strong ones, tend to perish over time. Thus, most co-authorships are symbiotic without positive concerns. Also, real co-authorship social networks from different research areas have more weak and random ties than strong and bridge ties.

We propose the 3c-index that measures the influence degree of researchers by evaluating the links they establish between communities. We evaluate its performance against well known metrics. The results show 3c-index outperforms them in most cases and can be employed as a complementary metric to assess researchers' productivity.

Online social networks have become a platform for running and optimizing classical algorithms. Here, we introduce a tool for finding paths between social network users in real-time, a task that classical solutions are not tailored for.

Scientific studies are usually developed by contributions from different researchers. Analyzing such collaborations is often necessary, for example, when evaluating the quality of a research group. Also, identifying new partnership possibilities within a set of researchers is frequently desired, for example, when looking for partners in foreign countries. Both analysis and identification are not easy tasks, and are usually done manually. This work presents VRRC, a new approach for visualizing recommendations of people within a co-authorship network (i.e., a graph in which nodes represent researchers and edges represent their co-authorships). VRRC input is a publication list from which it extracts the co-authorships. VRRC then recommends which relations could be created or intensified based on metrics designed for evaluating co-authorship networks. Finally, VRRC provides brand new ways to visualize not only the final recommendations but also the intermediate interactions within the network, including: a complete representation of the co-authorship network; an overview of the collaborations evolution over time; and the recommendations for each researcher to initiate or intensify cooperation. Some visualizations are interactive, allowing to filter data by time frame and highlighting specific collaborations. The contributions of our work, compared to the state-of-art, can be summarized as follows: (i) VRRC can be applied to any co-authorship network, it provides both net and recommendation visualizations, it is a Web-based tool and it allows easy sharing of the created visualizations (existing tools do not offer all these features together); (ii) VRRC establishes graphical representations to ease the visualization of its results (traditional approaches present the recommendation results through simple lists or charts); and (iii) with VRRC, the user can identify not only new possible collaborations but also existing cooperation that can be intensified (current recommendation approaches only indicate new collaborations). This work was partially supported by CNPq, Brazil.

In this paper, we propose that various keyword-based queries be processed over XML streams in a multi-query processing way. Our algorithms rely on parsing stacks designed for simultaneously matching terms from several distinct queries and use new query indexes to speed up search operations when processing a large number of queries. Besides defining a new problem and novel solutions, we perform experiments in which aspects related to performance and scalability are examined.

This tutorial introduces the concept of XML Structural Summaries and describes their role within XML retrieval. It covers the usage of those summaries for Database-style query processing and Information Retrieval-style search tasks in the context of both centralized and distributed environments. Finally, it discusses new retrieval scenarios that can potentially be favorably supported by those summaries.

Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.

In response to the widespread use of the XML format for document representation and message exchange, major database vendors support XML in terms of persistence, querying and indexing. Specifically, the recently released IBM DB2 9 (for Linux, Unix and Windows) is a hybrid data server with optimized management of both XML and relational data. With the new option of storing and querying XML in a relational DBMS, data architects face the the decision of what portion of their data to persist as XML and what portion as relational data. This problem has not been addressed yet and represents a serious need in the industry. Hence, this paper describes ReXSA, a schema advisor tool that is being prototyped for IBM DB2 9. ReXSA proposes candidate database schemas given an information model of the enterprise data. It has the advantage of considering qualitative properties of the information model such as reuse, evolution and performance profiles for deciding how to persist the data. Finally, we show the viability and practicality of ReXSA by applying it to custom and real usecases.

In XML databases, new schema versions may be released as frequently as once every two weeks. This poster describes a taxonomy of changes for XML schema evolution. It examines the impact of those changes on schema validation and query evaluation. Based on that study, it proposes guidelines for XML schema evolution and for writing queries in such a way that they continue to operate as expected across evolving schemas.

Structural summaries are data structures that preserve all structural features of XML documents in a compact form. We investigate the applicability of the most popular summaries as textitaccess methods within XML query processing. In this context, issues like space and false positives introduced by the summaries need to be examined. Our evaluation reveals that the additional space required by the more precise structures is usually small and justified by the considerable performance gains that they achieve.

XML and semi-structured data is usually modeled using graph structures. Structural summaries, which have been proposed to speedup XML query processing have graph forms as well. The existent approaches for evaluating queries over tree structured data (i.e. data whose underlying structure is a tree) are not directly applicable when the data is modeled as a random graph. Moreover, they cannot be applied when structural summaries are employed and, to the best of our knowledge, no analogous techniques have been reported for this case either. As a result, the potential of structural summaries is not fully exploited.In this paper, we investigate query evaluation techniques applicable to graph-structured data. We propose efficient algorithms for the case of directed acyclic graphs, which appear in many real world situations. We then tailor our approaches to handle other directed graphs as well. Our experimental evaluation reveals the advantages of our solutions over existing methods for graph-structured data.

A temporal data model can be used to specify not only static but also dynamic aspects of an application, allowing us to record the evolution of its objects. Conventional applications commonly use the concept of transaction to update information, while unconventional applications require managing many database states to keep the update history. To fulfil this request, the concept of version is presented. The main purpose of the work is to present an alternative for the union of temporal data and a version model. The result, the Temporal Versions Model, is able to store the object versions and for each version, the history of its dynamic properties and relationship values. One of the most interesting features of our model is the possibility of having two different time orders, branched time for the object and linear time for each version. The model still supports integration with existing databases by allowing the modeling of normal classes among the temporal versioned classes.