Abstract
Determining subgraph frequencies is at the core of several graph mining methodologies such as discovering network motifs or computing graphlet degree distributions. Current state-of-the-art algorithms for this task either take advantage of common patterns emerging on the networks or target a set of specific subgraphs for which analytical calculations are feasible. Here, we propose a novel network generic framework revolving around a new data-structure, a Condensed Graph, that combines both the aforementioned approaches, but generalized to support any subgraph topology and size. Furthermore, our methodology can use as a baseline any enumeration based census algorithm, speeding up its computation. We target simple topologies that allow us to skip several redundant and heavy computational steps using combinatorics. We were are able to achieve substantial improvements, with evidence of exponential speedup for our best cases, where these patterns represent up to 97% of the network, from a broad set of real and synthetic networks.

Abstract
Counting subgraph occurrences in complex networks is an important analytical task with applicability in a multitude of domains such as sociology, biology and medicine. This task is a fundamental primitive for concepts such as motifs and graphlet degree distributions. However, there is a lack of online algorithms for computing and updating subgraph counts in dynamic networks. Some of these networks exist as a streaming of edge additions and deletions that are registered as they occur in the real world. In this paper we introduce StreamFaSE, an efficient online algorithm for keeping track of exact subgraph counts in dynamic networks, and we explain in detail our approach, showcasing its general applicability in different network scenarios. We tested our method on a set of diverse real directed and undirected network streams, showing that we are always faster than the current existing methods for this task, achieving several orders of magnitude speedup when compared to a state-of-art baseline. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
There are countless reasons cited in scientific studies to explain the difficulties in programming learning. The reasons range from the subject's complexity, the ineffective teaching and study methods, to psychological aspects such as demotivation. Still, learning programming often boils down to practice on exercise solving. Hence, it is essential to understand that the nature of a programming exercise is an important factor for the success and consistent learning. This paper explores different approaches on the creation of a programming exercise, starting with realizing how it is currently formalized, presented and evaluated. From there, authors suggest variations that seek to broaden the way an exercise is solved and, with this diversity, increase student engagement and learning outcome. The several types of exercises presented can use gamification techniques fostering student motivation. To contextualize the student with his peers, we finish presenting metrics that can be obtained by existing automatic assessment tools. 2012 ACM Subject Classification Applied computing ! Education.

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Abstract
The Web Ontology Language (OWL) is a World Wide Web Consortium standard, based on the Resource Description Format standard. It is used to define ontologies. While large ontologies are useful for different applications, some tools require partial ontologies, based mostly on a hierarchical relationship of classes. In this article we present bOWL, a basic OWL browser, with the main goal of being pluggable into others, more significant, web applications. The tool was tested through its integration on LeXmart, a dictionary editing tool.

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