Abstract

Abstract
The growth of social media as an information medium without restrictive measures on the creation of new accounts led to the rise of malicious agents with the intend to diffuse unreliable information in the network, ultimately affecting the perception of users in important topics such as political and health issues. Although the problem is being tackled within the domain of bot detection, the impact of studies in this area is still limited due to 1) not all accounts that spread unreliable content are bots, 2) human-operated accounts are also responsible for the diffusion of unreliable information and 3) bot accounts are not always malicious (e.g. news aggregators). Also, most of these methods are based on supervised models that required annotated data and updates to maintain their performance through time. In this work, we build a framework and develop knowledge-based metrics to complement the current research in bot detection and characterize the impact and behavior of a Twitter account, independently of the way it is operated (human or bot). We proceed to analyze a sample of the accounts using the metrics proposed and evaluate the necessity of these metrics by comparing them with the scores from a bot detection system. The results show that the metrics can characterize different degrees of unreliable accounts, from unreliable bot accounts with a high number of followers to human-operated accounts that also spread unreliable content (but with less impact on the network). Furthermore, evaluating a sample of the accounts with a bot detection system shown that bots compose around 11% of the sample of unreliable accounts extracted and that the bot score is not correlated with the proposed metrics. In addition, the accounts that achieve the highest values in our metrics present different characteristics than the ones that achieve the highest bot score. This provides evidence on the usefulness of our metrics in the evaluation of unreliable accounts in social networks. Copyright

Abstract
Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%.

Abstract
Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present JAY, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. JAY is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of JAY with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.

Abstract
Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to summarize semantic graphs and this research explores a particular one, both to reduce the graph's size and to focus on its path patterns. A-graphs are a novel kind of graph designed to highlight path patterns using this kind of summarization. They are composed of a-nodes connected by a-edges, and these reflect respectively edges and nodes of the semantic graph. A-graphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph's content. A-graphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, a-graphs are not expected to compete with other forms of semantic graph visualization but rather to be used as a complementary tool. This paper provides a precise definition both of a-graphs and of the mapping of semantic graphs into a-graphs. Their visualization is obtained with a-graphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are presented to illustrate the use of agraphs for discovering path patterns in different settings, such as the visualization of massive semantic graphs, the codification of SPARQL or the definition of semantic measures. The validation with large semantic graphs is the basis for a discussion on the insights provided by a-graphs on large semantic graphs: the difference between a-graphs and ontologies, path pattern visualization using a-graphs and the challenges posed by large semantic graphs.

Abstract
One of the great challenges in programming education is to keep students motivated while working on their programming assignments. Of the techniques proposed in the literature to engage students, gamification is arguably the most widely spread and effective method. Nevertheless, gamification is not a panacea and can be harmful to students. Challenges comprising intrinsic motivators of games, such as graphical feedback and game-thinking, are more prone to have longterm positive effects on students, but those are typically complex to create or adapt to slightly distinct contexts. This paper presents Asura, a game-based programming assessment environment providing means to minimize the hurdle of building game challenges. These challenges invite the student to code a Software Agent to solve a certain problem, in a way that can defeat every opponent. Moreover, the experiment conducted to assess the difficulty of authoring Asura challenges is described.