CRACS
Permanent URI for this community
This service develops its activity in the areas of programming languages, parallel and distributed computing, data mining, intelligent systems and software architecture, with emphasis on solving concrete problems in areas of multidisciplinary collaboration, such as Biology, Medicine and Chemistry.
Browse
Browsing CRACS by Author "Álvaro Figueira"
Results Per Page
Sort Options
-
ItemANALYSING RELEVANT INTERACTIONS BY BRIDGING FACEBOOK AND MOODLE( 2016) Luciana Gomes Oliveira ; Álvaro Figueira
-
ItemAnalyzing Social Media Discourse An Approach using Semi-supervised Learning( 2016) Álvaro Figueira ; Luciana Gomes OliveiraThe ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics' applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an "editorial model" that characterizes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.
-
ItemAn Approach to Relevancy Detection: contributions to the automatic detection of relevance in social networks( 2016) Álvaro Figueira ; Miguel Oliveira Sandim ; Paula Teixeira FortunaIn this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.
-
ItemAutomated Assessment in Computer Science Education: A State-of-the-Art Review( 2022) Álvaro Figueira ; José Paulo Leal ; José Carlos Paiva ; 5088 ; 5125 ; 6251Practical programming competencies are critical to the success in computer science (CS) education and goto-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.
-
ItemAutomated Assessment in Computer Science Education: A State-of-the-Art Review( 2022) Álvaro Figueira ; José Paulo Leal ; José Carlos Paiva ; 5088 ; 5125 ; 6251Practical programming competencies are critical to the success in computer science (CS) education and goto-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.
-
ItemAutomated Assessment in Computer Science: A Bibliometric Analysis of the Literature( 2022) José Paulo Leal ; Álvaro Figueira ; 5125 ; 5088
-
ItemBenchmarking Analysis of Social Media Strategies in the Higher Education Sector( 2015) Oliveira,L ; Álvaro FigueiraThe adoption of social media networks by organizations has been increasing, mainly by using more social networks but also by constantly increasing on the number of messages and received comments posted on these channels. Interestingly, this process apparently has not been accompanied by a carefully planned and strategically design process to provide the essential alignment with organizational goals. This study is framed in the tertiary sector, the Higher Education Sector (HES), which despite its peculiarities, is no exception to the above limitations, and is facing an increased competitive environment. In this paper we present a sector benchmarking process, and the respective analysis, to provide insights on the sector's tendency, as well as a threefold classification of the sector's social media strategies being pursued. The analysis builds upon a regulatory communication framework and respective editorial model. We describe the results of our automatic text-mining and categorization information system, specifically developed to address and analyze the seven categories of HES' social media messages. Our results show that social media strategies have been focusing essentially on mediatization and building/maintaining the organizational image/reputation as well as on advertising educational services, but completely neglecting the dialogical dimension intrinsically linked to social media environments. © 2015 The Authors. Published by Elsevier B.V.
-
ItemBibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback( 2023) Álvaro Figueira ; José Paulo Leal ; 5088 ; 5125Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.
-
ItemBibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback( 2023) Álvaro Figueira ; José Paulo Leal ; 5088 ; 5125Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.
-
ItemClustering and classifying text documents a revisit to tagging integration methods( 2013) Cunha,E ; Álvaro Figueira ; Mealha,OIn this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.
-
ItemClustering Documents Using Tagging Communities and Semantic Proximity( 2013) Cunha,E ; Álvaro Figueira ; Mealha,OEuclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown.
-
Item
-
ItemCommunity detection by local influence( 2013) Cravino,N ; Álvaro FigueiraWe present a new algorithm to discover overlapping communities in networks with a scale free structure. This algorithm is based on a node evaluation function that scores the local influence of a node based on its degree and neighbourhood, allowing for the identification of hubs within a network. Using this function we are able to identify communities, and also to attribute meaningful titles to the communities that are discovered. Our novel methodology is assessed using LFR benchmark for networks with overlapping community structure and the generalized normalized mutual information (NMI) measure. We show that the evaluation function described is able to detect influential nodes in a network, and also that it is possible to build a well performing community detection algorithm based on this function. © 2013 Springer-Verlag.
-
ItemThe community structure of a multidimensional network of news clips( 2013) José Luís Devezas ; Álvaro FigueiraWe analysed the community structure of a network of news clips where relationships were established by the co-reference of entities in pairs of clips. Community detection was applied to a unidimensional version of the news clips network, as well as to a multidimensional version where dimensions were defined based on three different classes of entities: places, people, and dates. The goal was to study the impact on the quality of the identified community structure when using multiple dimensions to model the network. We did a two-fold evaluation, first based on the modularity metric and then based on human input regarding community semantics. We verified that the assessments of the evaluators differed from the results provided by the modularity metric, pointing towards the relevance of the utility and network integration phases in the identification of semantically cohesive groups of news clips. Copyright © 2013 Inderscience Enterprises Ltd.
-
ItemThe Complementary Nature of Different NLP Toolkits for Named Entity Recognition in Social Media( 2017) Batista,F ; Álvaro FigueiraIn this paper we study the combined use of four different NLP toolkits—Stanford CoreNLP, GATE, OpenNLP and Twitter NLP tools—in the context of social media posts. Previous studies have shown performance comparisons between these tools, both on news and social media corporas. In this paper, we go further by trying to understand how differently these toolkits predict Named Entities, in terms of their precision and recall for three different entity types, and how they can complement each other in this task in order to achieve a combined performance superior to each individual one. Experiments on two publicly available datasets from the workshops WNUT-2015 and #MSM2013 show that using an ensemble of toolkits can improve the recognition of specific entity types - up to 10.62% for the entity type Person, 1.97% for the type Location and 1.31% for the type Organization, depending on the dataset and the criteria used for the voting. Our results also showed improvements of 3.76% and 1.69%, in each dataset respectively, on the average performance of the three entity types. © Springer International Publishing AG 2017.
-
ItemCreating and analysing a social network built from clips of online news( 2013) Álvaro FigueiraCurrent online news media are increasingly depending on the participation of readers in their websites while readers increasingly use more sophisticated technology to access online news. In this context, the authors present the Breadcrumbs system and project that aims to provide news readers with tools to collect online news, to create a personal digital library (PDL) of clips taken from news, and to navigate not only on the own PDL, but also on external PDLs that relate to the first one. In this article, the authors present and describe the system and its paradigm for accessing news. We complement the description with the results from several tests which confirm the validity of our approach for clustering of news and for analysing the gathered data.
-
ItemCreating Interopearable e-Portfolios for Different Educational Levels( 2013) Soares,S ; Álvaro Figueirain this article we present a system capable of creating, managing and presenting digital portfolios. Our system innovates by using roles and states during its creation phase. This allows for high quality elements in the portfolio and promotes the students' reflection over them before full integration. The system also complies with the existing standards for e-portfolios. Moreover, it adds an extension to integrate previous created portfolios from different educational levels. In the article we show the need for such extension and describe how the system deals with integration of such diverse portfolios into a single one.
-
ItemThe current state of fake news: challenges and opportunities( 2017) Álvaro Figueira ; Luciana Gomes Oliveira
-
ItemDetecting Journalistic Relevance on Social Media: A two-case study using automatic surrogate features( 2017) Álvaro Figueira ; Nuno Ricardo Guimarães
-
ItemDISCOVERING SIMILAR ORGANIZATIONAL SOCIAL MEDIA STRATEGIES USING CLASSIFICATION AND CLUSTERING( 2016) Álvaro Figueira ; Luciana Gomes Oliveira