CRACS - Indexed Articles in Conferences

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Browsing CRACS - Indexed Articles in Conferences by Title

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    Accelerating Recommender Systems using GPUs
    ( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra
    We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).
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    Access Control and Obligations in the Category-Based Metamodel: A Rewrite-Based Semantics
    ( 2015) Sandra Alves ; Degtyarev,A ; Fernandez,M
    We define an extension of the category-based access control (CBAC) metamodel to accommodate a general notion of obligation. Since most of the well-known access control models are instances of the CBAC metamodel, we obtain a framework for the study of the interaction between authorisation and obligation, such that properties may be proven of the metamodel that apply to all instances of it. In particular, the extended CBAC metamodel allows security administrators to check whether a policy combining authorisations and obligations is consistent.
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    Active Manifold Learning with Twitter Big Data
    ( 2015) Silva,C ; Mário João Antunes ; Costa,J ; Ribeiro,B
    The data produced by Internet applications have increased substantially. Big data is a flaring field that deals with this deluge of data by using storage techniques, dedicated infrastructures and development frameworks for the parallelization of defined tasks and its consequent reduction. These solutions however fall short in online and highly data demanding scenarios, since users expect swift feedback. Reduction techniques are efficiently used in big data online applications to improve classification problems. Reduction in big data usually falls in one of two main methods: (i) reduce the dimensionality by pruning or reformulating the feature set; (ii) reduce the sample size by choosing the most relevant examples. Both approaches have benefits, not only of time consumed to build a model, but eventually also performance-wise, usually by reducing overfitting and improving generalization capabilities. In this paper we investigate reduction techniques that tackle both dimensionality and size of big data. We propose a framework that combines a manifold learning approach to reduce dimensionality and an active learning SVM-based strategy to reduce the size of labeled sample. Results on Twitter data show the potential of the proposed active manifold learning approach.
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    An adjustable sensor platform using dual wavelength measurements for optical colorimetric sensitive films
    ( 2014) Carlos Manuel Machado ; Gouveia,C ; João Ferreira ; Kovacs,B ; Pedro Jorge ; Luís Lopes
    We present a new and versatile sensor platform to readout the response of sensitive colorimetric films. The platform is fully self-contained and based on a switched dual-wavelength scheme. After filtering and signal processing, the system is able to provide self-referenced measures of color intensity changes in the film, while being immune to noise sources such as ambient light and fluctuations in the power source and in the optical path. By controlling the power and the switching frequency between the two wavelengths it is possible to fine tune the output gain as well as the operational range of the sensor for a particular application, thus improving the signal conditioning. The platform uses a micro-controller that complements the analog circuit used to acquire the signal. The latter pre-amplifies, filters and conditions the signal, leaving the micro-controller free to perform sensor linearization and unit conversion. By changing the sensitive film and the wavelength of the light source it is possible to use this platform for a wide range of sensing applications. © 2014 IEEE.
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    ANALYSING RELEVANT INTERACTIONS BY BRIDGING FACEBOOK AND MOODLE
    ( 2016) Luciana Gomes Oliveira ; Álvaro Figueira
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    Analyzing Social Media Discourse An Approach using Semi-supervised Learning
    ( 2016) Álvaro Figueira ; Luciana Gomes Oliveira
    The 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.
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    An Approach to Relevancy Detection: contributions to the automatic detection of relevance in social networks
    ( 2016) Álvaro Figueira ; Miguel Oliveira Sandim ; Paula Teixeira Fortuna
    In 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.
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    An architecture for seamless configuration, deployment, and management of wireless sensor-actuator networks
    ( 2014) Edgard Santos Neto ; Mendes,R ; Luís Lopes
    The goal of this work is to provide (non-specialist) users with the means to seamlessly setup and monitor a Wireless Sensor-Actuator Network (WSN) without writing any code or performing subtle hardware configurations. Towards this goal, we present an architecture that allows the seamless configuration, deployment and management of applications over WSN. We explore the fact that most deployments have a common modus operandi: (a) simple data readers running on the nodes periodically gather and send data to sinks, and; (b) sinks process incoming data and, accordingly, issue actuation commands to the nodes. We argue that, given the knowledge of a platform's capabilities, its sensors and actuators and their respective programming interfaces, it is possible to fully automate the process of configuring, building, and deploying an application over a WSN. Similarly, monitoring and managing the deployment can be vastly simplified by using a middleware that supports user defined tasks that process data from the nodes, divide the WSN into regions, defined by simple boolean predicates over data, and eventually issue actuation commands on regions.
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    An architecture for the rapid development of XML-based web applications
    ( 2009) José Paulo Leal ; 5125
    Our research goal is the generation of working web applications from high level specifications. Based on our experience in using XML transformations for that purpose, we applied this approach to the rapid development of database management applications. The result is an architecture that defines of a web application as a set of XML transformations, and generates these transformations using second order transformations from a database schema. We used the Model-View-Controller architectural pattern to assign different roles to transformations, and defined a pipeline of transformations to process an HTTP request. The definition of these transformations is based on a correspondence between data-oriented XML Schema definitions and the Entity-Relationship model. Using this correspondence we were able produce transformations that implement database operations, forms interfaces generators and application controllers, as well as the second order transformations that produce all of them. This paper includes also a description of a RAD system following this architecture that allowed us to perform a critical evaluation of this proposal.
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    Asura: A Game-Based Assessment Environment for Mooshak (Short Paper)
    ( 2018) José Paulo Leal ; José Carlos Paiva ; 5125 ; 6251
    Learning to program is hard. Students need to remain motivated to keep practicing and to overcome their difficulties. Several approaches have been proposed to foster students’ motivation. As most people enjoy playing games of some kind and play on a regular basis, the use of games is one of the most widely spread approaches. However, taking full advantage of games to teach specific concepts of programming requires much effort. This paper presents Asura, a game-based assessment environment built on top of Mooshak that challenges students to code Software Agents (SAs) to play a game, allowing them to test the SAs against each others’ SAs and watch a movie of the test. Once the challenge development stage ends, teachers are able to organize game-like tournaments among SAs. One of the key features of Asura is that it provides a means to reduce the required effort of building game-based challenges up to that of creating traditional programming exercises. © José Carlos Paiva and José Paulo Leal.
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    Authoring Game-Based Programming Challenges to Improve Students’ Motivation
    ( 2020) José Paulo Leal ; José Carlos Paiva ; Ricardo Queirós ; 5125 ; 6251 ; 5695
    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. © 2020, Springer Nature Switzerland AG.
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    Automated Assessment in Computer Science: A Bibliometric Analysis of the Literature
    ( 2022) José Paulo Leal ; Álvaro Figueira ; 5125 ; 5088
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    Automated Assessment of Simple Web Applications (Short Paper)
    ( 2023) José Paulo Leal ; 5125
    Web programming education is an important component of modern computer science curricula. Assessing students’ web programming skills can be time-consuming and challenging for educators. This paper introduces Webpal, an automated assessment tool for web programming exercises in entry-level courses. Webpal evaluates web applications coded in HTML, CSS, and Javascript, and provides feedback to students. This tool integrates with Virtual Learning Environments (VLEs) through an API, allowing the creation, storage, and access to exercises while assessing student attempts based on the created exercises. The evaluation process comprises various subcomponents: static assessment, interface matching, functional testing, and feedback management. This approach aims to provide feedback that helps students overcome their challenges in web programming assignments. This paper also presents a demo showcasing the tool’s features and functionality in a simulated VLE environment. © Luís Maia Costa, José Paulo Leal, and Ricardo Queirós; licensed under Creative Commons License CC-BY 4.0.
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    Automatic Generation and Delivery of Multiple-Choice Math Quizzes
    ( 2013) José Paulo Leal ; 5125
    We present an application of constraint logic programming to create multiple-choice questions for math quizzes. Constraints are used for the configuration of the generator, giving the user some flexibility to customize the forms of the expressions arising in the exercises. Constraints are also used to control the application of the buggy rules in the derivation of plausible wrong solutions to the quiz questions. We developed a prototype based on the core system of AGILMAT [18]. For delivering math quizzes to students, we used an automatic evaluation feature of Mooshak [8] that was improved to handle math expressions. The communication between the two systems - AgilmatQuiz and Mooshak - relies on a specially designed LATEX based quiz format. This tool is being used at our institution to create quizzes to support assessment in a PreCalculus course for first year undergraduate students.
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    Automatic network configuration in virtualized environment using GNS3
    ( 2015) Emiliano,R ; Mário João Antunes
    Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.
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    Batched Evaluation of Full-Sharing Multithreaded Tabling
    ( 2015) Miguel Gonçalves Areias ; Ricardo Rocha
    Tabling is a technique that overcomes some limitations of traditional Prolog systems in dealing with redundant sub-computations and recursion. When tabling is combined with multithreading, we have the best of both worlds, since we can exploit the combination of higher declarative semantics with higher procedural control. To support this combination, the Yap Prolog system has, at engine level, multiple designs that vary from a No-Sharing design, where each thread allocates fully private tables, to a Full-Sharing (FS) design, where threads share the complete table space. In this work, we propose an extension to the table space data structures, which we named Private Answer Chaining (PAC), as way to support batched scheduling evaluation with the FS design. Batched scheduling is one of the most successful tabling scheduling strategies, known to be useful when a tabled logic program requires an eager propagation of answers and/or do not requires the complete set of answers to be found. Experimental results show that PAC is a good first approach, since with it the FS design remains quite competitive.
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    Benchmarking Analysis of Social Media Strategies in the Higher Education Sector
    ( 2015) Oliveira,L ; Álvaro Figueira
    The 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.
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    Benchmarking Wireless Protocols for Feasibility in Supporting Crowdsourced Mobile Computing
    ( 2016) João Filipe Rodrigues ; Joaquim Magalhães Silva ; Rolando Martins ; Luís Lopes ; Drolia,U ; Narasimhan,P ; Fernando Silva
    Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.
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    Beyond blocks: Hyperbolic community detection
    ( 2014) Miguel Ramos Araújo ; Gunnemann,S ; Mateos,G ; Faloutsos,C
    What do real communities in social networks look like? Community detection plays a key role in understanding the structure of real-life graphs with impact on recommendation systems, load balancing and routing. Previous community detection methods look for uniform blocks in adjacency matrices. However, after studying four real networks with ground-truth communities, we provide empirical evidence that communities are best represented as having an hyperbolic structure. We detail HyCoM - the Hyperbolic Community Model - as a better representation of communities and the relationships between their members, and show improvements in compression compared to standard methods. We also introduce HyCoM-FIT, a fast, parameter free algorithm to detect communities with hyperbolic structure. We show that our method is effective in finding communities with a similar structure to self-declared ones. We report findings in real social networks, including a community in a blogging platform with over 34 million edges in which more than 1000 users established over 300 000 relations. © 2014 Springer-Verlag.
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    Can a Content Management System Provide a Good User Experience to Teachers? (Short Paper)
    ( 2023) José Paulo Leal ; 5125
    The paper discusses an ongoing project that aims to enhance the UX of teachers while using e-learning systems. Specifically, the project focuses on developing the teacher’s user interface (UI) for Agni, a web-based code playground for learning JavaScript. The goal is to design an intuitive UI with valuable features that will encourage more teachers to use the system. To achieve this goal, the paper explores the use of a headless Content Management System (CMS) called Strapi. The primary research question the paper seeks to answer is whether a headless CMS, specifically Strapi, can provide a good UX to teachers. A usability evaluation of the built-in Strapi UI for content creation and management reveals it to be generally consistent and user-friendly but challenging and unintuitive to create courses with programming exercises. As a result, the decision was made to develop a new teacher’s UI based on the existing Agni UI for students in an editable version. Once the development is complete, a new usability evaluation of the fully developed teacher’s UI will be conducted with the Strapi UI evaluation as a baseline for comparison. © Yannik Bauer, José Paulo Leal, and Ricardo Queirós; licensed under Creative Commons License CC-BY 4.0.