Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

A biophysical model for valence-based navigational learning: the role of D-Serine

Recent experiments have revealed the fundamental importance of neuromodulatory action on activity-dependent synaptic plasticity underlying behavioral learning and spatial memory formation. Neuromodulators affect synaptic plasticity through the modification of the dynamics of receptors on the synaptic membrane. However, chemical substances other than neuromodulators, such as receptors co-agonists, can influence the receptors' dynamics and thus participate in determining plasticity. Here we focus on D-serine, which has been observed to affect the activity thresholds of synaptic plasticity by co-activating NMDA receptors. We use a computational model for spatial value learning with plasticity between two place cell layers. The D-serine release is CB1R mediated and the model reproduces the impairment of spatial memory due to the astrocytic CB1R knockout for a mouse navigating in the Morris water maze. The addition of path-constraining obstacles shows how performance impairment depends on the environment's topology. The model can explain the experimental evidence and produce useful testable predictions to increase our understanding of the complex mechanisms underlying learning.

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

A shift from passive teaching at medical conferences to more interactive methods improves physician learning

Conferences that deliver interactive sessions designed to enhance physician participation, such as role play, small discussion groups, workshops, hands-on training, problem- or case-based learning and individualised training sessions, are effective for physician education.

Making distance learning E.R.O.T.I.C.: Applying interpretation principles to distance learning

Distance learners are self-directed learners traditionally taught via study books, collections of readings, and exercises to test understanding of learning packages. Despite advances in e-Learning environments and computer-based teaching interfaces, distance learners still lack opportunities to participate in exercises and debates available to classroom learners, particularly through non-text based learning techniques. Effective distance teaching requires flexible learning opportunities. Using arguments developed in interpretation literature, we argue that effective distance learning must also be Entertaining, Relevant, Organised, Thematic, Involving and Creative—E.R.O.T.I.C. (after Ham, 1992). We discuss an experiment undertaken with distance learners at The University of Queensland Gatton Campus, where we initiated an E.R.O.T.I.C. external teaching package aimed at engaging distance learners but using multimedia, including but not limited to text-based learning tools. Student responses to non-text media were positive.

A B-learning strategy for Therapeutics at the Bachelor Level

Background Information:The incorporation of distance learning activities by institutions of higher education is considered an important contribution to create new opportunities for teaching at both, initial and continuing training. In Medicine and Nursing, several papers illustrate the adaptation of technological components and teaching methods are prolific, however, when we look at the Pharmaceutical Education area, the examples are scarce. In that sense this project demonstrates the implementation and assessment of a B-Learning Strategy for Therapeutics using a “case based learning” approach. Setting: Academic Pharmacy Methods:This is an exploratory study involving 2nd year students of the Pharmacy Degree at the School of Allied Health Sciences of Oporto. The study population consists of 61 students, divided in groups of 3-4 elements. The b-learning model was implemented during a time period of 8 weeks. Results:A B-learning environment and digital learning objects were successfully created and implemented. Collaboration and assessment techniques were carefully developed to ensure the active participation and fair assessment of all students. Moodle records show a consistent activity of students during the assignments. E-portfolios were also developed using Wikispaces, which promoted reflective writing and clinical reasoning. Conclusions:Our exploratory study suggests that the “case based learning” method can be successfully combined with the technological components to create and maintain a feasible online learning environment for the teaching of therapeutics.

Mobile and web tools for participative learning

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, para a obtenção do grau de Mestre em Engenharia Informática

Ferramenta de estudo para alunos de medicina na especialidade de dermatologia

A vulgarização do uso de dispositivos móveis promoveu a proliferação de aplicações dos mais diversos âmbitos para estes dispositivos não sendo a área clínica uma excepção. Tanto a nível profissional, como a nível de ensino, as tecnologias móveis foram já há muito adoptadas nesta área para as mais diversas finalidades. O trabalho aqui apresentado pretende essencialmente provar a real importância desempenhada pelo mobile learning no contexto da aprendizagem clínica. Mais do que implementar um simples recurso educativo, pretendeu-se conceber um sistema integrado que respondesse a todas as necessidades do aluno quer durante o estudo nas suas diversas fases e locais, como também no próprio serviço hospitalar onde se encontre a desempenhar funções como interno da especialidade. Após uma exaustiva análise das aplicações móveis relevantes da área médica, verificou-se a inexistência de uma ferramenta integradora de vários módulos de aprendizagem com um custo comportável para a maioria dos alunos. Desta forma, idealizou-se uma aplicação capaz de superar esta lacuna que será detalhada ao longo desta tese. Para o desenvolvimento deste trabalho contou-se com a preciosa colaboração dos possíveis utilizadores finais desta ferramenta uma vez que a escolha dos módulos a integrar foi essencialmente baseada nas suas opiniões. Ainda no âmbito desta tese, encontra-se a avaliação do protótipo por parte dos alunos. Esta avaliação pretende validar a efectiva importância de uma ferramenta desta natureza para um aluno de medicina assim como o impacto que o protótipo teve na sua opinião acerca do conceito de mobile learning na aprendizagem clínica. Com vista a uma futura implementação de um recurso educativo deste âmbito, foram também recolhidos os pontos negativos e positivos mais relevantes para o aluno. Em suma, este trabalho valida a importância do papel que as aplicações de aprendizagem para dispositivos móveis podem desempenhar para um aluno de medicina tanto nos seus locais de estudo, como no serviço onde se possa encontrar.

The European Project Semester at ISEP: Learning to Learn Engineering

The European Project Semester at ISEP (EPS@ISEP) is a one semester project-based learning programme addressed to engineering students from diverse scientific backgrounds and nationalities. The students, organized in multicultural teams, are challenged to solve real world multidisciplinary problems, accounting for 30 ECTU. The EPS package, although focused on project development (20 ECTU), includes a series of complementary seminars aimed at fostering soft, project-related and engineering transversal skills (10 ECTU). This paper presents the study plan, resources, operation and results of the EPS@ISEP that was created in 2011 to apply the best engineering education practices and promote the internationalization of ISEP. The results show that the EPS@ISEP students acquire during one semester the scientific, technical and soft competences necessary to propose, design and implement a solution for a multidisciplinary problem.

RFID-based wireless passive sensors utilizing cork materials

This paper presents the design of low-cost, conformal UHF antennas and RFID tags on two types of cork substrates: 1) natural cork and 2) agglomerate cork. Such RFID tags find an application in wine bottle and barrel identification, and in addition, they are suitable for numerous antenna-based sensing applications. This paper includes the high-frequency characterization of the selected cork substrates considering the anisotropic behavior of such materials. In addition, the variation of their permittivity values as a function of the humidity is also verified. As a proof-of-concept demonstration, three conformal RFID tags have been implemented on cork, and their performance has been evaluated using both a commercial Alien ALR8800 reader and an in-house measurement setup. The reading of all tags has been checked, and a satisfactory performance has been verified, with reading ranges spanning from 0.3 to 6 m. In addition, this paper discusses how inkjet printing can be applied to cork surfaces, and an RFID tag printed on cork is used as a humidity sensor. Its performance is tested under different humidity conditions, and a good range in excess of 3 m has been achieved, allied to a good sensitivity obtained with a shift of >5 dB in threshold power of the tag for different humid conditions.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

Sistema de detecção de fogo e detecção e seguimento de objectos

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Fishing the key biological components of the bacterium Geobacter metallireducens for optimal biotechnological applications

Os resultados apresentados no capítulo 2 foram incluídos no artigo Dantas JM, Campelo LM, Duke NEC, Salgueiro CA, Pokkuluri PR (2015) "The structure of PccH from Geobacter sulfurreducens – a novel low reduction potential monoheme cytochrome essential for accepting electrons from an electrode", FEBS Journal, 282, 2215-2231.

An interactive learning approach to accounting classes

This working paper explores the use of interactive learning tools, such as business simulations, to facilitate the active learning process in accounting classes. Although business simulations were firstly introduced in the United States in the 1950s, the vast majority of accounting professors still use traditional teaching methods, based in end-of-chapter exercises and written cases. Moreover, the current students’ generation brings new challenges to the classroom related with their video, game, internet and mobile culture. Thus, a survey and an experimentation were conducted to understand, on one hand, if accounting professors are willing to adjust their teaching methods with the adoption of interactive learning tools and, on the other hand, if the adoption of interactive learning tools in accounting classes yield better academic results and levels of satisfaction among students. Students using more interactive learning approaches scored significantly higher means than others that did not. Accounting professors are clearly willing to try, at least once, the use of an accounting simulator in classes.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.