Math without stress: an open online learning project

MOOC (as an acronym for Massive Open Online Courses) are a quite new model for the delivery of online learning to students. As “Massive” and “Online”, these courses are proposed to be accessible to many more learners than would be possible through conventional teaching. As “Open” they are (frequently) free of charge and participation is not limited by the geographical situation of the learners, creating new learning opportunities in Higher Education Institutions (HEI). In this paper we describe a recently started project “Matemática 100 STRESS” (Math Without STRESS) integrated in the e-IPP project | e-Learning Unit of Porto’s Polytechnic Institute (IPP) which has created its own MOOC platform and launched its first course – Probabilities and Combinatorics – in early June/2014. In this MOOC development were involved several lecturers from four of the seven IPP schools.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Adaptive Learning in Games: Defining Profiles of Competitor Players

Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

The impact of firm-promoted accreditation of prior learning processes on the worker-organization relationship

The process of Competences Recognition, Validation and Certification , also known as Accreditation of Prior Learning (APL), is an innovative means of attaining school certificates for individuals without an academic background. The main objective of this process is to validate what people have learned in informal contexts, in order to attribute academic certificates. With the increasing interest of the qualification of workers and governmental support, more and more Portuguese organizations promote this process within their facilities and their work hours. This study explores the relationship between the promotion of this Human Resource Development Programme and employee’s attitudes (Job Satisfaction and Organizational Commitment) and behaviours (Extra-role Organizational Citizenship Behaviours) towards the organization they work for. Results of a cross-sectional survey of Portuguese Industrial Workers (N=135) showed that statistical significant results are in the higher levels of Voice Behaviours (a dimension of Extra-role Organizational Citizenship Behaviour in the groups of workers who were involved or had graduated from the firm promoted APL process.

Criteria for the selection of foster families and monitoring of placements.: Comparative study of the application of the Casey Foster Applicant Inventory-Applicant Version (CFAI-A)

Based on a literature review, this article frames different stages of the foster care process, identifying a set of standardized measures in the American and Portuguese contexts which, if implemented, could contribute towards higher levels of foster success. The article continues with the presentation of a comparative study, based on the application of the Casey Foster Applicant Inventory-Applicant Version (CFAI-A) questionnaire, in the aforementioned contexts. Taking a comparative analyses of CFAI-A's psychometric characteristics in four different samples as a starting point, one discovered that despite the fact that the questionnaire was adapted to Portuguese reality, it kept the quality values presented on the American samples. It specifically shows significant values regarding reliability and validity. This questionnaire, which aims to assess the potential of foster families, also supports the technical staff's decision making process regarding the monitoring and support of foster families, while it also promotes a better decision in the placement process towards the child's integration and development.

Students’ perceptions about assessment using an e-learning platform

ISCAP’s Information Systems Department is composed of about twenty teachers who have, for several years, been using an e-learning environment (Moodle) combined with traditional assessment. A new e-assessment strategy was implemented recently in order to evaluate a practical topic, the use of spreadsheets to solve management problems. This topic is common to several courses of different undergraduate degree programs. Being e-assessment an outstanding task regarding theoretical topics, it becomes even more challenging when the topics under evaluation are practical. In order to understand the implications of this new type of assessment from the viewpoint of the students, questionnaires and interviews were undertaken. In this paper the analysis of the questionnaires are presented and discussed.

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.

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Emerging (Bio)Sensing Technology for Assessing and Monitoring Freshwater Contamination – Methods and Applications

Ecological Water Quality - Water Treatment and Reuse

A biomimetic sensor for monitoring oxidative stress biomarker in point-of-care

1st ASPIC International Congress

Multifunctional Biosensor Based on Localized Surface Plasmon Resonance for Monitoring Small Molecule–Protein Interaction

We report an optical sensor based on localized surface plasmon resonance (LSPR) to study small-molecule protein interaction combining high sensitivity refractive index sensing for quantitative binding information and subsequent conformation-sensitive plasmon-activated circular dichroism spectroscopy. The interaction of α-amylase and a small-size molecule (PGG, pentagalloyl glucose) was log concentration-dependent from 0.5 to 154 μM. In situ tests were additionally successfully applied to the analysis of real wine samples. These studies demonstrate that LSPR sensors to monitor small molecule–protein interactions in real time and in situ, which is a great advance within technological platforms for drug discovery.