Learning to manage in knowledge-based organizations: the role of learning logs or portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm

LSP communication in a digital age: the outcomes of teaching and learning in tertiary education

This paper discusses the changes brought by the communication revolution in teaching and learning in the scope of LSP. Its aim is to provide an insight on how teaching which was bi-dimensional, turned into a multidimensional system, gathering other complementary resources that have transformed, in a incredibly short time, the ways we receive share and store information, for instance as professionals, and keep in touch with our peers. The increasing rise of electronic publications, the incredible boom of social and professional networks, search engines, blogs, list servs, forums, e-mail blasts, Facebook pages, YouTube contents, Tweets and Apps, have twisted the way information is conveyed. Classes ceased to be predictable and have been empowered by digital platforms, innumerous and different data repositories (TILDE, IATE, LINGUEE, and so many other terminological data banks) that have definitely transformed the academic world in general and tertiary education in particular. There is a bulk of information to be digested by students, who are no longer passive but instead responsible and active for their academic outcomes. The question is whether they possess the tools to select only what is accurate and important for a certain subject or assignment, due to that overflow? Due to the reduction of the number of course years in most degrees, after the implementation of Bologna and the shrinking of the curricula contents, have students the possibility of developing critical thinking? Both teaching and learning rely on digital resources to improve the speed of the spreading of knowledge. But have those changes been effective to promote really communication? Furthermore, with the increasing Apps that have already been developed and will continue to appear for learning foreign languages, for translation among others, will the students feel the need of learning them once they have those Apps. These are some the questions we would like to discuss in our paper.

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

In the last decade, both scientific community and automotive industry enabled communications among vehicles in different kinds of scenarios proposing different vehicular architectures. Vehicular delay-tolerant networks (VDTNs) were proposed as a solution to overcome some of the issues found in other vehicular architectures, namely, in dispersed regions and emergency scenarios. Most of these issues arise from the unique characteristics of vehicular networks. Contrary to delay-tolerant networks (DTNs), VDTNs place the bundle layer under the network layer in order to simplify the layered architecture and enable communications in sparse regions characterized by long propagation delays, high error rates, and short contact durations. However, such characteristics turn contacts very important in order to exchange as much information as possible between nodes at every contact opportunity. One way to accomplish this goal is to enforce cooperation between network nodes. To promote cooperation among nodes, it is important that nodes share their own resources to deliver messages from others. This can be a very difficult task, if selfish nodes affect the performance of cooperative nodes. This paper studies the performance of a cooperative reputation system that detects, identify, and avoid communications with selfish nodes. Two scenarios were considered across all the experiments enforcing three different routing protocols (First Contact, Spray and Wait, and GeoSpray). For both scenarios, it was shown that reputation mechanisms that punish aggressively selfish nodes contribute to increase the overall network performance.

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.

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.

Automatic musical instrument recognition for multimedia indexing

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Building a knowledge and learning society in Portugal - Adult students in technological schools and higher education institutions

Chapter in Merrill, Barbara (ed.) (2009) Learning to Change? The Role of Identity and Learning Careers in Adult Education. Hamburg: Peter Lang Publishers. URL: http://www.peterlang.com/ index.cfm?vID=58279&vLang=E&vHR=1&vUR=2&vUUR=1

Brokerage Platform for Media Content Recommendation

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

Biomimetic Sensor Potentiometric System for Doxycycline Antibiotic Using a Molecularly Imprinted Polymer as an Artificial Recognition Element

Molecular imprinting is a useful technique for the preparation of functional materials with molecular recognition properties. A Biomimetic Sensor Potentiometric System was developed for assessment of doxycycline (DOX) antibiotic. The molecularly imprinted polymer (MIP) was synthesized by using doxycycline as a template molecule, methacrylic acid (MAA) and/or acrylamide (AA) as a functional monomer and ethylene glycol dimethacrylat (EGDMA) as a cross-linking agent. The sensing elements were fabricated by the inclusion of DOX imprinted polymers in polyvinyl chloride (PVC) matrix. The sensors showed a high selectivity and a sensitive response to the template in aqueous system. Electrochemical evaluation of these sensors under static (batch) mode of operation reveals near-Nernstian response. MIP/MAA membrane sensor was incorporated in flow-through cells and used as detectors for flow injection analysis (FIA) of DOX. The method has the requisite accuracy, sensitivity and precision to assay DOX in tablets and biological fluids.

A learning environment for robotic production line simulation

This paper presents a framework for a robotic production line simulation learning environment using Autonomous Ground Vehicles (AGV). An eLearning platform is used as interface with the simulator. The objective is to introduce students to the production robotics area using a familiar tool, an eLearning platform, and a framework that simulates a production line using AGVs. This framework allows students to learn about robotics but also about several areas of industrial management engineering without requiring an extensive prior knowledge on the robotics area. The robotic production line simulation learning environment simulates a production environment using AGVs to transport materials to and from the production line. The simulator allows students to validate the AGV dynamics and provides information about the whole materials supplying system which includes: supply times, route optimization and inventory management. The students are required to address several topics such as: sensors, actuators, controllers and an high level management and optimization software. This simulator was developed with a known open source tool from robotics community: Player/Stage. This tool was extended with several add-ons so that students can be able to interact with a complex simulation environment. These add-ons include an abstraction communication layer that performs events provided by the database server which is programmed by the students. An eLearning platform is used as interface between the students and the simulator. The students can visualize the effects of their instructions/programming in the simulator that they can access via the eLearning platform. The proposed framework aims to allow students from different backgrounds to fully experience robotics in practice by suppressing the huge gap between theory and practice that exists in robotics. Using an eLearning platform eliminates installation problems that can occur from different computers software distribution and makes the simulator accessible by all students at school and at home.

Effects of Soil Compaction and Organic Carbon Content on Preferential Flow in Loamy Field Soils

Abstract: Preferential flow and transport through macropores affect plant water use efficiency and enhance leaching of agrochemicals and the transport of colloids, thereby increasing the risk for contamination of groundwater resources. The effects of soil compaction, expressed in terms of bulk density (BD), and organic carbon (OC) content on preferential flow and transport were investigated using 150 undisturbed soil cores sampled from 15 × 15–m grids on two field sites. Both fields had loamy textures, but one site had significantly higher OC content. Leaching experiments were conducted in each core by applying a constant irrigation rate of 10 mm h−1 with a pulse application of tritium tracer. Five percent tritium mass arrival times and apparent dispersivities were derived from each of the tracer breakthrough curves and correlated with texture, OC content, and BD to assess the spatial distribution of preferential flow and transport across the investigated fields. Soils from both fields showed strong positive correlations between BD and preferential flow. Interestingly, the relationships between BD and tracer transport characteristics were markedly different for the two fields, although the relationship between BD and macroporosity was nearly identical. The difference was likely caused by the higher contents of fines and OC at one of the fields leading to stronger aggregation, smaller matrix permeability, and a more pronounced pipe-like pore system with well-aligned macropores.

Learning sustainability by developing a solar dryer for microalgae retrieval

The development of nations depends on energy consumption, which is generally based on fossil fuels. This dependency produces irreversible and dramatic effects on the environment, e.g. large greenhouse gas emissions, which in turn cause global warming and climate changes, responsible for the rise of the sea level, floods, and other extreme weather events. Transportation is one of the main uses of energy, and its excessive fossil fuel dependency is driving the search for alternative and sustainable sources of energy such as microalgae, from which biodiesel, among other useful compounds, can be obtained. The process includes harvesting and drying, two energy consuming steps, which are, therefore, expensive and unsustainable. The goal of this EPS@ISEP Spring 2013 project was to develop a solar microalgae dryer for the microalgae laboratory of ISEP. A multinational team of five students from distinct fields of study was responsible for designing and building the solar microalgae dryer prototype. The prototype includes a control system to ensure that the microalgae are not destroyed during the drying process. The solar microalgae dryer works as a distiller, extracting the excess water from the microalgae suspension. This paper details the design steps, the building technologies, the ethical and sustainable concerns and compares the prototype with existing solutions. The proposed sustainable microalgae drying process is competitive as far as energy usage is concerned. Finally, the project contributed to increase the deontological ethics, social compromise skills and sustainable development awareness of the students.

Activity-Dependent Neurorehabilitation Beyond Physical Trainings: "Mental Exercise" Through Mirror Neuron Activation

The activity dependent brain repair mechanism has been widely adopted in many types of neurorehabilitation. The activity leads to target specific and non-specific beneficial effects in different brain regions, such as the releasing of neurotrophic factors, modulation of the cytokines and generation of new neurons in adult hood. However physical exercise program clinically are limited to some of the patients with preserved motor functions; while many patients suffered from paralysis cannot make such efforts. Here the authors proposed the employment of mirror neurons system in promoting brain rehabilitation by "observation based stimulation". Mirror neuron system has been considered as an important basis for action understanding and learning by mimicking others. During the action observation, mirror neuron system mediated the direct activation of the same group of motor neurons that are responsible for the observed action. The effect is clear, direct, specific and evolutionarily conserved. Moreover, recent evidences hinted for the beneficial effects on stroke patients after mirror neuron system activation therapy. Finally some music-relevant therapies were proposed to be related with mirror neuron system.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

A Multi–Agent Brokerage Platform for Media Content Recommendation

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.