Digital storytelling: student ID in higher education

Identity achievement is related to personality, as well as cognitive and interpersonal development. In tandem with the deep structural changes that have taken place in society, education must also shift towards a teaching approach focused on learning and the overall development of the student. The integration of technology may be the drive to foster the needed changes. We draw on the literature of multiple subject areas as basis for our work, namely: identity construction and self-representation, within a psychological and social standpoint; Higher Education (HE) in Portugal after Bologna, college student development and other intrinsic relationships, namely the role of emotions and interpersonal relationships in the learning process; the technological evolution of storytelling towards Digital Storytelling (DS) – the Californian model – and its connections to identity and education. Ultimately we propose DS as the aggregator capable of humanizing HE while developing essential skills and competences. Grounded on an interpretative/constructivist paradigm, we implemented a qualitative case study to explore DS in HE. In three attempts to collect student data, we gathered detailed observation notes from two Story Circles; twelve student written reflections; fourteen Digital Stories and detailed observation notes from one Story Show. We carried out three focus groups with teachers where we discussed their perceptions of each student prior to and after watching the Digital Stories, in addition to their opinion on DS in HE as a teaching and learning method and its influence on interpersonal relationships. We sought understandings of the integration of DS to analyze student selfperception and self-representation in HE contexts and intersected our findings with teachers’ perceptions of their students. We compared teachers’ and students’ perspectives, through the analysis of data collected throughout the DS process – Story Circle, Story Creation and Story Show – and triangulated that information with the students’ personal reflections and teacher perceptions. Finally we questioned if and how DS may influence teachers’ perceptions of students. We found participants to be the ultimate gatekeepers in our study. Very few students and teachers voluntarily came forth to take part in the study, confirming the challenge remains in getting participants to see the value and understand the academic rigor of DS. Despite this reluctance, DS proved to be an asset for teachers and students directly and indirectly involved in the study. DS challenges HE contexts, namely teacher established perception of students; student’s own expectations regarding learning in HE; the emotional realm, the private vs. public dichotomy and the shift in educational roles.

Explosion of smoothness from a point to everywhere for conjugacies between Markov families

For uniformly asymptotically affine (uaa) Markov maps on train tracks, we prove the following type of rigidity result: if a topological conjugacy between them is (uaa) at a point in the train track then the conjugacy is (uaa) everywhere. In particular, our methods apply to the case in which the domains of the Markov maps are Canter sets. We also present similar statements for (uaa:) and C-r Markov families. These results generalize the similar ones of Sullivan and de Faria for C-r expanding circle maps with r > 1 and have useful applications to hyperbolic dynamics on surfaces and laminations.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

Algoritmo genético para empacotamento de contentores

No âmbito da investigação operacional o problema de empacotamento de contentores é conhecido por procurar definir uma configuração de carga, de forma a otimizar a utilização de um espaço disponível para efetuar o empacotamento. Este problema pode ser apresentado em diversas formas, formas estas que variam em função das características de cada empacotamento. Estas características podem ser: o tipo de carga que se pretende carregar (homogénea ou heterogénea), a possibilidade de a carga poder sofrer rotações em todas as suas dimensões ou apenas em algumas, o lucro que está associado a cada caixa carregada ou restrições inerentes ao contentor como por exemplo dimensões. O interesse pelo estudo de problemas de empacotamento de contentores tem vindo a receber cada vez mais ênfase por várias razões, uma delas é o interesse financeiro dado que o transporte é uma prática que representa custos, sendo importante diminuir estes custos aproveitando o volume do contentor da melhor forma. Outra preocupação que motiva o estudo deste problema prende-se com fatores ambientes, onde se procura racionalizar os recursos naturais estando esta também ligada a questões financeiras. Na literatura podem ser encontradas varias propostas para solucionar este problema, cada uma destas dirigidas a uma variante do problema, estas propostas podem ser determinísticas ou não determinísticas onde utilizam heurísticas ou metaheurísticas. O estudo realizado nesta dissertação descreve algumas destas propostas, nomeadamente as metaheurísticas que são utilizadas na resolução deste problema. O trabalho aqui apresentado traz também uma nova metaheurísticas, mais precisamente um algoritmo genético que terá como objetivo, apresentar uma configuração de carga para um problema de empacotamento de um contentor. O algoritmo genético tem como objetivo a resolução do seguinte problema: empacotar várias caixas retangulares com diversos tamanhos num contentor. Este problema é conhecido como Bin-Packing. A novidade que este algoritmo genético vai introduzir nas diversas soluções apresentadas até à data, é uma nova forma de criar padrões iniciais, ou seja, é utilizada a heurística HSSI (Heurística de Suavização de Superfícies Irregulares) que tem como objetivo criar uma população inicial de forma a otimizar o algoritmo genético. A heurística HSSI tenta resolver problemas de empacotamento simulando, o comportamento da maioria das pessoas ao fazer este processo na vida real, contudo, tem um campo de busca reduzido entre as soluções possíveis e será então utilizado um algoritmo genético para ampliar este campo de busca e explorar novas soluções. No final pretende-se obter um software onde será possível configurar um dado problema de empacotamento de um contentor e obter, a solução do mesmo através do algoritmo genético. Assim sendo, o estudo realizado tem como principal objetivo contribuir com pesquisas e conclusões, sobre este problema e trazer uma nova proposta de solução para o problema de empacotamento de contentores.