Locomotion in Virtual Environments is currently a difficult and unnatural task to perform. Normally, researchers tend to devise ground-floor based metaphors, to constrain degrees of freedom (DoFs) during motion. These restrictions enable interactions closer to the way people walk in real life to provide high interaction fidelity. However, flying allows people to reach specific points in a virtual scene more expeditiously. Our experience suggests that high-fidelity techniques may also improve the flying experience, even though flying is not innate to humans, which requires the simultaneous control of multiple DoFs. We contribute the Magic Carpet, a family of methods that combines a floorproxy with a full-body representation, to avoid balance and cybersickness issues. This design allows separating degrees of freedom by addressing the indication direction and speed control travel phases travel separately. thereby promoting techniques with higher interaction fidelity. To choose the best representation suited for our design space, we implemented novel navigation metaphors while exploring different avatars with varying levels of either graphical or perspective fidelity. To validate our design space, we proposed two complementary studies, one for each travel phase. In our experimental evaluation, we present the results of both studies and identify the best suited techniques to be used in combination in the Magic Carpet approach. We applied both objective nd subjective measurements to evaluate efficiency, level of presence, and side-effects of the tested techniques inside our design space, such as physical fatigue and cybersickness. Our results show that the Magic Carpet family of methods supports novel techniques with a high degree of interaction fidelity for flying. While high interaction fidelity techniques are seemingly not the most efficient, the methods we explored allow a more precise speed control wrt other flying techniques. To prove the flexibility of our design space we conducted an additional evaluation with three Target-based techniques to assess both speed and transitions afforded by these techniques. Experimental Results show that methods that provide immediate user translation perform best and that phase transitions do not improve travel quality factors.
access_time April 17, 2019 at 09:30AM
place Sala Polivalente (0.17), Pavilhão de Informática II, IST, Alameda
local_offer Doctoral exam
person Candidate: Daniel Pires de Sá Medeiros
supervisor_account Advisor: Prof. Joaquim Armando Pires Jorge / Prof. Alberto Barbosa Raposo
Relatório da Unidade Curricular: Software Engineering Seminário: Measuring and Improving Software Fault Diagnosis
access_time April 15, 2019 at 02:30PM
place Anfiteatro PA-3 (Piso -1, Pavilhão de Matemática) IST, Alameda
local_offer Habilitation exam
person Candidate: Prof. Doutor Rui Filipe Lima Maranhão de Abreu
Cloud computing has enabled myriad of applications to benefit from dynamic provisioning of resources. These applications must adjust their resources in response to changes in the environment in order to satisfy business-defined goals about service quality. However, engineering decision-making mechanisms to help the deployment and management of resources in cloud contexts is a challenging task. In fact, cloud applications call for automated mechanisms that: (1) explore efficiently large solution spaces (defined by the combination of machine types, provisioning actions, and state transitions expected in the temporal horizon); (2) generate deliberate plans to operate the system in a way that satisfies requirements, maximizes performance and minimizes operational costs; and (3) support the definition and revision of policies to adapt the system under expected conditions. Automated Planning, the area of artificial intelligence concerned with synthesizing plans of actions to achieve a goal, offers opportunities to address these challenges. This thesis focuses on the design and evaluation of approaches that exploit automated planning to support the deployment and management of applications running in cloud environments. To this purpose, this thesis presents three contributions: (1) a solution to the (offline) generation of reactive policies, that exploits temporal planning languages and tools to support the definition and revision of policies, applicable under common conditions; (2) a solution to the (online) generation of proactive plans, that takes advantage of longterm temporal planning and behavioral predictions to reconfigure interactive applications; and (3) a solution to the (offline) generation of execution policies, that resorts to probabilistic planning to deal with the uncertainty caused by spot instance revocations in the deployment of workflow applications. These proposals have been evaluated using realistic case studies of elastic scaling and workflow executions in the cloud. Results support the claim that automated decision-making mechanisms that rely on planning are scalable and responsive, and able to guide the system to satisfy requirements, optimize performance and minimize operational costs.
access_time February 28, 2019 at 04:00PM
place Room 0.19, Pav. Inf. II, IST, Alameda
local_offer Doctoral exam
person Candidate: Richard Joaquin Gil Martinez
supervisor_account Advisor: Prof. Luís Eduardo Teixeira Rodrigues
Security is a crucial non-functionality requirement for software applications. However, building secure software is far from trivial as developers lack both the knowledge and tools to effectively address this concern. In this paper, we study the impact of changes to improve security on the maintainability of several open source applications. Using a dataset containing 607 security- oriented commits, we measure maintainability — as computed by the Software Improvement Group’s web-based source code analysis service Better Code Hub (BCH) — before and after the security refactoring. Results show that making software more secure comes at a cost on maintainability. This is particularly evident in refactorings to deal with Broken Authentication and Cross-Site Request Forgery attacks.
access_time February 07, 2019 at 02:00PM
place INESC-ID Room 418
local_offer Research topics
person Candidate: Sofia Oliveira Reis
supervisor_account Advisor: Prof. Rui Filipe Lima Maranhão de Abreu
The importance of automatic prosody assessment has been acknowledged, as it provides relevant information about the speaker, the languages, the pragmatics and paralinguistics of speech. Nevertheless, available data sets are often insufficient for the tasks aimed at, namely when it comes to the usage of DNNs, which require great amounts of data. Transfer learning is a state-of-the-art technique being used for several tasks and proven to be very informative, as training with diverse data sets and testing on distinct ones can assure robustness and cross-lingual analysis. In this case, we investigate whether transfer learning can be applied to L2 learners, either exclusively in learning contexts or in e-health ones too, which is a very challenging task.The main data set we use was built for an intonation imitation task with native speakers of Portuguese, whose assessment relied on a DTW algorithm only. Building upon previous work, a nativeness assessment task of L2 speakers of English, we applied a DNN for feature extraction. We have considered features with no temporal dependency and features with temporal dependency, corresponding to an LSTM layer.
access_time February 07, 2019 at 11:00AM
place INESC-ID, Room 336
local_offer Research topics
person Candidate: Mariana Dimas Julião
supervisor_account Advisor: Prof. Alberto Abad Gareta / Dra. Helena Moniz