Software dependability modeling using an industry-standard architecture description language

Performing dependability evaluation along with other analyses at architectural level allows both making architectural tradeoffs and predicting the effects of architectural decisions on the dependability of an application. This paper gives guidelines for building architectural dependability models for software systems using the AADL (Architecture Analysis and Design Language). It presents reusable modeling patterns for fault-tolerant applications and shows how the presented patterns can be used in the context of a subsystem of a real-life application

Data Modeling Patterns: A Method and Evaluation

Patterns capture abstractions of situations that occur frequently in data modeling. Effective use of data modeling patterns can lead to high quality designs and productivity gains. Data modeling patterns are widely available in the public domain, yet there is a lack of studies on usability of such patterns. In this exploratory study we examine the usability of data modeling patterns. Effective use of patterns presupposes the users’ ability to find similarities between task and pattern. We present and evaluate some heuristics for finding the similarities. The results of the empirical evaluation indicate that the heuristics are useful and can lead to accurate solutions. Future research as well as implications for researchers and practitioners is also discussed

On Universality in Human Correspondence Activity

Identifying and modeling patterns of human activity has important ramifications in applications ranging from predicting disease spread to optimizing resource allocation. Because of its relevance and availability, written correspondence provides a powerful proxy for studying human activity. One school of thought is that human correspondence is driven by responses to received correspondence, a view that requires distinct response mechanism to explain e-mail and letter correspondence observations. Here, we demonstrate that, like e-mail correspondence, the letter correspondence patterns of 16 writers, performers, politicians, and scientists are well-described by the circadian cycle, task repetition and changing communication needs. We confirm the universality of these mechanisms by properly rescaling letter and e-mail correspondence statistics to reveal their underlying similarity.Comment: 17 pages, 3 figures, 1 tabl

Adaptive object-modeling : patterns, tools and applications

Tese de Programa Doutoral. Informática. Universidade do Porto. Faculdade de Engenharia. 201

Quantification of maxillary ontogenetic processes using surface histology and geometric morphometrics

This thesis investigates the variability of ontogenetic maxillary bone modeling patterns in humans (Homo sapiens) and chimpanzees (Pan troglodytes). Along with sutural growth, bone modeling is the microscopic process by which bones grow in size and model their shape. It results from the simultaneous cellular activities of bone formation (produced by the osteoblasts) and bone resorption (produced by the osteoclasts) on bone surfaces. The study of these activities can bring new insights into our understanding of maxillary, and, more generally, facial ontogeny. However, bone modeling variability remains poorly understood. Using surface histology, we developed quantitative methods to objectively compare and visualize bone modeling patterns. In parallel, geometric morphometric methods were used to capture and quantify maxillary shape changes. Both methods were used for the first time together in an integrative approach. A large sample of H. sapiens individuals ranging from birth to adulthood, and originating from three geographically distinct areas (Greenland, Western Europe and South Africa), was used to infer the variation in maxillary bone modeling at the intraspecific level. We found that human populations express similar bone modeling patterns, with only subtle differences in the location of bone resorption. Moreover, differences in developmental trajectories were identified. This suggests that population differences in maxillary morphology stem from changes in timing and/or rates of the osteoblastic and osteoclastic activities. Adult individuals show similar maxillary bone modeling patterns to subadults, with both cellular activities expressed at reduced intensities. All human populations express high amounts of bone resorption throughout ontogeny, and high inter-individual variation. In contrast, we find low amounts of bone resorption and a low inter-individual variation in chimpanzees, which results in the anterior projection of their maxilla. In chimpanzees, resorption is predominant in the premaxilla, which has been found in some species of Australopithecus and Paranthropus. Other similarities in the location of bone resorption, mostly close to the sutures, suggest the preservation of shared ontogenetic patterns between the humans and chimpanzees. The low intraspecific variation in the location of bone resorption found in both species suggests that species-specific bone modeling patterns can be inferred from a limited number of individuals. This will allow future studies to discuss the bone modeling patterns in fossils for which subadult individuals are scarce

Modeling patterns of farm diversification in a Dutch landscape

In agrarische landschappen zijn het de beslissingen van de agrariërs die medebepalend zijn voor zowel het aanbod als de kwaliteit van de voortgebrachte landschapsdiensten. Het doel van dit proefschrift is inzicht te verschaffen in de ruimtelijke patronen van bedrijfsdiversificatie en na te gaan hoe deze patronen mogelijk in de toekomst zouden kunnen veranderen. De studie richt zich op de Gelderse Valle

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled 'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and was accepted at the ICLR 2016 worksho