Sociologia : um momento fundacional sob o signo da secularização

The present text focuses on “the foundational moment of sociology”, limiting it self to do it under the sign of “secularization”, in the frame of French society, and in relation with other two texts: one, previous by nature, dedicated to the European socio-historic process that goes from “the theocentric culture to the variations of enlightenment”; and the other, complementary, constituted by the analysis of the contribution of German sociology, especially the one represented by Max Weber. There was also the preoccupation of singularizing the contribution of each of the elected authors: the elaboration of “the new Christianism” in Saint-Simon holds a critique to traditional Christianism but it also implicates an unacknowledged tribute; in Comte, sociology structures through a direct con?ict with a theological-metaphysical and political vision of western culture; ?nally, to the strong secularization variant implicated in the work of the two preceding authors, Durkheim adds a sociological oeuvre marked by the tension between two processes: “the socialisation of the sacred” and “the sacralisation of the social

A WCS-based approach to integrate satellite imagery data in wildfire simulation

This paper describes the integration of multi-dimensional data from satellite sensors in a Civil Protection application that simulates fire spread. The approach uses standard Web Coverage Services from OGC to fetch and process land cover and recently burned areas, available in the form of satellite imagery data previously captured by the MODIS sensor, to automatically generate renovated fuel maps. The proposed architecture is based on rasdaman, a domain-independent database management system (DBMS) that offers a suite of WCS services on top of the DBMS. In the current work we extended rasdaman with facilities to: (i) insert and retrieve multi-layer coverages from WCS, (ii) support new formats, such as HDF, adequate for satellite imagery and multi-layer files, and (iii) support Coordinate Reference Systems. We also demonstrate that it is feasible to use MODIS datasets to automatically compute valuable and regularly updated fuel maps, used as input of fire spread simulations. The results also show that in spite of using inexpensive general and low resolution (500m) MODIS maps, we obtained quite acceptable results when compared with the static ones, which are tailored and higher resolution (80m).Fundação para a Ciência e a Tecnologia (FCT

Synchronous parallel kinetic Monte Carlo simulation of AL3SC precipitation

The main objective of the present work is to profound the applicability of a synchronous parallel kinetic Monte Carlo (spkMC) algorithm for simulating the nucleation of Al3Sc precipitates. Parallel processes communication is implemented through Message Passing Interface (MPI). Consequently, the capability of extending time and length scales of atomistic kinetic Monte Carlo (kMC) will be attested. Lastly, we present the results obtained from simulations of nucleation of Al3Sc precipitates, which include a comparative view between sequential and parallel algorithms

Improving the latency of Python-based web applications

This paper describes the process of optimizing the latency of Python-based Web applications. The case study used to validate the optimizations is an article sharing system, which was developed in Django. Memcached, Celery and Varnish enabled the implementation of additional performance optimizations. The latency of operations was measured, before and after the application of the optimization techniques. The optimization of the application was performed at various levels, including the transfer of content across the network and the back-end services. HTTP caching, data compression and minification techniques, as well as static content replication using Content Delivery Networks, were used. Partial update of the application’s pages on the front-end and asynchronous processing techniques were applied. The database utilization was optimized by creating indexes and by taking advantage of a NoSQL solution. Memory caching strategies, with distinct granularities, were implemented to store tem plates and application objects. Furthermore, asynchronous task queues were used to perform some costly operations. All of the aforementioned techniques favorably contributed to the Web application’s latency decrease. Since Django operates on the back-end, and optimizations must be implemented at various levels, it was necessary to use other toolsFCT – Fundaçãopara a Ciência e Tecnologia within the Project Scope:UID/CEC/00319/201

Converting web pages mockups to HTML using machine learning

Converting Web pages mockups to code is a task that developers typically perform. Due to the time required to accomplish this task, the time available to devote to application logic is reduced. So, the main goal of the present work was to develop deep learning models to automatically convert mockups of Web graphical interfaces into HTML, CSS and Bootstrap code. The trained model must be deployed as a Web application. Two deep learning models were built, resulting from two different approaches to integrate in the Web application. The first approach uses a hybrid architecture with a convolutional neuronal network (CNN) and two recurrent networks (RNNs), following the encoder-decoder architecture commonly adopted in image captioning. The second approach is focused on the spatial component of the problem being addressed, and includes the YOLO network and a layout algorithm. Testing with the same dataset, the prediction’s correction achieved with the first approach was 71.30%, while the se cond approach reached 88.28%. The first contribution of the present paper is the development of a rich dataset with Web pages GUI sketches and their captions. There was no dataset with sufficiently complex GUI sketches before we start this work. A second contribution was applying YOLO to detect and localize HTML elements, and the development of a layout algorithm that allows us to convert the YOLO result into code. It is a completely different approach from what is found in the related work. Finally, we achieved with YOLO-based architecture a prediction’s correction higher than reported in the literature.FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

A partition methodology to develop data flow dominated embedded systems

Comunicação apresentada no International Workshop on Model-Based Methodologies for Pervasive and Embedded Software (MOMPES 2004), 1, Hamilton, Ontario, Canada, 15-18 June 2004.This paper proposes an automatic partition methodology oriented to develop data flow dominated embedded systems. The target architecture is CPU-based with reconfigurable devices on attached board(s), which closely matches the PSM meta-model applied to system modelling. A PSM flow graph was developed to represent the system during the partitioning process. The partitioning task applies known optimization algorithms - tabu search and cluster growth algorithms - which were enriched with new elements to reduce computation time and to achieve higher quality partition solutions. These include the closeness function that guides cluster growth algorithm, which dynamically adapts to the type of object and partition under analysis. The methodology was applied to two case studies, and some evaluation results are presented

Simulation of the nucleation of the precipitate Al3Sc in an aluminum scandium alloy using the kinetic monte carlo method

This paper describes the simulation of the phenomenon of nucleation of the precipitate Al3Sc in an Aluminum Scandium alloy using the kinetic Monte Carlo (kMC) method and the density-based clustering with noise (DBSCAN) method to filter the simulation data. To conduct this task, kMC and DBSCAN algorithms were implemented in C language. The study covers a range of temperatures, concentrations, and dimensions, going from 573K to 873K, 0.25% to 5%, and 50x50x50 to 100x100x100. The Al3Sc precipitation was successfully simulated at the atomistic scale. DBSCAN revealed to be a valorous aid to identify the precipitates. The achieved results are in good agreement with those reported in the literature, but we went deeper in the evaluation of the influence of all the simulation and analysis parameters

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version

Simulation of precipitation in an aluminum scandium alloy using kinetic Monte Carlo and DBSCAN algorithms

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a kinetic Monte Carlo (kMC) method. The kMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the kMC simulation, the density-based clustering with noise (DBSCAN) method was employed. kMC and DBSCAN algorithms were implemented in the C language. The undertaken simulations were conducted in the SeARCH cluster at the University of Minho. The study covers temperatures, concentrations, and dimensions, ranging from 578K to 873K, 0.25% to 5%, and 50x50x50 to 100x100x100. The Al3Sc precipitation was successfully simulated at the atomistic scale. DBSCAN revealed to be a valorous aid to identify the precipitates. The achieved results are in good agreement with those reported in the literature, but we went deeper in the evaluation of the influence of all the simulation and analysis parameters. A parallel version of the kMC algorithm using OpenMP was evaluated, which has not proved advantageous compared to the optimized sequential implementation