The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

Automated interpretation of digital images of hydrographic charts.

Details of research into the automated generation of a digital database of hydrographic charts is presented. Low level processing of digital images of hydrographic charts provides image line feature segments which serve as input to a semi-automated feature extraction system, (SAFE). This system is able to perform a great deal of the building of chart features from the image segments simply on the basis of proximity of the segments. The system solicits user interaction when ambiguities arise. IThe creation of an intelligent knowledge based system (IKBS) implemented in the form of a backward chained production rule based system, which cooperates with the SAFE system, is described. The 1KBS attempts to resolve ambiguities using domain knowledge coded in the form of production rules. The two systems communicate by the passing of goals from SAFE to the IKBS and the return of a certainty factor by the IKBS for each goal submitted. The SAFE system can make additional feature building decisions on the basis of collected sets of certainty factors, thus reducing the need for user interaction. This thesis establishes that the cooperating IKBS approach to image interpretation offers an effective route to automated image understanding

A Study on Efficient Semantic Segmentation

Semantic segmentation extends classical image classification by attributing one class for each pixel in a given image. This approach requires a significant amount of resources to be performed. The majority of time, low­power resource devices are unable to deliver predictions on this task, because of its computational requirements. Some small robots lack inference speed, enough memory to inference a single instance at time or, even, battery life to delivery continuous predictions. Another aspect, is the incapability of training models on the edge, which can be a major limitation on the practicality of the solution. As if current networks were not big enough for this type of devices, novel architectures tend to be even more complex, which can be seen as a continuous divergence on the possibility of running this kind of models on low­power devices. With this in mind, the project has the goal of exploring efficient solutions to deploy segmentation models in the edge. To do so, the project aims at exploring efficient architectures and light convolutional layers, alternative segmentation methods and alternative methods of weight representation. In the end, by performing benchmarks on efficient networks with quantization, filter pruning along distillation and layer replacement, it is shown that these methods can be used to save computational resources, but to do so, they sacrifice precision points.O processo de segmentação semântica envolve uma enorme quantidade de recursos. Por consequência, este tipo de modelos são dificilmente ou, na grande parte dos casos, impossíveis de exportar para dispositivos eletrônicos de baixa capacidade computacional. Pequenos dispositivos, sendo alguns deles robots, não têm as capacidades computacionais necessárias para tornar o processo de inferência viável. Estes pequenos robots não têm muitas vezes memória RAM suficiente, ou noutros casos, bateria grande o suficiente para inferir de forma contínua durante curtos intervalos de tempo. Um outro aspecto consiste na impossibilidade de treinar os modelos nos próprios dispositivos, o que faz com que a sua aplicação seja ela mesma pouco prática. Por outro lado, as novas gerações de redes neuronais têm vindo a aumentar a escala dos recursos necessários, o que por um lado afasta ainda mais a possibilidade de usar estes pequenos dispositivos para tarefas de segmentação semântica. Com este problema em mente, o projecto foca­se em explorar métodos que tornem possível o uso deste modelos on the edge. Com este objectivo em mente, planeia­se explorar arquitecturas e camada convolucionais que fazem uso dos recursos de forma mais eficiente, métodos alternativos de segmentação e mecanismos de representação dos pesos para formatos mais leves

Architectures and GPU-Based Parallelization for Online Bayesian Computational Statistics and Dynamic Modeling

Recent work demonstrates that coupling Bayesian computational statistics methods with dynamic models can facilitate the analysis of complex systems associated with diverse time series, including those involving social and behavioural dynamics. Particle Markov Chain Monte Carlo (PMCMC) methods constitute a particularly powerful class of Bayesian methods combining aspects of batch Markov Chain Monte Carlo (MCMC) and the sequential Monte Carlo method of Particle Filtering (PF). PMCMC can flexibly combine theory-capturing dynamic models with diverse empirical data. Online machine learning is a subcategory of machine learning algorithms characterized by sequential, incremental execution as new data arrives, which can give updated results and predictions with growing sequences of available incoming data. While many machine learning and statistical methods are adapted to online algorithms, PMCMC is one example of the many methods whose compatibility with and adaption to online learning remains unclear. In this thesis, I proposed a data-streaming solution supporting PF and PMCMC methods with dynamic epidemiological models and demonstrated several successful applications. By constructing an automated, easy-to-use streaming system, analytic applications and simulation models gain access to arriving real-time data to shorten the time gap between data and resulting model-supported insight. The well-defined architecture design emerging from the thesis would substantially expand traditional simulation models' potential by allowing such models to be offered as continually updated services. Contingent on sufficiently fast execution time, simulation models within this framework can consume the incoming empirical data in real-time and generate informative predictions on an ongoing basis as new data points arrive. In a second line of work, I investigated the platform's flexibility and capability by extending this system to support the use of a powerful class of PMCMC algorithms with dynamic models while ameliorating such algorithms' traditionally stiff performance limitations. Specifically, this work designed and implemented a GPU-enabled parallel version of a PMCMC method with dynamic simulation models. The resulting codebase readily has enabled researchers to adapt their models to the state-of-art statistical inference methods, and ensure that the computation-heavy PMCMC method can perform significant sampling between the successive arrival of each new data point. Investigating this method's impact with several realistic PMCMC application examples showed that GPU-based acceleration allows for up to 160x speedup compared to a corresponding CPU-based version not exploiting parallelism. The GPU accelerated PMCMC and the streaming processing system can complement each other, jointly providing researchers with a powerful toolset to greatly accelerate learning and securing additional insight from the high-velocity data increasingly prevalent within social and behavioural spheres. The design philosophy applied supported a platform with broad generalizability and potential for ready future extensions. The thesis discusses common barriers and difficulties in designing and implementing such systems and offers solutions to solve or mitigate them