Sampling-Based Query Re-Optimization

Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and authors that appears in the Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2016

Identification and monitoring of violent interactions in video

This project shall help to bring a tool to fight against bullying in schools. It is also possible to use it in different scenes where a camera is recording a common area shared by people, such as companies, banks, prisons, or hospitals. To achieve that, the issue is approached from two main modules. The first one, a comparative study of approaches to detect violence in video, using image and video analyser Neural Networks (NN)s: a custom image analyser NN based on LeNet5, AlexNet, custom stacked long short-term memory (LSTM) and convolutional LSTM based NNs. The trainings are done with two datasets that have been subject to modifications to correct possible misinterpretations during the learning and pretraining is applied. The LeNet5 based NN is unsuccessful and tested with an independent dataset AlexNet is inaccurate. The best results are obtained with a stacked LSTM NN and a convolutional LSTM with dropout and a LSTM layer. Both NNs achieve over 90 % of accuracy with training and validation datasets, meanwhile the stacked LSTM and the convolutional NN achieve, respectively, 75 % and 100 % of accuracy with a small independent test dataset created. The convolutional LSTM needed 10 times less epochs to achieve the same result as the stacked LSTM. The second module consists of a violence detection system that applies the best solution obtained from the comparative study. The violence detection system saves the frames detected as violence with date, time and camera name and emits a sound alarm when more than a certain number of consecutive frames are evaluated as containing violence. This way the sensitivity of the system is reduced and avoids false alarms due to small mistakes done by the intelligence

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that addresses these issues in isolation. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection/hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.Comment: 9 pages, 3 figure

Analyzing the limits of deep learning applied to side channel attacks

Society is advancing by leaps and bounds in terms of technology in recent decades. These advances come with new products and services, which are generally designed within a few years, and potentially without undergoing tests to verify whether they are susceptible to physical or logical attacks. In an increasingly connected world, it is necessary to highlight the importance of cybersecurity. Within cybersecurity there is the field of hardware, where products can also have vulnerabilities. For instance, the information that cryptographic algorithms manage could be exploited by an attacker. This thesis is based on one of the most innovative techniques for analysing side-channel attacks: deep learning. In particular, the limits that may exist in the world of side-channel analysis techniques applying deep learning are explored, introducing the readers to the exciting world of hardware attacks. In addition, this thesis provides an introduction to neural computation. After gaining a detailed understanding of the functioning of ANN applied to SCA through the experiments carried out, the initial results have been improved by implementing two techniques proposed by researchers.La sociedad avanza a pasos agigantados en materia de tecnología en las últimas décadas. Estos avances vienen acompañados de nuevos productos y servicios, que generalmente se diseñan en pocos años, y potencialmente sin someterse a pruebas para verificar si son susceptibles de ataques físicos o lógicos. En un mundo cada vez más conectado, es necesario destacar la importancia de la ciberseguridad. Dentro de la ciberseguridad está el campo del hardware, donde los productos también pueden tener vulnerabilidades. Por ejemplo, la información que manejan los algoritmos criptográficos podría ser explotada por un atacante. Esta tesis se basa en una de las técnicas más innovadoras para analizar los ataques de canal lateral: deep learning. En particular, se exploran los límites que pueden existir en el mundo de las técnicas de análisis canal lateral aplicando aprendizaje profundo, introduciendo a los lectores en el apasionante mundo de los ataques por hardware. Además, esta tesis ofrece una introducción a la computación neuronal. Tras conocer en detalle el funcionamiento de la RNA aplicada a la SCA a través de los experimentos realizados, se han mejorado los resultados iniciales aplicando dos técnicas propuestas por los investigadores.La societat avança amb passes de gegant en matèria de tecnologia en les últimes dècades. Aquests avanços venen acompanyats de nous productes i serveis, que generalment es dissenyen en pocs anys, i potencialment sense sotmetre's a proves per a verificar si són susceptibles d'atacs físics o lògics. En un món cada vegada més connectat, és necessari destacar la importància de la ciberseguretat. Dins de la ciberseguretat està el camp del hardware, on els productes també poden tenir vulnerabilitats. Per exemple, la informació que manegen els algorismes criptogràfics podria ser explotada per un atacant. Aquesta tesi es basa en una de les tècniques més innovadores per a analitzar els atacs de canal lateral : deep learning. En particular, s'exploren els límits que poden existir en el món de les tècniques d'anàlisis de canal lateral aplicant l'aprenentatge profund, introduint als lectors en l'apassionant món dels atacs hardware. A més, aquesta tesi ofereix una introducció a la computació neuronal. Després de conèixer detalladament el funcionament de les ANN aplicades a SCA a través dels experiments realitzats, s'han millorat els resultats inicials aplicant dues tècniques proposades pels investigadors