Abstract
Secure databases have emerged to securely store and process sensitive data at untrusted infrastructures (e.g., Cloud Computing). To be secure and efficient, the encryption schemes used by these systems must be carefully chosen. Indeed, this task requires expertise both in databases and security, and is currently being done manually, which is time-consuming and error-prone and can lead to security violations, poor performance, or both. This paper presents ATOCS, a novel framework that analyses the applications' code and, from the inferred requirements, determines the best combination of encryption schemes and related configurations for the underlying secure NoSQL database. Its design is modular and extensible thus facilitating the support of different applications and database solutions. Our evaluation with real-world applications shows that ATOCS is fast (it takes 44 seconds to analyse more than 12K LoC), accurate, and simplifies the configuration of secure databases.

Abstract
The Giant Segmented Mirror Telescopes (GSMTs) including the Giant Magellan Telescope (GMT), the Thirty Meter Telescope (TMT), and the European Extremely Large Telescope (E-ELT), all have extreme adaptive optics (ExAO) instruments planned that will use pyramid wavefront sensors (PWFS). The ExAO instruments all have common features: a high-actuator-count deformable mirror running at extreme speeds (>1 kHz); a high-performance wavefront sensor (WFS); and a high-contrast coronagraph. ExAO WFS performance is currently limited by the need for high spatial sampling of the wavefront which requires large detectors. For ExAO instruments for the next generation of telescopes, alternative architectures of WFS are under consideration because there is a trade-off between detector size, speed, and noise that reduces the performance of GSMT-ExAO wavefront control. One option under consideration for a GSMT-ExAO wavefront sensor is a three-sided PWFS (3PWFS). The 3PWFS creates three copies of the telescope pupil for wavefront sensing, compared to the conventional four-sided PWFS (4PWFS), which uses four pupils. The 3PWFS uses fewer detector pixels than the 4PWFS and should therefore be less sensitive to read noise. Here we develop a mathematical formalism based on the diffraction theory description of the Foucault knife-edge test that predicts the intensity pattern after the PWFS. Our formalism allows us to calculate the intensity in the pupil images formed by the PWFS in the presence of phase errors corresponding to arbitrary Fourier modes. We use these results to motivate how we process signals from a 3PWFS. We compare the raw intensity (RI) method, and derive the Slopes Maps (SM) calculation for the 3PWFS, which combines the three pupil images of the 3PWFS to obtain the X and Y slopes of the wavefront. We then use the Object Oriented MATLAB Adaptive Optics toolbox (OOMAO) to simulate an end-to-end model of an AO system using a PWFS with modulation and compare the performance of the 3PWFS to the 4PWFS. In the case of a low read noise detector, the Strehl ratios of the 3PWFS and 4PWFS are within 0.01. When we included higher read noise in the simulation, we found a Strehl ratio gain of 0.036 for the 3PWFS using RI over the 4PWFS using SM at a stellar magnitude of 10. At the same magnitude, the 4PWFS RI also outperformed the 4PWFS SM, but the gain was only 0.012 Strehl. This is significant because 4PWFS using SM is how the PWFS is conventionally used for AO wavefront sensing. We have found that the 3PWFS is a viable WFS that can fully reconstruct a wavefront and produce a stable closed-loop with correction comparable to that of a 4PWFS, with modestly better performance for high read-noise detectors.

Abstract
Breaks in water consumption records can represent apparent losses which are generally associated with the volumes of water that are consumed but not billed. The detection of these losses at the appropriate time can have a significant economic impact on the water company's revenues. However, the real datasets available to test and evaluate the current methods on the detection of breaks are not always large enough or do not present abnormal water consumption patterns. This study proposes an approach to generate synthetic data of water consumption with structural breaks which follows the statistical proprieties of real datasets from a hotel and a hospital. The parameters of the best-fit probability distributions (gamma, Weibull, log-Normal, log-logistic, and exponential) to real water consumption data are used to generate the new datasets. Two decreasing breaks on the mean were inserted in each new dataset associated with one selected probability distribution for each study case with a time horizon of 914 days. Three different change point detection methods provided by the R packages strucchange and changepoint were evaluated making use of these new datasets. Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indices, a higher performance has been observed for the breakpoint method provided by the package strucchange.

Abstract
Designers often use physical hand-drawn mockups to convey their ideas to stakeholders. Unfortunately, these sketches do not depict the exact final look and feel of web pages, and communication errors will often occur, resulting in prototypes that do not reflect the stakeholder's vision. Multiple suggestions exist to tackle this problem, mainly in the translation of visual mockups to prototypes. Some authors propose end-to-end solutions by directly generating the final code from a single (black-box) Deep Neural Network. Others propose the use of object detectors, providing more control over the acquired elements but missing out on the mockup's layout. Our approach provides a real-time solution that explores: (1) how to achieve a large variety of sketches that would look indistinguishable from something a human would draw, (2) a pipeline that clearly separates the different responsibilities of extracting and constructing the hierarchical structure of a web mockup, (3) a methodology to segment and extract containers from mockups, (4) the usage of in-sketch annotations to provide more flexibility and control over the generated artifacts, and (5) an assessment of the synthetic dataset impact in the ability to recognize diagrams actually drawn by humans. We start by presenting an algorithm that is capable of generating synthetic mockups. We trained our model (N=8400, Epochs=400) and subsequently fine-tuned it (N=74, Epochs=100) using real human-made diagrams. We accomplished a mAP of 95.37%, with 90% of the tests taking less than 430ms on modest commodity hardware (approximate to 2.3fps). We further provide an ablation study with well-known object detectors to evaluate the synthetic dataset in isolation, showing that the generator achieves a mAP score of 95%, approximate to 1.5 x higher than training using hand-drawn mockups alone.

Abstract
Although new, renewed, and innovative museum experiences are constantly provided to the sighted public, millions of visually impaired persons worldwide are still deprived of access to enjoying and engaging with collections. This research aims to investigate the limitations of blind or partially sighted persons in visiting a museum nowadays. Semistructured interviews with seventy-two visually impaired Portuguese individuals revealed reasons and factors that contribute to positive and negative visiting experiences, and the assessment of sixteen accessibility resources demonstrates their frequency and potential usefulness in museum visits. It concludes that the actual limited experiences in museums could be surpassed and enhanced by moving beyond accessibility, embracing inclusion, and focusing on the creation of multimodal and multisensory approaches to promote exceptional, memorable, and exciting visiting opportunities for all.

Abstract
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.