Abstract

Abstract
This article proposes a Markov Chain Monte Carlo (MCMC) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

Abstract
When developing mobile applications, developers often have to decide when to acquire and when to release resources. This leads to resource leaks, a kind of bug where a resource is acquired but never released. This is a common problem in Android applications that can degrade energy efficiency and, in some cases, can cause resources to not function properly. In this paper, we present an extension of EcoAndroid, an Android Studio plugin that improves the energy efficiency of Android applications, with an inter-procedural static analysis that detects resource leaks. Our analysis is implemented using Soot, FlowDroid, and Heros, which provide a static-analysis environment capable of processing Android applications and performing inter-procedural analysis with the IFDS framework. It currently supports the detection of leaks related to four Android resources: Cursor, SQLite-Database, Wakelock, and Camera. We evaluated our tool with the DroidLeaks benchmark and compared it with 8 other resource leak detectors. We obtained a precision of 72.5% and a recall of 83.2%. Our tool was able to uncover 191 previously unidentified leaks in this benchmark. These results show that our analysis can help developers identify resource leaks.

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

Abstract
Programs with a continuous state space or that interact with physical processes often require notions of equivalence going beyond the standard binary setting in which equivalence either holds or does not hold. In this paper we explore the idea of equivalence taking values in a quantale V, which covers the cases of (in)equations and (ultra)metric equations among others. Our main result is the introduction of a V-equational deductive system for linear ?-calculus together with a proof that it is sound and complete (in fact, an internal language) for a class of enriched autonomous categories. In the case of inequations, we get an internal language for autonomous categories enriched over partial orders. In the case of (ultra)metric equations, we get an internal language for autonomous categories enriched over (ultra)metric spaces. We use our results to obtain examples of inequational and metric equational systems for higher-order programs that contain real-time and probabilistic behaviour.

Abstract
The prevalence and availability of cloud infrastructures has made them the de facto solution for storing and archiving data, both for organizations and individual users. Nonetheless, the cloud's wide spread adoption is still hindered by dependability and security concerns, particularly in applications with large data collections where efficient search and retrieval services are also major requirements. This leads to an increased tension between security, efficiency, and search expressiveness. In this article we tackle this tension by proposing BISEN, a new provably-secure boolean searchable symmetric encryption scheme that improves these three complementary dimensions by exploring the design space of isolation guarantees offered by novel commodity hardware such as Intel SGX, abstracted as Isolated Execution Environments (IEEs). BISEN is the first scheme to support multiple users and enable highly expressive and arbitrarily complex boolean queries, with minimal information leakage regarding performed queries and accessed data, and verifiability regarding fully malicious adversaries. Furthermore, BISEN extends the traditional SSE model to support filter functions on search results based on generic metadata created by the users. Experimental validation and comparison with the state of art shows that BISEN provides better performance with enriched search semantics and security properties.