Abstract
This article introduces the E-MANAFA energy profiler, a plug-and-play, device-independent, model-based profiler capable of obtaining fine-grained energy measurements on Android devices. Besides having the capability to calculate performance metrics such as the energy consumed and runtime during a time interval, E-MANAFA also allows to estimate the energy consumed by each device component (e.g. CPU, WI-FI, screen). In this article, we present the main elements that compose this framework, as well as its workflow. In order to present the power of this tool, we demonstrate how the tool can measure the overhead of the instrumentation technique used in the PyAnaDroid application benchmarking pipeline, which already supports E-MANAFA to monitor power consumption in its Android application automatic execution process. Video demo: shorturl.at/hmyz5

Abstract
Although machine learning (ML) is a field that has been the subject of research for decades, a large number of applications with high computational power have recently emerged. Usually, we only focus on solving machine learning problems without considering how much energy has been consumed by the different frameworks used for such applications. This study aims to provide a comparison among four widely used frameworks such as Tensorflow, Keras, Pytorch, and Scikit-learn in terms of many aspects, including energy efficiency, memory usage, execution time, and accuracy. We monitor the performance of such frameworks using different well-known machine learning benchmark problems. Our results show interesting findings, such as slower and faster frameworks consuming less or more energy, higher or lower memory usage, etc. We show how to use our results to provide machine learning developers with information to decide which framework to use for their applications when energy efficiency is a concern.

Abstract

Abstract
Strategic term rewriting and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies (recursion schemes) to apply term rewrite rules in defining transformations, while the latter is suitable for expressing context-dependent language processing algorithms. Each of these techniques, however, is usually implemented by its own powerful and large processor system. As a result, it makes such systems harder to extend and to combine. We present the embedding of both strategic tree rewriting and attribute grammars in a zipper-based, purely functional setting. The embedding of the two techniques in the same setting has several advantages: First, we easily combine/zip attribute grammars and strategies, thus providing language engineers the best of the two worlds. Second, the combined embedding is easier to maintain and extend since it is written in a concise and uniform setting. We show the expressive power of our library in optimizing Haskell let expressions, expressing several Haskell refactorings and solving several language processing tasks for an Oberon-0 compiler.

Abstract
Secure multiparty computation (SMC) allows for complex computations over encrypted data. Privacy concerns for cloud applications makes this a highly desired technology and recent performance improvements show that it is practical. To make SMC accessible to non-experts and empower its use in varied applications, many domain-specific compilers are being proposed.We review the role of these compilers and provide a formal treatment of the core steps that they perform to bridge the abstraction gap between high-level ideal specifications and efficient SMC protocols. Our abstract framework bridges this secure compilation problem across two dimensions: 1) language-based source- to target-level semantic and efficiency gaps, and 2) cryptographic ideal- to real-world security gaps. We link the former to the setting of certified compilation, paving the way to leverage long-run efforts such as CompCert in future SMC compilers. Security is framed in the standard cryptographic sense. Our results are supported by a machine-checked formalisation carried out in EasyCrypt.

Abstract
Password managers (PMs) are important tools that enable the use of stronger passwords, freeing users from the cognitive burden of remembering them. Despite this, there are still many users who do not fully trust PMs. In this paper, we focus on a feature that most PMs offer that might impact the user's trust, which is the process of generating a random password. We present three of the most commonly used algorithms and we propose a solution for a formally verified reference implementation of a password generation algorithm. We use EasyCrypt to specify and verify our reference implementation. In addition, we present a proof-of-concept prototype that extends Bitwarden to only generate compliant passwords, solving a frequent users' frustration with PMs. This demonstrates that our formally verified component can be integrated into an existing (and widely used) PM.