GCC-Plugin for Automated Accelerator Generation and Integration on Hybrid FPGA-SoCs

In recent years, architectures combining a reconfigurable fabric and a general purpose processor on a single chip became increasingly popular. Such hybrid architectures allow extending embedded software with application specific hardware accelerators to improve performance and/or energy efficiency. Aiding system designers and programmers at handling the complexity of the required process of hardware/software (HW/SW) partitioning is an important issue. Current methods are often restricted, either to bare-metal systems, to subsets of mainstream programming languages, or require special coding guidelines, e.g., via annotations. These restrictions still represent a high entry barrier for the wider community of programmers that new hybrid architectures are intended for. In this paper we revisit HW/SW partitioning and present a seamless programming flow for unrestricted, legacy C code. It consists of a retargetable GCC plugin that automatically identifies code sections for hardware acceleration and generates code accordingly. The proposed workflow was evaluated on the Xilinx Zynq platform using unmodified code from an embedded benchmark suite.Comment: Presented at Second International Workshop on FPGAs for Software Programmers (FSP 2015) (arXiv:1508.06320

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

A new automated workflow for 3D character creation based on 3D scanned data

In this paper we present a new workflow allowing the creation of 3D characters in an automated way that does not require the expertise of an animator. This workflow is based of the acquisition of real human data captured by 3D body scanners, which is them processed to generate firstly animatable body meshes, secondly skinned body meshes and finally textured 3D garments

The Adoption and Effectiveness of Automation in Health Evidence Synthesis

Background: Health systems worldwide are often informed by evidence-based guidelines which in turn rely heavily on systematic reviews. Systematic reviews are currently hindered by the increasing volume of new research and by its variable quality. Automation has potential to alleviate this problem but is not widely used in health evidence synthesis. This thesis sought to address the following: why is automation adopted (or not), and what effects does it have when it is put into use? / Methods: Roger’s Diffusion of Innovations theory, as a well-established and widely used framework, informed the study design and analysis. Adoption barriers and facilitators were explored through a thematic analysis of guideline developers’ opinions towards automation, and by mapping the adoption journey of a machine learning (ML) tool among Cochrane Information Specialists (CISs). A randomised trial of ML assistance in Risk of Bias (RoB) assessments and a cost-effectiveness analysis of a semi-automated workflow in the maintenance of a living evidence map each evaluated the effects of automation in practice. / Results: Adoption decisions are most strongly informed by the professional cultural expectations of health evidence synthesis. The stringent expectations of systematic reviewers and their users must be met before any other characteristic of an automation technology is considered by potential adopters. Ease-of-use increases in importance as a tool becomes more diffused across a population. Results of the randomised trial showed that ML-assisted RoB assessments were non-inferior to assessments completed entirely by human researcher effort. The cost-effectiveness analysis showed that a semi-automated workflow identified more relevant studies than the manual workflow and was less costly. / Conclusions: Automation can have substantial benefits when integrated into health evidence workflows. Wider adoption of automation tools will be facilitated by ensuring they are aligned with professional values of the field and limited in technical complexity

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system