Machine Learning (ML) technologies have shown great promise in many areas, but when used without proper oversight, they can produce biased results that discriminate against historically underrepresented groups. In recent years, the software engineering research community has contributed to addressing the need for ethical machine learning by proposing a number of fairness-aware practices, e.g., fair data balancing or testing approaches, that may support the management of fairness requirements throughout the software lifecycle. Nonetheless, the actual validity of these practices, in terms of practical application, impact, and effort, from the developers’ perspective has not been investigated yet. This paper addresses this limitation, assessing the developers’ perspective of a set of 28 fairness practices collected from the literature. We perform a survey study involving 155 practitioners who have been working on the development and maintenance of ML-enabled systems, analyzing the answers via statistical and clustering analysis to group fairness-aware practices based on their application frequency, impact on bias mitigation, and effort required for their application. While all the practices are deemed relevant by developers, those applied at the early stages of development appear to be the most impactful. More importantly, the effort required to implement the practices is average and sometimes high, with a subsequent average application. The findings highlight the need for effort-aware automated approaches that ease the application of the available practices, as well as recommendation systems that may suggest when and how to apply fairness-aware practices throughout the software lifecycle.

This paper presents a real time algorithm, based on a segmentation and labelling technique, for analog transducer signals, which extracts information from the signal. The process of sampling is extremely important because it is the only one that connects the real world with the digital world. In addition, the proposed technique can be embedded into sensors, allowing applications in sensor networks and sensors data fusion architectures like Internet of Things. A simplified version of these algorithms has been proposed as a standard for transducer signal feature extraction. These algorithms are analysed and experimental results are tackled for representative signals, in different fields.

Determining the correlation between biomedical terms is a powerful instrument to help scientist research activity, both to understand experimental results and to design new ones. In particular, a great potential comes from the integration of the many heterogeneous information sources currently available on the Web. In this article we focus on the correlation between genes and biological processes. In this context, we present a methodology for integrating information from biomedical literature with other heterogeneous types of structured information. In particular, the information sources integrated in this work are PubMed abstracts, pathway databases, and NCI thesaurus definitions. The integration is performed at the semantic analysis level using a customized approach we developed to modulate the impact of the different sources on the correlation score. We report the results of a study concerning the impact of the information integration on the correlation score and of the user-level parameters we introduced to modulate the impact of pathway data or NCI definitions with respect to biomedical literature information, depending on the context of the search. To evaluate the methodology, we performed correlation measures on six biological processes and nine genes by comparing the results with and without the integration of pathways and NCI definitions.

An effective knowledge extraction and quantification methodology from biomedical literature would allow the researcher to organize and analyze the results of high-throughput experiments on microarrays and next-generation sequencing technologies. Despite the large amount of raw information available on the web, a tool able to extract a measure of the correlation between a list of genes and biological processes is not yet available. In this paper, we present Gelsius, a workflow that incorporates biomedical literature to quantify the correlation between genes and terms describing biological processes. To achieve this target, we build different modules focusing on query expansion and document cononicalization. In this way, we reached to improve the measurement of correlation, performed using a latent semantic analysis approach. To the best of our knowledge, this is the first complete tool able to extract a measure of genes-biological processes correlation from literature. We demonstrate the effectiveness of the proposed workflow on six biological processes and a set of genes, by showing that correlation results for known relationships are in accordance with definitions of gene functions provided by NCI Thesaurus. On the other side, the tool is able to propose new candidate relationships for later experimental validation. The tool is available at >http://bioeda1.polito.it:8080/medSearchServlet/.

Massive Parallel Sequencing is a term used to describe several revolutionary approaches to DNA sequencing, the so-called Next Generation Sequencing technologies. These technologies generate millions of short sequence fragments in a single run and can be used to measure levels of gene expression and to identify novel splice variants of genes allowing more accurate analysis. The proposed solution provides novelty on two fields, firstly an optimization of the read mapping algorithm has been designed, in order to parallelize processes, secondly an implementation of an architecture that consists of a Grid platform, composed of physical nodes, a Virtual platform, composed of virtual nodes set up on demand, and a scheduler that allows to integrate the two platforms.

One of the major challenges in today's economy concerns the reduction in energy usage and CO2footprint in existing Public buildings and Spaces without significant construction works, by an intelligent ICT-based service monitoring and managing the energy consumption. In particular, interoperability between heterogeneous devices and networks, both existing and to be deployed is a key features to create efficient services and holistic energy control policies. In this paper we describe an innovative software infrastructure to provide a web-service based, hardware independent access to the heterogeneous networks of wireless sensor nodes, such as smart plugs for measuring energy motes for temperature, relative humidity and light monitoring. The proposed infrastructure allows easy extension to other networks, thus representing a contribute to the opening of a market for ICT-based customized solutions integrating numerous products from different vendors and offering services from design of integrated systems to the operation and maintenance phases.

MOTIVATION: Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model. RESULTS: We report the fusion genes discovered by the proposed framework on experimentally validated biological samples of chronic myelogenous leukemia (CML) and on public NCBI datasets, for which Bellerophontes is able to detect the exact junction sequence. With respect to state-of-art approaches, Bellerophontes detects the same experimentally validated fusions, however, it is more selective on the total number of detected fusions and provides a more accurate set of spanning reads supporting the junctions. We finally report the fusions involving non-annotated transcripts found in CML samples. AVAILABILITY AND IMPLEMENTATION: Bellerophontes JAVA/Perl/Bash software implementation is free and available at http://eda.polito.it/bellerophontes/.

The considerable improvement in the biotechnogical field and the adoption of screen techniques such as high throughput arrays produced a spread of biological and genetical data and scientific papers, mostly diffused on the Web. Even if the huge amount of available information represents a major step forward for the biomedical research field, the main effort for a scientist is to evaluate the correlation among the concepts both in qualitative and in quantitative terms. In the presented work, exploiting the richness of the UMLS Metathesaurus in combination with the novelty of the literature provided by PubMed, an automatic flow aimed at the scoring the semantic correlation among biomedical terms (e. g. biomolecules and biological processes) is proposed. The experiments show that obtained correlations are fully coherent with the information coming from the biological literature. The accuracy of the results remarks the importance of combining text mining techniques with complete and well structured thesaurus, such as UMLS.

Flash-based FPGAs are increasingly demanded in safety critical fields, in particular space and avionic ones, due to their non-volatile configuration memory. Although they are almost immune to permanent loss of the configuration data, they are composed of floating gate based switches that can suffer transient effects if hit by high energetic particles with critical consequences on the implemented logic. This paper presents a new way for the analysis of the impact of single event effects in flash-based FPGAs. We proposed a new methodology to identify the most critical switches inside the configuration logic block and the most redundant and robust configuration selection for each logic function. The experimental results achieved by fault injection demonstrated the feasibility of the proposed method and show that by using the most robust functional mapping it is possible to enhance the reliability of the entire design with respect to a not robust ones.

Reducing the cost of test (in particular by reducing its duration and the cost of the required ATE) is a common goal which has largely been pursued in the past, mainly by introducing suitable on chip Design for Testability (DfT) circuitry. Today, the increasing popularity of sophisticated DfT architectures and the parallel emergence of new ATE families allow the identification of innovative solutions effectively facing that goal. In this paper we face the increasingly common situation of SoCs adopting the IEEE 1149.1 and 1500 standards for the test of the internal cores, and explore the idea of storing the test program on the tester in a compressed form, and decompressing it on-the-fly during test application.