Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines

In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem. In this work we propose two hybrid algorithms using Integer Programming (IP) to generate a prioritized test suite. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of-the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time. Moreover, the nonlinear variant (the fastest one) is 27 and 42 times faster than PPGS in the two groups of instances analyzed in this work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2014-57341-R, the University of Málaga, Andalucía Tech and the Spanish Network TIN2015-71841-REDT (SEBASENet)

Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A novel monitoring system for fall detection in older people

Indexación: Scopus.This work was supported in part by CORFO - CENS 16CTTS-66390 through the National Center on Health Information Systems, in part by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC-03: ‘‘MONITORing for ehealth," FONDEF ID16I10449 ‘‘Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público’’, and in part by MEC80170097 ‘‘Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso’’. The work of V. H. C. De Albuquerque was supported by the Brazilian National Council for Research and Development (CNPq), under Grant 304315/2017-6.Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative. © 2013 IEEE.https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=842305

A parallel evolutionary algorithm for prioritized pairwise testing of software product lines

Lopez-Herrejon, R. Erick, Ferrer J., Chicano F., Haslinger E. Nicole, Egyed A., & Alba E. (2014). A parallel evolutionary algorithm for prioritized pairwise testing of software product lines. (Arnold, D. V., Ed.).Genetic and Evolutionary Computation Conference, GECCO '14, Vancouver, BC, Canada, July 12-16, 2014. 1255–1262.Software Product Lines (SPLs) are families of related software systems, which provide different feature combinations. Different SPL testing approaches have been proposed. However, despite the extensive and successful use of evolutionary computation techniques for software testing, their application to SPL testing remains largely unexplored. In this paper we present the Parallel Prioritized product line Genetic Solver (PPGS), a parallel genetic algorithm for the generation of prioritized pairwise testing suites for SPLs. We perform an extensive and comprehensive analysis of PPGS with 235 feature models from a wide range of number of features and products, using 3 different priority assignment schemes and 5 product prioritization selection strategies. We also compare PPGS with the greedy algorithm prioritized-ICPL. Our study reveals that overall PPGS obtains smaller covering arrays with an acceptable performance difference with prioritized-ICPL.Austrian Science Fund (FWF) project P25289-N15 and Lise Meitner Fellowship M1421-N15. Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2011-28194 and fellowship Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.BES-2012-055967. Project 8.06/5.47.4142 in collaboration with the VSB-Tech. Univ. of Ostrava and Universidad de Málaga, Andalucía Tech

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators