Towards the Usage of MBT at ETSI

In 2012 the Specialists Task Force (STF) 442 appointed by the European Telcommunication Standards Institute (ETSI) explored the possibilities of using Model Based Testing (MBT) for test development in standardization. STF 442 performed two case studies and developed an MBT-methodology for ETSI. The case studies were based on the ETSI-standards GeoNetworking protocol (ETSI TS 102 636) and the Diameter-based Rx protocol (ETSI TS 129 214). Models have been developed for parts of both standards and four different MBT-tools have been employed for generating test cases from the models. The case studies were successful in the sense that all the tools were able to produce the test suites having the same test adequacy as the corresponding manually developed conformance test suites. The MBT-methodology developed by STF 442 is based on the experiences with the case studies. It focusses on integrating MBT into the sophisticated standardization process at ETSI. This paper summarizes the results of the STF 442 work.Comment: In Proceedings MBT 2013, arXiv:1303.037

Multidimensional diagnosis of competitive anxiety in youth baseball team

Santiago de Cuba It is one of the provinces with the best sports results in Cuban history, baseball is one of the leading disciplines in this regard, however, in recent years, such achievements have been affected. Multiple are the answers that technicians, researchers, and specialists have tried to offer these unfortunate events. Youth baseball has not been exempt from this problem and, in this sense, Sports Psychology has played an important role in the study and search for scientific alternatives that contribute to reversing these results. During the last 3 years, high manifestations of competitive anxiety have been observed in baseball players in this category, this fact motivates the present investigation, it aims to rigorously diagnose how this negative emotion has been presented in said population. To complete the objective, the 25 members of the Santiago youth team were selected as a sample, the study is descriptive, being able to record, analyze and describe the general and observable characteristics of anxiety in real moments of training sessions and competitions, it is supported by the qualitative-quantitative methodology using psychological techniques such as observation, interview, CSAI-2, attitude tests for competition and appreciation of time and pulsometer

Performance impact of a slower main memory: a case study of STT-MRAM in HPC

In high-performance computing (HPC), significant effort is invested in research and development of novel memory technologies. One of them is Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) --- byte-addressable, high-endurance non-volatile memory with slightly higher access time than DRAM. In this study, we conduct a preliminary assessment of HPC system performance impact with STT-MRAM main memory with recent industry estimations. Reliable timing parameters of STT-MRAM devices are unavailable, so we also perform a sensitivity analysis that correlates overall system slowdown trend with respect to average device latency. Our results demonstrate that the overall system performance of large HPC clusters is not particularly sensitive to main-memory latency. Therefore, STT-MRAM, as well as any other emerging non-volatile memories with comparable density and access time, can be a viable option for future HPC memory system design.This work was supported by the Collaboration Agreement between Samsung Electronics Co., Ltd. and BSC, Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). This work has also received funding from the European Union's Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578).Peer ReviewedPostprint (author's final draft

Evaluation of intellectual disability in Ecuador: a challenge for psychology

One of the crucial aspects that Psychology assumes in the study of people with limitations in the Republic of Ecuador is the approach to intellectual disability. It is precisely and, within the framework of the implementation of the Manuela Espejo Solidarity Mission, it was necessary to address this issue of high social impact, this purpose the methodology used in conducting previous studies has taken as background in Cuba and Venezuela and of which the largest of the Antilles has been protagonist and manager. With this work, the objective has pursued, in the first instance, of reflecting on the complexity of the subject, due to the factors so diverse and dynamic that intervene in the classification of the different levels of intellectual affectation. Descriptively, the methods used in the 24 provinces of Ecuador have explained, verifying their effectiveness in the sample studied by the specialty. It has suggested, as a significant element, the implementation of new psychometric methods for subsequent studies, as well as the creation of diagnostic centers that detect, from earlier ages, people with intellectual disabilities for better treatment, intervention and quality improvement of life

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification

Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation

Diffusion models have shown impressive performance for image generation, often times outperforming other generative models. Since their introduction, researchers have extended the powerful noise-to-image denoising pipeline to discriminative tasks, including image segmentation. In this work we propose a conditional score-based generative modeling framework for medical image segmentation which relies on a parametric surface representation for the segmentation masks. The surface re-parameterization allows the direct application of standard diffusion theory, as opposed to when the mask is represented as a binary mask. Moreover, we adapted an extended variant of the diffusion technique known as the "cold-diffusion" where the diffusion model can be constructed with deterministic perturbations instead of Gaussian noise, which facilitates significantly faster convergence in the reverse diffusion. We evaluated our method on the segmentation of the left ventricle from 65 transthoracic echocardiogram videos (2230 echo image frames) and compared its performance to the most popular and widely used image segmentation models. Our proposed model not only outperformed the compared methods in terms of segmentation accuracy, but also showed potential in estimating segmentation uncertainties for further downstream analyses due to its inherent generative nature.Comment: 5 pages, 5 figures, conferenc