Abstract

Abstract
Entrepreneurial Ecosystems (EEs) have attracted the attention of academics, practitioners, and policymakers, that attempt to unlock 'a winning recipe' considering the different EEs pillars in order to ignite entrepreneurship at large. Therefore, understanding the degree of influence of each pillar on Entrepreneurial Initiative (EI) is helpful in framing more effective policies towards entrepreneurship. This study aims to bring a new facet to entrepreneurship research, specifically on decomposing the transformation of EEs and the influence of EEs pillars on EI. The transformation of EEs is shown by a balanced panel approach based on the Global Entrepreneurship Monitor (GEM) dataset over 8 years (2010-2017), comprising 18 countries. The study has several implications for entrepreneurship theory and practice as well as public policy since discusses three main issues, mainly supported by empirical results. First, the results show an unbalanced influence of EEs pillars on EI. Second, results also show the ineffectiveness of institutions in encouraging the desire to act entrepreneurially. Third, entrepreneurship needs to be part of the acculturation process evidencing the importance of collective normative. Therefore, providing the instruments and structures is not enough to encourage individuals to start an entrepreneurial journey. Generally, the results reveal that contextual determinants are significant in fostering entrepreneurial propensity to start a business. But the impact of the nine pillars is not equalized, revealing a fragmented influence with funding measures, R&D transfer, and cultural and social norms discouraging entrepreneurial initiative. Overall, the study contributes to the understanding of a multidimensional perspective on EEs and points future policy directions to overcome the lack of entrepreneurship and amend flawed entrepreneurship policies.

Abstract

Abstract
O presente artigo objetiva identificar estratégias metodológicas existentes no âmbito dos estudos métricos da informação que contribuem para o mapeamento de inovação. Em termos metodológicos, este estudo qualitativo de caráter exploratório e descritivo parte do levantamento da produção de conhecimento relacionada às tipologias dos estudos métricos na base de dados Web of Science, por meio da relação do termo inovação nas palavras-chave e keywords plus dos artigos. Os resultados destacam a aplicação da inovação nas metrias da informação no âmbito da altmetria, bibliometria, cientometria, patentometria e webometria.

Abstract
The vast datasets produced in human genomics must be efficiently stored, transferred, and processed while prioritizing storage space and restore performance. Balancing these two properties becomes challenging when resorting to traditional data compression techniques. In fact, specialized algorithms for compressing sequencing data favor the former, while large genome repositories widely resort to generic compressors (e.g., GZIP) to benefit from the latter. Notably, human beings have approximately 99.9 percent of DNA sequence similarity, vouching for an excellent opportunity for deduplication and its assets: leveraging inter-file similarity and achieving higher read performance. However, identity-based deduplication fails to provide a satisfactory reduction in the storage requirements of genomes. In this article, we balance space savings and restore performance by proposing GenoDedup, the first method that integrates efficient similarity-based deduplication and specialized delta-encoding for genome sequencing data. Our solution currently achieves 67.8 percent of the reduction gains of SPRING (i.e., the best specialized tool in this metric) and restores data 1.62x faster than SeqDB (i.e., the fastest competitor). Additionally, GenoDedup restores data 9.96x faster than SPRING and compresses files 2.05x more than SeqDB.

Abstract
Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).