Significance of Entrepreneurship

This paper investigates the degree of  current empirical evidence that  can communally and systematically authenticate the claim that entrepreneurship has important economic value. A systematic assessment is provided that can  answer the contribution of entrepreneurs to the economy in contrast to non-entrepreneurs? We study the comparative contribution of entrepreneurs to the economy based on four measures that have most widely been studied empirically. Hence, we answer the question: What is the contribution of entrepreneurs to (i) employment creation and dynamics, (ii) innovativeness and (iii) productivity and growth, relative to the contributions of the entrepreneurs’ counterparts, i.e. the ‘control group’? A fourth type of contribution studied is the role of entrepreneurship in escalating individuals’ effectiveness levels. 57 recent studies of high quality that contain 87 relevant separate analyses, were referred to  conclude that entrepreneurs have a very important – but specific – purpose in the economy that  they bring about relatively much employment creation, productivity growth and produce and commercialize towering quality innovations. They are more satisfied than employees. More importantly, recent studies show that entrepreneurial firms produce important externalities that affect regional employment growth rates of all companies in the region in the long run. However, the counterparts cannot be ignored either as they contribute to a relatively high value of GDP, a less unpredictable and more sheltered labor market, higher paid jobs and a greater number of innovations or they have a more dynamic role in the adoption of innovations. Keywords: entrepreneur, entrepreneurship, self-employment, productivity, economic development, growth, employment, innovation, patents, R&D, utility, remuneration, incom

FLECS: A Data-Driven Framework for Rapid Protocol Prototyping

Flecs is a framework for facilitating rapid implementation of communication protocols. Forwarding functionality of protocols can be modeled as a combination of packet processing components called abstract switching elements or Ases. The design of Ases is constrained by the axioms of communication which enables us to formally analyze forwarding mechanisms in communication networks. Ases can be connected in a directed graph to define complex forwarding functionality. We have developed Flecs on top of the Click modular router. The compilers in the Flecs framework translate protocol specifications into its Click implementation. We claim that the use of our framework reduces the implementation time by allowing the programmer to specify Ases and the forwarding configuration in a high-level meta-language and produces reasonably efficient implementations. It allows rapid prototyping through configuration, as well as specialized implementation of performance-critical functionality through inheritance

Towards MLOps: A DevOps Tools Recommender System for Machine Learning System

Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learning model and deploy the model to the production as well as store different versions of model and dataset. Benefits of MLOps is to make sure the fast delivery of the new trained models to the production to have accurate results. Furthermore, MLOps practice impacts the overall quality of the software products and is completely dependent on open-source tools and selection of relevant open-source tools is considered as challenged while a generalized method to select an appropriate open-source tools is desirable. In this paper, we present a framework for recommendation system that processes the contextual information (e.g., nature of data, type of the data) of the machine learning project and recommends a relevant toolchain (tech-stack) for the operationalization of machine learning systems. To check the applicability of the proposed framework, four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated where precision, recall and f-score is measured, the random forest out classed other approaches with highest f-score value of 0.66

Social relationship analysis using state-of-the-art embeddings.

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents

Pandemic-related emergency psychiatric presentations for self-harm of children and adolescents in 10 countries (PREP-kids): a retrospective international cohort study

To examine the differences in hospital emergency psychiatric presentations for self-harm of children and adolescents during the covid-19 lockdown in March and April 2020 compared with the same period in 2019. Retrospective cohort study. We used electronic patient records from 23 hospital emergency departments in ten countries grouped into 14 areas. We examined data on 2073 acute hospital presentations by 1795 unique children and adolescents through age 18. We examined the total number of emergency psychiatric hospital presentations and the proportion of children and adolescents presenting with severe self-harm as our two main outcome measures. In addition, we examined sociodemographic and clinical characteristics and clinical management variables for those presenting with self-harm. To compare the number of hospital presentations between 2020 and 2019 a negative binomial model was used. For other variables, individual participant data (IPD) meta-analyses were carried out. Emergency psychiatric hospital presentations decreased from 1239 in 2019 to 834 in 2020, incident rate ratio 0.67, 95% CI 0.62-0.73; p < 0.001. The proportion of children and adolescents presenting with self-harm increased from 50% in 2019 to 57% in 2020, odds ratio 1.33, 1.07-1.64; p = 0.009 but there was no difference in the proportion presenting with severe self-harm. Within the subpopulation presenting with self-harm the proportion of children and adolescents presenting with emotional disorders increased from 58 to 66%, odds ratio 1.58, 1.06-2.36; p = 0.025. The proportion of children and adolescents admitted to an observation ward also decreased from 13 to 9% in 2020, odds ratio 0.52, 0.28-0.96; p = 0.036. Service planners should consider that, during a lockdown, there are likely to be fewer emergency psychiatric presentations. Many children and adolescents with psychiatric emergencies might not receive any service. A focus on developing intensive community care services with outreach capabilities should be prioritised