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Entrepreneurship education serves a conduit for new venture creation as it provides the knowledge and skills needed to increase the self-efficacy of individuals to start and run new businesses and to grow existing ones. This study, therefore, sought to assess the relationship between the approaches to the teaching of entrepreneur-ship and entrepreneurial intention on a cohort of 292 respondents consisting of students who have studied entrepreneurship in three selected Universities. A structured questionnaire was used to obtain data randomly from students. The canonical correlation results indicate that education for and through entrepreneurship is the best approach to promoting entrepreneurial intensity among University students, if the aim of teaching entrepreneur-ship is to promote start-up activities. The findings provide valuable insights for institutions of higher learning and policy makers in Ghana with respect to the appropriate methodologies to be adopted in the teaching of entrepreneurship in our universities.
A plethora of architectural patterns and elements for developing service-oriented applications can be gathered from the state-of-the-art. Most of these approaches are merely applicable for single-tenant applications. However, less methodical support is provided for scenarios, in which multiple different tenants with varying requirements access the same application stack concurrently. In order to fill this gap, both novel and existing architectural patterns, architectural elements, as well as fundamental design decisions must be considered and integrated into a framework that leverages the devel- opment of multi-tenant application. This paper addresses this demand and presents the SOAdapt framework. It promotes the development of adaptable multi-tenant applications based on a service-oriented architecture that is capable of incorporating specific requirements of new tenants in a flexible manner.
In today’s business, culture plays a vital role or to a high degree influences the attitude, perception and decision making process of an individual. Culture is an unavoidable state of rules and regulations that defines people’s daily life in a particular environment or society. There are plenty examples of business failures or stagnation or failure of joint ventures, on account of the management's inability to recognize cross-cultural challenges and tackle them appropriately.
Recent work in image captioning and scene-segmentation has shown significant results in the context of scene-understanding. However, most of these developments have not been extrapolated to research areas such as robotics. In this work we review the current state-ofthe- art models, datasets and metrics in image captioning and scenesegmentation. We introduce an anomaly detection dataset for the purpose of robotic applications, and we present a deep learning architecture that describes and classifies anomalous situations. We report a METEOR score of 16.2 and a classification accuracy of 97 %.
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.
The non-farm sector is critical for the socio-economic development of Ghana especially the rural poor. Literature suggest that people engage in non-farm enterprises as a way out of poverty or a survival strategy, perhaps as a substitute for the landless. This paper analyses the determinants of individual participation in non-farm enterprises and the intensity of participation. The paper uses EGC/ISSER Socio-Economic Panel Survey data collected in 2009. The paper estimated the determinants of participation using a probit model and then estimated the intensity of participation using a truncated regression model. The results indicate that majority of women (about 73%) are engaged in non-farm enterprises in rural Ghana. The study found that females tended to participate more in non-farm self-employment and are less likely to participate in non-farm wage employment. The results further showed that individual characteristics such as the gender of the individual, being head of a household, being the spouse of a household head, having formal education, age of the individual, having access to credit, possessing a mobile phone, per capita landing holding and ownership of livestock influenced the participation of individuals in self-and wage employment. Results from truncated regression model for self-employed enterprises showed that having access to mobile phones, owning more livestock and electricity are important in determining the intensity of participation in self-employed enterprises. For wage-employment, being a household head, spouse of household head, having access to mobile phone and owning more livestock increased the number of days working on wage employment. Education is relevant for employment in the non-farm sector especially wage-employment. Government should play a lead role in making formal education accessible to the rural people. Deliberate policies should focus on addressing critical factors such as access to credit, mobile phone, electricity and education which are relevant for increasing participation intensity in rural enterprises.
Diese Arbeit beschäftigt sich mit der Effizienz der Seitenkanal-Kryptanalyse. In Teil II dieser Arbeit demonstrieren wir, wie die Laufzeit der wichtigsten Analysewerkzeuge mit Hilfe der CUDA Plattform erheblich gesteigert werden kann. Zweitens untersuchen wir neue Ansätze der profilierenden Seitenkanal-Kryptanalyse. Der Forschungszweig des maschinellen Lernens kann für deutliche Verbesserungen adaptiert werden, wurde jedoch wenig dahingehend untersucht. In Teil III dieser Arbeit präsentieren wir zwei neue Methoden, die einige Gemeinsamkeiten jedoch auch einige Unterschiede aufbieten, sodass sich Prüfergebnisse in einem vollständigeren Bild zeigen lassen. Darüber hinaus schlagen wir in Teil IV eine Seitenkanalanwendung zum Schutz geistigen Eigentums (IP) vor. In Teil V beschäftigen wir uns tiefergehend mit praktischer Seitenkanal-Kryptanalyse, indem wir Attacken auf einen Sicherheitsmikrokontroller durchführen, der Anwendung in einer, in Deutschland weit verbreiteten, EC Karte findet.
In Zeiten deutlicher Auswirkungen des Klimawandels und gravierender sozialer Missstände in Teilen der Welt drängen sich dringender denn je für die Weltgemeinschaft die Fragen nach Handlungsoptionen zum Erreichen nachhaltiger Entwicklung auf. Dementsprechend ist auch der öffentliche Sektor gefragt seinen Beitrag zu leisten. Als ein wirkungsvoller Beitrag ist die Etablierung nachhaltiger öffentlicher Beschaffung zu nennen. Um diesen Beitrag adäquat umzusetzen, müssen unter anderem die Richtlinien und Gesetze, die die öffentliche Beschaffung regeln, ausreichend Umsetzungsmöglichkeiten dafür bieten. Mit der jüngsten europäischen Vergaberechtsreform und der daraufhin verabschiedeten deutschen Anpassung der Gesetze ist der Grundstein dafür gelegt worden. Welche erfolgsversprechenden Umsetzungsmöglichkeiten diese rechtliche Basis nun den Kommunen in Deutschland als Hauptakteuren öffentlicher Beschaffung bietet und welche weiteren Erfolgsfaktoren für die Umsetzung nachhaltiger öffentlicher Beschaffung auf kommunaler Ebene entscheidend sind, ist Thema dieses Arbeitspapiers. Ein Praxischeck der Erfolgsfaktoren wird am Beispiel der Stadt Bonn, stellvertretend für die kommunale Ebene in NRW vorgenommen.