University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Tourism and the state in Cuba: from the past to the future

Abstract: It has long been recognised that nature and extent of state intervention in tourism development closely reflects the prevailing political-economy and ideology within the destination state. This is certainly the case with Cuba which, since the 1959 revolution and despite the collapse of communism elsewhere, remains the world's only centrally-planned economy that boasts a significant international tourism sector. Tracing the development of tourism since 1959, this paper explores the relationship between the evolution of Cuba's political-economic structures and processes and their subsequent influence on the planning, control, development and ownership of tourism on the island. In particular, it considers the potential future of tourism in Cuba, challenging the widespread belief that, in a post-Castro era, the island's tourism sector faces a bright future. It concludes that, even with a potential move towards market reform, significant improvements will be required with respect to the quality, value and diversity of the island's tourism product. Copyright © 2008 John Wiley & Sons, Ltd.

Determinants of livelihood choices and artisanal entrepreneurship in Nigeria

Abstract: Purpose – This study provides fresh insights into rural artisanal activities in a developing world context. It highlights key determinants of the decision to engage in an artisanal business and the challenges that impact upon the growth of these activities. Design/methodology/approach – The study adopts a mix-method research approach to explore a rural setting where most respondents (81 percent) combine farm and non-farm livelihood activities. Quantitatively, a multi-nominal regression is used to examine the determinants of diversified artisanal livelihoods. It modelled the differences between farming livelihoods that have not diversified, compared to those also involved in the artisanal activity or wage employment and the intensity of participation. Findings –The findings show that nearly half of artisanal businesses (45.4 percent) comprise only the owners and no employee, while 54.6 percent employ 1-3 workers. Also, some artisanal ventures were more gender-specific than the gender-neutral activities. Other observations were in age (most artisans were under the age of 46 years) and vocational training (most were self-trained followed by a third receiving training only in specific areas such as technical works, building and construction and general trading apprenticeships). Research limitations – The study is based on a relatively small sample size of 306 business owners, which makes it difficult to generalise despite the persuasiveness of the observations made. Practical implications – First, the use of econometric methods enabled development of valid data sets (and various descriptive statistical and logit regression) to analyse determinants of the decision to engage in artisanal work, and the intensity of participation. Second, the ambiguity in categorising artisanal activities is unravelled. The study characterises the local artisanal sector and examines the intensity of participation. Without these, targeted support would remain elusive for practical and policy interventions. Originality/value – Artisanal activities constitute a high proportion of small businesses in the study area – with more than half (54.2%) of respondents being classified as artisans, yet it is an overlooked area of entrepreneurship. Highlighted here are both types of activities and challenges regarding better conceptualising our understanding of artisans and regarding this mostly unarticulated base of practice.

Computer vision based fall detection by a convolutional neural network

Abstract: In this work, we propose a novel computer vision based fall detection system, which could be applied for the health-care of the elderly people community. For a recorded video stream, background subtraction is firstly applied to extract the human body silhouette. Extracted silhouettes corresponding to daily activities are applied to construct a convolutional neural network, which is applied for classification of different classes of human postures (e.g., bend, stand, lie and sit) and detection of a fall event (i.e., lying posture is detected in the floor region). As far as we know, this work is the first attempt for the application of the convolutional neural network for the fall detection application. From a dataset of daily activities recorded from multiple people, we show that the proposed method both achieves higher postures classification results than the state-of-the-art classifiers and can successfully detect the fall event with a low false alarm rate.

Improving social game engagement on facebook through enhanced socio-contextual information

Abstract: In this paper we describe the results of a controlled study of a social game, Magpies, which was built on the Facebook Online Social Network (OSN) and enhanced with contextual social information in the form of a variety of social network indices. Through comparison with a concurrent control trial using an identical game without the enhanced social information, it was shown that the additional contextual data increased the frequency of social activity between players engaged in the game. Despite this increase in activity, there was little increase in growth of the player-base when compared to the control condition. These findings corroborate previous work that showed how socio-contextual enhancement can increase performance on task-driven games, whilst also suggesting that it can increase activity and engagement when provided as context for non task-driven game environments.