A study into user acceptance of new technology: British Airways ground transport department Heathrow Terminal 5

This project was conducted with the help and encouragement of British Airways (BA) management. It was carried out at Heathrow Airport, Terminal 5 (T5) where a new Resource Management System (RMS) that is based upon Internet Protocol (IP) has been implemented. RMS has replaced traditional pen and paper and radio systems for allocating work tasks to 4,000 airport operational staff. This research project studied one application of the RMS system; the allocation of tasks to the coach drivers in the Ground Transport Services (GTS) department. The user acceptance of the RMS system by the drivers was evaluated. In the previous 20 years, user acceptance theories have been developed which have shown that increased user acceptance of new Information Technology (IT) projects significantly reduces costs and improves efficiency (Davis, 1980). The most comprehensive theory is that of Sun and Zhang (2006) who identify critical factors regarding individual user acceptance (gender, age, experience, cultural background and intellectual capability). This research project used a case study methodology: three days were spent airside at T5 observing and interviewing a sample of drivers. The project research question was: 'Can the degree of RMS acceptance by the GTS end-users be determined by factors identified in user acceptance theories?' Essentially, it was not possible to answer this question because of two reasons. First there was little difference in level of user acceptance; it was very high for all users. Second there was also very little difference in the sample and population. The drivers were all male, over 90% between 42 and 65 years of age, with similar levels of experience regarding the RMS technology and computers in general. In addition, it was not possible to measure any difference between the intellectual capabilities of the participants. A difference in the cultural background was identified; there were two ethnic groups, Asian and Caucasian. However, detailed analysis of the responses to the questionnaire demonstrated that there was no evidence of different levels of user acceptance of these groups. Recommendations to improve the testing of user acceptance theories are included in this report

Hybrid 2D and 3D face verification

Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%

An analysis and evaluation of the relationship between civilian-Navy interest groups and the Navy's public information program.

Thesis (M.S.)--Boston Universit

The Foodservice Industry\u27s Social Responsibility Regarding the Obesity Epidemic, Part II: Incorporating Strategic Corporate Social Responsibility into Foodservice Operations

Just as all types of business firms are now expected to go beyond their profit-oriented activities in boosting the well-being of the community, so, too, is corporate social responsibility (CSR) expected from foodservice firms. The significance of the obesity epidemic, combined with the foodservice industry\u27s role in the development of this epidemic, suggests that the industry has an ethical responsibility to implement CSR activities that will help reduce obesity, particularly among children. CSR should be seen as an efficient management strategy through which a firm voluntarily integrates social and environmental concerns into its business operations and its interactions with stakeholders. Although costs are associated with CSR initiatives, benefits accrue to the firm. Decisions regarding alternative CSR activities should be based on a cost-benefit analysis and calculation of the present value of the revenue stream that can be identified as resulting from the specific CSR activities. CSR initiatives should be viewed as long-term investments that will enhance the firms’ value. Key areas for foodservice firms\u27 CSR activities include marketing practices, particularly practices impacting advertising to children and marketing that will enhance the firms’ visibility; portion-size modification; new-product development; and consistent nutrition labeling on menus

The Foodservice Industry\u27s Social Responsibility Regarding the Obesity Epidemic, Part I: Parallels to Other Public Health Issues and Potential Legal Implications

The incidence of obesity among both children and adults in the United States (U.S.) has reached epidemic level. If not quickly curtailed, it represents significant long-term costs to all facets of the U.S. economy. The foodservice industry has contributed to this major public health issue. Parallels between the obesity epidemic and the public health issues of smoking and foodborne illnesses could influence the foodservice industry\u27s response to obesity concerns. Of particular note are the parallels between the liability litigation and legislative actions related to smoking and the tobacco industry. This industry has a history of taking socially responsible actions regarding public health issues. There is potential for costs to the foodservice industry from similar anti-obesity litigation and legislation if the industry does not once again assume social responsibility relative to the current obesity crisis and is not proactive in efforts to combat obesit

The Social responsibility of the foodservice industry: The need for action regarding the obesity crisis

Abstract: Obesity has reached epidemic proportions. Costs associated with obesity pose a severe threat to the U.S. economy. Evidence indicates the foodservice industry has had a major role contributing to the obesity crisis; thus it is argued that the industry has an ethical and social responsibility to now aggressively adopt socially responsible actions that will help alleviate the increasing incidence of obesity. Such actions might include innovative advertising initiatives, modification of portion sizes, and nutrition labeling so that consumers can make healthful food selections. Even though such actions might result in short-term profit losses, socially responsible actions have the potential to yield long-term economic value for the foodservice industry

Development Of Parameters For Incorporating Security Studies Into Hospitality Education

Worldwide concern with security issues and disaster management within the hospitality industry is creating opportunities for hospitality management education. This study investigated essential curriculum content, student characteristics, instructional modalities, and program competencies potentially important for hospitality industry based security studies programs. Findings indicate that such programs should have non-traditional, flexible structure as initial students will likely hold middle and upper management industry positions. Executive leadership, agencies involved in emergency preparedness, and integration of systems focused on security and disaster management, both as program content and resulting competencies are important program components

Symplectic structures on right-angled Artin groups: between the mapping class group and the symplectic group

We define a family of groups that include the mapping class group of a genus g surface with one boundary component and the integral symplectic group Sp(2g,Z). We then prove that these groups are finitely generated. These groups, which we call mapping class groups over graphs, are indexed over labeled simplicial graphs with 2g vertices. The mapping class group over the graph Gamma is defined to be a subgroup of the automorphism group of the right-angled Artin group A_Gamma of Gamma. We also prove that the kernel of the map Aut A_Gamma to Aut H_1(A_Gamma) is finitely generated, generalizing a theorem of Magnus.Comment: 45 page

Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.Comment: 5 pages, three figure