The Year 2000 Problem
Computers' Year-2000 problem (Y2K), whereby some computer software won't recognize any digits as representing years in the 21st century, may well strike hoteliers particularly hard due to hotels' reliance on date-sensitive data (room reservations, for example). Moreover, unless hotels' software is updated, Y2K will manifest itself sooner among hotels than in many other industries, as hotels begin to book rooms in the year 2000. Hoteliers can (1) choose to do nothing (i.e., wait and see what actually happens), (2) debug their existing computer applications, or (3) purchase and install all new systems software. Some methods to remove the Y2K problem include these five techniques: size expansion, data encoding, date shift, soft windows, and hard windows. Replacing existing application software with new, Y2K-compliant computer software will resolve the problem. That approach, however, means that all of a hotel's related computer-program requirements be accounted for—which is an enormous undertaking. Moreover, new computer systems are costly, and “off the shelf” business software rarely is of much help to hoteliers. As a result, hotels' computer systems and software most often require extensive customization. Time is short; hoteliers don't have until 2000 to solve their Y2K problems, since a hotel's existing reservation system may not be able to recognize reservations for the year 2000 (“00”). In practical terms, the time required for software development and testing is substantial. Therefore, hotels need to handle the Y2K issue immediately.
Attitudes to computers of managers in the hospitality industry
Abstract Service industries are an increasingly important part of most developed economies as employers of displaced manufacturing labour. Computers are currently used in fewer than 30% of British service organizations and it has been suggested that this low level of utilization is attributable to cost. A study of managers in the largest service industry in the United Kingdom, the hospitality industry, shows that it is not cost attitudes but management attitudes which are the major inhibitory factor. Furthermore, these findings are consistent with other studies of attitudes to computers of senior managers in British industry. A comparison with a study of the attitudes of some American professional persons suggests that this is not necessarily attributable simply to educational level. In such circumstances it seems probable that penetration levels will increase slowly and that creative applications of computers in service industries, which may reduce their propensity to absorb labour, are not imminent.
ImageCLEF 2017: ImageCLEF Tuberculosis Task - the SGEast Submission
In this paper, we describe our methodologies in an attempt to improve the diagnosis accuracy of drug resistant tuberculosis and also of identifying the type of tuberculosis present in the patient, as per the requirements of the ImageCLEF tuberculosis task of ImageCLEF 2017. Firstly, we employed the concept of Convolutional Neural Networks (CNN), which can be used to identify useful features in the Computerized Tomography (CT) scans that were provided in the competition and perform the classification based on them. Secondly, Recurrent Neural Networks (RNN) were used on top of CNNs by utilizing CNNs as a feature extractor and the RNNs as a classifier. In order for our model to produce acceptable results, proper preprocessing was performed prior to providing the input data to the models for training, such as image slicing and data augmentation. Our methods were able to reach rank 4 and 5 for the subtask involving drug-resistant tuberculosis and rank 1 and 2 for the other subtask of identifying the tuberculosis type, according to the evaluation performed by ImageCLEF.