Abstract
Mobile applications are proliferating in all business domains. In the tourism sector, that is information intensive, and a global phenomenon, the development of efficient solutions for deliver information and services for “information-starvingâ€� tourists is a challenge, and opportunity but mostly a necessity of modern competitive touristic destinations. This paper briefly discusses the role that mobile devices applications have in the support of information and services of Cabo Verde visiting tourists and presents the design and development of a prototype application Android-based that enable important information and services for all Cabo Verde tourists that need to know more about Cabo Verde islands and their important information and services, manly related with their culture, gastronomy, events and hospitality services. © 2019 International Business Information Management Association (IBIMA).

Abstract

Abstract
Esophageal cancer is a disease with a high prevalence that can be evaluated by a variety of imaging modalities, including endoscopy, computed tomography, and positron emission tomography. Computer-aided techniques could provide a valuable help in the analysis of these images, decreasing the medical workflow time and human errors. The goal of this paper is to review the existing literature on the application of computer vision techniques in the domain of esophageal cancer. After an initial phase where a set of keywords was chosen, the selected terms were used to retrieve papers from four well-known databases: Web of Science, Scopus, PubMed, and Springer. The results were scanned by merging identical entries, and eliminating the out of scope works, resulting in 47 selected papers. These were organized according to the image modality. Major results were then summarized and compared, and main shortcomings were identified. It could be concluded that, even though the scientific community has already paid attention to the esophageal cancer problem, there are still several open issues. Two majorfindings of this review are the nonexistence of works on MRI data and the under-exploration of recent techniques using deep learning strategies, showing the need for further investigation.

Abstract

Abstract
COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. These satellites can obtain ascending and descending synthetic aperture radar (SAR) images with very similar periods for use in the three-dimensional (3D) inversion of glacier velocities. In this paper, based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. The results show that applying the offset tracking technique to COSMO-SkyMed images can be used to derive the true 3D velocity of a glacier's surface. During the two periods, the Yiga Glacier had a stable velocity, and the maximum surface velocity, 2.4 m/d, was observed in the middle portion of the glacier, which corresponds to the location of the steepest slope.

Abstract
Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness in the developed world. With the increasing number of diabetic patients there is a growing need of an automated system for DR detection. We propose EyeWeS, a method that not only detects DR in eye fundus images but also pinpoints the regions of the image that contain lesions, while being trained with image labels only. We show that it is possible to convert any pre-trained convolutional neural network into a weakly-supervised model while increasing their performance and efficiency. EyeWeS improved the results of Inception V3 from 94:9% Area Under the Receiver Operating Curve (AUC) to 95:8% AUC while maintaining only approximately 5% of the Inception V3's number of parameters. The same model is able to achieve 97:1% AUC in a cross-dataset experiment.