Measuring hierarchy in elite networks of tourist destinations - A look beyond the measures of power and centrality

Tourist destination communities are often assumed as being represented by a loose and random combination of elites of individuals. So far, no research on the hierarchy of the informal organization of destination elites has been carried out. This paper challenges the common assumption that elites of community structured destinations are an unstructured group of individuals. The research analyzes six reputational elite networks in tourist destination communities in Europe. The application of the four graph theoretical dimensions (GTD), developed by Krackhardt (1994) indicates that there are strong hierarchical patterns. As a consequence, the identification of hierarchies, chiefs and lines of command increases effective destination management and development

Exploring the conditions for the effectiveness of DMO board of directors

This paper explores the conditions for DMO board effectiveness in destination management organizations (DMOs). First, three different measures for board effectiveness are identified: (1) good teamwork, (2) capability of realizing projects and initiatives, and (3) board strengthens the DMOs position in the destination. Second, a series of conditions as independent variables were selectively built from extant literature: (1) board size, (2) arguments, (3) dealing with crises, (4) mutual respect, (5) constructive discussions, and (6) taking the job seriously. Multiple regression results from 61 board members of 36 Swiss DMOs revealed that different conditions affect board effectiveness, depending on how the latter is identified. The paper concludes with indications for further research

Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

Identity verification is an increasingly important process in our daily lives, and biometric recognition is a natural solution to the authentication problem. One of the most important research directions in the field of biometrics is the characterization of novel biometric traits that can be used in conjunction with other traits, to limit their shortcomings or to enhance their performance. The aim of this work is to introduce the reader to the usage of heart sounds for biometric recognition, describing the strengths and the weaknesses of this novel trait and analyzing in detail the methods developed so far by different research groups and their performance.Comment: 18 pages, chapter to be published in the book "Biometrics / Book 1", ISBN 978-953-307-618-8, by InTec

Variable geometry for DMOs A principle for effective business development in tourist destinations

This paper proposes a new perspective of the role of DMOs in Europe, particularly in Switzerland, by bringing forward the concept of variable geometry with overlapping strategic business fields (sbf’s). In the past, local and regional DMOs have served the needs of the enterprises and the community from a purely territorial perspective. We suggest tracing tourist destination boundaries from the viewpoint of activities and attractions, visited by a strategically relevant and rather similar group of tourists. For three DMOs, we have carried out a series of workshops with the local tourist elite to identify the current and future strategic business fields (sbf’s). The resulting boundaries of the sbf’s were the basis for discussing (1) the future geographic area of responsibility for the DMOs, (2) the role of the DMO for the sbf’s, (3) future cooperative initiatives with neighboring DMOs, (4) alternative and specific approaches of financing and governing the DMO

SOLO TRAVEL - EXPLORATIVE INSIGHTS FROM A MATURE MARKET (SWITZERLAND)

This study examines solo travel, and offers a conceptual framework of solo travelers, a profile of these types of travelers (by socio-demographic characteristics), and a profile of travels (by specific descriptors). The data for this study emerged from a comprehensive survey of Swiss travel behavior conducted 2004 by the University of St. Gallen (Switzerland). The conceptual model proposes an a priori segmentation of four types of solo travel, delineated on the combination of the departure status (a single, one-person household, compared to a collective, multi-persons household) and arrival status (solo travel, compared to group travel), thus creating a two-by-two matrix with four segments overall. The results of the profiling reveal significant differences between the solo travel groups, as well as towards a control group incorporating all other travel. They include income, profession, and age, as well as familiarity with the destination, choice of type of accommodation, expenditures and various types of trips. However, no significant differences can be reported with regard to the choice of destination

Adaptive V/UV Speech Detection Based on Characterization of Background Noise

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments

SMILE: Smart Monitoring IoT Learning Ecosystem

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior

A Smart UAV-Femtocell Data Sensing System for Post-Earthquake Localization of People

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