Suchergebnisse

Title Page -- Copyright Page -- Book Series -- Dedication -- Table of Contents -- Detailed Table of Contents -- Preface -- Chapter 1: The Economics of Resilient Global Supply Chains in the Post-COVID-19 Era -- Chapter 2: Internationalization, Sustainability, and Capacity-based Motives of Foreign Direct Investment: A Conceptual Framework -- Chapter 3: Competitiveness and Value Creation in the "New Normal" -- Chapter 4: Tracing the Path of International Business Research -- Chapter 5: Research on International Business and the COVID-19 Pandemic -- Chapter 6: The Future of Mobility

Bosnians show little faith in their state-level institutions, and with good reason, as the country ranks poorly on measures of corruption, regulatory quality, and government efficacy. However, the Central Bank of Bosnia and Herzegovina (CBBH) is a notable exception. In a country where the state is still often paralyzed by ethnically aligned obstructionism, the Central Bank is widely lauded as an effective state-level institution, and it backstops, and oversees, a stable, trusted, pan-Bosnian banking system. To explain the Bank's success, we draw on rational choice and institutionalist literature to propose and test a theory of the "CBBH as referee" in its three main functional areas: currency board maintenance, payment system operation, and the coordination of banking supervision. We find evidence for this mechanism in the context of currency board operations, and we also document a Haas-ian neofunctional process, in which the Bank has interacted cyclically with foreign banks to unintentionally de-ethnicize the Bosnian financial sector. Initial Bank reforms facilitated foreign banks' market entry, and their subsequent lack of interest in hewing to prior ethnic divisions served to cement a unified Bosnian financial space. We substantiate this argument with data drawn from interactive interviews with Bosnian policymakers, financial sector experts, and banking sector participants, and in so doing, also show how the commercial behavior of transnational actors can have unexpected policy impacts.

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area. ; This work was supported in part by the Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS) Project: Reduccion de Emisiones Vehiculares Mediante el Modelado y Gestion Optima de Trafico en Areas Metropolitanas, Caso Medellín, Area Metropolitana del Valle de Aburra, under Grant 111874558167 and Grant CT 049-2017, in part by the Universidad Nacional de Colombia under Project HERMES 25374, and in part by NVIDIA Corporation for the donation of GPUs. The work of Sergio A. Velastín was supported in part by the Universidad Carlos III de Madrid, in part by the European Union's Seventh Framework Programme for Research, Technological Development and Demonstration under Grant 600371, in part by the El Ministerio de Economía y Competitividad under Grant COFUND2013-51509, and in part by the Banco Santander.

This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18) ; This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP. ; S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors.

This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017) ; This paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic. ; S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors.

This paper has been presented at 8th International Conference of Pattern Recognition Systems. ; This paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. The extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. The obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification. ; S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander