Helping citizens to locate political parties in the policy space: A dataset for the 2014 elections to the European Parliament

We present a dataset which contains the positions of 231 political parties across 28 countries and on 30 policy issues that were considered salient for the 2014 elections to the European Parliament. The party position estimates were originally used in a voter information tool which compared the policy preferences of citizens to those of political parties. The paper discusses the estimation method in the context of the literature on estimating party positions, outlines the coding instructions of the method, and stresses the value of the dataset for third-party users interested in studying political participation and representation

The state of corporate governance - experience from country assessments

Corporate governance deals with the ways in which the rights of outside suppliers of equity finance to corporations are protected and receive a fair return. Good practices reduce the risk of expropriation of outsiders by insiders and thus the cost of capital for issuers. The authors review the experience of the preparation of 15 corporate governance country assessments across five continents. The assessments have been prepared under the umbrella of the joint World Bank/IMF initiative of the"Reports on the Observance of Standards and Codes"(ROSCs). The assessments focus on the rights of shareholders, the equitable treatment of shareholders, the role of stakeholders, disclosure and transparency, and the duties of the board of listed companies, and use the OECD Principles of Corporate Governance as benchmark. The authors give an overview of the actual and potential contribution of the assessments to policy dialogue, diagnostic and strategic work, lending and non-lending operations, and technical assistance and capacity,and presents the unfinished agenda.Decentralization,Payment Systems&Infrastructure,Small Scale Enterprise,International Terrorism&Counterterrorism,Small and Medium Size Enterprises,Small Scale Enterprise,Private Participation in Infrastructure,Financial Crisis Management&Restructuring,National Governance,Microfinance

Entrepreneurial Orientation In Management Buy-Outs And The Contribution Of Venture Capital

This paper focuses on the development of entrepreneurial orientation (EO)after a management buy-out (MBO) and on the role played by venture capitalfirms in enhancing EO. It presents results of two exploratory case studiesof divisional buy-outs with regard to their EO and the areas where theventure capital firm (VC) has been of greatest help. We discuss theircontribution to elements of the EO of the buy-out firm. The key output isexpected to be a better understanding of the functioning and operations ofthe VC with regard to their contribution to the EO of the firm after an MBO.This will also benefit the management team that seeks venture capitalsupport to improve the firm?s economic performance by using its upsidepotential.governance;venture capital;entrepreneurial orientation;management buy-outs

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Sentiment Analysis in the Era of Web 2.0: Applications, Implementation Tools and Approaches for the Novice Researcher

Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice. Keywords:   Sentiment Analysis; Web 2.0; Applications; Tools; Novic

The effectiveness of virtual facilitation in supporting GDSS appropriation and structured group decision making

Since their introduction a quarter of a century ago, group decision support systems (GDSS) have evolved from applications designed primarily to support decision making for groups in face-to-face settings, to their growing use for “web conferencing,” online collaboration, and distributed group decision-making. Indeed, it is only recently that such groupware applications for conducting face-to-face, as well as “virtual meetings” among dispersed workgroups have achieved mainstream status, as evidenced by Microsoft’s ubiquitous advertising campaign promoting its “Live Meeting” electronic meeting systems (EMS) software. As these applications become more widely adopted, issues relating to their effective utilization are becoming increasingly relevant. This research addresses an area of growing interest in the study of group decision support systems, and one which holds promise for improving the effective utilization of advanced information technologies in general: the feasibility of using virtual facilitation (system-directed multi-modal user support) for supporting the GDSS appropriation process and for improving structured group decision-making efficiency and effectiveness. A multi-modal application for automating the GDSS facilitation process is used to compare conventional GDSS-supported groups with groups using virtual facilitation, as well as groups interacting without computerized decision-making support. A hidden-profile task designed to compare GDSS appropriation levels, user satisfaction, and decision-making efficiency and effectiveness is utilized in an experiment employing auditors, accountants, and IT security professionals as participants. The results of the experiment are analyzed and possible directions for future research efforts are discussed

Trade in Financial Services--Has the IMF Been Involved Constructively?

This paper considers the key policy issues related to liberalization of trade in financial services that the IMF should be concerned with, and the role the IMF has played in advising on policies related to trade in financial services in its bilateral and multilateral surveillance and conditionality attached to lending programs. IMF staff were generally aware of the literature and country experiences showing the benefits of financial liberalization. But Fund advice in support of liberalization can be best interpreted to be in support of country unilateral policy actions and the dynamics of the WTO accession process.financial liberalization, foreign banks, GATS, IMF

Deep Learning Models For Biomedical Data Analysis

The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis. During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset. Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics. In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts. Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning