The increasing use of the Internet for service delivery has paralleled an increase of e-service users' privacy concerns as technology offers ample opportunities for organizations to store, process, ...and exploit personal data. This may reduce individuals' perceived ability to control their personal information and increase their perceived privacy risk. A systematic understanding of individuals' privacy concerns is important as negative user perceptions are a challenge to service providers' reputation and may hamper service delivery processes as they influence users' trust and willingness to disclose personal information. This study develops and validates a model that examines the effect of organizational privacy assurances on individual privacy concerns, privacy control and risk perceptions, trust beliefs and non-self-disclosure behavior. Drawing on a survey to 547 users of different types of e-services – e-government, e-commerce and social networking – in Rwanda, and working within the framework of exploratory analysis, this study uses partial least square-structural equation modeling to validate the overall model and the proposed hypotheses. The findings show that perceptions of privacy risks and privacy control are antecedents of e-service users' privacy concerns, trust and non-self-disclosure behavior. They further show that the perceived effectiveness of privacy policy and perceived effectiveness of self-regulations influence both perceptions of privacy risks and control and their consequences; users' privacy concerns, trust and non-self-disclosure behavior. The hypotheses are supported differently across the three types of e-services, which means that privacy is specific to context and situation. The study shows that the effect of privacy assurances on trust is different in e-government services than in other services which suggest that trust in e-government may be more complex and different in nature than in other contexts. The findings serve to enhance a theoretical understanding of organizational privacy assurances and individual privacy concerns, trust and self-disclosure behavior. They also have implications for e-service providers and users as well as for regulatory bodies and e-services designers.
•Perceptions of privacy risk-control influence privacy concerns, trust and self-disclosure behavior.•Privacy policy influences perceptions of privacy risk and/or control, privacy concerns, trust and self-disclosure behavior.•Organizational privacy self-regulations influence users’ privacy concerns, trust and non-self-disclosure behavior.•Organization's strategies in executing privacy policies may reflect how effective the organization is in protecting personal information.•Privacy and trust in e-government are influenced by the level of trust users have in the government and its organizations.
Holm J. R. and Østergaard C. R. Regional employment growth, shocks and regional industrial resilience: a quantitative analysis of the Danish ICT sector, Regional Studies. The resilience of regional ...industries to economic shocks has gained a lot of attention in evolutionary economic geography recently. This paper uses a novel quantitative approach to investigate the regional industrial resilience of the Danish information and communication technology (ICT) sector to the shock following the burst of the dot.com bubble. It is shown that regions characterized by small and young ICT service companies were more adaptable and grew more than others, while diversity and urbanization increased the sensitivity to the business cycle after the shock. Different types of resilient regions are found: adaptively resilient, rigidly resilient, entrepreneurially resilient and non-resilient regions.
We investigate whether a larger CEO employment network provides access to information that improves firms' earnings forecasts and find a significantly positive relation between CEO employment network ...size and management earnings forecast accuracy. Our results suggest that firms use information obtained from CEO contacts to increase the accuracy of their earnings forecasts. Our conclusion is further supported by evidence of positive associations between CEO employment network size and the likelihood, frequency, and precision of management earnings forecasts. We also find that CEO employment network size is positively related to analysts' reactions to the forecast news and the accuracy of management earnings forecasts relative to analyst forecasts. Overall, our results are consistent with a larger CEO employment network generating external information that increases the accuracy of firms' earnings forecasts.
Résumé
L’agrégation d’information pour créer les attentes de résultats : données relatives aux réseaux de PDG et à l’exactitude des prévisions de la direction
Les auteurs examinent si un plus vaste réseau de PDG fournit l’accès à des informations améliorant les prévisions de résultats des entreprises. Ils constatent une relation positive significative entre l’étendue du réseau professionnel des PDG et l’exactitude des prévisions de résultats de la direction. Les résultats tendent à démontrer que les entreprises utilisent les informations obtenues auprès des contacts des PDG pour augmenter le degré d’exactitude de leurs prévisions de résultats. Cette conclusion est aussi étayée par des associations positives entre l’étendue du réseau professionnel des PDG et la probabilité, la fréquence et l’exactitude des prévisions de résultats de la direction. Les auteurs constatent non moins que l’étendue du réseau professionnel des PDG est positivement reliée aux réactions des analystes face aux annonces de prévisions, ainsi qu’à l’exactitude des prévisions de résultats de la direction par rapport aux prévisions des analystes. Globalement, les résultats sont cohérents avec le fait qu’un plus vaste réseau professionnel de PDG fournit des informations externes qui augmentent le degré d’exactitude des prévisions de résultats de la direction.
Dimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such ...techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.
In this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions ...separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new publicly available and fully onset annotated database of monophonic recordings which is balanced in terms of included instruments and onset samples per instrument, while it contains different performance styles. Results show that the accuracy of onset detection depends on the type of instruments as well as on the style of performance. Combining the information contained in the three dimensions by means of a fusion at decision level leads to an improvement of onset detection by about 8% in terms of F-measure, compared to the best single dimension.
By 2002, all but a handful of countries were connected to the Internet. The intertwining of the Internet and the globalization of finance, corporate governance, and trade raises questions about ...national models of technology development and property rights. The sudden ability of hundreds of millions of users to gain access to a global communication infrastructure spurred the creation of new firms and economic opportunities. The Internet challenged existing institutions and powerful interests: Technology was global, but its economic and business development was molded in the context of prevailing national institutions. Comparing the experiences of seven countries—France, Germany, India, Japan, Sweden, South Korea, and the United States—this book analyzes the rise of the Internet and its impact on changing national institutions. Each country chapter describes how the Internet developed, evaluates the extent to which the Silicon Valley model was adopted, and suggests why certain sectors and technologies developed faster than others. The book also analyzes specific Internet sectors and regulations across countries. It shows that the Internet's effects are more evolutionary than revolutionary. At the same time, the impact of broad cultural change on entrepreneurial aspirations is clearly visible in certain nations, especially India and Sweden.
t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. ...Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination ...of similarity and dissimilarity measures: Mutual Information (MI), Disjoint Information (DI) and Local Dissimilarity Map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using respectively the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k -means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of Recall, Precision and F-measure.
Scale Transform in Rhythmic Similarity of Music Holzapfel, André; Stylianou, Yannis
IEEE transactions on audio, speech, and language processing,
2011-Jan., 2011, 2011-01-00, 20110101, Letnik:
19, Številka:
1
Journal Article
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As a special case of the Mellin transform, the scale transform has been applied in various signal processing areas, in order to get a signal description that is invariant to scale changes. In this ...paper, the scale transform is applied to autocorrelation sequences derived from music signals. It is shown that two such sequences, when derived from similar rhythms with different tempo, differ mainly by a scaling factor. By using the scale transform, the proposed descriptors are robust to tempo changes, and are specially suited for the comparison of pieces with different tempi but similar rhythm. As music with such characteristics is widely encountered in traditional forms of music, the performance of the descriptors in a classification task of Greek traditional dances and Turkish traditional songs is evaluated. On these datasets accuracies compared to non-tempo robust approaches are improved by more than 20%, while on a dataset of Western music the achieved accuracy improves compared to previously presented results.
In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called ..."stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.
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