Towards (spatially) unbalanced development? A joint assessment of regional disparities in socioeconomic and territorial variables in Italy

The present study assesses disparities in the spatial distribution of three indicators evaluating respectively economic growth (per capita value added), sustainable development (a sustainable development index composing 99 individual variables) and the quality of the natural capital (Environmental Sensitive Area Index composing 14 individual variables) in Italy. The analysis was carried out on three different geographical domains (3 divisions (north, central and south Italy), 20 administrative regions and 103 provinces) with municipalities as the elementary spatial unit. While the distribution of the three indicators was coherent across space, the coefficient of variation of the three indicators, taken as a proxy of regional disparities, showed a contrasting spatial pattern. Domains with higher average values of the sustainable development index showed a lower variability among municipalities, indicating a less divided territorial context. By contrast, income and natural capital disparities are decoupled from the average level of the respective indexes. Multivariate analysis identifies a north-south gradient reflecting the divide between competitive and economically-disadvantaged regions in Italy. Results provide an informative base to implement sustainability policies in countries characterized by persistent socioeconomic disparitie

An evolutionary and functional assessment of regulatory network motifs.

BackgroundCellular functions are regulated by complex webs of interactions that might be schematically represented as networks. Two major examples are transcriptional regulatory networks, describing the interactions among transcription factors and their targets, and protein-protein interaction networks. Some patterns, dubbed motifs, have been found to be statistically over-represented when biological networks are compared to randomized versions thereof. Their function in vitro has been analyzed both experimentally and theoretically, but their functional role in vivo, that is, within the full network, and the resulting evolutionary pressures remain largely to be examined.ResultsWe investigated an integrated network of the yeast Saccharomyces cerevisiae comprising transcriptional and protein-protein interaction data. A comparative analysis was performed with respect to Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii and Yarrowia lipolytica, which belong to the same class of hemiascomycetes as S. cerevisiae but span a broad evolutionary range. Phylogenetic profiles of genes within different forms of the motifs show that they are not subject to any particular evolutionary pressure to preserve the corresponding interaction patterns. The functional role in vivo of the motifs was examined for those instances where enough biological information is available. In each case, the regulatory processes for the biological function under consideration were found to hinge on post-transcriptional regulatory mechanisms, rather than on the transcriptional regulation by network motifs.ConclusionThe overabundance of the network motifs does not have any immediate functional or evolutionary counterpart. A likely reason is that motifs within the networks are not isolated, that is, they strongly aggregate and have important edge and/or node sharing with the rest of the network

Advanced Cloud Privacy Threat Modeling

Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat modeling as a part of requirements engineering in secure software development provides a structured approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities in a system . This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in relation to processing sensitive data in cloud computing environments. It describes the modeling methodology that involved applying Method Engineering to specify characteristics of a cloud privacy threat modeling methodology, different steps in the proposed methodology and corresponding products. We believe that the extended methodology facilitates the application of a privacy-preserving cloud software development approach from requirements engineering to design

Some considerations on research dissemination with particular reference to the audience and the authorship of papers.

Original article can be found at : http://www.informaworld.com/This paper suggests that some refinements might need to be considered to current codes of ethics for dissemination of research. The growth of research in music education over the last decade is reviewed, with examples from new journals, conferences and professional associations. It is argued that nowadays researchers have to address a multidisciplinary number of audiences and this should be taken into account in the regulations for conferences and publications with the incorporation of guidelines for contributors to address their specific audience and to explain any previous dissemination. The authorship of papers is also considered, in particular issues arising from multiple authorship, as well as the research participants' contribution to the final report. Some of these issues are discussed with reference to studies focussed on a particular topic (creativity in music education) within the context of music education research, but it is acknowledged that the discussion also applies to other fields of the humanities and social sciences.Peer reviewe

Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201

Testing Schenkerian theory: an experiment on the perception of key distances

The lack of attention given to Schenkerian theory by empirical research in music is striking when compared to its status in music theory as a standard account of tonality. In this paper I advocate a different way of thinking of Schenkerian theory that can lead to empirically testable claims, and report on an experiment that shows how hypotheses derived from Schenker’s theories explain features of listener’s perception of key relationships. To be relevant to empirical research, Schenker’s theory must be treated as a collection of interrelated but independent theoretical claims rather than a comprehensive analytical method. These discrete theoretical claims can then lead to hypotheses that we can test through empirical methods. This makes it possible for Schenkerian theory improve our scientific understanding of how listeners understand tonal music. At the same time, it opens the possibility of challenging the usefulness of certain aspects of the theory. This paper exemplifies the empirical project with an experiment on the perception of key distance. The results show that two features of Schenkerian theory predict how listeners rate stimuli in terms of key distance. The first is the Schenkerian principle of “composing out” a harmony, and the second is the theory of “voice-leading prolongations.” In a regression analysis, both of these principles significantly improve upon a model of distance ratings based on change of scalar collection alone.Accepted manuscrip

Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

Automated Detection of Non-Relevant Posts on the Russian Imageboard "2ch": Importance of the Choice of Word Representations

This study considers the problem of automated detection of non-relevant posts on Web forums and discusses the approach of resolving this problem by approximation it with the task of detection of semantic relatedness between the given post and the opening post of the forum discussion thread. The approximated task could be resolved through learning the supervised classifier with a composed word embeddings of two posts. Considering that the success in this task could be quite sensitive to the choice of word representations, we propose a comparison of the performance of different word embedding models. We train 7 models (Word2Vec, Glove, Word2Vec-f, Wang2Vec, AdaGram, FastText, Swivel), evaluate embeddings produced by them on dataset of human judgements and compare their performance on the task of non-relevant posts detection. To make the comparison, we propose a dataset of semantic relatedness with posts from one of the most popular Russian Web forums, imageboard "2ch", which has challenging lexical and grammatical features.Comment: 6 pages, 1 figure, 1 table, main proceedings of AIST-2017 (Analysis of Images, Social Networks, and Texts