Human resources for Big Data professions: A systematic classification of job roles and required skill sets

The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.     

A unified representation of web logs for mining applications

The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information that, while hard to extract using standard IR techniques, can be useful in Web mining tasks. In the last few years, several approaches have been proposed to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search engines. However, the solutions proposed in the literature only partially represent the information available in the Web logs. In this paper, we propose to use a richer data structure, which is able to preserve most of the information available in the Web logs. This data structure consists of three groups of entities: users, documents and queries, which are connected in a network of relations. Query refinements correspond to separate transitions between the corresponding query nodes in the graph, while users are linked to the queries they have issued and to the documents they have selected. The classical query/document transitions, which connect a query to the documents selected by the users’ in the returned result page, are also considered. The resulting data structure is a complete representation of the collective search activity performed by the users of a search engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used in several Web mining tasks like discovering semantically relevant query suggestions and Web page categorization by topic.     

Whole body vibration: unsupervised training or combined with a supervised multi-purpose exercise for fitness?

Abstract

The aim of the study was to compare the effect of an unsupervised whole body vibration (WBV) training and two different supervised multi-purpose exercise programmes, with and without WBV, on body composition, functional fitness and self-reported well-being in middle-aged adults. Fifty-four healthy participants (age 48.6 ± 6.7 years) were randomly assigned to a vibration group (VG), a multi-purpose exercise group (MG) and a multi-purpose exercise with vibration group (VMG) and trained 3 days a week for 4 months. VG performed a standardised unsupervised WBV protocol, MG a supervised multi-purpose exercise and VMG a multi-purpose exercise including vibration. After training, drop out was significantly higher in VG group (P = 0.016) when compared to VMG group. In both MG and VMG, body composition, sit-up, push-up, sit and reach, agility test, hopping test and self-reported general health significantly improved (P < 0.05). No additive effects were generated by the vibration stimulus. Percentage of body fat and agility test in VG had a significant opposite trend compared to VMG group (P < 0.05). In summary, an unsupervised WBV training should not be chosen for training protocol. However, positive effects on physical fitness and the best results in adherence could be achieved integrating WBV practice into a multi-purpose exercise training.     

Innovative uses of 3D digital technologies to assist the restoration of a fragmented terracotta statue

This paper describes how some innovative methodologies have been designed and employed to support the restoration of the Madonna of Pietranico, a terracotta statue severely damaged in the 2009 earthquake. The statue, fragmented in many pieces, has undergone a complex restoration performed by a multidisciplinary working group. The contribution of digital technologies was planned from the very beginning, since the complexity of this restoration originated the design of innovative procedures for managing the reassembly and restoration process. The Madonna test bed was therefore an example of how technology innovation could be pushed by clear application needs. A first important contribution was the study of the recombination hypothesis of the fragments. This initial phase was performed on digitized 3D models of the statue fragments, with the aim of reducing fragments manipulation, preventing further damages and increasing the capabilities to rehearse and evaluate different reassembly options. The accuracy of the 3D scanned models and the new recombination procedure introduced in this paper allowed to manage this phase in the digital domain with successful results. The digital 3D models were also used to design and produce an innovative supporting structure, constructed with a rapid prototyping device. Another important contribution concerned the study and virtual restoration of the polychrome decoration of the statue; our aim was to reproduce and restore in the virtual 3D domain the very complex original polychrome decoration, on the base of the remaining traces. Consequently, new virtual painting functionalities have been designed on the MeshLab platform (an open-source tool for 3D models visualization and manipulation) for reproducing pictorial decorations over digital 3D models and have been assessed on this specific test bed. This allowed us also to investigate the complexity of the virtual repainting process and to identify further technology enhancements. Finally, computer graphics technologies have been also used to produce a video that tells the story of the restoration.     

Theoretical aspects of physical-chemical parameters for the correct conservation of mummies on display in museums and preserved in storage rooms

This study is aimed at evaluating physical and chemical parameters which are considered as the most appropriate for the long-term preservation of mummies, distinguishing between those which are on display in museums and those which are preserved in storage rooms. The objective of this study is also to compare and discuss differences among the most widely known guidelines and norms of the field and to highlight the single phenomena which affect the decay of this kind of cultural asset.     

Teachers’ preferences for educational planning: Dynamic testing,teaching’ experience and teachers’ sense of efficacy

This study surveyed a sample of 188 elementary teachers with respect to their preference for information regarding educational planning, in particular information captured with dynamic testing procedures. The influence of teachers’ experience and sense of efficacy on teachers’ preferences was also investigated. Results indicated teachers’ preferences for dynamically gathered information regarding children’s learning processes, next to standard information such as a diagnosis. Appreciation for dynamic testing information appeared to be relatively higher for those teachers with longer teaching experience, but not related to teachers’ sense of efficacy. Findings are discussed with regard to their implications for both diagnostic and teaching practices.     

Students’ Learning Activities While Studying Biological Process Diagrams

Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students’ learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each student completed three learning tasks. Verbal data and eye-tracking data were collected as indications of students’ learning activities. For the verbal data, we applied a fine-grained coding scheme to optimally describe students’ learning activities. For the eye-tracking data, we used fixation time and transitions between areas of interest in the process diagrams as indices of learning activities. Various learning activities while studying process diagrams were found that distinguished between more and less successful students. Results showed that between-student variance in comprehension score was highly predicted by meaning making of the process arrows (80%) and fixation time in the main area (65%). Students employed successful learning activities consistently across learning tasks. Furthermore, compared to unsuccessful students, successful students used a more coherent approach of interrelated learning activities for comprehending process diagrams.