Usage analytics: a process to extract and analyse usage data to understand user behaviour in cloud

Usage in the software field deals with knowledge about how end-users use the application and how the application responds to the users’ action. Understanding usage data can help developers optimise the application development process by prioritising the resources such as time, cost and man power on features of the application which are critical for the user. However, in a complex cloud computing environment, the process of extracting and analysing usage data is difficult since the usage data is spread across various front-end interfaces and back-end underlying infrastructural components of the cloud that host the application and are of different types and formats. In this paper, we propose usage analytics, a process to extract and analyse usage to understand the behavioural usage patterns of the user with the aim to identify features critical to user. We demonstrate how to identify the features in a cloud based application, how to extract and analyse the usage data to understand the user behaviour

Preface to the special issue on harnessing personal tracking data for personalization and sense-making

Increasingly, people are making use of diverse digital services that create many types of personal data. The most recent addition to such services are self-tracking devices that are capable of creating very detailed personal activity records. The focus of this special issue is to explore how such activity records can be exploited to provide user-centric personalization services

DAF-16 and Δ9 Desaturase Genes Promote Cold Tolerance in Long-Lived Caenorhabditis elegans age-1 Mutants

In Caenorhabditis elegans, mutants of the conserved insulin/IGF-1 signalling (IIS) pathway are long-lived and stress resistant due to the altered expression of DAF-16 target genes such as those involved in cellular defence and metabolism. The three Δ9 desaturase genes, fat-5, fat-6 and fat-7, are included amongst these DAF-16 targets, and it is well established that Δ9 desaturase enzymes play an important role in survival at low temperatures. However, no assessment of cold tolerance has previously been reported for IIS mutants. We demonstrate that long-lived age-1(hx546) mutants are remarkably resilient to low temperature stress relative to wild type worms, and that this is dependent upon daf-16. We also show that cold tolerance following direct transfer to low temperatures is increased in wild type worms during the facultative, daf-16 dependent, dauer stage. Although the cold tolerant phenotype of age-1(hx546) mutants is predominantly due to the Δ9 desaturase genes, additional transcriptional targets of DAF-16 are also involved. Surprisingly, survival of wild type adults following a rapid temperature decline is not dependent upon functional daf-16, and cellular distributions of a DAF-16::GFP fusion protein indicate that DAF-16 is not activated during low temperature stress. This suggests that cold-induced physiological defences are not specifically regulated by the IIS pathway and DAF-16, but expression of DAF-16 target genes in IIS mutants and dauers is sufficient to promote cross tolerance to low temperatures in addition to other forms of stress

Correlation of gene expression and protein production rate - a system wide study

<p>Abstract</p> <p>Background</p> <p>Growth rate is a major determinant of intracellular function. However its effects can only be properly dissected with technically demanding chemostat cultivations in which it can be controlled. Recent work on <it>Saccharomyces cerevisiae </it>chemostat cultivations provided the first analysis on genome wide effects of growth rate. In this work we study the filamentous fungus <it>Trichoderma reesei </it>(<it>Hypocrea jecorina</it>) that is an industrial protein production host known for its exceptional protein secretion capability. Interestingly, it exhibits a low growth rate protein production phenotype.</p> <p>Results</p> <p>We have used transcriptomics and proteomics to study the effect of growth rate and cell density on protein production in chemostat cultivations of <it>T. reesei</it>. Use of chemostat allowed control of growth rate and exact estimation of the extracellular specific protein production rate (SPPR). We find that major biosynthetic activities are all negatively correlated with SPPR. We also find that expression of many genes of secreted proteins and secondary metabolism, as well as various lineage specific, mostly unknown genes are positively correlated with SPPR. Finally, we enumerate possible regulators and regulatory mechanisms, arising from the data, for this response.</p> <p>Conclusions</p> <p>Based on these results it appears that in low growth rate protein production energy is very efficiently used primarly for protein production. Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).</p

Discovering prerequisite relations from educational documents through word embeddings

Inferring prerequisite relations among educational documents, in terms of prior knowledge required to understand and complete assignments about certain topics, is a crucial task for instructional designers and teachers. Massive open online courses, electronic textbooks, public encyclopedias and repositories of learning objects and other forms of informative content create a huge availability of educational material, which can be exploited in online platforms for distance education, both for recommending specific resources and personalized learning paths. But public taxonomies of prerequisites, or learning object metadata useful to trace down prerequisites are not generally available. A description of a new approach for prerequisite discovering in educational documents is given. It is based on word embeddings, that is, statistical language models for the representation of text-based learning objects in low-dimensional latent spaces. It takes advantage of the latent representations to identify prerequisites in a binary classification setting. The accuracy of the approach is validated by means of an experimental benchmark covering multiple datasets of educational material

A Web-based Training System for Business Letter Writing

As with the growing degree of office automation and diffuse use of electronic media, such as e-mails, written business communication is becoming a key element to promote synergies, relationships and disseminating information about products and services. Task recognition and the definition of strategies and suitable vocabularies are some of the activities that office workers deal with each time a communicative intent has to be effectively transferred and understood by a given addressee. This paper introduces a web-based intelligent training system based on the constructivism theory and self-directed learning paradigms for assisting company workers in the drafting business letters-writing task. A case-based engine suggests ad hoc rhetorical letters that users have the chance to adapt to their particular contexts and save them into user-defined case libraries

Using Social Media for Personalizing the Cultural Heritage Experience

This article presents a personalized recommendation approach of textual and multimedia resources related to artistic and cultural points of interest (POIs). This approach exploits linked open data to retrieve content related to POIs and social media to personalize their recommendation to the target user. The similarity evaluation between the social user profile and the related material occurs based on the classic doc2vec model. A preliminary comparative analysis conducted on 20 real users showed encouraging experimental results in terms of perceived accuracy and beyond-accuracy metrics