A blockchain-based Decentralized System for proper handling of temporary Employment contracts

Temporary work is an employment situation useful and suitable in all occasions in which business needs to adjust more easily and quickly to workload fluctuations or maintain staffing flexibility. Temporary workers play therefore an important role in many companies, but this kind of activity is subject to a special form of legal protections and many aspects and risks must be taken into account both employers and employees. In this work we propose a blockchain-based system that aims to ensure respect for the rights for all actors involved in a temporary employment, in order to provide employees with the fair and legal remuneration (including taxes) of work performances and a protection in the case employer becomes insolvent. At the same time, our system wants to assist the employer in processing contracts with a fully automated and fast procedure. To resolve these problems we propose the D-ES (Decentralized Employment System). We first model the employment relationship as a state system. Then we describe the enabling technology that makes us able to realize the D-ES. In facts, we propose the implementation of a DLT (Decentralized Ledger Technology) based system, consisting in a blockchain system and of a web-based environment. Thanks the decentralized application platforms that makes us able to develop smart contracts, we define a discrete event control system that works inside the blockchain. In addition, we discuss the temporary work in agriculture as a interesting case of study.Comment: Accepted for publication in the proceedings of the "Computing Conference 2018" - 10-12 July 2018 - London, United Kingdo

Error estimation and adaptive moment hierarchies for goal-oriented approximations of the Boltzmann equation

This paper presents an a-posteriori goal-oriented error analysis for a numerical approximation of the steady Boltzmann equation based on a moment-system approximation in velocity dependence and a discontinuous Galerkin finite-element (DGFE) approximation in position dependence. We derive computable error estimates and bounds for general target functionals of solutions of the steady Boltzmann equation based on the DGFE moment approximation. The a-posteriori error estimates and bounds are used to guide a model adaptive algorithm for optimal approximations of the goal functional in question. We present results for one-dimensional heat transfer and shock structure problems where the moment model order is refined locally in space for optimal approximation of the heat flux.Comment: arXiv admin note: text overlap with arXiv:1602.0131

Feedback on Academic Essay Writing through pre-Emptive Hints: Moving Towards "Advice for Action"

This paper adopts an “advice for action” approach to feedback in educational practice: addressing how provision of “hints” to participants before they write academic essays can support their understanding and performance in essay-writing tasks. We explored differences in performance by type of hint, and whether there was a transfer of better performance in subsequent essays. Fifty participants were recruited, consisting of eight men and 42 women aged 18-80. Participants were assigned in rotation to four groups, and asked to write two essays. Groups 1 and 3 received hints before Essay 1, whilst Groups 2 and 4 received hints before Essay 2. Groups 1 and 2 received essential hints; Groups 3 and 4 received helpful hints. Essays were marked against set criteria. The results showed that an “advice for action” approach to essay-writing, in the form of hints, can significantly improve writers’ marks. Specifically higher marks were gained for the introduction, conclusion and use of evidence: critical components of “good” academic essays. As the hints given were content-free, this approach has the potential to instantly benefit tutors and students across subject domains and institutions and is informing the development of a technical system that can offer formative feedback as students draft essays

Quality Assessment for E-learning: a Benchmarking Approach (Third edition)

The primary purpose of this manual is to provide a set of benchmarks, quality criteria and notes for guidance against which e-learning programmes and their support systems may be judged. The manual should therefore be seen primarily as a reference tool for the assessment or review of e-learning programmes and the systems which support them. However, the manual should also prove to be useful to staff in institutions concerned with the design, development, teaching, assessment and support of e-learning programmes. It is hoped that course developers, teachers and other stakeholders will see the manual as a useful development and/or improvement tool for incorporation in their own institutional systems of monitoring, evaluation and enhancement

Open Source Software for Automatic Detection of Cone Photoreceptors in Adaptive Optics Ophthalmoscopy Using Convolutional Neural Networks

Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online