The precariousnesses of young knowledge workers. A subject-oriented approach

Over the past decades, a number of EU member states have recorded large rises in the use of temporary employment. Young people are far more likely than other groups to be employed in precarious jobs, independently of their education and skills. In the midst of the global economic-financial crisis, in fact, the assault on the conditions of knowledge workers goes on, according to the different lines of the neoliberalistic logics, which juxtapose with the current precarisation processes like underpayment and misalignment between subjects’ educations and their working activities. How do young precarious knowledge workers recount their experiences? What relation holds between a high education level and the possibility of effectively deploying the competences and skills acquired? How do knowledge workers represent and deal with their precarious conditions? To answer these questions, this article proposes a definition of the concepts of ‘precarity’, ‘precariousness’ and ‘precariat’ and then focuses specifically on the precariousness experienced by young knowledge workers in Italy and the importance of investigating precarisation processes in light of their experiences. Hence, the present article discusses the invisible face of the conditions of young knowledge workers, which collides with the official face. The latter superficially presents them as ‘independent professionals’, although they increasingly experience conditions similar to those of dependent workers and at the same time suffer the effects of the further precarisation brought about by the crisis, but missing trade-unions support or political representation

A two-tiered 2D visual tool for assessing classifier performance

In this article, a new kind of 2D tool is proposed, namely ⟨φ δ⟩ diagrams, able to highlight most of the information deemed relevant for classifier building and assessment. In particular, accuracy, bias and break-even points are immediately evident therein. These diagrams come in two different forms: the first is aimed at representing the phenomenon under investigation in a space where the imbalance between negative and positive samples is not taken into account, the second (which is a generalization of the first) is able to visualize relevant information in a space that accounts also for the imbalance. According to a specific design choice, all properties found in the first space hold also in the second. The combined use of φ and δ can give important information to researchers involved in the activity of building intelligent systems, in particular for classifier performance assessment and feature ranking/selection

The precariousness of knowledge workers: hybridisation, self-employment and subjectification

In knowledge - based industries, work is circumscribed by the cognitive frames of creativity in the representations of subjects, but simultaneously demands adaptability, in a context in which deregulation and individualisation are now normal. The ethics of self - activation are therefore inextricably intertwined with the demands of intensification, standardisation and self - commodification. The first volume of this Special Issue – which is composed of two different parts – is focused on the phenomena of hybridisation, self - employment and subjectification, at the core of the experiences of precarious workers in the knowledge societies. This article introduces the first of a two - part Special Issue on the precariousness of knowledge workers

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models

A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19

COVID-19 is an ongoing global pandemic of coronavirus disease 2019, which may cause severe acute respiratory syndrome. This disease highlighted the limitations of health systems worldwide regarding managing the pandemic. In particular, the lack of diagnostic tests that can quickly and reliably detect infected patients has contributed to the spread of the virus. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and antigen tests, which are the main diagnostic tests for COVID-19, showed their limitations during the pandemic. In fact, RT-PCR requires several hours to provide a diagnosis and is not properly accurate, thus generating a high number of false negatives. Unlike RT-PCR, antigen tests provide rapid diagnosis but are less accurate in detecting COVID-19 positive patients. Medical imaging is an alternative diagnostic test for COVID-19. In particular, chest computed tomography allows detecting lung infections related to the disease with high accuracy. However, visual analysis of a chest scan generated by computed tomography is a demanding activity for radiologists, making widespread use of this test unfeasible. Therefore, it is essential to lighten their work with automated tools able to provide accurate diagnosis in a short time. To deal with this challenge, in this work, an approach based on 3D Inception CNNs is proposed. Specifically, 3D Inception-V1 and Inception-V3 models have been built and compared. Then, soft-voting ensemble classifier models have been separately built on these models to boost the performance. As for the individual models, results showed that Inception-V1 outperformed Inception-V3 according to different measures. As for the ensemble classifier models, the outcome of experiments pointed out that the adopted voting strategy boosted the performance of individual models. The best results have been achieved enforcing soft voting on Inception-V1 models

Personalized Text Categorization Using a MultiAgent Architecture

In this paper, a system able to retrieve contents deemed relevant for the users through a text categorization process, is presented. The system is built exploiting a generic multiagent architecture that supports the implementation of applications aimed at (i) retrieving heterogeneous data spread among different sources (e.g., generic html pages, news, blogs, forums, and databases); (ii) filtering and organizing them according to personal interests explicitly stated by each user; (iii) providing adaptation techniques to improve and refine throughout time the profile of each selected user. In particular, the implemented multiagent system creates personalized press-revies from online newspapers. Preliminary results are encouraging and highlight the effectiveness of the approach

The precariousnesses of young knowledge workers : A subject-oriented approach

Over the past decades, a number of EU member states have recorded large rises in the use of temporary employment. Young people are far more likely than other groups to be employed in precarious jobs, independently of their education and skills. In the midst of the global economic-financial crisis, in fact, the assault on the conditions of knowledge workers goes on, according to the different lines of the neoliberalistic logics, which juxtapose with the current precarisation processes like underpayment and misalignment between subjects\u2019 educations and their working activities. How do young precarious knowledge workers recount their experiences? What relation holds between a high education level and the possibility of effectively deploying the competences and skills acquired? How do knowledge workers represent and deal with their precarious conditions? To answer these questions, this article proposes a definition of the concepts of \u2018precarity\u2019, \u2018precariousness\u2019 and \u2018precariat\u2019 and then focuses specifically on the precariousness experienced by young knowledge workers in Italy and the importance of investigating precarisation processes in light of their experiences. Hence, the present article discusses the invisible face of the conditions of young knowledge workers, which collides with the official face. The latter superficially presents them as \u2018independent professionals\u2019, although they increasingly experience conditions similar to those of dependent workers and at the same time suffer the effects of the further precarisation brought about by the crisis, but missing trade-unions support or political representation