Exploiting Parameters Learning for Hyper-parameters Optimization in Deep Neural Networks

In the last years, the Hyper-parameter Optimization (HPO) research field has gained more and more attention. Many works have focused on finding the best combination of the Deep Neural Network's (DNN's) hyper-parameters (HPs) or architecture. The state-of-the-art algorithm in terms of HPO is Bayesian Optimization (BO). This is because it keeps track of past results obtained during the optimization and uses this experience to build a probabilistic model mapping HPs to a probability density of the objective function. BO builds a surrogate probabilistic model of the objective function, finds the HPs values that perform best on the surrogate model and updates it with new results. In this work, a system was developed, called Symbolic DNN-Tuner which logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, therefore improving performance. Symbolic DNN-Tuner improve BO applied to DNN by adding an analysis of the results of the network on training and validation sets. This analysis is performed by exploiting rule-based programming, and in particular by using Probabilistic Logic Programming (PLP)

Structural validity and classification performance of the Italian Short Negative Acts Questionnaire: A Structural Equation Modeling approach for building ROC curves

We investigated the structural (internal) validity and classification performance of the Italian Short Negative Acts Questionnaire (SNAQ), a 9-item self-report instrument assessing bullying at work. Consistent with recent attention of researchers to control measurement error in predictive models (Jacobucci & Grimm, Perspectives on Psychological Science, 15(3), 809–816 2020), classification performance was investigated through a proposed novel procedure that uses Structural Equation Modeling for building ROC curves. Participants included 357 workers (females = 50.4%) from various sectors. Our results showed that (a) the Italian SNAQ demonstrates adequate levels of structural validity; (b) its classification performance (in terms of self-labeled bullying) is outstanding; and (c) the ROC curves estimated by means of Structural Equation Modeling outperform those estimated with classical observed-variable approaches. In conclusion, we provided further evidence regarding the good psychometric properties of the Italian SNAQ and we also offered a novel approach for estimating ROC curves that does not neglect the issue of measurement quality

Multidomain Fault Models Covering the Analog Side of a Smart or Cyber-Physical System

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer

Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic

We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning and dictionary-based methods on the text of daily tweets geolocated within and near the locked-down areas, before and after the treatment. We classify tweets into four categories--economics, health, politics, and lockdown policy--to examine how the policy affected emotions heterogeneously. Using a staggered difference-in-differences approach, we show that the lockdown did not have a significantly robust impact on economic uncertainty and sentiment. However, the policy came at the price of higher uncertainty on health and politics and more negative political sentiments. These results, which are robust to a battery of robustness tests, show that lockdowns have relevant non-health related implications

Quantitative abilities in a reptile (Podarcis sicula)

The ability to identify the largest amount of prey available is fundamental for optimizing foraging behaviour in several species. To date, this cognitive skill has been observed in all vertebrate groups except reptiles. In this study we investigated the spontaneous ability of ruin lizards to select the larger amount of food items. In Experiment 1, lizards proved able to select the larger food item when presented with two alternatives differing in size (0.25, 0.50, 0.67 and 0.75 ratio). In Experiment 2 lizards presented with two groups of food items (1 versus 4, 2 versus 4, 2 versus 3 and 3 versus 4 items) were unable to select the larger group in any contrast. The lack of discrimination in the presence ofmultiple items represents an exception in numerical cognition studies, raising the question as to whether reptiles' quantitative abilities are different from those of other vertebrate groups

International Perspectives on the Legal Environment for Selection

Perspectives from 22 countries on aspects of the legal environment for selection are presented in this article. Issues addressed include (a) whether there are racial/ethnic/religious subgroups viewed as "disadvantaged,” (b) whether research documents mean differences between groups on individual difference measures relevant to job performance, (c) whether there are laws prohibiting discrimination against specific groups, (d) the evidence required to make and refute a claim of discrimination, (e) the consequences of violation of the laws, (f) whether particular selection methods are limited or banned, (g) whether preferential treatment of members of disadvantaged groups is permitted, and (h) whether the practice of industrial and organizational psychology has been affected by the legal environmen

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Structural modelling of multilayer skis with an open source FEM software

The design process of a ski is characterized by a short time of development due to continuous advancements in the material science and in the manufacturing processes as well as in customer’s requirements. Nowadays, the development process is very often still based on several physical prototypes and trials and Finite Elements Analysis (FEA) is a significant method to reduce times needed. The aim of this work is to develop a reliable numerical simulation of an existing mountaineering ski, able to predict the performance of the real element. For this purpose, an initial mechanical characterization of all the constituents used in the ski manufacturing was performed. Tensile tests in two directions were performed on flat bone-shaped samples laser cut from sheets. Combining the results of the tensile tests with Digital Image Correlation (DIC) data it was possible to approximate the four in-plane (XY) elastic properties, namely, the two elastic modules, the shear module and the Poisson ratio (Ex, Ey, Gxy, νxy). The DIC free software used is GOM Correlate. Results of the combined “tensile tests – DIC” approach were after verified with FEM simulations reproducing the testing configuration. The digital model of the ski was created starting from the nominal geometry. The whole procedure of modelling, meshing and FE analysis was performed in the open source software Code_Aster/Salome-Meca. Using this kind of software, which code is free to use and modify, permits to reduce costs due to its free license. The real component was tested in a three-point bending and torsion test. This kind of experiments were replicated on the FEM model and results were compared. The comparison highlighted discrepancies of 2.5%–10% with respect to the real component

Challenges in the New Economy: A New Era for Work Design

Models of work design emerged in the twentieth century to address workplace changes created by the industrial revolution. However, the world of work is currently undergoing a new, profound revolution in terms of technological, demographic, and environmental changes, leading to a new economy, within which organizations and employees must function. The field of work design currently includes robust theories with a deep understanding of how work design affects employee outcomes, many with relevance to this new economy. However, the new economy also includes issues never before considered (e.g., algorithmic management and gig and lone work), and the field of work design must tackle the implications of these emerging issues. In this article, we review the general findings on work design and then examine a range of contextual, economic, technological, and diversity issues and their relevance to work design. We conclude with an agenda for future work design research and implications for work analysis and work design interventions and policies