ClusterFix: A Cluster-Based Debiasing Approach without Protected-Group Supervision

The failures of Deep Networks can sometimes be ascribed to biases in the data or algorithmic choices. Existing debiasing approaches exploit prior knowledge to avoid unintended solutions; we acknowledge that, in real-world settings, it could be unfeasible to gather enough prior information to characterize the bias, or it could even raise ethical considerations. We hence propose a novel debiasing approach, termed ClusterFix, which does not require any external hint about the nature of biases. Such an approach alters the standard empirical risk minimization and introduces a per-example weight, encoding how critical and far from the majority an example is. Notably, the weights consider how difficult it is for the model to infer the correct pseudo-label, which is obtained in a self-supervised manner by dividing examples into multiple clusters. Extensive experiments show that the misclassification error incurred in identifying the correct cluster allows for identifying examples prone to bias-related issues. As a result, our approach outperforms existing methods on standard benchmarks for bias removal and fairness

Can adversarial networks hallucinate occluded people with a plausible aspect?

When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world

Social groups detection in crowd through shape-augmented structured learning

Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups, by injecting in the model socially plausible shape configurations. By taking into account social groups patterns, the system is able to outperform state of the art methods on two publicly available benchmark sets of videos. © 2013 Springer-Verlag

Aircraft dynamic lateral stability analytic formulation for dutch roll mode stability prediction

Currently, the civil aircraft manufacturers during the development have strong will to relax the natural aircraft stability, in order to optimize mainly performances in term of fuel consumption, by reducing lifting control surfaces, which is not without consequences on aircraft stability. The study of new innovating configurations, as the flying wing aircraft with the engines in rear position, requires a deepening of the predictive methods of static and dynamic stability linked with the new aerodynamic aspects. On the topic of dynamic stability, the first presentation was made with conference EUCASS 2007 in Brussels. This presentation focused on the longitudinal aspect that highlighted new analytical expressions of the manoeuvre point, likely to improve the prediction of the dynamic margin at the stage of future project studies. In the same spirit, this paper describes a similar work that investigated the study of lateral dynamic stability. This work studied dynamic lateral stability according to the center of gravity location and allowed to determine on the Dutch roll damping curve a point beyond which the lateral model is instable. The first part of this study presents considerations on Dutch roll damping mode leading to analytic resolution. The second part will propose examples of this method applied to A/C design phase and perspectives of this method for new A/C concepts

Improving working conditions in platform work in the light of the recent proposal for a directive

This collective work has the aim to bring together several contributions by scholars from different Countries through the leitmotif of the analysis of work through digital platforms, also in the light of the latest proposal for a European Union directive. The first section focuses on the analysis of digital platform work, in various aspects, including issues concerning the use of artificial intelligence. The second section analyses issues related to the development of workers’ rights through digital platforms. In the third section, the authors made considerations on the intervention of the draft directive on qualification

Foreword - Improving working conditions in platform work in the light of the recent proposal for a directive

The title of the book “Improving working conditions in platform work in the light of the recent proposal for a directive” evokes the intent to encourage a comparative reflection focused on modern work. For reasons of consistency, it was decided to divide the writings into three subgroups

Improving working conditions in platform work in the light of the recent proposal for a directive

This collective work has the aim to bring together several contributions by scholars from different Countries through the leitmotif of the analysis of work through digital platforms, also in the light of the latest proposal for a European Union directive. The first section focuses on the analysis of digital platform work, in various aspects, including issues concerning the use of artificial intelligence. The second section analyses issues related to the development of workers’ rights through digital platforms. In the third section, the authors made considerations on the intervention of the draft directive on qualification