fabsam @ AMI: A Convolutional Neural Network Approach

The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pre-trained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020

Patient-reported symptom burden of Charcot-Marie-Tooth Disease Type 1A: findings from an observational digital lifestyle study

Objectives: This study aims to explore the impact of Charcot-Marie-Tooth disease type 1A (CMT1A) and its treatment on patients in European (France, Germany, Italy, Spain, and the United Kingdom) and US real-world practice. Methods: Adults with CMT1A (n = 937) were recruited to an ongoing observational study exploring the impact of CMT. Data were collected via CMT&Me, an app through which participants completed patient-reported outcome measures. Results: Symptoms ranked with highest importance were weakness in the extremities, difficulty in walking, and fatigue. Almost half of participants experienced a worsening of symptom severity since diagnosis. Anxiety and depression were each reported by over one-third of participants. Use of rehabilitative interventions, medications, and orthotics/walking aids was high. Conclusions: Patient-reported burden of CMT1A is high, influenced by difficulties in using limbs, fatigue, pain, and impaired quality of life. Burden severity appears to differ across the population, possibly driven by differences in rehabilitative and prescription-based interventions, and country-specific health care variability

Management of hepatitis C virus genotype 4: recommendations of an international expert panel.

HCV has been classified into no fewer than six major genotypes and a series of subtypes. Each HCV genotype is unique with respect to its nucleotide sequence, geographic distribution, and response to therapy. Genotypes 1, 2, and 3 are common throughout North America and Europe. HCV genotype 4 (HCV-4) is common in the Middle East and in Africa, where it is responsible for more than 80% of HCV infections. It has recently spread to several European countries. HCV-4 is considered a major cause of chronic hepatitis, cirrhosis, hepatocellular carcinoma, and liver transplantation in these regions. Although HCV-4 is the cause of approximately 20% of the 170 million cases of chronic hepatitis C in the world, it has not been the subject of widespread research. Therefore, this document, drafted by a panel of international experts, aimed to review current knowledge on the epidemiology, natural history, clinical, histological features, and treatment of HCV-4 infections

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

TOBE-LearneD: Compensating users' contributions in Federated Learning with Fungible Tokens

Federated Learning allows multiple participants to collaboratively train a shared prediction model without exposing private datasets. However, it is vulnerable to adversarial attacks led by malicious users that aim to decrease the performance of the global model. Moreover, users are not encouraged to participate in collaborative learning because of the absence of incentives. This thesis presents TOBE-LearneD, a decentralized framework that implements a mechanism to encourage users to collaborate and solve proposed federated learning tasks. We studied a contribution-based aggregation method to avoid poisoning attacks from malicious users. Exploiting public Blockchain technology, we implement a token-based economy to assign rewards weighted on participants' contributions, compensating the computational cost needed to train local models. Our framework allows users to propose a customizable Federated Learning task utilizing Smart Contracts, ensuring transparency and tamper-proof properties, thus preventing denial of reward to the participants from malicious manufacturers. In addition, we evaluate a prototype of TOBE-LearneD from a performance, economic and security perspective in a real-world use case related to the SmartGrid field

History, practical aspects and contemporary research on witness and victim interviewing in France

International audienc