Profiling Cryptocurrency Influencers with Few-Shot Learning Using Data Augmentation and ELECTRA

With this work we propose an application of the ELECTRA Transformer, fine-tuned on two augmented version of the same training dataset. Our team developed the novel framework for taking part at the Profiling Cryptocurrency Influencers with Few-shot Learning task hosted at PAN@CLEF2023. Our proposed strategy consists of an early data augmentation stage followed by a fine-tuning of ELECTRA. At the first stage we augment the original training dataset provided by the organizers using backtranslation. Using this augmented version of the training dataset, we perform a fine tuning of ELECTRA. Finally, using the fine-tuned version of ELECTRA, we inference the labels of the samples provided in the test set. To develop and test our model we used a two-ways validation on the training set. Firstly, we evaluate all the metrics on the augmented training set, and then we evaluate on the original training set. The metrics we considered span from accuracy to Macro F1, to Micro F1, to Recall and Precision. According to the official evaluator, our best submission reached a Macro F1 value equal to 0.3762

The limited reach of fake news on Twitter during 2019 European elections

The advent of social media changed the way we consume content, favoring a disintermediated access to, and production of information. This scenario has been matter of critical discussion about its impact on society, magnified in the case of the Arab Springs or heavily criticized during Brexit and the 2016 U.S. elections. In this work we explore information consumption on Twitter during the 2019 European Parliament electoral campaign by analyzing the interaction patterns of official news outlets, disinformation outlets, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the elections, held between 23rd and 26th of May, 2019. We collected almost 400,000 tweets posted by 863 accounts having different roles in the public society. Through a thorough quantitative analysis we investigate the information flow among them, also exploiting geolocalized information. Accounts show the tendency to confine their interaction within the same class and the debate rarely crosses national borders. Moreover, we do not find evidence of an organized network of accounts aimed at spreading disinformation. Instead, disinformation outlets are largely ignored by the other actors and hence play a peripheral role in online political discussions

CrisMap: A Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis- related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy

Nowcasting of Earthquake Consequences Using Big Social Data

Messages posted to social media in the aftermath of a natural disaster have value beyond detecting the event itself. Mining such deliberately dropped digital traces allows a precise situational awareness, to help provide a timely estimate of the disaster’s consequences on the population and infrastructures. Yet, to date, the automatic assessment of damage has received little attention. Here, the authors explore feeding predictive models by tweets conveying on-the-ground social sensors’ observations, to nowcast the perceived intensity of earthquakes

Evolução dos pacientes com condrossarcoma grau I em relação ao tipo de tratamento cirúrgico

PURPOSE: To evaluate the oncological outcome of patients with grade I chondrosarcomas according to the type of surgical treatment performed, since there is still controversy regarding the need for aggressive resections to reach a successful outcome. MATERIALS AND METHODS: The records of 23 patients with grade I chondrosarcomas were reviewed. The mean age was 38.4 years, ranging from 11 to 70 years; 52% were men and 48% were women. The femur was the site of 13 tumors. The tumors were staged as IA (17, 74%) and IB (6, 26%). Regarding tumor location, 74% (17) were medullary, 22% (5) were peripheral, and 4% (1) was indeterminate. Tumor size ranged from 2 to 25 cm, mean 7.9 cm. Regarding the surgical procedure, 11 patients underwent intralesional resection, 9 patients underwent wide resection, and 3 underwent radical resection. The follow-up period ranged from 24 to 192 months. RESULTS: None of the patients developed local recurrence or metastases; 7 patients had other general complications. CONCLUSIONS: This data supports the use of less aggressive procedures for treatment of low-grade chondrosarcomas.OBJETIVO: Avaliar a evolução oncológica de portadores de condrossarcomas grau I de acordo com o tipo de tratamento cirúrgico efetuado. Existe controvérsia em relação à necessidade de ressecções agressivas para obtenção de uma evolução clínica favorável. MATERIAIS E MÉTODOS: Os prontuários de 23 portadores de condrossarcoma grau I foram analisados. A idade dos pacientes variou de 11 a 70 anos com média de 38,4 anos, 52% eram homens e 48% mulheres. O local mais acometido foi o fêmur com 13 pacientes. Dezessete lesões (74%) foram classificadas como IA e seis (26%) como IB. Setenta e quatro por cento dos tumores eram medulares, 22% eram periféricas e uma lesão indeterminada. O tamanho dos tumores variou de 2 a 25 cm, média de 7,9 cm. Onze pacientes foram submetidos a ressecção intralesional, nove a ressecção ampla e três a ressecção radical. O seguimento variou de 24 a 192 meses. RESULTADOS: Complicações não oncológicas ocorreram em sete pacientes. Nenhum dos pacientes apresentou recidiva local ou metástase. Estes dados sugerem que os procedimentos cirúrgicos menos agressivos são seguros para o tratamento dos pacientes com condrossarcoma grau I

Histological Study of Fresh Versus Frozen Semitendinous Muscle Tendon Allografts

OBJECTIVE: The purpose of this study was to histologically analyze allografts from cadaveric semitendinous muscle after cryopreservation at -80°C in comparison to a control group kept at only -4°C to test the hypothesis that the histological characteristics of the tissue are maintained when the tendons are kept at lower temperatures. METHODS: In a tissue bank, 10 semitendinous tendons from 10 cadavers were frozen at -80ºC as a storage method for tissue preservation. They were kept frozen for 40 days, and then a histological study was carried out. Another 10 tendon samples were analyzed while still "fresh". RESULTS: There was no histological difference between the fresh and frozen samples in relation to seven variables. CONCLUSIONS: Semitendinous muscle tendon allografts can be submitted to cryopreservation at -80ºC without suffering histological modifications

Propuesta de modelo de negocio de un food truck de venta de desayunos en una universidad privada de Chiclayo, 2016

El presente trabajo tiene como objetivo establecer un modelo de negocio para un food truck de desayunos en una universidad privada de Chiclayo. La metodología aplicada para la investigación es cualitativa – exploratoria, se fundamenta en un proceso inductivo (explorar, describir y luego generar perspectivas teóricas), es decir va de lo particular a lo general; esta metodología permite obtener información en base a entrevistas realizadas a la comunidad universitaria. La investigación busca conocer la aceptación del modelo de food truck de venta de desayuno, se basó en el modelo Lean Canvas, desarrollado en el libro Running Lean de Ash Maurya, nos da un enfoque de nueve (9) dimensiones para tener en cuenta y poder lograr un modelo de negocio de éxito. La propuesta de valor obtenida, consiste en vender productos saludables que les ayude a promover la calidad y bienestar de la salud de nuestros clientes, por ello se ofrecerán desayunos elaborados a base de frutas, cereales andinos y sándwich preparados al instante, ofrecidos en unos envases biodegradables, cumpliendo con los estándares de salubridad. Asimismo se tendrá variedad en los productos a ofrecer, para que el cliente pueda escoger y se brindará una atención rápida y personalizada con la finalidad de cumplir con uno de los aspectos que los clientes valoran.Tesi

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Text Enrichment with Japanese Language to Profile Cryptocurrency Influencers

From a few-shot learning perspective, we propose a strategy to enrich the latent semantic of the text provided in the dataset provided for the Profiling Cryptocurrency Influencers with Few-shot Learning, the task hosted at PAN@CLEF2023. Our approach is based on data augmentation using the backtranslation forth and back to and from Japanese language. We translate samples in the original training dataset to a target language (i.e. Japanese). Then we translate it back to English. The original sample and the backtranslated one are then merged. Then we fine-tuned two state-of-the-art Transformer models on this augmented version of the training dataset. We evaluate the performance of the two fine-tuned models using the Macro and Micro F1 accordingly to the official metric used for the task. After the fine-tuning phase, ELECTRA and XLNet obtained a Macro F1 of 0.7694 and 0.7872 respectively on the original training set. Our best submission obtained a Macro F1 equal to 0.3851 on the official test set provided