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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023 -- 18 September 2023 through 21 September 2023 -- 193170, We describe the outcome of the 2023 edition of the CheckThat!Lab at CLEF. We focus on subjectivity (Task 2), which has been proposed for the first time. It aims at fostering the technology for the identification of subjective text fragments in news articles. For that, we produced corpora consisting of 9,530 manually-annotated sentences, covering six languages —Arabic, Dutch, English, German, Italian, and Turkish. Task 2 attracted 12 teams, which submitted a total of 40 final runs covering all languages. The most successful approaches addressed the task using state-of-the-art multilingual transformer models, which were fine-tuned on language-specific data. Teams also experimented with a rich set of other neural architectures, including foundation models, zero-shot classifiers, and standard transformers, mainly coupled with data augmentation and multilingual training strategies to address class imbalance. We publicly release all the datasets and evaluation scripts, with the purpose of promoting further research on this topic. © 2023 Copyright for this paper by its authors., PE00000013, PNRR-M4C2-Investimento 1.3; Qatar Foundation, QF; Qatar National Research Fund, QNRF; Bundesministerium für Bildung und Forschung, BMBF: 01FP20031J; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 120E514; Università di Bologna, UNIBO: 2021-15854, DOT1303118, NPRP 14C-0916-210015, NPRP13S-0206-200281

24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023 -- 18 September 2023 through 21 September 2023 -- 193170, We describe the outcome of the 2023 edition of the CheckThat!Lab at CLEF. We focus on subjectivity (Task 2), which has been proposed for the first time. It aims at fostering the technology for the identification of subjective text fragments in news articles. For that, we produced corpora consisting of 9,530 manually-annotated sentences, covering six languages —Arabic, Dutch, English, German, Italian, and Turkish. Task 2 attracted 12 teams, which submitted a total of 40 final runs covering all languages. The most successful approaches addressed the task using state-of-the-art multilingual transformer models, which were fine-tuned on language-specific data. Teams also experimented with a rich set of other neural architectures, including foundation models, zero-shot classifiers, and standard transformers, mainly coupled with data augmentation and multilingual training strategies to address class imbalance. We publicly release all the datasets and evaluation scripts, with the purpose of promoting further research on this topic. © 2023 Copyright for this paper by its authors., PE00000013, PNRR-M4C2-Investimento 1.3; Qatar Foundation, QF; Qatar National Research Fund, QNRF; Bundesministerium für Bildung und Forschung, BMBF: 01FP20031J; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 120E514; Università di Bologna, UNIBO: 2021-15854, DOT1303118, NPRP 14C-0916-210015, NPRP13S-0206-200281

This study deals with the problem of the recognition of dermatological and exanthematic diseases through the use of deep learning techniques in order to diagnose malignant diseases at an early stage and in general to bring the pathology identified by the models to the attention of the person. A fundamental part of the research was the study of the methodologies present in the state of the art and for this reason in this paper we report the studies considered as most relevant. In this paper, two different types of models are reported, the Convolutional Network Disease (CND) model and the CND-InceptionV3 model using the transfer learning technique. The use of these two models made it possible to carry out an experimental phase in which the performance that can be achieved using the ISIC-archive dataset was analysed. Subsequently, the description of the work carried out for the improvement of the dataset through the association of syntactic-semantic information is reported. Finally, in the last section, conclusions are drawn on the values obtained and future developments that can be made to improve the performance of the models reported are reported.

This study stems from the observation of the growing demand, interest and incredible potential offered by Artificial Intelligence and Machine Learning techniques in the fields of content website or ecommerce. In particular, we will study the Content Management System (CMS), a system capable of creating and maintaining websites, forums and applications for customers. These systems have an intrinsic predisposition to provide services based on Machine Learning (ML), thus being able to add to the solutions offered by the most innovative ML systems. These include Sentiment Analysis, the Recommender System and the more innovative Chatbot . Integrating them into a website or web platform very often leads to significantly improved results for the user experience offered to the user. This study aims to analyse the new artificial intelligence techniques and how to implement them in a company. Reporting also a case of Study based on a real agency

Bayesian techniques are being quickly adopted in sports settings. The volume and variety of data produced in sports activities over the past years and the availability of software packages for Bayesian computation have contributed positively to this growth. This article provides a brief review of the latest advances in Bayesian statistics in sports, covering methods and applications. In the literature surveyed, we found a similar number of publications between 2013 and 2017 and those published in the three previous decades, which is an indication of the rapid adoption rate of Bayesian methods in sports.

We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online.

The article deals with the centrality of social work and its consequences in terms of educational investments. It is done highlighting the intensity and radicality of social transformations in the contemporary and developed countries. The consequence of those transformations is a systematic weakening of the general population: in this sense it is possible to talk of a socially produced disability. It derives the centrality of social work, especially in the direction of re-building the users' capability; it derives, also, the centrality of users' role in the functioning of social agencies. In that perspective, the relevance of the educational processes becomes clear; for which a stronger, more articulate and also theoretical knowledge becomes necessary. In particular, we need an education oriented to the organizational issues, taking into account the scenario of increasing governance in the social Services.

Increased computer speed and memory have encouraged simulation analysts to develop ever more realistic stochastic models. Despite these advancements in computing hardware, the most significant gains in the speed of stochastic simulation are still the result of methodological advances and clever algorithmic design. In this article, we survey a few of the most important methods for variance reduction and speedup that will benefit any simulation, no matter what the capabilities of the computing hardware are.

Clustering is often considered as the first step in the analysis when dealing with an enormous amount of Single Nucleotide Polymorphism (SNP) genotype data. The lack of biological information could affect the outcome of such procedure. Even if a clustering procedure has been selected and performed, the impact of its uncertainty on the subsequent association analysis is rarely assessed. In this research we propose first a model to cluster SNPs data, then we assess the association between the cluster and a disease. In particular, we adopt a Dirichlet process mixture model with the advantages, with respect to the usual clustering methods, that the number of clusters needs not to be known and fixed in advance and the variation in the assignment of SNPs to clusters can be accounted. In addition, once a clustering of SNPs is obtained, we design an individualized genetic score quantifying the SNP composition in each cluster for every subject, so that we can set up a generalized linear model for association analysis able to incorporate the information from a large-scale SNP dataset, and yet with a much smaller number of explanatory variables. The inference on cluster allocation, the strength of association of each cluster (the collective effect on SNPs in the same cluster), and the susceptibility of each SNP are based on posterior samples from Markov chain Monte Carlo methods and the Binder loss information. We exemplify this Bayesian nonparametric strategy in a genome-wide association study of Crohn’s disease in a case-control setting.

Dreissena polymorpha is awidespread ?lter-feeder species, resistant to a broad range of environmental conditions and different types of pollutants,which has recently colonized Italian freshwaters. Althoughwidely used tomonitor pollution in freshwater environments, this species is also an important food source for some ?sh and water birds. It can also be used to concentrate or remove particulate organicmatter to interrupt avian-to-human transmission of pollutants and control health risks for animals and humans. In this study, the accumulation/inactivation in D. polymorpha of human health-related spiked enteric viruses was described. The removal of endogenous Escherichia coli, the classical indicator of fecal contamination,was tested aswell.Our preliminary lab-scale results demonstrate that zebra mussels can reduce signi?cantly poliovirus titer after 24 h and rotavirus titer after 8 h. E. coli countswere also reduced in the presence of zebramussels by about 1.5 log after 4 h and nearly completely after 24 h. The fate of the two enteric viruses after concentration by zebra mussels was also investigated after mechanical disruption of the tissues. To our knowledge, the accumulation fromwater and inactivation of human health-related enteric viruses by zebra mussels has never been reported.

Dreissena polymorpha is awidespread ?lter-feeder species, resistant to a broad range of environmental conditions and different types of pollutants,which has recently colonized Italian freshwaters. Althoughwidely used tomonitor pollution in freshwater environments, this species is also an important food source for some ?sh and water birds. It can also be used to concentrate or remove particulate organicmatter to interrupt avian-to-human transmission of pollutants and control health risks for animals and humans. In this study, the accumulation/inactivation in D. polymorpha of human health-related spiked enteric viruses was described. The removal of endogenous Escherichia coli, the classical indicator of fecal contamination,was tested aswell.Our preliminary lab-scale results demonstrate that zebra mussels can reduce signi?cantly poliovirus titer after 24 h and rotavirus titer after 8 h. E. coli countswere also reduced in the presence of zebramussels by about 1.5 log after 4 h and nearly completely after 24 h. The fate of the two enteric viruses after concentration by zebra mussels was also investigated after mechanical disruption of the tissues. To our knowledge, the accumulation fromwater and inactivation of human health-related enteric viruses by zebra mussels has never been reported.

The aim of the paper is to discuss the association between SNP genotype data and a disease. For genetic association studies, the statistical analyses with multiple markers have been shown to be more powerful, efficient, and biologically meaningful than single marker association tests. As the number of genetic markers considered is typically large, here we cluster them and then study the association between groups of markers and disease. We propose a two-step procedure: first a Bayesian nonparametric cluster estimate under normalized generalized gamma process mixture models is introduced, so that we are able to incorporate the information from a large-scale SNP data with a much smaller number of explanatory variables. Then, thanks to the introduction of a genetic score, we study the association between the relevant disease response and groups of markers using a logit model. Inference is obtained via an MCMC truncation method recently introduced in the literature. We also provide a review of the state of art of Bayesian nonparametric cluster models and algorithms for the class of mixtures adopted here. Finally, the model is applied to genome-wide association study of Crohn’s disease in a case-control setting.

The aims of the study were to evaluate the occurrence of behavioral addictions (BAs) in alcohol use disorder (AUD) subjects and to investigate the role of impulsivity, personality dimensions and craving.

Numerous viruses of human or animal origin can spread in the environment and infect people via water and food, mostly through ingestion and occasionally through skin contact. These viruses are released into the environment by various routes including water run-offs and aerosols. Furthermore, zoonotic viruses may infect humans exposed to contaminated surface waters. Foodstuffs of animal origin can be contaminated, and their consumption may cause human infection if the viruses are not inactivated during food processing. Molecular epidemiology and surveillance of environmental samples are necessary to elucidate the public health hazards associated with exposure to environmental viruses. Whereas monitoring of viral nucleic acids by PCR methods is relatively straightforward and well documented, detection of infectious virus particles is technically more demanding and not always possible (e.g. human norovirus or hepatitis E virus). The human pathogenic viruses that are most relevant in this context are nonenveloped and belong to the families of the Caliciviridae, Adenoviridae, Hepeviridae, Picornaviridae and Reoviridae. Sampling methods and strategies, first-choice detection methods and evaluation criteria are reviewed. Virus hazards from food, water and the environment, their reservoirs and routes of transmission; Sampling methods and sampling strategies thereof, including the first choice test methods, and criteria for data evaluation are described. © 2011 Federation of European Microbiological Societies.

Because secondary transmission masks the connection between sources and outbreaks, estimating the proportion of foodborne norovirus infections is difficult. We studied whether norovirus genotype frequency distributions (genotype profiles) can enhance detection of the sources of foodborne outbreaks. Control measures differ substantially; therefore, differentiating this transmission mode from person-borne or food handler-borne outbreaks is of public health interest. Comparison of bivalve mollusks collected during monitoring (n = 295) and outbreak surveillance strains (n = 2,858) showed 2 distinguishable genotype profiles in 1) human feces and 2) source-contaminated food and bivalve mollusks; genotypes I.2 and I.4 were more frequently detected in foodborne outbreaks. Overall, ≈21% of all outbreaks were foodborne; further analysis showed that 25% of the outbreaks reported as food handler-associated were probably caused by source contamination of the food.

Background The food-borne viruses in Europe (FBVE) network database was established in 1999 to monitor trends in outbreaks of gastroenteritis due to noroviruses (NoVs), to identify major transmission routes of NoV infections within and between participating countries and to detect diffuse international food-borne outbreaks. Methods We reviewed the total of 9430 NoV outbreak reports from 13 countries with date of onset between 1 January 2002 and 1 January 2007 for representativeness, completeness and timeliness against these objectives. Results Rates of reporting ranged from a yearly average of 1.8 in 2003 to 11.6 in 2006. Completeness of reporting of an agreed minimum dataset improved over the years, both for epidemiological and virological data. For the 10 countries that provided integrated (epidemiological AND virological) reporting over the 5-year period, the completeness of the minimum dataset rose from 15 in 2003 to 48 in 2006. Two countries have not been able to combine both data types due to the structure of the national surveillance system (England and Wales and Germany). Timeliness of reporting (median days between the onset of an outbreak and the date of reporting to the FBVE database) differed greatly between countries, but gradually improved to 47 days in 2006. Conclusion The outbreaks reported to the FBVE reflect the lack of standardization of surveillance systems across Europe, making direct comparison of data between countries difficult. However, trends in reported outbreaks per country, distribution of NoV genotypes, and detection of diffuse international outbreaks were used as background data in acute questions about NoV illness and the changing genotype distribution during the 5-year period, shown to be of added value. Integrated reporting is essential for these objectives, but could be limited to sentinel countries with surveillance systems that allow this integration. For successful intervention in case of diffuse international outbreaks, comp

We will pursue a Bayesian semiparametric approach for an Accelerated Failure Time regression model, usually consid- ered in survival analysis, when the error distribution is a mix- ture of parametric densities with a nonparametric mixing mea- sure. The Dirichlet process is a popular choice for the mix- ing measure, yielding a Dirichlet process mixture model for the error; the paper considers the same model, but here, as an alternative to the Dirichlet process, the mixing measure is equal to a normalized inverse-Gaussian prior, built from nor- malized inverse-gaussian finite dimensional distributions, as recently proposed in the literature. A comparison between the two models will be carried out. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. The efficiency of computational meth- ods will also be compared, using both real and simulated data.

Based upon failure data (date and odometer reading) in the early operating time, a public transportation company is interested in checking the actual reliability of the door opening system of its subway trains before their warranty expires. We consider non‐homogeneous Poisson processes with a double scale because both time and kilometres are recorded for each failure. Different choices to model the relation between operated time and kilometres run are possible: in this paper the kilometres run are incorporated within the intensity function as a random function of time, modelled as a gamma process. Furthermore a periodic component is introduced to deal with the seasonality in the data. Bayesian inference is then carried out via Monte Carlo simulation, obtaining prediction intervals for the expected number of failures during periods of desired length, using only part of the data. The predictions are then compared with the observed data.