The accelerated dissemination of disinformation often outpaces the capacity for manual fact-checking, highlighting the urgent need for Semi-Automated Fact-Checking (SAFC) systems. Within the Portuguese language context, there is a noted scarcity of publicly available datasets ( corpora) that integrate external evidence, an essential component for developing robust AFC systems, as many existing resources focus solely on classification based on intrinsic text features. This dissertation addresses this gap by developing, applying, and analyzing a methodology to enrich Portuguese news corpora (Fake.Br, COVID19.BR, MuMiN-PT) with external evidence. The approach simulates a user's verification process, employing Large Language Models (LLMs, specifically Gemini 1.5 Flash) to extract the main claim from texts and search engine APIs (Google Search API, Google FactCheck Claims Search API) to retrieve relevant external documents (evidence). Additionally, a data validation and pre-processing framework, including near-duplicate detection, is introduced to enhance the quality of the base corpora. The main results demonstrate the methodology's viability, providing enriched corpora and analyses that confirm the utility of claim extraction, the influence of original data characteristics on the process, and the positive impact of enrichment on the performance of classification models (Bertimbau and Gemini 1.5 Flash), especially with fine-tuning. This work contributes valuable resources and insights for advancing SAFC in Portuguese.

In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between the real total number of infected and the number of infected that were documented in the official statistics. Due to many factors, like governmental decisions, several variants circulating, opening and closing of schools, the typical assumption that the parameters of the model stay constant for long periods of time is not realistic. Thus our objective is to create a method which works for short periods of time. In this scope, we approach the estimation relying on the previous 7 days of data and then use the identified parameters to make predictions. To perform the estimation of the parameters we propose the average of an ensemble of neural networks. Each neural network is constructed based on a database built by solving the SIRD for 7 days, with random parameters. In this way, the networks learn the parameters from the solution of the SIRD model. Lastly we use the ensemble to get estimates of the parameters from the real data of Covid19 in Romania and then we illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths. The main goal was to apply this approach on the analysis of COVID-19 evolution in Romania, but this was also exemplified on other countries like Hungary, Czech Republic and Poland with similar results. The results are backed by a theorem which guarantees that we can recover the parameters of the model from the reported data. We believe this methodology can be used as a general tool for dealing with short term predictions of infectious diseases or in other compartmental models.

Since open social platforms allow for a large and continuous flow of unverified information, rumors can emerge unexpectedly and spread quickly. However, existing rumor detection (RD) models often assume the same training and testing distributions and can not cope with the continuously changing social network environment. This paper proposed a Continual Prompt-Tuning RD (CPT-RD) framework, which avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. Specifically, we propose the following strategies: (a) Our design explicitly decouples shared and domain-specific knowledge, thus reducing the interference among different domains during optimization; (b) Several technologies aim to transfer knowledge of upstream tasks to deal with emergencies; (c) A task-conditioned prompt-wise hypernetwork (TPHNet) is used to consolidate past domains. In addition, CPT-RD avoids CF without the necessity of a rehearsal buffer.

Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552. Both results improve on the baseline results of the `AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. There is an imbalance in the education system during the Covid19 pandemic and most of the students don't even have access to educational tools and online learning platforms.

Fake news is an infodemic, a disease worse than anything else we've ever seen. And it's been around for longer than Covid19. Sometimes, fake news has little impact. But when times are uncertain, and a global crisis is in effect, people look for information that alleviates their fear. Unfortunately, fear leaves people more susceptible to accepting misleading information as the real deal.

Artificial intelligence is a vital component in the fight against COVID-19. Healthcare benefits greatly from machine learning and artificial intelligence techniques that allow for better and faster mapping of the virus as well as for more comprehensive research to administrate the right treatment and create a vaccine. The National Institutes of Health has launched the Medical Imagining and Data Resource Center (MIDRC) to deliver AI-based solutions for the new type of problems the world is facing in the actual climate. The goal is to combine the power of AI and medical imaging to better understand and retaliate against COVID-19. Moreover, their goal is to be able to use medical imaging to create personalized treatments for patients with COVID-19.

It was three months ago when my friend Kimonas asked me if I can help him with a huge secret project that he had on his mind. It was kind of a cheap shot as "huge secret project" are my trigger words. He asked me if I can join him on a Zoom at 6am. I told him that I am not going to wake up that early even if the President of Greece was on that Zoom call. It turned out that the Prime Minister and his team of scientists were on the call and I was there, 7am in Los Angeles, half awake, wearing my "A.I pays my bills" t-shirt.

In this post I want to present my modest contribution to the war against the COVID19, the virus that has been changing the way we live for almost a year now. In my latest post I explained how to connect a FLIR Lepton 3 thermal camera to a NVIDIA Jetson Nano to acquire thermal images. With this post I want to explain how they can be used for an useful application for this strange period. The "Jetson Fever Control" is an application that detects the 3D position of people, calculates the body temperature of each of them and emits an alarm if the nearest one has a temperature above 37.5 C, the well know fever threshold for COVID19 screening. I added to the system a Stereolabs ZED2 3D camera to detect people and retrieve their 3D position.

In any given year, 1 in 5 employed US adults experience a mental health issue like depression, anxiety, and insomnia. COVID19 has pushed the world into an uncharted territory as it has proved to be a perfect storm of stressors -- Right from job loss, economic instability, home schooling, food & health insecurity to the uncertainty of when (or even if) life will return to normal. A simple example -- 33 million jobs lost as of May 7, 2020 – huge financial stress, lock down multiplied domestic violence etc. In just a few months, Covid19 has just doubled the stat of the population affected mentally. Hence, mental health needs urgent addressing and some cool innovative technology solutions are coming to the rescue.