Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

False medical information on social media poses harm to people's health. While the need for biomedical fact-checking has been recognized in recent years, user-generated medical content has received comparably little attention. At the same time, models for other text genres might not be reusable, because the claims they have been trained with are substantially different. For instance, claims in the SciFact dataset are short and focused: "Side effects associated with antidepressants increases risk of stroke". In contrast, social media holds naturally-occurring claims, often embedded in additional context: "`If you take antidepressants like SSRIs, you could be at risk of a condition called serotonin syndrome' Serotonin syndrome nearly killed me in 2010. Had symptoms of stroke and seizure." This showcases the mismatch between real-world medical claims and the input that existing fact-checking systems expect. To make user-generated content checkable by existing models, we propose to reformulate the social-media input in such a way that the resulting claim mimics the claim characteristics in established datasets. To accomplish this, our method condenses the claim with the help of relational entity information and either compiles the claim out of an entity-relation-entity triple or extracts the shortest phrase that contains these elements. We show that the reformulated input improves the performance of various fact-checking models as opposed to checking the tweet text in its entirety.Comment: Accepted at The 9th Workshop on Argument Minin

An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking

Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical entities in the text. However, their study has two limitations: First, it relies on gold-annotated entities. Therefore, its feasibility for a real-world application cannot be assessed since this requires detecting relevant entities automatically. Second, they represent claim entities with the original tokens. This constitutes a terminology mismatch which potentially limits the fact-checking performance. To understand both challenges, we propose a claim extraction pipeline for medical tweets that incorporates named entity recognition and terminology normalization via entity linking. We show that automatic NER does lead to a performance drop in comparison to using gold annotations but the fact-checking performance still improves considerably over inputting the unchanged tweets. Normalizing entities to their canonical forms does, however, not improve the performance.Comment: Accepted at The Sixth FEVER Worksho

How automation, machine learning, and DNA barcoding can accelerate species discovery in “dark taxa”: Robotics and AI

Robotics and artificial intelligence are two methods that are suitable for improving processes that are normally done manually. Therefore, these techniques also can be used when examining specimen-rich invertebrate samples, where traditional sorting methods are to slow and require expert knowledge. For that reason, we developed the DiversityScanner: a classification, sorting, and measurement robot for invertebrates. The 500 x 500 x 500 mm robot has three linear axes that enable a camera unit and an automated pipette to be moved over a square Petri dish, containing up to 150 specimens. After starting the DiversityScanner the image taken by an overview camera mounted directly above the Petri dish is utilized to calculate the position of the insects. Then the camera unit is moved over one specimen to capture high resolution detailed images. Convolutional neuronal networks (CNNs) are then used to classify the specimen into 14 different insect taxa (mostly families) and the specimen length and volume are estimated. In a final step, the specimen is moved into a microplate using an automated pipette. In this talk we show how the DiversityScanner uses automation and artificial intelligence to take advantage of previously nearly untapped resources in the study of specimen-rich invertebrate samples

DiversityScanner 4K: A High Resolution Extended Focus Camera Setup as Extension for the DiversityScanner

Manual examination of invertebrate species diversity and abundance in Malaise trap samples is a time-consuming and costly task that requires expert knowledge. Automated solutions based on robotics and artificial intelligence can assist experts in evaluating the large number of samples collected, especially when the phenotype of individual species in a sample needs to be assessed and classified. Therefore, we have developed the DiversityScanner, a robotic solution that provides the ability to automatically image, measure, classify, and sort invertebrates (< 3 mm) into 96-well microplates for barcoding. Because it is necessary to document even the smallest details, such as tiny bristles, on a specimen, we have significantly improved the image quality of the detailed images in the DiversityScanner 4k. This is achieved by using an extended focus system and a 12-megapixel camera. By using an electrically focus tunable lens from Optotune, extended focus images can be created from multiple z-stack images with different focus planes. An algorithm then automatically aligns the images, detecting sharp areas in each image, and produces high-resolution extended-focus images. Finally, the object can be classified by a convolutional neural network and the biomass of the insect can be estimated from the image

SDR mint lehetséges drónzavaró eszköz = SDR as a Potential Jamming Tool

Napjainkban a drónok felhasználása és alkalmazása szinte az életünk minden területén megjelenik. A felhasználási intenzitás növekedése egyre nagyobb, akár katonai, akár civil vonatkozásban. A drón alkalmazása egy bizonyos nézőpontból lehet „hasznos”, az emberiség és társadalom fejlődését elősegítő, és lehet „káros” is. Az adott nézőpontból „hasznos” kategóriába sorolt drónt és annak küldetésteljesítését meg kell védeni, míg a „káros” kategóriába sorolt működését és küldetésvégrehajtását korlátozni kell. Cikkünkben a drónok tájékozódását segítő műholdas navigációs rendszer kiiktathatóságát és zavarhatóságát vizsgáljuk. Vizsgálatainkhoz szoftverdefiniált rádiót (SDR) használunk. Nowadays, the use and application of drones appear in almost every area of our lives. The intensity of use is increasing in both military and civilian contexts. From a certain point of view, the use of drones can be “useful” for the development of humanity and society, and it can also be “harmful”. From a given point of view, a drone classified in the “useful” category and its mission performance must be protected, while the operation and mission performance of a “harmful” category must be limited. In our article, we examine the discontinuity and interference of the satellite navigation system that helps with drone orientation. We use software defined radio (SDR) for our tests

Massive Open Online Courses as enablers of service learning

MOOCs offer the possibility of flexible and independent learning processes. Using MOOCs at universities is often seen in the context of blended learning and inverted learning. But the use of MOOCs in other didactic formats, such as service-learning, is less common. Service-learning describes the combination of social engagement with the training of students, i.e., the teaching of technical, methodological and social skills. The aim of this article is to reflect on the use of MOOCs in service-learning and to provide suggestions for further researc

DiversityScanner: Robotic handling of small invertebrates with machine learning methods

Invertebrate biodiversity remains poorly understood although it comprises much of the terrestrial animal biomass, most species and supplies many ecosystem services. The main obstacle is specimen-rich samples obtained with quantitative sampling techniques (e.g., Malaise trapping). Traditional sorting requires manual handling, while molecular techniques based on metabarcoding lose the association between individual specimens and sequences and thus struggle with obtaining precise abundance information. Here we present a sorting robot that prepares specimens from bulk samples for barcoding. It detects, images and measures individual specimens from a sample and then moves them into the wells of a 96-well microplate. We show that the images can be used to train convolutional neural networks (CNNs) that are capable of assigning the specimens to 14 insect taxa (usually families) that are particularly common in Malaise trap samples. The average assignment precision for all taxa is 91.4% (75%–100%). This ability of the robot to identify common taxa then allows for taxon-specific subsampling, because the robot can be instructed to only pick a prespecified number of specimens for abundant taxa. To obtain biomass information, the images are also used to measure specimen length and estimate body volume. We outline how the DiversityScanner can be a key component for tackling and monitoring invertebrate diversity by combining molecular and morphological tools: the images generated by the robot become training images for machine learning once they are labelled with taxonomic information from DNA barcodes. We suggest that a combination of automation, machine learning and DNA barcoding has the potential to tackle invertebrate diversity at an unprecedented scale