DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases.Comment: 5 pages, MED-NeurIPS 2022 worksho

Silicon as a ubiquitous contaminant in graphene derivatives with significant impact on device performance

Silicon-based contaminants are ubiquitous in natural graphite, and they are thus expected to be present in exfoliated graphene. Here, the authors show that such impurities play a non-negligible role in graphene-based devices, and use high-purity parent graphite to boost the performance of graphene sensors and supercapacitor microelectrodes

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic

Mechanism design for distance metric learning

Metric learning is an important area of machine learning, in which a similarity measure between sets of objects is learned from data. In a modern context, this data may come from crowd-sourcing problem instances to a set of users (or workers), who are likely to have different abilities and levels of commitment to the task. In this thesis, we first try to look over some of the main methods developed in this field of machine learning. Finally, we present a mechanism design approach for incentivizing workers to produce accurate data for a metric learning task. We show how to incorporate the data provided using this mechanism into a metric learning algorithm, and establish the theoretical properties of this approach. Results on some simulated problems show the promise of the algorithm.L’apprentissage me ́trique est un domaine important de l’apprentissage automatique, sur lequel une mesure de similarite ́ entre des ensembles d’objets est apprise a` partir de donne ́es. Dans un contexte moderne, ces donne ́es peuvent provenir de certains cas de proble`mes d’externalisation ouverte a` un ensemble d’utilisateurs (ou de travailleurs), qui sont susceptibles d’avoir des capacite ́s et des niveaux d’engagement diffe ́rents pour la taˆche. Dans cette the`se, nous essayons d’abord d’examiner quelques-unes des principales me ́thodes de ́veloppe ́es dans ce domaine de l’apprentissage automatique. Enfin, nous pre ́sentons une approche de conception de me ́canisme pour inciter les travailleurs a` produire des donne ́es pre ́cises pour la taˆche d’apprentissage me ́trique. Nous pre ́sentons comment incorporer les donne ́es re ́ve ́le ́es en utilisant ce me ́canisme dans un algorithme d’apprentissage me ́trique et e ́tablir les proprie ́te ́s the ́oriques de cette approche. Les re ́sultats sur certains proble`mes simule ́s re ́ve`lent la promesse de l’algorithme

Recovery of molybdenum and rhenium in scrub liquors of fumes and dusts from roasting molybdenite concentrates

The present work addresses the recovery of rhenium from scrub liquor of molybdenite concentrate roasting fume and dust by solvent extraction method. According to the results, recovery of rhenium from the scrub liquor is not practical unless molybdenum is removed in advance. The extraction of molybdenum was carried out using a D2EHPA-TBP system which resulted in up to 99.8% Mo extraction in a two-stage solvent extraction at pH = 1 and O/A = 1. Up to 99.6% of Re was extracted subsequently using TOA in a single-stage extraction at pH = −0.3 and O/A = 1:20. The organic phase was stripped by ammonium hydroxide 32% and the resultant liquor was further subjected to evaporation as a result of which, an enriched purified solution was obtained. Ammonium perrhenate was precipitated from the enriched liquor by adjusting the pH to 6.5–7