Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN-based Deepfake detectors into classifying fake videos as real. Finally, we study the extent to which adversarial perturbations transfer across different Deepfake detectors and create more accessible attacks using universal adversarial perturbations that pose a very feasible attack scenario since they can be easily shared amongst attackers.1
Regulatory Mechanisms of L-Lactic Acid and Taste Substances in Chinese Acid Rice Soup (Rice-Acid) Fermented With a Lacticaseibacillus paracasei and Kluyveromyces marxianus

Rice-acid has abundant taste substances and health protection function due to the various bioactive compounds it contains, including organic acids. L-lactic acid is the most abundant organic acid in rice-acid, but the regulatory mechanisms of L-lactic acid accumulation in rice-acid are obscure. In this study, we analyzed the dynamic changes in organic acids and taste substances in rice-acid in various fermentation phases and different inoculation methods. We identified the key genes involved in taste substance biosynthesis by RNA-Seq analysis and compared the data of four experimental groups. We found that the interaction of the differences in key functional genes (L-lactate dehydrogenase and D-lactate dehydrogenase) and key metabolism pathways (glycolysis, pyruvate metabolism, TCA cycle, amino acid biosynthesis, and metabolism) might interpret the accumulation of L-lactic acid, other organic acids, and taste substances in rice-acid fermented with Lacticaseibacillus paracasei. The experimental data provided the basis for exploring regulatory mechanisms of taste substance accumulation in rice-acid.
Does Universal Screening for Gestational Diabetes Mellitus Improve Neonatal Outcomes in a Socially Vulnerable Population: A Prospective Study in French Guiana

Aims/Introduction French Guiana has a high prevalence of metabolic diseases, which are risk factors for gestational diabetes mellitus. Despite routine screening for gestational diabetes, treatment is still challenging because of health inequalities and different cultural representations of disease and pregnancy. This study was conducted to assess the role of early and universal GDM screening on obstetrical and neonatal complications in a socially deprived population. Materials and Methods A prospective study was conducted, in the level III maternity in French Guiana. Of 2136 deliveries, 223 had gestational diabetes mellitus, 110 of whom were followed-up for 6 month to detail their social and laboratory parameters. Results The prevalence of gestational diabetes in French Guiana (Cayenne Hospital) was estimated at 10.3%. The study population was very precarious with 70% of patients on welfare (universal health coverage or state medical assistance). The following obstetrical complications were observed: cesarean delivery (32%), history of miscarriage (26%) and preeclampsia (7.4%). Nevertheless, neonatal complications were rarely present and included hypoglycemia (2.8%) and macrosomia (2.8%). Conclusion In French Guiana, gestational diabetes mellitus is very common. However, in a context of widespread poverty and diverse cultural representations, universal screening and monitoring limited the risk of macrosomia.
A Dynamic Transcription Factor Signature Along the Colorectal Adenoma-Carcinoma Sequence in Patients With Co-Occurrent Adenoma and Carcinoma

Background Colorectal carcinoma (CRC) often arises from benign adenoma after a stepwise accumulation of genetic alterations. Here, we profiled the dynamic landscapes of transcription factors (TFs) in the mucosa-adenoma-carcinoma progression sequence. Methods The transcriptome data of co-occurrent adenoma, carcinoma, and normal mucosa samples were obtained from GSE117606. Identification of differentially expressed TFs (DE-TFs) and subsequent function annotation were conducted in R software. Expression patterns of DE-TFs were clustered by Short Time-series Expression Miner software. Thereafter, modular co-expression analysis, Kaplan-Meier survival analysis, mutation profiling, and gene set enrichment analysis were conducted to investigate TF dynamics in colorectal tumorigenesis. Finally, tissue microarrays, including 51 tumors, 32 adenomas, and 53 normal tissues, were employed to examine the expression of significant candidates by immunohistochemistry staining. Results Compared to normal tissues, 20 (in adenoma samples) and 29 (in tumor samples) DE-TFs were identified. During the disease course, 28 expression patterns for DE-TFs and four co-expression modules were clustered. Notably, six DE-TFs, DACH1, GTF2IRD1, MEIS2, NR3C2, SOX9, and SPIB, were identified as having a dynamic signature along the colorectal adenoma-carcinoma sequence. The dynamic signature was of significance in GO enrichment, prognosis, and co-expression analysis. Among the 6-TF signature, the roles of GTF2IRD1, SPIB and NR3C2 in CRC progression are unclear. Immunohistochemistry validation showed that GTF2IRD1 enhanced significantly throughout the mucosa-adenoma-carcinoma sequence, while SPIB and NR3C2 kept decreasing in stroma during the disease course. Conclusions Our study provided a dynamic 6-TF signature throughout the course of colorectal mucosa-adenoma-carcinoma. These findings deepened the understanding of colorectal cancer pathogenesis.
Producing Athletes and Centering Whiteness: Reexamining Non-Revenue Generating Collegiate Athletic Cultures

Abstract Creating racially just campuses requires dismantling the cultural imperialism of Whiteness—how Whiteness becomes the norm to which all else is measured, compared, and excluded. Little is known about how Whiteness shapes the most prevalent athletic cultures: non-revenue teams. Using critical Whiteness studies and life-history interviews with 47 rowers and track and field college athletes, I propose three components of athletic cultures: isolation, compliance, and competition. Athletes learn these elements are neutral and necessary for athletics; however, they normalize White persons’ overrepresentation in sport, require loyalty to one (White) cultural form, and position social outcomes as individually and fairly won.
Understanding Muslim College Students’ Sense of Belonging and Mattering at HBCUs

Abstract With a sample of 160 Muslim college students attending 34 historically Black colleges and universities (HBCUs), ordinary-least-squares regression analyses were conducted to examine students’ sense of belonging and mattering. Conceptually grounded in the minoritized religious and spiritual campus climate framework (MRSCC), the findings indicated that Muslim students who felt comfortable talking with non-Muslim peers about critical social issues (i.e., racism, sexism, Islamophobia, etc.) were unlikely to feel like an outsider. Muslim students who indicated that they felt supported by administrators and faculty were more likely to report feeling like they matter. Support from administrators was also significant for Muslim students’ belonging.
An Evaluation of Saga Pattern Implementation Technologies

The Saga pattern is frequently mentioned in the literature to structure communication workflows in Microservices Architectures. To ease the implementation of the Saga pattern frameworks and tools have emerged. By implementing an exemplary use case, we qualitatively evaluate two of such technological solutions in this paper according to criteria relevant for Microservices Architectures. This evaluation can be considered when deciding on which technology to use for implementing the Saga pattern, or also as a more general insight into what should be kept in mind when implementing the Saga pattern in Microservices Architectures.
SOIL CHEMICAL CHANGES AFTER NINE YEARS OF DIFFERENTIAL N FERTILIZATION IN A NO-TILL DRYLAND WHEAT-CORN-FALLOW ROTATION

Intensively cropped dryland systems in the central Great Plains require adequate N fertilization for optimum residue and grain production. However, this N fertilization could be slowly changing the chemistry of the surface soil because of a decrease in soil pH and an increase in soil organic matter (SOM) and basic cations, even in previously well buffered calcareous soil systems. We investigated the effects of five increasing ammonium-N fertilizer rates in a Platner loam, on physical and chemical changes at the 0 to 5, and 0 to 15-cm depths after three cycles of no-till wheat (Triticum aestivum L.)-corn (Zea mays L.)-fallow rotation. We measured soil pH, texture, bulk density, cation exchange capacity (CEC), total P, soluble and total soil organic carbon (SOC), nitrate-N to a depth of 60 cm, and grain yields. No significant changes were found with soil texture, bulk densities, CEC, and total P. The data showed a significant reduction in surface (0-5 cm) soil pH (6.5 to 5.1) with the highest N rate (112 kg/ha), but this was accompanied by a 40% increase in SOC. Although there were significant increases in Al and Mn and decreases in Ca concentrations in the surface 0 to 5 cm at the highest N rate, no reduction in grain yields occurred relative to lower N levels with near neutral pHs. Because only a shallow depth of the soil was affected, residue, SOM, and rapid root growth could be compensating for surface acidity. Over the longer term, we need to monitor the effects of ammoniacal-N on downward soil acidity and yield trends under these new intensive cropping systems.
Stories from Blogs: Computational Extraction and Visualization of Narratives

Social media platforms are designed to serve as an avenue for people to connect with like-minded individuals to discuss their views and promote democracy. However, they have become a tool for deviant actors to undermine the same. Due to the anonymity and perceived less personal risk, deviant groups coordinate on these platforms to spread fake news, misinformation, and disinformation. Social media platforms such as blogs that are unregulated and provide richer space for content generation are strategically used for agenda setting, content framing, and weaponizing narratives to radicalize mobs and provoke hysteria. The recent events and protests coordinated through social media demonstrate the critical need for tools to identify these fringe narratives early on. In this paper, we demonstrate a narrative visualization tool that provides an analyst the ability to identify prominent themes and associated narratives. The tool builds upon published framework to extract narratives from blogs and is available for public use through the Blog-trackers application.
Hybridizing AI and Domain Knowledge in Nanotechnology: the Example of Surface Roughness Effects on Wetting Behavior

In this paper, we propose a scheme for the hybridization of domain modeling and theoretical knowledge in nanotechnology with Artificial Intelligence(AI) techniques and evaluate the success of its application to predict the relationship between nanosurface morphology and wettability. We utilize domain knowledge consisting of two parts. The first part is a mathematical modeling based on the inverse Fourier transform for the generation of rough surfaces with Gaussian or non-Gaussian height distributions, characterized by their first moments (Rms, skewness, kurtosis) and the correlation lengths along x and y-axes. The second part lies in the assumption that the Wenzel scenario for wetting of rough surfaces holds where the critical parameter for contact angle determination is the roughness ratio 𝑟 , defined as the ratio of true (active) area of the solid surface to the apparent (projected) area. By creating different types of surfaces with a variety of input parameters, we create a database linking surface roughness parameters to the ratio 𝑟 . This database is used to train Machine Learning (ML) models and validate them appropriately. Specifically, we train deep, feed-forward neural networks and random forest models and validate them on a separate (held-out) test dataset. We investigate systematically the amount of input data needed to get accurate predictions on the test data. We also evaluate the importance of different input roughness parameters with respect to their effects on surface wettability. To this end, we study the weights that the learning AI models assigned to roughness parameters through training and discuss the findings with respect to experimental expectations.
Opportunistic postpartum depression symptom screening at well-child clinics in Alberta, 2012–2016

The study objective was to assess the reach and delivery of opportunistic postpartum depression (PPD) symptom screening at well-child clinic (WCC) immunization appointments in Alberta. The relationship between socio-demographic factors and PPD symptom screening status, and PPD symptom scores was explored. In this retrospective population-based cohort study, administrative health data from WCC immunization appointments were used to assess the PPD symptom screening delivery and scores from January 1, 2012 to December 31, 2016. The associations with maternal age and area-level material deprivation were determined by multivariable statistics. The number of births ranged from 51,537 to 55,787 annually. The percentage of mothers screened for PPD symptoms using the Edinburgh Postnatal Depression Scale decreased between 2012 and 2016, from 80.1% to 69.7%. Of those screened, 3–3.2% of the mothers were identified to be at high risk for PPD, annually. Screening status varied according to maternal age: mothers ≤29 years were more likely to be screened than mothers 30–34 years, while mothers ≥35 years were the least likely to be screened. Logistic regression analyses, adjusting for age, found the odds of not being screened increased with increases in area-level material deprivation. Language/cultural barriers were the most commonly reported reasons for not screening. Opportunistic PPD symptom screening at WCCs can be an efficient method to identify mothers who need postpartum support and to inform population-level public health surveillance. Additional work is needed to further understand barriers to PPD symptom screening, especially language, cultural, and socio-demographic factors.
The Synthesis of Amphiphilic Luminescent Graphene Quantum Dot and Its Application in Miniemulsion Polymerization

Although emulsion applications of microscale graphene sheets have attracted much attention recently, nanoscale graphene platelets, namely, graphene quantum dots (GQDs), have been rarely explored in interface science. In this work, we study the interfacial behaviors and emulsion phase diagrams of hydrophobic-functionalized graphene quantum dots (C18-GQDs). Distinctive from pristine graphene quantum dots (p-GQDs), C18-GQDs show several interesting surface-active properties including high emulsification efficiency in stabilizing dodecane-in-water emulsions. We then utilize the C18-GQDs as surfactants in miniemulsion polymerization of styrene, achieving uniform and relatively small polystyrene nanospheres. The high emulsification efficiency, low production cost, uniform morphology, intriguing photoluminescence, and extraordinary stability render C18-GQDs an attractive alternative in surfactant applications.
Clinical, Pathological and Prognostic Features of Rare BRAF Mutations in Metastatic Colorectal Cancer (mCRC): A Bi-Institutional Retrospective Analysis (REBUS Study)

Simple Summary Somatic BRAF mutations occur in approximately 10% of metastatic colorectal cancers (mCRCs) and, according to the involved codon, are classified as V600E and in non-V600, accounting for 80% and 20%, respectively. Being the most frequent mutation, the BRAF V600E mutation has been extensively investigated and up to now its clinical, pathological and molecular phenotype and its prognostic impact have been clearly described. On the contrary, evidence concerning BRAF non-V600 is weaker. We retrospectively evaluated 537 mCRC patients treated at two Italian Institutions. This study corroborates and strengthens available evidence concerning phenotype and prognostic performance of BRAF non-V600 compared to BRAF V600E and BRAF wild-type mCRCs. This deeper insight on rare BRAF non-V600 mutated mCRC is a primary issue in the precision oncology era, since the wider application of NGS is expected to increase the identification of those aberrations. Abstract Recently, retrospective analysis began to shed light on metastatic colorectal cancers (mCRCs) harboring rare BRAF non-V600 mutations, documenting a distinct phenotype and a favorable prognosis. This study aimed to confirm features and prognosis of rare BRAF non-V600 mCRCs compared to BRAF V600E and BRAF wild-type mCRCs treated at two Italian Institutions. Overall, 537 cases were retrospectively evaluated: 221 RAS/BRAF wild-type, 261 RAS mutated, 46 BRAF V600E and 9 BRAF non-V600. Compared to BRAF V600E mCRC, BRAF non-V600 mCRC were more frequently left-sided, had a lower tumor burden and displayed a lower grade and an MMR proficient/MSS status. In addition, non-V600 mCRC patients underwent more frequently to resection of metastases with radical intent. Median overall survival (mOS) was significantly longer in the non-V600 compared to the V600E group. At multivariate analysis, only age < 65 years and ECOG PS 0 were identified as independent predictors of better OS. BRAF V600E mCRCs showed a statistically significant worse mOS when compared to BRAF wild-type mCRCs, whereas no significant difference was observed between BRAF non-V600 and BRAF wild-type mCRCs. Our study corroborates available evidence concerning incidence, clinicopathologic characteristics and prognosis of BRAF-mutated mCRCs.
Exploratory Data Analysis of Human Activity Recognition Based on Smart Phone

In a smart urban environment, providing accurate information about human activities is an important task. It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on. However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities. One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead. The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained. The HAR method based on smart phone and EDA proposed in this paper is a high precision method. Compared with other classifiers, its accuracy is 96.56%. This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames. The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology. The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.
Compensation for changes in AC voltage at the building terminals using a hybrid transformer

The article discusses the model predictive control of a hybrid transformer (HT) with matrix converter (MC) together with the algorithm of turn-on and turn-off the compensator when it working with the power grid with small voltage changes from nominal values. The proposed solution of HT is used to resolve power quality issues and power flow control in the electrical power distribution system and can be mounted on the connection terminals of buildings. The advantages of predictive control are its very simple realization and possibility to optimize various predefined criteria. However, the proposed algorithm based on the measurement of rms phase voltage values allows for effective management of compensator operation. The operation of both algorithms has been verified in simulation studies.
Deep Batch-normalized eLU AlexNet For Plant Diseases Classification

In early work, the automatic recognition problem of plant diseases relied on traditional machine learning techniques such as Multilayer Perceptrons (MLP) and Support Vector Machines (SVM). However, in recent years new approaches have moved towards the application of Deep Learning (DL) and convolutional neural network which is described as a dominant tool in this field. In this work, we introduce a model with an architecture based on the AlexNet model for the plant diseases classification from leaf images. We present a deeper version of AlexNet with size (3x3) convolution, normalization, regularization, and linear exponential unit (eLU) layers. The training and testing of the proposed model was performed on a PlantVillage dataset. This proposed model obtained precision and a high gain in convergence learning speed. It achieved 99.48% classification accuracy with 17.54x fewer parameters compared to AlexNet.
Pose Consistency KKT-Loss for Weakly Supervised Learning of Robot-Terrain Interaction Model

We address the problem of self-supervised learning for predicting the shape of supporting terrain (i.e. the terrain which will provide rigid support for the robot during its traversal) from sparse input measurements. The learning method exploits two types of ground-truth labels: dense 2.5D maps and robot poses, both estimated by a usual SLAM procedure from offline recorded measurements. We show that robot poses are required because straightforward supervised learning from the 3D maps only suffers from: (i) exaggerated height of the supporting terrain caused by terrain flexibility (vegetation, shallow water, snow or sand) and (ii) missing or noisy measurements caused by high spectral absorbance or non-Lambertian reflectance of the measured surface. We address the learning from robot poses by introducing a novel KKT-loss, which emerges as the distance from necessary Karush-Kuhn-Tucker conditions for constrained local optima of a simplified first-principle model of the robot-terrain interaction. We experimentally verify that the proposed weakly supervised learning from ground-truth robot poses boosts the accuracy of predicted support heightmaps and increases the accuracy of estimated robot poses. All experiments are conducted on a dataset captured by a real platform. Both the dataset and codes which replicates experiments in the paper are made publicly available as a part of the submission.