Miracle-dz/UniFAR-FTUs huggingface dataset:
Beneath the radar: Exploring the economics of business fraud via underground markets

Underground marketplaces have emerged as a common channel for criminals to offer their products and services. A portion of these product comprises the illegal trading of consumer products such as vouchers, coupons, and loyalty program accounts that are later used to commit business fraud. Despite its well-known existence, the impact of this type of business fraud has not been analyzed in depth before. By leveraging longitudinal data from 8 major underground markets from 2011–2017, we identify, classify and quantify different types of business fraud to then analyze the characteristics of the companies who suffered from them. Moreover, we investigate factors that influence the impact of business fraud on these companies. Our models show that cybercriminals prefer selling products of well-established companies, while smaller companies appear to suffer higher revenue losses. Stolen accounts are the most transacted items, while pirated software together with loyalty programs create the heaviest revenue losses. The estimated criminal revenues are relatively low, at under $600 000 in total for the whole period; but the total estimated revenue losses are up to $7.5 millions.
Synthesis of Thiazoles Bearing Aryl Enamine/Aza‐enamine Side Chains: Effect of the π‐Conjugated Spacer Structure and Hydrogen Bonding on Photophysical Properties

Abstract: An efficient synthesis of a new series of di- and trisubstituted thiazoles (TZs) bearing aryl enamine side chains (ETZs) was developed, and the optical properties of these compounds were compared with structural analogues containing the isoelectronic aza-enamine group (ATZs). Spectral characterization has demonstrated the difference in the absorption and fluorescence of the ETZs and ATZs in solution and emergence of fluorescence ETZs in a solid state. The optimized structural geometries and weak intramolecular interactions, electronic characteristics of the ground and excited states, HOMO and LUMO analysis, changes in electron density after the S0 → S1v excitation, and map of the electrostatic potential (MEP) calculated at DFT level allowed us to estimate the particularities in the geometric and electronic structures of the ETZs and ATZs in the ground and excited states. The availability, synthetic simplicity, stability, large Stokes shift, and high sensitivity to the microenvironment (fluorosolvatochromic behaviour) make the obtained TZs a useful platform for the further design and synthesis of new effective compounds for applications in the field of fluorescence imaging.
Anatomy and Correlation of the Coracoid Process and Coracoclavicular Ligament Based on Three-Dimensional Computed Tomography Reconstruction and Magnetic Resonance Imaging

Background The anatomy of the coracoid process and coracoclavicular (CC) ligament have been described and the correlation between them has been assessed based on 3-dimensional computed tomography (CT) reconstruction and magnetic resonance imaging (MRI), which provide a guide for coracoclavicular ligament reconstruction. Material/Methods Data were collected from 300 patients who underwent both CT and MRI of the shoulder joint from January 2017 to January 2019 at the Jiang’an Hospital of Traditional Chinese Medicine. The coracoid process was observed and classified and parameters of the CC ligament were measured according to different corneal types. All of the statistics were collected and classified by 2 radiologists, and average values were determined. Measurements of segments were taken as follows: ab – In the coronal plane, the length of the CC ligament from the central point of the CC ligament at the clavicular attachment to the CC ligament at the center of the CC attachment); ac – The distance from the center point of the CC ligament at the supraclavicular attachment to the acromioclavicular joint; de – In the sagittal plane, the length of the CC ligament from the center of the clavicular attachment to the coracoid attachment point; fg – The maximum diameter of the CC ligament at the anterior and posterior margins of the clavicle attachment; hi – The largest diameter of the CC ligament at the anterior and posterior edge of the coracoid process attachment; dj – The distance of the coracoclavicular ligament from the center point of the coracoid process attachment to the coracoid process tip; kl – The distance in the supraclavicular plane from the coracoclavicular ligament to the subcoracoid process. Results The analysis showed that there are 5 types of coracoid process: gourd (31%), short rod (20%), long rod (22.3%), wedge (10.3%), and water drop (6.3%). There were statistically significant differences between the lengths of the ac and hi segments in the among the wedge and gourd-type and the short rod and water drop-type coracoid processes. There were statistically significant differences between the lengths of the ab, de, and fg segments in the short rod, gourd, and long rod-type coracoid processes. There were statistically significant differences between the lengths of the ac, fg, hi, dj, and kl segments in the water drop, gourd, and long rod-type coracoid processes. Conclusions The present study indicated that measurement of the CC ligament and the different shapes of the coracoid process provide an anatomical basis for the diagnosis and treatment of shoulder diseases and the data can be used to improve the safety of CC ligament reconstruction.
Effect of pH, Dosage and Concentration on the Adsorption of Congo Red onto Untreated and Treated Aluminium Dross

The adsorption of congo red onto aluminium dross was studied in batch process. The objective of this study is to adsorption capacity between untreated and treated aluminium dross in the removal of congo red. Aluminium dross was leached with 250 ml of 1% of NaOH and and precipitated with 30% H2O2. The treated aluminium dross being calcined at 600°C for 3 hours. The surface area for untreated and treated aluminium dross was 10.06 m2/g and 79.80 m2/g respectively. Then the adsorption process was carried out on an orbital shaker at 200 rpm for 4 hours. In the effect of pH, it was found that untreated removes more congo red compared to the treated while in the effect of concentration solution and dosage of adsorbent, treated aluminium dross removes more congo red. In conclusion, this adsorbent was found to be effective and economically viable in the removal of congo red in waste water treatment.
SODBeans

Comprehending and debugging computer programs are inherently difficult tasks for sighted programmers. These tasks are even more difficult for non-sighted programmers, who rely on audio-based representations of programs. The state-of-the-art approach to building program execution and debugging environments for non-sighted programmers is to retrofit existing visual environments with screen readers. Because of intrinsic differences in the ways humans process auditory and visual information, we argue that these environments are insensitive to the needs of the blind community. We present an alternative: SODBeans (the SonifiedOmniscient Debugger in Netbeans), a compiler, debugger, and accessibility architecture for Netbeans 6.5.
Fingerprint Matching Using Minutiae Feature

-Fingerprints have been used in identification of individuals for many years because of the famous fact that each finger has a unique pattern. Fingerprint is a pattern of ridges on a finger. Fingerprint matching is the process used to determine whether two sets of fingerprint detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, while others look for similarities in the bigger structure of the fingerprint. This paper involves comparison between fingerprint matching based on minutiae matching method and analysis using performance parameter False Acceptance Rate and False Rejection Rate.
Emerging mycotoxins in botanicals: benefit and risk

Background: Fusarium species are responsible for production of emerging mycotoxins with cyclic hexadepsispeptides structures, like enniatins and beauvericin. Although these mycotoxins have not been regulated yet, their high prevalence in food and feed, as well as their potential toxic effects in humans and animals have made of them a burning issue and a threat to food security. Besides its inophoric properties, these mycotoxins may induce cells damages such as oxidative stress, mitochondrial modifications and the disruption on the cell cycle related to several health adverse effects such as immunotoxicity, genotoxicity, endocrine toxicity and neurotoxicity. Moreover, they showed interesting activity against various microorganisms and insects in several studies, leading to a potential use in pesticide and medicine research, as potential candidates for anticancer therapy. Botanicals can go contaminated by mycotoxins when the harvesting practices or manufacturing conditions are inadequate, which make mandatory their study. Methods: This review explores emerging mycotoxins occurrence in several botanicals forms and discuss their possible prejudicial and beneficial effects. Results: Several researchers have reported mycotoxins occurrence at low incidences and levels in botanicals. Despite emerging mycotoxins toxic effects, beneficial properties have been attributed to these mycotoxins by several authors. These compounds are related to insecticidal activity, antibacterial properties, and antifungal and antiviral activities. Cell organelles or enzyme systems are the targets in its antimicrobial activity. Data collected showed also that emerging mycotoxins are involved in capacity of the multidrug transport protein in human cancer cells modulation and apoptotic cell death induction, several researchers are explaining the possibility to employ them with medicinal purpose. Conclusion: More studies are required to explore enniatins and beauvericin possible applications in medicinal and pesticide research. Furthermore, it is important to highlight that the regulation of emerging mycotoxins in food must be revised and updated.
The Autodidactic Universe

We present an approach to cosmology in which the Universe learns its own physical laws. It does so by exploring a landscape of possible laws, which we express as a certain class of matrix models. We discover maps that put each of these matrix models in correspondence with both a gauge/gravity theory and a mathematical model of a learning machine, such as a deep recurrent, cyclic neural network. This establishes a correspondence between each solution of the physical theory and a run of a neural network. This correspondence is not an equivalence, partly because gauge theories emerge from N → ∞ limits of the matrix models, whereas the same limits of the neural networks used here are not well-defined. We discuss in detail what it means to say that learning takes place in autodidactic systems, where there is no supervision. We propose that if the neural network model can be said to learn without supervision, the same can be said for the corresponding physical theory. 1 ar X iv :2 10 4. 03 90 2v 1 [ he pth ] 2 9 M ar 2 02 1 We consider other protocols for autodidactic physical systems, such as optimization of graph variety, subset-replication using self-attention and look-ahead, geometrogenesis guided by reinforcement learning, structural learning using renormalization group techniques, and extensions. These protocols together provide a number of directions in which to explore the origin of physical laws based on putting machine learning architectures in correspondence with physical theories.
Post-Hoc Domain Adaptation via Guided Data Homogenization

Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings. A general approach to this problem involves the adjustment of models to a new domain through transfer learning. However, in many cases, this is not applicable in a post-hoc manner to deployed models and further parameter adjustments jeopardize safety certifications that were established beforehand. In such a context, we propose to deal with changes in the data distribution via guided data homogenization which shifts the burden of adaptation from the model to the data. This approach makes use of information about the training data contained implicitly in the deep learning model to learn a domain transfer function. This allows for a targeted deployment of models to unknown scenarios without changing the model itself. We demonstrate the potential of data homogenization through experiments on the CIFAR-10 and MNIST data sets.
Electrical Properties of Selective-Area-Grown Superconductor-Semiconductor Hybrid Structures on Silicon

We present a superconductor-semiconductor material system that is both scalable and monolithically integrated on a silicon substrate. It uses selective area growth of Al-InAs hybrid structures on a planar III-V buffer layer, grown directly on a high resistivity silicon substrate. We characterized the electrical properties of this material system at millikelvin temperatures and observed a high average field-effect mobility of $\mu \approx 3200\,\mathrm{cm^2/Vs}$ for the InAs channel, and a hard induced superconducting gap. Josephson junctions exhibited a high interface transmission, $\mathcal{T} \approx 0.75 $, gate voltage tunable switching current with a product of critical current and normal state resistance, $I_{\mathrm{C}}R_{\mathrm{N}} \approx 83\,\mathrm{\mu V}$, and signatures of multiple Andreev reflections. These results pave the way for scalable and high coherent gate voltage tunable transmon devices and other superconductor-semiconductor hybrids fabricated directly on silicon.
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data

Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this article, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive (AR) model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to time-series regression and classification tasks for Alzheimer’s disease progression modeling, intensive care unit (ICU) mortality rate prediction, human activity recognition, and event-based digit recognition, where the proposed model based on a gated recurrent unit (GRU) in all cases achieves significantly better predictive performance than the state-of-the-art methods using RNNs, GRUs, and long short-term memory (LSTM) networks.
Digital Epidemiology: A review

The epidemiology has recently witnessed great advances based on computational models. Its scope and impact are getting wider thanks to the new data sources feeding analytical frameworks and models. Besides traditional variables considered in epidemiology, large-scale social patterns can be now integrated in real time with multi-source data bridging the gap between different scales. In a hyper-connected world, models and analysis of interactions and social behaviors are key to understand and stop outbreaks. Big Data along with apps are enabling for validating and refining models with real world data at scale, as well as new applications and frameworks to map and track diseases in real time or optimize the necessary resources and interventions such as testing and vaccination strategies. Digital epidemiology is positioning itself as a discipline necessary to control epidemics and implement actionable protocols and policies. In this review we address the research areas configuring current digital epidemiology: transmission and propagation models and descriptions based on human networks and contact tracing, mobility analysis and spatio-temporal propagation of infectious diseases and infodemics that comprises the study of information and knowledge propagation. Digital epidemiology has the potential to create new operational mechanisms for prevention and mitigation, monitoring of the evolution of epidemics, assessing their impact and evaluating the pharmaceutical and non-pharmaceutical measures to fight the outbreaks. Epidemics have to be approached from the lens of complexity science as they require systemic solutions. Opportunities and challenges to tackle epidemics more effectively and with a human-centered vision are discussed here.
The Observed Mass Distribution of Galactic Black Hole LMXBs Is Biased against Massive Black Holes

The discovery of gravitational-wave radiation from merging black holes (BHs) also uncovered BHs with masses in the range of ≈20–160 M ⊙. In contrast, the most massive Galactic stellar-mass BH currently known has a mass of ≈21 M ⊙. While low-mass X-ray binaries (LMXBs) will never independently evolve into a binary BH system, and binary evolution effects can play an important role in explaining the different BH masses found through studies of X-ray binaries and gravitational-wave events, (electromagnetic) selection effects may also play a role in this discrepancy. Assuming BH LMXBs originate in the Galactic plane, we show that the spatial distributions of the current samples of confirmed and candidate BH LMXBs are both biased to sources that lie at a large distance from the plane. Specifically, most of the confirmed and candidate BH LMXBs are found at a Galactic height larger than three times the scale height for massive star formation. In addition, the confirmed BH LMXBs are found at larger distances to the Galactic center than the candidate BH LMXBs. Interstellar absorption makes candidate BH LMXBs in the plane and bulge too faint for a dynamical mass measurement using current instrumentation. Given the observed and theoretical evidence for BH natal and/or Blaauw kicks, their relation with BH mass and binary orbital period, and the relation between outburst recurrence time and BH mass, the observational selection effects imply that the current sample of confirmed BH LMXBs is biased against the most massive BHs.
The trouble with “puddle thinking:” a user’s guide to the Anthropic Principle

Are some cosmologists trying to return human beings to the centre of the cosmos? In the view of some critics, the so-called"anthropic principle"is a desperate attempt to salvage a scrap of dignity for our species after a few centuries of demotion at the hands of science. It is all things archaic and backwards - teleology, theology, religion, anthropocentrism - trying to sneak back in scientific camouflage. We argue that this is a mistake. The anthropic principle is not mere human arrogance, nor is it religion in disguise. It is a necessary part of the science of the universe.
Functional L-Optimality Subsampling for Massive Data

Massive data bring the big challenges of memory and computation for analysis. These challenges can be tackled by taking subsamples from the full data as a surrogate. For functional data, it is common to collect multiple measurements over their domains, which require even more memory and computation time when the sample size is large. The computation would be much more intensive when statistical inference is required through bootstrap samples. To the best of our knowledge, this article is the first attempt to study the subsampling method for the functional linear model. We propose an optimal subsampling method based on the functional L-optimality criterion. When the response is a discrete or categorical variable, we further extend our proposed functional L-optimality subsampling (FLoS) method to the functional generalized linear model. We establish the asymptotic properties of the estimators by the FLoS method. The finite sample performance of our proposed FLoS method is investigated by extensive simulation studies. The FLoS method is further demonstrated by analyzing two large-scale datasets: the global climate data and the kidney transplant data. The analysis results on these data show that the FLoS method is much better than the uniform subsampling approach and can well approximate the results based on the full data while dramatically reducing the computation time and memory.
QD-GCN: Query-Driven Graph Convolutional Networks for Attributed Community Search

—Recently, attributed community search, a related but different problem to community detection and graph clustering, has been widely studied in the literature. Compared with the community detection that ﬁnds all existing static communities from a graph, the attributed community search (ACS) is more challenging since it aims to ﬁnd dynamic communities with both cohesive structures and homogeneous node attributes given arbitrary queries. To solve the ACS problem, the most popular paradigm is to simplify the problem as two sub-problems, that is, structural matching and attribute ﬁltering and deal with them separately. However, in real-world graphs, the community structure and the node attributes are actually correlated to each other. In this vein, current studies cannot capture these correlations which are vital for the ACS problem. In this paper, we propose Query-Driven Graph Convolutional Networks ( QD - GCN ), an end-to-end framework that uniﬁes the community structure as well as node attribute to solve the ACS problem. In particular, QD - GCN leverages the Graph Convolutional Networks, which is a powerful tool to encode the graph topology and node attributes concurrently, as the backbones to extract the query-dependent community information from the original graph. By utilizing this query-dependent community information, QD - GCN is able to predict the target community given any queries. Experiments on real-world graphs with ground-truth communities demonstrate that QD - GCN out-performs existing attributed community search algorithms in terms of both efﬁciency and effectiveness.
Video Question Answering with Phrases via Semantic Roles

Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models’ application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string. To reduce the influence of language bias in VidQA datasets, we retrieve a video having a different answer for the same question. To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models. We perform extensive analysis and ablative studies to guide future work. Code and data are public.