Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Deep Clustering Variational Auto-Encoder for Anomaly-based Network Intrusion Detection

The role of semi-supervised network intrusion detection systems is becoming increasingly important in the ever-changing digital landscape. Despite the boom in commercial and research interest, there are still many concerns over accuracy yet to be addressed. Two of the major limitations contributing to this concern are reliably learning the underlying probability distribution of normal network data and the identification of the boundary between the normal and anomalous data regions in the latent space. Recent research has proposed many different ways to learn the latent representation of normal data in a semi-supervised manner, such as using Clustering-based Autoencoder (CAE) and hybridized approaches of Principal Component Analysis (PCA) and CAE. However, such approaches are still affected by these limitations, predominantly due to an overreliance on feature engineering, or the inability to handle the large data dimensionality. In this paper, we propose a novel Cluster Variational Autoencoder (CVAE) deep learning model to overcome the aforementioned limitations and increase the efficiency of network intrusion detection. This enables a more concise and dominant representation of the latent space to be learnt. The probability distribution learning capabilities of the VAE are fully exploited to learn the underlying probability distribution of the normal network data. This combination enables us to address the limitations discussed. The performance of the proposed model is evaluated using eight benchmark network intrusion datasets: NSL-KDD, UNSW-NB15, CICIDS2017 and five scenarios from CTU13 (CTU13-08, CTU-13-09, CTU13-10, CTU13-12 and CTU13-13). The experimental results achieved clearly demonstrate that the proposed method outperforms semi-supervised approaches from existing works.
The dissipation distance for a 2D single crystal with two symmetric slip systems

We solve a model problem from single crystal plasticity.We consider 4 slip systems in the plane with orthogonal slip-directions and equal slip rates, forward as well as backwards. We compute the associated dissipation distance by solving an optimal control problem. It turns out that from a computational point of view computing the distance is inexpensive. We put special emphasis on visualization of the metric spheres and the associated length-minimizing curves. As a byproduct we also solve a related problem, optimal path planning for a car driving forwards and backwards with limited turning radius in the hyperbolic plane. This is a hyperbolic version of the Reeds-Shepp-Car-Problem first discussed in (Pac. J. Math. 145:367-393, 1990).
DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation

The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don’t effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net.
Quantum Markov chains: A unification approach

In this paper, we study a unified approach for quantum Markov chains (QMCs). A new quantum Markov property that generalizes the old one, is discussed. We introduce Markov states and chains on general local algebras, possessing a generic algebraic property. We stress that this kind of algebras includes both Boson and Fermi algebras. Our main results concern two reconstruction theorems for quantum Markov chains and for quantum Markov states. Namely, we illustrate the results through examples.
The High Energy Cosmic-Radiation Detection (HERD) facility for direct cosmic-ray measurements.

The High Energy cosmic-Radiation Detection (HERD) facility is a future space experiment which is designed for the direct measurement of cosmic-rays (CR). The instrument will be installed aboard the China’s Space Station around 2027 and is based on a homogeneous, deep, 3D segmented calorimeter. The calorimeter is surrounded by scintillating fiber trackers, anti-coincidence scintillators, silicon charge detectors, and a transition radiation detector. The HERD instrument is designed to feature a very large acceptance, thus allowing the extension of current measurements up to much higher energies. e.g. the few PeV region in the case of light nuclei, depending on their actual flux. Fundamental progress in our understanding of propagation and acceleration of CR inside the Galaxy will be achieved by measuring the flux of protons and nuclei above hundreds of TeV per nucleon. By exploring the electron flux in the multi-TeV region, it will be possible to search for the signature of dark matter and nearby astrophysical sources. Finally, thanks to the large field of view, the experiment will also monitor the gamma-ray sky from a few hundred of MeV up to 1 TeV. In this paper a review of the current status of the experiment is presented
Influence of Fracture Reduction on the Functional Outcome after Intramedullary Nail Osteosynthesis in Proximal Humerus Fractures

Background: Optimal treatment of proximal humeral fractures (PHF) is still controversial. Therefore, we aim to investigate the influence of fracture reduction after intramedullary nailing of a PHF on the clinical outcome. Methods: Patients treated with intramedullary nail for PHF from 2013–2018, (minimum follow-up 12 months) were retrospectively included. Constant Score (CS), DASH and Simple Shoulder Test (SST) were collected. Postoperative radiographs were used to determine head-shaft-alignment (HSA), head-shaft-displacement (HSD), integrity of the medial hinge and the cranialization of the greater tuberosity (CGT). The results of fracture reduction were categorized as either “anatomical”, “acceptable” or as “malreduction”. Malreduction exists when at least one of the following parameters are present: HSA > 150/< 110°, HSD >5 mm, CTM > 5 mm or lack of integrity of the medial hinge. Results: 42 patients (mean age 65.5 ± 14.1 years, 15 male, 27 female) with a mean follow-up time of 43 months were included. The average CS was 60 ± 30, DASH 49.8 ± 24.3 and SST 62.9 ± 26.9. There was an “anatomic” reduction in 9 (21.4%), “acceptable” in 7 (16.6%) and a “malreduction” in 26 (62%) patients. Comparing the combined “anatomical” and “acceptable” reduction group with the “malreduction” group, worse scores were observed in the “malreduction” group (CS 67.2 vs. 55.2, DASH 45.2 vs. 51.9, SST: 69.3 vs. 58.6) without statistical significance (CS: p = 0.095, DASH: p = 0.307, SST: p = 0.400). By means of multiple logistic regression analyses no statistically significant risk factors were identified for lower DASH, CS and SST scores. Conclusions: Anatomical fracture reduction using intramedullary nails has a positive effect on postoperative outcome for the 3 scores recorded, without reaching statistical significance.
3D Surface Reconstruction of Mathematical modelling Used for Controlling the Generation of Different Bi-cubic BSpline in Matrix Form without Changing the Control Points

This paper introduces a 3D surface reconstruction of mathematical modelling by using blossoming, dependent on a parameter in the coefficient of the control points, the bicubic B-spline surface in matrix form scheme can be utilized to obtain a better quality of reconstructed surface, by using different values of parameter of coefficients used for controlling generating 3D complex surface, which is not easy to recreate. The de-Boor method is used to upgrade the matrix form of 2D to bicubic 3D- b-spline surface depends on parameter value. The model can be seen more efficient of surface in comparison with that needed in conventional methods. The effectiveness of the proposed algorithm is illustrated through several comparative examples of 3D matrices surface. The change in surface is made without any change in the control points. Applications of the modeling in both the curve and surface can be used in many fields, such as banknote design, shape design, decorations, governmental document, and other documents, all of which are of high impotence. And it is used to find the appropriate solutions that prevent or reduce the forgery and counterfeiting of the important and vital documents
Has the COVID-19 Pandemic Led to Changes in the Tasks of the Primary Care Workforce? An International Survey among General Practices in 38 Countries (PRICOV-19)

The COVID-19 pandemic has had a large and varying impact on primary care. This paper studies changes in the tasks of general practitioners (GPs) and associated staff during the COVID-19 pandemic. Data from the PRICOV-19 study of 5093 GPs in 38 countries were used. We constructed a scale for task changes and performed multilevel analyses. The scale was reliable at both GP and country level. Clustering of task changes at country level was considerable (25%). During the pandemic, staff members were more involved in giving information and recommendations to patients contacting the practice by phone, and they were more involved in triage. GPs took on additional responsibilities and were more involved in reaching out to patients. Problems due to staff absence, when dealt with internally, were related to more task changes. Task changes were larger in practices employing a wider range of professional groups. Whilst GPs were happy with the task changes in practices with more changes, they also felt the need for further training. A higher-than-average proportion of elderly people and people with a chronic condition in the practice were related to task changes. The number of infections in a country during the first wave of the pandemic was related to task changes. Other characteristics at country level were not associated with task changes. Future research on the sustainability of task changes after the pandemic is needed.
A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

Due to the problem of single factor and no benchmark in the design of image characteristic index, the measurement results of machine vision-based grinding surface roughness measurement are greatly influenced by the light source brightness, and the accuracy is limited. To address this problem, a method based on the combination of a full-reference (FR) image quality algorithm visual saliency-induced index (VSI) and a backward propagation (BP) neural network for grinding surface roughness measurement is proposed. First, the VSI is applied to characterize grinding surface roughness. Its performance is then compared with the mainstream color image-based indices. Meanwhile, the ability of anti-interference to light source is analyzed for the VSI. Furthermore, in order to improve the prediction accuracy, the BP neural network is selected and applied to construct the roughness prediction model. The experimental results show that the VSI has significant advantages in the abilities of measurement accuracy and anti-interference to light source brightness levels. BP can significantly improve the prediction accuracy for VSI.
Orchestrated Response of Intracellular Zwitterionic Metabolites in Stress Adaptation of the Halophilic Heterotrophic Bacterium Pelagibaca bermudensis

Osmolytes are naturally occurring organic compounds that protect cells against various forms of stress. Highly polar, zwitterionic osmolytes are often used by marine algae and bacteria to counteract salinity or temperature stress. We investigated the effect of several stress conditions including different salinities, temperatures, and exposure to organic metabolites released by the alga Tetraselmis striata on the halophilic heterotrophic bacterium Pelagibaca bermudensis. Using ultra-high-performance liquid chromatography (UHPLC) on a ZIC-HILIC column and high-resolution electrospray ionization mass spectrometry, we simultaneously detected and quantified the eleven highly polar compounds dimethylsulfoxonium propionate (DMSOP), dimethylsulfoniopropionate (DMSP), gonyol, cysteinolic acid, ectoine, glycine betaine (GBT), carnitine, sarcosine, choline, proline, and 4-hydroxyproline. All compounds are newly described in P. bermudensis and potentially involved in physiological functions essential for bacterial survival under variable environmental conditions. We report that adaptation to various forms of stress is accomplished by adjusting the pattern and amount of the zwitterionic metabolites.
Mechanisms of ionospheric mass escape

[1] The dependence of ionospheric O+ escape flux on electromagnetic energy flux and electron precipitation into the ionosphere is derived for a hypothetical ambipolar pickup process, powered the relative motion of plasmas and neutral upper atmosphere, and by electron precipitation, at heights where the ions are magnetized but influenced by photo-ionization, collisions with gas atoms, ambipolar and centrifugal acceleration. Ion pickup by the convection electric field produces “ring-beam” or toroidal velocity distributions, as inferred from direct plasma measurements, from observations of the associated waves, and from the spectra of incoherent radar echoes. Ring beams are unstable to plasma wave growth, resulting in rapid relaxation via transverse velocity diffusion, into transversely accelerated ion populations. Ion escape is substantially facilitated by the ambipolar potential, but is only weakly affected by centrifugal acceleration. If, as cited simulations suggest, ion ring beams relax into nonthermal velocity distributions with characteristic speed equal to the local ion-neutral flow speed, a generalized “Jeans escape” calculation shows that the escape flux of ionospheric O+ increases with Poynting flux and with precipitating electron density in rough agreement with observations.
An Intelligent System for DDoS Attack Prediction Based on Early Warning Signals

Among the different threats causing significant losses in cyberspace, the distributed denial of service (DDoS) attack is one of the most dangerous. The literature shows that the most reasonable manner to reduce the impacts of a DDoS attack is to prevent an attacker from launching it. Prevention is essential because attack sophistication allows them to reach massive traffic volumes, bypassing defenses. Defense mechanisms need time to detect and mitigate attacks. Hence, it is paramount to manage signals of the attack preparation before the attacker effectively launches it. This work presents COOPRED DDoS, a cooperative system for predicting DDoS attacks based on early warning signals extracted from the preparation of DDoS attacks. Its goal lies in increasing the time to prevent DDoS attacks. This work has followed four experiments utilizing two datasets widely employed in the literature. The results show that COOPRED DDoS identifies signals of attacks before the attacker effectively launches them. The system predicts one of the investigated attacks up to 3 minutes and 49 seconds in advance and the other attack up to 3 minutes and 55 seconds. The accuracy of the experiments varies from 99.60% to 99.87%.
Patterns of Diurnal Marine Stratocumulus Cloud Fraction Variability

AbstractThe spatial patterns of subtropical marine stratocumulus cloud fraction variability on diurnal time scales are examined using high-temporal-resolution cloud masks that are based on 30-min, 4 km × 4 km geosynchronous infrared data for 2003–10. This dataset permits comparison of the characteristics of variability in low cloud fraction among the three subtropical marine stratocumulus regions in the northeastern (NE) Pacific, southeastern (SE) Pacific, and SE Atlantic Oceans. In all three regions, the largest diurnal cycles and earliest time of cloud breakup occur on the edges of the cloud field where cloud fractions are generally lower. The rate at which the cloud breaks up during the day is tied to the starting cloud fraction at dawn, which determines the amount of longwave cooling that is initially available to offset shortwave radiative fluxes during the day. The maximum rate of cloud breakup occurs near 1200 LT. Cloud fraction begins to increase by 1600 LT (before the sun sets) and reaches its ma.
Simulations of the spectrum from a photoionized Si plasma

In order to interpret the spectrum of a photoionized Si plasma, we calculate the level populations and emissions using a collisional-radiative equilibrium model with detailed atomic data. In our calculations, levels with principal quantum number up to 9 are included and the electron impact processes are also taken into account. The peak around 1855 eV, which is not reproduced in the previous simulation, is obvious in the present results. Spectra analyzing shows that this peak mainly comes from the intercombination line of He-like Si. Our calculations indicate that the electron impact processes enhance this intercombination line about once, although the electron temperature is pretty low.
Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps’ privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.
Computing Significant Cliques in Large Labeled Networks

Mining cohesive subgraphs and communities is a fundamental problem in network analysis and has drawn much attention in the last decade. Most existing cohesive subgraph models mainly consider the structural cohesion but ignore the subgraph significance. In this article, we formulate a new model, called statistically significant clique, to mine significant cohesive subgraphs in large vertex-labeled graphs. A statistically significant clique is a complete subgraph with a significance value exceeding a given threshold. The subgraph significance is evaluated by a widely used metric called chi-square statistic. We study the problem of enumerating all maximal statistically significant cliques. The problem is proved to be NP-hard. We propose an efficient branch-and-bound algorithm with several elegant pruning strategies to solve our problem. We conduct extensive experiments on seven large real-world datasets to show the practical efficiency of our algorithms. We also conduct a case study to evaluate the effectiveness of our proposed model.
DISCRETE ANALOGUES OF THE LAGUERRE INEQUALITY

It is shown that , m = 0,1,…, where f(x) is either a real polynomial with only real zeros or an allied entire function of a special type, provided that the distance between two consecutive zeros of f(x) is at least . These inequalities are surprisingly similar discrete analogues of higher degree generalizations of the Laguerre and Turan inequalities. Applied to the classical discrete orthogonal polynomials, they yield sharp, explicit bounds, uniform in all parameters involved, on the polynomials and their extreme zeros. This is illustrated for the case of Krawtchouk polynomials.
Reassessing Neolithic Diets in Western Scotland

Although marine resources are known to have been exploited by both foragers and early farmers in Scotland, the importance of seafood to the diets of Neolithic groups has been widely debated. Here we present paired stable isotope (δ13C and δ15N) and radiocarbon measurements on Early Neolithic human remains from Raschoille Cave in Oban. These are compared with published data for other sites in western Scotland and used to re-evaluate the use of marine resources by the first farmers. The diets of Late Mesolithic foragers and Early Neolithic farmers were modelled from stable isotope data using both Linear and Bayesian (FRUITS) mixing models. Our FRUITS dietary models indicate that Mesolithic foragers obtained much of their dietary protein and calories from marine resources, consistent with the predominance of shellfish, fish and sea mammal remains in their shell middens. Of note is the large proportion of dietary calories obtained from plant foods, which is like that of the early farming groups. The diets of Early Neolithic farmers appear relatively homogeneous across Scotland. Plant foods were the primary source of calories. Meat and/or dairy from terrestrial mammals were the most important source of dietary protein. Marine resources were, at most, a minor component of the ‘lifetime’ diet.
Lattice Boltzmann Simulations for Thermal Vapor-Liquid Two-Phase Flows

Lattice Boltzmann model with a double distribution functions are used to simulate thermal vapor-liquid two-phase flow. In this model, the single component multi-phase lattice Boltzmann model proposed by Zhang and Chen is used to simulate the fluid dynamics. The internal energy is simulated by using a energy distribution functions. By coupling the fluid dynamics and internal energy field through a suitably defined density term, the thermal gas-liquid phase flows are simulating. Numerical results on phase separation show the coexistence curves of numerical simulations are in good agreement with the theoretical predictions for the density of gas-liquid phase, and the internal energy of gas is higher than the internal energy of liquid at the same reduced temperature.