Miracle-dz/UniFAR-FTUs huggingface dataset:
Payload-based Anomaly Detection for Industrial Internet Using Encoder Assisted GAN

Payload-based anomaly detection has been proved effective in discovering Internet misbehavior and potential intrusions, but highly relies on the unstructured feature engineering to generalize the distribution of normal payloads. This kind of generalization may not adapt well to the emerging industrial Internet, where the normal behaviors are more diverse and usually embedded in the raw payloads' local structures. In this paper, we tackle this generalization problem and propose a very different solution to payload-based anomaly detection without the need of feature engineering. Our basic idea is to learn the raw structures of normal payloads directly by a generative adversarial network (GAN), in which we have a generator (i.e., a reversed convolutional decoder) to sample raw payloads from a latent space as well as a discriminator (i.e., a convolutional classifier) to guide the generator produce raw payloads approximating the normal structures. We also deploy an assisted convolutional encoder to map the true payloads back to the latent space and combine with the GAN's decoder (i.e., generator) to reconstruct the payload structures. We consider anomalies appear in condition the re-constructed payloads are largely deviated from the true ones, since our encoder-decoder architecture is trained able to rebuild only the normal payload structures. We have evaluated our solution using extensive experiments on real-world industrial Internet datasets, and confirmed its effectiveness in detecting industrial Internet anomalies in the raw payloads.
Clock Drawing Test Evaluation via Object Detection for Automatic Cognitive Impairment Diagnosis

Dementia refers to a group of syndromes with cognitive impairment as the main clinical manifestation. Amongst various neuropsychological tests for clinical diagnosis of dementia, the clock drawing test (CDT) is frequently used due to its simplicity and effectiveness. In order to extricate physicians from their involvement in CDT scoring, we propose an automatic scoring method via object detection for accurate and efficient dementia screening. Given the drawn clock picture by a testee, this end-to-end method for CDT scoring directly produces the final score without any human interventions. Moreover, the proposed method can handle two ways of data acquisition scanned data set which contains the scanned version of drawn clocks and camera data set which consists of the drawn clocks taken from normal cameras. Extensive experimental results demonstrate the validity and feasibility of the proposed method. Specifically, the precision of the scanned dataset scored by a seven-point method and the camera dataset by a three-point method is 94.64% and 92.36% respectively, while that of junior outpatient physicians is 92.03% within the camera dataset.
Four-Wheel Anti-Lock Braking System With Robust Adaptation Under Complex Road Conditions

Because of the nonlinear feature of tire force, the anti-lock braking system (ABS) research based on wheel slip control (WSC) is widely used even without the measured wheel slip and road friction. Combined with the WSC technology, the four-wheel ABS with the fuzzy sliding mode (FSM) control method is introduced in this paper. Unlike most single-wheel independent ABS, this design has more advantages when braking under the unusual road conditions, such as the transition of road conditions, the split-road conditions between the left-side wheels and the right-side wheels, even the extreme situation in which road conditions of each wheel are different, as it has the smooth-braking judgment module that is designed for wheel-to-wheel control. Besides, based on the nonlinear function of the tire-road interaction (TRI) model, the designed road condition detection module provides characteristics of the braking road that are required in the ABS operation. The model of vehicle and tire is established in Simulink by using parameters of a vehicle: BYD F0. The effectiveness of the proposed four-wheel ABS is validated through the combined use of MATLAB and RT-lab and via substantial simulations and RT-LAB co-simulations.
BCES: A BlockChain based Credible E-Bidding System

In recent years, blockchain is experiencing a rapid development and has the huge potential of revolutionizing the information platform in the deposit application field. In this paper, a blockchain based credible e-bidding system (BCES) is presented to address operational compliance, multi-party coordination, and cybersecurity problem in the process of distribution, verification and backtracking of bidding data files. BCES is comprised of basic configuration management, contract business deployment, data service processing, system service support, data visualization. Through detailed design of a set of blockchain integration and transformation mechanism for bidding business, different stakeholders are connected together with the help of the alliance blockchain, forming a flat trust management model. The experiments demonstrate that the average TPS of BCES is stable at around 2600–2800, and the response time of the blockchain deposit platform is within the acceptable time range of 3s, and the error rate is very low. Currently, BCES has been deployed and applied in China Telecom Corporation and has achieved remarkable results.
Pilot Allocation Algorithm Based on K-means Clustering in Cell-Free Massive MIMO Systems

In this letter, a pilot allocation algorithm based on K-means clustering is proposed for the cell-free massive MIMO systems. Firstly, to avoid the communication between users and “invalid” access points (APs), a user-centered virtual cell division is adopted. Users select AP according to the large-scale factor of the channel to form a specific AP set which serves the specific users. Then, K value is determined by combining multiple features and the initial centroid is selected to cluster users. Finally, based on the results of user clustering, users in the internal cluster are assigned to orthogonal pilots, and users in different clusters are reused pilots, which can avoid pilot contamination (PC) caused by pilot reusing among users from the space perspective. Simulation results show that the proposed algorithm can reduce channel estimation error and enhance system throughput.
Fast HEVC Selective Encryption Scheme Based on Improved CABAC Coding Algorithm

Context-based adaptive binary arithmetic coding (CABAC) is the only entropy coding method in HEVC. According to statistics, CABAC encoders account for more than 25% of the high efficiency video coding (HEVC) coding time. Therefore, the improved CABAC algorithm can effectively improve the coding speed of HEVC. On this basis, a selective encryption scheme based on the improved CABAC algorithm is proposed. Firstly, the improved CABAC algorithm is used to optimize the regular mode encoding, and then the cryptographic algorithm is used to selectively encrypt the syntax elements in bypass mode encoding. The experimental results show that the encoding time is reduced by nearly 10% when there is great interference to the video information. The scheme is both safe and effective.
Decision-Making and Planning Method for Autonomous Vehicles Based on Motivation and Risk Assessment

In order to improve the real-time and computational efficiency of autonomous vehicles’ decision-making process, this paper draws on the decision-making behavior of human drivers with the motivation as the core and proposes a decision-making and planning method based on motivation and risk assessment. On the one hand, it analyzes and determines the motivations that cause the driving state to change for decision-making and planning. On the other hand, on the basis of the lateral trajectory prediction of surrounding vehicles, the longitudinal trajectory propensity prediction of different drivers is added to construct a risk assessment model that can reflect risk of the future time domain. Based on this, the motivation-based decision method is mapped into the risk assessment model, and a cost function is established to decouple the path and speed, so that the geometry and speed can be flexibly adjusted according to environmental risks. The simulation results show that the proposed method can effectively make driving behavior decisions and plan the trajectory in real time according to the current environment, which can improve the computational efficiency of the decision-making process and guarantee the safety at the same time.
CNN-based Intrusion Classification for IEEE 802.11 Wireless Networks

WiFi has been widely deployed to facilitate home, office, or even stadium-scale wireless access to the Internet, and will be an essential part of the future wireless network through being integrated with 5G cellular networks. However, security threats are still a big concern for WiFi due to the open share nature of wireless medium and the easy-access to WiFi intrusion tools. Therefore, it plays a critical role to identify the experienced attacks for applying further intrusion handling methods. However, existing intrusion detection methods based on traditional machine learning technique usually bears low detection accuracy and need a lot of human intervention. In this backdrop, this paper presents a convolutional neural network (CNN)-based intrusion detection algorithm for WiFi networks. Firstly, we present the detection framework, which first conducts data pre-processing, and then a CNN is trained for attack identification. To reduce both the risk of data overfit and the network training time, Dropout technique is adopted and different network structures are investigated. Experimental results on an open data set named AWID have shown that our algorithm greatly outperform existing ones, and its identification accuracy is higher than 99%.
Oxygenation Strategies in Critically Ill Patients With COVID-19

Background Severe acute respiratory syndrome coronavirus 2 is the virus that causes coronavirus disease 2019 (COVID-19). COVID-19 is a disease characterized by a range of clinical syndromes including variable degrees of respiratory symptoms from mild respiratory illness and severe interstitial pneumonia to acute respiratory distress syndrome, septic shock, coagulopathies, and multiorgan dysfunction. This virus carries the potential to manifest in a wide range of pulmonary findings and hypoxemias, from mild respiratory symptoms to more severe syndromes, such as acute respiratory distress syndrome. The rapid accumulation of evidence and persistent gaps in knowledge related to the virus presents a host of challenges for clinicians. This creates a complex environment for clinical decision-making. Objective To examine oxygenation strategies in critically ill patients with hypoxia who are hospitalized with COVID-19. Discussion These proposed strategies may help to improve the respiratory status and oxygenation of those affected by COVID-19. However, additional high-quality research is needed to provide further evidence for improved respiratory management strategies. Areas of future research should focus on improving understanding of the inflammatory and clotting processes associated with the virus, particularly in the lungs. High-level evidence and randomized controlled trials should target the most effective strategies for improving oxygenation, time requiring mechanical ventilation, and survival for hospitalized patients with COVID-19 presenting with hypoxemia.
Adsorption of carbon dioxide and xenon by porous glass over a wide range of temperature and pressure-applicability of the Langmuir case VI equation

Data on the adsorption of CO 2 and Xe by Vycor porous glass have been analyzed in terms of Langmuir's case VI equation. Measurements over a wide range of temperature and pressure enable a thermodynamic analysis to be made which identifies the parameters of the equation with those postulated in its theoretical derivation
Removal of Dissolved VOCs from Water with an Air Stripper/Membrane Vapor Separation System

Treatment of water contaminated with volatile organic compounds (VOCs) is a major problem for the United States chemical industry. Currently, VOCs are removed from moderately contaminated wastewater streams by processes such as steam stripping and from dilute wastewaters by air stripping combined with a carbon adsorption off-gas treatment system. This paper describes the development and performance of a hybrid process that combines air stripping with membrane organic-vapor separation to recover VOCs from the stripper off-gas. A number of prototype systems have been constructed and evaluated. The optimum system appears to be a tray stripper fitted with a high-pressure compression-condensation membrane separation unit. Such a system can remove 95 to 99% of the VOCs present in contaminated water; the removed VOCs are recovered as a liquid condensate. The economics of the technology are competitive with alternative processes, particularly for streams containing more than 500 ppm VOC and having flow rates less than 10 to 30 gal/min.
One-dimensional model for nonlinear stress control in cable terminations

A one-dimensional model for field control in cable terminations is analyzed mathematically both for linear and nonlinear field grading materials. Far an applied ac voltage, the concept of the penetration length allows for a useful characterization of the voltage profile, even for materials where both the conductivity and the permittivity are field dependent. For lightning impulse events an analytical solution for purely resistive field control is presented, allowing for an arbitrarily strong nonlinearity of the conductivity. The model results yield a useful input for more detailed finite element calculations. The results show that conclusions for nonlinear field control based on linear calculations in general are misleading.
Effects of organophosphate insecticide, malathion on the liver and kidney of male Wistar albino rats by α- tocopherol and selenium

Effects of organophosphate insecticide, malathion on the liver and kidney of male Wistar albino rats by αtocopherol and selenium Abdulaziz M. Al-Othman, Khaled S. Al-Numair, Gaber E. El-Desoky, Kareem Yusuf, Zeid A. Al Othman, Mourad A. M. Aboul-Soud* and John P. Giesy 1 Department of Community Health Sciences, College of College of Applied Medical Science, King Saud University, Kingdom of Saudi Arabia. 2 Department of Chemistry, College of Science, King Saud University, Kingdom of Saudi Arabia. 3 Biochemistry Department, Faculty of Agriculture, Cairo University, 12613 Giza, Egypt. 4 College of Science, King Saud University, P. O. BOX 2245, Riyadh 11451, Kingdom of Saudi Arabia. 5 Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. 6 Department of Zoology, and Center for Integrative Toxicology, Michigan State University, East Lansing, MI, USA. 7 School of Biological Sciences, University of Hong Kong, Hong Kong, SAR, China. 8 Zoology Department, College of Science, King Saud University, P. O. Box 2455, Riyadh 11451, Saudi Arabia.
Optimization Method for Extracting Stabilizer Geometry and Properties of REBCO Tapes

A good knowledge of material properties is a critical aspect for modeling high-temperature superconductor (HTS) devices. However, the knowledge of the electrical resistivity of coated conductors above the critical current is limited. The major challenge in characterizing this regime lies in the fact that for $I>I_{\rm {c}}$, heating effects and thermal instabilities can quickly destroy the conductor if nothing is done to protect it. In previous work we extracted overcritical current data, obtained by combining fast pulsed current measurements with finite element analysis (Uniform Current (UC) model). In this work, we assessed the impact of the uncertainties of the input parameters on the quantities calculated with the UC model (temperature, current in each layer of the tape and resistivity of HTS). Firstly, sensitivity and uncertainty analyses were performed and it was found that the input parameters that mostly affect the accuracy of the UC model are the electrical resistivity and the thickness of the silver layer. Afterwards, an optimization method was developed to correctly estimate the geometry and the resistivity of the silver layer. This method combines experimental measurements of resistance $R(T)$ of the tape and pulsed current measurements. The development of this strategy allowed us to determine the parameters that significantly impact the results of the UC model and helped to minimize their uncertainties. This enables a more accurate estimation of the resistivity in the overcritical current regime.
Enhancement of 3D Seismic Images using Image Fusion Techniques

Seismic images are data collected by sending seismic waves to the earth subsurface, recording the reflection and providing subsurface structural information. Seismic attributes are quantities derived from seismic data and provide complementary information. Enhancing seismic images by fusing them with seismic attributes will improve the subsurface visualization and reduce the processing time. In seismic data interpretation, fusion techniques have been used to enhance the resolution and reduce the noise of a single seismic attribute. In this paper, we investigate the enhancement of 3D seismic images using image fusion techniques and neural networks to combine seismic attributes. The paper evaluates the feasibility of using image fusion models pretrained on specific image fusion tasks. These models achieved the best results on their respective tasks and are tested for seismic image fusion. The experiments showed that image fusion techniques are capable of combining up to three seismic attributes without distortion, future studies can increase the number. This is the first study conducted using pretrained models on other types of images for seismic image fusion and the results are promising.
Potential role of copper availability in nitrous oxide accumulation in a temperate lake

Denitrifying bacteria require copper for the synthesis of nitrous oxide reductase. In the absence of sufficient bioavailable Cu, nitrous oxide (N2O) may accumulate in natural waters during denitrification. Cultures of Paracoccus denitrificans and natural bacterial assemblages collected from a mesotrophic lake (Linsley Pond) were grown at varying Cu concentrations to determine the Cu speciation that results in elevated N2O accumulation. P. denitrificans experienced Cu limitation beginning at inorganic Cu concentrations of 0.6 fmol L−1 (−log of inorganic Cu, pCu′, = 15.2). The natural community did not show an effect until pCu′ was reduced to 23.7 with 8‐hydroxyquinoline, resulting in an approximate 10‐fold increase in N2O concentrations during denitrification. Additions of ethylenediaminetetraacetic acid alone to natural communities did not affect N2O concentrations. Natural copper‐binding ligands detected with competitive ligand exchange‐cathodic stripping voltammetry occurred at concentrations of 6 to 25 nmol L21 with conditional stability constants (KCu2+ L) between 1014.4 and 1015.1. Although more than 99% of total Cu in Linsley Pond was bound to these ligands, inorganic Cu concentrations remained 10 orders of magnitude above those found to increase N2O accumulation during denitrification incubations. Measurements of nitrogen species, dissolved oxygen, and sulfide in the water column of Linsley Pond over the spring growing season revealed that N2O was produced by assimilatory nitrate reduction and nitrification in addition to denitrification, with nitrification generating most of the N2O found in the surface waters of the lake. The results suggest that inorganic Cu concentrations in Linsley Pond are sufficient to support denitrification. Moreover, some denitrifying bacteria may be able to access organically bound Cu, reducing the potential for this metal to affect N2O production in other aquatic environments.
Data Science Canvas: Evaluation of a Tool to Manage Data Science Projects

Data emerged as a central success factor for companies to beneﬁt from digitization. However, the skills in successfully creating value from data – especially at the management level – are not always profound. To address this problem, several canvas models have already been designed. Canvas models are usually created to write down an idea in a structured way to promote transparency and traceability. However, some existing data science canvas models mainly address developers and are thus unsuitable for decision-makers and communication within interdisciplinary teams. Based on a literature review, we identiﬁed inﬂuencing factors that are essential for the success of data science projects. With the information gained, the Data Science Canvas was developed in an expert workshop and ﬁnally evaluated by practitioners to ﬁnd out whether such an instrument could support data-driven value creation.
Process Mapping and Modeling: A Theoretical tool Analysis

The technological development and continuous changes in economic policies have motivated looking for greater efficiency in all levels of an organization. Organizations include in their management improvements that facilitate the control of their processes and activities. In this sense, management tools for process mapping and modeling have a positive influence on the companies’ performance since their inclusion encourages the improvement and optimization of daily activities. However, there is evidence of disadvantages in the application of some adopted methods. This paper analyzes the weaknesses and strengths of the available management models and tools to cover three specific purposes. Firstly, exploring different tools and models for process mapping and modeling used in business management. Secondly, prioritizing tools that enable their integration with the ISO standard and process costing systems. Finally, establishing the advantages and disadvantages of prioritized tools, as well as their main characteristics. To this end, this work contemplates a theoretical analysis and comparison of the methods Business Process Management (BPM)
Predisposition of Obesity through Genetic and Non-Genetic Risk Factors

Globally there is an increase in the number of people affected by obesity.This has increased the count of individuals to double,triple, and even quadruple. Obesity is a complex disease that has a genetic, behavioural,socioeconomic, and environmental effect. This raises morbidity and mortality in obesity. It is an important predisposition for diabetes as well as the current pandemic COVID-19. The rationale of this case-control observational study is to identify obese individuals among the diabetic and non-diabetic population. The study includes non - genetic factors like lipid profiles with genetic factors in form of SNP as a predisposition factor. The amplified portion of the ADIPOQ gene sequence revealed the presence of SNPs rs2241767 in 46.3% population and showed increased lipid profile values. It can be concluded that these are important predisposing factors for obesity .
A New Traffic Distribution Routing Algorithm for Low Level VPNs

Virtual Private Networks (VPN) constitute a particular class of shared networks. In such networks, the resources are shared among several customers. The management of these resources requires a high level of automation to obtain the dynamics necessary for the well-functioning of a VPN. In this paper, we consider the problem of a network operator who owns the physical infrastructure and who wishes to deliver VPN service to his customers. These customers may be Internet Service providers, large corporations and enterprises. We propose a new routing approach referred to as Traffic Split Routing (TSR) which splits the traffic as fairly as possible between the network links. We show that TSR outperforms Shortest Path Routing (SPR) in terms of the number of admitted VPN and in terms of Quality of Service. Keywords—Virtual Private Networks (VPN); Quality of Service (QoS); NS-2; Simulations; Shortest Path Routing (SPR); Traffic Split Routing (TSR); Routing algorithm