Miracle-dz/UniFAR-FTUs huggingface dataset:
The North American Soil Moisture Database: Development and Applications

AbstractSoil moisture is an important variable in the climate system that integrates the combined influence of the atmosphere, land surface, and soil. Soil moisture is frequently used for drought monitoring and climate forecasting. However, in situ soil moisture observations are not systematically archived and there are relatively few national soil moisture networks. The lack of observed soil moisture data makes it difficult to characterize long-term soil moisture variability and trends. The North American Soil Moisture Database (NASMD) is a new high-quality observational soil moisture database. It includes over 1,800 monitoring stations in the United States, Canada, and Mexico, making it the largest collections of in situ soil moisture observations in North America. Data are collected from multiple sources, quality controlled, and integrated into an online database (soilmoisture.tamu.edu). Here we describe the development of the database, including quality control/quality assurance, standardization, and .
Audit committee characteristics and firm performance: Evidence from the insurance sector in Oman

This study aims to determine the effect of Audit Committee characteristics on the performance of the eleven insurance companies listed in Oman between 2015 and 2019. This study focuses on the audit committee meetings and their frequency, the size of the committee, their independence, and the knowledge or expertise they possess. After conducting data analysis on the specified dataset, the regression results revealed that ACSIZE is statistically significant at a p-value less than.05. This indicates that the size of an audit committee has a significant impact on an insurance company's performance. In addition, the ACINDE was statistically significant at p.05, demonstrating that the independence of an audit committee plays a significant role in determining the performance of a company. Moreover, the ACEXP variable had a p-value of less than 0.05, indicating that it is statistically significant. This can be interpreted as meaning that the frequency of meetings has a statistically significant impact on the performance of a company. The statistical significance of only three of the four variables was determined. The variable was excluded from the regression model because its p-value was greater than.05. Therefore, the three characteristics of audit committees in the Omani market have a significant impact on the performance of an insurance company, and senior management should ensure that audit committees have approximately four members, are fully independent to limit control by the firm's management, and have extensive financial experience in order to function effectively. This study will be very useful to financial practitioners and policymakers since it contains practical ideas and recommendations. The research results may also contribute to the creation and implementation of strategic policies for improving corporate governance practices with the goal of maximizing profit and wealth.
Vehicle identification and speed calculation method for general road camera perspective

Vehicles are closely related to human life, and the problem of road vehicles has always existed. In order to make road travel safer, the role of developing vehicle-road collaborative technology has become more and more important. However, there are still many problems in the monitoring of road vehicles, such as the integration of other single-point speed monitoring and video-based license plate recognition. In order to solve the problems related to vehicle speed monitoring, adapt to the current situation of insufficient utilization of road image data, this paper introduces a target detection algorithm based on YOLOv5, Dilb 's tracking algorithm, and perspective change speed solution. It is suitable for vehicle recognition tracking and speed calculation technology in general road camera perspective. The program designed by this technology has achieved high results in the actual test of the road. The mAP of the vehicle detection model after parameter adjustment can reach 94.7, and the speed measurement accuracy up to 90.3%, which has high usability.
Reinforcement learning multi-hop reasoning method with GAN network

At present, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.
présentée pour obtenir le grade de docteur de l'Ecole Nationale Supérieure des Télécommunications Spécialité : Informatique et Réseaux

Abstract This thesis is concerned with quantum key distribution (QKD), a cryptographic prim-itive allowing two distant parties, Alice and Bob, to establish a secret key, in spite ofthe presence of a potential eavesdropper, Eve. Here, we focus on continuous-variableprotocols, for which the information is coded in phase-space. The main advantage ofthese protocols is that their implementation only requires standard telecom components.The security of QKD lies on the laws of quantum physics: an eavesdropper willnecessary induce some noise on the communication, therefore revealing her presence.A particularly di cult step of continuous-variable QKD protocols is the reconcilia-tion where Alice and Bob use their classical measurement results to agree on a commonbit string. We rst develop an optimal reconciliation algorithm for the initial protocol,then introduce a new protocol for which the reconciliation problem is automatically takencare of thanks to a discrete modulation.Proving the security of continuous-variable QKD protocols is a challenging problembecause these protocols are formally described in an in nite dimensional Hilbert space. Asolution is to use all available symmetries of the protocols. In particular, we introduce andstudy a class of symmetries in phase space, which is particularly relevant for continuous-variable QKD. Finally, we consider nite size eects for these protocols. We especiallyanalyse the in uence of parameter estimation on the performance of continuous-variableQDK protocols.Keywords: quantum cryptography, quantum key distribution, quantum optics, quan-tum communication, quantum bit commitment, phase-space representation, information
A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

Statistical process monitoring (SPM) provides a way, by means of monitoring schemes, to be alerted when a process significantly changes, and to initiate investigations for potential causes of variation. However, these schemes can only determine if the process is stable or unstable and they cannot provide any additional information. Several researchers have recommended the integration of machine learning (ML) approaches into SPM as a solution for the shortfalls of the techniques used to construct traditional monitoring schemes. This paper introduces a new multivariate extended homogeneously weighted moving average (MEHWMA) monitoring scheme and investigates its performance in terms of the run‐length distribution using simulation. In addition, the proposed MEHWMA scheme is integrated with a support vector machine to allow for the classification of the out‐of‐control events which facilitates the identification of the root causes of variation in the process. A numerical illustrative example is provided using real‐life data.
Substantial Upper Plate Faulting Above a Shallow Subduction Megathrust Earthquake: Mechanics and Implications of the Surface Faulting During the 2016 Kaikoura, New Zealand, Earthquake

The 2016 moment magnitude 7.8 Kaikoura, New Zealand, earthquake occurred at the southern end of the Hikurangi subduction zone where the upper plate above the shallow megathrust is exposed sub‐aerially. As a result, the substantial co‐seismic deformation in the upper plate above the megathrust rupture was observed geologically and geodetically. We explore the relationship between this surface faulting and the subduction megathrust rupture and find that the greatest upper plate fault slip occurred coincident (in time and location) with the megathrust rupture. Models of Coulomb stress change demonstrate that these surface faults become positively loaded as the upper plate rebounds during the megathrust event, favoring fault slip. In addition, during the megathrust rupture these faults terminate against an uncoupled subduction plate interface. We simulate the effects of decoupling at the base of these faults and find that very large fault slip is an expected consequence of this decoupling, allowing near‐complete strain release. In contrast, typical strike‐slip faults, pinned at their base, would have lower amounts of fault slip. These two conditions—increased Coulomb stress and basal decoupling—combine to produce the extreme co‐seismic upper plate faulting observed above the shallow Kaikoura megathrust earthquake. Similar conditions occur in other global subduction zones, but in most subduction zones the region above the coupled megathrust is underwater and poorly observed. Our analysis of the Kaikoura earthquake indicates a need to reevaluate patterns of strain accumulation and release in these regions, rather than assuming simple models of elastic rebound.
Improved algorithm for motion image analysis based on optical flow and its application

For the problems of low motion estimation accuracy and easy to be affected by various interferences such as object surface structure change,background light disturbance etc,when solving the basic optical flow constraint equation with the traditional algorithm,an improved optical flow algorithm with five-confidence-point restraint based on gradient threshold has been put forward in this paper.The five-confidence-point weighted method was applied to calculate the time and space gradient value of each image pixel,reducing noise effect on it,and gradient and gradient threshold was adopted to process the basic optical flow constraint equation,so that the interference data which will disturb the motion estimation in the optical flow field were eliminated,and the motion estimation accuracy of motion image was improved.The experiments have been carried out by the traditional algorithm and the improved one respectively for global and local movements.The contrastive analysis shows that this improved optical flow algorithm,which can figure both scene global motion and local motion in the scene,has the advantages of not only a high accuracy of motion estimation and a strong anti-interference,but also a better robustness and convergence compared with the traditional one,as well as one-third of computaion time shortened.It can be expected that this algorithm will be of potential application in driving environment sensing and traffic flow analysis.
Verified calculation of nonlinear dynamics of viscous detonation

It is a common notion in detonation theory that the effects of diffusion can be neglected in comparison to those of reaction and advection [1][8]. However, there are indic ations that such an assumption can be problematic. For example, using grid sizes around 10 m for their three-dimensional simulations of unsteady H2-air detonations, Tsuboi et al. [8] report wave dynamics that show strong sensitivity to the fin ness of the grid. While apparent convergence of some structures was reported, they also note with regard to some particulars of the detonation structure “The present results cannot resolve such cross-hatchings in the ribbon because of a lack of grid resolution. ” The presence of reaction dynamics and steep gr adients at micron length scales suggest that physical diffusion has an important role to play. Indeed, Powers [9] s howed that two-dimensional detonation patterns are strongly grid-dependent for simulations of reactive Euler equations, but relax to a grid-independent dissipative structure for a comparable reactive Navier-Stokes calcula tion. This suggests numerical diffusion is playing a significant role in the inviscid limit and that one should consid er the introduction of physical diffusion to properly capture the dynamics.
Duration of type 2 diabetes and liver-related events in nonalcoholic fatty liver disease: A landmark analysis

In a landmark analysis of 30,360 patients with NAFLD, we showed that the risk of liver-related events (cirrhotic complications, HCC or liver-related death) and all-cause mortality increased with the duration of type 2 diabetes at each landmark age of interest. Because of the small number of events in young patients, the HRs represent unadjusted univariate analysis at the age of 40 and 50 years. Background and Aims: We aimed to determine the impact of the duration of type 2 diabetes (T2D) on the risk of liver-related events and all-cause mortality in patients with NAFLD. Approach and Results: We conducted a territory-wide cohort study of adult patients with NAFLD diagnosed between January 1, 2000, and July 31, 2021, in Hong Kong. T2D was defined by the use of any antidiabetic agents, laboratory tests, and/or diagnosis codes. The primary endpoint was liver-related events, defined as a composite endpoint of HCC and cirrhotic complications. To conduct a more granular assessment of the duration of T2D, we employed landmark analysis in four different ages of interest (biological age of 40, 50, 60, and 70 years). By multivariable analysis with adjustment of non–liver-related deaths, compared with patients without diabetes at age 60 (incidence rate of liver-related events: 0.70 per 1,000 person-years), the adjusted subdistribution HR (SHR) of liver-related events was 2.51 (95% CI: 1.32–4.77; incidence rate: 2.26 per 1,000 person-years) in patients with T2D duration < 5 years, 3.16 (95% CI: 1.59–6.31; incidence rate: 2.54 per 1,000 person-years) in those with T2D duration of 6–10 years, and 6.20 (95% CI: 2.62–14.65; incidence rate: 4.17 per 1000 person-years) in those with T2D duration more than 10 years. A similar association between the duration of T2D and all-cause mortality was also observed. Conclusions: Longer duration of T2D is significantly associated with a higher risk of liver-related events and all-cause mortality in patients with NAFLD.
A comparative study of stock price prediction based on BP and LSTM neural network

In recent years, stock price prediction has become a research hotspot. The price of the stock market is unstable, which often rises or falls sharply due to the national policies, which makes it difficult for investors to achieve stable returns in the stock market. With the rapid rise of artificial intelligence, computers have become flexible in dealing with mathematical problems. Therefore, the extraordinary computing power of computers has been used to analyze and predict the trend of the stock market. More and more computer professionals began to enter the financial market and use neural network to study the trend of the stock market. This paper uses BP neural network and LSTM neural network to learn and predict the stock data of Shanghai Composite Index from January 2012 to June 2022. LSTM is a kind of RNN, but it is superior to other neural networks. It can effectively deal with data forgetting and gradient explosion problems and bring reliability to the prediction results of the model. The two models are evaluated by analyzing MAE, MSE and the time required for model training. The results show that LSTM model can not only learn longer time span than BP model, but also better than BP model in MAE and MSE indexes, which provides some reference and guidance for the prediction of medium and long-term stocks.
Evaluation of an artificial intelligence project in the software industry based on fuzzy analytic hierarchy process and complex adaptive systems

PurposeArtificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.Design/methodology/approachBased on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.FindingsThe outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.Originality/valueThis study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.
Regularized marginal Fisher analysis and sparse representation for face recognition

When Marginal Fisher Analysis(MFA) is applied to face recognition,it suffers the small size sample problem.If principal component analysis is used to deal with the problem,some useful components will get lost for classification.If replacing the objective function of MFA with maximum margin criterion,it would be difficult to find the optimal parameter.Therefore,in this paper,the regularized MFA method was proposed.It constructed a regularized item by a small number multiplying the identity matrix,and the regularized item was added to within-class scatter matrix so that the resulting matrix was not singular.This method does not lose any useful component for classification and is easy to determine the parameter.Because a sample usually can be linearly represented by few neighbors in the same class,the sparse representation classification was used to further improve the recognition accuracy after regularizing MFA.Experiments were carried out FERET and AR database,and results show that the proposed method can significantly improve the recognition accuracy compared with some classic dimensionality reduction methods.
PENGARUH PENGALAMAN PELANGGAN DAN KEPERCAYAAN TERHADAP NIAT BELI ULANG MELALUI KEPUASAN PELANGGAN SEBAGAI VARIABEL MEDIASI

This study aims to determine the effect of customer experience and trust on customer repurchase intentions of products sold at Lazada mediated by customer satisfaction. Repurchase intention is a post-purchase action caused by the satisfaction felt by consumers for products that have been previously purchased or consumed. Repurchase intention can be understood with satisfaction and trust, as consumers tend to rely on their experience after the initial transaction.The data collection method used in this research is literature study and field study. The data used in this research is primary data. The variables used in this study are customer experience and trust as independent variables, while repurchase intention as the dependent variable and customer satisfaction as the mediating variable. The sample in this study using purposive sampling technique. The data in this study were processed using AMOS. Based on the results of this study, it shows that the variables of customer experience and trust have a positive effect on customer satisfaction. Customer experience variables have no effect on customer repurchase intentions. The trust variable has a positive effect on customer repurchase intention. Customer satisfaction variables have a positive effect on customer repurchase intentions. Customer satisfaction variables can mediate the indirect effect of customer experience and trust in Lazada.coc. id customer repurchase intentions. Lazada can continue to build customer trust so that customer repurchase intentions remain high. Lazada as an online sales website should be able to guarantee customer safety in making purchases and online payments so that buyer satisfaction increases.
An optimal experimental design criterion for discriminating between non‐normal models

Summary. Typically T‐optimality is used to obtain optimal designs to discriminate between homoscedastic models with normally distributed observations. Some extensions of this criterion have been made for the heteroscedastic case and binary response models in the literature. In this paper, a new criterion based on the Kullback–Leibler distance is proposed to discriminate between rival models with non‐normally distributed observations. The criterion is coherent with the approaches mentioned above. An equivalence theorem is provided for this criterion and an algorithm to compute optimal designs is developed. The criterion is applied to discriminate between the popular Michaelis–Menten model and a typical extension of it under the log‐normal and the gamma distributions.
Blind Source Separation Based on Multistage Independent Component Analysis

A hands-free speech recognition system and a hands-free telecommunication system are essential for realizing an intuitive, unconstrained, and stress-free human-machine interface. In real acoustic environments, however, the speech recognition performance and a speech recording performance significantly degraded because we cannot detect the user’s speech with a high signal-to-noise ratio (SNR) owing to the interference signals such as noise. In this thesis, we introduce blind source separation (BSS), which is an approach for estimating original source signals only from the information of the mixed signals observed in each input channel. Many BSS methods based on independent component analysis (ICA) have been proposed for the acoustic signal separation. However, the performances of these methods degrade seriously particularly under extreme reverberant conditions. The ICA-based BSS can be classified into two groups in terms of the processing domain, i.e., frequency-domain ICA (FDICA) and time-domain ICA (TDICA) From the experimental study using the conventional FDICA, the source-separation performance is saturated because the independence assumption collapses in each narrow-band. In TDICA, the convergence degrades because the iterative learning rule becomes more complicated as the reverberation increases. In order to resolve the problems, I newly propose multistage ICA (MSICA), in which FDICA and TDICA are cascaded. In the proposed method, the separated signals of FDICA ∗ Doctoral Dissertation, Department of Information Processing, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DD0261017, March 24, 2005.