Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Forecasting Using Hybrid VARIMA Neural Network in JCI Case

Hybrid methods is one of relatively new modern methods for multivariate forecasting. This paper focuses on the comparison between VARIMA (Vector Autoregressive Integrated Moving Average), neural network, and hybrid methods for multivariate forecasting of stock prices. RMSE (Root mean of squares errors) are used for evaluating the forecast accuracy. Four variables are used in this paper, i.e. open, high, low, and close of JCI (Jakarta Composite Index). The result show that hybrid methods with nine input neurons yield more accurate than VARIMA and neural network in training datasets. Whereas in testing datasets show that hybrid methods with eight input neurons yield more accurate than individual VARIMA and neural network. The three methods can’t be use for long term forecasting.
Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.
Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network

With the continuous development of Web attacks, many web applications have been suffering from various forms of security threats and network attacks. The security detection of URLs has always been the focus of Web security. Many web application resources can be accessed by simply entering an URL or clicking a link in the browser. An attacker can construct various web attacks such as SQL, XSS, and information disclosure by embedding executable code or injecting malicious code into the URL. Therefore, it is necessary to improve the reliability and security of web applications by accurately detecting malicious URLs. This paper designs a convolutional gated-recurrent-unit (GRU) neural network for the detection of malicious URLs detection based on characters as text classification features. Considering that malicious keywords are unique to URLs, a feature representation method of URLs based on malicious keywords is proposed, and a GRU is used in place of the original pooling layer to perform feature acquisition on the time dimension, resulting in high-accuracy multicategory results. The experimental results show that our proposed neural network detection model is very suitable for high-precision classification tasks. Compared with other classification models, the model accuracy rate is above 99.6%. The use of deep learning to classify URLs to identify Web visitors’ intentions has important theoretical and scientific values for Web security research, providing new ideas for intelligent security detection.
A GPU-Accelerated ADI Method for Transient Thermal Simulation with Parallel Cyclic Reduction

An efficient thermal simulation approach based on Graphics Processing Units (GPU) accelerated alternating-direction-implicit (ADI) method with parallel cyclic reduction (PCR) is presented in this paper. Compared with the conventional Thomas algorithm, the PCR can accelerate the ADI method by parallel computing on GPUs. The accuracy and efficiency of the proposed method are illustrated by the numerical example.
Locally accurate MPS approximations for ground states of one-dimensional gapped local Hamiltonians

A key feature of ground states of gapped local 1D Hamiltonians is their relatively low entanglement --- they are well approximated by matrix product states (MPS) with bond dimension scaling polynomially in the length N of the chain, while general states require a bond dimension scaling exponentially. We show that the bond dimension of these MPS approximations can be improved to a constant, independent of the chain length, if we relax our notion of approximation to be more local: for all length-k segments of the chain, the reduced density matrices of our approximations are ϵ-close to those of the exact state. If the state is a ground state of a gapped local Hamiltonian, the bond dimension of the approximation scales like (k/ϵ)1+o(1), and at the expense of worse but still poly(k,1/ϵ) scaling of the bond dimension, we give an alternate construction with the additional features that it can be generated by a constant-depth quantum circuit with nearest-neighbor gates, and that it applies generally for any state with exponentially decaying correlations. For a completely general state, we give an approximation with bond dimension exp⁡(O(k/ϵ)), which is exponentially worse, but still independent of N. Then, we consider the prospect of designing an algorithm to find a local approximation for ground states of gapped local 1D Hamiltonians. When the Hamiltonian is translationally invariant, we show that the ability to find O(1)-accurate local approximations to the ground state in T(N) time implies the ability to estimate the ground state energy to O(1) precision in O(T(N)log⁡(N)) time.
Analysis of n(87Sr)/n(86Sr), δ88Sr/86SrSRM987 and elemental pattern to characterise groundwater and recharge of saline ponds in a clastic aquifer in East Austria

ABSTRACT Elemental and isotopic pattern of n(87Sr)/n(86Sr) and δ88Sr/86SrSRM987 were used to characterise groundwater and recharge of saline ponds in a clastic aquifer in East Austria. Therefore, shallow, artesian and thermal groundwaters of the investigated aquifer along with rainfall and rivers were analysed using (MC) ICP-MS. The n(87Sr)/n(86Sr) ratio and elemental pattern changed with aquifer depth as a result of progressing bedrock leaching and dissolution with increasing groundwater residence time. The n(87Sr)/n(86Sr) ratio of shallow groundwater below saline ponds of 0.71019 ± 0.00044 was significantly different from thermal groundwater of 0.71205 ± 0.00035 (U, k = 2). In contrast to previous theories, this result suggested no recharge of saline ponds by upwelling paleo-seawater. Isotope pattern deconvolution revealed that rainfall accounted to about 60% of the n(87Sr)/n(86Sr) ratio of shallow groundwater below saline ponds. The δ88Sr/86SrSRM987 values of groundwater decreased from about 0.25 ‰ in most shallow, to predominantly negative values of about –0.24 ‰ in artesian groundwater. This result indicated leaching and dissolution of weathered minerals. In turn, the δ88Sr/86SrSRM987 of deep thermal groundwater showed positive values of about 0.12 ‰, which suggested removal of 86Sr from solution by carbonate precipitation. These results highlight the potential of δ88Sr/86SrSRM987 signature as an additional geochemical tracer.
Plant Classification in the Wild: A Transfer Learning Approach

Datasets specialized in wildlife usually contain imbalanced classes of natural wild images such as, for instance, plant images, which are acquired from the surrounding environment with natural scene background. Deep neural networks have proven their efficiency in classifying such datasets. However, such an approach requires a workaround to approximately balance the classes in order to prevent the occurrence of overfitting during the training phase of the neural network. Many approaches exist to overcome this problem includes over-sampling, undersampling, generating synthetic samples, data augmentation, etc. The iNaturalist species classification and detection dataset represents a good example of vastly imbalanced datasets. It contains 13 superclasses. This work focuses on the Plantae superclass and builds a Convolutional Neural Network to distinguish a subset of the subclasses of Plantae. Our model benefits from cutting-edge techniques such as transfer learning and data augmentation to obtain a reasonably high level of accuracy (78.76%).
Red Blood Cells Segmentation: A Fully Convolutional Network Approach

Red blood cell segmentation in microscopic images is the first step for various clinical studies carried out on blood samples such as cell counting, cell shape identification, etc. Conventional methods while often showing a high accuracy are heavily depending on the acquisition modality. Deep learning approaches have shown to be more robust regarding such modalities and still showing a comparable accuracy. In this paper, we first investigate necessary steps to apply a specific type of deep learning methods, namely fully convolutional networks, to red blood cell segmentation. Based on data given and constraints imposed by our partners mainly regarding a high throughput of their data we then describe an exemplary application. First results show, that even with a focus on high performance a good accuracy above 90% can be reached.
New Weak Error Bounds and Expansions for Optimal Quantization

We propose new weak error bounds and expansion in dimension one for optimal quantization-based cubature formula for different classes of functions, such that piecewise affine functions, Lipschitz convex functions or differentiable function with piecewise-defined locally Lipschitz or $\alpha$-Holder derivatives. This new results rest on the local behaviors of optimal quantizers, the $L^r$-$L^s$ distribution mismatch problem and Zador's Theorem. This new expansion supports the definition of a Richardson-Romberg extrapolation yielding a better rate of convergence for the cubature formula. An extension of this expansion is then proposed in higher dimension for the first time. We then propose a novel variance reduction method for Monte Carlo estimators, based on one dimensional optimal quantizers.
Preliminary Studies of SEMG-Based Finger Gesture Classification for Smart Watch Application Using Deep Learning

To control smart watches available in the market, both hands should be used. A surface electromyography (SEMG)-based interface will enable controlling the watch with the hand where it is worn. However, fewer studies have been conducted on SEMG-interface for healthy subjects for smart watch applications. This study developed an algorithm to classify 3–5 finger gestures from SEMG signals recorded on the upper wrist. Also, we compared the classification accuracies between intra-subject models and an inter-subject model. We concluded that when there is a low number of gestures, factory calibration for matching SEMGs to gestures is sufficient. However, when there are a high number of gestures, individual calibration is required just after purchasing the watch.
Design and Analysis Considerations for Stepped Wedge Cluster Randomized Trials with Decayed Correlation

A stepped wedge cluster randomized trial is a type of longitudinal cluster design that sequentially switches clusters to intervention over time until all clusters are treated. While the traditional posttest-only parallel design requires adjustment for a single intraclass correlation coefficient, the stepped wedge design allows multiple outcome measurements from the same cluster and so additional correlation parameters are necessary to characterize the within-cluster dependency structure. Although a number of studies have differentiated between the concepts of within-period and inter-period correlations, few studies have allowed the inter-period correlation to decay over time. In this article, we consider the proportional decay correlation structure for a cohort stepped wedge design, and provide a matrix-adjusted quasi-least squares (MAQLS) approach to accurately estimate the correlation parameters along with the intervention effect. We further develop a corresponding sample size procedure accounting for the correlation decay, and numerically validate it for continuous outcomes in a simulation study. We show that the empirical power agrees well with the prediction even with a small number of clusters, when data are analyzed with MAQLS concurrently with a suitable bias-corrected sandwich variance. Two trial examples are provided to illustrate the new sample size procedure.
Iterative Methods for Solving Sixth Order Time-Fractional Cahn-Hillard Equation

The objective of this paper is to obtain analytical-approximate solutions to the time-fractional Cahn-Hilliard equation of sixth order using two types of iteration methods: the new iterative method (NIM), which depends on the decomposition of the non-linearity term and the q-homotopy analysis method (q-HAM), a modification of homotopy analysis method (HAM).
Good orientations of 2T-graphs

In this paper we study graphs which admit acyclic orientations that contain a pair of arc-disjoint out-branching and in-branching (such an orientation is called good) and we focus on edge-minimal such graphs. A 2T-graph is a graph whose edge set can be decomposed into two edge-disjoint spanning trees. Vertex-minimal 2T-graphs with at least two vertices which are known as generic circuits play an important role in rigidity theory for graphs. We prove that every generic circuit has a good orientation. Using this result we prove that if $G$ is 2T-graph whose vertex set has a partition $V_1,V_2,\ldots{},V_k$ so that each $V_i$ induces a generic circuit $G_i$ of $G$ and the set of edges between different $G_i$'s form a matching in $G$, then $G$ has a good orientation. We also obtain a characterization for the case when the set of edges between different $G_i$'s form a double tree, that is, if we contract each $G_i$ to one vertex, and delete parallel edges we obtain a tree. All our proofs are constructive and imply polynomial algorithms for finding the desired good orderings and the pairs of arc-disjoint branchings which certify that the orderings are good. We also identify a structure which can be used to certify a 2T-graph which does not have a good orientation.
Early exercise boundary and option prices in Lévy driven models

Pricing and hedging of European, American, barrier options and interest rate derivatives for wide classes of Lévy driven models is considered in situations where qualitative and quantitative differences between Gaussian and Lévy modelling are most prominent, and the dependence on the choice of a family of Lévy processes is analysed. Asymptotics of option prices near the barrier and expiry are calculated; for American options, two fast numerical methods are constructed. It is shown that for many classes of Lévy processes, the early exercise boundary of the American put is separated from the strike by a non-vanishing margin, and as the riskless rate vanishes, the early exercise boundary tends to 0 uniformly over the interval [0, T). Implications for fitting of parameters are discussed.
Effective Clustering Analysis Based on New Designed Clustering Validity Index and Revised K-Means Algorithm for Big Data

Clustering tries to find the natural structure of input datasets and partitions them into groups or clusters. As an unsupervised pattern classification method, it has been widely used in data mining, pattern recognition, image processing and so on. However, many of the existing clustering algorithms are suffering from many obstacles, such as low efficiency, poor clustering accuracy, more sensitive to noise points and cannot deal with complex big data properly. Aiming at these problems, an improved K-means algorithm (Grid-K-means) is firstly proposed. In the algorithm, dynamically changing grids operations are used to substitute data point operations to improve the clustering efficiency and reduce the number of manually setting initial parameters. Meanwhile, by utilizing grids with the highest density to determine the initial clustering centers, more accurate and stable clustering results are acquired. Then, based on the idea of utilizing grid as the weighted representative point to process the dataset, a new clustering validity index (BCVI) is introduced to better evaluate the quality of clustering results. BCVI can quickly determine the optimal clustering number especially for large-scale datasets. Experimental results on testing 5 simulated datasets (including two large sample data sets) have demonstrated that the Grid-K-means algorithm is faster and more accurate than the traditional ones. Meanwhile, the clustering results are evaluated by our BCVI and 6 other existing clustering validity indexes. The experimental results have also shown that the new BCVI is superior to traditional indexes in data processing speed and stability.
The FDTD-based Time-Reversal Method for Diagnosis of Antenna Arrays

Array element failure is one of the challenges in application of antenna arrays. The existing methods are of frequency-domain, and they can be complicate, expensive or time consuming. In this paper, we propose the use of the time-reversal method for array diagnosis. The method can directly reconstruct both operating and defect elements without matrix operations. Both theoretical analysis of the time-reversal method and computation of an example are presented to verify the effectiveness of the time-reversal method.
Uncoupled isotonic regression via minimum Wasserstein deconvolution

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown non-decreasing regression function $f$ from independent pairs $(x_i, y_i)$ where ${\mathbb{E}}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much less is known about its uncoupled counterpart, where one is given only the unordered sets $\{x_1, \ldots , x_n\}$ and $\{y_1, \ldots , y_n\}$. In this work, we leverage tools from optimal transport theory to derive minimax rates under weak moments conditions on $y_i$ and to give an efficient algorithm achieving optimal rates. Both upper and lower bounds employ moment-matching arguments that are also pertinent to learning mixtures of distributions and deconvolution.
Research on Vehicle Identification Based on High Resolution Satellite Remote Sensing Image

Traditional traffic information acquisition methods had many limitations, so the acquisition of road traffic information from high-resolution satellite images had become a research hotspot in intelligent traffic system. In this paper, the object oriented classification method is used to establish the vehicle detection and processing process of high resolution satellite image. First, the high resolution satellite image was processed by median filtering and denoising, and the enhancement of vehicle information in remote sensing images was realized by using the stretch of gray histogram. Secondly, the image was segmented to the optimal scale, and the optimal segmentation scale was determined by the method of the maximum average area of the vehicle. Then according to different color characteristics of vehicles with dark establish a separate classification rule set, based on the object-oriented classification of vehicle classification, by extracting feature threshold classification more bright color vehicles, using relationship between classes of vehicles using fuzzy classification method to extract the dark color vehicles, finally formed a high-resolution satellite remote sensing overall framework of vehicle detection.
Vehicle/Pedestrian Localization System Using Multiple Radio Beacons and Machine Learning for Smart Parking

In recent years, the number of casualties and injuries at intersections and roads has been decreasing due to wide spread of safe driving support systems, but the number of casualties and injuries due to low-speed traffic accidents in parking lots has not decreased. In the parking lot, it is necessary to drive while looking for an empty slot, which may result in contact accidents with pedestrians. Therefore, in this research, we propose a new smart parking system that prevents low-speed contact accidents by estimating availability of slots in the parking lot and the position of pedestrians. The proposed system attempts to estimate positions of user’s smartphones by deploying a small number of beacon devices on the parking lot, and by analyzing the radio wave intensity measured by the smartphones. In addition, estimation accuracy of the position of the pedestrian / driver is evaluated by experimental evaluation in a parking lot. Through the performance evaluation, estimation accuracy of the vehicle’s position to higher than 98%, and estimation accuracy of the pedestrian’s position is about 70%.
git2net - Mining Time-Stamped Co-Editing Networks from Large git Repositories

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication, from the commit history of projects. Most of the studied networks are based on the co-authorship of software artefacts defined at the level of files, modules, or packages. While this approach has led to insights into the social aspects of software development, it neglects detailed information on code changes and code ownership, e.g. which exact lines of code have been authored by which developers, that is contained in the commit log of software projects. Addressing this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. This information allows us to construct directed, weighted, and time-stamped networks, where a link signifies that one developer has edited a block of source code originally written by another developer. Our tool is applied in case studies of an Open Source and a commercial software project. We argue that it opens up a massive new source of high-resolution data on human collaboration patterns.
Semantic Analysis of NIH Stroke Scale using Machine Learning Techniques

In particular, stroke is a major disease leading to death in adults and elderly people, as well as disability. Rapid detection of stroke is very difficult because the cause and cause of the onset are different for each individual. In this paper, we design and implement a system for semantic analysis of early detection of stroke and recurrence of stroke in Koreans over 65 years old, based on the National Institutes of Health (NIH) Stroke Scale. Using C4.5 of the decision tree series represented by the analytics algorithm of machine learning technique, we conduct a semantic interpretation that analyzes and extracts the semantic rules of the execution mechanism that are additionally provided by C4.5. The C4.5 algorithm is used to construct a classification and prediction model using the information gain of the NIH stroke scale features, and to obtain additional NIH Stroke Scale feature reduction effects.