Reviewerly/exHarmony huggingface dataset:
Dynamic Spatial Predicted Background

We present a novel method for online background modeling for static video cameras - Dynamic Spatial Predicted Background (DSPB). Our unique method employs a small subset of image pixels to predict the whole scene by exploiting pixel correlations (distant and close). DSPB acts as a hybrid model combining successful elements taken from two major approaches: local-adaptive that propose to fit a distribution pixelwise, and global-linear that reconstruct the background by finding a low-rank version of the scene. To our knowledge, this is the first attempt to combine these approaches in a unified system. DSPB models the scene as a superposition of illumination effects and predicts each pixel's value by a linear estimator comprised of only 5 pixels of the scene and can initialize the background starting from the 5th frame. By doing so, we keep the computational load low, allowing our method to be used in many real-time applications using simple hardware. The suggested prediction model of scene appearance is novel, and the scheme is very accurate and efficient computationally. We show the method merits on an application for video FG-BG separation, and how some of the main existing approaches may be challenged and how their drawbacks are less dominant in our model. Experimental results validate our findings, by computation speed and mean F-measure values on several public datasets. We also examine how results may improve by analyzing each video individually according to its content. DSPB can be successfully incorporated in other image processing tasks like change detection, video compression and video inpainting.
Software Verification and Utilization for Direct and Indirect Preventive Maintenance Models

Purpose: The purpose of this paper is to present verification processes for software that implements the direct-indirect preventive maintence (PM) model of Park et al. [11]. Furthermore, an example of an application of the software involving field data is provided.BRMethods: The software was tested by varying the model parameters, and we confirmed that the test results conformed to the general characteristics of the direct-indirect PM model.BRResults: We could not find any abnormalities in the test results with respect to model parameters, and the optimal maintenance scheduling suggested by the software was also reasonable.BRConclusion: The verification tests presented in this paper do not guarantee the integrity of the software, However, the results presented in this paper are expected to be useful for the design of maintenance model software.
IMBALANCED DATA IN SENSIBLE KERNEL SPACE WITH SUPPORT VECTOR MACHINES MULTICLASS CLASSIFIER DESIGN

The utilization of various assessment measures for grouping different tasks have picked up a lot of consideration in previous decades extraordinarily for such issues through different and redundant classes. A classifier is proposed particularly to advance one of the conceivable measures, to be specific, the hypothetical G-mean method. In any case, the method is general, and it very well may be utilized to streamline bland assessment measures. The streamlining calculation to prepare the classifier is depicted, and the numerical plan is tried demonstrating its ease of use and power. The proposed oversampling calculation alongside a cost- reduction SVM classification is appeared to enhance execution when contrasted with other estimated strategies on numerous benchmark imbalanced informational indexes. What's more, a various leveled system is produced for multiclass imbalanced issues that have a dynamic class arrange. A novel structure for kernel space instruction in a limited space named Sensible Kernel Space (SKS) is introduced in this manuscript. The SKS can be expressly worked by utilizing any optimistic clear bit counting Gaussian BCG bit by means of an exact portion outlined. The proposed sensible Kernel space can ideally choose various subsets of recently mapped datasets in SKS keeping in mind the end goal to enhance the speculation execution of the classifier.
Research on Digital Watermarking Technology Based on Discrete Wavelet Transform

Abstract By using the advantages of discrete wavelet transform in digital watermark embedding, extraction and detection, a corresponding basic model was established. Using MATLAB as a tool for simulation experiments, the experimental results show that the discrete wavelet transform can extract the watermark signal under the common JPEG compression, noise, shear and rotation, filtering and other attacks, and has high robustness.
Dynamic Hand Gesture Recognition Based On 3D Skeleton

In this paper, we propose a dynamic hand gesture recognition method based on 3D Skeleton. According to the characteristics of dynamic gestures, we use the distance between different joints to represent hand shape feature, the movement of some certain joints to represent trajectory feature and the relationship between some certain joints to describe hand rotation feature. These three features are then input onto the SVM classifier. The proposed method is evaluated on a challenging dataset called DHG-14/28, experimental result shows that our proposed method is effective and outperforms start-of-the-art methods.
IDENTIFY OF FRAUDULENT FINANCIAL OPERATIONS USING THE MACHINE LEARNING ALGORITHM

Current work is devoted to the problem of automatic detection of fraudulent financial transactions. The article describes the causes of fraudulent transactions their typical attributes, as well as the basic principle of detection. The concepts of fraudulent and honest transactions are defined. Examples of algorithms for determining suspicious financial transactions in antifraud systems are given. Modern approaches to monitoring and detecting cases of fraud in remote banking systems are considered. The positive and negative aspects of each approach are described. Particular attention is paid to the problem of optimal recognition of transaction classes in highly unbalanced data. Methods for solving the problem of unbalanced data are considered. The choice of means for evaluating the operation of the machine learning model is justified considering the specifics of data distribution. As a solution, we propose an approach based on the use of ensemble classifiers in conjunction with balanced sampling algorithms, the key feature of which is to create a balanced sample not for the entire classifier, but for each student in the ensemble separately. Based on data on fraud in the field of bank credit cards, a comparison is made and the best classifier is selected among such ensemble algorithms as random forest, adaptive boosting and bagging of decision trees. To create balanced subsets of evaluators of ensemble algorithms, the algorithm of random insufficient sampling is used. To search for the optimal parameters of the classifiers, the random search algorithm on the grid is used. The results of experimental comparison of the selected methods are presented. The advantages of the proposed approach are analyzed, and the boundaries of its applicability are discussed.
Analysis of Spatial Dependence of Ore-Forming Elements Using Geostatistics and Moran Correlogram

The spatial dependence of data obtained from the geochemical prospecting process can provide useful information for evaluating mineralization potential. This study proposes two approaches to study the spatial dependence of ore-forming elements. To reduce the influence of extreme values and outliers, a semi-variogram was first used to study spatial variability and degree of spatial dependence of geochemical data using Cressie robust semi-variogram estimator. The Moran spatial correlogram was then employed to describe spatial heterogeneity and to test for the presence of spatial autocorrelation in geochemical data. The Moran’s I statistics is strongly sensitive to positively skewed distribution, therefore, geochemical data were Box-Cox transformed before computing spatial correlograms. Results from a case study of Ag and Au elements in Jiurui Copper districts (southeast China) have shown that moderate spatial dependence was found for both of the Au and Ag variables, the maximum spatial variability was 20 km for Au and 10 km for Ag, respectively. The degree of spatial dependence among geochemical data decreases as distances increase. These findings demonstrate that the spatial dependence of ore-forming elements can be effectively measured using geostatistics and Moran correlogram.
An Analysis of the Middle School Mathematics Curriculum in France: Focusing on ‘Algorithms and Programming’

In this study, I have investigated the middle school mathematics curriculum in France, which was reformed according to the change of ‘Socle commun’ in 2015. I have analyzed the area of ‘algorithms and programming' that was newly introduced into the curriculum. The result shows that the area of ‘algorithms and programming' is effectively connected with all the other mathematical content areas rather than simply being added to the list of existing mathematics content areas. In this process, scratch plays a crucial role. In other words, scratch interweaves traditional mathematical contents with the area of ‘algorithms and programming' and thus creates a new type of mathematics curriculum. The curricular reform in France was made by pursuing algorithmic thinking rather than CT(Computational Thinking). In the age of artificial intelligence, the reform in the curriculum of middle school mathematics in France raises the necessity of introducing this area and scratch into the middle school mathematics curriculum in Korea.
A new approach of image encryption based on dynamic substitution and diffusion operations

This work proposes a solution that provides good security for the transmission of color images through unsecured channels. To achieve this objective, two enhanced chaotic maps, which present more complicated dynamics behaviors are used. In substitution phase, both chaotic maps are employed to create a dynamic substitution table, which not only depends on level grey but also the dimension of the original image, thereby, the table served to substitute each pixel. In diffusion mechanism, the keystream generated by a combination of both chaotic maps is used to cache the random numbers generated and each image pixel, on the one hand, and to link each encrypted pixel to its adjacent, on the other hand. The security analysis in terms of correlation coefficient, entropy, histogram, UACI and NPCR show that the suggested algorithm is secure enough to resist statistical attack, differential attack, and exhaustive attack.
Tamper-Proof Certificate Management System

Certificates are a proof of achievement/membership like University degrees and school certificates etc. Certificates as a proof are essential in the society but our current certificate management system is mostly analog, inefficient and amenable to forgery. Due to the ineffective anti-forge mechanism, forged certificates are becoming prevalent. To solve this problem, many secure certificate management are proposed, then also security problems like privacy, transparency and forgeries still exist. We proposes a tamper-proof certificate management using hyper-ledger which provides secure anti-forge mechanism. Hyperledger has unmodifiablity and other suitable properties of the blockchain that helps to minimize the problem of forgery. We use IPFS (Inter Planetary File System) for storing the certificate. The procedure for issuing the certificates is to first generate the degree of a student using portal, meanwhile then calculate its hash value and encrypt it using asymmetric encryption. Then store file into IPFS. Finally, we make a transaction that contains metadata of certificate which stores in the blockchain system. Then, chaincode used for verification of user's document. we also reduce the time complexity of searching and verifying the multiple of document of a same user. Our proposed work enhances the credibility of paper-based certificates, and also reduces the risk of forging certificates. We also show the performance of generating transactions in hyperledger.
WiHRR: CSI based English Alphabet Handwriting Recognition

Human Computer Interaction has been an important and vital subject of research since last few years and new and innovative techniques are being implemented in this field with each passing day.This research paper tries to improve existing samples of Channel frequency Response (CFR) that is obtained from Wi-Fi devices in the form of channel state information (CSI).In this research article, handwriting is used as an input but character recognition is not done on the paper but in the air.It discusses all the important procedures which are undertaken to accomplish this task which include fetching the input, pre-processing , feature extraction and classification of characters using machine learning algorithms.Current wireless devices can measure the interference caused by handwritten English alphabet in the range of signal coverage during the transmission of wireless signals in their own channels, and these measured interference s can be applied to handwritten recognition.Compared with traditional behavior recognition using cameras or wearable sensor devices, handwritten recognition using Wi-Fi wireless signals has more unexpected benefits: first, the wireless signals do not need LOS (line of sight) monitoring; second, no need to wear redundant sensor devices; and, at the same time, no need to wear redundant sensor devices.Wireless signals can be deployed in various scenarios without dead corners, such as home, office, public area and so on.Compared with the camera, the behavior recognition based on wireless signal is not affected by the weather.
A Super Scalable Algorithm for Short Segment Detection

In many applications such as copy number variant (CNV) detection, the goal is to identify short segments on which the observations have different means or medians from the background. Those segments are usually short and hidden in a long sequence, and hence are very challenging to find. We study a super scalable short segment (4S) detection algorithm in this paper. This nonparametric method clusters the locations where the observations exceed a threshold for segment detection. It is computationally efficient and does not rely on Gaussian noise assumption. Moreover, we develop a framework to assign significance levels for detected segments. We demonstrate the advantages of our proposed method by theoretical, simulation, and real data studies.
An Enhanced Similarity Measure for Collaborative Filtering-based Recommender Systems

In this era of the Internet and ubiquitous computing, huge information is being generated every moment. Obtaining useful information from the World Wide Web (WWW) has become too difficult. Recommender Systems appear to handle this problem of information overload to save user effort and time by recommending items of potential interest based on other similar users' ratings of the relevant items. Numerous algorithms have been proposed to recommend the potential items of interest. The most widely used methods include collaborative filtering (CF), content-based filtering and combination (hybrid) of two or more methods to get advantages of them. A CF-based recommender system method follows two main steps: computation of the similarity between two users/items and prediction of the unknown ratings to recommend items to a user. Several algorithms have been proposed for each of the steps. However, these methods may not be accurate in some situations, and hence the accuracies of predictions in CF-based RSs can be improved by overcoming those drawbacks. In this work, we propose a new method to compute the similarity between two users/items to overcome the shortcomings of the existing measures, and thereby improve the accuracy of prediction in the CF-based RSs. We evaluate our work, in comparison with other existing methods, based on recommendation accuracies in terms of different performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).
A Robust Image Watermarking Scheme Based on the Laplacian Pyramid Transform

This paper is concerned with the digital image watermarking techniques to protect intellectual property and to authenticate digital images. Different from the most conventional methods using the discrete cosine transforms (DCT) and discrete-wavelet transforms (DWTs), this paper exploits the improved Laplacian pyramid transform to develop a new image watermarking scheme in which the improved Laplacian pyramid transform is used to decompose and reconstruct the host image. Then, to select an appropriate watermarking solution, we investigate the various frequency sub-band regions with different the levels and strength factors to perform the watermark embedding. Finally, we conduct experiments to investigate the invisibility and robustness of the proposed algorithm in terms the peak signal-to-noise ratio (PSNR), normalized correlation (NC), and structural similarity index (SSIM). Experimental results showed that our proposed scheme offers good robustness and invisibility. As compared to the watermarking schemes using the curvelets, our watermarking scheme is more robust for the lossy JPEG compression and Gaussian low pass filtering attacks. In addition, our method is also efficient in terms of computational time.
Elliptic curve and Lattice cryptosystem

In this work, we will present a new hybrid cryptography method based on two hard problems: 1- The problem of the discrete logarithm on an elliptic curve defined on a finite local ring. 2- The closest vector problem in lattice and the conjugate problem on square matrices. At first, we will make the exchange of keys to the Diffie-Hellman. The encryption of a message is done with a bad basis of a lattice.
Design of Visual Gaze Target Locating Device Based on Depth Camera

Based on the research of machine vision positioning application in recent years, this paper discusses the common machine vision positioning system and its principle, and designs a visual target positioning device to detect people's target intention. It mainly includes dynamic following and target positioning functions. Dynamic following can determine the fixation direction of the tester, and target positioning can obtain the spatial position information of the fixation target. The device can recognize the intention of people by observing the visual fixation direction of the subject and obtain the spatial position information of the target, which is a human-computer interaction path with a widely used scene.
Comparative study on crowd counting with deep learning

This paper aims at comparing 4 top models for crowd counting and evaluating their highlights based on their performance. In DSNet, the distended convolution block network was proposed, where the distended layers are densely connected to each other in order to preserve information from continuously varied scales. Three blocks are cascaded and linked to dense residual connections to widen the range of levels covered by network and also a novel loss of consistency at multi-scale density level was introduced to improve performance. In SFANet, two foremost elements with VGG backbone CNN and two-way path multi-scale fusion networks were suggested for the front end feature extractor and back end to make density map in which one path highlights crowded regions present in images. The other direction is responsible for the fusion of multi-scale features and for the generation of the final high-quality high-density maps. In MANet (Multi-scale Attention Network), a new mechanism of soft attention was presented, which learns a series of masks and a level-conscious loss feature was introduced to regularize and direct the learning of different branches to specialize on a specific scale. In Bayesian Loss, a novel loss function was used to generate a density contribution model from the point annotations. We also analyzed the results of the 4 convolutional neural networks, extracted the pattern of convolutional neural network structure and found promising pathways for researchers in this fast-growing area.
CloudMonitor: Data Flow Filtering as a Service

The primary concern of cloud service consuming organizations is the kind of control that they may have over their data. Companies are legally required to monitor a subset of the data that is crucial both to their business and customers. Conversely, the cloud service providers are not showing commitment towards securely handling enterprise data, and this may put sensitive data at risk. Hence, consumers are expected to maintain control over their sensitive data both at their local infrastructure and in the cloud. To achieve this, companies are practicing methods that would typically block access to cloud service that stores sensitive data at the network level. However, such restrictions may limit employee performance, and at the same time may not combat the malicious activities of bad employees. In this paper, we propose a model that allows consumers and providers the ability to transparently track the data in the cloud environment. The model allows consumers to have control over their data and conduct an audit to the treatment of their data by third-party services, while employees are allowed to use the cloud service.
A proposal for a healthcare environment with a real-time approach

The increased use of IoT has contributed to the popularisation of environments that monitor the daily activities and health of the elderly, children or people with disabilities. The requirements of these environments, such as low latency and rapid response, corroborate the usefulness of associating Fog Computing with healthcare environments since one of the advantages of Fog Computing is to provide low latency. Because of this, we propose the use of a hardware and software infrastructure capable of storing, processing and presenting monitoring data in real-time, based on Fog Computing paradigm. Additionally, we propose the structuring of sensors for the implementation of a simulated healthcare environment, as well as the processing logic for the presentation of results referring to the health of the user.
Grey Island: Immersive tangible interaction through augmented reality

Along with augmented reality maturing, there have been more possibilities about digital space and real space. By recognizing real space, augmented reality appends virtual objects and information to the recognized real space. However, most of today's augmented reality interactive narrative models are still in the stage of recognize space - append information. What users can do is only to watch a space with virtual information appended, or to interact with virtual objects through screens or gestures. This paper proposed a new interactive narrative design method in augmented reality, which means that the audience can simultaneously manipulate both tangible props and virtual avatar, and the augmented reality environment responds to the audience's interaction behavior through virtual and physicallybased feedback. An interactive installation Grey Island was built in this paper, by which the user can see a floating island on the augmented reality screen. On the plane of the floating island stands a virtual avatar that has been given the gravity property. When the user manipulates the avatar to walk to a different position, the floating island will be inclined at a relative angle. The user can guide the virtual ball to the scoring area by moving the avatar and the tangible props. This augmented reality interactive narrative is preferable for its direct interactive feedback, intuitive interactive feedforward stimuli and richer narrative methods.