Reviewerly/exHarmony huggingface dataset:
Observations from the FooWire Project: Experimenting with DLT for Payments Use

In 2019, a team at the Federal Reserve Board (Board) conducted small-scale experimentation, named the "FooWire project," to build a payment system using distributed ledger technology (DLT). The team built the system using Hyperledger Fabric, a popular DLT platform, because it generally met the team's design requirements of a closed network, mature technology, and enterprise readiness.
Knowledge dimension in investment of the human capital as perceived by staff working in Jordanian insurance companies

The main objective of this research is to address knowledge dimension of human capital in insurance sector in Jordan. There is an urgent need for studies on human capital, including intellectual capital (IC). After a thorough review of the research literature, the researchers were able to develop a questionnaire to apply to insurance companies in Jordan. In this study, the sample of 30 companies has been randomly selected from Amman, Zarqa, and Irbid in the insurance sector in Jordan. Means, SD , ANOVA analysis, and “Tukey Test” were used. Multiple regression was extracted for the first hypothesis, for subhypothesis simple regression is used. For the correlation indicator, we used Multicollinearity, finally Stepwise Regression was used. The values of the calculated t ‐ratios for the staff position show that the nonsupervisory staff feel more than the supervisory staff in Staff Recruitment and Job Environment variables. And the supervisory staff feel more than the nonsupervisory staff in Staff Development. The values of the calculated t ‐ratios for the gender show that the male staff feel more than the female staff in Staff Recruitment and Job Environment variables. And the female staff feel more than the male staff in Staff Development. The values of the calculated F ‐ratios for each of the factors affecting knowledge dimension of the investment in human capital high and it can be concluded that there is significant statistical evidence that differences between factors exist according to the staff Work Place in variables Staff Recruitment, Job Environment, and Staff Development. Result of the test showed that staff who's working in Irbid feel in Staff Recruitment variable more than staff who's working in Zarqa. And the staff who's working in Zarqa feel in Staff Development variable more than staff who's working in Amman.
Sparse minimum average variance estimation via the adaptive elastic net when the predictors correlated

Abstract A new model-free variable selection method was proposed in this article, which is called SMAVE-AdEN. We combined the effective sufficient dimension reduction method MAVE with the variable selection method which is called adaptive Elastic Net (AdEN) to introduce SMAVE-AdEN. The SMAVE-AdEN produces a sparse and accurate estimate when the predictors are highly correlated. The advantage of SMAVE-AdEN is that SMAVE-AdEN extended Adaptive Elastic net (AdEN) to nonlinear and multi-dimensional regression. Also, the SMAVE-AdEN enables MAVE to work with problems were the predictors are highly correlated. In addition, SMAVE-AdEN can exhaustively estimate dimensions, while selecting informative covariates simultaneously. The performance of SMAVE-AdEN is evaluated by both simulation and real data analysis.
Classification Algorithms for Unbalanced High-Dimensional Data with Hyperbox Vertex Over-Sampling Iterative Support Vector Machine Approach

This paper is written for addressing the bisectional classification problem for big and high-dimensional data sets with unbalanced characteristics distribution, whenever the support vector machine (SVM) algorithm is used. As is well known in conventional SVM, calculating large-scale covariance matrices and biased support vectors due to unbalanced characteristics are thorny issues that severely affect accuracy and efficiency of SVM classification algorithm in general. To surmount these problems, a class of modified SVM classification algorithm (hyperbox vertex over-sampling iterative SVM: HVS-ISVM) based on hyperbox vertex over-sampling data compensating and active iterative learning are innovatively proposed. The algorithm procedures are explained briefly and the main features are summarized, which are conducive to implementing and revealing deeper data indices. Numerical simulation results show that the proposed HVS-ISVM algorithm have higher calculation speed and better classification accuracy, and the obtained iterative support vectors and their corresponding hyperplanes are also robust against numerical noise due to over-sampling.
New Attributed Graph Clustering by Bridging Attribute and Topology Spaces

We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs. We propose Non-linear Attributed Graph Clustering by Symmetric Non-negative Matrix Factorization with Positive Unlabeled Learning. The features of our method are three holds. 1) it learns a non-linear projection function between the different cluster assignments of the topology and the attributes of graphs so as to capture the complicated relationship between the topology and the attributes in real-world graphs, 2) it leverages the positive unlabeled learning to take the effect of partially observed positive edges into the cluster assignment, and 3) it achieves efficient computational complexity, $O((n^2+mn)kt)$, where $n$ is the vertex size, $m$ is the attribute size, $k$ is the number of clusters, and $t$ is the number of iterations for learning the cluster assignment. We conducted experiments extensively for various clustering methods with various real datasets to validate that our method outperforms the former clustering methods regarding the clustering quality.
Byzantine-Resilient High-Dimensional Federated Learning

We study stochastic gradient descent (SGD) with local iterations in the presence of Byzantine clients, motivated by federated learning. The clients, instead of communicating with the server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own datasets and then communicate the net update with the server, thereby achieving communication efficiency. Furthermore, only a subset of clients communicates with the server at synchronization times. The Byzantine clients may collude and send arbitrary vectors to the server to disrupt the learning process. To combat the adversary, we employ an efficient high-dimensional robust mean estimation algorithm at the server to filter-out corrupt vectors; and to analyze the outlier-filtering procedure, we develop a novel matrix concentration result that may be of independent interest. We provide convergence analyses for both strongly-convex and non-convex smooth objectives in the heterogeneous data setting. We believe that ours is the first Byzantine-resilient local SGD algorithm and analysis with non-trivial guarantees. We corroborate our theoretical results with experiments for neural network training.
Overview of Input-based Facial Expression Generation and Latest Applications

Expression generation technology is an important part of the field of artificial intelligence research. As a hot issue in computer applications today, its results have been widely used in human-computer interaction, digital entertainment, communications, video conferencing, and medical fields.This article summarizes the published research methods based on input methods into image and text input control expression generation methods, and reviews them. The image input control expression generation method is divided into two cases according to the presence or absence of constraints. Most model algorithms propose solutions to improve the coincidence of natural images and application models in pixel space. This article discusses typical successes in terms of theory, technology, etc., analyzes the characteristics of different algorithms, current problems, and future development directions, and provides references for related research.
Research on Adaptive Segmentation of Typical Objects in Remote Sensing Images

The high-resolution remote sensing image contains rich information of surface features. However, the same surface features collected in different regions at different times still have large appearance differences. This problem makes the network parameters trained in a single environment difficult to adapt to the extraction of similar objects in different environments. In order to solve these problems, this paper proposes a domain adaptive method based on CNN to solve the single class target segmentation problem in different feature domains for remote sensing images. The model consists of two parts. Firstly, this paper selects a segmentation framework suitable for remote sensing image feature extraction as a generator. Different feature layers of the generator are extracted to describe the source domain and the target domain image in multiple layers. Second, the features are used as the input of the discriminator to adversarial learning to distinguish whether the input is from the source image or the target image. In order to verify the effectiveness of the algorithm, this paper takes the extraction of planting greenhouses in remote sensing images as an example, first this paper proposes a data set for the extraction of planting greenhouses and carries out pixel level annotation. Then, the remote sensing image of the greenhouse in Chenggong District, Kunming City, Yunnan Province, South China was made into the labeled source data, and the remote sensing image of the greenhouse in the Inner Mongolia Autonomous Region of the North was used as the target data of the unmarked. The experimental results on the database show that the recall rate and accuracy of the proposed algorithm are good, and the MAE is 0.544, which is better than the existing feature extraction method based on migration learning.
Research on Smelting Temperature Control of Wear-resistant Ring Raw Materials Based on Stepped Fuzzy PID

For the problem that the smelting temperature is difficult to control caused by the nonlinear, strong coupling and large time delay of raw materials' smelting process in the production of wear-resistant ring, a fuzzy PID temperature controller based on stepped control is proposed. According to the difference in temperature error, the heating process is divided into three stages, corresponding to three heating methods to form a stepped fuzzy PID control system structure. Through simulation experiments, it is verified that the developed controller can speed up the response speed, shorten the adjustment time, reduce overshoot and temperature fluctuations, improve system dynamic performance, so as to effectively optimize the smelting temperature control process of wear-resistant ring raw materials.
TSBP: Tangent Space Belief Propagation for Manifold Learning

We present Tangent Space Belief Propagation (TSBP) as a method for graph denoising to improve the robustness of manifold learning algorithms. Dimension reduction by manifold learning relies heavily on the accurate selection of nearest neighbors, which has proven an open problem for sparse and noisy datasets. TSBP uses global nonparametric belief propagation to accurately estimate the tangent spaces of the underlying manifold at each data point. Edges of the neighborhood graph that deviate from the tangent spaces are then removed. The resulting denoised graph can then be embedded into a lower-dimensional space using methods from existing manifold learning algorithms. Artificially generated manifold data, simulated sensor data from a mobile robot, and high dimensional tactile sensory data are used to demonstrate the efficacy of our TSBP method.
Development of Online Ticket Booking Application for Ferry Crossing Website Based in Toba Lake Area

Tourist area of Lake Toba is one of the international class destinations in Indonesia. It is time to improve the quality of Lake Toba through the development of infrastructure based on information technology. Every holiday arrives, the queue of cars for ferry ticket crossing to Samosir Island becomes very congested. The problem is, tickets are only given to passengers who have a car to avoid the practice of brokers while on the other hand the number of ferry and capacity is very limited. Conventional ticket reservations usually have waiting times ranging from 5-10 hours. This study aims to build ticket reservations online. The application development model used was the waterfall model. The design of data flow in an online ticket booking system application uses a structured approach, which was visually illustrated with Context and Data Flow Diagrams. The programming language used was PHP with the Laravel 5.7 framework using the MVC concept. Website design uses a CSS framework called Bootstrap and JQuery Javascript Library. RDBMS used was MySQL with XAMPP tools. Online ticket system simulation test results show the passenger waiting time was only about 1 to 2 hours.
Type-2 Fuzzy Adaptive Control For Uncertain Robot Manipulator System With Time-Varying Output Constraints

A novel adaptive control design method based on type-2 fuzzy logic system is proposed in this paper. The controller is to solve the stability problem of n-link manipulator system with time-varying output constraints in complex environment. Through the analysis of type-2 fuzzy logic systems, a new and original universal approximator with parameter is proposed to equalize the un-certainties in robot manipulator system. The main advantages of this design method one is that the computational burden of online learning can be reduced greatly, another merit is more smaller tracking error can be ensured, and the output or position of the robot manipulator system are constrained by time-varying output. In addition to these, the idea of this paper extends the semi-global stability to global stability compares to the traditional fuzzy adaptive controls. The effectiveness of the method is demonstrated by simulation results.
Comparative Performance of Random Forest Algorithms and Discriminant Analysis in Classifications

The use of computer-based systems plays a leading role today as an analytical technique in diagnosing diseases, including predicting autism disorders. This research aimed to predict the class or target by using related variables accurately. The prediction process involved the number of attributes and records from the dataset. The dataset used in this study was the autism disorder dataset taken from the UCI repository machine learning; the data was already in number (binary) thus, no dummy variable was necessary is not needed. In this study, we used the Random Forest algorithm and discriminant analysis. The purpose of this study was to predict the type of autism disorder in children according to their prescribed symptoms. Two types of disorders used as target variable; communication disorders and behavioral disorders. Meanwhile, the independent variables are symptom 1, symptom 2, symptom 3, symptom 4, and symptom 5. The results from these are later processed using the SPSS software. The results from the Random Forest method indicated that 74.2% of forming a classification and a sensitivity value of 26.3%. Meanwhile, the percentage in the discriminant analysis was 70.1%. This value indicated that the decision tree was more reliable in classifying target types
An Experimental Study on the Effect of Noise in CCITT Group 4 Compressed Document Images

Consultative Committee for International Telephony and Telegraphy (CCITT) group 4 compressed binary document images support Modified Read (MR) and Modified Modified Read (MMR) compression schemes, which use three modes such as horizontal, pass, and vertical modes for encoding the data, along with run-length and modified Huffman codes. This compression scheme is widely used because of its efficiency in storage and transmission of data. Noise is a part of every data. In order to study the effect of noise in CCITT group 4 compressed binary document images, we performed the analysis of three modes, without using the decompression operation. From the analysis, it was found that noise, in general, affects the vertical mode at rapid rate compared to the other two modes. This experimental study would pave a way to perform automatic detection, analysis, and removal of noise directly in the compressed domain.
Linear SVM for Classifying Breast Cancer Data Encrypted Using Homomorphic Cryptosystem

Support Vector Machine (SVM) is widely used for data classification in many fields, including in biomedical area. While data privacy is a requirement for patients' health records, most existing classification models that are based on SVM cannot be operated on encrypted data. With the increasing use of cloud system, the need to process encrypted data cannot be avoided, especially for data stored in cloud system. This paper proposes a classification model that is based on Linear SVM. The model works on data that is encrypted using Paillier homomorphic encryption. Modification on Linear SVM classifier must be conducted to suit the requirement. Based on the evaluation, the accuracy, precision and recall of the proposed model is as good as those in the model that is applied on un-encrypted data. In terms of processing time, the application of modified Linear SVM classifier on encrypted data does not increase the time complexity.
DSP-Based Uniaxial Solar Tracking System

Solar tracking system is a device which can automatically locate and track the sun. It is mainly used to improve the power generation efficiency of solar array. At present, due to the development of the photovoltaic industry, more and more attention has been paid to solar tracking system. However, the previous solar tracking system can not meet the precision requirements, or the cost is too high to promote. Aiming at these two problems, this paper proposes a new solar tracking system, which uses DSP as microcontroller, collects illumination signals from the sensor to determine the position of solar illumination, and drives a motor to make the sensor rotate with the position of solar illumination, so that the sensor can always follow the sun illumination. It has the characteristics of high precision, simple structure and low cost.
Multi-task Learning and Thai Handwritten Text Recognition

Written languages have some commonalities and differences. Knowing one language can help learning other languages better. While labelled data is limited in offline Thai handwritten text recognition problem domain, multi-task learning is chosen to address the problem in our study. The multi-task model is trained from Thai, Latin and Devanagari writing scripts. The model consists of three layers employing Convolutional Neural Network, Recurrent Neural Network and Connectionist Temporal Classification, respectively. Recognition accuracies are compared against three corresponding single-task models. Thai handwritten text recognition performance is considerably improved by multi-task learning. Learning multiple languages helps the model generalize better when trained by large enough datasets.
The Development and Innovation of Financial Enterprises Based on Artificial Fish Swarm Algorithm

By constructing projection index function to optimize the design, a projection pursuit comprehensive evaluation model (AFSA-PP) based on artificial fish swarm algorithm is established to evaluate the development and innovation of financial enterprises. The results show that the interpolation of development and innovation has a good adaptability for the comprehensive evaluation model, and it is in the same accuracy, compared with raga-pp evaluation model, AFSA-PP needs shorter optimization time and is more suitable for the rapid evaluation of innovation in modern financial enterprises.
Review: A comparison Steganography Between Texts and Images

Abstract The steganography is branch from information hiding, the speed of evolve communications Internet and networks in wide areas in the world. This evolve make to most people tends for work in security data through transmit across networks from sender to receiver. The major aim from steganography uses to protect the substantial data, such as text, image, video, and audio during transmit between sender and receiver. The problems in steganography, because the people to increase uses internet in the present time therefore, needs to protected information during transmitted from sender to receiver. And solve this problem in steganography, in here many techniques is used in this article. This article offers comparing in steganography techniques between texts and Images, when hiding secret message in texts and Images. In this study, several techniques it uses by researcher in domain of steganography. The outcomes to obtained comparing between steganography texts or images. The results that shown the compression between text and image steganography are good and efficiency together without sensitive by attackers.
Improved Encryption Algorithm for Digital Image of Knight Tour Based on Cell Block

This paper presents an improved algorithm based on digital image encryption of knight tour. The efficient greedy algorithm is used to generate the knight tour matrix to improve the time efficiency of image encryption; the process of image encryption is specifically improved, and two different knight tour matrices are used to encrypt the image. Firstly, every pixel of the image is scrambled one by one, then the scrambled dense image is divided into cell array blocks, and the cell array is used as the operation object to scramble the blocks, and the image is encrypted again. The algorithm is simple, easy to implement, and has strong real-time operability, which can be used for real-time sharing of images; only the combination of the knight patrol matrix used for encryption and scrambling is properly matched, then a better encryption effect picture can be obtained, just like random noise, which cannot be distinguished. The experimental results show that the improved algorithm has strong ability of anti cutting attack and anti noise attack, good image restoration, high security performance and good reliability.
Energy efficient workload processing in distributed computing environment modeling

Global digital transformation requires more productive large scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency, reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, what makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node's individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload, and then adjust these patterns to match the current workload, while using PCPB scheduling strategy to optimize hardware utilization. An approach is tested using Matlab modelling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service level agreement (SLA).