Reviewerly/exHarmony huggingface dataset:
Design Optimization of Ultra-Thin Asphalt Pavement on Cement Concrete Bridge Deck based on Artificial Intelligence

In the design of cement concrete bridge deck ultra-thin layer asphalt pavement, due to the insufficient design of construction method and asphalt preparation ratio, the adhesion and firmness of the bridge deck ultra-thin layer are poor. Therefore, the optimization method of asphalt pavement design based on artificial intelligence is designed. Through the setting of coarse and fine aggregate technology and asphalt index, the preparation process of ultra-thin asphalt on cement concrete bridge deck is completed. Artificial intelligence technology is used to construct a visual finite element model of the ultra-thin asphalt pavement on the bridge deck, and the paving unit is set according to this model. Use asphalt macadam seal bridge deck construction technology to complete the paving process. At this point, the design optimization method of ultra-thin asphalt pavement on cement concrete bridge deck based on artificial intelligence is completed. Constructing the experimental link, through comparison, it can be seen that the adhesion and firmness of the ultra-thin bridge deck designed by this method are better than the other two methods. It can be seen that this method is more effective.
Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition

This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. The recently proposed Convolutional Neural Network (CNN) model is MobileNet, which has both offline and real-time accuracy and speed to provide fast and predictable real-time results. This also solved a problem related to the face that occurs in the identification and recognition of the face. This paper presents the different methods and models used by numerous researchers in literature to solve the issue of faces. They get a better result in using the highest number of layers. It is also noted that the combination of a machine learning approach with multiple image-based dataset increases the efficiency of the classifier to predict knowledge related to face detection and recognition
PELAPORAN AKUNTANSI PADA UMKM CV. CITRA ABADI BATU

Ipteks for the community on the CV.Agro Citra Abadi Batu with the aim of helping partners in order to have a reliable accounting information systems that can provide important information, accurate and timely, so that owners and parties concerned can analyze and take the right decision. Problems of the partners is not yet able to evaluate his performance for not having a system of accounting information to make financial statements. The purpose of this activity is to improve the understanding and knowledge of human resources. The lasting company has to information system of accounting and financial reporting. Besides helping partners in designing the accounting system in accordance with the conditions of the partners. Method to solving the problem: advising system of accounting information based SAK EMKM, discussing, accounting software design,and implementation of accounting software.The results of these activities shows that there is an increased understanding and ability of partners in the field of accounting. Partners can understand and apply the concept of entity references separation of business transactions with private transactions. Proven partners may draw up financial statements, can use the financial statements as the basis for evaluation of performance and can use for materials management decision-making
Solar energy forecasting using intelligent techniques: A step towards sustainable power generating system

The rapid advancements in the technology, increase in comfort levels, movement of population to urban areas, depletion of fossil fuels and increasing greenhouse gas emissions have invigorated the use of renewable energy resources for power generation in the last few years. The major renewable energy resources which have potential to fulfill the requirements includes solar energy, wind energy, small hydro and biomass etc. Among these major resources, solar energy-based technology is considered as one of the fastest growing technology because of its various advantages and ubiquitous availability of the resources. However, there are certain challenges in the utilization of solar energy for power generation because of various uncertainties in the atmosphere. As a result, the power generated from solar based power plants is fluctuating in nature which is not desirable. Therefore, the utilities are adopting the smart grid approach which has ability to integrate the solar power plants efficiently and the solar energy forecasting is one of the essential tools for this new model. In this paper, AI based techniques are utilized to forecast solar energy using high quality measured solar irradiance data. The forecasting accuracy of the developed models is evaluated based on statistical indices such as absolute relative error and mean absolute percentage error. The results obtained from the developed models are compared to observe the forecasting ability and performance with the high-quality measured data and found accurate.
Pandemic Literature Search: Finding Information on COVID-19

Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. We investigate how to better rank information for pandemic information retrieval. We experiment with different ranking algorithms and propose a novel end-to-end method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search. This work could lead to a search system that aids scientists, clinicians, policymakers and others in finding reliable answers from the scientific literature.
Novel hash function using Zaslavsky map

In this paper, we propose a novel hash function based on Zaslavsky chaotic map. Exact study has been provided on the novel algorithm using distribution, sensitivity, statistic analysis of diffusion and confusion, and collision analysis. The experimental data shows that the proposed hash function satisfied a high level of data security.
Warped Document Image Correction Based on Checkboard Pattern and Geometric Transformation

Abstract— Document image warping problem refers to the process of geometrically transforming 2D images. In this work we aim to solve the warped document image problems. The idea of the proposed method is inspired from the concept of image registration to align two images (distorted document and undistorted ground truth checkboard). In this paper, we propose document image dewarping method based on checkboard calibration pattern and geometric transformation. Some pairs of control points are selected between the two images to define the mapping function between them. The dewarping process transforms the warped document image according to the geometric transformation defined by the calculated mapping function. Results on document dewarping dataset CBDAR 2007 demonstrate the success of our strategy. OCR (Optical Character Recognition) error metrics are also used to gauge the success of the suggested approach. Based on the quantitative and qualitative metrics the proposed method outperforms the compared state of the art method.
Entanglement of General Two-Qubit States in a Realistic Framework

In the present paper, we examine the quantum entanglement for more general states of two-qubit system in the context of spin coherent states (SCSs). We consider the concurrence as a quantifier of entanglement and express it in terms of SCSs. We determine new set of maximally entangled conditions that provide the maximal amount of entanglement for certain values of the amplitudes of SCSs for the case of pure states. Finally, we examine the entanglement of a class of mixed states of the two qubits and provide the range in which the entanglement value is maximal with respect to the values of the amplitudes of SCSs.
Domain Adaptation for Tibetan-Chinese Neural Machine Translation

The meaning of the same word or sentence is likely to change in different semantic contexts, which challenges general-purpose translation system to maintain stable performance across different domains. Therefore, domain adaptation is an essential researching topic in Neural Machine Translation practice. In order to efficiently train translation models for different domains, in this work we take the Tibetan-Chinese general translation model as the parent model, and obtain two domain-specific Tibetan-Chinese translation models with small-scale in-domain data. The empirical results indicate that the method provides a positive approach for domain adaptation in low-resource scenarios, resulting in better bleu metrics as well as faster training speed over our general baseline models.
Implementation of E-Government Information System Application for Village Service

The rapid development of information technology has a significant impact, especially on government offices and agencies in managing data to improve services to the public. Using technology, of course, greatly facilitates the government apparatus in processing data and reports. The government agency
Kernelized Correlation Filtering algorithm fused with Kalman Filter

The KCF algorithm updates the model by sampling each frame of the image. When the object is obscured by the object, the wrong sample will be introduced in the model update, making the tracking effect worse or even the target lost. Aiming at the above problems, KCF algorithm is fused with Kalman filter. But in fact, the target motion in the image is nonlinear due to the translation and rotation of the camera, while Kalman filter can only deal with the linear motion of the target. Through the transformation of world coordinate system - camera coordinate system - image coordinate system - pixel coordinate system, the two-dimensional coordinates of the target in the pixel coordinate system of KCF algorithm are converted into the three-dimensional coordinates in the world coordinate system by combining the distance from the camera to the target and the camera's attitude angle and offset. The nonlinear motion of the target in the image is converted into the linear motion in the world coordinate system. According to the assumed target motion model, the Kalman filter is used to estimate the optimal three-dimensional coordinates of the target, and then the target position is converted into the target position modifier in the KCF algorithm. The experimental results show that the improved algorithm is more robust than KCF when the target is occluded.
Image classification using local binary patterns

An image-classification algorithm based on an alphabet of local binary patterns is described. The efficiency of the proposed algorithm is demonstrated using a test image set of handwritten digits (MNIST). A comparison of the algorithm’s properties with similar results to those of convolutional artificial neural networks is presented.
PDEs on graphs for semi-supervised learning applied to first-person activity recognition in body-worn video

This paper showcases the use of PDE-based graph methods for modern machine learning applications. We consider a case study of body-worn video classification because of the large volume of data and the lack of training data due to sensitivity of the information. Many modern artificial intelligence methods are turning to deep learning which typically requires a lot of training data to be effective. They can also suffer from issues of trust because the heavy use of training data can inadvertently provide information about details of the training images and could compromise privacy. Our alternate approach is a physics-based machine learning that uses classical ideas like optical flow for video analysis paired with linear mixture models such as non-negative matrix factorization along with PDE-based graph classification methods that parallel geometric equations from PDE such as motion by mean curvature. The upshot is a methodology that can work well on video with modest amounts of training data and that can also be used to compress the information about the video scene so that no personal information is contained in the compressed data, making it possible to provide a larger group of people access to these compressed data without compromising privacy. The compressed data retains information about the wearer of the camera while discarding information about people, objects, and places in the scene.
The Character of the Keys in the Music of the Classical and Romantic Period

To discuss the character of keys in tonal music is a task between speculation and science based on facts. The purpose of this essay is to identify empirical facts on which individual traits of a given key are based. At the same time, I was keen on leaving room for the imagination, which plays an essential role in this context. On the one hand, it was my aim to conduct research which yields concrete results; on the other, I wanted to maintain an open-minded approach so as not to lose or narrow down the great range of feelings associated with individual keys. I also discuss a number of varying and contradictory approaches to the subject. In addition, I have included some thoughts on the much-disputed interconnectedness of key and colour. I examined a limited number of examples - by Joseph Haydn, Franz Schubert, Robert Schumann and others - because I was primarily interested in pointing out relatable reasons for a given key’s perceived character and not necessarily in comparing a maximum number of compositions.
Measuring the Quality Of the Development Process Academic System with E-GQM Method

In a software development project, aspects of software quality are fundamental; all stakeholders expect high-quality software. To ensure the quality of software products, it is necessary to ensure the software quality process. A software process is essential to be assessed from their quality. In the software development process, the developer needs guidance in carrying out every aspect of it. The goals to achieve and the procedure to measure for each aspect's goals performance must be determined. One method that can be used is the Extended Goal Question Metric method. This method determines what aspects must be achieved for each development process. A few goals to measure are defined for each aspect. For each goal, one or more goals determine one or more relevant questions. For each question, an appropriate metric is Â determined. The next step is mapping between G to Q and Q to M. The measurement was conducted by calculating the goal value obtained from the metric calculation. From this metric, each goal's value could be obtained, whether it is achieved or not. The tests were carried out on the software process to develop the academic Directorate of Technology and Information System Development of Institut Teknologi Sepuluh Nopember Surabaya, Indonesia. Each goal's value exceeded 0.51 (for a scale of 0-1), which achieved the Software development process's quality. The total average score was 0.889.
A Secret data sharing Model for Agriculture Experts in Federated Cloud based on Polynomial based Encrypted Scheme

A shift in computation from PC’s to Cloud allows more number of users to involve in cooperative computation on various categories of data wish to merge their expertise and thereby gain more useful information without leaking their own sensitive information. In the case of data collected from various sensors in an agricultural farm IoT device, the cloud and customers can cooperate to provide adequate services; benefits to experts, research stations related to agriculture. Enormous Agriculture Data generated is related to Soil, weather, Research, crop, farmers, Agriculture marketing, Agri-IOT, fertilizers and pesticide makes cloud as a centralized resource. The exchange of information and research will inculcate a healthy competitive atmosphere in the country in agriculture. Sharing of data, computation, services across cloud boundaries with different clients at different places will enhance expertise suggestions and results to farming field which benefit to improve countries economy. Federation of cloud will allow resource and data sharing, but the security threats severely limit the application development as the usage of data processing or sharing mechanisms will leak private information. So in this research paper, a Polynomial Based Encryption Secret Sharing Scheme (PBESSS)is proposed as Federated cloud data exchange system with multiple cloud instances of the same cloud host or separate computing hosts.
Analisis Kepuasan Masyarakat Terhadap Kualitas Pelayanan Pada Pengadilan Negeri Simalungun Menggunakan Metode Algoritma C4.5

This study aims to analyze the level of community satisfaction with services in the District Court Simalungun using the C4.5 Algorithm method. The data source used in this study was taken with an instrument in the form of a questionnaire with closed and open answers, where the data is in the form of numbers and analyzed static descriptive. The study population was all service users in the District Court Simalungun, including Procedures, Service Time, Costs/ Tariffs, Implementing Behavior, etc. After doing the calculations manually, the verification is also done using the application, namely RapidMiner. From the analysis process, it can be seen that the responsive aspect is the most dominant aspect in determining the level of community satisfaction at the District Court Simalungun
Analisa Kelayakan Penerima Program Keluarga Harapan (PKH) Menggunakan Algoritma C4.5

In overcoming the problem of poverty, the Government implements the Family Hope Program (PKH) which is a program assistance that provides assistance (subsidies) in cash to poor households as long as they meet the requirements set out in the program. The purpose of this study is to determine whether the family is still eligible or not receive the Family Hope Program (PKH) assistance, where there are many other disadvantaged families who have not had the opportunity to receive this assistance program. Sources of data obtained from the Martoba Village Head Office. The method used in the study is a data mining technique with the C4.5 algorithm which is implemented with the RapidMiner application. The attributes used in determining the family's eligibility are still feasible or not receiving assistance from this assistance program, namely income, number of family dependents, vehicle ownership and residence status. The results of the classification algorithm C4.5 and testing with Rapid Miner software, it is found that the factor that most influences the eligibility of the Family Hope Program (PKH) recipients is the number of stages (C1) with an acquisition value of 0.51827179
Parallel Implementation of a Target Detection and Tracking System on a Graphics Processing Unit

In these days, with spreading imaging systems, target detection and tracking become an important utility of the systems. In literature, detecting and tracking targets that have various different features in military or civilian applications are common research are of the community. The main purpose on target detecting is detecting objects that can be disassociated from background. They can be stationary or moving. After detecting targets, state vectors of the targets that can include position, velocity, magnitude etc. are predicted via tracking algorithm. In this paper, for detecting targets, salient region extracting is aimed so, Boolean Map Salient, (BMS), approach is used. For tracking targets, Bayesian approach based particle filter is used. Particle filter tries to find probability density function, (PDF), using particles with different weights. The main purpose of choosing particle filter is nonlinear behaviors can be represented. There are limited time for finding salient objects and tracking them in an embedded system. For this reason, to decrease processing time of the application, it is decided to use graphics processing unit, (GPU). It is observed that parallel implementation on a graphics processing unit can provide significant improvements on processing time.
Effect Of Family Environment, Home Literacy Environment, and Self Efficacy On Student Literation Skills

This study aims to determine the Effect of Family Environment, Home Literacy Environment, and Self Efficacy on Literacy Skills. There is a significant influence between the influence of the environmental environment, Home Literacy Environment and Self Efficacy on students' literacy skills need to be considered in order to be functional in shaping the habits of students to have reading skills so that from these habits can make students become positive personalities. The research method used is quantitative research. The technique used in data collection was carried out by the method of collection by distributing questionnaires to respondents totaling 230 and then processed using SPSS version 23 and amos version 23.
American Privacy Perceptions in the COVID Pandemic

The on-going COVID-19 pandemic has brought surveillance and privacy concerns to the forefront, given that contact tracing has been seen as a very effective tool to prevent the spread of infectious disease and that public authorities and government officials hope to use it to contain the spread of COVID-19. On the other hand, the rejection of contact tracing tools has also been widely reported, partly due to privacy concerns. We conducted an online survey to identify participants’ privacy concerns and their risk perceptions during the on-going COVID-19 pandemic. Our results contradict media claims that people are more willing to share their private information in a public health crisis. We identified a significant difference depending on the information recipient, the type of device, the intended purpose, and thus concretize the claims rather than suggesting a fundamental difference. We note that participants’ privacy preferences are largely impacted by their perceived autonomy and the perceived severity of consequences related to privacy risks. Contrarily, even during an on-going COVID-19 pandemic, health risk perceptions had limited influence on participants’ privacy preference, given only the perceived newness of the risk could weakly increase their comfort level. Finally, our results show that participants’ computer expertise has a positive influence on their privacy preference while their knowledge to security make them less comfortable with sharing.