Reviewerly/exHarmony huggingface dataset:
Indonesian Language Learning with Structural Analitic Sintetic (Sas) Method for 1st Grade Student in Brilliant Integrated Islamic Elementary School of Mojoagung

The focus in this research is learning Indonesian with Synthetic Analytical Structural (SAS) method. The main purpose of this research was to improve participant’s knowledge about Indonesian with SAS method. It used pretest and posttest to know the effect of SAS method in learning Indonesian and use the result as the main indicator. It used quantitative and qualitative research design. The subject of this research is the first grade students and teachers of SDIT Brilliant, Mojoagung, Jombang joining. There were two meetings conducted. The instruments used in each meetings was consisted of observation sheet of participants activity, study record, interview, and documentation. The researh showed that there was a significant effect of using SAS method to learn Indonesian on Tematic 5, subtheme 1, at the second meeting. It is shown from the pretest which get 70,2%, categorized as enough and the posttest get 85,8%, categorized as excellent. Based on the data, it can be concluded that learning Indonesian with SAS method have a good impact on students performance and it is proven with the good score as the outcomes. Keywords : Indonesian subjects, Synthetic Analytical Structural (SAS) method.
Review of &amp;quot;Multiple technical observations of the atmospheric boundary layer structure of a red warning haze episode in Beijing&amp;quot;

Within this study, the authors discuss a weeklong period when there was extremely poor air quality in Beijing in December of 2016.The authors use data from a 325-m tower, a backscatter lidar, 12-hourly radiosondes, and a wind profiling radar to analyze the atmospheric boundary-layer structure during this time frame.Boundary-layer heights are determined from the lidar, wind profiler, and radiosonde, which are intercompared and used to evaluate how the height changes depending on the air quality.The authors also find that the TKE and u_* are inversely correlated with PM2.5 concentrations.Unfortunately, there are numerous major concerns with this study which are listed be-
Depth Motion Maps and Log-Gabor Features Based Human Action Recognition Using Support Vector Machine

In this paper, an efficient scheme is introduced for the human action recognition from depth video sequence.In particular, we have proposed an effective feature extraction technique which is based on Depth Motion Maps (DMMs) and Log-Gabor wavelet.Firstly, three Depth Motion Maps (DMMs) are projected from front, side and top views to accumulate DMMs features.Then, the Log-Gabor filter is employed on DMMs to attain complex wavelet features illustration.In Log-Gabor feature, we have evaluated two parts of a depth sequence called real and imaginary information for front, side and top sequence of DMMs projections.After that, we have adopted principal component analysis to reduce the dimension of the feature matrix.Finally, support vector machine (SVM) is utilized for action classification.The experimental results on the public Microsoft Research Action3D dataset suggest that our proposed DMM-Log-Gabor features based action recognition method is efficiently achieved the best performance on human action recognition.
Work in Progress: The Role of Student Counselors at Cybersecurity Summer Camps

conference papers, in addition to making numerous other conference presentations.Straub's research spans the gauntlet between technology, commercialization and technology policy.In particular, his research has recently focused on cybersecurity topics including intrusion detection and forensics, robotic command and control, aerospace command and 3D printing quality assurance.
Efficient Alphabetic Text-based Tree Indexing

The Indexing data structure is used to store and retrieves data in database.It is frequently being used to speed up access to particular data items.Creating an index is a very important part of databases.Tree is a best option to create indexing for database.Appropriate indexing techniques supports in reducing the space and time complexity of storage and retrieval data.Traditional index sequencing method is used to static database and homogeneous data.To retrieve heterogenous big data, conventional indexing techniques is not useful.For heterogenous big data, optimized decision-making indexing is required.If there is a requirement to create the index for string or character for searching, tree-based indexing is the best option.In this paper, alphabetic text and character-based indexing techniques have proposed for searching purpose.This proposed index technique is very useful for insertion and updating database quickly.The alphabetic text and character-based indexing serve the "Starts with" operations for autocomplete function.Text-based indexing will be more beneficial than hash-based index.Alphabetic text and Character based indexing means the indices for characters are simply the order of alphabets like 'a' -> 0, 'c' -> 2 and so on.The proposed technique can be used to quickly get some important information of one of the fields or combination and proceed with Create, Read, Update and Delete operations.Experimental results show that alphabetic text indexing is better then hash-based indexing for insertion, deletion and updating of database.
Structured Dirichlet Smoothing Model for Digital Resource Objects

Typically, the results of digital resource object retrieval are made up of whole documents. The problem is that each document contains a large number of metadata units. These units are located in a single document describing different topics. Therefore, retrieving the entire document means retrieving all these units which may be mostly irrelevant to the user‘s query. The Dirichlet smoothing model usually calculates the probability of having the query in each document and then displays the related documents basedon the query.It is therefore best to retrieve the nearest and relevant metadata units for the query regardless of which document they belong to.To achieve this, astructured Dirichlet smoothing model is proposed in this paper thatcalculates the likelihood between the query and the metadata units instead of between the query and the whole document. The experiments which were conducted on the cultural heritage CHiC2013 collectionhave shown a statistically significant improvement over the traditional Dirichlet smoothing model.
Attitudes of university professors towards the scientific uses of Facebook in the Kurdistan region Sulaymaniyah and Sulaymaniyah Technical University as a model

This research deals with a cognitive topic on the ground of the knowledge relationship that exists between Facebook and university professors in the Kurdistan region, as it aims to reveal the trends of university professors in the Kurdistan region towards the scientific uses of Facebook, in order to realize a set of communicative habits and their motives, through access to these trends And what it is, which is basically the structural problem of this study. This is by obtaining field answers to the following questions: What are the scientific motives for university professors' use of Facebook? What are the academic groups that university professors communicate with for this purpose? What are the academic gratifications achieved by university professors' use of Facebook? &nbsp;&nbsp; The research is considered an appropriate descriptive research to discover what its structural variables and the interconnections that bring together, that is, those variables, using the survey methodology to address its basic questions in the field, relying on the professors of human sciences at the University of Sulaymaniyah and Sulaymaniyah Technical University as a collective for research and comprehensive inventory, and its most important results are: -The existence of a good relationship between university professors and the Facebook social network. -The presence of good interactive experience among university professors regarding this social network, i.e. Facebook.
The Academic Use Of Smartphone In English Classes

Ample researches about smartphone use problems have been conducted for years recently. Despite it, however, researches of academic use of smartphone is limited, particularly related to English classes in Indonesian context. This research was conducted to scrutinize how teachers of English take benefits of the smartphone use in the delivery of both teaching and testing activities. Sample comprised 129 teachers derived from three school clusters i.e., Junior Secondary, General Senior Secondary, and Vocational Senior Secondary schools in the greater city of Jakarta. To confirm it, data of student’s practice of the smartphone use to learn English was also scrutinized. For that purpose, diverse students from the three school clusters were involved. The research reveals the findings, such as (1) all teachers are familiar with the features and application available in the smartphone, (2) all teachers utilize the available application to blend the delivery of teaching activities, but kinds and numbers of the application vary, (3) reading is the most preferred skills to deliver using smartphone, (4) almost all teachers utilize the available application to assess student’s learning achievement, and google application is the most preferred choice, (5) in addition to teaching and testing, all teachers utilize smartphone to motivate students to learn English, but kinds of application is limited, (6) rule reinforcement is significantly important to enact as far as smartphone use during schools hours is concerned. This research implies that blended teaching is obligatory in the millennial age nowadays.
Unemployment Alleviation: Does Social Overhead Capital Matter? An Empirical Evidence From SAARC Countries

The main objective of the present study is to reduce unemployment considering the role of social overhead capital in the SAARC countries.The study is based on the panel data covering the time period 2000 to 2018.Different forms of social over head capital like Transportation infrastructure, Telecommunication sector infrastructure, social sector infrastructure, Financial penetration and renewable energy sources are used in the research.Panel cointegration techniques and Fully Modified OLS method are employed for estimation.The research concludes that the all forms of infrastructure are compatible with theory and highly significant for unemployment mitigation.It is suggested that the SAARC economies should develop social overhead capital and formulate the joint policies to investment more in telecommunication sector energy sector and social sector infrastructure like health and education.
Development of Text Clustering Method with K-Means for Analysis of Text Data

Clustering is a widely used unsupervised data mining technique. In clustering, the main aim is to put similar data objects in one cluster and dissimilar in another cluster. The k-implies is the most famous clustering algorithm because of its effortlessness. But the performance of the k-means clustering algorithm depends upon the parameter selection. Parameter selection like number of cluster and initial cluster center are key of k-means algorithm. Distance augmentation method, density method quadratic clustering methods are utilized to initial cluster selection. This paper examination five unique methods, for example, improved k-means text clustering algorithm, revisiting k-means, LMMK algorithm, SELF-DATA architecture, Clustering Approach for Relation e.t.c. But these techniques have some limitations. To improve these approach, this paper has proposed the development of text clustering method with k-means for analysis of text data.
Improved RRT Algorithm Path Planning Combined with Artificial Potential Field Algorithm

In order to solve the problem of low path planning efficiency in complex obstacle environment, an improved Rapidly-exploring Random Trees (RRT) algorithm is proposed.It utilizes artificial potential field to guide the fast expanding random tree to growing towards the target and away from obstacles.Considering the gravitational field of the target node and the repulsion field of the obstacle in the artificial potential field, a certain number of random nodes generated in one iteration are evaluated and selected.The simulation results show that the improved RRT algorithm has strong advantages over the basic RRT algorithm in search ability and computation time.
Object Detection using K-Means Clustering – A Research

Clustering is an unsupervised machine learning technique and the task is to group a set of objects in such a way that objects in the same group are more similar to each other than those in other groups. There are different clustering techniques, each with its own advantages and disadvantages. The K-means clustering algorithm is our main focus in this paper. K-means is mostly used when there is a large number of unlabeled data. The difficulties in the path of K-means clustering are a) Different initial points can lead to different final clusters. b) It does not work with clusters of different sizes and densities. c) With the global cluster, it does not work well and is difficult to determine K Value. Our main aim is to try and modify the K-means clustering algorithm to get rid of the above-mentioned drawbacks.
Estimation of Weibull parameters using artificial neural network

&lt;p&gt;Weibull distribution is an important distribution in the field of reliability. In this distribution usually there are two parameters. The usual parameter estimation method is maximum likelihood estimation. Maximum likelihood estimation requires mathematical formulation and prior assumption. Non parametric method such as neural network does not require prior assumption and mathematical formulation. They need data to formulate the model. In this report feed forward neural network with back propagation is used to estimate the parameters of a two-parameter Weibull distribution based on four Scenarios. The Scenario consists of training and test data set. Training and test data set generated through simulated time to failure events using wblrnd function in MATLAB. The input to the network is time to failure, and the output is shape and scale parameters. The network is trained and tested using trainbr algorithm in MATLAB. The network performed better on Scenario 2 which has the larger number of training examples of shape and scale.&lt;/p&gt;
A Combined Horizontal Parallel Apriori Algorithm and Adaptive Frequent Pattern Growth Algorithm for Big Data Mining

Due to the massive data size and complexness, big data mining using a sole computer is a problematic task. With the rapid increase in the database size, parallel and distributed computing systems can yield better benefits in the data mining applications. Parallelization of the Association Rule Mining (ARM) algorithms is a significant task in the data mining application for effectively mining the frequent itemsets from the large-size databases. These mining algorithms allocate the database in a horizontal manner or increase the number of processors to decrease the overall time necessary for mining the frequent itemsets. In this paper, a combined Horizontal Parallel-Apriori (HP-Apriori) and Adaptive Frequent Pattern (FP) Growth algorithm is proposed to divide the database both horizontally and vertically into four sub-processes, for parallel processing of all four tasks. The Horizontal Parallel-Apriori algorithm increases the speed of the mining process using an index file. Adaptive Binomial Distribution (ABD) is applied to the Frequent Pattern Growth Algorithm to find the minimum support for mining the optimal frequent itemsets. Experimental analysis established that the combined algorithm outperforms in terms of minimizing the overall execution time and increasing the computational speed in high scalability.
Licensing Electronic Products at the American Psychological Association

SUMMARY. The American Psychological Association (APA) has been licensing electronic products since 1987 and in 2004 plans to license five major databases. The licensing process has evolved over time in response to customer concerns and the need to make it more efficient for the customer, vendor, and APA. Increasingly, institutions are required by university regulations or state law to add very substantive clauses before they can sign a license. Issues include liability, indemnification, governing law, security, and perpetual access among others. Although licensing is time consuming for all parties, the process will unlikely be eliminated; therefore, it’s necessary to make it as painless as possible for all. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
Virtual Reality, Augmented Reality and beyond: Laboratory of Computer Applications for Health Care at University of São Paulo

The Laboratory of Computer Applications for Health Care at University of São Paulo develop multidisciplinary research involving Computer Science areas and other knowledge fields such as Physiotherapy, Psychiatry, Radiology, and Cardiology. Most studies use concepts besides Computer Science, by aggregating knowledge to solve real problems. In this paper we present the main projects in Virtual Reality and Augmented Reality areas, highlighting serious games, computer-aided diagnosis and therapy, simulation and training. Besides the social and scientific impact inherent to the type of the developed research, some systems are available to health professionals in order to allow technology transfer and consequent technological and economic impact in daily activities of health tasks.
About the perspectivity or unsufficiency of introducing the institute of chipping the population

This article is devoted to an urgent problem – the issues of feasibility, legality and expediency of population chipping. The author provides a deep and comprehensive analysis of the positive and negative sides of the problem under consideration, using the example of foreign countries in which there is a studied practice. The article proposes a scientific polemic of published works on the problem of microchips, offers its own point of view and draws its own conclusions. The key point of this article is, developed on the basis of research, SWOT analysis, which includes the strengths and weaknesses of the problem under study. In conclusion, the author draws his own conclusions and makes proposals aimed at committing the crimes, which may have theoretical and / or practical value. The article is intended for persons interested in the forensic technique, forensic characteristics of crimes, methods of investigation of certain types (groups) of crimes, as well as for a wide range of readers.
Designing nonlinearity

Qubits cannot exist without nonlinearity, but nonlinear elements in superconducting circuits lead to losses. A superconducting qubit has now been realized by nonlinearly coupling two microwave resonators, offering the promise of long coherence times.
Predicting Ranking for Scientific Research Papers using Scalable Tensor Flow Library and Learning to Rank

Scientific research papers play a vital role for innovation of new technology. It is the future of the development where a novice person can understand the technology and tries to develop a new idea. In this paper, concentrated on relative order for a group of items applied to scientific research paper. In this process we identify how LTR differs from standard supervised learning in the sense that instead of looking at a precise score or class for each sample, it aims to discover the best relative order for a group of items. Firstly we identified the work of ranking of scientific research papers using traditional method know as supervised learning. Secondly we evaluated and made the comparison between the supervised learning and the scalable Tensor flow library for learning to rank. Apart from solving information retrieval problems, Learning to Ranking is mostly used in areas like Natural language processing (NLP), Machine translation, Computational biology or Sentiment analysis.
Artificial Intelligence Technology Supports the Following Research on the Generation of Subtitles for College English Teaching

In the process of college English teaching, the generation of subtitles in English class has gradually received more attention. The accuracy of subtitle generation determines the quality of English teaching. In order to optimize the subtitle generation technology for English teaching in university classrooms, this study uses the self attention mechanism of artificial intelligence technology to construct the subtitle generation model, and applies it to the sequence segmentation and paragraph segmentation of news data set. The results show that the subtitle generation model based on self attention mechanism can effectively complete the corresponding tasks with high accuracy, recall and FI-Score, and provide strong support for English teaching.
The impact of modern information technology on customer satisfaction considering the mediating role of organisational health

Today, an organisation's survival and continuity is based on use of information technology (IT). Without IT, using modern approaches in the organisation and even competition with others would be impossible. With respect to the many developments in IT, the customers of each organisation would expect more from them. The goal of current research is assessing the impact of modern IT on customer satisfaction with mediating role of organisational health. This research is categorised among the descriptive-correlational and an applied in the nature. All consumers of oil products including 267 people have been chosen. The presented model tested by use of structural equation modelling, specifically, partial least squares (PLS). Based on the hypothesis testing, ease of use, speed, accuracy, reliability and accountability as six components of modern IT have positive and meaningful impact on organisational health. Finally, the impact of organisational health on customer satisfaction confirmed. Based on these findings, it can be said that the modern technologies have positive influence on organisational health which leads to customer satisfaction. Our approach and findings have significant implications for managing customer satisfaction in the services industry.
Image Compression and Detection Technique Using Principal Component Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.