Reviewerly/exHarmony huggingface dataset:
Towards the Construction of Web Services the Perception of Transformative Programming

Various pros would agree that, had it not been for blockage control, the amalgamation of IPv7 may never have occurred. Following a long time of ordinary examination into multi-processors, we demonstrate the examination of superblocks, which epitomizes the tremendous guidelines of frameworks organization. We show that the acclaimed set up figuring for the cognizance of correspondence by O. Nehru is recursively enumerable.
Partial Least Square – Structural Equation Model for Effect of Employee Engagement on Job Satisfaction and Job Performance in Automobile Industries

The major challenges organizations facing today’s competitive world is diversified work force and ever changing environment and technology. It is, therefore, imperative to utilize the employees ‘capabilities to the maximum possible extent to achieve individual and organizational goals. In this perspective, it has been discussed that job satisfaction and employee motivation are wide influencer of employees’ performance. Employee dedication and commitment towards work leads to improved outcomes in performance, organizational productivity and profit. Employees are more loyal and satisfied to the organization when they get recognition from their employers. The understanding of organizations on the factors satisfying employees will lead to deal with the people and get the extra ordinary results from them. The employee engagement and business performances are complimentary to each other. Higher is the employee engagement then greater the business performance. The present research makes an attempt to investigate the concept of job satisfaction and its impact on performance among the employees in the automobile industry, which is one of the key drivers that boost the economic growth of the country. In the time of recessions, the survival of the organization depends on the fully engaged employee. There is no agreement between defining the employee engagement by the earlier researchers and also its impact on job satisfaction and performance. In this concern, this study is significant in the definition of employee engagement and to find its relationship with job satisfaction and job performance in automobile industry.
Influential Components of People's Relationship with Environment in the Formation of Social Interactions (Case Study: Tarbiat Pedestrian and Valiasr Pedestrian )

Influential Components of People's Relationship with Environment in the Formation of Social Interactions (Case Study: Tarbiat Pedestrian and Valiasr Pedestrian )
Social Problems in Short Stories entitled Mata Yang Enak Dipandang by Ahmad Tohari and its Relevance in Senior High School

This research was based on the view that literature is basically a reflection of society. So in this study, researchers used the approach to the sociology of literature. This paper aimed to describe (1) short story becomes one of the literary works that are used as teaching material in schools, (2) the aspect of characrter education based on the collection of short stories entitled Mata Yang Enak Dipandang by Ahmad Tohari, and (3) social problems collection of short stories entitled Mata Yang Enak Dipandang. The method used in this research is qualitative descriptive explaining the data that has been found in research. The main data sources in this study the quotations contained in the novel and the results of interviews with literary experts and learning experts. The results of this study is the main characters in this short story are varied and its can be utilized in the study of literary appreciation for Class XI in Senior high school semester with the standard literary discourse understanding competence through reading poetry and short stories in the aspects of reading and basic competence that is analyzing the intrinsic elements of a short story linkages with everyday life
A Fast and Efficient Lossless Compression Technique for Greyscale Images

The growth of cloud based remote healthcare and diagnosis services has resulted, Medical Service Providers (MSP) to share diagnositic data across diverse environement. This medical data are accessed across diverse platforms, such as, mobile and web services which needs huge memory for storage. Compression technique helps to address and solve storage requirements and provides for sharing medical data over transmission medium. Loss of data is not acceptable for medical image processing. As a result, this work considers lossless compression for medical in particular and in general any greyscale images. Modified Huffman encoding (MH) is one of the widely used technique for achieving lossless compression. However, due to longer bit length of codewords the existing Modified Huffman (MH) encoding technique is not efficient for medical imaging processing. Firstly, this work presents Modified Refined Huffman (MRH) for performing compression of greyscale and binary images by using diagonal scanning method. Secondly, to minimize the computing time parallel encoding method is used. Experiments are conducted for wide variety of images and performance is evaluated in terms of Compression Ratio, Computation Time and Memory Utilization. The proposed MRH achieves significant performance improvement in terms of Compression Ratio, Computation Time and Memory Usage over its state-of-the-art techniques, such as, LZW, CCITT G4, JBIG2 and Levenberg–Marquardt (LM) Neural Network algorithm. The overall results achieved show the applicability of MRH for different application services.
Resource allocation and task admission control in cloud systems

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.
Accuracy of the Neurons Number in the Hidden Layer of the Levenberg-Marquardt Algorithm

Backpropagation, as a learning method in artificial neural networks, is widely used to solve problems in various fields of life, including education. In this field, backpropagation is used to predict the validity of questions, student achievement, and the new student admission system. The performance of the training algorithm is said to be optimal can be seen from the error (MSE) generated by the network. The smaller the error produced, the more optimal the performance of the algorithm. Based on previous studies, we got information that the most optimal training algorithm based on the smallest error was Levenberg–Marquardt with an average MSE = 0.001 in the 5-10-1 model with a level of α = 5%. In this study, we test the Levenberg-Marquardt algorithm on 8, 12, 14, 16, 19 neurons in hidden layers. This algorithm is tested at the learning rate (LR) = 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1. This study uses mixed-method, namely development with quantitative and qualitative testing using ANOVA and correlation analysis. The research uses random data with ten neurons in the input layer and one neuron in the output layer. Based on ANOVA analysis of the five variations in the number of neurons in the hidden layer, the results showed that with α = 5% as previous research, the Levenberg–Marquardt algorithm produced the smallest MSE of 0.00019584038 ± 0.000239300998. The number of neurons in the hidden layer that reaches this MSE is 16 neurons at the level of LR = 0.8.
A Movie Recommender System using Ontology Based Semantic Similarity Measure

Continuous growth in information available on the Internet overwhelms the users during navigation. This information overload may result in users’ dissatisfaction which is undesirable. Users’ satisfaction is very important aspect in every domain. Recommender systems play a vital role in dealing with information overload problems. The recommender systems filter the huge information on the Internet to generate limited and personalized information to users. This helps in increasing users' satisfaction by retaining his/her interests during navigation. Pure Web usage data based recommender systems have been used from last few years. However, they lag in precise recommendations because of absence of domain knowledge. Further, the similarity measures play a vital role in recommendation process and hence affect the performance of the recommender systems. The performance of recommender systems can be enhanced through integration of domain knowledge with usage data. This paper presents an approach to movie recommender system that integrates domain knowledge with usage data. The ontology is used to represent domain knowledge. The proposed approach is based on a new ontology based semantic similarity measure. The experimental results prove that the recommendations’ quality andaccuracy of prediction can be enhanced through integration of ontological domain knowledge with Web usage data.
Machine Learning Methods for Keyword Extraction and Indexing

The digital age results in the creation of massive information. It is a common tradition among the users to digitalize almost every moment of daily life, since it has become convenient to fetch the information as and when needed from the Internet. User can able to retrieve information by providing query keyword. The objective of the search is to quickly return the set of most relevant documents given a search string. Accomplishing this task for a fixed query involves determining the most relevant documents form the big-data. Queries given to the IR systems are enabled by the keywords. Keyword extraction is a process of identifying the document. Manual keyword extraction is cumbersome and it is in feasible to efficiently identify all the keywords in the document. Therefore the machine learning approaches for keyword extraction are proposed. In this paper various machine learning approaches have discussed along with its merits and de-merits. Here we are also proposing a trained index structure which is efficient to identify the specific locus of the record.
Context Aware Quick Sensor Service (CQSS) to Remote Patients

Using sensors in healthcare can greatly improve the quality of life, especially for elderly patients. The data from the sensors of the patients is constantly monitored for abnormalities at a server. Whenever this data crosses a threshold value, the information is notified to the corresponding doctor. The doctor can then take the necessary action. However an inspection of historical data has shown that some conditions of patients have cyclic patterns and the medications are often repeated. The proposed system is designed to assist the doctor in diagnosis by retrieving those patterns. We have compared the times taken for receiving responses from the two different systems and a significant amount of improvement was noticed. We have introduced a Dynamic Context Aware Technique (DCAT) which can improve the quality of 24 hour monitoring patient. This paper presents the design and implementation of a system based on DCAT using SAMSUNG GEAR S (Heart rate monitor sensor.The backend remote centralized computation and data storage can decreases the workload of the remote health care provider by avoiding of sending the identical and similar cases data to the doctors. This improves the processing speed and also gives solutions in case of the unavailability of doctors in some cases. Experimental results based on real datasets show that our system is highly efficient and scalable to a long time monitoring patients.
Seismology

The authors test the existence of magnitude correlations for a self-similar earthquake occurrence rate model.As a first observation I would like to remark that magnitude correlations are intrinsic to this kind of models simply because the occurrence probability cannot be factorized.
A Correlation Study of the Effectiveness of Teaching Practice between Taught Programs and Personal In-Built Performance Talents

The study aims to identify the types of relationships related to the conversation, teaching methods, language testing, syntax, micro-teaching, classroom management, and teaching practice. It is hypothesized that there is no linear relationship between the two variables, r= 0. In other words, no connection is there between the teaching programs at the university and performing well at schools during practising. Thus, and by contrast, there is a linear relationship between the two variables, r ≠0. In other words, the teaching programs at the university and teaching practice are related. However, there is an uncertainty that performing well at schools could be solely due to the effectiveness of taught programs and personal in-built talents. Specifically, the study tries to answer these questions: 1. Is there any relationship between the teaching programs and teaching well at schools during practising? If yes, how strong is that? 2. Which teaching program is relatively strongly related to the teaching practice module? And 3. Does personal in-built talent bring about an escalation in performing well at schools during practicing? Also, the study aims to figure out any connection between programs at university and teaching performance at schools by identifying the type and the magnitude of the relationship available. Also, it attempts to highlight the most influential positive or negative connection between the taught program variables. Further, it reveals the rationale beyond teaching practice achievements and, consequently, relates them to the actual causes. The study revealed that there is a positive relationship between all the selected modules and teaching practice based on the Pearson Correlation test calculating the coefficient value output at 0.494 with a p. value of 0.000. Since correlation does not mean causation, the findings report a kind of confusion about whether teaching programs are beyond teaching well at schools.
Antarctic Ocean Whaling Photo Album : Cataloguing And Preservation Of A Japanese Album

The Antarctic Ocean whaling photo album is an album published during the 1940's by a Japanese marine product company. The album contains 52 gelatin silver prints of a whaling expedition to the Antarctic Ocean and is a fascinating visual record of the Japanese whaling industry. Using this album as a case study, this thesis project is a study of the cataloguing process and preservation of a photographic album. The goal of this project is to make the album more accessible to researchers through translation, cataloguing and digitization, as well as to provide a preservation strategy through condition assessments. This paper summarizes the research conducted on the album, outlines the cataloguing process, the condition assessment of the album and provides a treatment proposal and a handling guideline for the album. The paper also includes a Romanization chart and a translation table of the album texts as aids for further research.
Classification of Categorical Outcome Variable Based on Logistic Regression and Tree Algorithm

Logistic regression is most popular techniques incorporated in traditional statistics. Usually, this regression is applicable when the dependent variable is of categorical binary in nature. In the field of Statistics and Machine learning, classification of data is critical to discriminate to which set of clusters a new observation belongs, in the base of training set of a data containing observation whose group relationship is known. In this paper, we are focusing on the concepts of Logistic regression and classification tree. A large data taken from UCI (Machine learning Repository) incorporated for this research work. The aim of study is to distinguish the results obtained from Logistic regression and decision tree. At the end, decision tree gives better results than Logistic regression.
PERAN PENGELOLAAN ARSIP DIGITAL UNIVERSITAS MUHAMMADIYAH PONOROGO DALAM UPAYA PENINGKATAN KUALITAS ADMINISTRASI UNIVERSITAS

Management of documents, hereinafter referred to as archives, is an activity that must be carried out by the entire organization. But not infrequently the problem of document management becomes a problem that has a negative impact on the quality of the administration of the organization. The development of technology eventually became an important moment in the management of documents / archives in an organization in order to improve the quality of their administration through the management of digital archives. Using a qualitative descriptive method the author intends to provide a real picture of managing digital archives in an effort to improve the quality of administration at Muhammadiyah University, Ponorogo. Sources of data obtained from administrative staff who act as users as well as archives manager as well as several other supporting documents. The results of this study indicate that the management of documents / archives digitally is very helpful for administrative staff in the creation, storage, supervision and provide opportunities for them to learn more about mastering IT equipment.
A Weighted Frequent Item-Set Mining using WD-FIM Algorithm

Smart systems are the one of the most significant inventions of our times. These systems rely on powerful information mining techniques to achieve intelligence in decision making. Frequent item set mining (FIM), has become one of the most significant research area of data mining. The information present in databases is in-general ambiguous and uncertain. In such databases, one should think of weighted FIM to discover item sets which are significant from end user’s perspective. Be that as it may, with introduction of weight-factor for FIM makes the weighted continuous item sets may not fulfil the descending conclusion property anymore. Subsequently, the pursuit space of successive item set can't be limited by descending conclusion property which prompts a poor time effectiveness. In this paper, we introduce two properties for FIM, first one is, weight judgment downward closure property (WD-FIM), it is for weighted FIM and the second one is existence property for its subsets. In view of above two properties, the WD-FIM calculation is proposed to limit the looking through space of the weighted regular item sets and improve the time effectiveness. In addition, the culmination and time productivity of WD-FIM calculation are examined hypothetically. At last, the exhibition of the proposed WD-FIM calculation is confirmed on both engineered and genuine data sets.
Pythagorean Fuzzy Labeling Graph

The objective of this paper is to introduce a new concept of Pythagorean Fuzzy Labeling. If a fuzzy labeling is a Pythagorean Fuzzy Labeling, then the membership value of the edges is strictly less than the corresponding membership value of its incident vertices and the non - membership value of the edges is strictly greater than the corresponding non - membership value of the incident vertices of a graph. The sub graphs and union of Pythagorean Fuzzy Labeling Graphs are defined.
ANALISIS YURIDIS PERATURAN TERKAIT ARSITEKTUR BANGUNAN BERCIRIKHAS DAERAH ISTIMEWA YOGYAKARTA

ABSTRACT&#x0D; This study examines the juridical analysis of regulations related to the architecture of the Yogyakarta Special Region. Whereas the regulation of architectural buildings with cultural characteristics has been regulated in several regional regulations, including regional regulations, governor regulations, and mayor regulations. This research is a library research (library research) and combined with field research that is descriptive analytical. So that bringing up the form of approach is a normative approach. The results of this study are regulations related to architectural regulations that characterize the Special Region of Yogyakarta experiencing strengthening in terms of its jurisdiction, especially with the birth of Regional Regulation with the birth of Yogyakarta Special Region Regulation Number 1 Year 2017 Concerning Distinctive Characteristics of Yogyakarta Special Region. In terms of the substance of the existing regulations will strengthen the legal umbrella of Yogyakarta's architectural pluralism, strengthen the area of ​​cultural heritage and the area of ​​cultural heritage. Then in terms of the laws and regulations, it is necessary to harmonize regulations related to building architecture with special character of Yogyakarta Special Region so that the implementation of architectural buildings unique to the Special Region of Yogyakarta can be maintained and optimally maintained.
Image Dehazing Based on Physical Model and Gray Projection

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.