Reviewerly/exHarmony huggingface dataset:
Influencing Factors in Determining Research Data Repository Infrastructure for Research Data Management

Increasing volumes of data are rapidly being produced by researchers with the advancement of digital technologies. In order to manage these data, a suitable research data repository infrastructure is needed by the higher learning institutions. Apart from storing the data, these data repository need to support the research data life-cycle that include the tasks of data creation, processing, analysis, preservation, access and reuse. The objective of this research is to deeply investigate the influencing factors fordata repository infrastructure in managing research data. A systematic literature review is conducted to perform the investigation where research papers are searched over three electronic journal databases. Selected papers are then analysed and a quality assessment has been conducted to identify the relevant infrastructure for research data repository. As a result, we identified the important components of research data repository infrastructure development.
The Application of Genetic Algorithm and an Evaluation Algorithm in Online Examination System

Online examination system plays a significant role in education. However, there are varieties of disadvantages in non-optimized systems, such as randomly selecting questions that make the exam paper has an imbalance difficulty, the unanticipated weight of knowledge points, and so on. A genetic algorithm is an efficient and achievable way to improve the ability to generate exam paper. Besides, a massive amount of data are generated when the system is running. Nevertheless, some of the systems only store the data, in another word, they do not make full use of the generated data. An evaluation algorithm is put forward in this essay to give objective and scientific evaluations on students’ learning and teachers’ teaching via using the data that are generated in examinations, which is based on the degree of difficulty. To make this algorithm working well, the degree of difficulty of questions stored in the database is supposed to be updated dynamically when the samples of questions’ answers become large enough.
Hamming Code Converter using Reversible Toffoli Netlist

Technological advancements in the last two decades have witnessed a plethora of emerging technologies gaining global research attention. One of them is reversible logic due to its capability to arrest tremendous amount of heat generated in modern digital conversions at the architecture level. With CMOS reaching near capping of threshold parameters, researchers are imperatively exploring the reversible domain for possible alternatives. Telecommunication is one area which expects appreciable share of digital resources. This communication proposes the reversible version of Hamming Code converter, decoder, and Corrector unit. Hamming codes form one of the perfect codes attaining the Hamming Bound. The designs have been implemented using Toffoli Netlist. A state-of-the-art four variable reversible gate in literature has been used to design the high level schematic for part of the proposals. The authors provide the results in terms of primitive Quantum Metrics.
Hybrid Feature Extraction and Multiclass SVM Based Ancient Character Recognition

Ancient character recognition is the most difficult task due to their different formats and less frequent knowledge about the ancient characters. This is performed in the previous research work namely Shape and Size aware Character Recognition and Conversion System (SSCR-CS). This research work proved better character recognition outcome. However this research work might be reduced in performance with lesser detailed information in the images. This is resolved in the proposed research work by introducing the most recent techniques for the character recognition outcome. This is attained by introducing the method namely Hybrid Feature Extraction and Multiclass SVM based recognition method (HFE-MCSVM). In this research work, initially image pre-processing is performed by using Gabor filter. After pre-processing segmentation of characters is performed by using overlapped character segmentation method. After segmentation character recognition is done by introducing the method namely hybrid feature extraction with Multiclass SVM classification approach. The overall assessment of the research work is done in the matlab simulation environment and then it is proved the proposed HFE-MCSVM shows better performance.
JPR volume 56 issue 2 Cover and Back matter

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Institutionalization of stock status report in the management of HIV/AIDS programme: experience from Nigeria

Incomplete and poor quality of data generated by routine health information systems limit their use in program planning, implementation and monitoring.Prior to 2012, there was no central structure in place in Nigeria for the collation, analysis and presentation of HIV/AIDS Logistics Management Information System (LMIS) data in a usable format.Following the resolution to unify multiple HIV/AIDS supply chains, the country embarked on an aggressive effort to improve the quantity and quality of LMIS reports.This effort culminated in the periodic generation of Stock Status Report (SSR).The report ensures that LMIS data are collated, analysed and presented in a format that enables stakeholders make informed decisions.In the course of implementation, the country also simplified LMIS tool and adopted a mix of report collection models.The SSR has ensured that Nigeria has a repository of LMIS data that aids program planning, implementation and monitoring.In addition, report generation has stimulated improvement in LMIS data quality for HIV/AIDS and other public health programmes.Our experience suggests that data from routine health information systems (RHIS) is of value in public health programming in resource limited settings.Positive spillover effect is also feasible while simplification of data collection tools can indeed help improve quantity and quality of reports generated through RHIS.
The Affiliation Between Markov Models and Wide-Area Networks with BOSS

Many security experts would agree that ,had it not been for the Construction of model checking, the deployment of accesspoints might never have occurred.In this paper, weverifythede- ployment of the UNIV AC computer. In this position paper wever ifyth atthough the acclaimed train able algorithm for the deployment of hash tables by Brown[21]is recursively enumerable, context- free grammar and the World Wide Web are generally incompatible. We leave out these results for an onymity
Automated Emergency Room Triage: Helping Patients Get the Best Treatment

We describe an intelligent triage system for emergency rooms; the system interacts with patients and classifies them by priority level and with respect to medical specialty. The system consists of a conversational interface, coupled with sensors and a physical robot-like platform, and classifiers that operate on symptoms and measurements so as to select a medical specialty and to output a priority level. Tests with human subjects demonstrated that our Healthbot system was well received and in fact preferred to alternatives by most people. Tests have also shown that the classifiers reached accuracy consistent with a doctor's output.
Simultaneous approximation of images applications to image and video compression

Given a set of images we propose an algorithm that approximates all images simultaneously. The algorithm finds the best common partition of the images' domain at each step, this is accomplished by maximizing an appropriate inner product. The algorithm is a pursuit algorithm constrained to build a tree, the optimization is done over a large dictionary of wavelet-like functions. The approximations are given by vector valued discrete martingales that converge to the input set of images. Several computational and mathematical techniques are developed in order to encode the information needed for the reconstruction. Properties of the algorithm are illustrated through many examples, comparisons with JPEG2000 and MPEG4-3 are also provided.
IDETIFIKASI UKURAN PAKAIAN BERBASIS IMAGE PROCESSING

Data becomes a valuable asset today. With so much data, current technology can open up the possibility to process and make predictions. Machine Learning (ML) is part of Artificial Intelligence (AI) which has become an important tool in software systems in optimizing running systems. Image Classification is one of the topics of Machine Learning that is widely used in machine learning. The application of image classification on the server-side is often done in every complex classification problem because it requires large computing power. This application uses the ml5.js library as a javascript library to process data in machine learning. The application of machine learning for clothing measurement allows users to make it easier to determine the size of clothes at the Reevant Supply IND store. Meanwhile, to support the use of machine learning on the website, a pre-trained model is needed. This study examines neural network settings such as epoch and optimal learning rate. In addition, to prove that you can be superior in training time and loss function values. The results of this study prove that it is easier for users to determine the size of clothes purchased at the Reevant Supply IND store much faster than prior consultation with the store admin
Exploiting Manipulated Region in an Image using Integrated Convolution Neural Network and LRW Segmentation Features

To locate the manipulated region in digital images, we suggest to use Convolution Neural Networks and the segmentation based analysis. A unified CNN architecture is designed with set of training procedures for sampled training patches. Tampering map can be generated for the above said Convolution Neural Networks with the help of tampering detectors. In the other hand, a segmentation using lazy random walk based method is second-hand to generate the tampering chance map, finally integrate the maps and generate the final decision map. This can help to locate the manipulated region accurately. Experiments are conducted using the various datasets to prove the efficiency of the suggest method.
Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments

This dissertation aims to develop an effective and practical method to forecast chaotic time series. Chaotic behaviour has been observed in the areas of marketing, stock markets, supply chain management, foreign exchange rates, weather forecasting and many others. An effective forecasting model can reduce the potential risks and uncertainty and facilitate planning and decision making in chaotic systems. In this study, residual analysis using a combination of the embedding theorem and ensemble artificial neural networks is adopted to forecast chaotic time series. Based on the embedding theorem, the embedding parameters are determined and the time series is reconstructed into proper phase space points. The embedded phase space points are fed into the first neural network and trained. The weights and biases are kept to predict the future values of phase space points and accordingly to obtain future values of chaotic time series. The residual of the predicted time series is further analyzed; and, if a chaotic behaviour is observed, then the residuals are processed as a new chaotic time series and predicted. This iterative residual analysis can be repeated several times depending on the desired accuracy level and computational efficiency. Finally, the last neural network is trained using neural networks' result values of the time series and the residuals as input and the original time series as output. The initial weights and biases of the neural networks are improved using genetic algorithms. Taguchi's design of experiments is adopted to identify appropriate factor-level combinations to improve the result of the proposed forecasting method. A systematic approach is proposed to improve the combination of ensemble artificial neural networks and their parameters. The proposed methodology is applied to a number of benchmark and some real life chaotic time series. In addition, the proposed forecasting method has been applied to financial sector time series, namely, the stock markets and foreign exchange rates. The experimental results confirm that the proposed method can predict the chaotic time series more effectively in terms of error indices when compared with other forecasting methods in the literature.
Disease Prognosis by Machine Learning Over Big Data from Healthcare Communities

With huge information headway in biomedical and healthcare communities, appropriate examination of therapeutic information helps early sickness identification, tolerant consideration and network administrations. Prediction accuracy is diminished when the nature of medicinal information is inadequate. At that point the various areas appear, one of kind qualities of certain local infections, which may debilitate the expectation of illness episodes. In this paper, machine learning method is applied for viable forecast of interminable disease in the history of predicting diseases. The main intension is to have different prediction models over genuine medical clinic information gathered from focal China in 2013-2015. To conquer the trouble of deficient information, a latent factor model is used to regenerate the irrecoverable data. Here, experiment on a territorial chronic infection of cerebral localized necrosis is done. CNN-MDRP (convolutional neural system based multimodal infection chance prediction) algorithm is explained utilizing organized and unstructured information from medical clinic. Apparently, none of the current work establishes on the two information types in the zone of therapeutic enormous information investigation. Contrasted with numerous prediction algorithm, the precision accuracy of the proposed method arrives at 94.8% with a combination speed which is quicker than that of the CNN-UDRP(convolutional neural network based unimodal disease risk prediction) technique.
ANALISIS PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH (BOS) PADA PENINGKATAN PELAYANAN PENDIDIKAN DI SMA NEGERI 4 JAYAPURA

The education budget is a critical aspect to coincide schools operational requirements mainly how to meet the needs for investment, operations, and school personnel (facilities and infrastructure). It serves to protect the community, especially those who still need assistance in striving for their right to get proper education services. The objective of this study is to examine the governance of the School Operational Assistance (BOS) budget in improving education services in Senior High School (SMA) 4 Jayapura. This research used descriptive statistical analysis. Qualitative data is in the form of in-depth interviews with several key informants from the School, consisting of the Education and Teaching Office, the Committee, and the parents/guardians of students as well as school documents/archives in the form of School Profiles and Reports Accountability (K-7). The data collection method used in this study is in-depth interviews and documentation. The results show that most of the school budget used for spending on interaction and processes of education. Also, we find that BOS funds had decreased in the last few years. The evaluation of the BOS funds gained attention from the school principal, and cooperation of the inspectorate of the local government.Keywords: School Operational Assistance, School Income and Expenditure Budget, School performance
Inter-analyst comparison and reproducibility of apatite fission track analysis

Factors influencing data reproducibility of fission-track (FT) thermochronology can be summarized into three main categories associated with data acquisition steps.(1) Sample preparation involves mineral separation, mounting, polishing and etching; (2) data revelation relates to instrumentation (microscope, LAICPMS, etc.) and software settings; and (3) execution depends on feature selection by the analyst.
On generalized random environment INAR models of higher order: Estimation of random environment states

The behavior of a generalized random environment integer-valued autoregressive model of higher order with geometric marginal distribution and negative binomial thinning operator is dictated by a realization {zn}?,n=1 of an auxiliary Markov chain called random environment process. Element zn represents a state of the environment in moment n ? N and determines all parameters of the model in that moment. In order to apply the model, one first needs to estimate {zn}?,n=1, which was so far done by K-means data clustering. We argue that this approach ignores some information and performs poorly in certain situations. We propose a new method for estimating {zn}?,n=1, which includes the data transformation preceding the clustering, in order to reduce the information loss. To confirm its efficiency, we compare this new approach with the usual one when applied on the simulated and the real-life data, and notice all the benefits obtained from our method.
Neural networks and technical analysis for price prediction: the case of Borsa Italiana SP MIB

The paper analyses the relationship between common technical analysis indicators and the returns of an index for the period considered. It is expected to find correlation between indicators and index prices, as well as showing clear patterns and potential strategies for investment analysis and portfolio management. As an innovative methodology a mixed analysis is carried out, trying to combine classic signals offered by the indicators with the power of neural networks. The neural network plays an important role in that allows for an accurate regression with efficient error minimisation, while giving indications about the concentration of results obtained around some reference values. Through a simple hidden-layer, back-propagation algorithm, regressions give interesting result, in term of the forecasting potential of the analysed indicators. The final step of the project is to conclude about results and summarise the indication coming from the multivariate stage analysis, commenting on the power of the indicators to reveal potential investment opportunities.
Sistem Pelaporan Identifikasi Karakter Seorang Anak Berdasarkan Pola Sidik Jari dan Sudut Tangan

Hand-fingerprint analysis is one of the biometric methods for knowing a person's character. Fingerprint analysis was developed as one of the methods to find out the most dominant brain functions affecting one's genetic potential. Through the interpretation of fingerprint patterns and measurements of the lines at the end of each finger can be read potential, motivation, learning style, work style, even the potential stress of a person who was born from birth. The existence of the relationship between the pattern of fingerprint with the character of a person raises the idea to create a system that can be used to analyze the characteristics of a child based on the type of fingerprint pattern it has. The system can perform analysis based on the fingerprint and hand angle patterns to get the type of spelling, learning style and character in the child in order to facilitate user in analyzing automatically based on fingerprint pattern and hand corner of input to get the output type of spiritedness, learning style and character in child.
"Research on Decryption Methodologies and Key Aggregate Searchable Encryption for Data Security Storage in Cloud"

Numerous firm’s architectures management of data guarantees substantially alter method, gain access to maintain private commercial business. Occur additional facts protection problems. Existing statistics safety methods have limits in preventing records legal assaults, in particular these performed by utilizing companion diploma business executives to the cloud dealer to monitor data get right of entry to within the cloud and find out unusual records get right of entry to. Sorts require method of building a machine robust protection privateer’s statistic through files person team customer’s technique much documents. Not simply the encryption keys but also the search keys need to be utilized and consumers have to keep their keys resistant and disseminated. The cutting-edge current gadget the place the facts proprietor can solely share one key with the user, whether or not it is any kind of document, massive or small variety of documents, and the function of the person is to cross a lure in the cloud of overall performance checking out and safety evaluation of shared documents, which is an impenetrable and superb proposed gives schemes. And consumers are also extremely concerned about data sharing storage, inexplicable information leak in the cloud, and harmful attackers. In the article, an experiment is done to format a method for encryption for impenetrable statistics allocation.
Differential Technique of Training on the Basis of Diagnostics of Students’ Learning Capabilities

An author's differential technique of training has been developed, which presupposes the identification of individual typological differences of students (learning capabilities) and the development on this basis of strategies for organizing interaction between the teacher and the learner (student).Differential technique includes the following components: invariant (heuristic algorithmic prescription) and variable (learning strategies for five typological groups of students).The effective implementation of the differential technology is provided by a set of diagnostic training facilities with computer support, aimed at training the teacher and at ensuring the learning process.
Organizational Performance of Companies in Saudi Arabia: The Mediating Effect of Knowledge Sharing

Organizational performance (OP) is important for the company, employment and the country. Previous studies deployed secondary data approach in developed countries while few studies examined the OP using primary data approach. Using the theories of resource based view, knowledge based view, this study aims to examine the effect of organizational culture (OC), organizational structure (OS), and technology infrastructure (TI) on OP of companies in Saudi Arabia. In addition, the study aims to examine the mediating role of knowledge sharing (KS). Using a purposive sampling, the data was collected from managerial level employees in Saudi. The results of Smart Partial Least Square showed that the OC is the most important variable followed by OS and TI. KS mediated the effect of OC and TI on OP. Companies in Saudi Arabia should establish a KS culture and enhance their TI to achieve competitive advantage and better OP.