Reviewerly/exHarmony huggingface dataset:
A Survey of Time-varying Structural Modeling to Accountable Cloud Services

Cloud service has the computing characteristics of self-organizing strain on demand, which is prone to failure or loss of responsibility in its extensive application. In the prediction or accountability of this, the modeling of cloud service structure becomes an insurmountable priority. This paper reviews the modeling of cloud service network architecture. It mainly includes: Firstly, the research status of cloud service structure modeling is analyzed and reviewed. Secondly, the classification of time-varying structure of cloud services and the classification of time-varying structure modeling methods are summarized as a whole. Thirdly, it points out the existing problems. Finally, for cloud service accountability, research approach of time-varying structure modeling is proposed.
Denoising of signals and noise extraction by sparse autoregression

Denoising is the process of removing noise of a noise contaminated signal, noise extraction is to destill out noise. This can be done by using autoregressive (AR) filters, if the signal was generated by an AR process and if the AR coefficients are known. We introduce a sparse AR denoising/noise extraction method by using Yule-Walker (YW) equations in combination with l1-regularization and compare the results with a ‘classical’ YW based denoising/noise extraction. Simulations show that the novel approach is superior compared to the classical one for short sparse AR signals. The novel approach does not require AR order estimation and may be useful for supervised or automated denoising/noise extraction of sparse AR signals.
SPE Awards Program Focused on Industry Professionals and Their Achievements

Plastics EngineeringVolume 76, Issue 3 p. 5-7 SPE Awards SPE Awards Program Focused on Industry Professionals and Their Achievements Six honored in 2020 for their achievements in the plastics industry Chris Barry, Search for more papers by this author Chris Barry, Search for more papers by this author First published: 02 March 2020 https://doi.org/10.1002/peng.20267Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume76, Issue3March 2020Pages 5-7 RelatedInformation
An Analysis of Thematic Progression on Academic Essays Writing by the Fifth Semester Students of UIN Antasari Banjarmasin

This study aimed to identified of thematic progression pattern that used on students’ academic essay seen from three levels of achievement: low, middle, and high. Descriptive qualitative method was used as the method of this study. The data of the study were twelve students’ academic essays from low, middle, and high level of achievement. The steps in analyzing the data are first: taking texts written by the students, second: reading the texts and segmenting each text into clauses, third: identifying theme and rhyme of each clause of the students’ texts, fourth: identifying thematic development pattern of the students’ texts by using thematic progression pattern proposed by Eggins (2004), fifth: consulting the identified thematic progression pattern to the experts, and sixth: calculating the amount of the occurrence of each thematic progression pattern. The result shows that Simple Linear Progression (SLP) became the most applied pattern on high achievers’ text with the total number of occurrence 10 times. Simple Linear Progression (SLP) also became the most applied pattern on low achievers’ text with the total number of occurrence 12 times, while on middle achievers’ text, Constant Theme Progression became the most applied pattern with the total number of occurrence 6 times.
One-qubit purity in terms of the discrete Wigner transform

An explanation and an illustration of the meaning of a discrete phase-space is given. The class of a discrete Wigner transform (DWT) for the specific case of a one-qubit state is introduced. We derive the one-qubit state formalism around its formulation in terms of the DWT in detail. A novel structure of a one-qubit purity in terms of the DWT is introduced. We find a criterion for stating when a one-qubit state is either mixed or pure.
An Improved Approach to Robust Digital Image Watermarking Algorithm Using Lifting Wavelet Transform Technique for Copyright Protection

This research work has developed a robust digital image watermarking algorithm using lifting wavelet transform technique for copyright protection to address the problem of watermark distortion during embedding and extracting processes and easy removal of watermark. The improvement in this work is predicated on the replacement of conventional discrete wavelet transform with lifting wavelet transform for more efficient decomposition and minimal embedding and extraction time without significant trade-off in the performance indices as compared to other image watermarking algorithms that used 2-D transformation such as DWT, DCT, SVD or combination of two or all of these transform methods for image transformation. These transforms have been identified to possess limited properties that make them less efficient. The watermark embedding was carried out by embedding the watermark in the Lifting Wavelet Transform (LWT) coefficients of each 2 x 2 blocks decomposed. The algorithm was implemented using MATLAB R2013a software and evaluated using Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). Based on the simulation results obtained, the improved model gave robustness improvement of 18.83%, 54.33% and 59.93% for Jpeg compression attack, cropping attack, and Gaussian noise attack respectively. A Peak Signal to Noise Ratio (PSNR) of 38.4073dB was obtained representing 2.64% imperceptibility improvement. These indicate that the improved model has better performance. Furthermore, the simulated result of the improved model were validated using ITUT-T JI47 recommendations benchmark of < 1 for Normalized Correlation (NC) when subjected to attacks and Peak Signal to Noise Ratio (PSNR) of 35dB or above for good robustness and imperceptibility respectively.
Research on an Improved Association Rule Mining Algorithm

Data mining association rules is an important role of data mining because of its wide applicability in market analysis by expressing how tangible products and services relate to each other and how rend to group together. The paper proposed Apriori algorithm of riddling compression. And has carried on the simulation, the result demonstrated the Apriori algorithm of riddling compression can improve the efficiency greatly. It can greatly reduce the candidate frequent itemsets, keeps the completion of frequent itemsets, reduces the cost of computing, and improve the efficiency of algorithm.
How to Change Default Settings in Chrome

Google Chrome is not only the most popular web browser today, its also one of the most versatile. Chrome offers numerous settings you can configure to ...
소화기 영역의 빅데이터 연구 - 빅데이터연구위원회 활동 및 성과 중심으로 -

The Big Data Research Committee of the Korean Society of Gastroenterology conducted activities and researches with three goals. First, it provides the basis for proper and cost-effective treatment of digestive diseases in Korea. Second, it carries out pop­ulation-level global research by establishing a system of big data analysis related to gastroenterology. Third, it provides the members of the Korean Society of Gastroenterology with the opportunity to plan and assess the public interest related to big data. The studies published by the committee members in this paper were carried out under these objectives, and the findings are believed to have achieved the public interest goals that may be helpful in the current medical and health policy. The construction of the big data infrastructure for digestive drugs is also underway, and we expect to see meaningful results pertaining to important digestive drugs. Research using public health medical big data, such as the National Health Insurance Corporation data base, should ulti­mately provide a basis for reflecting public messages and policies for the public. To this end, it is necessary for Korean re­searchers to lead efforts to lower the barriers and to approach relevant information and opportunities using big data research.
Guides: Unit Readings FAQs: Create reading list

Unit Readings is UWA's system for sharing reading lists with students Reusing an existing list or creating a new one.
Anomaly Detection Combining Discriminative and Generative Models

This paper proposes a method to accurately detect anomaly from an image by combining features extracted by discriminative and generative models. Automatic anomaly detection is a key factor for reducing operation costs of visual inspection in a wide range of domains. The proposed method consists of three sub-networks. The first sub-network is convolutional neural networks as a discriminative model for extracting features to distinguish between anomaly and normal. The second subnetwork is a variational autoencoder as a generative model to extract features representing normal. The third sub-network is neural networks to discriminate between anomaly and normal on the basis of features from the discriminative and generative models. Experiments were conducted using pseudo anomalous images generated by superimposing anomaly which was manually extracted from real images. Results of the experiments show that the proposed method improves the area under the curve by 0.08-0.33 points compared with that of a conventional method. With high accuracy, automatic visual inspection systems can be implemented for reducing operation costs.
THE USING THIEVES STRATEGY TO TEACH READING COMPREHENSION TO EIGHTH GRADE STUDENTS OF SMPN 9 KENDAR

The objective of this research is to investigate is there a significant difference in students’ reading comprehension between those taught using THIEVES strategy and those attending conventional instruction and also to investigate the motivation and students’ perception in reading comprehension. This research applied treatment and control class with mix method design which analyzed using quantitative and qualitative analysis. The hypothesis of the research is there a significant difference in students’ reading comprehension between those taught using THIEVES strategy and those attending conventional instruction. Using independent t-test to evaluate the hypothesis of the research (sig.2-tailed = 0.00<0.05), reveals that there is a significant difference of reading comprehension between those taught using THIEVES strategy and those attending conventional instruction. This research also covers the students’ motivation in reading comprehension between THIEVES strategy and conventional instruction . The result of my research is there is no significant difference between those taught using THIEVES strategy and conventional instruction and motivation in treatment classes that using treatment THIEVES strategy those increased than conventional instruction or control class. In perceived about these strategies the students prefer in treatment class or using THIEVES strategy.
Keystone Academy Libraries: MS Language & Literature: MS Language & Literature Home

A research guide for Middle School Language & Literature classes. The home page for Middle School I&S.
Evaluation of Credit Card Fraud Detection Using SVM and ANN

A system for credit card fraud detection is essential for today’s technology-driven market since majority of population uses the facility of credit card in the economic system. Here we use two algorithms in machine learning and compare the accuracy of system in credit card fraud detection. It uses full historical transactions of a person including normal transaction data or fraudulent to get normal/fraud transaction features and then these features are used to check whether a transaction is normal or not. The two machine learning supervised algorithms are Support Vector Machine (SVM) and Artificial Neural Network (ANN). The final result will be based on the most accurate algorithm used.
Organisational Citizenship Behaviour and its Influence on Manufacturing Firms Survival in Nigeria: A Systematic Review

Objective: The importance of firm survival in a highly competitive business environment cannot be over emphasized. Organisational Citizenship Behaviour (OCB) is however considered to be one of the fundamental behavioural initiatives that can help firms achieve sustainable survival in the midst of economic turbulence as organisations now depend on their employees for success. Recent studies on the sustainability of firms shows that many Nigerian manufacturing firms are into extinction and few operate at a sustainable level as a result of the operating environment of business occasioned by global capitalism which has been unpredictable, complex and competitive. The main objective of this study is to explore extant studies on OCB and its influence on firm survival specifically in the Nigerian manufacturing sector.Methods: In order to fill this lacuna, this study explored reputable data bases of both empirical and non-empirical extant studies on OCB between 2009 and 2019. Studies were selected from business, management, Industrial/organisational psychology and human resource data bases. OCB was observed to have many dimensions which are overlapping and were re-categorized based on their objectives and characteristics.Results: OCB dimensions identified have significant effect on positive behavioural outcomes. It is therefore recommended that organisations should challenge their employees by providing them with positive work climate that will foster extra role behaviour in order to ensure organisational survival. Also, a proposed study model integrating the dimensions of OCB was developed which is expected to serve as hypothetical basis for future research and practice. More so, some theoretical frameworks were linked to OCB and positive employee behavioural outcomes that can foster firm survival.
Preface: International Scientific and Practical Conference “Modeling in Education - 2019”

First Page
Writer Identification Based on Combination of Bag of Words Model and Multiple Classifiers

In this paper, an efficient approach for text-independent writer identification using bag of words model and the combination of multiple classifiers is proposed. First of all, a bag of words model is established by extracting sub-images from the original handwriting image. Then, features are extracted by moment method, direction index histogram method and simplified Wigner method respectively to calculate the distance between the sub images having the same labels. Finally, the handwriting classification task is completed by means of feature fusion and multi-classifier combination. To evaluate this approach, writer identification is conducted on IAM English database. Experimental results revealed that the proposed writer identification algorithm with small number of characters and unconstrained contents achieves interesting results as compared to those reported by the existing writer recognition systems.
Exploration of temperature effect on videogrammetric technique for displacement monitoring

There has been a sustained interest towards the non-contact structural displacement measurement by means of videogrammetric technique. On the way forward, one of the major concerns is the spurious image drift induced by temperature variation. This study therefore carries out an investigation into the temperature effect of videogrammetric technique, focusing on the exploration of the mechanism behind the temperature effect and the elimination of the temperature-caused measurement error. 2D videogrammetric measurement tests under monotonic or cyclic temperature variation are first performed. Features of measurement error and the casual relationship between temperature variation and measurement error are then studied. The variation of the temperature of digital camera is identified as the main cause of measurement error. An excellent linear relationship between them is revealed. After that, camera parameters are extracted from the mapping between world coordinates and pixels coordinates of the calibration targets. The coordinates of principle point and focal lengths show variations well correlated with temperature variation. The measurement error is thought to be an outcome mainly attributed to the variation of the coordinates of principle point. An approach for eliminating temperature-caused measurement error is finally proposed. Correlation models between camera parameters and temperature are formulated. Thereby, camera parameters under different temperature conditions can be predicted and the camera projective matrix can be updated accordingly. By reconstructing the world coordinates with the updated camera projective matrix, the temperature-caused measurement error is eliminated. A satisfactory performance has been achieved by the proposed approach in eliminating the temperature-caused measurement error.
Inference Based on Continuous Linear Inequalities via Semi-Infinite Programming

I develop a consistent, asymptotically normal estimator of bounds on functions of parameters partially identified by the intersection of continuous linear inequalities. The inference strategy uses results from the semi-infinite programming literature to form a convenient estimator. Aside from allowing for continuous constraints, an advantage of the estimator is that it can be used to compute a closed form confidence interval, without numerically inverting a hypothesis test. So it is easy to compute confidence intervals even if the number of parameters is very large, especially when we are interested in a linear function of parameters. I also consider the dual problem of bounding a linear function of a sequence, an infinite dimensional parameter, partially identified by finitely many linear restrictions on the sequence.
Detection and tracking of sphere markers

Tracking and detection of sphere markers captured by a GoPro Hero 4 Silver camera at 240 frames per second is studied and discussed in this report. Different tools are designed such as, optimized color detection, optimized edge circle detection, and point tracking. A number of tools have been used such as Kernelized Correlation Filters, Optical Flow and Circle Hough Transform. The major problem of KCF trackers is their incapability of adjusting the scale changes of the target. It is solved by designing a point tracker unit. The marker used for this study is spherical and gray. Color detector algorithms are normally reliable for specific colors. In the worst case they cannot detect gray objects or it comes with high error. A learning algorithm was designed to optimize the color range in HSV as a signature of the marker. The Circle Hough Transform is a well-known method for detecting circles. This function accepts many inputs which effect the position of the circle. A learning method is developed to optimize its inputs by minimizing a defined error function. Finally, the results have been smoothen by applying Kalman Filter, an extremely accurate filter used in control industry and robotics to smooth or predict the position of robots. In this work we discuss the robustness of the developed algorithm against changes in brightness that shows how it would perform in more real-world conditions. The final results have been compared in accuracy of the algorithm versus three other well-known algorithms: CSRT, Boosting, and Median Flow. This comparison shows that the proposed method is reliable and the developed algorithm is in fact more accurate in detection and more reliable with fewer failures.
Analysis on Credit Card Fraud Identification based on KNN and Outlier Detection

Popular payment mode which is credit card is acquired both offline and online that provides us with its great advantage of cashless transaction. A credit card is a convenient financial product that can be used for everyday purchases such as gas, groceries, and other goods and services. It becomes very easy, suited and groovy to make payments and other transactions through credit card. Credit card fraud is also growing along with the development in technology. Along with improvement in the global communication the economic fraud is remarkably increasing in the global communication. It is being recorded every year that the loss due to these fraudulent actions is billions of dollars. These activities are carried out so gracefully which looks similar to authentic transactions. Therefore, to have a systematized method of fraud detection has become a need for all banks in order to minimize heu and cry and bring order in place. Thus techniques of KNN and outlier detection are implemented to optimize the best result for the fraud detection problem. These methods are proved to slash the false alarm rates and enlarge the fraud detection rate. KNN and Outlier detection are quiet familiar area of research. Outlier Detection is absolutely important task in various application domains. Earlier outliers were considered as noisy data and now it has become severe difficulty in various areas of research. The discovery of outlier is useful in detection of unpredicted and unidentified data in certain areas like fraud detection of credit cards, calling cards, discovering computer intrusion and criminal behaviors etc. Whereas KNN (k-Nearest Neighbor) is used for classification because of its interpretation and low calculation time.
Metode Forward Chaining Untuk Mendiagnosa Penyakit Kanker Payudara

Cancer is a disease caused by abnormal growth of body tissue cells that turn into cancer cells. One of them is breast cancer, cancer is often suffered by women. Some women tend not to know it has been exposed to the disease. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques in solving problems commonly solved by an expert. Expert system is an artificial intelligence that learns how to imitate the way an expert thinks in solving a problem, or take a decision based on existing facts. In this study, we will design an expert system used to diagnose cancer using forward chaining method. This expert system is used as an early diagnosis to know the stadium suffered by the patient and the solution
Trusted and Secured E-Voting Election System Based on Block Chain Technology

The blockchain is an open, distributed ledger, which is running under a common software application that must agree before making any change to the network’s ledger. The number of blockchain platforms is already available and many companies have already begun applying blockchains to their business. It is not only used in business, but it can also be applied as a service and has the potential to make the voting more accessible. Therefore, NRIs and adult internet users can cast their votes in a matter of seconds despite their location on the day of the election. This will be easy and more secure. In the current system, there’s a high risk of false votes and damage to property. People don’t want to stand in big queues and wait for a long time. Also, people who conduct the election have to follow a completely different procedure for voting which takes more time and there’s no transparency. So we present a novel blockchain technique for voting with a guarantee of a safe and secure election. The election system using Blockchain makes it not only easier for the committee to keep a count, but also for the citizens to vote. The Blockchain system will allow the citizens to cast their votes despite their location. Every year a large group of Indians ends up residing outside the country due to which they are unable to practice their voting rights most of the time. The Blockchain system will be a medium for them to cast their votes even though they are not physically present in India during election time.