Reviewerly/exHarmony huggingface dataset:
Enumeration of a special class of irreducible polynomials in characteristic 2

$A$-polynomials were introduced by Meyn and play an important role in the iterative construction of high degree self-reciprocal irreducible polynomials over the field $\mathbb F_2$, since they constitute the starting point of the iteration. The exact numb
A SURVEY ON WORKLOAD PREDICTION MODELS IN CLOUD BASED ON SPOT INSTANCES FOR PROACTIVE AUTO SCALING STRATEGY

Auto scaling techniques help exploiting the elastic nature of cloud by provisioning resources on demand. Very often, the application hosted in a cloud tend to face workload surges which causes the application to respond slow or deny requests. There are reactive and proactive strategies for auto scaling to the rescue which helps provisioning instances based on the demand. When the demand is met after the workload surge the auto scaling is reactive in nature. Otherwise, the demand can be predicted beforehand to provision instances that serve during the surge is proactive in nature. Off late , the proactive auto scaling is gaining more attention amongst researchers. This survey presents a comprehensive detail about workload prediction in cloud and bidding for spot instances in cloud. The survey results uncover a possible concept which requires more attention and the one which helps tapping into resource heterogeneity of the spot market with a cost oriented benefit.
Detection of Lesions in Mammograms Using FRFT-Enhanced Homomorphic Filter and Adaptive Thresholding

In this paper, a novel scheme for multilevel detection and segmentation of suspicious lesions from mammograms is proposed. First, non-decimated wavelet transform (NDWT) of selective scale is applied to input mammograms. In coarse segmentation, NDWT sub-images go through decimated wavelet transform, and thereafter multiscale analysis of image probability distribution function is used. In order to improve the morphological feature of coarse segmented image, a morphological filter is applied to NDWT transformed sub-image and convolved with it. Fractional Fourier transform (FRFT) domain homomorphic filter is applied on convoluted image for improved fine segmentation. FRFT domains are used to enhance the intensity gradient by controlling the reflectance slope with the help of fractional order $$\upomega$$. Second level local adaptive thresholding is used to obtain final segmented image. Efficiency of the proposed scheme is verified by simulating mammograms in the mammographic image analysis society (MIAS) database. The proposed algorithm obtains improved detection with 100% sensitivity and 0.667 false positive per image for CIRC and MISC lesions and comparable results for other masses in comparison to the nearest schemes.
A Survey: To Explore the Factors Behind Inaccuracy in Cost Effort Estimation and Providing Improvements and Suggestions

Accuracy of software cost estimation plays vital role in software development industries. Although after lot of research still this area is challenging. In my previous survey investigated the accuracy of cost effort estimation and the current estimation techniques in software industries of NCR. This paper is also analyzed 596 software development team member’s data on likert scale and conducted questionnaire survey on various organizations to investigate the reasons behind in accuracy as well assuggestions and improvements to avoid these factors. The survey presents the suggestions related to small and large software project estimation in NCR and provides evidence-based estimation improvement suggestions. The inspiration of research is to improve the effort estimation process and increase accurate estimates. Poor estimation accuracy is a big issue on running projects. Many kinds of effort estimation methods have been developed to improve the situation through algorithm based and data driven techniques to machine learning. Different kinds of methods seem to work the best in different kinds of contexts (Boehm, Abts, C Shepperd&Kadoda, 2001), so a study is needed to determine how to improve effort estimation in the context of these software companies team members.
File Tracking System

Locating files is one of the biggest problems in universities nowadays. Time is wasted in searching files, energy is wasted chasing misspelled files, deadlines are missed. Due to this , I decided to make a system for file tracking that will solve all problems in the best way.File Tracking System is a internet application that manages all the file movement from one desk to another one and helps them in managing flow of files effectively and efficiently. All the files such as: reports, decisions, requests, reminders, and others can be processed and can also tracked by the system at any time. The system helps in online tracking or locating information and pendency monitoring more effectively by providing a plethora of data at a glance. It has large facilities for managing classification of all files. The system follows a steps of file unique numbering and enables file management, file status monitoring, etc. It also has a very powerful search form to locate a file and analyze the history of movement of that file. It also has a very powerful search form to locate a file/data and analyse the history of movement of that file. Other than this, the system will allow the administrator to create faculties of that university and assign to them all the departments . Each department must have a user who will be responsible for that department. In the other hand.
Does Culture Play a Role on Underpricing? A Study on 47 Capital Markets

Culture can be defined as all living and thinking assets that separate one country from others.These material and spiritual features that come from the past and encompass the future determine the viewpoints and characteristics of nations.Although increasing globalization with the influence of the internet age brings each society a little closer, the cultural differences of the countries are preserved.In recent studies, the influence of culture has been observed in the fields of personal finance and capital markets, as well as in other areas of life.Initial public offerings are a way for companies to enter capital markets and this is a turning point for many firms.Companies initiate IPOs in order to increase their prestige, enhance their exporting volume, or maintain a better position among their competitors.It is important to manage this process efficiently with the appropriate pricing.Considering the public offerings that take place worldwide, underpricing draws attention almost anywhere.Underpricing, which is the difference between the public offering price and the first day closing price, has found wide coverage in the financial literature.Although, studies on the role of culture in underpricing have increased only in recent years.In this study, the potential cultural impact on underpricing in the initial public offerings at 47 countries was measured by the cultural dimensions of Hofstede.Results show that, power distance and underpricing has positive relationship at the level of 39%, with 0.01 significance.In countries where democracy hasn't felt enough and inequality has increased, underpricing has been boomed due to the faulty trust among capital market players.In addition, 32.6% negative relationship was determined between individualism and underpricing, with 0.05 significance.Compared to countries where individuals are living in groups, it has been found that there is less underpricing at countries where individuals are more confident personally.Also, in the regression analysis of Hofstede's six cultural dimensions, it was observed that the power distance explained the underpricing by 39%.
The main critical issues of the gym environment in an Italian city.

Background The lack of a consistent national regulation regarding gym facilities, combined with the growth and transformation of the world of fitness, has led to an uncontrolled situation, where, especially in metropolitan areas, low cost gyms are continuously popping up, often not respecting the structural and hygienic requirements. Aim of the study Objective of this study is to evaluate the results of a monitoring programme about the gym environment, to highlight the main critical issues. Methods In 2018 a randomized sample of 90 gyms was inspected in Milan, using a checklist with three sections of inquiry and the resulting data were analysed through a series of multivariate regression models. Results As per the various aspects analysed, many outcomes with low scores concerned franchised gyms, which have shown to be unsatisfactory in many respects; in addition, the lack of L. pneumophila risk containment procedures has been observed in the facilities without a swimming pool, compared to those with it. Conclusions The study results offer a clear picture of the gym environment, identifying many inadequacies for different hygienic and safety aspects; therefore, it has been possible to understand which issues need particular attention in order normalize the situation, which should be checked by future investigative steps.
Design of Route Search Algorithm Based on Station Map Information and Depth-First-Search

Route search is an important part in the interlocking system. The result can affect the work in the station. Through the study of information such as turnouts, track number and the coordinates of equipment, this article shows a new method to search the route. By adding a variety of conditions, the Depth-First-Search algorithm is modified and applied for the route searching process. The existing route search methods usually select multiple paths and compare them to select the best one. The result usually is uncertain, and searching progress has low efficiency. The improved algorithm in this article imitates how the people search routes. Thus, only one path will be selected, which is the best one. Also, this algorithm can improve the efficiency in the searching process, and the storage of data is reduced. By simulating the interlocking system, the correctness of the algorithm is verified. And the performance comparison between different methods is showed in this article.
Opportunities of silicon photonics for calculation and machine learning applications (Conference Presentation)

Computing enabled by electronics has been improved so extensively as to make machine learning algorithms such as deep neural network so powerful than ever before. Data communications enabled by optics have been one of the cornerstones of the modern society built upon Internet. The rapidly increasing demands for communication bandwidth due to numerous emerging applications such as AI-based cloud computing have significantly increased the bisection bandwidth of intra-datacenter networks. This trend will necessitate the optics-electronics co-packaging on board, which can only be realized by the substantial development of integrated photonics such as silicon photonics. This opportunity, making optics and electronics so close to each other, will in turn offer a chance to reconsider building EO-hybrid computational and intelligent systems. Although optical computing and optical neural networks have been proposed since a while ago, recent demonstrations of deep neural networks implemented on silicon photonics reactivate the study of exploiting photonics for machine learning. The current program-based deep neural networks are powerful, but consume huge computational resources. Suppose that just optical propagation in compact photonic chips could realize similar functions, it would greatly decrease the power and latency. Even though the scalability and the integration of nonlinear activation functions on photonic chips are still challenging, for some applications such as classifier and digit recognition, photonics systems could be viable and beneficial from such aspects. This talk will introduce our efforts to develop the topology concepts, algorithms, and applications in order to implement silicon photonics for calculation and machine learning applications. First, a high-bit reconfigurable DAC based on generic photonic circuits will be presented. Second, without using any nonlinear activation functions or building deep neural networks, we demonstrate a photonic classifier based on only linear optical components. At last, we will show several ways of implementing silicon photonic circuit to recognize digits.
Empirical uncertainty estimates for classification by deep neural networks

While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree. Uncertainty methods are designed to provide an estimate of class probabilities when predicting class assignment. While there are a number of proposed methods for estimating uncertainty, they all suffer from a lack of calibration: predicted probabilities can be off from empirical ones by a few percent or more. By restricting the scope of our predictions to only the probability of Top-1 error, we can decrease the calibration error of existing methods to less than one percent. As a result, the scores of the methods also improve significantly over benchmarks.
REFERENCE EVAPOTRANSPIRATION IN THE MUNICIPALITY OF INHAMBANE, MOZAMBIQUE: EMPIRICAL METHODS AND SUPPORT VECTOR MACHINE

Reference evapotranspiration (ETo) is useful for calculating crops water demand and irrigation scheduling. Thus, eleven empirical methods for ETo estimation (four based on temperature, three on mass transfer and four on solar radiation) and sixteen on Support Vector Machine models: SVM (eight with the Radial Basic Kernel function: RBFK and eight with the Kernel polynomial: PK) were evaluated against the Penman Montheith FAO 56 method (PM FAO 56) based on the following statistical indicators: MBE (Mean Bias Error), RMSE (Root Mean Square Error), R2 and t test. The RMSE was used as the main selection criterea of methods/ models. Monthly meteorological data of Inhambane Municipality (maximum, minimum and average temperature: T, relative humidity: RH, wind speed and sunshine hour: n), from 1985 to 2009 were used. The results showed the following classification: RBFK7 = PK7 > PK7 > PK6 > PK6 > RBFK8 = PK8 > Makm > PK3> Turc = RBFK3 > RBFK1 = RBFK5 = PK5 > BenL = RBFK4 = 24 > Ham > Lin > Pen > Mah > Wmo. The RBFK7 (MBE = 0.26 mm d-1, RMSE = 0.36 mm d-1 and R2 = 0.96) and PK7 (MBE = 0.26 mm d-1, RMSE = 0.36 mm d-1 and R2 = 0.96) models require the measurement of T, RH and n. In the absence of n, which is common, the PK3 (MBE = -0.02 mm d-1, RMSE = 0.42 mm d-1 and R2 = 0.83) and RBFK1 (MBE = -0.01 mm d-1, RMSE = 0.52 mm d-1 and R2 = 0.74) models can be used as alternatives, which require T and RH data; and T, respectively. Contrary to SVM, the empirical methods were statistically different from the PM FAO 56 method at 5% significance level.
Gini Index and Entropy-Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation

Image segmentation is an old and ever-growing field in computer vision. Several image segmentation algorithms with diverse approach methodologies have been proposed over the years. As a result, several evaluation criteria have also been proposed. Many of these segmentation and evaluation methods are based on the famous Gini index. Several have tried using entropy values as well. Methods based on these have been tuned and modified for betterment over the years. This paper does a thorough literature survey on the growth and usage of Gini index and entropy for segmentation and primarily evaluation. Realizing the potential as well as the limitations, the paper proposes an evaluation criteria based on Gini index and entropy. The proposed algorithm uses the concept of maximum intra-region homogeneity and inter-region heterogeneity in segments to evaluate a segmentation technique. Evaluation is done on segments as seen by the segmentation technique in the original input image.
BUKU GOOGLE SEBAGAI PERPUSTAKAAN DIGITAL ERA GENERASI MILENIUM DAN GENERASI ZET

Today's Information Users prefer information that  is  available  online.  This online information is clearly easier to find, faster and cheaper and more suitable for hobbies with those who like to surf the internet.  Google books as one of online resources can be hernessed to find information for academic needs, especially writing scientific papers. The Google ebook database is not yet widely known and utilized by today's information seekers.  In order for this facility to be eye catching for today's information seekers there needs to be a deliberate effort by librarians and library managers. The purpose of this reset is how to use google books online efficiently and effectively
An Analysis of Students’ Errors in Writing Descriptive Text at Tenth Grade of SMAN 1 Tulungagung in Academic Year 2018/2019

The students usually make error on the use of grammar when they produce an English writing, also in writing descriptive text. It is due to the difference between Indonesia and English grammatical system. This research aims to find out the types of grammatical errors based on (Caroline Mei Lin Ho, 2005) theory of taxonomy grammatical errors, the most frequently error occurred, and the causes of errors made by the tenth grade students of MIPA 4 at SMAN 1 Tulungagung. The researcher uses qualitative method in order to obtain valid findings from 25 participants of tenth grade students. Based on the error analysis conducted, the researcher found that errors regarding sentence structure are the most dominant errors, with the biggest number of errors are in incomplete or fragmented sentences (36,20%). Then it is followed by errors regarding verb and verb groups with the biggest number of errors are in omission of suffix –s, -es, -ed, and -ing (20,69%). Then, it is followed by errors regarding preposition with the biggest number of errors are in inappropriate selection or usage of preposition (6,03%). Last, the errors regarding noun and noun groups with the biggest number of errors are in inappropriate selection of quantifier, article, determiner (2,58%). The findings show that the students have difficulties in using correct sentence structure, especially in incomplete or fragmented sentences. This problem may occur due to their lack understanding of grammatical rules.
A Study of Self- Organizing Maps(SOM) Neural Network Using Matlab

Kohonen's Self-Organizing Map (SOM) is one of the most popular artificial neural network algorithms.Bing unsupervised neural network , Self Organizing Maps(SOM) have applications in different fields such as speech recognition, image processing and so on .This project includes a study of Self Organizing Maps neural network using MATLAB The structure, characteristics, implementation, applications and testing of this neural network for styles of one dimension and two dimensions have been considered It includes finding out the functions used for topology, and then finding out the distance function for this network throughout illustrative examples.Neural network implementation consists of three stages: Initialization (creation), Training, and Simulating.Explanation of the neighborhood concept is done.MATLAB software is used to perform how creating, training and simulating of a Self Organizing Map.Creation process consists of choosing a network parameters, plotting the results, then illustrating and identifying all functions used to create a Self Organizing Map.Training consists of weight initialization and weight vector creation.Simulating means testing the neural network using the initialization parameters and training vector created in the past two stages.The Study includes also four tests: The first test is used manual calculation procedure of the network mathematically.The other three tests are procedures for different applications using MATLAB language.It becomes evident from the graphs of the results that it's essential to have the weight vectors for the coordination field greater than the density of input vectors in the case of employing the network to seclude the training styles in output cells.
NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison

In the current scenario, handling of the unstructured data and extracting information is one of the most crucial aspects. This paper proposes a novel system, Natural Language Information Interpretation and Representation System (NLIIRS) which can accept information in natural language text and can answer to the queries without storing or converting the data into natural language text. Nowadays the presence of available information is large in number in the light of unstructured data. The unstructured information that we get is in the shape of natural languages texts. Protection is needed for the government operative information or any delicate data so that it might be best used when the data can be extricated effectively and effortlessly. Natural Language Information Interpretation and Representation System (NLIIRS) acknowledges the data as characteristic natural language text, process the data and enables the client to recover data by rendering question in natural language. The inquiries subsequently asked are reacted by NLIIRS as expression based answers. The entire frameworks have been composed on Natural Language Tool Kit (NLTK) of Stanford University which helped us to create POS tag, tokenize the information, and shaping the tree structure. The Noval content handling calculation uses the lemmatizer, stemmer and ne chunker to set up the content for data recovery through Q&A. The upside of this framework is that it need not bother with preparing or training. This framework will empower the client to recover any data of his/her decision from the accessible unstructured data.
Recognition of an Analog Display Instrument Based on Deep Learning

With the development of remote automatic recording of utility meter readings, the problems of locating an analog display instrument against a complex background and recognition instruments with inconsistent scales are highlighted. This paper presents an automatic recognition method based on deep learning using the Faster-RCNN algorithm, which can locate the instrument position quickly and accurately in images with large amounts of noisy interference. By using the LeNet-5 convolutional neural network and the proportional relation between the pointer position and both ends of the scale, the number on the dial is recognized automatically. Experimental results showed that the Faster-RCNN algorithm can effectively detect the instrument position in images with large amounts of interference, thus laying an important foundation for subsequent pointer processing and reading recognition. Furthermore, the combination of number recognition and pointer location can make this method effective for different types of instruments with different ranges, which achieves better recognition than traditional approaches.
The Polar-ICE Project: Using Authentic Polar Data to Teach Data Literacy Skills Across a Variety of Grades and Levels

At Princeton High School, located in Princeton, NJ, we have implemented the Sci I Polar-ICE project at three levels: Biology I In-Class Resource Program (ICRP), Biology I Accelerated, and the Science Research Program (see Hunter-Thomson article for more information on the Sci-I program). It was also used as the basis for a summer science experience for middle school students, focused on building scientific reasoning skills in underrepresented populations. This project allowed students to collaborate with polar scientists, analyzing real-time data (from https://pal.lternet.edu/data) as it was generated in Palmer Station, Antarctica This project was in collaboration with Rutgers University, through a grant funded by the National Science Foundation, and data was collected on students’ perceptions of scientists and the scientific process. The unique nature of having real-time, authentic data available to our students and the flexibility to use it, as deemed appropriate, made this project accessible across a variety of grades and class levels.