Reviewerly/exHarmony huggingface dataset:
Comprehensive Optimal Fir Filter Design Procedures With Various Impacts

Digital-signal-processing (DSP) is one of the recent emerging techniques contain more filtering operations. It may an image type or audio/ video signal processing. Each processing unit has filtering sections to filter noise elements. Hence, there is a need for efficient and secure algorithmic scheme. Here, a exhaustive scrutiny use of complex optimization algorithms towards the digital-filter construction is conferred. In appropriate, the scrutiny target on the identification of various suggestions and limitations in FIR system design. For exact representations, the infinite impulse response adaptive filters and finite impulse response models are considered for estimation. It is designed to review a various swarm and evolutionary computing structures employed for filter design schemes. Some popular computing algorithms are noticed to recover characteristics of percolate design approach. Further, compared with recent research for identifying the updating features in optimization schemes. Finally, this review suggested that the swarm intelligence based researchers improved the constraints and its attributes.
An Associative Ring Memory to Support Decentralised Search

Centroid terms are single, descriptive words that semantically and topically characterise text documents and thus can act as their very compact representation in automated text processing tasks that strongly rely on the semantic similarity of texts. Algorithms to classify and cluster them make use of this information. In this book, the novel, brain- and physicsinspired concept of centroid terms is introduced and deeply discussed. Furthermore, their unique properties and practical usage in major natural language processing and text mining tasks are covered. In this regard, a new graph-based method for their fast calculation is presented as well. In contrast to methods relying on the bag-of-words model, the derived centroid distance measure can uncover
A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification

Recent research has shown that Particle Swarm Optimisation is a promising approach to feature selection. However, applying it on high-dimensional data with thousands to tens of thousands of features is still challenging because of the large search space. While filter approaches are time efficient and scalable for high-dimensional data, they usually obtain lower classification accuracy than wrapper approaches. On the other hand, wrapper methods require a longer running time than filter methods due to the learning algorithm involved in fitness evaluation. This paper proposes a new strategy of combining filter and wrapper approaches in a single evolutionary process in order to achieve smaller feature subsets with better classification performance in a shorter time. A new local search heuristic using symmetric uncertainty is proposed to refine the solutions found by PSO and a new hybrid fitness function is used to better evaluate candidate solutions. The proposed method is examined and compared with three recent PSO based methods on eight high-dimensional problems of varying difficulty. The results show that the new hybrid PSO is more effective and efficient than the other methods.&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;div&gt;&lt;table&gt;&lt;tr&gt;&lt;td&gt;© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;
FORGERY DETECTION OF FAKE CURRENCY USING IMAGE DERIVATIVE BASED SEGMENTATION TECHNIQUE

Fake currency is a threat to each country throughout the globe.This paper is centered to detect the fake currency use of the image segmentation technique.Fake foreign money aims in discovering the forgery currency.The first step in image processing is preprocessing of the image involve containing cropping smoothing and adjustment of the image.The second step contains the conversion of the image into a gray coloration image.The conversion is done after image segmentation.The features include removal and mitigation.In the last step, compare the image to the original or the forgery image.India is more vulnerable to this problem which is results in a big hurdle.The empirical simulation is carried out using Rupees 100, 500 note of Indian Currency.
Conventional AI Chatting Robot

In This era of innovative world, an emerging technology called ChatBot is in huge demand as it’s is Customer experience in the future and the opportunity in the not too distant future to substitute search windows and many apps; ChatBot is a technology that communicates with people automatically by supplying them with details about your company and notifications directly through your messaging system.. ML allows the bot to learn itself without programming whereas NLP gives the bot an ability to understand human speech or text. ChatBot is an additional and new way for people to interact with your business more effectively. The answers are given by a ChatBot through a mix of predefined scripts and machine learning. Current chatbots are significantly complex because of the sophisticated underlying software and the data it accesses. We are building a Multilingual Chatbot as it engages users in their native language and boosts conversions.
Google Us!

Capital Area District Libraries enrolled in Googe Ad Grants in 2018 and receives up to $10,000 of in-kind Google Ads each month. This article describes how we obtained the grant, the campaigns we've developed, and the impact it has made on visits to our online branch.
Design of the Competence Model for Staff Demanded by PSEDA Industrial Enterprises

The article proposes to consider the scheme of the competence model of staff demanded by PSEDA industrial enterprises (located in the territories of priority social and economic development - PSEDA). It identifies a need for a new type of personnel, presents an attempt to build a competence model of a specialist, which contributes to defining a refined list of competencies for a new type of specialist demanded by PSEDA enterprises. The complex of “significant” competencies, confirming the uniqueness of the professional activity of specific PSEDA team, should be applied to conducting various personnel procedures that evaluate professionalism and to designing programs for professional instruction offered to PSEDA personnel [9]. The proposed solution involves the implementation of a two-circuit model, which clearly defines the sequence of stages to design and evaluate the quality of educational programs, as well as establishes the relationship between the internal processes of quality assurance of training and the external environment.
Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression

When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
An Improved Method for Electromagnetic Streaming Data Anomaly Detection

The electromagnetic data is a kind of real-time, streaming data, the usage of wireless devices can alter the energy value at the corresponding frequency point.To detect the usage of wireless devices timely, an improved CUSUM method(H-CUSUM) is proposed, which combined a nonparametric CUSUM algorithm with an adaptive sliding window and tolerance factor.Four experimental data sets are collected to evaluate the proposed method, The result shows that the proposed method can detect the change in electromagnetic streaming data with high coverage and low latency.
A User-Centric QoS-Based Web Service Selection Framework

With the proliferation of web services, the selection process, especially the one based on the non-functional properties (e.g. Quality of Service – QoS attributes) has become a more and more important step to help requestors locate a desired service. There have been many research works proposing various QoS description languages and selection models. However, the end user is not generally the focal point of their designs and the user support is either missing or lacking in these systems. The QoS language sometimes is not flexible enough to accommodate users’ various requirements and is too complex so that it puts extra burden on users. In order to solve this problem, in this thesis we design a more expressive and flexible QoS query language (QQL) targeted for non-expert users, together with the user support on formulating queries and understanding services in the registry. An enhanced selection model based on Mixed Integer Programming (MIP) is also proposed to handle the QQL queries. We performed experiments with a real QoS dataset to show the effectiveness of our framework.
Theoretical Foundations for Native AI and Machine Learning

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The Formation of Digital Competence by Means of Open Educational Resources

The article deals with the problem of the formation of person’s digital competence. The analysis of the European frame of digital competence allowed specifying the main tasks for the educational system: acceptance of those competences that are relevant for modern digital community and are basic in educational process during the whole life; formation of teacher’s digital competence; creation of open educational systems. The analysis of the level of formation of digital competence among primary school teachers and university lecturers shows the gap between the achieved results and challenges of the modern society. One of the ways in solving of the given problemis the creation and testing of open educational resources, as well as unified means for monitoring of digital quotient.
Development of a Graduate Cybersecurity Research Methods Course

Abstract This paper presents the North Dakota State University's (NDSU) new graduate-level research methods course focused on cybersecurity. This course combines scholarly article and popular technical press readings, an online discussion board, seminar-style discussions during class meeting times and a semester-long research project. The paper explains the context for this course, its role in NDSU's graduate programs and the factors that led to course creation. The types of content that were included are discussed and the discussion board questions and resources are included as an appendix.
Development Employee Creativity in Work Place: Measurement the Role Transformational Leadership

Any organizations need creative employees who generate new ideas to develop product or process innovation. Observing the implications of leadership style, the study examines the role of transformational leadership in predicting employee creativity .A study was carried out using 240 employees as respondents. The findings indicate that leadership style can foster employee’s creativity on work environment. The results also supported the hypothesis that leadership style enhances creative performance.
User Story Estimation based on the Complexity Decomposition using Bayesian Networks

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers.
Denoising of Medical Ultrasound Images using Wavelet Transform with Bilateral Filter

Medical image degradation contains a significant impact on image quality and therefore affects the human interpretation and also the accuracy of computer assisted diagnostics techniques, unfortunately ultrasound images are principally degraded by an intrinsic noise known as speckle noise. Therefore, de- speckle filtering may be pre-processing step in medical ultrasound images. During this paper we propose a new image de-noising technique is the combination of bilateral filter and wavelet transform. The main contribution of this paper is within the use of a new neighborhood relationship to develop a new multi-scale bilateral filter. Experimental outcomes validate the usefulness and also the correctness of the proposed filter in edge preservation and speckle noise reduction for medical ultrasound images.
An experiment in rural education

India encountered Industrial Revolution partially during its struggle for freedom. The already established culture of compliance was more deeply ingrained in Indian society through colonial rule. But conflicts of caste, religion, and language added layers of complication. The process of state-run industrialisation that began after Independence rested on this social setting. India’s journey on this road began only after Independence. In 1909, Mahatma Gandhi presented an alternative to Western civilisation in his classic work Hind Swaraj. Mahatma Gandhi called it ‘Rural National Education through village handicrafts’ in the foreword of the curriculum of the scheme that Dr Zakir Husain termed ‘Basic National Education’. Displacement of the rural population by making the village, where a majority of Indians live, irrelevant in modern society has led to a situation of crisis. Anand Niketan was a village school founded by Mahatma Gandhi in 1938 in the Sevagram ashram premises.
Human Emotion Recognition Using Mean of Average and Maximum Pooling

Currently a the very beginning's of the unsolved difficulty in pc imaginative and prescient is perceiving or understanding different people' feelings and sentiments. Albeit ongoing strategies accomplish close to human exactness in controlled conditions, the acknowledgment of emotions inside the wild remains a hard difficulty. On this paper we proposed MAM Pooling (mean of common and maximum) strategy with CNN to perceive human feelings. We center round programmed distinguishing evidence of six emotions constantly: Happiness, Anger, unhappiness, surprise, fear, and Disgust. Convolutional Neural network (CNN) is a certainly propelled trainable layout that may study invariant highlights for numerous programs. Whilst all is said in carried out, CNNs include of rotating convolutional layers, non-linearity layers and highlight pooling layers. In this artwork, a Novel include pooling approach, named as MAM pooling is proposed to regularize CNNs, which replaces the deterministic pooling obligations with a stochastic system thru taking the ordinary of max pooling and regular pooling strategies. The benefit of the proposed MAM pooling technique lies in its firstrate capability to address the over fitting problem skilled with the resource of CNN age.
The uses of Smart Devices in Teaching English for Palestine

This study explored the uses of smart devices among EFL teachers in teaching English for Palestine and find out if there are differences due to gender, years of experience, and teaching level.To fulfill this aim, the descriptive approach was adopted.A questionnaire of (33) uses which was distributed into two main parts (smart phone uses and smart board uses) was utilized to find out the uses of smart devices in teaching English for Palestine.Each part of the questionnaire has two domains: the presentation domain and practice, and feedback one.A sample of ( 386) EFL teachers was requested to fill out the questionnaire.The Pearson Correlation, Cronbach Alpha, T-test, One Way ANOVA and Scheffe were used to analyze EFL teachers' responses on the questionnaire.The findings showed that EFL teachers sometimes used smart phone and smart board in teaching English for Palestine.There were no statistically significant differences in the uses of smart phone due to gender and teaching experiences but there were differences in the uses due to secondary level in the practice and feedback stage.There were no statistically significant differences in the uses of smart board among EFL teachers due to teaching experiences and there were statistically significant differences due to gender in favor of male teachers and teaching level for the sake of secondary level.The researcher recommended responsible parties to encourage the uses of EFL teachers to smart devices by making competition among teachers, holding workshops for teachers to exchange their experiences.
Tensors

The main objects of multi-linear algebra that we will use in the study of Algebraic Statistics are multidimensional matrices, that we will call tensors.
Native Hawaiian Career and Technical Education Program (ED)

Federal Grants & ContractsVolume 45, Issue 6 p. 1-1 Grants alerts Native Hawaiian Career and Technical Education Program (ED) First published: 15 February 2021 https://doi.org/10.1002/fgc.31556Read the full textAboutPDF ToolsRequest permissionExport 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume45, Issue618 February 2021Pages 1-1 RelatedInformation
Student Activities

Higher Education AbstractsVolume 55, Issue 3 p. 287-287 STUDENT ACTIVITIES Student Activities First published: 30 July 2020 https://doi.org/10.1111/hea.36_12154Read the full textAboutPDF ToolsRequest permissionExport 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume55, Issue3July 2020Pages 287-287 RelatedInformation
WBC Analysis using Data Augment Method and Convolutional Neural Network

WBC is a White Blood Cell or White Blood Corpuscle also known as Leucocytes. The normal count of WBC ranges from 4000 to 11000/mm3. It plays a vital role in the human body. Many diseases in human start with the abnormal balance of WBC, which acquire the part of immunity. To have adequate knowledge about WBC, we have to have a clinical test like blood count test which gives the count of RBC, hemoglobin, WBC, etc. RBC is otherwise known as Erythrocyte and it does not have a nucleus, with pigment hemoglobin. Due to the presence of this pigment, blood is red in color. In RBC, O2 and CO2 are transported in and out of the tissues. Recent research explains about diseases like cancer, allergy, breast cancer, etc are caused due to lack or abnormal WBC. This comes with the solution of finding the count of WBC in two types: Manually and automated way. In our paper, we are concentrating on collecting the WBC count using the Data augmentation method and Convolutional Neural Network.The Quality of image is improved in comparison with number of augmented images. This explains that we have 12500 sample images in the dataset in which 9957 samples are trained, validated on 2487 samples and training accuracy is high with increasing epoch value.
Social interaction technology in the context of inclusive education

The article presents the main directions of the development of inclusive education and ways to implement them. The application of the technology of social interaction aimed at the formation of an inclusive culture between the participants of the educational process is considered relevant in the joint educational process.
Email Thread Identification and Management

Nowadays, Email communication is use as primary communication tool in the business domain as well as in education sector. Due to massive incoming emails, overflowing inbox is one of the problems faced by email users. There are several reasons for such a situation, one of them being the unnecessary mass of thread emails. They are retained in inbox even when they are not necessary. Even if this email is deleted from inbox, the next message as thread email will hit your inbox. Wrong use of „reply-all‟ tab adds to this situation called “Email storm”. Thread emails are often generated because of users‟ careless habit to click on „Replyall‟ button. It is almost like a reflex action on their part. This work intends to solve the problem of email storm on two fronts :  Identification of thread emails  Automatically controlling thread email The three datasets Din, Dadm and Dexam from academic domain are used as training data. The experimental outcome shows that „In-Reply-To‟, „References‟ and additionally „threadindex‟ are the dominant features in identifying thread emails. We have used these features to derive thread classification strategy. The developed algorithm is used to test four datasets Dcor, DCS, DF1 and DF2. Using this method accuracy upto 99.91% is achieved. Further, the paper also suggests access control rights strategy to control email storm. The model is proposed for controlling thread emails in education domain. The control mechanism will help system administrators to control email traffic.