Reviewerly/exHarmony huggingface dataset:
Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders. DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs). And then, subclusters are merged into individuals by comparing the density of edge samples. By iteration of the merger, the suitable dc and cluster centers are ensured. Finally, we compared state-of-the-art methods with our proposal in public datasets. Experiments show that our algorithm automatically determines cutoff distance and centers accurately.
Quantum noise of a harmonic oscillator under classical feedback control*

Quantum sensing has been receiving researcher’s attention these years due to its ultrahigh sensitivity and precision. However, the bandwidth of the sensors may be low, thus limiting the scope of their practical applications. The low-bandwidth problem is conquered by feedback control methods, which are widely utilized in classic control fields. Based on a quantum harmonic oscillator model operating near the resonant point, the bandwidth and sensitivity of the quantum sensor are analyzed. The results give two important conclusions: (a) the bandwidth and sensitivity are two incompatible performance parameters of the sensor, so there must be a trade-off between bandwidth and sensitivity in practical applications; (b) the quantum white noise affects the signal to be detected in a non-white form due to the feedback control.
Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is Naive Bayes, with an accuracy rate of 88% and an average precision of 90%.
Machine Learning Models for Automatic Labeling: A Systematic Literature Review

Automatic labeling is a type of classification problem. Classification has been studied with the help of statistical methods for a long time. With the explosion of new better computer processing units (CPUs) and graphical processing units (GPUs) the interest in machine learning has grown exponentially and we can use both statistical learning algorithms as well as deep neural networks (DNNs) to solve the classification tasks. Classification is a supervised machine learning problem and there exists a large amount of methodology for performing such task. However, it is very rare in industrial applications that data is fully labeled which is why we need good methodology to obtain error-free labels. The purpose of this paper is to examine the current literature on how to perform labeling using ML, we will compare these models in terms of popularity and on what datatypes they are used on. We performed a systematic literature review of empirical studies for machine learning for labeling. We identified 43 primary studies relevant to our search. From this we were able to determine the most common machine learning models for labeling. Lack of unlabeled instances is a major problem for industry as supervised learning is the most widely used. Obtaining labels is costly in terms of labor and financial costs. Based on our findings in this review we present alternate ways for labeling data for use in supervised learning tasks.
Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors

Smartphones with gym exercises predictors can act as trainers for the gym-goers. However, various available solutions do not have the complete set of most practiced exercises. Therefore, in this research, a complete set of most practiced 26 exercises was identified from the literature. Among the exercises, 14 were unique and 12 were common to the existing literature. Furthermore, finding suitable smartphone attachment position(s) and the number of sensors to predict exercises with the highest possible accuracy were also the objectives of the research. Besides, this study considered the most number of participants (20) as compared to the existing literature (maximum 10). The results indicate three key lessons: (a) the most suitable classifier to predict a class (exercise) from the sensor-based data was found to be KNN (K-nearest neighbors); (b) the sensors placed at the three positions (arm, belly, and leg) could be more accurate than other positions for the gym exercises; and (c) accelerometer and gyroscope when combined can provide accurate classification up to 99.72% (using KNN as classifier at all 3 positions).
A Novel Transfer Learning Approach to Detect the Location of Transformers in Distribution Network

One of the key requirements for designing reliable and scalable electrical distribution networks is the identification of the correct location of its electrical components. The location of transformers in the distribution network is crucial as they are the backbone for transforming one form of electricity into another. In this study, a transfer learning approach based on artificial intelligence is presented to determine the location of transformers in distribution networks. To implement the concept, a dataset of transformer images is collected from public platforms such as Google images. The collected dataset is then used to train the transfer learning model developed in Python. A custom object detection model for transformers is developed using the YOLOv3 architecture. To evaluate the potential of proposed approach, the developed model is tested on real-world images of transformers. The experimental results show that the transfer learning method can achieve a high detection accuracy for the detection of transformer locations. The presented research would be useful in the educational and practical fields as it could be used by electricity companies to develop, extend, and prepare modern distribution networks.
Securing Smart Grid Communication using Ethereum Smart Contracts

Smart grids are being continually adopted as a replacement of the traditional power grid systems to ensure safe, efficient, and cost-effective power distribution. The smart grid is a heterogeneous communication network made up of various devices such as smart meters, automation, and emerging technologies interacting with each other. As a result, the smart grid inherits most of the security vulnerabilities of cyber systems, putting the smart grid at risk of cyber-attacks. To secure the communication between smart grid entities, namely the smart meters and the utility, we propose in this paper a communication infrastructure built on top of a blockchain network, specifically Ethereum. All two-way communication between the smart meters and the utility is assumed to be transactions governed by smart contracts. Smart contracts are designed in such a way to ensure that each smart meter is authentic and each smart meter reading is reported securely and privately. We present a simulation of a sample smart grid and report all the costs incurred from building such a grid. The simulations illustrate the feasibility and security of the proposed architecture. They also point to weaknesses that must be addressed, such as scalability and cost.
Influencing factors in online tourism service quality: a fuzzy cognitive map based on customers’ perceptions

Purpose This study aims to explore the influencing factors of online tourism service quality to clarify the relationship between such factors and the degree of influence so that targeted and effective measures to improve service quality can be suggested. Design/methodology/approach A questionnaire is used to obtain original data, establish the fuzzy cognitive map (FCM) structure chart model and modify the model. Findings The results reveal that comprehensive service types, comprehensive information provided, true and accurate, upgrade and update, payment security, data and information security, customer rights and interests protection, service friendliness and evaluation processing are the key influencing factors in online tourism service quality. In addition, the FCM can also predict the improvement of service quality. Originality/value To establish an FCM model, this study establishes the evaluation framework of influencing factors of online tourism service quality and identifies the cause and effect of 26 indicators. The mechanism of influencing factors of online tourism service quality is explored through the iteration of the model.
Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing.Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system.In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information.Firstly, we integrate auxiliary information with rating information.Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information.Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods.Experimental results demonstrate that our proposed model is superior to other recommendation methods.
Design of Book Recommendation System Using Sentiment Analysis

In this paper, we propose four-level process to recommend the best book to the users. The levels are named as grouping of similar sentences by the semantic network, sentiment analysis (SA), clustering of reviewers and recommendation system. In the first level, grouping of similar sentences by the semantic network is done taking pre-processed data using parts of speech (POS) tagger from the datasets of reviewers and books. In the second level, SA is done in two phases which are training phase and testing phase by using deep learning methodology like convolutional neural networks (CNN) with n-gram method. The outcome of this level is given as input to the third level (clustering) which clusters the reviewers based on their age, locality and gender using K-nearest neighbor (KNN) algorithm. In the last level, a recommendation of books is done based on top-n interesting books using collaborative filtering (CF) algorithm. The system of book recommendation is to be done to get the best accuracy within less elapsing time.
Enhancement of Two-Tier ATM Security Mechanism: Towards Providing a Real-Time Solution for Network Issues

In the current scenario, the crime rate has tremendously increased with respect to the Automatic Teller Machine (ATM).During the last few years, criminals are becoming more sophisticated and paid more attention to ATMs.The majority of ATMs in India are working on a single authentication technique.The attacks, such as skimming, shimming, card cloning, card swapping, shoulder surfing, etc. works due to the use of minimal authentication in ATMs.So, the concern about the security of ATMs is reached to its peak level.Nowadays, banks have moved towards the two-tier authentication level.Recently in India, some banks have adopted the One Time Password (OTP) mechanism along with a UID number to perform the transaction in ATMs.In such a case, dependency on the cellular network for OTP is also a significant concern.To overcome these types of issues researcher proposed a two-tier authentication mechanism.The paper addresses the recent problems and their solution with the help of a two-way authentication method.To resolve the network issue, the researcher also proposed a novel technique, i.e., Security Question-based verification mechanism.
Measuring and Mapping Data Reuse: Findings From an Interactive Workshop on Data Citation and Metrics for Data Reuse

Widely adopted standards for data citation are foundational to efforts to track and quantify data reuse.Without the means to track data reuse and metrics to measure its impact, it is difficult to reward researchers who share high-value data with meaningful credit for their contribution.Despite initial work on developing guidelines for data citation and metrics, standards have not yet been universally adopted.This article reports on the recommendations collected from a workshop held at the Future of Research Communications and e-Scholarship (FORCE11) 2018 meeting titled Measuring and Mapping Data Reuse: An Interactive Workshop on Metrics for Data.A range of stakeholders were represented among the participants, including publishers, researchers, funders, repository administrators, librarians, and others.Collectively, they generated a set of 68 recommendations for specific actions that could be taken by standards and metrics creators; publishers; repositories; funders and institutions; creators of reference management software and citation styles; and researchers, students, and librarians.These specific, concrete, and actionable recommendations would help facilitate broader adoption of standard citation mechanisms and easier measurement of data reuse.
A Survey on Content Aware Image Resizing Methods

With the advancement in the field of image processing, images are being processed using various image processing algorithms.Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing.Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm.This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods.After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators.This survey is useful for the researchers interested in content-aware image retargeting.
Critical Theory of Educational Administration

The purposes of educational administration are taken for granted as being those of adjusting the individual to the society or economy, or both. A critical educational theory of administration would be committed to ensuring that technical, practical and emancipatory interests were considered with regard to each of these message systems. Teachers are resistant to the privatisation and marketing of education, for quite good reasons. The role of educational administrators in protecting such space against the encroachment of technocratic consciousness is vital to the process of social evolution through mutual learning. A critical theory of educational administration would have precisely such a goal. The problem is a central and crucial one for education, because education is fundamentally concerned with the construction of meaning and identity—that is, with psychological and cultural processes. Education must produce a more skilled workforce and impose tighter social discipline so that the economy can be more competitive.
Decision Tree Method with C4.5 Algorithm for Students Classification Who is Entitled to Receive Indonesian Smart Card (KIP)

Abstract The research intends to create an application program which is able to make analysis data in a school to classify students who is entitled to accept Indonesian Smart Card. It was a government program that aims to finance school education free of charge, for children from poor families. Problems arose when the distribution to students, some children from poor families did not get it but rich families actually got it. Analysis and the right way are needed so that the distribution can be right on target to eligible students. This study used a decision tree method with C4.5 algorithm to classify students who are entitled to receive the Indonesian Smart Card. This application is developed with C4.5 algorithm to determine the decision tree method. Research data was carried out at Junior High School One of Jatibarang, Brebes. Data record of 300 students, with 240 as training data while 60 as testing data. From the test results were accuracy was 97%, it is proven this method has a high accuracy so that the application can help the decision makers solved the distribution problems to eligible students. The impact is that poor students will get the right to receive Indonesian Smart Card.
Practice and Innovations of Inclusive Education at School

The article has carried out a meta-analysis of the research concerning practice and innovations of inclusive education at school. Investigation of the practice of inclusive education at schools has been intensified since the 1990s, after identifying the need to implement inclusion strategies and concepts at the international level. The first studies of inclusive education (until the 2000s) concerned beliefs and values as a factor, influencing the effectiveness of inclusion, strategies of inclusive education. Investigations after the 2000s have been aimed at more focused subject matter of the research at the local level in different countries: principals’ beliefs, teachers’ self-efficacy, the role of parental support, school ideology, models of inclusion at private schools, the severity of disability as a factor determining teachers’ beliefs concerning inclusion. Various inclusive models have been formed as a practice result of implementing inclusion. Two key effective approaches to integration of inclusion have been highlighted: integrated and differentiated. An integrated approach involves the introduction of innovations in inclusive education in the following elements of the educational system, namely: the concept (strategy) that defines the model, external preconditions and stages of inclusion; a school that defines the internal prerequisites for inclusion; a community. A differentiated approach is used in combination with theintegrated one in order to identify the internal prerequisites for inclusion: values, beliefs and attitudes of teachers, the competence of educators.
Morphological Segmentation for Textures and Particles

This chapter is concerned with image segmentation via the methods of morphological image processing. It is also concerned with regional texture-oriented segmentation and particle segmentation. The chapter focuses primarily on watershed segmentation, which has the advantages of being very general, usually accurate and fast, and applicable to both binary and gray-scale images. While granulometries can be employed to describe shape, more directly related to texture segmentation is their ability to measure changes in surface condition and in particle distribution processes, for instance electrophotographic images. In texture segmentation, an image is partitioned into regions, each of which is defined by a set of features characteristic to the microimage structure within it, this structure typically being viewed in terms of the small texture primitives composing it. As originally conceived for binary images, granulometries are employed to characterize size and shape information within granular binary images via the manner in which they are sieved through sieves of various sizes and shapes.
Fast and Lightweight Text Line Detection on Historical Documents

We introduce a novel method for handwriting text line detection, which provides a balance between accuracy and computational efficiency and is suitable for mobile and embedded devices. We propose a lightweight convolutional neural network with only 22K trainable parameters which consists of two parts: downsampling module and context aggregation module (CAM). CAM uses stacked dilated convolutions to provide large receptive field for output features. The downsampling module allows a further reduction of latency. Our network has two output channels, one for baseline prediction and the other for detection of text lines without ascenders/descenders. This modification has a positive impact on accuracy. The proposed method achieves an F-measure of 0.873 on the ICDAR 2017 cBAD complex track with a network inference time of 8 ms on NVIDIA GeForce GTX 1080Ti card and another 12 ms for subsequent postprocessing operations on CPU, which is significantly faster than other existing methods with comparable accuracy.
Experimental investigations on flexural behaviour of delaminated carbon/epoxy composite using three dimensional digital image correlation

In semi automated manufacturing processes of multilayered carbon fiber reinforced composites, defects are induced. These defects cause reduction in service life of the products and lead to decrease in structural performance. In the present work, carbon fiber reinforced epoxy composite laminate with a stacking sequence of [0/0//45/−45/90/45/−45/90/90/−45/45/90/−45/45/0/0] was fabricated by hand lay-up method using vacuum bagging technique. An artificial defect to create pre-delamination was located in this composite between 0° and 45° plies at the centre of laminate. Experiments were conducted in four-point bending to determine the stiffness behavior of composite laminate and study other damage patterns occurred due to the existence of pre-delamination. The probable reasons for occurrence of other damage forms are explained in detail. Three-dimensional digital image correlation (DIC) technique was employed to determine displacements and strains of the specimen. The out of plane displacement due to local buckling against the bending load was also quantified.