Reviewerly/exHarmony huggingface dataset:
A conceptual comparison of metaheuristic algorithms and applications to engineering design problems

This paper presents conceptual comparison among spotted hyena optimiser, grey wolf optimiser, particle swarm optimisation, ant colony optimisation, gravitational search algorithm, bat algorithm, moth flame optimisation, and whale optimisation algorithm. The behaviour of these algorithms is mathematical modelled to show the optimisation process. Twenty-three benchmark test functions are used to validate the performance of these algorithms. The exploration and exploitation of these algorithms are analysed using convergence curve. The experimental results depict that spotted hyena optimiser and grey wolf optimiser give optimal solutions as compared to the other algorithms. Furthermore, these algorithms are tested on five constrained engineering design problems. Experimental results reveal the applicability of these algorithms in real-life engineering design problems.
IEEE Transactions on Quantum Engineering

based on the engineering applications of quantum phenomena, including quantum computation, information, communication, software, hardware, devices, and metrology.
System for Identifying and Correcting Invalid Words in the Devanagari Script for Text to Speech Engine

The Text To Speech (TTS) system takes text as an input and generates speech as an output. If input text is incorrect then overall quality of speech output may degrade. The main aim of the proposed system is to provide correct input text to the TTS. The system takes Unicode word as an input, identifies invalid word and corrects it by inserting, deleting or updating characters of the word. In this system, the State Machine is used to identify and correct invalid word in the Devanagari script which in turn is based on rules. Rules are developed for converting character to input symbol. Actions and States are identified for State Machine. Finally, the state transition table is developed for validation and correction of word. Using this system, incorrect words of the Devanagari script can be corrected to valid words (word contains all the valid Devanagari syllables) based on Devanagari script grammar. Since, all Devanagari characters are not present in Hindi language; this system will correct these nonHindi characters to Hindi.
Biped Robots

The C# programming language was designed for developing programs for Microsoft’s .NET Framework.
Are we there yet?

The purpose of this study is to prepare a source of realistic looking images in which optimal steganalysis is possible by enforcing a known statistical model on image pixels to assess the efficiency of detectors implemented using machine learning. Our goal is to answer the questions that researchers keep asking: &#x201C;Are our empirical detectors close to what can be possibly detected? How much room is there for improvement?&#x201D; or simply &#x201C;Are we there yet?&#x201D; Our goal is achieved by applying denoising to natural images to remove complex statistical dependencies introduced by processing and, subsequently, adding noise of simpler and known statistical properties that allows deriving the likelihood ratio test in a closed form. This theoretical upper bound informs us about the amount of further possible improvement. Three content-adaptive stego algorithms in the spatial domain and non-adaptive LSB matching are used to contrast the upper bound with the performance of two modern detection paradigms: a convolutional neural network and a classifier with the maxSRMd2 rich model. The short answer to the posed question is &#x201C;We are much closer now but there is still non-negligible room for improvement.&#x201D
Methods you might meet, but not every day

This chapter comments briefly on a wide variety of methods that are sometimes, but not often, encountered in the medical literature. Addressed are some extensions of methods already in the book, including analysis of variance, regression, nonparametric tests, survival analysis, Bonferroni methods, and testing for normality. Other methods are multivariate methods, missing data correction, treating outliers, curve fitting, and data mining. A method that was treated in the third edition of this book has been reassessed as appropriate to this chapter: Markov chain Monte Carlo.
Construction of Personalized English Teaching Model Driven by Big Data

With the acceleration of educational reform and the advancement of information-based education, the traditional teaching mode of English teaching in China has neglected the cultivation of students' individualized learning.In the era of big data, it is urgent to make corresponding reforms in personalized English teaching in China.Firstly, it introduces the meaning and basic characteristics of big data.Secondly, it elaborates the basic requirements of individualized English teaching.Then it analyses the impact of big data on individualized English teaching.On this basis, it puts forward the strategy of constructing individualized English teaching mode based on big data.At this stage, China is in a big data era of information and knowledge explosion, and the long-distance technology exchange of mobile electronic devices and the Internet has also developed rapidly.In the era of big data, all data can be quantified as a huge advantage for us to learn knowledge, providing unlimited possibilities for creation.English teaching in China is facing more development challenges and opportunities due to the arrival of the big data era, but it also provides a huge space for the development of individualized English teaching.In addition, the development of individualized English teaching helps to solve the problems existing in English teaching in China.The problem.Therefore, this paper focuses on personalized English teaching based on big data to carry out research on teaching strategies.
The results of needs analysis for a mobile application which will be developed with the purpose of supporting the intra-faculty communication and professional development of academics

The purpose of this study is to determine the needs for a mobile application which will be developed so that the problems faced by academics in intra-faculty communication can be solved and their professional development can be supported. A questionnaire form consisting of two sections was used by researchers as a data collection tool of the study. Frequency analysis was conducted on the data obtained from the academics who were included in the study. At the end of the study, it was concluded that academics experienced problems as regards the communication means they used in the faculty, that phone costs increased, lines were mostly occupied, meetings took too much time and e-mails occasionally were not delivered. It was found out that academics requested that intra-faculty communication should be performed through a mobile device and should be given educative information as regards their professional development.
Blockchain Technology Factor for Improve Good Distribution Practice in the Pharmaceutical Industry

The distribution process is one of the processes that occur in all industries, including the pharmaceutical industry. The distribution of medicines in the pharmaceutical industry is the most important to provide quality pharmaceutical products to patients or consumers. With several problems that occur in the distribution of drugs in things that currently should be considered. This happens a lot due to the many drugs that disagree with the provisions or such illegal or false drugs. This research investigates the use of blockchain technology for drug distribution in Indonesia. This research uses a systematic literature review method with related literature. The results of this research found 54 blockchain factors that were used to improve nine aspects of good distribution practices and to ensure the good quality of medicines distributed to consumers.
SENTIMENT ANALYSIS ON GOOGLE CLOUD PLATFORM

This project explores two services available on the Google Cloud Platform (GCP) for performing sentiment analysis: Natural Language API and AutoML Natural Language.The former provides powerful prebuilt models that can be invoked as a service allowing developers to perform sentiment analysis, along with other features like entity analysis, content classification, and syntax analysis.The latter allows developers to train more domain specific models whenever the pre-trained models offered by the Natural Language API are not sufficient.Experiments have been conducted with both of these offerings and the results are presented herein.
Effectiveness of Irrigation Water Distribution System in Enhancing Rice Cropping Index at Subak Padanggalak, Kedewatan Irrigation Area, Bali

One way that can be done to increase rice production is the expansion of the harvest area by increasing the rice crop index.Irrigation water supply at certain times is increasingly scarce so that proper management of irrigation water by subak is needed to increase agricultural productivity.Therefore, this study aimed to determine the effectiveness of irrigation water distribution systems in Subak Padanggalak, under the Kedewatan Irrigation Area, Bali.The effectiveness parameter of the water distribution system was specified by using the percentage of area covered from water distribution in Subak Padanggalak throughout the year.The results of the analysis show that the water distribution system is useful as it was able to irrigate the all-area Subak Padanggalak throughout the year adequately.The existing rice cropping index is 250%, and the potential index of food crops is 300%.
An Efficient Forgery Image Detection Method using Hybrid Feature Extraction and Multiclass SVM

The advancement of image editing software tools in the image processing field has led to an exponential increase in the manipulation of the images. Subjective differentiation of original and manipulated images has become almost impossible. This has kindled the interest among researchers to develop algorithms for detecting the forgery in the image. ImageSplicing, Copy-Move and Image Retouching are the most common image forgery techniques. The existing methods to detect image forgery has drawbacks like false detection, high execution time and low accuracy rate. Considering these issues, this work proposes an efficient method for detection of image forgery. Initially, bilateral filter is used to remove the noise in pre-processing, Chan-Vese Segmentation algorithm is used to detect the clumps from the filtered image utilizing both intensity and edgeinformation, followed by hybrid feature extraction technique. Hybrid feature extraction technique comprises of Dual Tree Complex-Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Gray-Level-Co-Occurrence Matrix (GLCM). The DWT has dual-tree complex wavelet transform with important properties, it is nearly shift invariant and directionally selective in two and higher dimensions. Principal Component Analysis (PCA) finds the eigenvectors of a covariance matrix with the highest eigenvalues and uses these values to project the data into a new subspace of equal or less dimensions. Gray-Level-Co-Occurrence Matrix (GLCM) extracts the Feature values such as energy, entropy, homogeneity, standard deviation, variance, contrast, correlation and mean. Classification is done based on the texture values of training dataset and testing dataset using Multi Class-Support Vector Machine (SVM). The performance analysis is done based on the True positive, False positive and True negative values. The experimental results obtained using the proposed technique shows a better performance compared to the existing KNN classifier model.
Review of Robot Navigation Inspired by the Localization Cells' Cognitive Mechanism

Abstract：Fast and accurate target navigation is an important ability for the mammals, and the hippocampus and entorhinal cortex are the main regions of brain to realize the spatial cognition and navigation ability.The coorperation of grid cells, place cells and other related cells provides the brain an integrated positioning system, which realizes that mammals can perform higher-level cognitive functions.The latest research in the field of robot autonomous navigation with mammals' localization cells were reviewed, especially focus on the localization cells' cognitive mechanism inspired robotic environment modelling, map building and behavior planning and controlling.At last, the deficiencies of current research and the future trend of autonomous robot navigation are analyzed and discussed.
Ornamental

In 1948, Claude Shannon's A Mathematical Theory of Information laid the groundwork for the digital age. It defined the term information, introduced the unit bit and revolutionized how we communicate. Shannon's work gives us physical parameters to define an often perceivably abstract entity: information. It's a seemingly strange and wonderful truth in the digital age that information is physical---where one is often led to believe that information lives in the ether of clouds.
Object description

Objects are represented as a collection of pixels in an image. Thus, for purposes of recognition we need to describe the properties of groups of pixels. The description is often just a set of numbers – the object’s descriptors. As such, we should be able to recognise similar objects when they have similar descriptors. Descriptions should have invariant properties: to orientation, scale and position and also to affine and perspective changes. We present the characterisation of objects by two forms of descriptors: region and shape descriptors characterise an arrangement of pixels within the area and the arrangement of pixels in the perimeter or boundary, respectively. This region versus perimeter representation is common in image analysis. There are actually many techniques that can be used to obtain descriptors of an object’s boundary. Here, we shall just concentrate on three forms of descriptors: chain codes and two forms based on Fourier characterisation. For region descriptors we shall distinguish between basic descriptors and statistical descriptors defined by moments, introducing Zernike and Krawtchouk moments in detail.
WhatsApp with English language teaching Some practical ideas and strategies

Mobile assisted language learning offers tremendous opportunities for language learning. The use of short message service and, more recently, integration of the WhatsApp application into language classrooms has generally been reported to positively impact on language learning and learning experience for learners as well as for teachers. As a result, use of WhatsApp is gaining immense popularity in English as a foreign language classrooms across the globe. However, it is important to acknowledge that, while technology plays a critical role, it is how the tool is utilised per se that determines the successful integration of the new technology. How to use technology is pre-requisite to successful integration. Despite the popularity of the potential use of WhatsApp in language learning, currently there are no guidelines on how to use it for language teaching and learning. This paper attempts to offer some practical ideas and strategies for using WhatsApp for teaching English as a foreign language as well as for teaching other languages.
Real-time 3D Pose Estimation of Small Ring-Shaped Bin-picking Objects using Deep Learning and ICP Algorithm

Bin picking is an important task in smart manufacturing and intelligent robotics. For a robot to pick or grip an object with a human-like gripping action, it needs to know the accurate 3D pose of the object. In this paper, we propose a method for estimating the 3D pose of a small ring-shaped object using infrared and depth images generated by a depth camera. The proposed method consists of two algorithm modules, the first to recognize an object in a 2D infrared image and the second to estimate the 3D pose by applying the ICP (iterative closest point) algorithm to 3D depth data. In the first module, we propose a method to generate a three-channel integrated image with features from the depth and infrared images. Next, we introduce a method for training an object detector based on deep-learning. Because the bin-picking test object in this paper is small and ring-shaped, it is difficult to detect and find 3D poses of individual objects when many such objects are piled up. We solved this problem with a depth-based filtering method. Using the filtered image, each object region is separated by the deep learning approach. In the second module, the ICP algorithm is employed to estimate the 3D pose of the ring object. We match a 3D reference model of the object and the real object using the point-to-point ICP algorithm. Performance of the proposed method is evaluated by using two different types of depth camera in the experiments.
Whole-Slide Imaging in Cytopathology

Whole-slide imaging (WSI) is the digitization of glass slides allowing users to remotely examine, share, and analyze digital slides using computer monitors instead of microscopes. Since WSI scanners have become commercially available, many clinical and non-clinical applications have been established, mostly for surgical pathology. The application of WSI to cytopathology is less pervasive, largely because of certain unique challenges related to cytology image acquisition (e.g., focus) and cytology workflow (e.g., screening). The aim of this chapter is to review current digital cytology use cases, examine specific challenges, and discuss future advances in the field of WSI.
A collaborative content-based movie recommender system

In this paper, Pearson's correlation coefficient is employed for collaborative filtering due to its ability to manipulate numerical data as well as determine linear relationship among existing users. Its steps involve a user-user representation, similarity generation and prediction generation with a goal to produce a predicted opinion of the active user about a specific item. Concept of parental control is also incorporated for enhancement. Evaluation of the system was done using precision, recall, F-measure, discounted cumulative gain (DCG), idealised discounted cumulative gain (IDCG), normalised discounted cumulative gain (nDCG) and mean absolute error (MAE). Three hundred fortysix datasets were used, out of which 126 were gathered from local video shops and 220 were extracted from internet movie database (IMDb). These were used for the experiments and the results generated through mining of data obtained from profiles and ratings of system users prove the system's average ranking quality of the collaborative filtering algorithm is 95.9%.
Adoption of mobile technologies in learning: an agribusiness case

The purpose of this study is to identify the impact of mobile learning technologies in agribusiness education by examining the effectiveness of a DISCOVERe mobile technology classroom program on students' participation in class activities, their use of instructional materials, their interaction with peers, and their overall learning experience. Online survey data collected from agribusiness students at a California State University during the spring 2018 semester were analysed, hypothesis tests were conducted and a three-stage learning adoption framework was estimated. The results demonstrate that taking the DISCOVERe course significantly improves students' participation in class activities, enhances their access of course materials, makes it easier for them to interact with fellow students, and improves students' overall learning experience. The conclusion is that as students become more familiar with new technologies, they will be more confident in their ability to learn and will be more efficient in acquiring knowledge and exchanging ideas.
Improved Conditional Differential Analysis on NLFSR-Based Block Cipher KATAN32 with MILP

In this paper, a new method for constructing a Mixed Integer Linear Programming (MILP) model on conditional differential cryptanalysis of the nonlinear feedback shift register- (NLFSR-) based block ciphers is proposed, and an approach to detecting the bit with a strongly biased difference is provided. The model is successfully applied to the block cipher KATAN32 in the single-key scenario, resulting in practical key-recovery attacks covering more rounds than the previous. In particular, we present two distinguishers for 79 and 81 out of 254 rounds of KATAN32. Based on the 81-round distinguisher, we recover 11 equivalent key bits of 98-round KATAN32 and 13 equivalent key bits of 99-round KATAN32. The time complexity is less than <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mn>31</mn> </mrow> </msup> </math> encryptions of 98-round KATAN32 and less than <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mn>33</mn> </mrow> </msup> </math> encryptions of 99-round KATAN32, respectively. Thus far, our results are the best known practical key-recovery attacks for the round-reduced variants of KATAN32 regarding the number of rounds and the time complexity. All the results are verified experimentally.
Wavelet based Reversible Image Watermarking with Logistic Encryption for Health Informatics Systems

Health Informatics systems preserves the patient’s digital records. Two techniques that help in this process are watermarking and encryption. In this paper a reversible image watermarking scheme with logistic encryption is presented. The reversible watermarking is utilizing the concept of integer wavelet transform. The image is divided into sub bands and then the binary data is hidden in these sub bands. The watermarked wavelet sub bands are passed through logistic encryption module which scrambles the coefficients. These coefficients are then sent to inverse wavelet transform for image reconstruction. This process helps encrypt the image though spectral scrambling, thus resulting in faster and better encryption. The proposed algorithm outperforms the exiting algorithms in terms of execution time and the level of encryption.
Multimedia auto-annotation via label correlation mining

How to automatically determine the label for multimedia object is crucial for multimedia retrieval. The neighbour voting mechanism is known to be effective for multimedia object annotation. However, it estimates the relevance of a label with respect to multimedia content by labels' frequency derived from its nearest neighbours, which does not take into account the assigned label set as a whole. We propose LSLabel, a novel algorithm that achieves comparable results with label correlation mining. By incorporating the label correlation and label relevance with respect to multimedia content, the problem of assigning labels to multimedia object is formulated into a joint framework. The problem can be efficiently optimized in a heuristic manner, which allows us to incorporate a large number of feature descriptors efficiently. On two standard real world datasets, we demonstrate that LSLabel matches the current state-of-the-art.