Reviewerly/exHarmony huggingface dataset:
Agent-based Modeling and Simulation of Digital Learning Environment

In this paper, we propose a novel multiscale approach to modeling the Digital Learning Environment (DLE) by the introduction of a design pattern, which links students, educational organizations, resources, teachers, targeted skills, audience characteristics, constraints, and existing environment. For this purpose, we have to define an organizational structure and adopt management strategies to improve the performance of DLE. This organizational structure is an important element that has to be taken into account to simulate a digital learning environment. To facilitate the design of these simulations, we propose an agent-based methodological framework for this complex system.
Data Sharing Algorithm of Fusion Information System of Party Building and Business Work in Private Colleges under Background of Data Collection Mining

Data sharing algorithm of the fusion information system of party building and business work in private colleges under the background of data collection mining is studied in this paper. In data sharing applications, it is very common to use data from multiple data providers for the fusion statistics and comprehensive decision-making. Data disseminators play the role of dissemination media, including collecting and discovering scientific datasets, realizing persistent storage and persistent access to datasets, statistics and analysis of data dissemination traces, and reporting on dissemination effects. With this theoretical basis, the designed model is focused on the fusion information system of party building and business work in private colleges under the background of data collection mining. The system is implemented and tested under different scenarios.
A Novel Technique for Ensembled Learning based on Convolution Neural Network

Content-Based Medical Image Retrieval (CBMIR) systems primarily aid in extracting useful information from a large set of medical images. CBMIR procedures are useful in biomedicine, education, the military, web image organization, and search. Many studies in the field of medical image retrieval proposed various methodologies. The CBMIR is currently looking for a source of precise and fast retrieval. A large number of strategies have been developed to improve CBIR execution. These techniques are useful for categorizing images. Compared to other models such as BoW and Convolution Neural Network, the proposed method has a higher accuracy and output retrieval rate (CNN). The proposed system’s execution considers both grouping and recovery by implementing CNN based on SVM and bag of words. CNN based on SVM performs significantly better. The system was effectively prepared for 27 classes of CT scan images with a normal order exactness of 97.1 percent obtained using Bag-of-words and high accuracy of 97.3 percent obtained by comparing with state-of-the-art MATLAB software the proposed Convolution Neural Network (CNNH).
Possible limits on superconducting quantum computers from spontaneous wave-function collapse models

The continuous spontaneous localization (CSL) model is an alternative formulation of quantum mechanics, which introduces a noise-coupled nonlinearly to the wave function to account for its collapse. We consider CSL effects on quantum computers made of superconducting transmon qubits. As a direct effect CSL reduces quantum superpositions of the computational basis states of the qubits: we show the reduction rate to be negligibly small. However, an indirect effect of CSL, dissipation induced by the noise, also leads transmon qubits to decohere, by generating additional quasiparticles. Since the decoherence rate of transmon qubits depends on the quasiparticle density, by computing their generation rate induced by CSL, we can estimate the corresponding quasiparticle density and thus the limit set by CSL on the performances of transmon quantum computers. We show that CSL could spoil the quantum computation of practical algorithms on large devices. We further explore the possibility of testing CSL effects on superconducting devices.
Classification and model reconstruction method of non‐cooperative multifunctional radar waveform unit

Abstract There are many variations in the form of multifunctional radar signals. At present, the problem associated with the highest concern in the field of electronic reconnaissance is the inversion of its working mode from the reconnaissance data. A method is proposed for waveform unit classification and model reconstruction using the non‐cooperatively extracted waveform units as input data based on the hierarchical structure of multifunctional radars. The method performs multiparametric matching on waveform units subject to the constraint of the match level to obtain the similarity measure between waveform units; it then recursively expands the waveform unit pairing score matrix to complete the classification of the waveform units. Further, based on similar waveform units, the matching state matrix of the waveform unit model can be reconstructed. The feasibility and performance of the proposed method are verified by setting up typical multitype simulation experiments. The experimental results show that the proposed method can complete the classification of waveform units in the small‐data content and extensive noise intensity cases and supports the reconstruction of waveform unit models with low errors.
Do we need Diagnostic Decision Support For Pathologists in Anatomic Pathology Laboratory Information System (APLIS)?

Abstract Introduction/Objective On a typical day, pathologists spend time not just looking at the slides but have various information needs in order to facilitate the sign-out. This study was performed to find out various resources utilized by pathologists to facilitate the sign-out and if having the diagnostic decision support in APLIS will improve the sign-out experience? To answer these questions a survey was created. Methods/Case Report An anonymous Google survey of 10 questions inquiring about practice setting, training level, work experience, various resources utilized to facilitate daily sign-out, time spent to find the information, and ways the sign-out experience can be enhanced was created and distributed among the pathologists via a social media platform. Results (if a Case Study enter NA) 85 pathologists participated in the survey, and the results showed that 66% of respondents worked in an academic setting while 34% worked in a community practice setting. 37% of respondents had work experience &amp;gt;5 years while 23% had work experience &amp;lt; 5 years and the remainder of them were in training. 44% of them had a subspecialty sign-out system. EPICBeaker (27.3%) was the most commonly utilized APLIS system, followed by Cerner (22%) and Copath(16%), and reminder used Novopath, soft path, Med-tech, and Power path. The pathologists use various resources to facilitate daily sign out which include textbooks, ebooks, PubMed, CAP protocols, WHO books, own notes, and internet searches. Reasons to refer to these resources include finding the grossing guidelines, differential diagnosis, special or immunohistochemical stains, practices guidelines, correct tumor stage, and grade or molecular tests. On a typical day, 41% of the respondents spend 1-2 hours/day, and 25% of the respondents spend 2-3hrs/day finding the correct information to sign out their cases. 81% of the pathologists responded that if all the resources are available in APLIS will increase their productivity. Conclusion Having all the resources in one place in APLIS in the form of diagnostic decision support will enhance the sign-out experience for pathologists and will increase productivity.
Robust simultaneous estimation of location parameters

In this paper, we propose two robust non-parametric estimators for population location parameters via a shrinkage idea. Improvements of the proposed estimators over traditional estimators are demonstrated through simulation studies and empirical data analysis.
A lightweight encryption and message authentication framework for wireless communication

Confidentiality and authentication are two important security properties of wireless communication. There are currently two types of research in wireless communication security: one is based on cryptography, which can be too complicated for some wireless systems; the other is based on physical layer characteristics, which has some security problems and cannot work as well as the cryptography-based method. In this paper, a lightweight encryption and message authentication framework for wireless communication is proposed. The proposed framework has lower complexity than the cryptography-based method and works more securely and efficiently than the classical physical layer method. For the encryption, a physical layer offered chain key (PHYLOCK) structure is proposed to generate one-time keys, which are then XOR-ed with the plaintext to generate the ciphertext. A security analysis of PHYLOCK is performed and it is proved that it has better security features than the traditional physical layer key generation. For the message authentication, three types of PHYLOCK-CRC-based message authentication schemes are developed whose generator polynomial is determined by one-time keys generated by PHYLOCK. PHYLOCK-CRC-based message authentication detects not only random errors introduced by noises but also malicious attacks. The error-detecting and anti-attack capabilities are analyzed mathematically. Compared to the standard CRC, PHYLOCK-CRC-based message authentication has a decline in error correction capability but is enhanced with cryptographic security features. Monte-Carlo simulations are provided to evaluate the proposed scheme.
Knowledge Management Using Blockchain Technology for Digital Resources

Blockchain is a shared distributed database and cryptographically ensures that it cannot be tampered with, is unforgeable, decentralized, and trustless shared general ledger, and can safely store simple and self-verifiable data in the system. It is a new decentralized, overall maintained infrastructure and distributed computing paradigm, with the four major characteristics of decentralization, trustlessness, collective maintainability, and reliable database. This research will mainly use the literature survey method and the inductive analysis method to analyze the relevant documents of the blockchain technology in the CNKI database to find out the trends and characteristics of the development and application of the blockchain technology and explore the use of the blockchain technology-based on these characteristics. Here, we need to analyse the feasibility of creating a knowledge management process model for digital resources. The blockchain itself has the feature of decentralization, self-operation, and sharing, which is highly compatible with the knowledge management of digital resources, especially in the field of knowledge sharing.
Aerial Image Classification Using Convolution Neural Network

Image classification task has been an important area in the field of computer vision study as it is applied in varied applications. The approaches for image classification is based on feature selection of image class and effectively applied through various low level feature algorithms for matching with a particular class and yielding a classification result identifying one from another. The task for feature selection particularly become extremely challenging to spatial images such as aerial images, as they contain varied complex feature, scale challenges as well as image orientation. A more accepted approach of image classification for aerial image is the use of Convolution Neural Networks (CNN). CNN models are capable of producing higher accuracy compared to contemporary feature based algorithm as they tend to utilize higher local features. Transfer learning approach using readily available and proven CNN for image classification makes the task a step closer to capturing and designing a CNN which adapt to user dataset and classification requirements. There is however the need to create user dataset with sufficient images to retrain the network for fine-tuning and testing its accuracy. This paper presents such experiment using GoogLeNet pre-trained network which is subject to replacing of the fully-Connected layer and output layer as per the classification requirement. The network is further subject to training and testing using test dataset other than that have never been exposed to the network. An accuracy of 98.33% was achieved.
Approach Toward Word Sense Disambiguation for the English-To-Sanskrit Language Using Naïve Bayesian Classification

Natural language processing (NLP) refers to the ability of a computer program capacity to understand both spoken and written human languages. Word Sense Disambiguation (WSD) is a method for separating words with similar meanings and determining the words with the precise significance of meaning. It is an essential and critical application for all NLP tasks. Several methodological approaches come up in the context of WSD. There are supervised and unsupervised WSD approaches that are widely used in the disambiguation process. Supervised WSD approaches have shown better results than unsupervised approaches. The Naïve Bayesian (NB) classifiers approach is known as one of the best methods among all the supervised approaches for WSD. NB is a classification algorithm that is based on the Bayes theorem and it simplifies learning by accepting that features are independent of a given class. In this paper, we use an NB classifier to disambiguate ambiguous English words by predicting part-of-speech inclusive of “noun,” “verb,” “adverb,” and “adjective.” This disambiguation module is an enhancement in machine translation. The system reported the performance measure of eighty-five percent of the scale of F1-measure.
Improving the Quality of English-Bharati Braille Machine Translation Using Syntax Analysis

Life is tough for people who are blind or deafblind. Braille is a medium through which they can read the text available in the world. Unfortunately, due to the lack of resources that can transcribe textual material into Braille, they are deprived of a lot of knowledge available in books. This paper is an attempt to address this issue. We have developed a system that can transcribe the text available in English into Hindi Braille. We have developed a hybrid machine assisted translation system, which can translate English text into Bharati(Hindi) Braille. This research would be helpful to people in our country who have a desire to learn and gain knowledge but are unable to do so due to the unavailability of literature in Bharati Braille. The developed system is critically important to the lives of visually impaired people because the ability to read and write in Braille opens the door to literacy, intellectual freedom, equal opportunity, and personal security for them. We have evaluated our MT system against standard MT evaluation metrics which provides promising results. We also did quality analysis of the MT output and found that for almost all the simple sentences, and the translations were good while for complex sentences. The system produced above average translations.
A Short Overview on Various Bio-Inspired Algorithms

Nature never fail to inspire us; we can learn a lot from them. Based on the inspiration acquired from the biological activities of nature, bio-inspired algorithms are developed to compete with modern rival techniques. An optimal solution can be obtained for intricate engineering and scientific problems when a bio-inspired algorithm is embedded with machine learning techniques. The problems are proposed with multiple nonlinear constraints which require huge time and high dimensionality. In this overview, various bio-inspired algorithms like artificial bee colony (ABC) algorithm, fish swarm algorithm (FSA), cat swarm optimization (CSO), whale optimization algorithm (WOA), artificial algae algorithm (AAA), elephant search algorithm (ESA), chicken swarm optimization algorithm (CSOA), moth flame optimization (MFO), and gray wolf optimization (GWO) algorithm are discussed precisely.
Sentiment Analysis of Reviews Using Bi-LSTM Using a Fine-Grained Approach

In the era of digitization, where everything is available online, and further, information about everything is online, we have transcended into a new phase—finding subjective information about everything in the form of reviews. Performing sentiment analysis on reviews left by customers can prove to be essential for service providers as it can help them engineer better products/services, understand sales dynamics, and target audiences appropriately. The more detailed the analysis, the deeper the insight gained. Due to this, sentiment analysis of online reviews has become a project taken up frequently, prompting data scientists to compile and share numerous data repositories. For this research, we have selected one such dataset consisting of reviews for several products and attempted to build an intelligent model using Bidirectional LSTM that can classify reviews as positive, negative, and neutral, rather than categorizing them only as positive and negative.
Cross-Modal Retrieval Using Deep Learning

Cross-modal retrieval intends to empower adaptable recovery across various modalities. The center of cross-modal retrieval is the manner by which to quantify the substance similitude between various sorts of information. In this work, we deal with a cross-modal retrieval technique, called Canonical Correlation Analysis (CCA). It accepts one sort of information as the question to recover pertinent information of another sort. The given indexed lists across different modalities can be useful to the clients to get exhaustive data about the objective occasions or points. With the quick development of various kinds of media information like texts, pictures, and recordings on the Internet, cross-modal retrieval turns out to be progressively significant in true applications. As of late, cross-modal retrieval has drawn in the significant consideration of the analysts from both scholarly communities also, industry. The test of cross-modal retrieval is the ticket to gauge the substance closeness between various kinds of information since they, which is alluded to as the heterogeneity hole. After data preprocessing and learning the mappings in the same space, we will try to find out the most similar samples based on pre calculated features of the samples in a given format. We will keep the features learned by VGG-16 precalculated and the features learned by text model would then be used to search for the most similar image that best explains the caption.
Student Invigilation System Using Modified Faster RCNN

Cheating during exams is a fraudulent act that sabotages hard-working students’ academic integrity and efforts and damages the standard of education. Traditional ways of invigilation are restricted to physical monitoring of students by humans, and most of the time, invigilators are few in number as compared to students. In this article, we have proposed a computer vision-based deep learning cheating detection model. The proposed deep learning model is based on the highly fast region-based convolution neural network (RCNN) with the MobileNet as the feature extractor. Because of its higher level of examination to detect fraudulent behavior in students, this feature extractor makes it so lightweight that it can process the camera steam with limited computational resources. The model can detect the neck and head movements of the students. The proposed model manages to achieve 92.4% accuracy on neck and head movement detection as compared to the original Faster RCNN and various proposed approaches for detecting these two cheating states.
Security and Privacy Challenges in Big Data

Huge amounts of data are being generated on a regular basis as a result of technological advancements such as social media, smart phones, GPS signals, and sensors. Different types of data, such as structured, semi-structured, and unstructured data, are represented in the datasets. Big data is a word that refers to data that has been collected from many sources and in various volumes. Big data encompasses the vast majority of data generated nowadays. Due to the unstructured form of data, there are a number of concerns with this type of data, including data processing, security, and privacy. This paper will look at some of the characteristics of big data as well as the security concerns it faces. This paper also contains a comprehensive review of the literature on security measures and a comparison of different strategies.
A Survey on Video Description and Summarization Using Deep Learning-Based Methods

Video description is basically a method of automatic creation of natural language sentences which can illustrate the insides of a given video. Several applications areas are there where we need summarization of description of video such as in video subtitling, human–robot interaction, helping the visually impaired person etc. In this present paper, we review different state-of-the-art techniques for video description using deep learning techniques. This paper summarizes the deep learning based state-of-the-art methods for video description. The paper also identifies the pros and cons of each evaluation metrics employed in the task. Most of these methods generally uses a 3D CNN model to convert videos into multi-dimensional arrays, then uses a word embedding techniques like GLOVE, etc., for featurization of text description and then finally trains an RNN or LSTM model or a variant of the two to perform final classification. The paper also describes the benchmark datasets of each of these methods along with evaluation metrics and state-of-the-art performance is reported on the same. Wherever applicable, we also list down the advantages and drawbacks of each of these methods as stated earlier. This survey paper can act as a preparatory read for anyone entering the field.
Learning Techniques for Prediction of Breast Cancer Disease: A Comparative Analysis

Breast cancer and its detection is always one of the trending topics of all time. Many research activities have been done in this field using different technologies to make it effective. In this research, it has taken machine learning and deep learning algorithms to develop an effective breast cancer classifier. It has implemented ten best machines learning and deep learning classification algorithms like logistic regression, decision tree, random forest, support vector machine, K-nearest neighbor, Naïve Bayes, multi-layered perceptron, stochastic gradient descent, AdaBoost, and artificial neural network classifier for prediction of breast cancer disease. These classifiers are successfully able to classify whether the given data are coming under benign cell or malignant cell. It has also got different accuracy for every classifier. At the end, it has successfully implemented all the approaches and has got the good accuracy.