Reviewerly/exHarmony huggingface dataset:
Construction of q-ary(n,M,d)-codes in PG(2,16)

The goal of this paper was to study the applications of the projective plane PG (2, q) over a Galois field of order q in the projective q-ary (n, M, d) -code such that the parameters length of code n, the maximum value size code M, and the minimum distance d with the error-correcting e according to an incidence matrix have been calculated. Also, this research provides examples and theorems of links between the combinatorial structures and coding theory. The method of the research depends on the classification of the points and lines in PG (2, q).
Acquiring spectral scattering properties of seawater from an RGB image

For accurate color restoration of underwater images, it is necessary to acquire the spectral scattering properties of seawater. In this study, we propose a method for acquiring spectral scattering properties from an RGB image. In the proposed method, we first obtain scattering properties (scattering and extinction coefficients) in RGB space. Then, we estimate spectral scattering properties from these RGB coefficients. We conduct simulation experiments using seawater datasets with various spectral scattering properties, and evaluate the accuracy of the proposed method.
NN-based embedment of watermark in end-to-end image compression

This paper proposes a method of embedding and extracting watermark in end-to-end (E2E) image compression that is an emerging image compression framework. Although end-to-end image compression algorithms are recently developed based on deep neural networks to beat the conventional image compression methods, there are no research to support watermarking embedment and extraction for contents protections. This paper proposes a NN-based watermarking algorithm for end-to-end image coding. At the end-to-end image coder and decoder, the GDN-based multi-layer modules for embedment and extraction of watermark are equipped to support watermarking for E2E image compressor. The proposed method shows an average BD-rate gain of 8.07%, over the method of directly connecting the existing watermarking network and end-to-end image compression network. In addition, bit error rate (BER) of watermark is also improved by more than 2.7% in terms of robustness.
Private Blockchain in the Field of Health Services

Blockchain is a technology that is quite popular and has been adopted in various fields in recent years. This technology has caught the attention of researchers in the health sector because of its innovation which is considered capable of providing the necessary guarantees for the safe processing, sharing, and management of sensitive patient data. There are many problems with falsifying reports and withholding important information from patients, which is considered medical fraud. Hyperledger, a type of private Blockchain, is very suitable for healthcare applications. A private blockchain is a restricted type of blockchain network created by an entity. This type of network is limited to those with access permissions. In addition, private blockchains usually use a centralized verification system and are controlled by the network's creators. Hyperledger Fabric is one example of a permissioned blockchain that can play a role in implementing patient-centric, interoperable healthcare systems
Edge-side task scheduling: Auction mechanism and genetic algorithm based methods

Edge computing is an emerging computing architecture. The scheduling and optimization of the tasks on the edge side of the smart factory can effectively reduce the processing delay and improve the utilization efficiency of servers. This study focuses on the problem of edge-side task scheduling with the goal of minimizing the maximum completion time of the tasks. A first-price sealed-bid auction based algorithm and a genetic algorithm with elite retention strategy are designed to solve the problem. The experimental results indicate that the auction-based scheduling algorithm has better real-time performances compared to the genetic algorithm.
The effect of numbered heads strategy on the achievement of intermediate second-grade students in mathematics and their attitudes toward it

The aim of the research was to identify (the effect of numbered heads strategy on the achievement of intermediate second-grade students in mathematics and their attitudes towards it); the research sample consisted of (64) intermediate-grade students, distributed into two groups: experimental by (30) students, and control by ( 34) Students .
ICPDS: Design and Analysis of PSSOA and Blockchain Fusion Scheme for Cross-Hospital Medical Systems

&lt;p&gt;With the rapid development of information technology, medical treatments are gradually transforming into digitalization and informatization. The diverse sources and scopes of the medical data are sensitive and significant. As important data assets, medical data are constantly being analyzed and mined. Because of the huge commercial value of medical data, the problems of privacy leakage exist in the centralized information system. They cannot adapt to complex medical environment. Most of the current authentication mechanisms use passwords for verification. It is not easy for users to remember all usernames and passwords. Based on these problems, we design single-sign-on (called SSO) authentication (Password Single-Sign-On Authentication scheme, PSSOA) and blockchain (Improved Cross-Blockchain based Privacy-Preserving Date Sharing scheme, ICPDS) fusion schemes. Users are verified by multiple identity servers in this scheme. Then identity servers issue authentication tokens with threshold manners. The protected identity servers regularly update their master secret shares to defend against malicious adversaries.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Further Results on Autocorrelation of Vectorial Boolean Functions

In this paper, we study the properties of the sum-of-squares indicator of vectorial Boolean functions. Firstly, we give the upper bound of $\sum_{u\in \mathbb{F}_2^n,v\in \mathbb{F}_2^m}\mathcal{W}_F^3(u,v)$. Secondly, based on the Walsh-Hadamard transform, we give a secondary construction of vectorial bent functions. Further, three kinds of sum-of-squares indicators of vectorial Boolean functions are defined by autocorrelation function and the lower and upper bounds of the sum-of-squares indicators are derived. Finally, we study the sum-of-squares indicators with respect to several equivalence relations, and get the sum-of-squares indicator which have the best cryptographic properties.
Comparative Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection

Banks are the backbone of the economy of any country, and hence, it is essential that they function properly so as to achieve a booming and sustainable economy. The major barrier for banks is the defaults on the credits they give to the customer. As banks profit mostly from the loans they give to customer, hence, it is required that they possess a solid and efficient model to minimize the losses they incur via the credit defaults. This article assesses the credit card default prediction’s performance. Earlier the credit card defaults were tallied using standard tools such as FICO scores, but with the development of machine learning, it became much easier to build highly effective risk prediction model. Anyone with a bit of machine learning knowledge would know that credit risk prediction is nothing but a mere binary classification problem. Thus, various machine learning models such as KNN, decision tree, random forest, and logistics regression have been applied. When we look into the credit risk of credit card clients, it indicates that random forest best specifies the aspects to examine with an accuracy of 82% and an area under curve of roughly 77%.
AR/VR/3D Technologies: Enabling Future Business Growth and Scale-Up

Augmented Reality and Virtual Reality (AR/VR) technologies have been in existence for some time, but recent advancements in hardware, storage, graphics processing, and high-resolution displays have made them more accessible and practical. As a result, an increasing number of organizations are exploring these immersive technologies for various purposes. This research focuses on organizations that have already initiated their AR/VR/3D journey, either through experimentation or implementation, and aims to answer the following questions. ❖ How is AR/VR/3D being employed today? ❖ What are the latest trends in AR/VR/3D technology? ❖ How can firms either begin or evolve their initiatives by implementing AR/VR/3D tech?
Identification of Breast Tumors With Image Processing Using Canny Edge Detection

Breast tumor is one of the leading causes of death in women worldwide. The term tumor is often used for all lumps found in the human body. The increase in the number of cases of breast tumors that occur each year is due to the absence of prevention or early detection. The research was carried out by utilizing the development of information technology to create a breast tumor detection system with digital image processing. The application system will process mammogram images to detect edges with the canny method and will be classified using the SVM method. The data used is 176 data obtained from the Kaggle dataset. The test results revealed that 64 patients were classified as having malignant breast tumors (M), and 113 did not have breast tumors (B), with a classification accuracy rate of 95%. From the results obtained, the application system is very good for the early identification of breast tumors.
Authorship Attribution using Sequential Part-of-Speech Pattern Mining

Given an anonymous text, automatically attributing a name from a group of known writers is called "Authorship Attribution" (AA).It is a classification problem, and feature extraction techniques are initially applied, followed by the training of a model using a collection of texts whose authors are known.Numerous features, such as lexical, semantic, structural, n-grams, etc., can be used to identify the stylistic characteristics of writers.The authors of this research propose a novel approach to this problem by using sequential pattern mining on part-of-speech (PoS) tags.This paper introduces and discusses the concept of a Part-of-Speech Skip-Gram (PoSSG) that is different from traditional n-gram.A sequential pattern mining algorithm is applied to obtain PoSSG patterns, which are then used for authorship attribution tasks.Experimental studies on two different datasets: novels extracted from Project Gutenberg and Stamatatos06
Modern Problems of Face Recognition Systems and Ways of Solving Them

Currently, the face recognition system is applied in various fields such as classified materials, file transferring, and general human interaction and innovative technologies, including cell phones with owner recognition function.The relevance of this research lies in the need to consider problems touching upon the increase in the efficiency of such technologies.The objective of this study was to improve the algorithm of the face identification system from different sides in order to demonstrate appropriate results.In the course of the study, a method based upon deep neural networks was applied through the projection of layers.As a result, a receiver operating characteristic curve was constructed to evaluate the quality of binary classification, and loss functions for deep learning, data distribution, and algorithm accuracy were demonstrated.Proceeding from the results obtained, it was found that the selected face identification system is resistant to each of the influencing factors (make-up, lighting, posture, etc.) considered separately in the work.The operation principle represents an original technical solution for face recognition problems when images in the database have discrepancies, which is a typical scenario in the actual world.The testing accuracy in this work reaches 95.04%.
Improving Existing CMMS Software Packages Using Association Rules

Since their inception, industries have experienced the negative effects of downtime, lost productivity, lost revenue, as well as layoffs.The prediction of an item's remaining useful life (RUL) enables maintenance techniques to avoid costly and serious damage.As a result, a prognostic is now acknowledged as a crucial activity.Thanks to the Internet of Things (IoT) and IT solutions like Computer Aided Maintenance Management (CMMS) software packages, industries today have a vast amount of data gathered from on-site sensors.This offers real-time data on the equipment's state as well as each piece of equipment's history of interventions from the CMMS software database.By utilizing the vast amount of data that has accumulated over the years, we will be able to extract even more crucial information.The use of artificial intelligence (AI) methods can open up new possibilities for CMMS software packages.In this study, we try to predict RUL (Remaining Useful Time) using an artificial intelligence technique called association rules.This strategy is applied to enhance existing CMMS software programs.Experiment is carried out with a well-known dataset provided by the NASA Ames Research Center and the CoE "Center of Excellence".Experiment results indicate that our suggested approach performs well in forecasting the RUL of turbojet engines and that it also significantly improves the outcomes of predictive maintenance.
Action Recognition Using Segmental Action Network

Human action recognition refers to the task of recognizing and categorizing human actions in video or image sequences.This is a complex problem in computer vision and has a wide range of applications, including video surveillance, human-computer interaction, and sports analysis.In this work, A novel approach to action recognition using Segmental Action Networks is presented.The proposed approach utilizes 2D and 3D convolutional neural networks to extract spatiotemporal features from video frames, which are used to train a Segmental Action Network.To improve the model's accuracy, Different voting and feature extraction techniques, such as Space-Time Interest Points, (STIP) and Optical flow have been applied.The proposed model has been tested on the HMDB51 dataset and has achieved better results than existing models.The results demonstrate the effectiveness and robustness of our proposed approach for action recognition.Furthermore, our model is computationally efficient and can be deployed on edge devices with low computational and memory capacity, making it a promising approach for real-world applications.
Image Counterfeiting Detection and Localization Using Deep Learning Algorithms

As social networking services such as Whatsapp, Facebook, Twitter, and Instagram have grown in popularity over the past two decades, the volume of picture data created throughout the globe has exploded.Images that have been altered or doctored using editing software such as Adobe Photoshop, GIMP, and Paint-3D are a major source of concern in the digital age.As a result, it is essential to verify the validity of suspect images before taking action against people who fabricate them.Copy-move forgery and spliced image fraud are two of the most extensively used picture forgery methods in the field.Recent Deep Learning (DL) algorithms have simplified tasks like categorization, localization, segmentation, and other comparable studies.With the use of Residual Neural Networks (ResNet), copy-move forgery and spliced fraud in photographs may be discovered and classified.Experimental results on benchmark datasets such as CASIA-2, MICC-F2000, and CoMoFoD indicate significant gains over state-of-the-art approaches.Gradient Class activation mappings (Grad-CAM) were applied to find forged regions in tampered photographs, and the suggested approach was also proven to be successful in predicting tampered images.On the CoMoFoD dataset, a classification accuracy of 99.9% was attained, while on the MICC-F 2000 dataset, it was 97%.
An Analysis of the Status of High School Music Elective Course Offerings under the 2015 Revised Curriculum

본 연구는 2015 개정 교육과정을 바탕으로 음악과 선택 과목 운영 현황을 파악하고자 2018년부터 2021년까지 나이스를 사용하고 있는 전국의 모든 유형별 고등학교의 음악과 교육과정을 전수조사하여, 설립별⋅유형별⋅학과별⋅과목별로 분석하였다. 연구 결과 2015 개정 교육과정 적용 이후 우리나라 고등학교의 음악 선택 교과개설률은 모든 학교 유형별로 증가하였으나, 일반고에 비해 자사고, 특목고, 특성화고는 다과목 개설 비율이 낮았다. 또 국⋅공립교에 비해 사립교의 다과목 개설수가 적었다. 이를 해결하기 위해서는 예술 과목 선택권 보장 체계를 마련하고, 유형별⋅설립별로 제반사항을 공유하여 협력하도록 제도적으로 지원하여야 한다.This study aimed to identify the current status of music elective courses based on the 2015 revised curriculum. A complete enumeration survey on the music curriculum of all types of high schools across the nation using NEIS from 2018 to 2021 was conducted. Course offerings were categorized by educational foundation, type, department, and subject. The result of the study revealed that, after applying the 2015 revised curriculum, the rate of offering music electives in Korean high schools increased for all school types. Compared to general high schools, private, special-purpose, and vocational high schools, and vocational high schools had a lower rate of opening multiple subjects. Also, compared to national and public schools, the number of courses opened in private schools were small. In order to solve this problem, a system for guaranteeing the right to choose art subjects needs to be provided and all matters by educational foundation and type shared for cooperation.
Interactive User Interface for Dialogue Summarization

Summarization is one of the important tasks of natural language processing used to distill information. Recently, the sequence-to-sequence method was applied, in a general manner, to summarization tasks. The problem is that a large amount of information must be pre-trained for a specific domain, and information other than input statements cannot be utilized. To compensate for this shortcoming, controllable summarization has recently been in the spotlight. We introduced three properties into controllable summarization: 1) a new human-machine communication input format, 2) a robust constraint-sensitive summarization method for these formats, and 3) a practical interactive summarization interface available to the user. Experiments on the Wizard-of-Wikipedia dataset show that applying this input format and the constraint-sensitive method enhances summarization performance compared to the typical method. A user study shows that the interactive summarization interface is practical and that participants are evaluating it positively.
A Study on Deep Learning based Classification and Identification of offensive memes

Sarcasm is a complicated language device that is regularly utilised on e-commerce and social media websites. Failure to recognise sarcastic statements would confuse classification algorithms and result in incorrect findings in Natural Language Processing applications like sentiment analysis and opinion mining. Studies on sarcasm detection have included a variety of learning techniques. However, the majority of these teaching strategies have always focused their attention entirely on the concepts that were communicated, ignoring the context. As They thereby missed the meaning and context of the sarcastic statement. Second, the emotional polarity of words is not taken into account when using the word embedding learning methodology, which is a common way for convolutional feature vector encoding in NLP deep learning approaches. To overcome the issues mentioned above,This research presents a context-based feature strategy for sarcasm detection that combines deep learning, BERT, and conventional machine learning. Two benchmark datasets from the Internet Argument Corpus (IAC-v2) and Twitter were used for the categorisation. Employed are all three learning models. Deep learning and embedding-based representation are both utilised by the initial model. Word embedding and context are being developed utilising global vector representation (GloVe) learning and Recurrent Neural Network (RNN) with a Bidirectional Long Short Term Memory (Bi-LSTM). A pre-trained Bidirectional Encoder representation (BERT) is used to build the second model, which is based on Transformer. In contrast, the third model is founded on the BERT feature’s feature fusion. a function that incorporates sentiment-related, syntactic, and GloVe embedding
College classroom attendance and teaching evaluation method based on face recognition

Classroom attendance in colleges and universities takes up teaching time, and classroom quality evaluation data is highly subjective. This paper develops classroom attendance and teaching quality evaluation methods in colleges and universities based on face recognition, detects and tracks the head based on face detection algorithm, and realizes dynamic attendance, attention analysis, classroom state curve and other functions. The results show that the accuracy of the evaluation method can reach 80%.
Pre Encryption Data Hiding Techniques using Reserving Room Approach

Reversible data hiding is a method that embeds hidden data covertly and reversibly into media (such as text, pictures, and video). The cover media can be fully retrieved together with the embedded data. Hidden data is extremely and losslessly recovered from the encrypted image and the visible watermarking using pixel prediction. Even though the recipient is uninformed of the original photograph, to add the visible watermark, a data-hider may change a portion of encrypted data, such as the black pixels in a binary watermark image. Data extraction and/or image restoration faults might result from this change in the encrypted data but those flaws can be removed by employing error-correcting codes. In this article, the existing methods are compared to the proposed algorithm. The suggested approach has a higher embedding rate than the earlier techniques. This article demonstrates that the suggested algorithm will produce superior outcomes to another algorithm.
Patients’ Medical History Summarizer using NLP

Text summarization is the process of extracting the meaning and important points from the text. It helps gain important information from the text while separating futile data. For generating a lot of textual data manually a person will be required to go through all the documents and then generate the summary which can be time taking and tiresome. Here Automatic text summarization (ATS) comes into the picture which takes text as input and generates the summary of that text with the help of machine learning algorithms and natural language processing techniques or NLP techniques. The use of ATS in the medical field can help doctors go through a patient’s medical history in a shorter period of time and take better decisions about the diagnosis of the patient.