Reviewerly/exHarmony huggingface dataset:
Noisy QR Code Smart Identification System

The resurrection of the quick-response (QR) code has been made possible by the expansion of mobile network coverage combined with a rise in smartphone online content over the years. They have become much more accessible by integrating a code reader in smart devices, thus removing several unpleasant procedures and providing faster access to crucial information. However, noise in the printed images is unavoidable owing to printer processes and restricted printing technology, thus may decrease the quality of a QR code image during digital image collection and transmission which may eventually cause failure while scanning and extracting actual information. As a result, this study proposes an intelligent image classification strategy to correctly identify noisy and original QR code types. For this, a new dataset is built, containing 20,000 images pertaining to the original QR code and noisy QR codes. Later, the study exploited three well-known machine learning algorithms (logistic regression (LG), support vector machine (SVM), and convolutional neural network (CNN)) to segregate noisy images among original QR code images. The experimental results show that SVM outperformed others by attaining an overall performance accuracy of 97.5%, precision of 97.50%, recall of 97.5%, and F1-score of 97.5%, while LG almost competes by achieving 97.25% accuracy, 97.31% precision, 97.22% recall, and 97.25% F1-score.
Prostate Cancer Grading Using Multistage Deep Neural Networks

Prostate cancer is the second most commonly occurring cancer in men with a high incidence to mortality ratio. Accurate prostate cancer grading is the foremost step in determining the precise treatment process for the patient in preventing mortality of the patient. Currently, the grading is carried out by pathologists, which has limitation of availability super specialist doctors across world to grade it at affordable price, and non-super specialist doctor grading is error prone. This paper evades the need for an expert pathologist by proposing a novel deep learning method for automatic screening of prostate images to detect and assign a grade severity of cancer based on the images. The explainability of classification model imbibed using gradient-weighted class activation mapping (GradCAM) visualization, which generate heatmap of image, which influenced the decision of the model. The proposed method has three stages with ensemble deep neural networks to grade the prostate cancer. Firstly, a UNet is used for the segmentation of the histopathological image. Subsequently, the segmented image is overlaid on the original image, which helps underscore the most critical regions determining the grade of cancer. Finally, the overlaid image is used by an ensemble model consisting of Xception, Resnet-50, EfficientNet-b7 to predict the final grade of the histopathological image. The dataset containing 10,000 histopathological images obtained from Karolinska and Radboud that are made publicly available through the Prostate Cancer Grade Assessment Challenge hosted in Kaggle is used for training and evaluation. This method achieves a classification accuracy of 92.38% and outperforms many state-of-the-art methods.
Cyber Crimes Against Women

Indian civilisation is one of the world's oldest civilizations. Women in our nation are given particular treatment. They have been regarded as if they were goddesses. However, as society has modernised, these fundamental rights have been trampled. India is rapidly expanding in the sector of information technology. People rely on computers for most of their daily tasks. The year 2000 was regarded as a year of technological change. With the widespread use of computers, cybercrime is on the rise. Furthermore, women are the most common victims of cybercrime in our country. Cybercrime is a brand-new type of crime. Cybercrime may take many forms, including online stalking, morphing, and cyber defamation. Emails are harassing females. They are subjected to harassment, which is very frequent in todayâ€™s era. Tough we have the Information and Technology Act of 2000 to combat such crimes, but it is ineffective unless individuals reform their thinking.
Cancer Detection Using Deep Neural Network Differentiation of Squamous Carcinoma Cells in Oral Pathology

In this modern era, computer-aided differentiation of normal and cancer cells helps in assisting oral pathologists for the diagnosis of oral cancers. The most common modality in the diagnosis of oral cancer is histopathology. This chapter presents the data engineering tasks involved in the diagnosis of anomalous tissues. The advent of modern tools and techniques used for feature selection and extraction using microscopic images of the tissues will be explained. Deep learning workflow for the well differentiation of squamous carcinoma cells using histopathologic images is illustrated with the implementation of using deep learning modules and technologies. Toward the end, summary on research challenges in digital pathology is presented. Finally, this chapter concludes with future research directions.
Adaptive Dendritic Cell-Negative Selection Method for Earthquake Prediction

Earthquake prediction (EQP) is an extremely difficult task, which has been overcome by adopting various technologies, with no further transformation so far. The negative selection algorithm (NSA) is an artificial intelligence method based on the biological immune system. It is widely used in anomaly detection due to its advantages of requiring little normal data to detect anomalies, including historical seismic-events-based EQP. However, NSA can suffer from the undesirable effect of data drift, resulting in outdated normal patterns learned from data. To tackle this problem, the data changes must be detected and processed, stimulating fast algorithmic adaptation strategies. This study proposes a dendritic cell algorithm (DCA)-based adaptive learning method for drift detection and negative selection algorithm (DC-NSA) that dynamically adapts to new input data. First, this study adopts the Gutenberg–Richter (GR) law and other earthquake distribution laws to preprocess input data. Then, the NSA is employed for EQP, and then, the dendritic cell algorithm (DCA) is employed to detect changes to trigger gradient descent strategies and update the self-set in NSA. Finally, the proposed approach is implemented to predict the earthquakes of MW &gt; 5 in Sichuan and the surroundings during the next month. The experimental results demonstrate that our proposed DC-NSA is superior to the existing state-of-the-art EQP approaches.
Evaluation model of college students' online learning level difference based on support vector machine

There are some problems in the evaluation of online learning level differences among college students, such as low accuracy and long evaluation time. This paper proposes a new research method by extracting online learning behaviour characteristics to determine behaviour evaluation indexes and evaluation standards. The depth of the residual neural network method is used to remove interference index. Using the Lagrange multiplier method, the evaluation problem is transformed into a dual problem. The nonlinear transformation causes us to strive for the optimal classification plane. Difference in evaluation data was accessed by using big data technology, through the normalised processing the data, in order to obtain the optimal classification function. Finally, the final evaluation results are obtained, and the online learning evaluation level differences are completed. Through comparison, the accuracy of this method is 97%, and the time cost of assessment is always less than 9.5 ms.
Authentic learning environments for in-service training in cybersecurity: a qualitative study

Today's rapidly digitalising world has led to business processes becoming digitalised, which necessitates paying attention to the cybersecurity issues inherent in those digital processes. The multi-disciplinary nature of working life and the complexity of cybersecurity issues place demands on learning environments. The present study examined the requirements for optimal in-service training to ensure individual and organisational learning of the competencies that are crucial for dealing with cybersecurity incidents. Building on theories of authentic learning and qualitative research methods, the study identified three fundamental components and four elements of optimal in-service cybersecurity training. The research found that practicing organisational actions increased readiness and competence to act in the face of a real cybersecurity incident. A comprehensive cyber arena supports the implementation of optimal training and thus the efficiency of in-service training.
L’ordine delle parole nel Chronicon Vulturnense

"Word Order in the Chronicon Vulturnense. The aim of this paper is to present a linguistic analysis of the Chronicon Vulturnense, a “roborated chronicle” written in Latin in the first half of the 12th century by a monk of the monastery of San Vincenzo at Volturno. The work, which is characterised by the repeated interweaving of chronological and documentary parts, offers interesting insights for the study of the interaction and co-presence of different linguistic repertoires in the written forms of the centuries immediately following the first attestations of vernacular Italian. In particular, the study of word order shows a tendency towards constructions with the verb in a final position in narrative parts and in a central position in documentary parts, while throughout the chronicle proper there is a high frequency of constructions with postverbal subjects, as occurs in many texts of ancient Italian. Keywords: medieval chronicles, latin-romance transition, medieval latin, word order, pragmatics, postverbal subject, ancient Italian "
Accelerating the Convex Hull Computation with a Parallel GPU Algorithm

The convex hull is a fundamental geometrical structure for many applications where groups of points must be enclosed or represented by a convex polygon. Although efficient sequential convex hull algorithms exist, and are constantly being used in applications, their computation time is often considered an issue for time-sensitive tasks such as real-time collision detection, clustering or image processing for virtual reality, among others, where fast response times are required. In this work we propose a parallel GPU-based adaptation of heaphull, which is a state of the art CPU algorithm that computes the convex hull by first doing a efficient filtering stage followed by the actual convex hull computation. More specifically, this work parallelizes the filtering stage, adapting it to the GPU programming model as a series of parallel reductions. Experimental evaluation shows that the proposed implementation significantly improves the performance of the convex hull computation, reaching up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times$</tex> of speedup over the sequential CPU-based heaphull and between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times\sim 4\times$</tex> over existing GPU based approaches.
Plataforma Moodle y la aplicación WhatsApp, recursos didácticos en tiempos de COVID-19

Despite the advantages that digital technologies offer to the teaching-learning process there are still limitations in their use, which was evidenced, when online education was assumed as an alternative to continue training processes in times of Covid-19. The objective of this article is to analyze the usefulness of the Moodle didactic platform during the sanitary crisis caused by COVID-19 and how this experience has served for its better use in post-pandemic face-to-face courses. For which a descriptive study with mixed focus was carried out, based on hermeneutical, analytical-synthetic and statistical methods. In addition, a survey was applied to the 16 students of the second year of the Sociocultural Management for Development career at the University of Granma. The results obtained indicate that regardless of the initial difficulties and uncertainties, the students quickly adopted online education for the development of the teaching-learning process, achieving, not only the fulfillment of the objectives of the curriculum, but also the quality of learning. They currently advocate maintaining the experience of using ICT in the post-pandemic face-to-face modality.
An Efficient Convolutional Neural Network for Classification of Multi-Class Colorectal Tissue Using Histopathological Images

Colorectal cancer (CRC) has a high fatality rate that continuously influences human lives worldwide. Early diagnosis of CRC prolongs human life and helps to prevent the disease. Histopathological inspection is routinely utilized to diagnose CRC. Manual inspection of histological diagnosis needs more assessment time, and the decision is based on clinicians' subjective opinions. In this work, a less complex, lightweight convolutional neural network (CNN)-based model is proposed for the automatic classification of multiclass colorectal tissue utilizing histopatho-logical images. Histopathological images are denoised using a median filter. The obtained denoised histopathological images are fed as input to the developed network and two pre-trained models. The proposed CNN achieved a classification accuracy of 93.50%. Compared to Xception and ResNet50 the proposed CNN is lightweight as it requires lesser learnable parameters, which makes it computationally fast. The effectiveness of the developed model is compared with the other existing methods to classify colorectal tissue on a similar dataset.
Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM

Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.
Prediction of corn crop yield using backpropagation neural network algorithm

Corn is one type of food crop commodity in Indonesia. Malang Regency is one of the producers that ranks 10th in corn production in the East Java region. People are very interested in planting corn because this crop commodity has many benefits so as to make the demand for production increase. There was a significant increase in market demand, but the uncertain amount of production made the supply of corn plants unable to be fulfilled properly. In this study, it predicted the demand for corn by using the Backpropagation Neural Network algorithm in Malang Regency. The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 every month using maize data from the Malang area. The results showed that the backpropagation algorithm produced an MSE value of 0.00004178.
3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.
Envisaging Global Regulations on Cryptocurrency

We live in an extremely fast-paced and opportunity-rich world. Every step provides an opportunity to innovate and challenge the conventional methods. Cryptocurrencies first appeared on the scene nearly a decade ago. And, since its inception, it has played a pivotal role in advancing the global financial infrastructure. The breakthrough made by cryptocurrencies has been so encouraging that many countries have considered incorporating them into their financial infrastructure. Despite their rapid growth and numerous benefits, they are still widely misunderstood. There is a widespread belief that cryptocurrencies are associated with risky investment schemes, terrorist funding, and laundering of ill-earned money. As a result, we require a proper framework to govern these cryptocurrencies. This research paper aims to provide readers with an overview of the impending and existing global regulations & regulatory frameworks for cryptocurrencies. Additionally, this piece of work shall also talk about the various illicit activities that are conducted through using cryptocurrency and regulations in the pipeline to combat the same. And lastly, this paper shall also briefly analyse the taxation policies of different nations and compare the same.
CLASSIFICATION REVIEW OF RAW SUBJECTIVE SCORES TOWARDS STATISTICAL ANALYSIS

We proposed a no reference model for quality estimation based on compression artifacts which are principal factor for determining the QoE compared to varying depth range within compression. Moreover, in our past research work, contents with strong compression have been rejected by the Subjects who graded quality of distorted videos and judged unacceptable. The results of our proposed approach conrm that our subjective scores of 120 videos are related to control of visible spatial artifacts of reconstructed videos not distorted and with consideration of User Experience (UX), It was traced out by rening raw subjective scores, it happened due to coding of spatial and temporal perceptual information according to H.264 standards mentioned by ITU only for video quality assessment, i.e., instead of coding spatial of chrominance and temporal information of luminance plane, selected test sequences were coded within luminance plane for both spatial and temporal information
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning

Abstract Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD) system. Its goal is to decide the next action of the dialogue system, given the dialogue state at each turn based on a learned dialogue policy. Reinforcement learning (RL) is widely used to optimize this dialogue policy. In the learning process, the user is regarded as the environment and the system as the agent. In this paper, we present an overview of the recent advances and challenges in dialogue policy from the perspective of RL. More specifically, we identify the problems and summarize corresponding solutions for RL-based dialogue policy learning. In addition, we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in RL. We believe this survey can shed light on future research in DPL.
A Study on the Mediating Effect of Career Preparation Behavior in the Relationship between Educational Service Quality and Major Satisfaction in Fisheries and Shipping High School

이 연구는 수산·해운계고등학교 학생들이 인식하는 교육서비품질과 전공만족도의 관계에서 진로준비행동의 매개효과를 연구하여, 수산·해운계고등학교 학생들이 자신의 전공에 대한 만족도와 자부심을 가질 수 있도록 도움을 주기 위함에 있다. 아울러 학령인구감소에 따른 학생모집으로 어려움을 겪고 있는 수산·해운계고등학교에 경영학적 요소인 교육서비스품질을 통한 유·무형의 환경 조성을 통해 학생선발에 도움을 주며, 수산·해운계고등학교의 미래정책설계에 보탬이 되고자 한다. 연구는 수산·해운계고등학교 재학생 269명을 대상으로 하였다. 본 연구의 결과를 요약하면 다음과 같다. 첫째, 성별에 따른 교육서비스품질, 전공만족도, 진로준비행동의 차이는 없었으나, 학년에 따라서는 교육서비스품질의 하위변인 중 유형성과 신뢰성, 전공만족도 중 일반만족과 교과만족 및 인식만족에서 유의미한 차이가 나타났다. 둘째, 수산·해운계고등학생이 인식하는 교육서비스품질과 전공만족도의 관계는 유의미한 양의 상관관계를 나타낸다. 셋째, 수산·해운계고등학생이 인식하는 교육서비스품질과 진로준비행동의 관계는 유의미한 양의 상관관계를 나타낸다. 넷째, 수산·해운계고등학생이 인식하는 교육서비스품질과 전공만족도의 관계에서 진로준비행동은 부분매개효과를 보인다.The purpose of this study is to study the mediating effect of career preparation behavior in the relationship between educational service quality and major satisfaction recognized by fisheries and shipping high school students so that they can have satisfaction and pride in their major. In addition, it aims to help select students by creating a tangible and intangible environment through educational service quality, which is a management element, at Fisheries and Shipping High School, which is suffering from student recruitment due to a decrease in the school-age population. The study was conducted on 269 students enrolled in fisheries and shipping high schools. The results of this study are summarized as follows. First, there were no differences in educational service quality, major satisfaction, and career preparation behavior according to gender, but there were significant differences in general satisfaction, subject satisfaction, and perception satisfaction among sub-variables of educational service quality. Second, the relationship between educational service quality and major satisfaction recognized by fisheries and shipping high school students shows a significant positive correlation. Third, the relationship between educational service quality and career preparation behavior recognized by fisheries and shipping high school students shows a significant positive correlation. Fourth, career preparation behavior has a partial mediating effect in the relationship between educational service quality and major satisfaction recognized by fisheries and shipping high school students.