Reviewerly/exHarmony huggingface dataset:
Leukemia detection system using convolutional neural networks by means of microscopic pictures

All over the world, there are a significant number of patients suffering every year from blood cancer. Most of the people are unaware of the risk involved in such a disease. A majority of these diseases are dangerous and may cause death. The patient who have been diagnosed with such a disease, feels very afraid. The patient may feel that the disease is very uncontrolled. Such diseases are very uncommon, and the patient may get very less assistance and information available about this disease. This symptom is called as acute lymphocytic leukemia (ALL) in medical science. In such a kind of cancer, white blood cells are mostly affected. In case of children, this disease is mainly detected i.e. children are more prone to this disease. If the disease is diagnosed in the early stage, the chances of recovery are maximum. Hence, there should be an accurate and guaranteed mechanism available to detect such type of blood cancers in the patients. This work proposes a system to distinguish the three different types of ALL using a convolutional neural network (CNN) by means of microscopic pictures of peripheral blood smears (PBS) and obtain accuracy levels that surpass those of practicing physicians.
Word difficulty classification prediction based on ARIMA and DBSCAN

Wordle is an online word-guessing game, where players are given a five-letter word each day and are asked to guess the word within six tries. Since the launch of the game, it has been loved by a wide range of players. To classify the experiment data and point the ‘EERIE’ at an appropriate label, the article uses unsupervised learning (with DBSCAN and ARIMA), cluster “sum(x)” and “rank(y)” and obtain the values (5.18185152, 8.83924243). Meanwhile, according to the image ‘The word positions by DBSCAN’, we define the ‘EERIE’ in moderately easy.
Steganlysis on data embedded medical image

Cybercrime around the world is increasing day by day abruptly. Stego attack is one of the popular methods of transferring the message secretly through the cover medium. Most of the sources are using this process for covert communication. The paper discusses the importance and analysis of data hiding theft and covert communication process through steganalysis where it talks about the ”covered writing” in such a way that the mutation of an image is not discernible. The main motive of this paper is to attack and analyze the Digital Imaging and Communications in Medicine (DICOM) file format. The file format DICOM discussed in this research is mostly supported in the medical field. CT Scans and MRI Scans are widely used in hospitals to view the human body. The random pixels is chosen to hide the text on the DICOM formats and the performance is measured using different metrics. The proposed approach produces 100% recovery of the hidden text inside an image with maximum payload capacity.
Library supervision in participating in library accreditation in Magelang City, Indonesia

The Library and Archives Office of Magelang City can be said to be successful in supervising 30 libraries in Magelang City, with the result that 19 libraries got a grade of “A” and 11 libraries got a grade of “B”. It was interesting to study their activities in supervising the libraries in Magelang City. This study aimed to determine how to supervision process was carried out by the Library and Archives Office of Magelang City to supervise libraries in Magelang City that participated in library accreditation. The study used qualitative research with a case study approach conducted in Magelang City. Data collection techniques used interviews with related parties, namely the Library and Archives Office of Magelang City and Library Officers in Magelang City who participated in the accreditation. The selection of informants purposefully used the snowball technique until the data experienced a saturation point. The results showed that the Library and Archives Office of Magelang City carried out supervision consisting of 3 categories: supervising individuals, managing groups, and leading organizations. However, two items were not implemented in supervision activities; the first was managing reward items in the category of supervising individuals. The supervised library considered the accreditation certificate as a reward. The second was budgeting basics items in the category of managing groups. It resulted in the managing reward, which was not implemented; therefore, library supervision has succeed implement the supervision process in participating in library accreditation in Magelang City.
Automated offensive language classification through “emotional” comments from network users

In recent years, online hate speech has become widespread as a result of the ease with which any online opinion can be presented on social media. This trend is mainly due to the rapid growth of mobile devices, computers and the Internet. There is a lot of interest in research on automatic hate speech detection. However, there is not a lot research on whether social networking sites can help identify hate crimes and related communities. Hate speech has a negative impact on the mental level of any person. However, some people are more prone to emotional instability than others. It is difficult for racial or ethnic groups whose language is not popular automatically collecting and evaluate texts created on the Internet. In this article, using artificial intelligence techniques in text processing and natural language processing, for the first time, a database was collected using various keywords to identify offensive words in social networks.
Making Waves for Nigerian Youths Through Education and Technology: The Harsh Fantasy of the Environment

The goal of this paper is to bring to light an adequate understanding of the concepts of "making waves," to capture the minds of parents, students, and other members of society, on how education and technology can help to excel, and to educate one on the negative effects of social vices on the environment. However, the benefits of education and technology to both individuals and society were examined as a catalyst for social and economic sustainability. Hence, education and technology have been unraveled as a medium for achieving sustainable development when youths strive for excellence in their academic pursuits and eschew vices. The paper looked at technology and its emergence in the twenty-first century, how to make waves through digitalisation, the importance of digital education in Nigerian schools, challenges to the digitalisation of education in Nigeria, and ways to strive for excellence. This paper suggested, among others, that government should sufficiently fund education from the primary to the high school levels. Drugs, sex abuse, cults, and other vices must be rejected by every member of the community. A decrease in the number of children who are not in school must also be ensured by the government at all levels of the nation, along with provisions for the necessary infrastructure, learning, and teaching tools.
Research and implementation of image enhancement method based on improved Retinex algorithm

In view of the phenomenon of detail loss and edge blurring in color images collected underwater, noise reduction is mostly ignored in underwater image enhancement research. In this paper, an improved image enhancement method based on Retinex algorithm is proposed. First, the original underwater image is transferred from RGB channels to HSI color space, and the saturation component is adaptively linearly stretched by gamma correction, The improved Retinex decomposition network based on homomorphic filtering is used to decompose and enhance the Lightness component separately. Eventually, the fused image is transferred from HSI to RGB color space, and the processed image is output. According to the experimental results, this algorithm can not only eliminate the halo artifacts, but also improve the brightness and contrast of the image, restore the original details of the image, and effectively remove the image noise. It greatly improves the visual effect and objective evaluation results of images.
Revival of quantum steering in correlated noisy channels

Multipartite quantum steering has crucial applications in the field of asymmetric secure quantum networks. In the actual quantum communication transmission process, the inevitable loss and noise in the channels will lead to reduction or even disappearance of quantum steering, which is extremely unfavorable to the transmission of information, so it has important practical significance for research of the revival of quantum steering. We theoretically studied the influence of noise on steering and investigated the revival of quantum steering by correlated noisy channels. The results show that excessive noise can cause sudden death of steering, whereas the disappeared steering can be revived by introducing correlated noise. Simultaneously, we also confirmed that the steerability and direction of steering can be manipulated by changing the transmissivity of the revived beam splitter. Our work provides a feasible reference for secure quantum communication in real noisy environment.
Digital Rights Management System of Media Convergence Center Based on Ethereum and IPFS

In order to solve the problems of copyrights infringement, high cost and complex process of rights protection in current media convergence center, a digital rights management system based on blockchain technology and IPFS (Inter Planetary File System) technology is proposed. Considering that large files such as video and audio cannot be stored on the blockchain directly, IPFS technology is adopted as the data expansion scheme for the data storage layer of the Ethereum platform, IPFS protocol is further used for distributed data storage and transmission of media content. In addition, smart contract is also used to uniquely identify digital rights through NFT (Non-fungible Tokens), which provides the characteristics of digital rights transferability and traceability, and realizes an open, transparent, tamper-proof and traceable digital rights management system for media convergence center. Several experimental results show that it has higher transaction success rate, lower storage consumption and transaction confirmation delay than existing scheme.
Self-assessment of managerial restrictions among the heads of internal affairs bodies of the Russian Federation

The subject of research in this article is the managerial restrictions of the heads of the internal affairs bodies of the Russian Federation. The purpose of the work is to identify the features of managerial restrictions among the heads of the internal affairs bodies of the Russian Federation. The methodology of the work was based on the subject-system approach. The research methods were the test method (questionnaire "Self-assessment of managerial restrictions") and mathematical processing of the obtained data. The study involved 197 respondents. The results of 17 respondents were not taken into account due to the impossibility of data processing. The study involved the heads of structural units of territorial, educational, scientific, medical organizations of the system of the Ministry of Internal Affairs of Russia and their deputies from 73 constituent entities of the Russian Federation. Self-assessment of managerial restrictions revealed that with an increase in seniority in the position of a leader, destructive changes occur: firstly, in the value system, namely its narrowing and inconsistency with the conditions of official life; secondly, the goals of personal or professional life become less realistic and compatible with the conditions of modern service and life; thirdly, the leader becomes immune to new situations and opportunities, the ability to solve modern professional problems decreases; as well as the ability to generate enough new ideas and the ability to implement new ideas.
University Students’ Attitudes toward Adoptionof Mobile Health Application for Serving Older People

In this quantitative study, we examined the awareness of mobile health (mHealth) applications among Taiwan's university students, particularly the government-operated My Health Bank application.The My Health Bank application allows users to access their personal health information simply through their smartphone's sensor-assisted facial recognition camera and fingerprint sensor.Whether these university students might help older adults to use this application if they perceive it to be helpful was also explored.Two instruments were used in this study, namely, the Perceptions toward My Health Bank (PMHB) and Geriatric Attitudes Scale (GAS).Two hundred twenty-five (225) university students from the Departments of Senior Citizen Service Management (n = 136) and Nursing (n = 89) at two universities participated in this study.The findings indicated low adoption of My Health Bank and related mHealth applications among students even though they had high adoption rates of smartphones.However, they perceived that the functions of this application are useful and should be promoted to older adults.Thus, building a basic foundation of mHealth and related technology courses is recommended to assist healthcare students in being well-prepared for working with older adults in the digital society.
IoT Autonomous Dynamic Spectrum Sharing by using Blockchain

It is effective to utilize the spectrum resources currently in use as well as to develop new spectrum resources in order to realize more advanced mobile communication systems. One of the goals of 5G is efficient connection to a large number of IoT devices. It is needed to automate a spectrum sharing for many IoT devices rather than the conventional manual centralized management. On the other hand, blockchain is a high availability autonomous decentralized service platform. This paper first provides an overview of the major spectrum sharing technologies that use blockchain. Next, we propose the autonomous decentralized dynamic spectrum sharing for IoT. This method uses blockchain to coordinate the automatic transfer of spectrum rights from PUs to SUs. It issues more crypto assets to IoT devices that share a large amount of sensing data. Since this crypto asset can be used for spectrum auctions, IoT devices that share a lot of data can get more spectrum sharing opportunities. We also propose the use of carrier aggregation to use shared spectrum and show the implementation method using OSS in O-RAN environment. Finally, we discuss processing time, use cases, changes in spectrum usage data, and changes in the number of bidders.
A Digital Forensic Taxonomy For Programmable Logic Controller Data Artefacts

The growing complexity of industrial control systems (ICS) and increasing cyber attacks targeting critical infrastructures demand bespoke forensics techniques for Programmable Logic Controllers (PLCs). As they control their critical physical processes, PLCs form the backbone of many ICS. However, due to their unique characteristics and constraints, which include heterogeneous architectures, proprietary technologies and stringent real-time operational requirements, traditional digital forensic techniques may not be directly applicable.PLCs are intricate embedded devices with numerous distinct internal data artefacts, ranging from proprietary firmware to logic codes, safety logs, and process I/O values. Therefore, those tasked with PLC investigation must understand these intricacies and their underlying implications to effectively answer the forensic questions in the aftermath of an incident.To address this need, our paper presents the first tailored taxonomy for digital forensics on PLCs, systematically categorizing the various characteristics, forensic processes and considerations based on the stages involved in a forensic investigation. Furthermore, we employ our developed taxonomy to establish mappings between identified PLC data artefacts and their corresponding attributes, offering a contextualised interrelationships between these artefacts and the PLC forensic investigation steps.
DARTScore: DuAl-Reconstruction Transformer for Video Captioning Evaluation

Video captioning evaluation aims at assessing the semantic consistency between video and candidate text, which should include measurement from two aspects: faithfulness (whether the information conveyed by candidate is correct w.r.t. video) and comprehensiveness (whether the main video content is covered by candidate). However, previous approaches have difficulty in evaluating faithfulness and comprehensiveness due to heavy reliance on references or heterogeneous of visual and textual data. In this paper, we propose a vision-involved evaluation metric based on a novel DuAl-Reconstruction Transformer, named DARTScore. DARTScore formulates the caption evaluation task as a dual-reconstruction problem to evaluate both faithfulness and comprehensiveness explicitly. Since the word in a candidate is usually related to several frames, DARTScore adaptively collects relevant frames to reconstruct the word and computes the reconstruction accuracy as faithfulness to inherently reflect whether the word information is contained in the video. In the inversive way, DARTScore reconstructs each frame with relevant words to evaluate comprehensiveness. By integrating fine-grained bidirectional reconstruction accuracies, DARTScore drills into each word in candidate and each frame in video to fully evaluate the semantic consistency. Furthermore, we collect and annotate two Chinese datasets with a large domain gap, named CRAETE-EVAL and VATEX-ZH-EVAL, to systematically evaluate existing metrics and fill the blank of Chinese video captioning evaluation. Experimental results show that DARTScore achieves higher correlation with human judgments, has lower reference reliance, and generalizes well to data from different domains.
Building Domain-Specific Sentiment Lexicon Using Random Walk-Based Model on Common-Sense Semantic Network

Sentiment lexicons are an essential resource for research in the sentiment analysis field. Many existing sentiment lexicons like SentiWordNet, SenticNet, etc. are available, but they do not cover words of every domain with their polarity. They contain only a finite number of words. Thus, the automatic construction of domain-specific sentiment lexicons becomes an important task. In this paper, we present a framework to build a domain-specific sentiment lexicon based on common-sense knowledge automatically. The proposed work is the first work, that provides sentiment values to words, word phrases, foreign words, and out-of-vocabulary (OOV) words. For lexicon generation, the sentiment value of the seed words is propagated using a random walk model over the network constructed from ConceptNet. In this work, we provide a sentiment weight to every relation of ConceptNet based on the assumption that concepts propagate their sentiment values to their neighbors depending on the type of relationships connecting them. In the experimental results, we show that the proposed strategy can improve the polarity accuracy of the sentences having word phrases, foreign words, and out-of-vocabulary (OOV) words.
Classifying Breast Cancer Using Deep Convolutional Neural Network Method

The efficacy of conventional classification systems is contingent upon the accurate representation of data and a substantial portion of the effort invested in feature engineering, which is a laborious and timeconsuming process requiring expert domain knowledge. In contrast, deep learning has the capacity to automatically identify and extract discriminative information from data without the need for manual feature creation by a domain expert. In particular, Convolutional Neural Networks (CNNs), a type of deep feedforward network, have garnered attention from researchers. This study conducts several preliminary experiments to classify breast cancer histopathology images using deep learning, given the small number and high resolution of training samples. The proposed approach is evaluated on the publicly available BreaKHis dataset, utilizing both a scratch model and transfer learning pre trained models. A comparison of the proposed scratch method to alternative techniques was carried out using a suite of performance evaluation metrics. The results indicate that the scratch model, with its independent magnification factor, achieved greater accuracy, with a binary classification accuracy of 99.5% and a multiclass classification accuracy of 96.1%.
Machine Learning Algorithms for Prediction of Mobile Phone Prices

The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbor’s 94% accuracy was close to that of the support vector machine.
Biometrics

Abstract In biometrics, physiological characteristics (e.g., fingerprints, facial features, voices) are measured to identify individuals. Since biometry does not share the same characteristics as other authentication techniques, such as passwords or PIN codes, it is widely used as a second-factor authentication. A fully integrated solution offers significant advantages as it aligns biometric hardware, key generation, validation, and storage with the cryptographic provider, providing a coherent control environment. As a result, designing and implementing secure biometric authentication methods requires highly specialized technical expertise and experience. In addition, as biometrics become prevalent and their integration with artificial intelligence systems increases, ethical and regulatory questions become more pressing.
The Problem of Deviant Behavior Among Modern University Students

The article deals with the problems of deviant behavior of modern university students. The historical, socio-economic, medical and biological prerequisites for the occurrence of deviations are analyzed. The frequency of manifestation of various forms of deviant behavior is compared. The problem of students missing classes without valid reasons is considered, including the impact of the pandemic caused by the "New coronavirus infection Covid-19" on non-attendance at the university. Special emphasis is placed on the digitalization of modern society as a factor predisposing to skipping classes. Conclusions are drawn about the possibilities of improving the educational process and ways to solve problems related to the deviant behavior of modern students.