Reviewerly/exHarmony huggingface dataset:
A random exploration based fast adaptive and selective mean filter for salt and pepper noise removal in satellite digital images

The digital image is one of the discoveries that play an important role in various aspects of modern human life. These findings are useful in various fields, including defense (military and non-military), security, health, education, and others. In practice, the image acquisition process often suffers from problems, both in the process of capturing and transmitting images. Among the problems is the appearance of noise which results in the degradation of information in the image and thus disrupts further processes of image processing. One type of noise that damages digital images is salt and pepper noise which randomly changes the pixel values to 0 (black) or 255 (white). Researchers have proposed several methods to deal with this type of noise, including median filter, adaptive mean filter, switching median filter, modified decision based unsymmetric trimmed median filter, and different applied median filter. However, this method suffers from a decrease in performance when applied to images with high-intensity noise. Therefore, in this research, a new filtering method is proposed that can improve the image by randomly exploring pixels, then collecting the surrounding pixel data from the processed pixels (kernel). The kernel will be enlarged if there are no free-noise pixels in the kernel. Furthermore, the damaged pixels will be replaced using the mean data centering statistic. Images enhanced using the proposed method have better quality than the previous methods, both quantitatively (SSIM and PSNR) and qualitatively.
Methods for Predicting the Rise of the New Labels from a High-Dimensional Data Stream

In data engineering, multi-label learning (MLL) has emerged to classify the instances through a specific characteristic that associates with the set of class labels (CLs).Mostly, the learning design was adaptive and newer views may exist in a data stream (DS); so, MLL has to classify the features with newer CLs.To tackle this issue, the MLL with emerging new labels (MuENL) and handling high-dimensional DSs (MuENLHD) technique has been adopted which considers the CLs in the test data were similar to that in the learning data.But, it was not able to deal with the adaptive situation where multiple newer CLs exist since it can manage only a single newer CL in one iteration.Hence this article proposes an MLL with emerging multiple new labels (MuEMNL) and MuEMNLHD versions to combat the issues in a complex scenario wherein several newer CLs are found.The main idea of this technique is to split the newer CL group into many newer CLs independently for changing the complex scenario.In this technique, four different steps are executed: i) creates a linear classification model to adjust the pairwise CL sorting error and the categorization error on the given CLs, ii) develops a novel outlier identifier depending on the primary and test DS, iii) discovers the cluster for the MuEMNL and MuEMNLHD depending on the OPTICS clustering and iv) employs a classifier updating scheme to integrate newer CLs for designing a robust classifier.Finally, the experimental outcomes exhibit that these techniques on low-dimensional databases attain an overall mean precision of 67.08% and an overall F1-score of 64.02% compared to the classical MLL techniques.Similarly, these techniques on high-dimensional databases achieve mean precision of 64.6% and a mean F1-score of 63.1% compared to the classical MLL techniques.
Rare and Valuable Copies from the Library of N. V. Ustyugov in the Department of Rare Books and Manuscripts of the State Public Scientific Technological Library of the SB RAS

The article deals with the problem of identifying and studying personal book collections of researchers in the funds of state libraries on the example of the documents from the collection of the Soviet historian N. V. Ustyugov, stored in the department of rare books and manuscripts (RBMD) of the State Public Scientific and Technological Library of the Siberian Branch of the Russian Academy of Sciences (GPNTB SB RAS). The purpose of the article is to introduce valuable documents from this collection into scientific circulation, to attract the attention of the research community to it. The most notable documents from this collection obtained in 2018 are considered. They are: books of the XVIII – first third of the XIX centuries, belonging to the category of book monuments, the oldest of which (“Tsarstvennyj Letopisets”) was printed in 1769. In the process of studying copies stored in RBMD from the Ustyugov book collection , more than 250 books with donation records of outstanding scientists (A. A. Zimin, V. T. Pashuto, N. I. Pavlenko, A. M. Sakharov etc.) were also revealed.; records of 150 donors have been identified in total). They reflect the professional and human relations of the owner with colleagues, students, various aspects of his activities. The value of this collection as a curios reflection of a certain period of Soviet historical science is emphasized.
Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features

This paper proposed a framework for estimating human age using facial features.These features exploit facial region information, such as wrinkles on the eye and cheek, which are then represented as a texture-based feature.Our proposed framework has several steps: preprocessing, feature extraction, and age estimation.In this research, three feature extraction methods and their combination are performed, such as Local Binary Pattern (LBP), Local Phrase Quantization (LPQ), and Binarized Statistical Image Feature (BSIF).After extracting the feature, Principle Component Analysis (PCA) was performed to reduce the feature size.Finally, the Support Vector Regression (SVR) method was used to predict age.In evaluation, the estimation error will be based on mean average error (MAE).In the experiment, we utilized the well-known public dataset, faceage.zip,and UTK Face datasets, containing 15,202 facial image data.The data were divided into the training of 12,162 images and the testing of 3,040 images.Our experiments found that combining BSIF and LPQ with PCA achieved the lowest MAE of 9.766 and 9.754.The results show that the texture-based feature could be utilized for estimating the age on facial image.
Clustering-based Automated Requirement Trace Retrieval

The benefits of requirement traceability are well known and documented.The traceability links between requirements and code are fundamental in supporting different activities in the software development process, including change management and software maintenance.These links can be obtained using manual or automatic means.Manual trace retrieval is a time-consuming task.Automatic trace retrieval can be performed via various tools such as Information retrieval or machine learning techniques.Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced.This study proposes an approach that addresses the term mismatch problem to obtain the greatest improvements in the trace retrieval accuracy.The proposed approach uses clustering in the automated trace retrieval process and performs an experimental evaluation against previous benchmarks.The results show that the proposed approach improves the trace retrieval precision.
A Cascaded Feature Extraction for Diagnosis of Ovarian Cancer in CT Images

This paper proposed ovarian cancer detection in the ovarian image using joint feature extraction and an efficient Net model. The noise of the input image is filtered by using Improved NLM (Improved Non-Local Means) filtering. The deep features are extracted using Deep CNN_RSO (Deep Convolutional Neural Network Rat Swarm Optimization), and the low-level texture features are extracted using ILBP (Interpolated Local Binary Pattern or Interpolated LBP). To improve the feature extraction and reduce the error, use a cascading technique for the feature extraction. RSO also helps to efficiently optimize the DCNN features from the images. Finally, the extracted image is classified using the Efficient Net classifier, which performs a global average summary and classification of ovarian cancer (normal and abnormal). The system’s performance is implemented on the Cancer Genome Atlas Ovarian Cancer (TCGA-OV) dataset. The system’s performance, like sensitivity, specificity, accuracy and error rates, shows better with respect to other techniques.
KukArt Magazine as a Cultural Project

The present research raises a question on functioning of magazines in the digital era. The author of the paper addresses the KukArt magazine (1992–2006) which used to be a hallmark in the Russian intellectual life at the turn of the century and has not lost its relevance at present. The research proposes a discursive analysis to identify features of the KukArt magazine strategy. According to the author of the paper, the KukArt longevity phenomenon can be explained by the fact that the magazine became a testing ground to try some mutually exclusive ideas, and the place where different discourse practices coexisted. The article formulates the idea that the purpose of the magazine was to form a new cultural space which synthesizes various fi elds of art. The author gives a detailed description of all issues of the magazine in the synchronous and diachronic aspects. The main feature of KukArt is absence of a unifi ed framework (making features which were based on objectives set and problems solved: the puppet and the avant-garde, the puppet and literature, the puppet and folklore, the puppet and the masquerade mask, the puppet and home theatre, the puppet and the mannequin, etc.) Originally targeted at the puppet, the magazine shifted gradually towards an anthropological pole trying to answer the question: ‘What is a human?’ Due to such an editorial strategy KukArt drew a wide reading audience attention and went beyond theatre environment bounds. Limited edition and absence of a digital format led to the rise of the magazine ‘aura value’, for the unique and non-reproducible things only have had the greatest value in human culture system.
Characterization of Buleleng clay and improvement of its ceramic properties

Purpose. The research focuses on the improvement of local clay-based ceramic pottery bodies from Alas Angker in Buleleng Regency in Bali. The main purpose is to utilize the clay which meets the standards of ceramics such as smooth and hard earthenware, especially to reduce its water absorption property. Methods. Firstly, the clay is analyzed for its physical and chemical composition. Two ceramic body formulations are made using the clay with the BWNAA1 and BWNAA2 codes. The specimens are measured in terms of clay ceramics properties including plasticity, dry shrinkage, firing shrinkage and water absorption. The cracked ceramic products are also analyzed for a comparison. Findings. The ceramic properties show similar results, except for the water absorption percentage. The BWNAA2 code formulation has lower water absorption rate by 10.8% and meets the requirements for the type of smooth and hard earthenware body in compliance with the national standard SNI:7275-2018. The water absorption rate is about half of the existing products, which is effective in avoiding the problem of cracks. Originality. Comparison of the existing ceramic products and specimens made in this study shows a significant difference in water absorption and crack growth. Adding a certain amount of kaolin, feldspar, ball clay and quartz sand has significantly reduced the water absorption rate of ceramic bodies. Practical implications. This research can be a potential solution to increase the quality of ceramic pottery products in the vicinity of Buleleng Regency which use the clay of this kind.
A survey of prosecutors and investigators using digital evidence: A starting point

Digital evidence is essential to criminal investigations and prosecutions, but its use is fraught with challenges: rapid changes in technology, the need to communicate those changes to stakeholders, and a sociopolitical landscape that leaves little room for error, particularly regarding electronic data privacy. In the criminal justice system, these challenges can affect the admissibility of evidence and its proper introduction at trial, as well as how cases are charged and resolved. A survey of 50 United States (U.S.)-based prosecutors, contextualized by data from a second survey of 51 U.S.-based investigators, explores these issues for the present and future, finding that crucial factors include training, prosecutors who specialize in digital evidence issues, and strong relationships between prosecutors and investigators.
ParaDiSE: Efficient Threshold Authenticated Encryption in Fully Malicious Model

Threshold cryptographic algorithms achieve robustness against key and access compromise by distributing secret keys among multiple entities. Most prior work focuses on threshold public-key primitives, despite extensive use of authenticated encryption in practice. Though the latter can be deployed in a threshold manner using multi-party computation (MPC), doing so incurs a high communication cost. In contrast, dedicated constructions of threshold authenticated encryption algorithms can achieve high performance. However to date, few such algorithms are known, most notably DiSE (distributed symmetric encryption) by Agrawal et al. (ACM CCS 2018). To achieve threshold authenticated encryption (TAE), prior work does not suffice, due to shortcomings in definitions, analysis, and design, allowing for potentially insecure schemes, an undesirable similarity between encryption and decryption, and insufficient understanding of the impact of parameters due to lack of concrete analysis. In response, we revisit the problem of designing secure and efficient TAE schemes. (1) We give new TAE security definitions in the fully malicious setting addressing the aforementioned concerns. (2) We construct efficient schemes satisfying our definitions and perform concrete and more modular security analyses. (3) We conduct an extensive performance evaluation of our constructions, against prior ones.
The Effect of Virtual Reality Assisted Robotics Coding Teaching on Spatial Visualization and Coding Skills

The aim of this study is to examine the effect of virtual reality (VR) assisted robotic coding teaching, which is a different platform used in robotic coding education, on spatial visualization and coding skills. In order to achieve this aim, a research was designed according to the quasi-experimental design with pre-test post-test control group. 56 sophomore students studying in the Elementary Mathematics Teaching program were divided into experimental and control groups. In the experimental application, which lasted for 4 weeks in total, 8 hours of VR assisted robotic coding teaching was given to the experimental group. Students both created and coded the robots using virtual reality versions of the LEGO® Mindstorms EV3 robot sets. In the control group, the students physically used the LEGO® Mindstorms EV3 robot sets and coded the robots they prepared. Before and after the instruction, the spatial visualization and coding skills of the participants in the experimental and control groups were measured and their changes at the end of the process were examined. As a result of the research, it was determined that there was a significant increase in the coding skills and spatial development, spatial rotation and spatial view skills of the pre-service teachers in the Elementary Mathematics Teaching program, who designed and codes robots using virtual reality technology. Accordingly, it was concluded that the use of virtual reality technology instead of the physical sets used in robotic coding teaching had a similar effect on students' coding skills.
MCPR: A Chinese Product Review Dataset for Multimodal Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA), which aims to analyze the sentiments toward the extracted aspects, has been attracting considerable interest in the last decade. Most of the existing studies concentrate on determining the sentiment polarity of the given aspect according to only textual content, while there is little research on multimodal aspect-based sentiment analysis (MABSA) due to the scarcity of datasets consisting of multimodality content, such as both texts and images. In this paper, we design and construct a Multimodal Chinese Product Review dataset (MCPR) to support the research of MABSA. MCPR is a collection of 1.5k product reviews involving clothing and furniture departments, from the e-commercial platform JD.com. After aspect-base sentiment annotation and text-image matching, we obtain 2,719 text-image pairs and 610 distinct aspects in total. It is the first aspect-based multimodal Chinese product review dataset.
Metaverse: A New Digital Marketing Trend

Modern society is seeing a fast uptick in technology adaptation and incorporation. The development of the internet and other modern devices has made it possible for systems to become further adaptable, diverse, and sophisticated, enabling them to handle the bulk of humanity's duties and burdens. The most successful technological advances in internet adaptation are virtual worlds and augmented reality. But the increased search for technical breakthroughs has made room for the most recent virtual world, called Metaverse. Metaverse has created the presence of the digital space and changed it into the actual world so that we are no longer just dependent on the internet via our screens. Utilizing virtual or augmented reality systems, it enables users to actively involve themselves in the virtual space where they may engage and innovate everyday choices in a time-based setting. The Metaverse has improved its accessories throughout the years. In order to provide customers with a holistic and true experience with the item or commodity they desire to purchase, companies and businesses are making use of automation's special and distinguishing features. Due to the rising competitiveness in the marketing industry, the Metaverse has given businesses and traders the opportunity to investigate technology aspects for more creativity. An introduction to the metaverse and its developments is presented in this paper.
The Effect of Applying Information Security Awareness Concept of MOH Employees on Cybersecurity Department – Ministry of Health –Riyadh

The proposed study focuses on the effect of applying the concept of information security awareness of MOH employees on the cybersecurity department at the Ministry of Health in Riyadh. The researcher used the descriptive analytical method in order to achieve the study objectives and used a questionnaire for collecting data. The study sample consisted of around (430) of MOH employees. The results of the study showed a high level of agreement on answering its questions. The study yielded numerous recommendations; it stressed that spreading the culture of awareness on the importance of personal information, through holding workshops, is considered as the most effective way to reduce cybersecurity risks. Also, it showed that the cybersecurity department is keen to develop guidelines to be followed by employees in order to limit the sharing of personal information and that paramount importance should be attached to the human element by familiarizing it with the tricks used by cybercriminals. In addition, the cybersecurity department is keen to create an electronic archive that includes monitoring and recording of cybersecurity incidents and should encourage employees to view this archive and consider it as a means of exchanging knowledge and raising awareness. Moreover, it is imperative to use the contribution of information security experts in order to design awareness programs. In addition, advanced technical training should be directed to employees to keep pace with the rapid development in methods and techniques of information crime. The researcher achieved various design of training and education program.
Application of SAW Method in Decision Support System for Determination of Exemplary Students

The decision support system for determining exemplary students using the SAW method is a solution to existing problems so that the results of the assessment of exemplary students become more effective and efficient in terms of time and selection of exemplary students. Decision-making in the determination of exemplary students can be more detailed and more accurate from existing candidates to truly exemplary prospective students who meet the established criteria. This SAW method can help teachers in making decisions using weights that have been determined by the school. The results of the decision support system for determining exemplary students using the SAW method for Rank 1 were obtained by M. Pebi Ramadhan with a value of 0.992857. Rank 2 was obtained by Adittyo Yunanta with a value of 0.984286. Rank 3 was obtained by Aldo Al Farigi with a value of 0.982857.
Artificial intelligence, ethics, law: a view on the Italian and American debate (and on their differences)

Introduction. Legal scholars invading the pitchIn the past ten years the scientific discourse on artificial intelligence (AI) 1 has thrived. 2 Legal scholars, not immune to this trend, jumped on the AI bandwagon, joining philosophers, ethicists, and computer scientists. 3This is not surprising: not 1There is no generally accepted definition of AI.This essay will not account for all the different definitions of artificial intelligence theorised in the past fifty years.We will limit ourselves to acknowledge that there is debate on the (legal) definition of AI and that the term AI can be used to refer both to a set of technologies and to a specific scientific discipline, which branches from computer science.For a systematic analysis of the issue of defining 'artificial intelligence' see inter alia:
DISTANCE EDUCATION METHODS: VIDEO ANALYSIS IN TEACHING PHYSICS

Formation of the skills of conducting an experiment and analyzing its results during laboratory work in natural science has always been an important didactic problem, which has significantly increased in the conditions of distance and mixed learning. The study of approaches to the effective use of software for the analysis of video recordings of observations of real physical processes and phenomena is one of the tasks of instrumental digital didactics. The affordable and regularly updated software Tracker: Video Analysis and Modeling Tool is a popular didactic tool for the analysis of physical quantities based on the processing of static and dynamic images followed by comparison with the corresponding mathematical model. The rules for creating educational videos suitable for analysis in a digital environment are summarized. On the examples of laboratory classes on many topics of physics (mechanics, hydrodynamics, molecular and atomic physics, and optics) and astronomy, the general features of creating video recordings, laboratory works, and problem tasks based on video analysis are shown. The STEM laboratory of the Junior Academy of Sciences of Ukraine has created numerous reference videos about physical experiments and the rules for their use; a collection of video tasks was also created; innovative methods of educational physical experiments were developed. The methods of video analysis were tested during distance and mixed education in the conditions of threats, also in formal and informal education formats, such as the summer science school for students and the All-Ukrainian natural science online tournament "Open Natural Science Demonstration". Instrumental digital didactics is a component of training courses for teachers and is regularly discussed at seminars and conferences on science education. The stemua.science source of the "MANU" NC is popular among Internet users.