Reviewerly/exHarmony huggingface dataset:
Measurement of the amount of chromosome aberration through object detection

Nature of the radiation which results in DNA damage is utilized to measure the extent of irradiation by quantification of the chromosome aberration. In this research, we applied You Only Look Once (YOLO), to measure the amount of the chromosome aberration in the chromosome slide automatically, based on the consideration that it requires both of identification and classification of the chromosome aberrations. Among the various models derived from YOLO, we selected YOLOv5. The parameters were updated by the objective function which is calculated as a summation of the complete intersection over union (CIoU) and classification loss. It is trained with the dataset, generated from attaching the single dicentric and normal chromosome images to the chromosome slide which contains only normal chromosome. The proposed model is shown to be localize and classify the chromosomes successfully. In conclusion, we confirmed the applicability of the object detection method for the purpose of measuring the amount of chromosome aberration.
Track Coalescence Avoidance in Multi-target Tracking

Multi-target tracking suffers from track coalescence due to closely spaced tracks and so, it avoids coupling between the tracks in clutter and uncertain measurements environment. When the motion of the multi-targets are in close vicinity, their track becomes cross-merged so that the multi-target turn out to be one target. We propose two ideas; one is to refine the target estimates by applying smoothing method, and the other is to ignore the influence of target measurement being tracked in a vicinity of a potential track. The proposed smoothing method uses the linear multi-target based on integrated track splitting filter (sLM-ITS) to avoid joint (common) multi-target measurements association while allowing them as pretended clutters. Hence, sLM-ITS updates a potential track without impact of the other tracks in its vicinity. The proposed method avoids track coales-cence significantly in a difficult multi-target situation. The sLM-ITS method provides improved smoothing as well as false-track discrimination (FTD) capabilities in comparison to the existing algorithms as illustrated in the simulation results.
Machine Learning Curriculums Generated by Classifier Ensembles

Image classification is a fundamental task that attempts to classify the images into the classes they belong to. CNN is commonly used for the classification due to its flexibility and stability. This paper treats Curriculum Learning as an assistant method that decides the procedure the CNN ought to be trained. Curriculum Learning is generally defined as a method that divides and orders the training datasets into several different levels of learning difficulties. This paper utilizes the idea of ensemble classification that trains according to the order based on the consensus of different machine learning models. As a result, the proposed Ensemble-Generated Curriculum Learning model achieves higher accuracy compared to that of a single-stage CNN learning with dog face recognition and CIFAR-10 datasets.
A Novel Real-time Automated Face Classification and Detection system using Machine Learning Technique

In recent days, Image classification and detection technique has become an important and more essential in the Image processing research field. Creating effective face detection is an essential aspect of handling the detection mechanism, Tracking mechanism and Validation mechanism. The classical methods used for face detection do not have sufficient output. This research paper presents various studies and how machine learning methods are become to solve many challenges present in the face detection system. The first phase of work has a classification model with support vector machines, decision trees and Hybrid Ensemble Transfer learning algorithm. The second phase of work is investigated with real-the world’s most popular dataset from World Masked Face Image Dataset and Label Faces in the wild (RMFD). Moreover, the experiment, results show how better accuracy and fast computation which has been achieved by Hybrid Ensemble algorithm with SVM and Decision Trees machine learning techniques. This research helps to assist many social applications such as during pandemics like covid-19 and personal identity, it can be verifying the mask-worn persons.
Factorized multi-Graph matching

In recent years, multi-graph matching has become a popular yet challenging task in graph theory. There exist two major problems in multi-graph matching, i.e., the cycle-consistency problem, and the high time and space complexity problem. On one hand, the pairwise-based multi-graph matching methods are of low time and space complexity, but in order to keep the cycle-consistency of the matching results, they need additional constraints. Besides, the accuracy of the pairwise-based multi-graph matching is highly dependent on the selected optimization algorithms. On the other hand, the tensor-based multi-graph matching methods can avoid the cycle-consistency problem, while their time and space complexity is extremely high. In this paper, we found the equivalence between the pairwise-based and the tensor-based multi-graph matching methods under some specific circumstances. Based on this finding, we proposed a new multi-graph matching method, which not only avoids the cycle-consistency problem, but also reduces the complexity. In addition, we further improved the proposed method by introducing a lossless factorization of the affinity matrix in the multi-graph matching methods. Synthetic and real data experiments demonstrate the superiority of our method.
Flooding

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 2 p. 24193A-24193C Policy and Practice Flooding First published: 05 April 2023 https://doi.org/10.1111/j.1467-6346.2023.11009.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue2April 2023Pages 24193A-24193C RelatedInformation
GEO 2023 Learning Conference set for May 22–23

Board & Administrator for Administrators OnlyVolume 39, Issue 9 p. 6-6 Resources GEO 2023 Learning Conference set for May 22–23 First published: 05 April 2023 https://doi.org/10.1002/ban.31526Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume39, Issue9May 2023Pages 6-6 RelatedInformation
Metaverse Librarians

Intelligent libraries have been a hot topic in academia, and scholars have offered many insights into the future development of library intelligence. However, there has been little research on the concept and competency characteristics of intelligent librarians as an important driver of library intelligence, which is viewed as a new profession. Meanwhile, the emergence of metaverse has opened up new ideas and impetus for the development of library intelligence, while demanding the professional competence of intelligent librarians. Based on previous research, this chapter examines the evolution of metaverse, its application in libraries, and its advantages. It also discusses the requirements and training routes for intelligent librarians, as well as the concept of intelligent librarians in the context of metaverse. Intelligent libraries, in effect, create a new need for increased cooperation and collaboration between librarians and users. Each party must be aware of their respective roles, responsibilities, and privileges.
Design and Development of an E-Healthcare Records Management System using Blockchain Algorithm

The characteristics of a country's healthcare system are thought to be among the most crucial factors in this paper's description of the electronic health record using block chain technology. Currently, the patient's medical information is kept in electronic health records (EHR). EHR does, however, have some drawbacks, including interoperability, security, and privacy. All these problems result in a deterioration in the standard of the medical system and an expand in healthcare costs because data are split among various medical facilities in various locations. Medical data management presents several special difficulties, such as data access and the frequency with which data is accessed outside of a medical facility. The potential for blockchain technology to help improve the current healthcare system exists. This paper aims to show how EHR can be used in the healthcare industry. EHR sharing systems can be made more effective, secure, and private with the help of blockchain technology. On the other hand, current solutions that depend on a central bibliography are liable to conventional safety problems like Denial of Service (DoS) attacks and alone bit of defeat, as are conventional bibliography systems. Additionally, earlier solutions did not address performance and scalability issues, leaving users vulnerable to privacy linking attacks.
Intelligent Optimization Algorithm based Data Storage and Management: An Empirical Study

Due to developments in digital world, security is developed into an inseparable problem but transferring images. Visual Cryptography (VC) is a present cryptographic approach that is utilized to confidential images shared safely and also its data kept with utmost secrecy. A sender transfers the confidential image that is separated into shares and it holds hidden data. If share is arranged and stacked together, it is inclined to represent the confidential image data to receiver. Previous VC method, the confidentiality of shares could not be retained because all other fake shares are easily modified or inserted remain that continuing challenges. This article develops an Intelligent Optimization Algorithm Based Data Storage and Management (IOA-DSAM) technique. The IOA-DSAM technique intends to securely and effectively store the data. To attain this, the IOA-DSAM technique splits the images into different shares. Next, the shares are encrypted using Homomorphic Encryption (HE) technique where the key generation process takes place via modified fruitfly optimization (MFFO) algorithm. Upon reception, the decryption of shares is performed followed by share reconstruction process. To exhibit the performance of the IOA-DSAM system, a widespread experimental study is performed. The resultant outcomes demonstrate the improvement of the IOA-DSAM technique over other models.
Automated Essay Scoring (AES) System in COVID 19 Pandemic: A Contactless Academia Approach

The objective of this scoping review is to identify the suitability of AES products in higher&nbsp; education and examine the&nbsp; type&nbsp;&nbsp; of&nbsp; methods&nbsp;&nbsp; used&nbsp;&nbsp; to&nbsp; present&nbsp;&nbsp; these products. The 2019 SAR COV-2 outbreak introduced the concept of "contactless" and established it as the norm for the majority of businesses as a risk-reduction measure against coronavirus exposure. In higher education, there are numerous exposure scenarios where contact presents a risk. Handling and grading essay scripts from assignments, tasks, research outputs, and more is one of them. The inclusion of the "Automated Essay Scoring" (AES) system in the mitigation toolkits for higher education institutions is a crucial step that should be taken into consideration. This study was undertaken in the form of a scoping&nbsp;&nbsp; review&nbsp;&nbsp; using the Prisma&nbsp;&nbsp; flow sequence of literature search and selection from 6 databases. Different AES products,&nbsp;&nbsp; literatures&nbsp;&nbsp; and research&nbsp;&nbsp; designs were employed&nbsp;&nbsp; in the investigation of AES products.&nbsp; The outcome of reviewed literatures varied on suitability of AES in scoring essay task in Higher Institution of Learning. Academics and scorers should use the appropriate AES to evaluate and score assignments, written tasks, essays, dissertations, and any other academic written output, with exceptional cases assessed manually as may be required by the AES.
On Learner-Based Teaching in Modern Educational Contexts

The concept of learner-based teaching has been gaining popularity in recent years as a way to engage students in their own learning process and to promote active learning. It is of great significance to explore on learner-based teaching in China to promote lifelong learning. In this paper, we explain what learner-based teaching is, the advantages of learner-based teaching, how to introduce learner-based teaching into your classroom, what the potential of learner and the potential problems are. It is expected to address these points in the paper and raise other questions. It encourages you to ask yourself some more questions, to think about how you already use the potential of your learners, how you could use it more, and explore how learner-based teaching can be implemented in the classroom. It hopes to cast some enlightenment on further research in this field.
Research on the Impact of Mobile-Assisted Language Learning by Means of Tiny Cards App on the Vocabulary Learning and Retention of Intermediate EFL Learners

Emerging technologies have significantly altered the teaching and learning processes, and there is no doubt that the rapid advancement of technology has altered the way English teachers transfer knowledge to their students. As a result, this study looked into the impact of MALL using the Tiny Cards app on the vocabulary learning and retention of intermediate EFL learners. Vocabulary&nbsp; is a very important language component that is practically used in improving and boosting all other language skills. In this quantitative study, a quasi-experimental design was used. A Quick Placement Test was used to choose 40 intermediate EFL students who were enrolled in an English course at Farzan English Language Institute in Rasht, Iran, as homogenous participants. They were then split into equal experimental and control groups in a non-random manner. To confirm the learners' initial knowledge of vocabulary items, a pretest of vocabulary was given before the start of treatment. The experimental group then received vocabulary instruction via Tiny Cards app over eight sessions, while the control group received those items via traditional method of instruction without MALL. Overall, the outcomes of independent-samples t-tests for the posttest and delayed posttest of vocabulary revealed a statistically significant difference in performance between the two groups on the posttest and delayed posttest of vocabulary learning and retention. The study came to the conclusion that teachers, material developers, and educational authorities might use the findings to teach MALL vocabulary to EFL students.
Network Data

In Chapter 1, we defined a network representation as a formal abstraction created for the purposes of visualization or analysis. Such network representations are created using network data. In this chapter, we define network data, its diverse types and data formats, and we give a wide range of examples of how it can be used to represent abstractions of archaeological data and relational theories. We conclude this chapter with best practice guidelines for how to go about collecting, documenting, storing, and sharing your network data.
Introduction

The interface between digitalized information (Data), intellectual property, privacy regulations, and competition law in the ‘Internet of Things’ (IoT) scenario is currently triggering the interest of politicians, businessmen, the academic community, and, even, the general public. The groups are interested for different reasons; for example, businessmen see an opportunity for the creation of wealth; researchers see the possibility of gaining, analyzing, and distributing knowledge efficiently; and everyone acknowledges that the collection and distribution of data may raise several concerns in reference to private and public power, freedom, privacy, and data protection concerns.
Introduction

Countries are competing ever more fervently to attract the best and brightest, whether highly skilled migrant workers, students with potential, or athletes and others boasting exceptional talent. It should therefore come as little surprise that they should vie with each other to lure in the wealthy as well. This chapter introduces the key characteristics, debates, and stakes in citizenship by investment and residence by investment programs.
Cluster cooperation as a vector for the development of modern secondary vocational education

The experience of participation of railway clusters in the experiment on the development, testing and implementation of a new educational technology for designing educational programs of secondary vocational education (EP SVE) within the framework of the federal project (FP) “Professionalitet” is analyzed. The purpose of the study is to assess the potential of cluster cooperation using the innovative platform “ProfTranSPOrt. Leaders Boiling Point” to solve the problems of educational and production centers (clusters) of railway transport. In 2022, the educational and production centers of railway transport began implementing the activities of the federal project “Professionalitet”, which is one of the strategic initiatives for the socio-economic development of the Russian Federation until 2030 and is aimed at creating a flexible model for training qualified personnel in the system of secondary vocational education in accordance with current the needs of organizations in the real sector of the economy. At the initial stage of the implementation of the “Professionalitet” JSC “Russian Railways” became the main coordinator of the joint activities of the educational and production centers of railway transport. The most effective result in the design of uniform exemplary educational programs “Professionalitet” in four railway specialties, the modernization of the material and technical base of educational organizations, the development of a universal thematic classifier for the digital constructor of the “Professionalitet”, was achieved by consolidating the efforts of clusters railway transport. At present, there is a need to create tools for the development of cluster cooperation in order to improve the quality of implemented and develop joint solutions in the interests of the development of modern open source software. One of these tools is the creation of an innovative industry platform “ProfTranSPOrt. Leaders Boiling Point” on the basis of the Yaroslavl branch of PGUPS with the support of the Federal State Budgetary Institution DPO “UMTS ZhDT”, which will help synchronize the work of basic OOs to improve the quality and content of the EP SVE.
The Korean Movie Minari: A Big Data Analysis

The ultimate goal of this paper is to analyze 42 articles of Google regarding the Korean movie Minari. As a research tool for our goal, we used the software package NetMiner. A major point to note is that one word has the highest frequency (1,247 tokens) and the highest proportion (57.8%). More specifically, it occurs 1,247 times in 42 articles of Google. A further point to note is that topic 11 is the most occurred one in 40 articles, followed by topic 3, topic 4, and topic 10, in that order. When it comes to the use of keywords in 42 articles, the word film is the most widely used one, followed by the word Minari, the word family, the name Jacob, and the name Monica, in that order. Talking about degree centrality, the word family has the highest degree centrality, followed by the name Monica, the word Minari, and the word film, in that order. It is worthwhile noting, on the other hand, that the word family has the highest closeness centrality since the distance among the word family and the other words is the closest. Finally, this paper argues that the name Monica has the highest Eigenvector centrality since it has the most neighbors. Thus, the name Monica counts as the most influential and important.
APPLICATION OF BIPLOT ANALYSIS WITH ROBUST SINGULAR VALUE DECOMPOSITION TO POVERTY DATA IN SULAWESI ISLAND

Poverty is defined as an inability of the individual to meet basic needs for a decent life. According to BPS data in2020, Sulawesi Island ranks fifth as the poorest island in Indonesia. This study aims to find out the mapping of areas and indicators of poverty in Sulawesi Island using Biplot Analysis with Robust Singular Value Decomposition approach for outlier research data. Based on the results of the study, there are five objects that are outlier and the information provided by the biplot amounted 98.45%. District/city that have similar characteristics are divided into 4 groups. The indicator of poverty that has the most diversity is the School Old Expectations Numbers (Var 4) and the one with the least diversity is Poor Households Using Clean Water (Var 8). Indicators of poverty that are positively correlated are Literacy Numbers (Var 1) and Non-Working Poor Population (Var 5), while the negative correlated are The Non-Working Poor Population (Var 5) and Poor Households Using Clean Water (Var 8). There are 19 districts/cities that have literacy values above the average of all districts/cities and 11 districts/cities that have a per capita expenditure value below the average of all districts/cities.
Managing Non-functional Requirements in an ELASTIC Edge-Cloud Continuum

The ELASTIC European project addresses the emergence of extreme-scale analytics, providing a software architecture with a new elasticity concept, intended to support smart cyberphysical systems with performance requirements from extreme-scale analytics workloads. One of the main challenges being tackled by ELASTIC is the necessity to simultaneously fulfil the non-functional properties inherited from smart systems, such as real-time, energy efficiency, communication quality or security. This paper presents how the ELASTIC architecture monitors and manages such nonfunctional requirements, working in close collaboration with the component responsible for the orchestration of elasticity.
A Deep Learning Driven Architecture for Evolving Steganalysis

ABSTRACT In multimedia forensics, steganalysis is a challenging task. Researchers have proposed many deeper networks to efficiently detect the hidden information stored in digital media. Existing approaches are inclined towards the depth of the network based on the proposition that as the model gets deeper it will provide better results. Conversely, as the model gets deeper, gradient backpropagation cannot assure the ability to move through the weights of every layer which makes the model difficult to learn. Moreover, the deeper model will use more computational resources. We present a novel architecture that integrates the batch normalization layers with the outer layers and focuses on the acceleration of the training process and reduction of the use of computational resources. To protect the tiny steganographic traits from being lost, we diminish the depth of the network and make it wider with no pooling layer. We illustrate the effectiveness of our approach on stego images generated from two steganographic mobile applications Mobistego and Pictograph. The result shows that our model can perform efficiently on the StegoAppDB database.