Reviewerly/exHarmony huggingface dataset:
Research on Semi Supervised Image Segmentation Using Optimal Hierarchical Clustering by Selecting Interested Region as Prior Information

The initial stage in image processing, pattern recognition, and feature extraction is picture segmentation. This segmentation may be done using a variety of approaches. This is a crucial yet fundamental part of image analysis as it is the single factor that affects the characteristics of the finished image analysis output. The semi-supervised picture segmentation using the hierarchical clustering technique is discussed in this study. The prior information for the clustering process is given as an interested area selection from image using mouse. The picture qualities of intensity, colour, and texture are discussed here. The suggested technique provides more clarity of segmented regions than previous semi-supervised approaches.
Interested Learning

This chapter examines one of the most active areas where feature vectors play a central role: machine learning. The Bayes classifier is used as an entry point into the field, showing how a simple statistical technique introduced in the early 1960s is surprisingly instructive for understanding how machine learning operates more broadly. The goal is to shed light on the core principles at work and to explain how they are tweaked, adapted, and developed further into different directions. This chapter also develops the idea that contemporary information ordering represents an epistemological practice that can be described and analyzed as ‘interested reading of reality’, a particular kind of inductive empiricism.
Analisis Spasial Untuk Klasifikasi Pengembangan Tempat Penampungan Sementara Menggunakan Metode Jaringan Syaraf Tiruan

Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of waste. The distribution of temporary waste shelters (TPS) in each area is still insufficient to accommodate the volume of waste, and its availability is inadequate. The purpose of this study is to model spatial data through spatial analysis using artificial intelligence methods in classifying the development of integrated temporary shelter locations (TPST) and regional integrated temporary shelters (TPST Regions) by utilizing Web-based technology (Geographical Information System (Web-GIS). The Artificial Neural Network method with the Backpropagation algorithm is used for the spatial analysis process based on the parameters of the population, the amount of organic and inorganic waste, the amount of industrial waste, and the volume of the TPST and Regional TPST capacity. The spatial analysis results using the Artificial Neural Network method obtained an accuracy value of 7171.02%. The results of this study can be the basis for Department of Environment and Cleanliness policies for the development of TPST and TPST areas with information coverage at the village level.
Internet Financial News Text Classification Algorithm Based on Blockchain Technology

With the explosive growth of various Internet news text data in Internet funds, how to effectively use these text data and find out the true value behind it is very important. As the core of Internet word processing and word mining, text classification has become a major research topic in the field of natural language editing. For text classification problems, traditional methods are mainly shallow machine learning. With the rapid development of blockchain technology, huge research findings have been made in the field of image recognition and speech recognition, and the ability of blockchain to learn technical features has been further proved. This article is based on the blockchain technology model to classify Internet financial news text Research the problem. This article attempts to introduce blockchain technology related theories and design a blockchain technology model to complete the task of classifying Internet financial news texts. Overcome the shortcomings of the semantic connection between words in shallow machine learning. The final result of the research shows that the improved algorithm in this paper is 8% higher than the IG algorithm’s macro average recall rate and 2% higher than the PIG algorithm. The F1 index has increased by 6% and 2% respectively. The experiment proves the feasibility and effectiveness of the improved IG feature selection algorithm proposed in this paper.
A Method Towards Allocation of VM using a Cuckoo Search Algorithm

Cloud computing is a new technology created for complex systems with massive service sharing. It is not easy to analyze the cloud model due to its complex and large scale structure. To meet the user's requirement along with the quality of service (QoS) parameters become the most dominating research issue. The QoS parameters are affected due to various issues such as load balancing, Virtual Machine (VM), makespan, throughput, Utilization of Hosts’ Processing Capability (UHPC) and many more. The allocation policy of VM mainly used to place VMs on hosts at various datacenters. This article outlines the new VM allocation policy using Modified Best Fit Decreasing (MBFD) along with with Cuckoo Search (CS) algorithm that delivers the VM burden between hosts to improve the handling capability and the cloud system performance. The test results are obtained using CloudSim trace simulation and the QoS are compared with existing allocation policies of VM.
Retraction: Smart Chair System for Maintaining Health using Internet of Things (IOP Conf. Ser.: Mater. Sci. Eng. 1145 012048)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Supplemental Material: Similar curvature-to-width ratios for channels and channel belts: Implications for paleo-hydraulics of fluvial ridges on Mars

Additional description of previous work, our methods for curvature determination, a fractal geometry description of channel belts, martian ridge descriptions, and the measured data.&lt;br&gt;
Use of digital imaging correlation techniques for full-field strain distribution analysis of implantable devices and tissue in spinal biomechanics research

Few studies have used optical full-field surface strain mapping to study spinal biomechanics. We used a commercial digital imaging correlation (DIC) system to (1) compare posterior surface strains on spinal rods with those obtained from conventional foil strain gauges, (2) quantify bony vertebral body and intervertebral disc (IVD) surface strains on 3 L3-S cadaveric spines during gold-standard flexibility tests (7.5-Nm flexion-extension and 400-N compression), and (3) report our experience with the application and feasibility of DIC to comprehensively map strain in spinal biomechanics. Spinal rods were tested under zero load and using ASTM F1717 standard. For rod strain measures, the largest mean bias offset and baseline noise standard deviation under zero load for DIC were 7.6 με and 33.7 με, respectively. For tissue measures, the largest mean bias offset was 8 με for ε1 and -55 με for ε2 with baseline noise standard deviations of 19 με and 26 με, respectively. On average, DIC rod strain measurements were 5.3% less than strain gauge measurements throughout the load range. Principal IVD and bony surface strains were consistently measurable and showed marked regional differences in strain patterns under different load conditions. Strains measured on spinal rods using DIC techniques reasonably agreed with standard strain gauge measurements. Subregional strain analyses on soft and hard spinal tissues during standard flexibility tests were feasible. Optical strain mapping is a viable, accurate, and promising measurement technique for novel spinal biomechanical studies.
The development of clusterwise regression model on gamma-normal mixed distribution with genetic algorithm

Clusterwise regression is a statistical technique that combines clustering process and regression analysis. Cluster optimization was carried out using genetic algorithm (GA). GA is an optimization technique that adapts the theory of natural evolution starting from the formation of the initial population to produce the best generation. GA provides an optimal fitness value that describes the optimal clustering. This study aims to construct a clusterwise regression algorithm on Gamma-Normal mixed distribution using GA. Simulations were carried out to evaluate the results of the GA construction by generating data for various distributions. The simulation results on the Gamma distribution give an accuracy value 85% with cluster proportion 37% : 67%. Normal distribution with cluster proportion 51% : 49% yielded 95% accuracy. The highest accuracy is 98% in mixed distribution with cluster proportion 52% : 48%. Based on high accuracy value of the simulation results, it indicates the construction of an appropriate algorithm. This indicates that clusterwise regression Gamma-Normal mixed distribution with GA is able to cluster and model well.
O cliente oncológico em tempos de pandemia da COVID-19: uma análise bioética

The aim was to analyze the effects of excessive workload on nurses. This is an integrative literature review, through the study of multiple articles on the topic covered hours of work in nursing and the Burnout syndrome. This work allows general conclusions about a particular area of ​​study, carried out in a systematic and orderly manner, and contributes to the deepening of the topic to be studied and its knowledge. The research is bibliographical, and queries were made in medical literature and in journals from the last 5 years, in the Latin American Caribbean Literature in Health Sciences (LILACS) database, in the Virtual Health Library (VHL) database platform). Thus, consequently, the risks of the profession will be minimized or even eliminated, thus, the institution gains by being a reference in excellence in care, society gains by being in the hands of a serious, motivated, and committed professional, and the nurse who will feel valued and, in this way, accomplished in the chosen profession.
Method of developing professional competencies future teacher for labor training

The main goal of the research was to specify the features for the key competencies’ formation of teachers of labor education and technology and provide recommendations for their implementation based on the competence approach. The main method was a sociological survey among students majoring in “Labor Education and Technology”. As a result of research, the essential features which arise in the preparation of students of a specialty "Labor training and technologies" of the researched university are revealed. The differences between the competencies defined in the standard for the preparation of students majoring in “Labor Education and Technology” and the professional competencies of teachers are identified. As a result was shown that the success of the progress of forming qualified competencies is ensured by creating the following conditions: introduction of a systematic approach, implementation of a contextual approach through updating the possibilities of subjects for future qualified competence. Future teachers, according to their specialties and qualifications, stimulate the conducts and methods of their development. The practical significance of the study is to form recommendations and principles for the subject competence of students majoring in “Labor Education and Technology” taking into account their future professional needs.
Consent-based Implied Bare Licences

This chapter examines consent-based implied bare licences, considering situations where the copyright owner impliedly grants a licence gratuitously, meaning nothing (whether payment or any other form of consideration) flows to the copyright owner in return for the licence. The modern relevance of a consent-based implied bare licence lies in its application to the copyright work placed on the internet by its owner or with her consent. It examines the case law in the light of the framework proposed in Chapter 3, that separates the conduct of consenting separately from the knowledge required to consent. It also discusses the revocability of bare licences. A consent-based bare licence can be revoked, since the ability to grant such a licence and to revoke it (upon reasonable notice) both originate from the powers of the copyright owner. However, there are situations where these licences cannot be revoked, and the courts have ascribed proprietary estoppel as the reason for irrevocability.
Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.
The Model Development for Forecasting the NS1Ag Antigen Examination Results of Dengue Fever Patients Using the Multi-Layer Perceptron Neural Network

The objective of the article is to develop a model for forecasting the NS1Ag antigen examination results of dengue fever patients by using the Multi-Layer Perceptron Neural Network.The data used for developing the model was the results of the antigen type NS1Ag tests collected from a certain hospital's information system.The data dated from October 2018 -March2020 with the number of 1,916 records.The data featured with 11 attributes.The Resample and SMOTE techniques were applied to adjust the imbalance ofthe data by increasing the size of data from 100% to 200%.The effectivenessevaluation results of the model using the percentage split of 70:30 revealedthat the imbalance-adjusted model using the SMOTE method with the size of200% and the Multi-Layer Perceptron Neural Network technique provided theaccuracy of 93.54%.This provided higher accuracy compared to the modelusing the Multi-Layer Perceptron Neural Network solely.The results of thestudy show that the model could be used for forecasting the NS1Ag antigenexamination results of dengue fever patients.It can be applied as a tool tosupport doctors and nurses to diagnose the dengue fever accurately and filterpatient transfers to the laboratory appropriately.
A Longitudinal Dense Feature Pyramid Network for Object Detection

Feature Pyramid Network (FPN) is one of the most popular subnetworks in the current object detection model, which deals with the scale change problem in object detection through feature fusion. In feature fusion, when different layers of feature maps are merged into a new feature map, it will inevitably lead to noise increase and information loss. All of these will make a feature pyramid suboptimal. To address these challenges, this paper proposes a new feature fusion network called Longitudinal Dense BiFPN (LD-BiFPN) based on the bi-directional feature pyramid network (BiFPN). LD-BiFPN supplements the missing feature information by reusing the existing feature maps and improves the accuracy of the detector. The focal point of this work is to explore a more efficient feature pyramid network to improve the accuracy of object detection tasks without increasing a large number of parameters. Experiments have demonstrated that the EfficientDet-D0 detector using LD-BiFPN has an Average Precision (AP) improvement of 0.9 points than our re-implemented EfficientDet-D0 detector on the COCO val-2017 benchmark, without bells and whistles.
MitoDet: Simple and Robust Mitosis Detection

Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the colour representation of digitized images. In this method description, we present our submitted algorithm for the Mitosis Domain Generalization Challenge [1], which employs a RetinaNet [5] trained with strong data augmentation and achieves an F1 score of 0.7138 on the preliminary test set.
SISTEM PELAYANAN MASYARAKAT PADA KANTOR KEPALA DESA BALAM SEMPURNA BERBASIS WEB

The Village Service describes the identity of the village itself. So far, the service system for making letters at the office of the Village Head of Balam Sempurna, Rokan Hilir Regency uses services in the form of a manual information system, the service is carried out by a Village Secretary. Some of the problems faced by this service system are difficult and slow in the process of submitting proposals for new letters, permits and certificates. Meanwhile, people want friendly, fast and effective service. The self-service system can be used as a solution to provide excellent service to the community. In this self-service system, the public can access services such as taking care of certificates and cover letters for various purposes by entering or typing the Population Identification Number into the website. If the data is found, the community can immediately perform the desired service. The result of the application is in the form of a certificate or permit.
Estimating Children’s Intrinsic Motivation During a Class Based on Face Orientation and Gaze Information

Considering that non-cognitive abilities develop significantly during childhood, the Japanese early childhood education community has recently begun to focus on the study of non-cognitive abilities. However, measurement and estimation methods for non-cognitive abilities face quantitative and objective challenges. In addition, there is little research on the active use of artificial intelligence technology in education. Thus, this study proposed a method for quantitatively estimating concentration in the classroom, one of the children’s non-cognitive abilities. Specifically, it examined the number of intersections between children’s gazes using information on the direction of their faces and their gazes during class and determined whether all children should focus their gaze on a small area. Moreover, it quantitatively examined whether each child behaved appropriately in different situations by measuring the angle between each child’s gaze and the center of gravity of the intersections of all the children’s gazes. As a result, we found that we could capture the characteristics of each child’s behavior.
Automatic Utterance Selection Based on Prosodic Features of Children’s Vocalizations

There is a growing need for dialogue systems that can interact appropriately with children. In some dialogue systems, the system selects an utterance in a given scenario. Our goal is to use prosody to generate an appropriate dialogue without requiring a user’s dialogue selection. In this study, we constructed a learning system for an utterance selection in a scenario called “Introduction”. The developed system learns the dialogue selections from the prosody of a the mother’s selection of dialogues and a child’s responses. In our experiments, we found that by using prosodic information for the state, the system can learn new dialogue selections.
Getting Successful Performances

Even after the composition is finished, finding a group willing and able to perform it successfully, is another matter. Few student composers have the resources to hire performers, and it takes some networking to find volunteers to perform new works, unless the composer has already established a significant positive reputation. Building relationships with people who will perform original works is extremely important. It is hard to know exactly how a piece will sound until it’s actually being performed by humans, rather than by a computer. The software programs available are great tools but can never fully replicate the real experience. Remaining open to performer comments and criticism will greatly increase the likelihood of a successful performance.
Resilience In The Semantic Space Of Education For Sustainable Development

The success of education for sustainable development largely depends on the linguistic means used. Education for sustainable development sets the tasks for every person to master the knowledge and skills that contribute to the sustainable development of society. To make his language understandable for everyone, he cannot be limited only by scientific concepts. To change the assessments of the world, values, human behavior, education for sustainable development has to be connected with the human feelings, culture, traditions, everyday practice, include symbols, myths, metaphors that reflect the environment-saving type of relations between society and nature. The authors substantiate the necessity of developing linguistic means of education for sustainable development. The purpose of the research is to study the influence of sustainable development metaphors on the understanding of teachers of the essence of the new concept of resilience. In the second of these groups, teachers, unlike the first, consider the introduction to teaching of the new term “resilience” to be necessary, understand their responsibility for its formation in children, consider that not only material, but also spiritual factors are important for this (knowledge, values , worldview) and are confident that they can do it. The conclusion is made about the importance of metaphorical modeling of the language of instruction of teachers in the formation of the semantic space of education for sustainable development.