Reviewerly/exHarmony huggingface dataset:
Surface‐GCN: Learning interaction experience for organ segmentation in 3D medical images

Accurate segmentation of organs has a great significance for clinical diagnosis, but it is still hard work due to the obscure imaging boundaries caused by tissue adhesion on medical images. Based on the image continuity in medical image volumes, segmentation on these slices could be inferred from adjacent slices with a clear organ boundary. Radiologists can delineate a clear organ boundary by observing adjacent slices.Inspired by the radiologists' delineating procedure, we design an organ segmentation model based on boundary information of adjacent slices and a human-machine interactive learning strategy to introduce clinical experience.We propose an interactive organ segmentation method for medical image volume based on Graph Convolution Network (GCN) called Surface-GCN. First, we propose a Surface Feature Extraction Network (SFE-Net) to capture surface features of a target organ, and supervise it by a Mini-batch Adaptive Surface Matching (MBASM) module. Then, to predict organ boundaries precisely, we design an automatic segmentation module based on a Surface Convolution Unit (SCU), which propagates information on organ surfaces to refine the generated boundaries. In addition, an interactive segmentation module is proposed to learn radiologists' experience of interactive corrections on organ surfaces to reduce interaction clicks.We evaluate the proposed method on one prostate MR image dataset and two abdominal multi-organ CT datasets. The experimental results show that our method outperforms other state-of-the-art methods. For prostate segmentation, the proposed method conducts a DSC score of 94.49% on PROMISE12 test dataset. For abdominal multi-organ segmentation, the proposed method achieves DSC scores of 95, 91, 95, and 88% for the left kidney, gallbladder, spleen, and esophagus, respectively. For interactive segmentation, the proposed method reduces 5-10 interaction clicks to reach the same accuracy.To overcome the medical organ segmentation challenge, we propose a Graph Convolutional Network called Surface-GCN by imitating radiologist interactions and learning clinical experience. On single and multiple organ segmentation tasks, the proposed method could obtain more accurate segmentation boundaries compared with other state-of-the-art methods.
Super-resolution method for SEM images based on pixelwise weighted loss function

Scanning electron microscopy (SEM) has realized high-throughput defect monitoring of semiconductor devices. As miniaturization and complexification of semiconductor circuit patterns increase in recent years, so has the number of defects. There is thus a great need to further increase the throughput of SEM defect monitoring. Toward this end, we propose a deep learning-based super-resolution method that reproduces high-resolution (HR) images from corresponding low-resolution images. Image quality factors such as pattern contrast and sharpness are important in SEM HR images in order to evaluate the quality of printed circuit patterns. Our proposed method meets various image quality requirements by changing the loss calculation method pixelwise based on the pattern in the image. It realizes super-resolved images that compare favorably with actual HR images and can improve SEM throughput by 100% or more.
Sampling moiré method: A more exact solution

An important task in the sampling moiré method is phase measurement of a grating image by using the phase of the moiré fringe from a single-shot image. In the method, by thinning out the image, one gets N phase-shifted moiré fringes, where N is called the thinning-out index. In the conventional treatment, they are considered as phase-shifted patterns with shifts equal to multiples of 2π/N. This is not an exact supposition. It is shown in this work that the exact magnitude of the phase shift is 2π/T, where T is the period of the grating image. According to this fact, the formalism of the technique is modified. The simulation results verify the correctness of the theoretical results.
IMPLEMENTATION OF IMAGE AUTHENTICATION USING DIGITAL WATERMARKING WITH BIOMETRIC

The rapid global development of E-commerce in terms of digitalization and distribution of digital contents in the form of image, audio, video, increases the possibility of unrestricted duplication and broadcasting of copyrighted data and the protection of crucial documents is highly significant. Digital watermarking inserts watermark into the cover or host data by unnoticeable modification. In this work digital watermarking with biometric features is done. In this work a technique to implement the hiding of an image inside another image using biometric features namely signature and fingerprint using watermarking techniques is done. To accomplish this, a hybrid watermarking scheme consisting of Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DWT-DCT-SVD) is proposed for image authentication that is robust against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. By adding both the singular values of watermarks we acquire the transformed watermark. To improve the security, robustness and provide authenticity for the image, a two-step watermarking method is demonstrated. The evaluation parameters like PSNR (Peak Signal to Noise Ratio), SSIM (Structured Similarity Index Method), normalized correlation coefficient (NCC) are used for image quality assessment.
Analysis of Errors in English Narrative Writing of STIE Port Numbay Students

Background: The making of errors in second language acquisition, especially in writing, is natural, but the errors should not be ignored and need to be treated properly.&#x0D; Purpose: The purposes of this descriptive research are to identify the types of errors occured in the narrative writing of first-year students at STIE Port Numbay in Jayapura, explain the causes of the errors and the strategies applied to correct them and to prevent them from happening in the future.&#x0D; Design and methods: This research is a descriptive qualitative one that uses the content analysis method. The data were taken from 20 narrative writings about daily activities made by 20 first-year students of STIE Port Numbay in Jayapura who major in Development Economics and took the English 1 course in the Odd Semester of the 2020/2021 Academic Year.&#x0D; Results: The findings revealed that there are 9 types of error occured i.e. omission, addition, misselection, misordering, blending, misformations, misspellings, sentence errors and semantic errors in lexis. The errors are caused by interference of the first language or interlingual, inadequate mastery of the target language rules or intralingual and limited vocabulary. The strategies for the error correction are direct feedback and exposing the target language use by its native speakers so that the learners can compare it with their own L2 use. To prevent the same errors, the instructor can improve the learners' mastery of the target language rules by using creative and varied teaching methods and materials as well as encouraging the learners to develop the habit of reading English texts in order to improve their vocabulary and to obtain input about the use of English in writing.&#x0D; Keywords: Error analysis, interlingual, intralingual
Study on Personal Identification Which is Stable Against the Change of Room Temperature Using Thermal Image

Since the temperature distribution of the face varies among individuals, there is a possibility that it can be used for personal authentication. This paper proposes a method of using the high temperature region of the face, which is stable during personal authentication and resistant to changes in room temperature. The method obtained a 87% recognition rate in individual identification experiments under a variety of room temperature. In addition, this paper experimentally explains the method is stability against internal and external factors such as mental stress load and direct wind. Furthermore, combining this method with conventional face recognition methods using visible images, it will be possible to improve the authentication rate and avoid various risks.
Fast Color Image Matching Based on Occurrence Probability Analysis of Pixel Pair Hue Values

We propose a fast and reliable method for detecting objects using color information. The probability of the occurrence of a hue pattern in a template image is calculated for each two-pixel pair, and only those pixel pairs with extremely low probability are carefully selected for matching. Since such pixels have high distinctiveness, it can be used for highly reliable matching without being affected by ambient disturbances, and since only a small number of pixels are used, it is also fast. In real image experiments, we achieved a recognition success rate of 93% and a processing time of 47 msec by using only 0.5% (143 pixels) of the template image. When 0.1% (29 pixels) of the template image was used, the recognition success rate was 7 msec, confirming that practical matching with a good balance between reliability and speed was achieved.
Extending Neuro-fuzzy Techniques with Grey-Based Hybridisation

Real-world problems are commonly composed by interrelated components in many complex ways. They are usually dynamic, that is, they change with time through a series of interactions among related components. Classical decision-making techniques cannot support these kinds of challenges. For that reason, this paper focused on the extension of neuro-fuzzy techniques as Fuzzy Cognitive Maps with Grey Systems Theory.
Classification methods for handwritten digit recognition: A survey

Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit recognition problem, from linear classifiers to convolutional neural networks. Results: Handwritten digit recognition classification accuracy tested on the MNIST dataset while using training and testing sets is now higher than 99.5% and the most successful method is a convolutional neural network. Conclusions: Handwritten digit recognition is a problem with numerous real-life applications. Accurate recognition of various handwriting styles, specifically digits is a task studied for decades and this paper summarizes the achieved results. The best results have been achieved with convolutional neural networks while the worst methods are linear classifiers. The convolutional neural networks give better results if the dataset is expended with data augmentation.
Convolutional Neural Networks for Object Detection and Recognition

One of the essential technologies in the fields of target extraction, pattern recognition, and motion measurement is moving object detection. Finding moving objects or a number of moving objects across a series of frames is called object tracking. Basically, object tracking is a difficult task. Unexpected changes in the surroundings, an item's mobility, noise, etc., might make it difficult to follow an object. Different tracking methods have been developed to solve these issues. This paper discusses a number of object tracking and detection approaches. The major methods for identifying objects in images will be discussed in this paper. Recent years have seen impressive advancements in fields like pattern recognition and machine learning, both of which use convolutional neural networks (CNNs). It is mostly caused by graphics processing units'(GPUs) enhanced parallel processing capacity. This article describes many kinds of object classification, object racking, and object detection techniques. Our results showed that the suggested algorithm can detect moving objects reliably and efficiently in a variety of situations.
Meningkatkan Kognitif Anak Usia Dini melalui Media Flashcard

One of the cognitive abilities of early childhood (AUD) is symbolic thinking, which includes the ability to know, mention, and use the concept of numbers. The cognitive abilities of students of RA Sabilil Huda Sanglar, Reteh have not developed optimally. This study aims at improving students’ cognitive abilities through flashcard media. It is a Class Action Research (CAR), which was carried out in group B RA Sabilil Huda Sanglar, Reteh. This was designed for two cycles, where each cycle consisted of three meetings (two meetings for treatment, and one meeting for tests). Data were collected using observations, tests, and field notes. They were then subsequently analyzed descriptively. Based on the data, it is known that students’ cognitive abilities are at an average of 36.25 (45.31%) with the Start to Develop (SD) category in the pre-cycle, 56 (70%) with the Very Well Developed (VWD) category in the cycle I, and 75.6 (94%) with the Very Well Developed (VWD) category in the cycle II. Thus, the use of flashcard media can improve the cognitive abilities of group B students of RA Sabilil Huda Sanglar, Reteh.
Gamification of Environmental Monitoring. Technical and Informational Means of Supporting the Formation of Civil Society Institutions

A set of information and technical means is proposed to stimulate the formation of civil society institutions in countries such as Kazakhstan, where, for historical reasons, they remain relatively weak. One of the main tools of this set is a mobile application that measures an indicator reflecting the degree of negative impact of an unfavorable environmental situation on a specific user. The operation of this application is based on correlating the user's route, determined by geolocation data, with a map of the city's pollution and its dependence on time. It is shown that the introduction of such tools is a prerequisite for the introduction of developmental and educational games into mass use, ensuring monitoring by the efforts of schoolchildren-volunteers. Methods for verifying the set of measurements obtained by volunteers using neural networks are discussed. The possibility of pairing the means providing measurements of the above indicator with the methods, correction of the psychophysiological and psycho-emotional state is considered. It is shown that the introduction of such methods, focused on group use, is also an important step towards the establishment of civil society institutions that solve environmental problems.
On the Issue of the Marketing Model of the University Ecosystem as the Dominant of Higher Education in Targeting Activities

The purpose of the article is to review the current changes in domestic higher education against the background of trends in global educational markets and to develop a marketing model of the university ecosystem as a dominant one that defines modern economic relations of market entities involved in the educational process with a clear focus on target audiences. The comparative contexts of global trends in higher education are based on the intersection point of several models of countries such as France, Spain, Germany, Canada and the USA, which allowed us to prioritize the development of the author's model. Monitoring and author's survey of representatives of the university conglomerate were chosen as the research tools, which allowed us to conclude that it is advisable to introduce new constructs into the practice of universities, from the transformation of university marketing to the formation of stable public relations focused on the targeted development of higher education and Russian higher education institutions. The results of the marketing analysis were the basis for the development of the ecosystem of regional universities, which contributed to increasing competitiveness and customer orientation, was reflected in the educational process and the overall marketing activities of universities.
Extramural Studies: Harvesting and Analysis of Students’ Digital Footprint

The paper emphasizes the necessity of the design and implementation of individual educational trajectories of students and substantiates the need to use the possibilities of the students’ digital footprint in the digital educational environment. The investigations of Russian and foreign scientists on the role of the digital footprint in the design of individual educational trajectories are analyzed. The results of the harvesting and interpretation of digital data of the activities of extramural students in the disciplines Foreign Language and Business Foreign Language in Professional Activities in the Learning Management System Educon of the Industrial University of Tyumen, on the Zoom platform and in the Google Docs cloud service are presented. The involvement of extramural students of the 1st and 2nd study years in activities in the Learning Management System Educon, midpoint assessment tests, as well as students’ preferences in terms of the tasks performance way in practical classes were analyzed. Diagrams of involvement of extramural students in activities in electronic courses by disciplines, preferences by the number of attempts used when performing of midpoint assessment tests, as well as students’ preferences by the tasks performance way in practical classes are constructed. Conclusions are drawn about the possibility of identifying elements of work programs that are difficult to master, their possible adjustment as well as the formation of individual educational routes for students based on digital data, which ultimately will allow adjust the educational process as a whole and improve the quality of education.
Digital Pedagogy: Opportunities and Challenges of Learning in the Information Environment

Digitalization of society and all spheres of social production is rapidly changing the whole landscape of education. The time of learning generation Z, known as the network generation (zoomers), formed in the digitally oriented era, is coming. However, there are many challenges for learning in the digital environment. All these tasks for digital pedagogics, methodological possibilities of which are still in their infancy, require justification of their sufficiency and efficiency. This article shows the problems and new directions of pedagogical science and student teaching methodology development under the conditions of changes in the information educational space. It is noted that digital and information and communication technologies stimulate the development of digital didactics, opening up a lot of new principles and approaches to teaching and assessment of achievements, but, at the same time, bring many problems and risks. At the same time, didactic principles of digital pedagogy dictate significant changes in the role of the teacher, increasing the share of his/her work as a designer of a new form of educational process, taking into account electronic resources and big databases. The solution of these and other problems requires scientific approach and experimental testing.
Distance Learning at University: Trends, Problems and Prospects

This article is devoted to current research in the field of the influence of the digital economy on the formation of a set of key competencies and the training of specialists in the modern world. The article discusses a new type of competence in demand in the modern market - “technological”. The authors consider digital competences as a means of effective interaction between the student and the educational environment. To analyze the features of creating a digital educational environment in universities, an assessment of digital literacy was carried out and a mechanism for its formation and maintenance at an acceptable level should be created. The article presents the results of studies of students’ perception of distance learning in a regional university of the Central Federal District of the Russian Federation. This educational institution is an information platform within the framework of the formation of digital competences in demand in Industry 4.0. Many IC and IT developments of the system of higher and additional education are being implemented here.
Quantum Computing in Machine Learning

For physicists, statisticians, computer engineers, and other technical professionals, quantum information processing is an essential technology with many outstanding challenges. This chapter gives a brief overview of some well-known previous works on the areas of quantum AI, quantum ML, and quantum image handling. The primary goal of this work is to compile the current state of the art in various quantum image handling tasks, storage techniques along with various quantum supervised and unsupervised learning algorithms and summarize the recent developments in this field. Other from that, several of the available quantum approaches are examined based on their applications, distinctions, limitations, and potential uses. In this chapter, we study and describe an overview of how quantum principles can contribute to improving the performance of different machine learning and image processing techniques.
Quantum Computing in Image Processing

In current years, quantum image processing becomes one of the popular research topics along with the quantum machine learning.
Introduction

Data, which is shorthand for “information”, has always been gathered, reviewed, and/or analyzed as part of the running of the Head Start program. For children to enroll in the program, numerous pieces of information are needed. Information from screenings and any subsequent services are included in the delivery of health and dental services. The gathering and use of a significant amount of information are required in every aspect of a Head Start program, including content and management.
Virtual and Augmented Reality with Embedded Systems

Virtual reality (VR) is a computer-generated world that is presented to the viewer in such a way that it appears and feels genuine. The user must put on specialized gloves, ears, and goggles in order to enjoy a virtual reality world. These devices take input from the computer system. The user's actions are continuously seen and analyzed by the computer, which also modifies the data given to the devices they are wearing.
Virtual Customer Assistants in finance: From state of the art and practices to design guidelines

Virtual Customer Assistants (VCAs) are revolutionizing the way users interact with machines. VCAs allow a far more natural interaction, and are gaining an increasingly large role in customer service. The financial domain is especially susceptible of this change, because customer care is of paramount importance. Furthermore, VCAs have the potential of supporting customers in performing routine operations such as money transfers, or in more complex decision-making operations such as trading stocks, both of which require that VCAs display strong reliability. This survey has a two-fold goal. First, we perform an analysis of the state of the art and practices of VCAs in the financial domain. Second, we provide a sort of toolbox that collects the best practices for designing and developing a VCA in the financial domain, guaranteeing a high-quality user experience.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e110" altimg="si3.svg"><mml:mrow><mml:mi>i</mml:mi><mml:mi>S</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:msup><mml:mrow><mml:mi>E</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> — The Explanation Experiences Editor

Existing XAI libraries offer a good number of explanation and visualization methods. We refer to the combination of these XAI methods and their further customization to meet the actual needs of users as explanation strategies. The Explanation Experiences Editor (E3) is a software tool that allows capturing, editing, and executing explanation experiences through a user-friendly interface based on behaviour trees. To easily integrate third-party XAI methods, E3 is supported by a restful web service API that provides a unified access layer for them. The platform also provides an online repository to store the AI models and normalize their access by explanation methods.