Reviewerly/exHarmony huggingface dataset:
From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

Virtual unrolling or unfolding, digital unwrapping, flattening or unfurling - all these terms are used to describe the process of surface straightening of a tomographically reconstructed digital object. For many objects of historical heritage, tomography is the only way to obtain a hidden image of the original object without its destruction. Digital flattening is no longer considered a unique met hodology. It being applied by many research group, but AI-based methods are used insignificantly in such projects, despite the amazing success of AI in computer vision, in particular optical text recognition. It can be explained by the fact that the success of AI depends on large, broad and high quality datasets, but there are very few published CT-based datasets relevant to the task of digital flattening. Accumulation of a sufficient amount of data necessary for training models is a key point for the next technological breakthrough. In this paper, we present open and cumulative dataset CT-OCR-2022. Dataset includes 6 packages data for different model objects that help to enrich tomographic solutions and to train machine learning models. Each package contains optically scanned image of model objects, 400 measured X-ray projections, 2687 CT- reconstructed cross-sections of 3D reconstructed image, segmentation markups. We believe that CT-OCR-2022 dataset will serve as a benchmark for reconstructed object digital flattening and recognition systems, and that it will prove invaluable for advancement of the field of CT-reconstruction, symbols analysis and recognition. The data presented are openly available in Zenodo at doi:10.5281/zenodo.7123495 and linked repositories.
Real-time CNN-based keypoint detector with Sobel filter and descriptor trained with keypoint candidates

In this paper, we propose two separate and lightweight convolutional neural networks, SobelNet and DesNet, which work in parallel, as keypoint detector and descriptor respectively. Sobel filter provides the edge structure map of the grayscale image as the input of SobelNet. The locations of keypoints will be obtained after exerting the non-maximum suppression process on the output score map of SobelNet. Gaussian loss is designed to train SobelNet to detect corner points in the edge structure map as keypoints. In the meantime, a dense descriptor map is produced by DesNet which is trained with Circle loss. Besides, the output score map of SobelNet is utilized while training DesNet. The proposed method is evaluated on two widely used datasets, FM benchmark and ETH benchmark. Compared with other state-ofthe- art methods, SobelNet and DesNet can reduce more than half of the computation and achieve comparable or even better performance. The inference times of an image with the size of 640×480 are 7.59 ms and 1.09 ms for SobelNet and DesNet respectively on RTX 2070 SUPER, which meet the real-time requirement.
A brief introduction to using WebR and Quarto for client-side interactive lesson material

Last week an exciting new addition to the R ecosystem was announced: webR.
Multi-task Multi-instance Learning for Jointly Diagnosis and Prognosis of Early-Stage Breast Invasive Carcinoma from Whole-Slide Pathological Images

With the tremendous progress brought by artificial intelligence, many whole-slide pathological images (WSIs) based machine learning models are designed to estimate the clinical outcome of human cancers. However, most of the existing studies treat the prognosis and diagnosis tasks separately, which overlooks the fact that the diagnosis information indicating the severity of the disease that is highly related to the patients’ survival. In addition, it is still challenge to design machine learning models to analyze WSIs since a WSI is of large size but only annotate with coarse labels, it increasingly becomes a research hotspot for the development of automated WSI analysis tools in a scenario without fully annotated data. Based on the above considerations, we propose a multi-task multi-instance (WSI-MTMI) learning method that can simultaneously conduct the prognosis and diagnosis tasks from WSIs. Specifically, inspired by the fact that taking the associations between diagnosis and prognosis tasks can improve the generalization ability for each individual task, we firstly adopt multi-instance learning algorithms to aggregate the patches derived from WSIs by considering both the common and specific task information. Then, we design a novel cross-attention network that can effectively identify useful information shared across different tasks. To evaluate the effectiveness of the proposed method, we test it on the early-stage breast invasive carcinoma (BRCA) derived from the Cancer Genome Atlas project (TCGA), and the experimental results indicate that our method can achieve better performance on both diagnosis and prognosis tasks than the related methods.
Tips for an effective remote defense

Zoom works great: I’ve seen up to ~50 folks attending the talk remotely and slides with video. Everything connection wise worked well except for a single committee member with some minor freezing during the private defense. Have backup options: Give yourself time and backups in case things go wrong. Set up the connection early (15+ minutes) and ask the committee to show up early to check everything is working. Have one of more backups including a phone based conference call. Call manager...
False Detection for Online Reviews by Using Deep Neural Network

Online reviews usually have the most significant impact on a customer's decision to buy a good or service. Therefore, manufacturers and retailers are anxious and concerned about customer feedback and reviews regarding this critical component and its impact on items. Unfortunately, the greater reliance on online reviews has an immediate effect on product sales since it may encourage false reviews that promote or disparage the goods and services. Opinion (Review) Spam happens when spammers fabricate and manipulate negative and false reviews to make money or gain an advantage. Machine learning approaches for categorization, and evolutionary algorithms for feature selection are used to extract meaningful information from text.Supervised learning techniques, which need labelled data, have been the focus of the most recent research. However, there needs to be more when addressing online review spam. Customer reviews on a product are becoming exponentially in number. The integrity of online opinions is a serious issue that is routinely disregarded. To make appropriate and accurate decisions or to gather marketing and commercial knowledge, it is crucial to have a technique for evaluating the trustworthiness of reviews. The product manufacturer gains knowledge about the benefits and drawbacks of their own and their competitors' goods, as well as consumer preferences and interests, which may help them decide how to maximise profits. Furthermore, it's crucial to find trustworthy consumer reviews. Bio-inspired algorithm-based feature selection is proposed to address the problem of categorising spam reviews.
Topics in RRG: Complex Sentences

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PENENTUAN LOKASI ATM BANK SYARIAH INDONESIA MENGGUNAKAN METODE FUZZY C MEANS DI KABUPATEN JEMBER

Bank Syariah Indonesia was inaugurated on February 1, 2021, which is a combination of BNI Syariah, Mandiri Syariah, and BRI Syariah. One of the ease of transaction facilities for customers is using an ATM (Automated Teller Machine). BSI currently has 18,291 ATMs spread throughout Indonesia and 10 of them are located in Jember Regency. The purpose of the ATM is to increase customer convenience and public trust in BSI. Ease of access to ATMs will affect the number of customers because ATM locations that are easily accessible can increase the number of profits obtained by BSI. The main factors in determining the location of ATMs that need to be considered are easy to reach, close distances, and safe locations. The Fuzzy C Means (FCM) method is used to classify the right area in determining the location of ATMs. The results of this study, the Jember district can be grouped into 6 clusters, wherein each cluster there is one ATM with the first cluster and the sixth cluster with as many as 9 districts, the second cluster with as many as 6 districts, the third cluster as many as 5 districts, the fourth cluster as many as 7 districts, and the fifth cluster as many as 4 districts. The conclusion of this study, is the exact location of BSI ATMs in Jember Regency based on the number of clusters that have been determined to be in Patemon Village, Antirogo Village, Jubung Village, Jember Lor Village, and Sumbersari Village. Clustering validation uses the Silhoutte Index (SI) with an average silhouette coefficient of 0.83028.
Regression Quadratic Method untuk Menganalisis Hubungan Sudut Kemiringan terhadap Radiasi dan Energi Panel Surya

The potential of rooftop solar power plants at Semarang State Polytechnic Mechanical Engineering Workshop has been researched. This study discusses one of the factors that affect the radiation received and energy sent to the grid, namely the angle of inclination in the solar panels The average radiation data received by solar panels with variations in tilt angle between 0 to 30° and azimuth angles at 0° facing north and 180° facing south are simulated using Helioscope software. Based on the results of mathematical calculations, it is estimated that the relationship between the tilt angle of the solar panel and the radiation received and the energy sent to the grid will form a quadratic curve. From the results of data processing with the Regression Quadratic Method, it was obtained that the data match was 99.6%. The results of calculations using the quadratic equation show that the angle of inclination of the solar panels for installation of a rooftop PLTS in Machine Workshop Polines is 11° with a radiation of 1775.6 kWh/m2 and a energy production of 13575.9kWh.
The Concept of Inclusive Higher Education and Approach to Its Implementation Based on Openness to Persons with Disabilities

The paper aims to develop an approach to implementing the concept of inclusiveness in higher education based on openness to persons with disabilities through the justification of key factors and the prospects of increasing inclusiveness based on their optimization. For this purpose, using the method of regression analysis, the authors carry out a factor analysis of the inclusiveness of higher education and select key factors contributing to the openness of universities towards persons with disabilities. The authors develop an approach to the implementation of the concept of inclusive higher education based on openness towards persons with disabilities and justify the prospects of its implementation through the optimized management of selected key factors of inclusiveness. The research novelty lies in the fact that it clarifies the essence of the influence of the main factors on the inclusiveness of higher education and provides a transition from a qualitative to a quantitative study of these factors. The author’s conclusions and recommendations allow for improving the effectiveness of managing the inclusiveness of higher education through targeted management of its openness to persons with disabilities, precisely focused on the selected key factors. The uniqueness and novelty of the author’s approach to the implementation of the concept of inclusive higher education lie in the isolated consideration of the influence of the main (generally accepted) factors on the openness of higher education to persons with disabilities.
IMPLEMENTATION OF EARTH STRUCTURE LEARNING APPLICATIONS USING MARKERLESS AUGMENTED REALITY USING SURFACE TRACKING METHOD

In science lessons at school there are materials that are taught, one of which is a lesson about the layers of the earth. As an alternative learning media, Augmented Reality technology is needed which can help teachers have a variety of learning media, so that they can accommodate the learning needs of students who have various types of learning characteristics. In this study the authors made augmented reality using markerless so that they could display or show how the structure of the earth's layers looks like seeing directly without markers or markers. Using EasyAR as a Software Development Kit (SDK) because it has many features and methods that can be used for creating augmented reality.
Evaluation of Polysynchronous Learning of Statistical Materials in High School

Evaluation of learning implementation is critical. The emphasis is mostly on polysynchronous learning. The purpose of this study is to assess polysynchronous learning of statistical material in high school. The type and approache of this study is descriptive-quantitative. The subjects of the study were 29 high school students. Observation, tests, and questionnaires were used to collect data on the implementation of polysynchronous learning, problem-solving ability, and student learning independence. Data analysis is performed in three stages: scoring, computing percentages, and generating conclusions based on the provided categories. The results demonstrated that polysynchronous learning can be implemented properly utilizing Whatsapp for asynchronous and Google Meet for synchronous. Pupils with very high, high, medium, and low problem-solving talents are represented by 11, 7, 5, and 6 students, respectively. The number of pupils with learning independence ranges from very high to high to medium, with as many as 6, 19, and 4 students in each category. Students have the ability to solve mathematical problems and the independence to learn high-level categories, which makes it possible for the implementation of polysynchronous learning of statistical material to be carried out in an appropriate manner. The findings of this study suggest that educators can make use of polysynchronous learning to enhance the problem-solving abilities and independent learning capabilities of their pupils, regardless of whether they are living in a pandemic or new-normal environment.
MC-Net: multi-scale Swin transformer and complementary self-attention fusion network for pancreas segmentation

The pancreas is located deep in the abdominal cavity, and its structure and adjacent relationship are complex. It is very difficult to treat it accurately. In order to solve the problem of automatic segmentation of pancreatic tissue in CT images, we apply the multi-scale idea of convolution neural network to Transformer, and propose a Multi-Scale Swin Transformer and Complementary Self-Attention Fusion Network for Pancreas Segmentation. Specifically, the multi-scale Swin Transformer module constructs different receptive fields through different window sizes to obtain multi-scale information; the different features of the encoder and decoder are effectively fused through a complementary self-attention fusion module. By comparing experimental evaluations on the NIH-TCIA dataset, our method improves Dice, sensitivity, and IOU by 3.9%, 6.4%, and 5.3% respectively compared to the baseline, which outperforms current state-of-the-art medical image segmentation methods.
Contour and key point based eye feature extraction

The use of eye movements for identifying intrinsic human psychological changes is a hot topic, and the identification of eye states in specific scenarios is the basis for analyzing the test taker's instantaneous psychological response state and thus for testing implicit driving quality deficits. Infrared eye images are difficult to visualize eye movements, and traditional eye image processing results are not suitable for eye state analysis. In order to make the eye images show the eye movements, a feature extraction method based on contours and key point is proposed for infrared eye images. To extract the eye contour features, the iris and eyelid contours are extracted based on the variation of gray values between the points located on the boundary and their surroundings; for the extraction of eye key point features, a network design with a combination of dlib and residual structure is used to extract the pupil and eyelid key points, respectively. The experimental results show that the method can represent the relative positions of the pupil, iris and eyelid separately with little computational effort, which can intuitively show the eye movement and thus reduce the stress of subsequent eye state recognition.
What do we know about 21st Century Skills, Technologies and Initial Teachers Training? A Scoping Literature Review

Twenty first century skills and digital technologies have become more relevant in educational sciences, with a greater emphasis on school education, but also in teachers training. Several studies highlight its relevance to Initial Teachers Training, but also some vulnerabilities. This study aims to identify the research main trends between 2000 and 2020 on the development of competencies and the integration of digital technologies in initial teachers training. A five-stage Literature Review was conducted, based on document selection and data charting to examine the scope and nature of the most relevant studies. This methodology results in 998 references that integrate the three dimensions analyzed, whereby the dimension of competencies stood out the most with 39.67% of the references, followed by Initial Teacher Training with 31.40% and technology with 28.94%. The results reveal what are the current needs of elementary school teachers regarding the development of competencies, the strengths and vulnerabilities of initial teachers training and the enabling and conditioning factors for the integration of technologies in training.
Extracting a function encoded in amplitudes of a quantum state by tensor network and orthogonal function expansion

Abstract There are quantum algorithms for finding a function f satisfying a set of conditions, such as solving partial differential equations, and these achieve exponential quantum speedup compared to existing classical methods, especially when the number d of the variables of f is large. In general, however, these algorithms output the quantum state which encodes f in the amplitudes, and reading out the values of f as classical data from such a state can be so time-consuming that the quantum speedup is ruined. In this study, we propose a general method for this function readout task. Based on the function approximation by a combination of tensor network and orthogonal function expansion, we present a quantum circuit and its optimization procedure to obtain an approximating function of f that has a polynomial number of degrees of freedom with respect to d and is efficiently evaluable on a classical computer. We also conducted a numerical experiment to approximate a finance-motivated function to demonstrate that our method works.
Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.
A Study on the Innovation Cases and Improvements of University Education

The purpose of this paper is to explore recent innovation cases and improvements related to university education. Universities in the current era are making every effort to quickly prepare and implement self-rescue improvements to fulfill their unique role and meet the trend of the times due to population decline and rapid technological development. In this study, innovation trends in domestic and foreign university education, which were investigated mainly in April-May 2023, were confirmed and related contents were presented. Recently, attempts and methods for university education innovation at home and abroad were examined, and educational discussions and improvements were sought to the extent practically possible. As a result of a comprehensively examining the contents of the research topic, it was confirmed that, in particular, in university education innovation, integrated selection, convergence education, industry-academia cooperation, flexible curriculum, building infrastructure, and learner-led in-depth learning induction methods and activities are appearing. Through this study, it is expected that we will be able to start in-depth discussions on the innovation of university education that practices learner-centered education while flexibly responding to the needs of the rapidly changing era.
A Qualitative Phenomenological Investigation of Pre-university English as a Second Language Learners’ Experiences in Malaysia

This research study explicitly analyses pre-university learners’ individual learning experiences of English as a second language in a public university in Malaysia. In an attempt to gauge participants’ individual learning experiences, this study drew from Skinner’s (1957) behaviourism, Krashen’s (2020) nonnative language acquisition and Vygotsky’s (1974) sociocultural theories of learning. The behaviourism theory puts forth that learning a second language is a mechanical process which relies heavily on habit formation (Skinner, 1957). Krashen’s (2020) nonnative language acquisition and Vygotsky’s (1978) sociocultural theories, on the other hand, posit that a new language is naturally and effortlessly acquired through social interactions that require cognitive reasoning (Vygotsky, 1978). The qualitative findings received from one to one unstructured interviews were analysed in relation to the aforesaid theories. The findings revealed that informants studied English for university admission and that their sociocultural environment inhibited them from speaking the language outside the classroom. Discerning that the conventional teaching of English would not help them to attain their goal, informants though asserted to experience a drop in motivation throughout their English course, established an artificial linguistic environment for them to receive comprehensible inputs of English from various sources. Four out of five informants affirmed their preference to learn English in English speaking nations to be able to practice English in the wider community, while one underlined his/her preference for non-English speaking nations due to the fear of having difficulties in understanding accented English and of being laughed for his/her poor command English by native English speakers. Frequent addition of the linguistic suffix ‘lah’ from Bahasa Malaysia was added to standard English by local informants, leading to the creation of a hybrid landscape. Pedagogical implications for second language teaching and learning are raised alongside a revision of the structure, content and teaching strategies of existing English as a second language course.