Reviewerly/exHarmony huggingface dataset:
Tumour segmentation with deep learning model trained on immunostain-augmented pixel-accurate labels – Application on whole slide images of breast carcinoma

Introduction: Whole slide images are high resolution and data intensive. Traditional manual annotation is unable to produce high-definition pixel accurate labels. Augmentation by corresponding immunostain images overcomes operator dependent annotation limits. Methods: H&E slides from breast carcinoma resection specimens were retrieved and reviewed for presence of tumour. The H&E slides with tumour were scanned using whole slide scanner Leica AT2 at 0.25 um per px. AE1/3 (pancytokeratin) stain was performed on the same slides after de-staining the H&E dyes. The scanned AE1/3 images were aligned to the H&E images and immunoreative areas were used to generate annotations of tumour areas to the corresponding H&E slides. The annotations were checked and modified by two independent assessors, converted into binary masks. The cases were randomly assigned to the training or testing cohort with a train-test split of 3:1. The masks and paired H&E images were fed into a U-Net based deep learning model for tumour detection and segmentation. Results: The annotations resulting from the AE1/3 image were pixel accurate and the assessors were only required to highlight the rough tumour areas in bulk only. A total of 101 specimens were included, 75 and 26 were assigned to the training and validation cohort. The algorithm achieved a per-pixel accuracy of 0.928 (0.827–0.998) for tumour segmentation in the testing cohort. The tumour segmentation results were able to precisely identify carcinoma areas from peritumoral stromal area. Conclusion: Immunostain-augmentation increases annotation accuracy and efficiency, resulting in pixel-accurate annotations, from which deep learning generates models that precisely maps tumour on whole slide images. Immuonstain-augmentation with deep learning has potential to provide large scale and reliable data for cancer staging, cellular and microenvironment spatial analysis and three-dimensional tumour reconstruction.
Balanced NUCOMP

Arithmetic in the divisor class group of a hyperelliptic curve is a fundamental component of algebraic geometry packages implemented in computer algebra systems such as Magma and Sage. In this paper, we present an adaptation of Shanks’ NUCOMP algorithm for split model hyperelliptic curves of arbitrary genus that uses balanced divisors and includes a number of enhancements to optimize its efficiency in that setting. Our version of NUCOMP offers better performance than Cantor’s algorithm in the balanced divisor setting. Compared with Magma’s built-in arithmetic, our Magma implementation shows significant speed-ups for curves of all but the smallest genera, with the improvement increasing as the genus grows.
Shared Vision Based Teamwork Cohesiveness: Catalyst for Organizational Commitment and Organizational Citizenship Behavior (Self Congruence Theory Perspective)

This paper reviews the important role of Shared Vision Based Teamwork Cohesiveness (SVBTC) in bridging Organizational Commitment (OC) and Organizational Citizenship Behavior (OCB) for employees of an organization. One of the positive behaviors that are expected to grow in an organization is the presence of high OC and OCB in employees. Several previous studies have shown that employees who have good OC are not always supported by good OCB. In this study, a catalyst in the form of Shared Vision Based Teamwork Cohesiveness (SVBTC) is presented from the point of view of Self Congruence Theory (SCT) which is believed to be able to strengthen the relationship between OC and OCB. It is hoped that this paper will provide a new perspective on the factors that can increase OCB for employees in an organization.
Image processing for positioning mechanical device with Backpropagation algorithm and separate handling of RGB components

Different approaches for the use of Artificial Neural Networks - ANNs, in the recognition of image patterns, have been used with variations ranging from the processing of the image data to the ANN architecture itself. This paper describes the development of a system that aims to recognize patterns of images with ANNs of three inputs that receive images decomposed into their RGB components. The ANNs have an architecture with two hidden layers of six neurons each, and use the algorithm Backpropagation. The built model normalizes RGB components with values ​​between zero and one. The Backpropagation algorithm is used for the purpose of functional approximation of these components, and after training, the numerical arrangements obtained in the three outputs corresponding to the inputs are denormalized to form the resulting training image. Six image pattern had training in different ANNs, forming a system to recognized each pattern. The feasibility of using the model was verified with the tests for its generalization capacity. Images used to position a mechanical device, which did not participate in the training, were inserted into the system and from them the positioning of the device was performed, with a high degree of accuracy.
Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM

Cervical cancer’s a gynecological malignancy in women that’s very dangerous, even causes death. Prevention through early detection of Pap smear test. It was carried out by pathologists with the help of a microscope still have obstacles in observations. There’re many studies on Pap smear image processing for helping pathologists in cell identification. Availability of Pap smear image dataset is needed in cervical cancer early detection research. The purpose of this study was to segment, feature extraction and classify 180 Pap smear images of RepoMedUNM. The method used to identify Pap smear images begins with preprocessing, namely changing the color in the image to L*a*b color, segmentation using the K-means method, extraction of 6 features, namely metric, eccentricity, contrast, correlation, energy, and homogeneity, and then identified by calculating the closest distance between the training data features and the test data features with the Euclidean distance. The result of identification ThinPrep Pap smear images in 3 classes achieve average accuracy of 93.33%, Non-ThinPrep Pap smear images in 2 classes achieve 90% average accuracy and the average accuracy of the overall in the 4 classes reached 92%. These results indicate that the proposed method can identify Pap smear images well.
Pengaruh Model Pembelajaran Word Square terhadap Hasil Belajar Tema 1 Sub Tema 1 Kelas 5 Di MI Nurul Huda

There are still many teachers who do not use interesting learning methods, as a result students become passive in learning. Teachers should use interesting methods and take advantage of existing facilities so that students are interested and enthusiastic about learning, The code square learning model affects students' learning. Analysis using word square as a test is proved this. The study was conducted at mi nurul huda sukaraja with the number of 60 students, classes a 30 and class b 30 respondents. The objectives of this study: 1. The learning outcomes of students who apply the word square learning model to the theme 1 sub-theme 1, 2. The learning outcomes of students who do not apply the word square learning model to the sub-theme 1 theme, 3. The effect of applying the word square learning model to friends 1 sub-theme 1. The research method used in this study is a quasi-experimental design with a true experimental design. This research can be concluded that the Word Square learning model has an effect on student learning outcomes
Perbandingan Cara Belajar Siswa dari Sekolah Pondok dan Sekolah Negeri Terhadap Hasil Belajar

Education during this pandemic is a challenge for all parties. Students are required to get good learning outcomes during online learning. The efforts of students in achieving these learning outcomes are by applying their respective ways of learning. This study aims to determine the effect of junior high school students' learning outcomes on learning outcomes. This type of research is survey research with a quantitative approach. The subjects of this study were 36 students of SMP Wahid Hayim and SMP Negeri 2 Nglipar grade 8 for the academic year 2021/2022 with samples taken using simple random sampling technique. The instrument in this study was a questionnaire on how to learn which was adopted from the questionnaire on how to learn in the thesis. The Effect of Learning Mathematics on Mathematics Learning Achievement in Grade IV Elementary Schools throughout the Imam Bonjol Cluster, Purbalingga District by Rofiqoh Nur Rokhmah, which was valid and reliable. The questionnaire was distributed in the form of a google form. The data is in the form of a data base of students' UTS scores as well as research instruments. Analysis of the data in the form of descriptive statistical analysis and simple linear regression analysis. The results of this study will show that the comparison of student learning from the two schools has a different effect on student learning outcomes.
Framework design research and practice of digital water spatial database system

In the promotion and construction of the Yangtze River digital waterway project, in order to solve the problem that lacking of standard and unified visual spatial information service in the waterway management business process, the framework of the spatial database system of the Yangtze River digital waterway is designed in combination with the maintenance management mode and business characteristics of the Yangtze River waterway, and proposed the system practice scheme of the function, data architecture and standard specification of the spatial database system, which provides effective technical support for the realization of unified intelligent service of spatial information of the Yangtze River waterway.
Hybrid Feature Approach of Face Recognition based on Pixel Binary Segmentation

The pose, illumination, and expression variations are challenging tasks in Facial Recognition (FR) and are a popular research area nowadays. We introduce novel nibbles of pixel technique and hybrid features from nibbles in this paper. The color images are converted into grayscale and then converts decimal values from each pixel into eight-bit binary values. The novel concepts of segmenting eight-bit binary into two groups of four-bit binary as Leftmost Nibble (LN) and Rightmost Nibble (RN) is presented. The nibble concept increases the computational speed and decreases the complexity of the system in the case of a real-time system as the 256 shades of grayscale images are decreased to 16 shades for LN and 16 shades for RN i.e., totally only 32 shades instead of 256 shades. The LN and RN binary is converted back to decimal values. The LL subband which is obtained from the applied Discrete Wavelet Transform (DWT) technique on the LN matrix is considered as the most important information while the Histogram of Oriented Gradients (HOG) is applied on the RN matrix to detect the edge information. The linear convolution of DWT and HOG results in the final hybrid features. In the matching part, the Euclidean Distance (ED) matching and the Artificial Neural Network (ANN) are selected to classify the features and calculate the proposed algorithm's performance parameters. The experimentation is implemented on six standard face databases, which demonstrates an outstanding performance by getting higher accuracy with less computation time compared with the existing techniques.
An Integrated Approach to Produce Robust Deep Neural Network Models with High Efficiency

Deep Neural Networks (DNNs) need to be both efficient and robust for practical uses. Quantization and structure simplification are promising ways to adapt DNNs to mobile devices, and adversarial training is one of the most successful methods to train robust DNNs. In this work, we aim to realize both advantages by applying a convergent relaxation quantization algorithm, i.e., Binary-Relax (BR), to an adversarially trained robust model, i.e. the ResNets Ensemble via Feynman-Kac Formalism (EnResNet). We discover that high-precision quantization, such as ternary (tnn) or 4-bit, produces sparse DNNs. However, this sparsity is unstructured under adversarial training. To solve the problems that adversarial training jeopardizes DNNs’ accuracy on clean images and break the structure of sparsity, we design a trade-off loss function that helps DNNs preserve natural accuracy and improve channel sparsity. With our newly designed trade-off loss function, we achieve both goals with no reduction of resistance under weak attacks and very minor reduction of resistance under strong adversarial attacks. Together with our model and algorithm selections and loss function design, we provide an integrated approach to produce robust DNNs with high efficiency and accuracy. Furthermore, we provide a missing benchmark on robustness of quantized models.
Entity Relations Based Pointer-Generator Network for Abstractive Text Summarization

The goal of automatic text summarization is to generate a shorter text containing the main ideas and key information of the original text. In recent years, sequence-to-sequence (Seq2Seq) models have made great progress in text summarization task. Many derived models appeared and successfully handled challenges of this task, such as fluency and readability. They also alleviate repetition and out-of-vocabulary (OOV) word problems. However, there remains an important issue to be solved, the factual consistency (also named factual coherency). Since important messages exist in the entities and their relations which appear in the original text sentences, this paper investigates the value of entity relations to boost performance of Seq2Seq abstractive text summarization models. To this end, we present Entity relations based Pointer-Generator Network (ERPG) which has 1) Informative OpenIE Relation Triples Selection Algorithm that generating non-redundant Open-domain relation triples from plain text by using Stanford OpenIE (Open Information Extraction); 2) Entity Relations Graph Attention network (ERGAT), a new graph attention neural network is designed to obtain structural features from entity relation triples in the text. 3) Entity-focused attention, a modified calculation of attention distribution is introduced to guide Seq2Seq model to focus on the salient words of the text. Experimental results show that ERPG can boost the performance of Pointer-Generator network, ERGAT is the main factor of improvement and the keyinfo attention can enhance the basic attention mechanism. The relation triples have high potential to improve abstractive text summarization models.
Predicting Cyber-Attack using Cyber Situational Awareness: The Case of Independent Power Producers (IPPs)

The increasing critical dependencies on Internetof-Things (IoT) have raised security concerns; its application on the critical infrastructures (CIs) for power generation has come under massive cyber-attack over the years.Prior research efforts to understand cybersecurity from Cyber Situational Awareness (CSA) perspective fail to critically consider the various Cyber Situational Awareness (CSA) security vulnerabilities from a human behavioural perspective in line with the CI.This study evaluates CSA elements to predict cyber-attacks in the power generation sector.Data for this research article was collected from IPPs using the survey method.The analysis method was employed through Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the proposed model.The results revealed negative effects on people and cyber-attack, but significant in predicting cyber-attacks.The study also indicated that information handling is significant and positively influences cyber-attack.The study also reveals no mediation effect between the association of People and Attack and Information and Attack.It could result from an effective cyber security control implemented by the IPPs.Finally, the study also shows no sign of network infrastructure cyber-attack predictions.The reasons could be because managers of IPPs had adequate access policies and security measures in place.
A Reversible and Rotational-Invariant Watermarking Scheme Using Polar Harmonic Transforms

Polar harmonic transformation (PHT) is a well-known family of rotation and scaling image-invariant descriptors. The PHT is frequently used in the watermarking applications. The watermark bits of the image are mixed into the image moments. The modified moments are then used to reconstruct the watermarked image. The ability of the image embedded with the watermark bits to resist an attack depends on the strength of the image moments to resist the attack. Therefore, the accuracy of the moments determines the robustness of the watermarking algorithm. An improved method based on the combination of analytical and numerical techniques is used for computing the image moments. The more accurate moments can be then used for the watermarking uses. The experimental results show that the improved moments have the ability to resist rotational attacks and are completely reversible. A comparative study also suggest that the improved moments have better robustness for rotational attacks than some of the existing image moment-based watermarking techniques.
A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes

In order to develop a non-contact and simple gesture recognition technology, a recognition method with a charge induction array of nine electrodes is proposed. Firstly, the principle of signal acquisition based on charge induction is introduced, and the whole system is given. Secondly, the recognition algorithms, including the pre-processing algorithm and back propagation neural network (BPNN) algorithm, are given to recognize three input modes of hand gestures, digital input, direction input and key input, respectively. Finally, experiments of three input modes of hand gestures are carried out, and the recognition accuracy is 97.2%, 94%, and 100% for digital input, direction input, and key input, respectively. The outstanding characteristic of this method is the real-time recognition of three hand gestures in the distance of 2 cm without the need of wearing any device, as well as being low cost and easy to implement.
Social Entrepreneurship Projects in the English Class: A Pandemic Multimodal Experience

This exploratory qualitative study shares an innovative experience with twenty-three EFL undergraduate students in the Business Administration Program at a private university in Colombia. It aimed at positioning them as problem solvers and connecting their background as sources for EFL learning. We explored how business administration students problematized their communities to propose social solutions during the pandemic. Data were collected from students’ reflective journals, students’ artifacts, and a survey. In groups, the students explored cultural practices for shopping, economy, and pet-related issues to propose a social entrepreneurship project. Eleven business projects emerged as a result of students’ socially-oriented concerns, critical skills, and multimodal experiences.
Lossy Image Compression Using Karhunen-Loeve Transform Based Methods

Karhunen-Loeve Transform (KLT) is generally not preferred much because it is a signal-dependent transform in signal compression.However, by developing effective algorithms, image compression that provides good performance can be achieved.In this study, two effective image compression methods based on eigenvector matrices obtained using KLT are applied.Image compression is performed by creating small-sized codebooks for the two methods.The first method is based on the number of eigenvector matrices used in the training phase.Some of the highest numbers of used eigenvector matrices are used for image compression.The second compression method uses eigenvectors of autocorrelation matrices for quantization.In this approach, quantization is performed using the principal components of the eigenvector matrices.Various codebook sizes have been tested for image compression.The qualities of the reconstructed test images were compared with DCT-based JPEG and Wavelet Transform-based JPEG2000 compression methods using the PSNR metric.Experimental results show that the PSNR values of the proposed methods give better results than that of JPEG.
Smart Hospitality and Secure Tourism Management using Blockchain Technology: BESHosTM Approach

Throughout the age of 5G technology, the majority of contactless banking is made via software that is enabled by a wide range of financial platforms. Several alternative financing channels provide access to a variety of services. The opportunity for hackers to engage in nefarious behaviour such as payment account hacking, identity theft, and payment system assaults stages of clearances with e-tourism, monetary information is kept in a database. Payment issues can be caused by a centralised cloud server. Throughout the periods of heavy congestion, the abovementioned problems are solvable by utilising a decentralised system like blockchain, it allows for the maintenance of trustworthiness between distinct groups of financial institutions, tour companies, airways, and trains are examples of consumers. Cruise ships, accommodations, cafes, as well as regional cabs are all available. Inspired mostly by following the foregoing debate, we suggest the blockchain Enables Secure Smart Hospitality and Tourism Management (BESHosTM) model.