Reviewerly/exHarmony huggingface dataset:
Automatic Detection System to Identify Invasive Ductal Carcinoma by Predicting Bloom Richardson Grading from Histopathological Images

After skin cancer, the most common type of cancer is breast cancer among the world population. Breast cancer is the leading cause of cancer-induced mortality among women. Breast cancer is frequently diagnosed by using biopsies in which tissue is removed from the breast and studied under a microscope. The results of these biopsies are based on the qualifications and experience of the pathologist who diagnoses the abnormal cell under the microscope. With the emergence of advancements in the fields of image processing and artificial intelligence, there is an area of interest in developing a deep learning model to improve and enhance the quality and accuracy of breast cancer diagnosis. This study proposed a deep learning model that automatically analyses the multiclass classification of hematoxylin and eosin-stained histological images of invasive ductal carcinoma (IDC) by discriminating the IDC into grades such as G-1, G-2, and G-3. The methodology is focused on a deep learning model to detect grades of invasive ductal carcinoma by adopting the Sequential Convolutional Neural Network Two-Dimensional (CNN2D). We used DataBiox, a public dataset taken from an internet source consisting of 922 images. We evaluate the result of multiclass classification by dividing 80% and 20% of the dataset into training and testing data, respectively. As a result of the training and testing of the pre-trained CNN model, sequential CNN yields the accuracy of the model of 92.81%. Our model accurately classifies a multi-class classification of histological images of grades of breast cancer, specifically IDC. It is ready to be tested with a more diverse and massive database in the future.
Use of Bidirectional Long Short Term Memory in Spoken Word Detection with reference to the Assamese language

Objectives :The proposed method is based on a unique technique of Deep learning for identifying spoken words with reference to Assamese language.Most of the DNN based algorithms have been successfully implemented in the field of image recognition, computer vision, natural language processing and medical picture analysis.Methods: The method used here is the Bidirectional Long Short Term Memory (BLSTM).BLSTM incorporates both past and future situations together.The speech database for this research work is hired from the repository of Indian Language Technology Proliferation and Development Center (ILTP-DC).This repository contains 32,335 utterances by 1000 numbers of male and female participants, which is comprised of 262 unique Assamese native words.The BLSTM based recognition model is using 10 out of the 262 unique words and the remaining words are used in construction or generation of synthesized sentences.The feature extraction module uses 39 feature coefficients, which are composed of MFCC, ∆MFCC and ∆∆MFCC coefficients.Findings: The Word Error Rate (WER) of the BLSTM based recognition model is 18.84% with an average accuracy of 98.12%, which sets one promising benchmark when compared to recent findings.Novelty: In this work an attempt has been made with a different approach to detect certain keywords of Assamese language by adopting deep learning methodology.The future objective of this proposed work is to improve the detection capability of this model by considering multiple DNN models together in a hybrid approach along with the inclusion of additional features.
Development of Augmented Reality (Ar) Learning Media of Natural Science Subject on Subject Matter of Water Cycle for MI Grade V Students

This development research aims to: (1) produce the learning medium of Augmented Reality (AR) on natural science subject on subject water recycle in class V MI Wahid Hasyim; (2) knowing the quality of the products the learning medium of Augmented Reality (AR) on natural science subject on the subject matter of water recycle ; (3) find out the results of the response of teachers and students so that decent media used in learning natural science. The development model used in this medium is the modified Thiagarajan 4D model. The stages carried out consist of three stages, namely Define (defining), Design (design), and Develop (development). The media for learning Augmented Reality (AR) was assessed by 1 media expert, 1 matter expert, 1 peer reviewer, 1 class V teacher, and 10 V grade students MI Wahid Hasyim to find out student responses. The instruments used were questionnaires and evaluation sheets using non-objective description forms for testing the quality of science learning media products. The results of the study are: (1) Augmented Reality (AR) media learning products have been developed for water cycle matter run on Android version 4.1 jelly bean, (2) Learning media for Augmented Reality (AR) water cycle matter with feasibility according to the overall assessment of reviewers and peers reviewers obtain very good quality ie 90.2%, (3) response or response of class V teachers is very good with a percentage of 82.57%. Likewise, the students' responses obtained a percentage of 90.2% with a positive response so that the learning media had a quality category on the Very Good criteria which was also supported by the results of students who experienced an increase of 35.8%. Based on the results of data acquisition shows that the learning media of Augmented Reality (AR) on natural science subjects water cycle matter in class V MI Wahid Hasyim is worthy of being used as a learning resource for students.
Multi-layer noise reshaping and perceptual optimization for effective adversarial attack of images

Abstract Adversarial attack aims to fail the deep neural network by adding a small amount of perturbation to the input image, in which the attack success rate and resulting image quality are maximized under the l p norm perturbation constraint. However, the l p norm is not accurately correlated to human perception of image quality. Attack methods based on l 0 norm constraint usually suffer from the high computational cost due to the iterative search for candidate pixels to modify. In this work, we explore how perceptual quality optimization can be incorporated into the adversarial attack design and propose a two-stage attack method to reshape the adversarial noise by an initial attack and optimize the visual quality of the attacked images without sacrificing the attack success rate. Specifically, we construct a visual attention network to generate a perceptual attention map to modulate the adversarial noise generated by a base attack method. The network is trained to maximize the visual quality in Structural Similarity Index Metric (SSIM) while achieving the same attack success rate. To improve the image perceptual quality further, we propose a fast search algorithm to perform an iterative block-wise pruning of the adversarial noise. We evaluate our method on the mini-ImageNet dataset against three different defense schemes. The results have demonstrated that our method can achieve better attack performance in image quality, attack success rate, and efficiency than the state-of-the-art attack methods.
Adversarial Augmentation for Enhancing Classification of Mammography Images

Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classification task (breast cancer recognition), we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier. By augmenting the data, performance on downstream classification tasks could be improved even with a relatively small training set. We show that this "adversarial augmentation" yields promising results compared to classical image augmentation on the example of breast cancer classification.
Learning to Infer Program Sketches

Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction. The key idea of this work is that a flexible combination of pattern recognition and explicit reasoning can be used to solve these complex programming problems. We propose a method for dynamically integrating these types of information. Our novel intermediate representation and training algorithm allow a program synthesis system to learn, without direct supervision, when to rely on pattern recognition and when to perform symbolic search. Our model matches the memorization and generalization performance of neural synthesis and symbolic search, respectively, and achieves state-of-the-art performance on a dataset of simple English description-to-code programming problems.
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"

This paper documents the release of the ELKI data mining framework, version 0.7.5. ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions of additional methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms. We will first outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version. We also include an appendix presenting an overview on the overall implemented functionality.
Content-based image retrieval system with most relevant features among wavelet and color features

Content-based image retrieval (CBIR) has become one of the most important research directions in the domain of digital data management. In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transformation and color features in RGB and HSV domains are proposed as representative feature vector for images in database followed by appropriate similarity measure for each feature type. In CBIR systems, retrieving results are so sensitive to image features. We address this problem with selection of most relevant features among complete feature set by ant colony optimization (ACO)-based feature selection which minimize the number of features as well as maximize F-measure in CBIR system. To evaluate the performance of our proposed CBIR system, it has been compared with three older proposed systems. Results show that the precision and recall of our proposed system are higher than older ones for the majority of image categories in Corel database.
A Quantum Approximate Optimization Algorithm for continuous problems

We introduce a quantum approximate optimization algorithm (QAOA) for continuous optimization. The algorithm is based on the dynamics of a quantum system moving in an energy potential which encodes the objective function. By approximating the dynamics at finite time steps, the algorithm can be expressed as alternating evolution under two non-commuting Hamiltonians. We show that each step of the algorithm updates the wavefunction in the direction of its local gradient, with an additional momentum-dependent displacement. For initial states in a superposition over many points, this method can therefore be interpreted as a coherent version of gradient descent, i.e., 'gradient descent in superposition.' This approach can be used for both constrained and unconstrained optimization. In terms of computational complexity, we show how variants of the algorithm can recover continuous-variable Grover search, and how a single iteration can replicate continuous-variable instantaneous quantum polynomial circuits. We also discuss how the algorithm can be adapted to solve discrete optimization problems. Finally, we test the algorithm through numerical simulation in optimizing the Styblinski-Tang function.
Reconstruction of 3D Porous Media From 2D Slices

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices), we recover the three-dimensional structure around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method provides a good reconstruction in terms of Minkowski functionals.
Meta-Learning Mean Functions for Gaussian Processes

When fitting Bayesian machine learning models on scarce data, the main challenge is to obtain suitable prior knowledge and encode it into the model. Recent advances in meta-learning offer powerful methods for extracting such prior knowledge from data acquired in related tasks. When it comes to meta-learning in Gaussian process models, approaches in this setting have mostly focused on learning the kernel function of the prior, but not on learning its mean function. In this work, we explore meta-learning the mean function of a Gaussian process prior. We present analytical and empirical evidence that mean function learning can be useful in the meta-learning setting, discuss the risk of overfitting, and draw connections to other meta-learning approaches, such as model agnostic meta-learning and functional PCA.
Kubernetes as an Availability Manager for Microservice Applications

The move towards the microservice based architecture is well underway. In this architectural style, small and loosely coupled modules are developed, deployed, and scaled independently to compose cloud-native applications. However, for carrier-grade service providers to migrate to the microservices architectural style, availability remains a concern. Kubernetes is an open source platform that defines a set of building blocks which collectively provide mechanisms for deploying, maintaining, scaling, and healing containerized microservices. Thus, Kubernetes hides the complexity of microservice orchestration while managing their availability. In a preliminary work we evaluated Kubernetes, using its default configuration, from the availability perspective in a private cloud settings. In this paper, we investigate more architectures and conduct more experiments to evaluate the availability that Kubernetes delivers for its managed microservices. We present different architectures for public and private clouds. We evaluate the availability achievable through the healing capability of Kubernetes. We investigate the impact of adding redundancy on the availability of microservice based applications. We conduct experiments under the default configuration of Kubernetes as well as under its most responsive one. We also perform a comparative evaluation with the Availability Management Framework (AMF), which is a proven solution as a middleware service for managing high-availability. The results of our investigations show that in certain cases, the service outage for applications managed with Kubernetes is significantly high.
Metadata for Creators

Metadata is generally viewed from a third-party perspective, standing aside from both the creator and the audience of a work. This paper looks at the problem from the creator's standpoint: metadata that is deeply intertwined with the process of creation. In additional to more conventional organization metadata, two other important classes of metadata stand out: signature metadata, which is for the creator of a work to claim authorship and communicate with their audience, and process metadata, which is to help the creator recover key details of the creation process for themselves to drive further creation. Some key techniques for capturing and embedding signature and process metadata are detailed for a particular use case are described, along with some lessons learned. It turns out that taking a metadata-first development approach can make is easier both to capture the metadata and implement and tweak the process itself.
Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region

With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges.An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020.19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts.The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.
Binarization Algorithm Based on Side Window Multidimensional Convolution Classification

Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.
Cognitive Trait Model: Measurement Model for Mastery Level and Progression of Learning

This paper seeks to establish a framework which operationalizes cognitive traits as a portion of the predefined mastery level, the highest level expected to successfully perform all of the relevant tasks of the target trait. This perspective allows us to use and interpret the cognitive trait levels in relative quantities (e.g., %s) of the mastery level instead of relative standings (i.e., rankings) on an unbounded continuum. To facilitate the proposed perspective, this paper presents an analytical framework that has support on the [0, 1] trait continuum with truncated logistic link functions. The framework provides a solution to cope with the chronic question of “relative standings or magnitudes of learning outcome?” in measuring cognitive traits. The proposed framework is articulated relative to the traditional models and is illustrated with both simulated and empirical datasets within the Bayesian framework, estimated with the Markov chain Monte Carlo method.
Sales Anomaly Detection Using Automatic Time Series Decomposition

This study proposes an automated time series decomposition (ATSD) technique for sales anomaly detection using weekly ice cream sale data from Google Trends with the caption "(Mobile AL-Pensacola (Ft. Walton Beach) FL. In this study, an empirical approach based on automatic time series decomposition (ATSD) was used to detect anomalies in sales data. A historical quantitative weekly ice cream sales data between periods of 9 April 2017 to 3 April 2022 consisting of 261 data points was used. The process for discovering the anomalous sales point includes importing of the necessary libraries, data visualization, construction of the ATSD model, creating the time series components from original data (seasonal, trend, residual), calculating the estimated from the original, and finally, the extraction of the anomalous sales. Anomalies were first discovered during the data visualization stage, which revealed some abnormal sales in mid-2019, late-2019, and early 2020. However, after applying the ATSD, our model detected anomalies with the specific dates of these anomalies. Following the application of the ATSD, our results indicate that the proposed anomaly detection approach can reliably detect anomalies with the dates of these anomalies. Because this technique worked well with our data, we assume it will work with any time series data.
Construction of Permutation Polynomials Using Additive and Multiplicative Characters

Permutation is a natural phenomenon useful for understanding and explaining the structural and functional behavior of objects or concepts. The mathematical formulation of permutation behavior can be readily achieved by permutation polynomials. Permutation polynomials are constructed by suitably modifying linearized polynomials and associated affine polynomials with the help of additive characters, multiplicative characters, and special types of Trace functions for the polynomials in one and more than one indeterminates. The permutation properties of the obtained polynomials are verified using the AGW criterion.