Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Breast Cancer Segmentation Using U-Net Model with Attention Mechanisms and FedProx

Breast cancer is a leading cause of death among women worldwide, emphasizing the need for early detection and accurate diagnosis. As such Ultrasound Imaging, a reliable and cost-effective tool, is used for this purpose, however the sensitive nature of medical data makes it challenging to develop accurate and private artificial intelligence models. A solution is Federated Learning as it is a promising technique for distributed machine learning on sensitive medical data while preserving patient privacy. However, training on non-Independent and non-Identically Distributed (non-IID) local datasets can impact the accuracy and generalization of the trained model, which is crucial for accurate tumour boundary delineation in BC segmentation. This study aims to tackle this challenge by applying the Federated Proximal (FedProx) method to non-IID Ultrasonic Breast Cancer Imaging datasets. Moreover, we focus on enhancing tumour segmentation accuracy by incorporating a modified U-Net model with attention mechanisms. Our approach resulted in a global model with 96% accuracy, demonstrating the effectiveness of our method in enhancing tumour segmentation accuracy while preserving patient privacy. Our findings suggest that FedProx has the potential to be a promising approach for training precise machine learning models on non-IID local medical datasets.
Revolutionizing Cancer Diagnosis Through Hybrid Self-supervised Deep Learning: EfficientNet with Denoising Autoencoder for Semantic Segmentation of Histopathological Images

Machine Learning technologies are being developed day after day, especially in the medical field. New approaches, algorithms and architectures are implemented to increase the efficiency and accuracy of diagnosis and segmentation. Deep learning approaches have proven their efficiency; these approaches include architectures like EfficientNet and Denoising Autoencoder. Accurate segmentation of nuclei in histopathological images is essential for the diagnosis and prognosis of diseases like cancer. In this paper, we propose a novel method for semantic segmentation of nuclei using EfficientNet and Denoising Auto-encoder on the PanNuke dataset. The denoising auto-encoder pre-processing step is used to enhance the feature representations of input images, and EfficientNet is the model that has been used as the semantic segmentation model. Our proposed method achieved state-of-the-art results, outperforming most of the previously proposed methods by a significant margin, as our proposed method achieved a higher Dice score of 83.33 compared to the previous related work methods, which achieved scores varying from 69.3 to 80.28. The efficiency of our proposed approach will be demonstrated in this paper by discussing, exploring, and comparing it with previously proposed methods and their results.
Super Images - A New 2D Perspective on 3D Medical Imaging Analysis

In medical imaging analysis, deep learning has shown promising results. We frequently rely on volumetric data to segment medical images, necessitating the use of 3D architectures, which are commended for their capacity to capture interslice context. However, because of the 3D convolutions, max pooling, up-convolutions, and other operations utilized in these networks, these architectures are often more inefficient in terms of time and computation than their 2D equivalents. Furthermore, there are few 3D pretrained model weights, and pretraining is often tricky. We present a simple yet effective 2D method to handle 3D data while efficiently embedding the 3D knowledge during training. We propose transforming volumetric data into 2D super images and segmenting with 2D networks to solve these challenges. Our method generates a super-resolution image by stitching slices side by side in the 3D image. We expect deep neural networks to capture and learn these properties spatially despite losing depth information. This work aims to present a novel perspective when dealing with volumetric data, and we test the hypothesis using CNN and ViT networks as well as self-supervised pretraining. While attaining equal, if not superior, results to 3D networks utilizing only 2D counterparts, the model complexity is reduced by around threefold. Because volumetric data is relatively scarce, we anticipate that our approach will entice more studies, particularly in medical imaging analysis.
Number Functions

This chapter delves into the realm of numeric data manipulation and analysis within Power Query. These functions allow you to perform various mathematical calculations and transformations on numeric data. These functions are essential for manipulating numerical values within your data, performing calculations, and generating insights. Number functions can be applied to individual numeric values and columns of numeric data, or they can be used in combination with other functions to create complex calculations. The functions in this chapter are categorized into six groups, which will be discussed in the rest of this chapter.
Digital Currency and Financial Markets in Nigeria: Impact and Policy Implications

&lt;p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: arial, helvetica, sans-serif;"&gt;The rise of privately issued digital currencies, which primarily serve as alternative investment assets poses a challenge to the traditional financial instruments traded in the financial market. This study examines the dynamic relationship between the major privately issued digital currency (Bitcoin) and two financial market securities in Nigeria. The paper employed Vector Autoregressive (VAR) model and presents three relevant findings. First, the impulse response function indicates the absence of a significant response of the Nigerian financial market to shocks emanating from the Bitcoin market, implying lower connectedness between the two markets. Secondly, the outcome of the variance decomposition reveals a lower contribution of Bitcoin to changes in stock prices and treasury bills, however, stock prices and treasury bills contributed higher impact to each other compared to the contribution of Bitcoin. Thirdly, a weak bi-directional causality between the Bitcoin and treasury bills was observed and a uni-directional causality running from treasury bills and stocks, implying the existence of portfolio rebalancing from the fixed income to the equities market. Despite the weak connection between digital currency and the financial market, the paper recommends that the Central Bank of Nigeria and the Securities and Exchange Commission should maintain monitoring the development of crypto exchanges and continue reviewing the existing policy restricting cryptocurrency transactions through banks to avoid its unsavoury effects.&lt;/span&gt;&lt;/p&gt;
Investigating the impact of representation features on decision model comprehension

Decision models play an important role in various areas of information systems research, including system analysis and design, compliance management, and various application domains. Decision models must be effectively presented so that analysts can assure their correctness and completeness. So far, empirical research on the cognitive effectiveness of decision models has provided partially inconclusive results. Our paper provides novel insights into the drivers of decision model comprehension by moving from a classification based on representation archetypes to granular representation features. Using an experimental research design, we discover that the decision model type must be assessed in conjunction with other representational factors, such as representation structure (expanded vs. frugal) and representation design (monochromatic vs. colours). In this way, we extend prior arguments of cognitive fit theory by demonstrating that colour can be used to compensate for a misfit of decision model and task, and structural features can further increase model comprehension. We further studied the root causes of the observed effects by using eye-tracking. Our findings have implications for both cognitive information systems research and practice, as they can be used to guide decision model users and tool vendors.
Estimation of Japanese word intelligibility by automatic speech recognition with noise adaptation

The purpose of this study is to estimate subjective word intelligibility using DNN-based speech recognition. The evaluation of intelligibility is important to guarantee high speech quality in ambient noise and reverberation. The Japanese Diagnostic Rhyme Test (JDRT) is one such subjective evaluation method. In this study, we estimated Japanese word intelligibility by simulating the JDRT with cloud-based speech recognition systems. Recent DNNs can improve recognition accuracy with their robust classification ability and efficient use of high-dimensional features. We built a DNN-based speech recognition system using cloud-based ASR APIs, and adaptively trained its model to several noise conditions to improve its intelligibility estimation accuracy. In this paper, we will report comparisons with other objective and subjective evaluations and discuss ideas for a more generic objective evaluation method.
BliMSR: Blind Degradation Modelling for Generating High-Resolution Medical Images

A persisting problem with existing super-resolution (SR) models is that they cannot produce minute details of anatomical structures, pathologies, and textures critical for proper diagnosis. This is mainly because they assume specific degradations like bicubic downsampling or Gaussian noise, whereas, in practice, the degradations can be more complex and hence need to be modelled “blindly”. We propose a novel attention-based GAN model for medical image super-resolution that models the degradation in a data-driven agnostic way (“blind”) to achieve better fidelity of diagnostic features in medical images. We introduce a new ensemble loss in the generator that boosts performance and a spectral normalisation in the discriminator to enhance stability. Experimental results on lung CT scans demonstrate that our model, BliMSR, produces super-resolved images with enhanced details and textures and outperforms recent competing models, including a diffusion model for generating super-resolution images, thus establishing a state-of-the-art. The code is available at https://github.com/Samiran-Dey/BliMSR .
Organizing Russian as a foreign language teaching in schools in Asia, Africa and Latin America on the basis of a Russian online training package as an education export strategy

The aim of the research is to substantiate the high relevance of expanding pre-university Russian language education programs abroad to increase the level of Russian higher education export in terms of boosting demand among young people from Asia, Africa and Latin America. The paper examines the current picture of pre-university forms of Russian teaching abroad and determines the most effective form; investigates the existing conditions for the implementation of this form in Asia, Africa and Latin America and identifies the type of Russian methodological support necessary for its scaling. The scientific novelty of the research lies in defining one of the key conditions for the successful expansion of pre-university Russian language teaching programs abroad that consists in the organization of teaching the subject “Russian language” in foreign schools through the implementation of digital methodological support in the form of a Russian online training package to assist countries that do not have long-term experience in the implementation of Russian as a foreign language training programs. As a result of the research, the prospects of promoting the inclusion of the subject “Russian language” in the programs of foreign schools as a second foreign language in Asia, Africa and Latin America have been substantiated, as this form of education contributes to the effective organization of pre-university Russian language learning and the creation of positive ideas about the scientific and technological development of Russia among schoolchildren, forming their motivation and communication competencies for admission and successful study in Russian universities.
A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Images

Gross pathology photography of surgically resected specimens is an often overlooked modality for the study of medical images that can provide and document useful information about a tumour before it is distorted by slicing. A method for the automatic segmentation of tumour areas in this modality could provide a useful tool for both pathologists and researchers. We propose the first deep learning based methodology for the automatic segmentation of tumour areas in gross pathological images of lung cancer specimens. The semantic segmentation models applied are Deeplabv3+ with both a MobileNet and Resnet50 backbone as well as UNet, all models were trained and tested with both a DICE and cross entropy loss function. Also included is a pre and post-processing pipeline for the input images and output segmentations respectively. The final model is formed of an ensemble of all the trained networks which produced a tumour pixel-wise accuracy of 69.7% (96.8% global accuracy) and tumour area IoU score of 0.616. This work on this novel application highlights the challenges with implementing a semantic segmentation model in this domain that have not been previously documented.
City Branding Design from the Identification of Local Values of Penajam Paser Utara Regency

Penajam Paser Utara Regency is in a strategic location to improve its brand identity. Its existence adjacent to the new State Capital (IKN) is an opportunity with great potential for developing all Penajam Paser Utara Regency (PPU) sectors. The natural wealth in the form of the sea, endemic fauna in the form of sambar deer, Mastogok traditional motifs and some rice-producing areas that can be said to be the granary of IKN food security are local values that will be visualized in the form of a logo that characterizes Penajam Paser Utara Regency, plus a location that can be said to be "Serambi Nusantara" which means that PPU is the front porch / front page of IKN. The method in designing the city branding logo of Penajam Paser Utara Regency is practice-based (Practice-based research) because the foundation at the beginning is the experience of directly observing and obtaining data directly from the community, regional officials, and local customary leaders. The stages in this method include observation, improvisation, and realization. Ultimately, this paper shows a comprehensive design process for creating a visual identity for a city/district, which begins with research, visual enrichment, and visual design finalization.&#x0D;
Analysis of Electrical Equipment at UNICAMP: Insights from the Inventory Database

Data plays a crucial role in understanding several problems, including those related to electrical micro, smart grids and power consumption. In addition to electrical meters, data can be sourced from different channels such as environmental conditions, user expertise, maintenance history, and inventory registries. This paper introduces a Python-based analysis tool designed to search for equipment categories that exhibit constant power consumption within the asset inventory database of the University of Campinas. The software effectively identifies and categorizes items as air conditioners, refrigerators, computers, uninterrupted power supplies and internet routers, providing detailed insights into their specific characteristics. The tool generates a comprehensive PDF report featuring item discrimination through values, charts, organized and university units. Additionally, the software incorporates identification item lists and logs, aiding in the identification of missing or mismatched data throughout the process. These reports has been utilized to establish internal guidelines for optimizing in power consumption and already help the university to improve its GreenMetric index.
Solar Energy Forecasting: Case Study of the UNICAMP Gymnasium

Within the spectrum of studies conducted by the São Paulo Center for Energy Transition Studies (CPTEn), time series from the Photovoltaic Energy Plant of the UNICAMP Multidisciplinary Gymnasium (GMU-PV) were analyzed. This plant is associated with the first implementation of a photovoltaic system in the context of the Sustainable Campus Project (PCS) at UNICAMP - as a consequence, it originated the most extensive and robust time series in the project. The research, structured according to the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, aimed to identify the patterns and parameters associated with the energy production of the aforementioned photovoltaic system. Based on Machine and Deep Learning techniques, forecasting models were developed to maximize the use of available resources and promote the sustainability of this energy system at UNICAMP. In evaluating the results, it was observed that the most effective model was the Orthogonal Matching Pursuit (OMP) built from the Python low-code library, PyCaret. This regression machine learning model led to a coefficient of determination ( $$R^2$$ ) of 0.935 494 and a root mean square error (RMSE) of 8.561 679.
TON-ViT: A Neuro-Symbolic AI Based on Task Oriented Network with a Vision Transformer

The objective of this paper is to present a neuro-symbolic AI based technique to represent field-medicine knowledge, referred as to TON-ViT. TON-ViT integrates a Deep Learning Model with an explicit symbolic manipulation, a task graph. This task graph describes the steps of each trauma resuscitation as denoted by a verb and noun pair. Through this representation, symbolic processing and manipulation on task graphs, we can find stereotypical procedures, regardless of style of the performer. Furthermore, we can use this technique to find differences in styles, errors, shortcuts and generate procedures never seen before. When used in combination with a transformer, it can help recognize actions in egocentric vision datasets. Last, through symbolic manipulations on the graph, it is possible to generate medical knowledge which the model has not seen before. We present preliminary results after testing the TON-ViT with the Trauma Thompson Dataset.
A Study on the Meaning of Minimum Achievement Level in Art Subjects: Focusing on the 2022 revised national curriculum

This study aims to examine the meaning of the minimum achievement level discussed in the art curriculum. For this purpose, the minimum achievement level in The high school credit system and The comprehensive plan to guarantee basic education were examined. In addition, the terms of the minimum level of achieve-ment mentioned in the 10 subjects of the 2022 revised national curriculum were searched. As a result, only the Korean language and art mentioned the minimum achievement level in the common curriculum. The minimum achievement level of the art department could be summarized into keywords such as solving the learning gap, guidance, individual diagnosis, customized management, appropriate teaching and learning, and online and offline supplementary learning. The minimum achievement level in the art curriculum shows inclusivity for all of universal learning beyond the completion standards mentioned by the High school credit system. Since the art department is also a subject that should discuss achievement, the discussion on learning in art should be expanded further through the concept of the minimum level of achievement. In addition, standards or example data related to the minimum achievement level should be widely distributed for meaningful application in the field.
A Real-Time Non-Invasive Anomaly Detection Technique for Cooling Systems

The cooling systems contribute to 40% of overall building energy consumption. Out of which, 40% After identifying the anomalies, we find the cause of the anomaly. Based on the anomaly, the solution recommends a fix. If there is a technical fault, our proposed technique informs the technician regarding the faulty component, reducing the cost and mean time to repair. In the first stage, we propose a domain-inspired statistical technique to identify anomalies in cooling systems. We observe the Area Under the Curve of the Receiver Operating Characteristic ( $$AUC-ROC$$ ) score of more than 0.93 in both simulation and experimentation. In the second stage, we propose using a rule-based technique to identify the anomaly’s cause and classify it into three classes. We observe an $$AUC-ROC$$ score of 1. Based on the anomaly classification, in the third stage, we identify the faulty component of the cooling system. We use the Nearest-Neighbour Density-Based Spatial Clustering of Applications with Noise (NN-DBSCAN) algorithm with transfer learning capabilities to train the model only once, where it learns the domain knowledge using simulated data. The overall accuracy of the three-stage technique is 0.82 and 0.86 in simulation and experimentation, respectively. We observe energy savings of up to $$68\%$$ in simulation and $$42\%$$ during experimentation.
Exploring Identity Competency in the 2022 Revised Korean National Art Curriculum

Identity was the most actively discussed issue in current society, which was characterized by delocaliza-tion, multiculturalization, and pluralization. Contemporary art has also faced various problems surrounding humans occurring in the same era and provided clues through images to solve them. The starting point was respect for humans, and it always started with a discussion about identity. While witnessing these changes in art, school art education has focused on identity as a starting point for leading an individual's life in the 21st century educational transition. However, identity was a concept that changes and is con-structed, and has gone through various transformations before being converted into an educational competency. Reflecting the overall spirit of the 2022 revised Korean National Curriculum with the vision of ‘leading talent with inclusiveness and creativity,’ ‘initiativeness’ was also emphasized in the 2022 revised Korean National Art Curriculum, which reflected self-identity. autonomy, self-development, self-reflection, self-exploration, self-understanding, self-orientation, and subjectivity understanding were proposed as iden-tity competencies with sub-competencies. This study examined the meaning of identity in education and various discussions related to identity based on the identity issue of contemporary art as a basis for under-standing the identity competency of the 2022 revised Korean National Art Curriculum. This study focused on explaining its meaning in detail and helping people understand and apply it in their educational settings.
A point-free perspective on lax extensions and predicate liftings

Abstract Lax extensions of set functors play a key role in various areas, including topology, concurrent systems, and modal logic, while predicate liftings provide a generic semantics of modal operators. We take a fresh look at the connection between lax extensions and predicate liftings from the point of view of quantale-enriched relations. Using this perspective, we show in particular that various fundamental concepts and results arise naturally and their proofs become very elementary. Ultimately, we prove that every lax extension is induced by a class of predicate liftings; we discuss several implications of this result.