Reviewerly/exHarmony huggingface dataset:
Research on online learning resource sharing model based on alliance chain

Aiming at the problems of centralized management of online learning resources, unbalanced resource construction and difficult sharing of system resources in Guangdong Open University system, the article introduces alliance chain technology, summarizes the difficulties in the construction of online learning resources in Guangdong Open University system, compares the application status of alliance chain in resource construction, and analyzes the technical compatibility between alliance chain and resource sharing. An online learning resource sharing model based on alliance chain is constructed, and its basic framework, functional features and sharing process are described and analyzed.
Research

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Adaptive Overclocking Mining Algorithm Selection Approach in the Cryptocurrency Mining Pool

Cryptocurrency is one of the hit topics under the web 3.0 era. The first and most widely used cryptocurrency is bitcoin. For example, miners use bitcoin mining mechanism to solve cryptographic puzzles in a mining competition and receive Bitcoins as a reward. Setting the ideal clock rate on the mining device can produce the highest hash rate and thus the highest Bitcoin reward, which is one of the key factors affecting the results of the mining competition. In this paper, we concentrate on the NiceHash platform, the biggest crypto-mining market with a significant number of active miners. For miners, NiceHash offers software that can automatically choose the most lucrative algorithm, removing the need for them to keep an eye on the market and each of their various wallets. However, the current software typically chooses a mining algorithm for a miner to use without any further updates for a considerable amount of time, even though the profit made by using a mining algorithm is highly variable. Additionally, it chooses a mining algorithm without considering the clock rate of the miner's mining equipment. Furthermore, a lot of miners on the NiceHash pool encountered frequent disconnects. To solve the aforementioned issues, we propose a system that automatically selects the most profitable mining algorithms, adaptively sets the ideal clock rate of the mining device, and monitors the connection between miners and the mining pool. Compared to the original algorithm used in NiceHash, experimental results demonstrate that our automated system can efficiently maximize mining profits by increasing them by 21%. The profit can increase by up to 42% by having the mining device's clock rate automatically adjusted.
Developing an Extreme Learning Machine Based Approach to Weed Segmentation in Pastures

Effective weed management in pastures is critical for maintaining the productivity of grazing land. Autonomous ground vehicles (AGVs) are increasingly being considered for weed localization and treatment in agricultural land. Weeds, however, can be difficult to distinguish from background plants, due to similarities in colour, shape and texture. While deep learning approaches can be used to solve the localization issue, they are computationally expensive, and require a large volume of training images in order to combat overfitting. In this paper we present a novel Extreme Learning Machine based network for segmenting weeds from the background pasture. The proposed method utilizes a combination of LBP, HOG and colour features, and is tested on four small datasets, achieving a high mean Intersection over Union of 87.1, 79.5, 81.6 and 87.6 for Bathurst burr, horehound, thistle and serrated tussock respectively.
Fast detection and localization of mitosis using a semi-supervised deep representation

Detection, characterization and counting of mitosis is a main biomarker in cancer, allowing diagnosis, histological grading and prognosis. Nonetheless, mitosis identification remains as a challenging task (inter-observer variability up to 20%). Even, the computational support strategies remain limited to include wide visual variability of mitotic patterns, with an inherent bias because labeled observations. This work introduces a semi-supervised scheme that learns to identify mitotic cells from an initial limited amount of labeled data. Then, the initial trained backbone is used to propagate pseudo-labels into training samples. The most challenging samples, i.e., false positive and false negative pseudo-labeled samples, are included in further batches to re-train the model. At each iteration, a set of complementary non-mitotic patches are generated from an auxiliary net. The proposed approach was validated with the public ICPR dataset, achieving competitive results of 0.74 accuracy and 0.78 sensitivity. In addition, the proposed approach achieves an average inference time of 5.21 seconds (on a batch of 240 candidate patches).
Interpretable graph convolutional network enables triple negative breast cancer detection in imaging mass cytometry

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging pathology technology which can detect the spatial distribution of up to 40 markers at cellular resolution. However, the high content and complexity of this spatial information is underutilized in downstream analysis. To overcome this limitation, we develop an interpretable graph convolutional network (GCN) trained by IMC data with 30 cell markers from 238 patient breast cancer samples, with both marker expressions and cell locations. The network enables triple negative breast cancer (TNBC) classification from other clinical types, including HR+HER2+, HR+HER- and HR-HER+. More importantly, with an embedded self-attention pooling module, cell communities with high diagnostic values can be detected based on the attention scores. The proposed GCN framework is benchmarked with a fully connected artifical neural network (ANN) without spatial information. With a stratified 8-fold cross validation, GCN performs slightly better than ANN for tissue-level classification (class balanced accuracy: 0.8283±0.0964 to 0.8123±0.0989; area under the curve: 0.8548±0.1252 to 0.8298±0.1407). Nevertheless, GCN remarkably outperforms ANN on potentially interested cell community detection, especially in TNBC tissues, regarding the spearman correlation coefficient (SCC) between attention scores and marker expressions. The average SCC differences between GCN and ANN range from 0.0532 to 0.1876 for Cytokeratin 5, 7, 14, 8/18, 19, and pan Cytokeration. With comparisons of selected markers on tissues with different clinical types and attention scores, the cell marker expressions correlate with their clinical types and diagnostic values, which further validate the proposed framework. Overall, our GCN enables interpretable triple negative breast cancer detection and has the potential to be widely implemented in other diseases and highly multiplexed imaging techniques for enhanced microenvironment analysis.
Predicting cell type counts in whole slide histology images using evidential multi-task learning

Predicting the presence and counting various cell types in the tumor micro-environment is crucial for identifying cell spatial and morphological features in computational pathology. Deep learning (DL) approaches have been used extensively in predicting and counting different cell types in whole slide images (WSIs). However, DL-based probabilistic approaches have limitations in classifying pixels and determining cellular boundaries. In this study, we propose an end-to-end evidential multi-task deep neural network for counting different cell types in hematoxylin and eosin-stained digital pathology images. The aim is to achieve a reasonable accuracy with lower uncertainties than state-of-the-art methods. Our proposed approach consists of one encoder and three task-specific decoders. The encoder utilizes a pre-trained ResNet50 model with ImageNet weights to extract contextual and spatial imaging features. The multi-tasking approach comprises of three tasks: predicting various cell type counts (main task), nuclei semantic segmentation and estimating cell-type density maps (auxiliary tasks). We evaluated the effectiveness of our proposed approach on two open-source datasets, PanNuke and MoNuSAC, which consist of 205,343 and 46,909 nuclei segmentation labels, respectively. Our results were compared with state-of-the-art methods, HoVer-Net, StarDist, and ALBRT. Our proposed approach showed superior performance in cell-type counting compared to HoVer-Net, ALBRT, and StarDist with relative improvements of 11%, 7%, and 5% on the PanNuke dataset test set, and 8.5%, 2%, and 11% on the MoNuSAC dataset test set, respectively, in terms of <i>R</i><sup>2</sup>. Our experiments suggest that our proposed approach can provide compelling interpretations of diverse cell types and is applicable for various downstream tasks in computational pathology.
Digital pathology whole slide image compression with vector quantized variational autoencoders

Digital pathology Whole Slide Images (WSIs) are large images (∼30 GB/slide uncompressed) of high resolution (0.25 microns per pixel), presenting a significant data storage challenge for hospitals wishing to adopt digital pathology. Lossy compression has been adopted by scanner manufacturers to address this issue - we compare lossy Joint Photographic Experts Group (JPEG) compression for WSIs and investigate the Vector Quantised Variational Autoencoder 2 variant (VQVAE2) as a possible alternative to reduce file size while encoding useful features in the compressed representation. We trained three VQVAE2 models on a Camelyon 2016 subset to the Compression Ratio (CR) of 19.2:1 (CR<sub>1</sub>), 9.6:1 (CR<sub>2</sub>) and 4.8:1 (CR<sub>3</sub>) and tested on a Camelyon 2016 (DS<sub>1</sub>) subset; University of California (DS<sub>2</sub>) and Internal Validation Set (DS<sub>3</sub>). We then compared compression performance to ImageMagick JPEG and JPEG 2000 implementations. Both JPEG and JPEG 2000 compression outperformed the VQVAE2 implementation within the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. The trained VQVAE2 models could visually reproduce WSI tissue structure, but used colours from the original training data within the reconstructions on other datasets.
3D-GAN to generate representative and realistic three-dimensional breast cancer models for virtual clinical trial applications

This paper implements a generative adversarial deep learning network (GAN) to automate the generation of realistic and representative 3D cancer models to investigate digital breast tomosynthesis (DBT) with virtual clinical trials (VCT). Initially, a series of mass lesions from wide-angle DBT cancer cases were manually segmented. We trained a 3D-GAN in two phases: the first phase utilized 105 manually created models of invasive ductal carcinoma (IDA) (including both microlobulated and spiculated lesions) as a training dataset; the second phase focused on enhancing the details of the borders of the GAN-models by using a smaller training set of 42 highly spiculated segmentations. To improve the realism of the generated models in VCTs from both phases, post-processing was carried out by removing disconnected pixels, filling holes, smoothing the borders and elongating existing spicules. Fifteen generated lesions were then simulated in acquired patient images and their realism was validated by a radiologist. 80% of the simulated cases received at least a realism score of 3 out of 5. While the average realism score of the generated voxel models was slightly lower than the average score of manually segmented lesions, the 3D-GAN successfully generated breast cancer models of spiculated masses even when trained with a limited dataset. This same method could be applied to generate other mass models of certain subgroups to allow lesion specific simulations, increasing the efficiency of the process to produce representative lesion models for VCTs.
Multi-modality GLCM image texture feature for segmentation and tissue classification

Humans and computer observer models often rely on feature analysis from a single imaging modality. We will examine benefits of new features that assist in image classification and detection of malignancies in MRI and X-Ray tomographic images. While the image formation principles are different in these modalities, there are common features like contrast that are often employed by humans (radiologist) in each of these when making decision. We will examine other features that may not be well-understood or explored such as grey level co-occurrence matrix (GLCM) texture features. As preliminary data, we show here the utility of some of these features along with classification methods aided by Gaussian mixture models (GMM) and fuzzy C-Means dimensionality reduction. GLCM maps characterize the image texture and provide a numerical and spatial tool of the texture signatures present in it. We will present pathways for using these in tissue classification, segmentation and development of task-based assessments.
Effect of color-normalization on deep learning segmentation models for tumor-infiltrating lymphocytes scoring using breast cancer histopathology images

Studies have shown that the increased presence of tumor-infiltrating lymphocytes (TILs) is associated with better long-term clinical outcomes and survival, which makes TILs a potentially useful quantitative biomarker. In clinics, pathologists’ visual assessment of TILs in biopsies and surgical resections result in a quantitative score (TILs-score). The Tumor-infiltrating lymphocytes in breast cancer (TiGER) challenge is the first public challenge on automated TILs-scoring algorithms using whole slide images of hematoxylin and eosin-stained (H&E) slides of human epidermal growth factor receptor-2 positive (HER2+) and triple-negative breast cancer (TNBC) patients. We participated in the TiGER challenge and developed algorithms for tumor-stroma segmentation, TILs cell detection, and TILs-scoring. The whole slide images in this challenge are from three sources, each with apparent color variations. We hypothesized that color-normalization may improve the cross-source generalizability of our deep learning models. Here, we expand our initial work by implementing a color-normalization technique and investigate its effect on the performance of our segmentation model. We compare the segmentation performance before and after color-normalization by cross validating the models on the three datasets. Our results show a substantial increase in the performance of the segmentation model after color-normalization when trained and tested on different sources. This might potentially improve the model’s generalizability and robustness when applied to the external sequestered test set from the TiGER challenge.
Deep modality association learning using histopathology images and immune cell sequencing data

Nowadays, the availability of different types of biomedical digital data offers many opportunities to investigate the relationships between the different modalities and thus develop a more comprehensive understanding of complex diseases such as cancer. In this paper, we propose a multi-modal model, called deep modality association learning (DMAL), that maps immune cell sequencing patterns to morphological tissue features of whole slide imageds (WSIs) in an embedding space. Useful information is extracted from T-cell receptor (TCR) sequences to guide the training process. DMAL maps the TCR features to the morphology features in histopathology images, which in turn enables the model to learn the association features between the two modalities. The discrimination power of the WSI-TCR association features has been assessed by classifying samples with different cancer subtypes. The conducted experiments have shown that DMAL generates more discriminative features compared to features obtained from single-modal data. In addition, DMAL has been utilized to predict TCR information from histopathology image representations without the need to have the actual TCR sequencing data.
Combining multiple ground truth annotations for segmentation training for oral cavity cancer

Annotation of true ground truth is a difficult task for many computational pathology problems. Types of ground truth labels in the field include bounding boxes, text labels, binary class labels, and full tissue maps. The compounding issue is when multiple different pathologists label the same image, and there is disagreement between them. In this work, we investigate multiply reannotated tumor maps for squamous cell carcinoma, and if different annotation fusion methods have an impact on tumor segmentation. We find in this work that tumor label maps with an average annotation similarity of 0.759, do not have a significant quantitative difference in tumor segmentation.
Solar Irradiance Forecasting Using Ensemble Models of Machine Learning

Ensemble learning is widely used in forecasting due to its unique ability to combine individual models to improve prediction power. It provides the composite prediction where the final accuracy is better than the accuracy of the individual models. This paper will discuss different ensemble learning models for solar irradiance forecasting. Nine daily average meteorological parameters are considered a feature input of Jaipur over three years for solar irradiance forecasting. Based on different parameters like Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2), comparative analysis of different ensemble learning models are made. The Principal Component Analysis and Feature Importance plot methodology have been used for input feature selection. The model with the lowest error metrics and greatest R-value is regarded as the best and is used to forecast Jaipur's daily solar irradiance for 2021.
Sentiment Analysis with 'syuzhet' using R

This lesson teaches you how to obtain and analyse narrative texts for patterns of sentiment and emotion.
Index/Impressum: Vakuum in Forschung und Praxis 2/2023

Vakuum in Forschung und PraxisVolume 35, Issue 2 p. 51-51 Magazin Index/Impressum: Vakuum in Forschung und Praxis 2/2023 First published: 06 April 2023 https://doi.org/10.1002/vipr.202370211AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume35, Issue2April/May 2023Pages 51-51 RelatedInformation
Scientific and Production Activity of Leningrad Higher Education Institutions in the Years of the Blockade (on the Example of M.I. Kalinin Leningrad Polytechnical Institute)

The article is devoted to the analysis of scientific, practical and production activities of employees of the Leningrad Polytechnic Institute named after M.I. Kalinin during blockade. The purpose of the scientific publication is to study examples of real practical assistance of employees of the Polytechnic Institute during years of the blockade through research work. In the course of the research, author used the method of comparative analysis, and presented types, features of technological improvement of performance of production operations are shown using descriptive technique. The principles of historicism and scientific objectivity helped the authors to reveal the objective factors that influenced the assistance of the industrial and labor collective of the city in production. The paper presents cases of participation of the scientific staff of the university in the organization of a number of events related to the conversion of peaceful production for military needs, participation in the development and construction of the ice route "Road of Life". In conclusion, it should be noted that the scientific and pedagogical staff of the Leningrad Polytechnic Institute made a huge contribution to the organization of the work of industrial enterprises during the blockade. Most of the scientific works of the university scientists were of a practical nature. Being in extreme conditions, despite all the difficulties, teaching staff of the university continued to work, publish works, conduct experiments that made it possible to ensure the work of enterprises of the Leningrad industry, thereby making a huge contribution to the Victory over fascism.
DESIGNING AND ORGANIZING EXPERIENTIAL LEARNING ACTIVITIES TO DEVELOP STUDENTS' ENVIRONMENTAL PROTECTION COMPETENCE THROUGH THE OPTIONAL TOPIC OF CHEMICAL FERTILIZERS OF CHEMISTRY 11

Chemistry is a close combination of theory and experiment which is a bridge of the Natural Sciences such as Physics, Biology, Medicine, and Geology. Experiential learning activities using experiments in teaching chemistry play a very important role in the new general education curriculum. Through these activities, students have more opportunities to apply their acquired knowledge and skills in real life to form and develop general and specific competencies and enhance their creativity and passion. Within the scope of this article, we have studied the theoretical basis of experiential learning and organized experiential learning activities using experiments (Test Kit) to identify and determine ammonium (NH4+) concentration in 7 fertilizer samples (Ure CO(NH2)2; NPK, organic fertilizers, natural water samples) to improve students' environmental protection competence. The results of pedagogical experimentation on 85 students (class 11) of Truc Ninh B High School, Nam Dinh province, and 87 students (class 11) of Tue Tinh High School in Hai Duong province have proved the feasibility and effectiveness of this study. This research has contributed to raising the awareness and responsibility of students about environmental issues and environmental pollution.
AI text detection is mathematically impossible

To better explain the cosmos we see, physicist Neil Turok has proposed the existence of a mirror-image universe stretching back in time from the big bang. He tells Thomas Lewton why it is such a compelling idea – and how it might be tested
A Neutrosophic Model for Blockchain Platform Selection based on SWARA and WSM

The blockchain as a distributed ledger with flourishing blocks are secured and linked with cryptographic hashes. The blockchain is a type of distributed database that is used in many vital business transactions of replication, sharing, tracking, synchronization data among various sites. Recently, the global technological and industrial revolution is accelerating, the bitcoin extends the industrial revolution to become a lot of interest from both the business world and academic circles. This paper aims to take the advantages of blockchain concepts to be applied in Enterprise Banking Systems (EBS). The EBS depend on smart contract and blockchain technologies for trust only the installation of a blockchain platform with a solid design and a proven user base. Unfortunately, only a few blockchain platforms (BP) have achieved stable design and confident implementation. The selection of appropriate BP is leading step for decision makers that pretended to be a real challenge. Therefore, any digital transformation project that makes use of blockchain must contend with the difficulty of selecting a BP that is suited to the requirements of EBS. In this study, a hybrid approach of a neutrosophic theory for uncertainty conditions in a multi-criteria decision-making problem with the use wise weight assessment ratio analysis (SWARA) and Weighted Sum Method (WSM) to select the appropriate and efficient BP. A case study is applied on EBS, as an uncertain environment, to show the efficiency for the proposed model in aiding decision makers to achieve to ideal BP according to challenges to achieve sustainability.