Reviewerly/exHarmony huggingface dataset:
A federated learning-based method for class imbalanced power big data fusion

Bureau class-imbalanced and device-local class- imbalanced data distributions tend to degrade the performance of standard federated averaging algorithms. A device selection algorithm based on an online learning algorithm framework is proposed, and a class estimation scheme is designed. In each round of communication, the subset of devices that is most complementary to the category test performance offset of the previous global model is selected, so that the test performance of each category of the linear combination of the global model after training is more balanced, thereby achieving faster convergence and more stability. better training process and better test performance. Numerical experiments fully explore the effects of different parameters on the class-imbalanced federated averaging algorithm, and verify the effectiveness of the proposed device selection algorithm. In order to realize the security fusion and sharing of big data under the premise of availability and practicability, a distributed data security fusion model based on federated learning is designed, and four methods of differential privacy, secure multi-party computation, homomorphic encryption and functional encryption are used to realize the realization., to ensure the security and feasibility of distributed data fusion.
Machine Learning-Based Classification of Leukemia Comparative Study

Leukemia disease designates a cancer of the bone marrow and lymphatic system. It occurs when certain blood cells acquire changes i.e. or mutations in their genetic material. Leukemias are classified according to their rate of progression and the type of cells involved. Acute Lymphocytic Leukemia (ALL), Acute Myelogenous Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myelogenous Leukemia are the four main kinds of leukemia (CML). The classification of the type of Leukemia is very important to diagnose the disease and determine its progression. In this context, we have used the classifiers of machine learning to identify different forms of leukemia., which facilitates the task of doctors and patients. The main objective of this paper is to determine the most effective methods for the detection of leukemia. According to this context, we have established a comparative study between five classifiers (Support Vector Machine, Random Forest, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes). We have evaluated our system with four metrics: Precision, Accuracy, Recall, and F1-score. The experimental results on Gene Expression Dataset demonstrate that the Support Vector Machine classifier obtains the highest accuracy; however, this accuracy varies depending on the algorithm used to classify the types of leukemia and also on the shape and size of the sample.
Enhancing Federated Learning With Server-Side Unlabeled Data by Adaptive Client and Data Selection

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, the data on each client may be insufficient for model training. Conversely, in cloud datacenters, there exist large-scale unlabeled data, which are easy to collect from public access (e.g., social media). Herein, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ada-FedSemi</i> system, which leverages both on-device labeled data and in-cloud unlabeled data to boost the performance of DL models. In each round, local models are aggregated to produce pseudo-labels for the unlabeled data, which are utilized to enhance the global model. Considering that the number of participating clients and the quality of pseudo-labels will have a significant impact on the training performance, we introduce a multi-armed bandit (MAB) based online algorithm to adaptively determine the participating fraction and confidence threshold. Besides, to alleviate the impact of stragglers, we assign local models of different depths for heterogeneous clients. Extensive experiments on benchmark models and datasets show that given the same resource budget, the model trained by Ada-FedSemi achieves 3% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 14.8% higher test accuracy than that of the baseline methods. When achieving the same test accuracy, Ada-FedSemi saves up to 48% training cost, compared with the baselines. Under the scenario with heterogeneous clients, the proposed HeteroAda-FedSemi can further speed up the training process by 1.3× <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 1.5\times$</tex-math></inline-formula> .
Classification-Oriented Distributed Semantic Communication for Multivariate Time Series

We present a many-to-one distributed semantic communication system for multivariate time series classification. The system adopts a federated learning-based architecture to achieve low-redundancy collaborative inference, where an unsupervised auxiliary task is designed to coordinate the feature vectors at different dimensions between semantic encoders and the classifier. For each transmitter, we design a scale-adaptive semantic encoder by applying weighted sum to a number of predefined convolutional layers. The scale-adaptive semantic encoder can extract multi-scale features from time series following various distributions. A dynamic channel encoder is developed to adapt to the scale-adaptive semantic encoder, converting semantic features to complex symbols appropriate for wireless transmission. For the receiver, we apply the same scale-adaptive structure to the semantic decoder to extract multi-scale semantic features from all transmitters for accurate classification. Simulation results show that the proposed distributed semantic communication system outperforms two baseline systems under AWGN, Rician, and Rayleigh channels and achieves excellent Top-1 accuracy performance on three UEA2018 datasets, especially when the signal-to-noise ratio is low.
Knowledge outcome level of exercise physiology and physical fitness of basketball coaches for the excellent clubs and the coaches of professional football clubs in Jordan

This study aims to identify the level of knowledge outcome in the exercise physiology and physical fitness of basketball coaches for the excellent clubs and the coaches of professional football clubs in Jordan, as well as to spot the difference of the cognitive outcome among coaches with some variables. The study sample consisted of (30) managers within the high level of the Premier Leagues for basketball and (47) for football coaches, were chosen randomly. The researchers used the cognitive outcome as a tool to combine different studies. The study concluded that the cognitive outcome of both coaches of the two games in the physiology of exercise, were at the very weak level, and in the field of physical fitness they were at the weak level. The researchers recommended that the training courses, should focus on the two fields of physiology, exercise physiology and physical fitness should be given greater concern, and importance during these courses and seminars, as well as the necessity of educating the trainers of the importance of being informed of all that is new and modern in the field of sciences related to modern sports training, the most important of which is the physiological aspects and physical fitness and that it be among the preparation plans. هدفت الدراسة إلى التعرف على مستوى الحصيلة المعرفية في فسيولوجيا الجهد البدني واللياقة البدنية لدى مدربي كرة السلة للأندية الممتازة ومدربي الأندية المحترفة لكرة القدم في الأردن .تكونت عينة الدراسة من (30)مدربا بكرة السلة و(47) مدربا لكرة القدم ، تم اختيارهم بالطريقة العشوائية ، وقد استخدم الباحثون الاختبار المعرفي كأداةً لجمع بيانات الدراسة . وقد توصلت الدراسة إلى أن الحصيلة المعرفية لدى مدربي كلا اللعبتين في فسيولوجيا الجهد البدني كانت في المستوى الضعيف جدا ، وفي مجال اللياقة البدنية كانت في المستوى الضعيف . و أوصى الباحثون بضرورة التركيز في الدورات التدريبية الخاصة بكلتا اللعبتين على مجالي فسيولوجيا الجهد البدني واللياقة البدنية خلال تلك الدورات والندوات، وكذلك ضرورة توعية المدربين بأهمية الإطلاع على كل ما هو جديد وحديث في مجال العلوم المرتبطة بالتدريب الرياضي الحديث واهمها النواحي الفسيولوجية واللياقة البدنية وان تكون من ضمن خطط الإعداد .
Deep learning-based “All-in-One” combined visualization strategy for disease screening in CT imaging

Computed tomography (CT) is a widely used medical imaging modality which is capable of displaying the fine details of human body. In clinics, the CT images need to highlight different desired details or structures with different filter kernels and different display windows. To achieve this goal, in this work, we proposed a deep learning based ”All-in-One” (DAIO) combined visualization strategy for high-performance disease screening in the disease screening task. Specifically, the presented DAIO method takes into consideration of both kernel conversion and display window mapping in the deep learning network. First, the sharp kernel, smooth kernel reconstructed images and lung mask are collected for network training. Then, the structure is adaptively transferred to the kernel style through local kernel conversion to make the image have higher diagnostic value. Finally, the dynamic range of the image is compressed to a limited gray level by the mapping operator based on the traditional window settings. Moreover, to promote the structure details enhancement, we introduce a weighted mean filtering loss function. In the experiment, nine of the ten full dose patients cases from the Mayo clinic dataset are utilized to train the presented DAIO method, and one patient case from the Mayo clinic dataset are used for test. Results shows that the proposed DAIO method can merge multiple kernels and multiple window settings into a single one for the disease screening.
Ink removal in whole slide images using hallucinated data

Pathologists regularly use ink markings on histopathology slides to highlight specific areas of interest or orientation, making it an integral part of the workflow. Unfortunately, digitization of these ink-annotated slides hinders any computer-aided analyses, particularly deep learning algorithms, which require clean data free from artifacts. We propose a methodology that can identify and remove the ink markings for the purpose of computational analyses. We propose a two-stage network with a binary classifier for ink filtering and Pix2Pix for ink removal. We trained our network by artificially generating pseudo ink markings using only clean slides, requiring no manual annotation or curation of data. Furthermore, we demonstrate our algorithm’s efficacy over an independent dataset of H&E stained breast carcinoma slides scanned before and after the removal of pen markings. Our quantitative analysis shows promising results, achieving 98.7% accuracy for the binary classifier. For Pix2Pix, we observed a 65.6% increase in structure similarity index, a 21.3% increase in peak signal-to-noise ratio, and a 30% increase in visual information fidelity. As only clean slides are required for training, the pipeline can be adapted to multiple colors of ink markings or new domains, making it easy to deploy over different sets of histopathology slides. Code and trained models are available at: <a href="https://github.com/Vishwesh4/Ink-WSI">https://github.com/Vishwesh4/Ink-WSI</a>.
An ensemble approach for histopathological classification of vulvar cancer

Light microscopy of tissue slides is an important tool for analyzing human diseases including cancer. In this work, we focus on classifying patches from a immunohistochemically stained tissue microarray (TMA) of vulvar cancer. We propose a novel ensemble-based deep learning technique to classify patches of tissue as cancerous, stroma, both, or none. Our ensemble model consists of a pre-trained data-efficient image transformer (DeiT) module to extract features of patches followed by a transformer block and graph convolutional networks (GCN). Transformer blocks aid the sequential learning of the extracted features from DeiT while the graph convolutional network (GCN) extracts neighborhood information. Our approach combines both methods for classification. In the evaluation, we show that our approach outperforms state-of-the-art architectures for the addressed application. We also show that is applicable when only small amounts of labelled data are available.
Deep-supervised adversarial learning-based classification for digital histologic images

High-resolution histopathological images have rich characteristics of cancer tissues and cells. Recent studies have shown that digital pathology analysis can aid clinical decision-making by identifying metastases, subtyping and grading tumors, and predicting clinical outcomes. Still, the analysis of digital histologic images remains challenging due to the imbalance of the training data, the intrinsic complexity of histology characteristics of tumor tissue, and the extremely heavy computation burden for processing extremely high-resolution whole slide imaging (WSI) images. In this study, we developed a new deep learning-based classification framework that addresses these unique challenges to support clinical decision-making. The proposed method is motivated by our recently developed adversarial learning strategy with two major innovations. First, an image pre-processing module was designed to process the high-resolution histology images to reduce computational burden and keep informative features, alleviating the risk of overfitting issues when training the network. Second, recently developed StyleGAN2 with powerful generative capability was employed to recognize complex texture patterns and stain information in histology images and learn deep classification-relevant information, further improving the classification and reconstruction performance of our method. The experimental results on three different histology image datasets for different classification tasks demonstrated superior classification performance compared to traditional deep learning-based methods, and the generality of the proposed method to be applied to various applications.
Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors

High-grade gliomas are aggressive forms of brain cancer associated with a poor prognosis of 12-15 months. The mutation of isocitrate dehydrogenase I (IDH) and O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation are biomarkers relevant to disease classification and prognosis. Current methods to identify patients’ molecular status are expensive and can be resource prohibitive. Morphological attributes as captured on histopathology contain rich phenotypic information indicative of the underlying molecular processes. Recently, deep learning methods have demonstrated diagnostic and prognostic value via computational analysis of histopathology to incorporate complex and subvisual morphometric features that may not be visually accessible to pathologists. We hypothesize that a computational deep learning approach applied to Hematoxylin and Eosin (H&E)-stained digitized tissue slides will be able to reliably predict the molecular (IDH, MGMT) status in high-grade glioma patients. Specifically, we present a deep learning approach that employs self-supervised and multiple instance learning, on a total of n=325 H&E stained high-grade glioma slides, for identifying (a) IDHmutant versus IDH-wild-type (WT) and (b) MGMT promoter methylated versus unmethylated tumors. The approach addressed challenges of patch selection and the unbalanced instances that arise from only subregions of whole-slide images providing diagnostic value. The deep learning approach achieved accuracy values of 91.17 (+/- 3.47) and 86.11 (+/- 4.45), for the prediction of IDH mutation and MGMT promoter methylation status respectively, demonstrating an improvement of over 5% compared to the reported accuracy values in previous studies.
A Novel Method for Handwritten Digit Recognition System

Character recognition is crucial in the contemporary world. It can resolve more difficult issues while also simplifying human tasks. Character identification from handwriting is one illustration. This system is used extensively throughout the globe to identify zip codes or postal codes for mail sorting. Handwritten symbols can be recognized using a variety of methods. In this paper, two methods—pattern recognition and convolutional neural networks—are studied. (CNN). Both techniques are described, and various implementations of each strategy are also covered. Methods for pattern recognition include Bayesian Decision Theory, Nearest Neighbor Rule, and Linear Classification or Discrimination. Neural networks are used for shape identification, Chinese character recognition, and handwritten digit recognition. Use of neural networks for training and identification Key Word: Digit Recognition, Handwritten digits recognition, Convolutional Neural Network.
A new algebraic attack on DASTA

As a fully homomorphic encryption friendly symmetric-key primitive, DASTA was invented by Hebborn at Fast Software Encryption 2020. A new fixed linear layer design concept is introduced in the DASTA stream cipher so that its AND depth and the number of ANDs per encrypted bit are quite small. Currently, the security of the DASTA stream cipher has received extensive attention. Note that the best-known attack (i.e., algebraic attack) on DASTA still has a very high data complexity. It appears to be an important task to reduce the data complexity of the attack on DASTA. In this article, a new algebraic attack on DASTA is proposed. More specifically, the key feed-forward operation, the properties of the nonlinear layer and the invariance from the linear layer are successfully utilized in the attack. In particular, the nonlinear relation of internal states in DASTA is linearized effectively. In this case, more secret key bit equations with low algebraic degrees are collected by fixing the bit. It is illustrated that four ( r − 1 )-round instances of the DASTA cipher family are theoretically broken by the attack, where r is the iterative number of round operations. Compared with the results of previous algebraic attacks, our approach achieves more favorable data complexity.
No-go result for quantum postselection measurements of a rank- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi></mml:math> degenerate subspace

We present a no-go result for postselection measurements where the conditional expectation value of a joint system-device observable under postselection is nothing else than the conventional expectation value. Such a no-go result relies on the rank-$m$ degenerate of the joint observable, where $m$ is the dimension of the device subspace. Remarkably, we show that the error and disturbance in quantum measurements obey the no-go result, which implies that the error-disturbance uncertainty is unaffected under postselection measurements.
Bell-nonlocality quantifiers and their persistent mismatch with the entropy of entanglement

For some commonly used nonlocality quantifiers, the maximally nonlocal state may not coincide with the maximally entangled state, what became known as the anomaly of nonlocality. More recently, nonanomalous quantifiers have been defined, for which maximal entanglement and maximal nonlocality do coincide. In this work we investigate in detail how these quantifiers behave for nonmaximal-resource states and show that some mismatch remains between the studied figures of merit. Besides the nonlocal volume (a quantifier that counts the volume of the set of behaviors accessible to a specific state that gives rise to nonlocality), we present an alternative quantity referring to states and based on behaviors, the trace-weighted nonlocal volume. The construction is based on the nonlocal volume integral, but weighted by a quantifier of nonlocality for behaviors (the shortest distance between the behavior and the local set). Although in all investigated scenarios the anomaly is not present, the nonlocality quantifiers as compared to the entropy of entanglement are inversely related for some set of states within some Bell scenarios. In the investigated situations this phenomenon occurs when the considered states go through a change of rank. This fact is discussed in general and illustrated through computations for the (2,3,2) scenario.
Teachers’ and Students’ Perception of Poetry Teaching and Learning in Class VI of Rangaytung Primary School, Chhukha Dzongkhag

The poetry question of class VI is marked out of 8 both in the mid-term as well as in the annual examination according to the Instructional Guide for English, classes IV-VI, of which the poem should be from outside the prescribed English textbook. After studying the marks scored by 14 students of class VI of Rangaytung Primary School, Chhukha in the annual examination, 2022, more than 75% of the students on average scored below 4 out of 8 in poetry. Therefore, this study found the perception of teachers and students in terms of lack of appropriate content in the selection of poems in the prescribed class VI English textbook. The study also touched on the difficulties faced by teachers and students in the teaching and learning of poetry in the classroom. The participants for the study were selected based on purposive sampling. The study adopted qualitative research design to collect data from the field. Qualitative thematic analysis was used to interpret the collected data. The findings of this study include lack of appropriate content in the selection of poems in the prescribed class VI English textbook. One of the major difficulties that the teachers faced was students’ lack of comprehension skills in poetry because of the lack of poetry reading at school as well as at home. The students also found it difficult to understand the meaning of new words and pronounce difficult words from the poems because of various reasons. Keeping all difficulties aside, the teacher played a crucial role in making poetry learning interesting to the children. Recommendations to subject specialists, curriculum developers and English teachers of class VI are discussed separately towards the end of the fifth chapter.
Research on Collaborative Fire Fighting Technology of Tunnel Fire Robot

Aiming at the complex fire environment in the tunnel, a track fire extinguishing robot with a suspended type in the tunnel is developed. Based on the leader-follower formation control framework and artificial potential field method to establish cooperative formation control model, through the state space of fire robot and fire task, in the form of ability vector to fire robot and task ability vector definition, set different threshold for the robot response ability to task difference, based on the size of the fire robot to fire point, proposed more mobile robot based on intelligent formation fire extinguishing strategy. Matlab tool is used to establish an appropriate mathematical model simulation of fire extinguishing robot under tunnel environment. The simulation results show that the established mathematical model conforms to the actual situation and can provide an important theoretical basis for the actual fire fighting work.