Reviewerly/exHarmony huggingface dataset:
Practical Considerations in PSD Upper Bounding of Experimental Data

In this paper, we develop a methodology to estimate the power spectrum of an experimentally-obtained data set for high-integrity modeling of Kalman filter (KF) input noise time correlation. In theory, power spectral density (PSD) upper-bounding can be used to determine time-correlated error models that guarantee bounds on the estimation error variance in recursive navigation algorithms such as KFs. This assumes that an empirical PSD is given. In practice, there is more than one way to determine a PSD from data. This PSD estimate depends on the number of samples in the data set, on the windowing process, and on the PSD frequency resolution. These parameters have an impact on the robustness of the PSD-upper-bounding model. In this paper, we analyze error model sensitivity to these parameters for example simulated random processes and for a one-year-long time-series of GPS orbit and clock ephemeris errors.
Development of Readiness of Future Special Teachers to Improve Professional Activities

The article deals with the issue of preparing future special teachers to improve their professional activities. Currently, the formation of the readiness of future special teachers to improve their professional activities is observed in ensuring the level of accessibility and improving the quality of education. Focusing on the special need to prepare future special teachers for professional activities, it was said that in the conditions of higher education, students can independently develop their intellectual potential, expand their knowledge base, and use them creatively. A comprehensive analysis of the theoretical and methodological literature was carried out, and the issue of systemic preparation of future special teachers for a new situation of professional activity in the pedagogical environment was considered. The results obtained indicate that educational issues in the formation of professional training of future special teachers are not given sufficient attention. Particular attention is paid to special courses and a variety of practical experience in this area. In the article, much attention is paid to the personal qualities of a special teacher based on the system of the modern stage of development of special education. On the basis of the conducted empirical research, the authors come to the conclusion that special attention should be paid to the professional training of future special teachers. The purpose of the article is to provide a theoretical basis for the formation of the readiness of future special teachers to improve their professional activities and to propose new technologies and methods.
Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

Breast cancer is the most common cancer among women. It occurs when few breast cells begin to grow abnormally. The national average for 2022 is 100.4 cases per 1,00,000 people, with a large number of women being diagnosed with breast cancer. The objective is to design a prediction system that can predict breast cancer at early stages using a set of attributes that have been selected from a critical dataset. The Wisconsin Kaggle dataset is used for this experiment. The goal of this work is to predict breast cancer utilizing hybrid machine learning methodologies, such as SVM and PCA. ML algorithms that could help to predict cancer, as the early detection of this disease would help to slow down the progression of other diseases. In our paper, we are implementing Hybrid algorithms like PCA and SVM and optimizing SVM with k-fold cross-validation for predicting Breast cancer at early stages with high accuracy. The goal is to raise the fraction of early-stage breast cancer detection and to reduce mistake rates with maximum precision, which are sustainable.
A Digital Evidences Preservation Framework for a Logic Based Smart Contract

Recently, smart contracts were introduced as a necessity to automatically execute specific operations within blockchain systems. The popularity and diversity of blockchain systems attracted intensive attentions from academia, industry and other sectors. Blockchain systems were implemented using different programming languages that used in defining the triggering events and their consequent actions within the smart contract. In this article, we propose a digital evidences preservation framework that supports logic-based smart contracts to manage entries associated with digital evidences. Combining logic-based approach and blockchain systems may result in ensuing contracts that have technical advantages over procedural coding. The paper shows the motivation for choosing logic-based approach to define a smart contract. We introduce the rules and structure of the proposed logic-based contract.
Synthetic Singleplex-Image Generation in Multiplex-Brightfield Immunohistochemistry Digital Pathology Using Deep Generative Models

Multiplex-brightfield immunohistochemistry imaging (MPx) enables quantification of various biomarkers in tissue while retaining morphological and spatial information. One of the critical challenges of MPx is the complexity of visually inspection-based assessment of multiple stain intensities when they are co-localized in a cell. Consequently, it requires digital unmixing methods to separate multiple staining components and remix individual staining elements together with counterstain to become synthetic singleplex images (SPx). Unmixing MPx images becomes even more challenging when more than three biomarkers co-localized. Conventional unmixing methods e.g.,color-deconvolution or Nonnegative-Matrix-Factorization are limited and error prone when separating staining intensities of co-localized biomarkers in membrane or nuclear-subcellular compartments. Here, we exploit advances in generative-adversarial networks (GANs) based on unpaired image-to-image translation (CycleGAN) to generate synthetic SPx from MPx for pathologists to read and score. Three tonsil tissues with a total of 36 wholeslide images, stained with CD3, Bcl2, and CD8 using chromogenic detection, were used for training and evaluating our framework. Adjacent SPx were used to evaluate the visual quality of our synthetic SPx images with the following experiments: 1) performed perceptual studies or “real-vs.-fake” based on Amazon-Mechanical Turk (AMT) with pathologist observers, where results showed synthetic SPx were indistinguishable from real-adjacent SPx; and 2) evaluated whether our synthetic SPx were realistic to be scored for intensity. Results showed similarity scores of 0.96, 0.96, and 0.97 overall intensity for each synthetic SPx, respectively. Our framework provides alternative methods to virtually unmix the stains in order to accurately and efficiently generate synthetic SPx images from MPx tissue slides. This can bring confidence and opportunities for MPx in-vitro diagnostics.
Stochastic dynamics and data science

Recent advances in data science are opening up new research fields and broadening the range of applications of stochastic dynamical systems. Considering the complexities in real-world systems (e.g., noisy data sets and high dimensionality) and challenges in mathematical foundation of machine learning, this review presents two perspectives in the interaction between stochastic dynamical systems and data science. On the one hand, deep learning helps to improve first principle-based methods for stochastic dynamical systems. AI for science, combining machine learning methods with available scientific understanding, is becoming a valuable approach to study stochastic dynamical systems with the help of observation data. On the other hand, a challenge is the theoretical explanations for deep learning. It is crucial to build explainable deep learning structures with the help of stochastic dynamical systems theory in order to demonstrate how and why deep learning works. In this review, we seek better understanding of the mathematical foundation of the state-of-the-art techniques in data science, with the help of stochastic dynamical systems, and we further apply machine learning tools for studying stochastic dynamical systems. This is achieved through stochastic analysis, algorithm development, and computational implementation. Topics involved with this review include Stochastic Analysis, Dynamical Systems, Inverse Problems, Data Assimilation, Numerical Analysis, Optimization, Nonparametric Statistics, Uncertainty Quantification, Deep Learning, and Deep Reinforcement Learning. Moreover, we emphasize available analytical tools for non-Gaussian fluctuations in scientific and engineering modeling.
The Relationship of Professional Burnout with Indicators of Age and Characteristics of the Educational Environment

&lt;p style="text-align: justify;"&gt;The article examines the dependence of professional burnout of secondary school teachers on the favorable educational environment and on the age characteristics of teachers. The study was carried out in two stages (the first stage included the study of the relationship between the professional burnout of teachers and age characteristics; the second stage was aimed at studying the relationship between the indicators of the favorable educational environment of the school and the professional burnout of teachers). The study involved 70 subjects (women, mean age 43.2 + 12.61 years, minimum age 23 years, maximum age 62). The main research methods were: diagnostic (MBI professional burnout questionnaire, educational environment safety questionnaire, psychotic atmosphere in the team questionnaire, assessment of the index of relative biological aging according to V.P. Voitenko) and statistical (correlation analysis and stepwise multiple regression with the inclusion of variables). The authors come to the conclusion that for teachers with a work experience of 1 year or more, there is no linear relationship between experience, calendar age and indicators of professional burnout. At the same time, there is a correlation between the indicators of burnout and the index of relative biological aging: the higher the indicators of burnout, the stronger the rate of biological aging of the body. The main conclusion in the work is the established fact of the relationship of professional burnout with an increase in biological age, and the absence of a linear relationship with calendar age and length of service. On the other hand, professional burnout depends on the degree of favorable educational environment: the more favorable the environment, the less burnout.&lt;/p&gt;
Cyberaggression and Сyberbullying in Forensic Practice

&lt;p style="text-align: justify;"&gt;The paper analyzes materials with the phenomena of cyberaggression and cyberbullying on the topic of history, received for a comprehensive forensic psycholinguistic study on the example of the cases received by the Moscow State Budgetary Institution &amp;ldquo;Moscow Research Centre&amp;rdquo;. The aim of the work is to describe the structural and the content characteristics of the information material with signs of cyberaggression. The structural characteristics include the features of the communication process, such as the function of an addressee and an addresser, the type of a message, the encoding and the channel of information, the purpose and the result of communication; the content psychological characteristics, taking into account various options for cyberaggression (flaming, trolling, hating, cyberstalking, cyberbullying, etc.), are the following: the type of the communicative situation presented in the material, the methods of psychological influence, the socio-psychological orientation of the material. The characteristics presented in the article will allow structuring the conduct of psychological and linguistic research on information materials in line with the study of cyberaggression, expand the possibilities of a comprehensive forensic psycholinguistic study on information materials.&lt;/p&gt;
PENERAPAN DATA MINING PADA PENJUALAN PRODUK ELEKTRONIK

Surya Jaya Electronic &amp; Furniture, the author found many obstacles due to the accumulation of annual sales data. This made it difficult for the company to know the availability of existing goods and could not predict which goods or products were most in demand by customers and sold the most. Implementation of data mining with the a priori algorithm on Surya Jaya Electronic &amp; Furniture found that in June there were 23 data points for item set 2, 8 data points for item set 3, and 35 data points for item set 4. In the July period, there were 19 data points for item set 2, 13 data points for item set 3, and 33 data points for item set 4, and in the August period, there were 15 data points for item set 2, 1 data point for item set 3, and 7 data points for item set 4.
ESTIMATION OF RETRIEVAL DATE IN ANTAGONIST PROTOCOL CYCLES AT DIFFERENT STAGES OF THE CYCLE, USING ARTIFICIAL INTELLIGENCE ALGORITHM

This study aims to develop an algorithm for predicting the retrieval date of antagonist protocol cycles as the cycle progresses. A retrospective cohort study including data of 5,956 antagonist protocol cycles performed in a large center serving over 50 physicians, from August 2018 to October 2022. Each cycle includes baseline characteristics, blood hormone levels, follicles’ sizes as determined by ultrasound exams, days of stimulation, Gonadotropin dosage, and the number of days GnRH antagonist was administered. The model was developed using XGBoost classifier, to evaluate the probability of retrieval performed within 3 to 10 days, following GnRH antagonist initiation. Evaluation was done using True Positive (TP), False Positive (FP), False Negative (FN), Precision (TP/(TP+FP)), Recall (TP/(FN+TP)) and F1 Score (2*Precision*Recall/(Precision+Recall)) metrics on 20% of the data that was designated as a test set. The best results of predicting the retrieval date for antagonist cycles received 3 days in advance with Precision of 0.84, Recall of 0.75 and F1 Score of 0.8. The performance of all days detailed in the following table:Tabled 1In X daysPrecisionRecallF1 ScoreNumber of samples30.840.750.80120640.690.630.66119950.470.620.54114960.460.550.50103370.440.400.42775 Open table in a new tab According to the total gain, the primary features utilized by the model were Estradiol levels, number of follicles, and Gonadotropin dosage. An AI based algorithm can accurately predict the retrieval date of antagonist cycles with increasing precision as the cycle progresses and approaches the retrieval day.
Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms.
Sustainable Hand Gesture Recognition for Speech Conversion, Empowering the Speech-Impaired

A sustainable language disorder affects an individual’s ability to reach out to others through speaking and listening. So utilizing sustainable hand gestures is among the most widespread means of non-verbal and visual communication used by people with speech disabilities worldwide. However, even though sustainable sign language is used everywhere by speech-impaired and hearing-impaired people, most of the populace who don't have any knowledge about sign language face difficulties in sustainably communicating with them. This sustainable problem requires better solutions that can successfully support communication for people with speech disabilities. This sustainable approach will reduce the communication gap for the speech-impaired population. There are many sustainable solutions in the market such as using sensors to make a sustainable device that gives a helpful output. But these sustainable solutions are expensive and not everyone can afford them. We are employing Convolutional Neural Networks to create a sustainable model that is trained on different gestures. This sustainable model enables speech-impaired individuals to convey their information using signs which get converted to human-understandable language, and sustainable voice is given as output. The sustainable hand gestures made are captured as a series of sustainable images which are processed using Python code. This sustainable endeavor introduces a solution that not only automates the identification of sustainable hand gestures but also transforms them into sustainable speech. By interpreting these recognized sustainable gestures, the corresponding recorded audio will be played sustainably. The focus of this sustainable paper is to offer accessibility, convenience, and safety to individuals with speech impairments in a sustainable manner. These sustainable individuals often experience societal discrimination solely due to their disabilities. This sustainable paper is aimed at innovating a sustainable device to help those without the knowledge of sign language sustainably communicate with the people who face difficulty in speech.
A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs instead of raw data, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC’s two primary benefits: integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify a near-optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.
Minimizing Parameter Overhead in Self Supervised Models for Target Task

Supervised deep learning models encounter two significant challenges: the labeled datasets for training and the parameter overhead, which leads to extensive GPU usage and other computational resource requirements. Several CNN models show state-of-the-art performance while compromising with either of the challenges. Self-supervised models reduce the requirement for labeled training data, however, the problems of parameter overhead and GPU usage are rarely addressed. This paper proposes a method to address the two challenges of the image classification task. We introduce a transfer learning approach for a target data set, in which we take the learned features from a self-supervised model after minimizing its parameters by removing the final layer. The learned features are then fed into a CNN classifier, followed by a multi-layer perceptron (MLP), where the hyperparameters of both the CNN and MLP are automatically tuned (autotuned) using a Bayesian optimization based technique. Further, we reduce GFLOPs by limiting the search space for the hyperparameters, not compromising the performance. The proposed approach effectively deals with above challenges. The first challenge is addressed by utilizing the learned representations from the self-supervised model as a foundation for knowledge transfer in the proposed model. Rather than relying solely on labeled data, we employ the insights from unlabeled data by transferring knowledge from the self-supervised model to the task at hand, hence reducing the cost and effort associated with data annotation. We address the second challenge by utilizing a minimized self-supervised backbone model and constraining the search space. We experiment with a wide variety of benchmark datasets, such as datasets with a large number of small-sized images (CIFAR-10 and CIFAR-100), fewer large-sized images (Oxford-IIIT Pet Dataset and Oxford 102 flowers), and large-sized images with a variety of classes (Caltech101).The proposed method outperforms the state-of-theart with fewer parameters and GFLOPs. The codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jk452/minimized-parameter-overload</uri>
Embracing Contact: Detecting Parent-Infant Interactions

We focus on a largely overlooked but crucial modality for parent-child interaction analysis: physical contact. In this paper, we provide a feasibility study to automatically detect contact between a parent and child from videos. Our multimodal CNN model uses a combination of 2D pose heatmaps, body part heatmaps, and cropped images. Two datasets (FlickrCI3D and YOUth PCI) are used to explore the generalization capabilities across different contact scenarios. Our experiments demonstrate that using 2D pose heatmaps and body part heatmaps yields the best performance in contact classification when trained from scratch on parent-infant interactions. We further investigate the influence of proximity on our classification performance. Our results indicate that there are unique challenges in parent-infant contact classification. Finally, we show that contact rates from aggregating frame-level predictions provide decent approximations of the true contact rates, suggesting that they can serve as an automated proxy for measuring the quality of parent-child interactions. By releasing the annotations for the YOUth PCI dataset and our code1, we encourage further research to deepen our understanding of parent-infant interactions and their implications for attachment and development.