Reviewerly/exHarmony huggingface dataset:
A Deep Learning Based Ensemble Model for Generalized Mitosis Detection in H &amp;E Stained Whole Slide Images

Identification of mitotic cells as well as estimation of mitotic index are important parameters in understanding the pathology of cancer, predicting response to chemotherapy and overall survival. This is usually performed manually by pathologists and there can be considerable variability in their assessments. The use of deep learning(DL) models can help in addressing this issue. However, most of the state-of-the-art methods are trained for specific cancer types, and often tend to fail when used across multiple tumor types. Hence there is a clear need for a more ‘pan-tumor’ approach to identifying mitotic figures. We propose a generalized DL model for mitosis detection using the MIDOG-2022 Challenge dataset. Using an ensemble of predictions from a transformer-based object detector and a separate classifier, our model makes final predictions. Our approach achieved an F1-score of 0.7569 and stood second in the MIDOG-2022 challenge. The predictions from the object detector alone achieved an F1-score of 0.7510. Our model generalizes well to address the domain shifts caused by variability in image acquisition, protocols and tumor tissue types.
Supply Chain Management Using Blockchain Security Enhancement

The cybersecurity of contemporary systems has drawn much more focus from both academic and industrial viewpoints. The blockchain-based approach has recently become more popular due to how straightforward it is, especially in the management of supply chains. This highlights how crucial the quality factors are from a supply chain management standpoint. Numerous sectors have come to realize how crucial it is to have reliable supply chain and logistics solutions. The introduction of blockchain technology has given rise to several possible breakthroughs in the management and monitoring of corporate operations, specifically supply chain procedures. The blockchain and, more precisely, a smart contract technology that was utilized to manage the process of creating, verifying, and inspecting data over the supply chain management process were both discussed in this study. Next, discuss blockchain cybersecurity in the context of supply chains. Security protection needs to be powerful sufficient to shield the assets and data from dangers, as the smart contract increasingly controls the movement of data over many places. The paper then examines the primary security breaches that have an impact on the data on the blockchain and offers a fix.
The need to form “hard skills” and “soft skills” for future pedagogues in the context of digitalization of education

The article is devoted to the actual problem of the organization of personal development of future pedagogues in the process of their professional training. As a solution to this problem, the authors pay attention to the study of the problem of the formation of hard skills and soft skills in future pedagogues, their role in professional career and personal development. These skills allow future pedagogues to success adaptation to the educational environment. The role of the formation of hard skills and soft skills in the process of professional development of future pedagogues in the context of digitalization of education is increasing every year. The article discusses the concepts of hard skills and soft skills and their importance for the development of future pedagogues in the professional field. The authors conducted a literary analysis on the topic of the study, noted common and distinctive features in approaches to the understanding of supra-professional and professional skills by various authors. The authors conducted a survey among students of the educational program 6B01101 — Pedagogy and psychology, the basis of the study was the pedagogical faculty of the Karaganda University of the name of academician E.A. Buketov. The results of the empirical research conducted by the authors are presented in the article. The results of the study allowed the authors to conclude that it is necessary to form not only theoretical knowledge, but also practical skills that will contribute to the creation and development of a competitive and competent society of pedagogues of a new formation. The problems raised by the authors are relevant for further research and will be continued in the search for forms and methods of forming hard skills and soft skills for future pedagogues in the conditions of digitalization of education.
Quantum metrology with quantum Wheatstone bridge composed of Bose systems

The quantum version of a special classical Wheatstone bridge built with a boundary-driven spin system has recently been proposed. We propose a quantum Wheatstone bridge consisting of Bose systems, which can simulate the general classical Wheatstone bridge. Unknown coupling can be obtained when the quantum Wheatstone bridge is balanced, which can be determined simply by homodyne detection. When the expectation value of the homodyne detection is 0, the quantum Wheatstone bridge is unbalanced. When the expectation value of the homodyne detection is proportional to the square root of the initial number of bosons by regulating a known coupling strength, the quantum Wheatstone bridge is balanced. By calculating the quantum Fisher information, we show that the measurement precision is optimal when the quantum Wheatstone bridge is balanced. And the homodyne detection is close to the optimal measurement in the case of low-temperature baths.
“Super-Microsurgical Anastomosis training using the chick embryos within the “Egg-in-Cube” system.”

Although supermicrosurgery techniques are essential skills for lymphatic surgery or perforator-flap surgery, an ideal training model is yet to be introduced. Living animal models, such as rodents, are considered to be ideal microsurgical training models. However, the use of living animal models is costly and involves bioethical considerations. Hence, the authors developed a novel, cost-effective, highly reproducible, and easy-to-handle supermicrosurgical anastomosis training system using the chicken embryo within the egg-in-cube system. Chick embryos were fertilized in the artificial cubic eggshell, which was fabricated by integrating a polycarbonate frame structure and five polydimethylsiloxane membranes. Seven days later, the trainees underwent supermicrosurgical training using the vitelline artery of the chick embryo. The trainees were able to perform supermicrosurgical training using all 11 surviving chicken embryos. The average diameter of the vitelline artery was 0.43 mm. Patency and pulsation were observed after the anastomosis in four of the 11 cases. The supermicrosurgical training system using the chicken embryo within the egg-in-cube system has several advantages. This system is ethically acceptable, less costly and easier to manage than other animal models, and suitable as a supermicrosurgical training model, such as for lymphovenular anastomosis, because the diameter of the vitelline artery was similar to that of the lymphatic vessels in patients with lymphedema. Moreover, the trainee can confirm patency and leakage after the anastomosis because this model has a circulation system. The trainee can practice the supermicrosurgical technique efficiently with simultaneous feedback on anastomosis results.This study introduces a novel, cost-effective supermicrosurgical training system using chicken embryos within an egg-in-cube, offering a practical and ethical alternative. Its close simulation to human lymphatic vessels supports skill enhancement for practicing surgeons.
ParaDime: A Framework for Parametric Dimensionality Reduction

Abstract ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high‐dimensional data items in a low‐dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter‐item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t‐SNE, and UMAP. It allows users to fully customize all aspects of the DR process. We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high‐dimensional data.
Quantum steering as a resource for secure tripartite quantum state sharing

Quantum state sharing (QSS) is a protocol by which a (secret) quantum state may be securely split into shares, shared between multiple potentially-dishonest players, and reconstructed. Crucially, the players are each assumed to be dishonest, and so QSS requires that only a collaborating authorized subset of players can access the original secret state; any dishonest unauthorized conspiracy cannot reconstruct it. We analyze a QSS protocol involving three untrusted players and demonstrate that quantum steering is the required resource enabling the protocol to proceed securely. We analyze the level of steering required to share any single-mode Gaussian secret which enables the states to be shared with the optimal use of resources.
Comparative Study of Optical Character Recognition Using Different Techniques on Scanned Handwritten Images

Optical Character Recognition (OCR) is always a prominent area of research and has gained attention in academia and industries, and more advancements are being performed to improve accuracy and quality of text and images. OCR is a technique that converts the digital or optical image (which can be image of typed or printed document or scanned handwritten document or scene photo) into machine-encoded format. OCR takes an image containing text as input, scans the text character by character, analyzes it, and generates a text corresponding to the text in the image, thus reducing the storage size and making accessibility of data easy and faster. In this paper, we will be studying OCR using different techniques like Convolutional Neural Network, Recurrent Neural Network, pytesseract, Keras-OCR, EasyOCR and comparing their results on handwritten scanned images.
IPTV Video Perception Quality Based on Packet Loss Distribution

IPTV (Internet Protocol Television) provides interactive function, which greatly improves subjective experience of users. However, IP network is a best-effort network and does not guarantee a reliable connection, which makes video transmission based on IP network prone to problems. This paper focuses on the relationship between packet loss and video perception quality in IPTV transmission. Through subjective evaluation experiments, the influence of different packet loss rate and packet loss distributions (burst loss and random loss) are quantitatively studied. Experimental results show: When packet loss rate is the same, video with burst loss has a better quality than one with random loss. The proportion of burst loss in total packet loss increases, and subjective perception quality increases exponentially. Based on experimental results, this paper also designs an objective evaluation model of video quality based on packet loss rate and packet loss distribution.
Logic of Differentiable Logics: Towards a Uniform Semantics of DL

Differentiable logics (DL) have recently been proposed as a method of training neural networks to satisfy logical specifications. A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions. These loss functions can then be used during training with standard gradient descent algorithms. The variety of existing DLs and the differing levels of formality with which they are treated makes a systematic comparative study of their properties and implementations difficult. This paper remedies this problem by suggesting a meta-language for defining DLs that we call the Logic of Differentiable Logics, or LDL. Syntactically, it generalises the syntax of existing DLs to FOL, and for the first time introduces the formalism for reasoning about vectors and learners. Semantically, it introduces a general interpretation function that can be instantiated to define loss functions arising from different existing DLs. We use LDL to establish several theoretical properties of existing DLs and to conduct their empirical study in neural network verification.
Subsumption Prediction for E-Commerce Taxonomies

Taxonomy plays a key role in e-commerce, categorising items and facilitating both search and inventory management. Concept subsumption prediction is critical for taxonomy curation, and has been the subject of several studies, but they do not fully utilise the categorical information available in e-commerce settings. In this paper, we study the characteristics of e-commerce taxonomies, and propose a new subsumption prediction method based on the pre-trained language model BERT that is well adapted to the e-commerce setting. The proposed model utilises textual and structural semantics in a taxonomy, as well as the rich and noisy instance (item) information. We show through extensive evaluation on two large-scale e-commerce taxonomies from eBay and AliOpenKG, that our method offers substantial improvement over strong baselines.
ANALYSIS OF THE IMPLEMENTATION ELEMENTS OF DISTANCE TRAINING

This article analyzes the results of distance learning for applicants for higher military education by correspondence. It (distance learning) took place using the tools of the distance learning platform MOODLE of the Ministry of Defense of Ukraine. The results of studying (performing the planned activities) subjects «Fundamentals of Heat Engineering» and «Higher Mathematics», as well as passing exams in these disciplines provided the basis for analysis of the quality of learning. It should be noted that 30-55% of students in higher mathematics and 55-75% in the basics of heat engineering used the opportunity to study and report. At the same time, 90% of students sent paper copies of their tests in higher mathematics and other disciplines of the department to the mailbox of the Academy, 50% of students used the opportunity of electronic reporting, duplicating the work. It is worth noting that 55-60% used the opportunity to take exams for tickets integrated into the system. In this case, 40% of the tasks were theoretical, the answers to which were evaluated by the teacher, and 60% - practical tasks, which students had to solve and enter answers to the platform themselves, or choose from a list of proposed. The correctness of such answers was «checked by the system». Therefore, it can be argued that this method adequately reflected the real skills of students and minimized the personal factor possible in the face-to-face exam. The average time of examination work was 1 hour and 28 minutes. The histogram of the received results of examinations on disciplines of a mathematical cycle testifies to similarity of the schedule of distribution of points to the schedule of normal distribution. At the same time there is no sharp increase in extreme results. In combination, these results may indicate that the students of the group performed the work using their knowledge and skills to a greater extent than the «hints» of the Internet. Therefore, such results can be interpreted as a positive consequence of distance learning.
Semi-Supervised Domain Generalization with Stochastic StyleMatch

Ideally, visual learning algorithms should be generalizable, for dealing with any unseen domain shift when deployed in a new target environment; and data-efficient, for reducing development costs by using as little labels as possible. To this end, we study semi-supervised domain generalization (SSDG), which aims to learn a domain-generalizable model using multi-source, partially-labeled training data. We design two benchmarks that cover state-of-the-art methods developed in two related fields, i.e., domain generalization (DG) and semi-supervised learning (SSL). We find that the DG methods, which by design are unable to handle unlabeled data, perform poorly with limited labels in SSDG; the SSL methods, especially FixMatch, obtain much better results but are still far away from the basic vanilla model trained using full labels. We propose StyleMatch, a simple approach that extends FixMatch with a couple of new ingredients tailored for SSDG: (1) stochastic modeling for reducing overfitting in scarce labels, and (2) multi-view consistency learning for enhancing domain generalization. Despite the concise designs, StyleMatch achieves significant improvements in SSDG. We hope our approach and the comprehensive benchmarks can pave the way for future research on generalizable and data-efficient learning systems. The source code is released at https://github.com/KaiyangZhou/ssdg-benchmark .
Efficient Computation of Optimal Thresholds in Cloud Auto-scaling Systems

We consider a horizontal and dynamic auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off to minimise energy consumption while meeting performance requirements. Finding cloud management policies that adapt the system to the load is not straightforward, and we consider here that virtual machines are turned on and off depending on queue load thresholds. We want to compute the optimal threshold values that minimize consumption costs and penalty costs (when performance requirements are not met). To solve this problem, we propose several optimisation methods, based on two different mathematical approaches. The first one is based on queueing theory and uses local search heuristics coupled with the stationary distributions of Markov chains. The second approach tackles the problem using Markov Decision Process (MDP) in which we assume that the policy is of a special multi-threshold type called hysteresis. We improve the heuristics of the former approach with the aggregation of Markov chains and queues approximation techniques. We assess the benefit of threshold-aware algorithms for solving MDPs. Then we carry out theoretical analyzes of the two approaches. We also compare them numerically and we show that all of the presented MDP algorithms strongly outperform the local search heuristics. Finally, we propose a cost model for a real scenario of a cloud system to apply our optimisation algorithms and to show their practical relevance. The major scientific contribution of the article is a set of fast (almost in real time) load-based threshold computation methods that can be used by a cloud provider to optimize its financial costs.
Transformer based Early Classification for Real-time Human Activity Recognition in Smart Homes

Human activity recognition (HAR) plays a key role in intelligent systems. Ambient sensors are utilized to avoid privacy concerns and to collect data streams in a less intrusive manner in smart homes. In scenarios requiring immediate intervention, the systems must perform HAR in real-time. Since it is difficult to segment the exact transition point in real-time, data unrelated to the target activity can appear at the beginning of the time series, which we call unrefined data. It leads us to a new challenge that the HAR model recognizes a user's activity as early as possible with unrefined data. To explore the impact of unrefined data on real-time HAR, we design an experimental system that consists of a Transformer-based filtering network and an LSTM-based early classifier. We evaluate the experimental system with 3 public datasets collected on testbeds with ambient sensors installed. Our results reveal that unrefined data degrade HAR performance in terms of accuracy and earliness, and the use of the filtering network that filters out unrefined data improves recognition performance.
Filtered Trajectory Recovery: A Continuous Extension to Event-Based Model for Alzheimer’s Disease Progression Modeling

Event-based model (EBM) is a flexible way to model the progression of Alzheimer’s disease. The core of EBM is to use an ordering of events to build the trajectory of disease progression. The ordering of events is usually inferred from the data using optimization, hence may suffer from local optima and computational complexity. This paper mathematically proves that this ordering can be directedly determined by the cumulative distribution function (CDF) of the data. From this standpoint, we formulate two properties—order preserving property (OPP) and distance preserving property (DPP)—that an estimated trajectory should satisfy. We show that a trajectory that satisfies these two properties is equivalent to a reparametrized version of the true trajectory. Furthermore, we show that one such reparametrized trajectory can be directedly obtained from the CDF of the data by filtering. We call the algorithm filtered trajectory recovery (FTR). Extensive experiments on simulated data and real data from ADNI show that FTR can retrieve a trajectory that provides a better estimation of the event order and stages, disentangle the progression heterogeneity, without assuming a parametric form of the trajectory function. The code is released at https://github.com/Jiangchuan-Du/FTR-master .
Innovating Elections Smart Voting through Facial Recognition Technology

An online voting system for elections in India is first described in this application. As the high-security password of the voter must be checked before the vote is tallied in the main database of the Election Commission of India, the recommended approach has more security. The additional feature of the model enables the voter to confirm that their vote was cast for the appropriate candidate party. Under this system, a voter has the choice to cast a ballot from a location different than the one that is assigned to them or from their preferred website. The recommended technology will automatically count the votes, saving a considerable amount of time and enabling the Indian Election Commissioner to announce the results in a very condensed time.
Multi-Stage Salient Object Detection in 360° Omnidirectional Image Using Complementary Object-Level Semantic Information

Recently, salient object detection (SOD) for 2D images has been extensively studied. However, due to the complexity of scene and the existence of geometric distortions, research on 360° SOD is still lacking with respect to the wide field-of-view. In this paper, we explore a multi-stage solution for SOD of 360° omnidirectional images, which considers the effects of RGB image and the complementary object-level semantic (OLS) information in locating the objects. Specifically, to effectively concatenate two types of features, we propose a novel Multi-level Feature Fusion and Progressive Aggregation Network (MFFPANet) for accurately detecting the salient objects in 360° omnidirectional images, which is mainly composed of a dynamic complementary feature fusion (DCFF) module and a progressive multi-scale feature aggregation (PMFA) module. First, the OLS and RGB images share the same backbone network for joint learning, and the DCFF module dynamically integrates the hierarchical features from the backbone network. In addition, the PMFA module includes multiple cascaded feature integration modules, which gradually integrate multi-scale features via deep supervision in a progressive manner. Experimental results show that the proposed MFFPANet achieves superior performances on two 360° SOD databases.
Age and Gender Prediction Using Deep Learning

Attribute information such as age and gender improve the performance of face recognition.A data of pertained images is taken that are used to train model using HAAR Feature-based Cascade Classifiers and our main objective is to train a model which can predict age and gender in most efficient way.We are using CNN deep learning method because it is time consuming and the process gives defined results of large dataset.