Reviewerly/exHarmony huggingface dataset:
A Novel Data Partitioning Method for Active Privacy Protection Applied to Medical Records

In recent years, cloud computing has attracted extensive attention from industry and academia due to its convenience and ubiquity. As a new Internet-based IT service model, cloud computing has brought revolutionary changes to traditional computing and storage services. More and more individual users and enterprises are willing to deploy their own data and applications on the cloud platform, but the accompanying security issues have also become an obstacle to the development of cloud computing. Multi-tenancy and virtualization technologies are the main reasons why cloud computing faces many security problems. Through the virtualization of storage resources, multi-tenant data are generally stored as shared physical storage resources. To distinguish the data of different tenants, labels are generally used to distinguish them. However, this simple label cannot resist the attack of a potential malicious tenant, and data still has the risk of leakage. Based on this, this paper proposed a data partitioning method in a multi-tenant scenario to prevent privacy leakage of user data. We demonstrate the use of the proposed approach in protecting patient data in medical records in health informatics. Experiments show that the proposed algorithm can partition the attributes more fine-grained and effectively protect the sensitive information in the data.
Kernel Fisher Dictionary Transfer Learning

Dictionary learning is an efficient knowledge representation method that can learn the essential features of data. Traditional dictionary learning methods are difficult to obtain nonlinear information when processing large-scale and high-dimensional datasets. While most dictionary learning algorithms are based on the assumption that the training data and test data have the same feature distribution, which is not always true in practical applications. To address the above problems, we propose the Kernel Fisher Dictionary Transfer Learning (KFDTL) algorithm. First, we map each sample to high-dimensional space through kernel mapping and use any dictionary learning algorithm to learn the essential features. Then, the feature-based transfer learning method is performed to predict the labels of the target samples. This method includes three main contributions: (1) KFDTL constructs a discriminative Fisher embedding model to make the same class samples have similar coding coefficients; (2) Based on the relationship between profiles and atoms, KFDTL constructs an adaptive model that adapts source domain samples to target domain samples; (3) The kernel method is used to efficiently solve nonlinear problems. Experiments on a large number of public image datasets have proved the effectiveness of the proposed method. The source code of the proposed method is available at https://github.com/zzfan3/KFDTL .
Explainable Machine Learning to Predict Successful Weaning of Mechanical Ventilation in Critically Ill Patients Requiring Hemodialysis

Lungs and kidneys are two vital and frequently injured organs among critically ill patients. In this study, we attempt to develop a weaning prediction model for patients with both respiratory and renal failure using an explainable machine learning (XML) approach. We used the eICU collaborative research database, which contained data from 335 ICUs across the United States. Four ML models, including XGBoost, GBM, AdaBoost, and RF, were used, with weaning prediction and feature windows, both at 48 h. The model's explanations were presented at the domain, feature, and individual levels by leveraging various techniques, including cumulative feature importance, the partial dependence plot (PDP), the Shapley additive explanations (SHAP) plot, and local explanation with the local interpretable model-agnostic explanations (LIME). We enrolled 1789 critically ill ventilated patients requiring hemodialysis, and 42.8% (765/1789) of them were weaned successfully from mechanical ventilation. The accuracies in XGBoost and GBM were better than those in the other models. The discriminative characteristics of six key features used to predict weaning were demonstrated through the application of the SHAP and PDP plots. By utilizing LIME, we were able to provide an explanation of the predicted probabilities and the associated reasoning for successful weaning on an individual level. In conclusion, we used an XML approach to establish a weaning prediction model in critically ill ventilated patients requiring hemodialysis.
MATHEMATICS AS AN ORGANIZING PRINCIPLE OF THE SYSTEM OF CHARACTERS IN A. BELY’S NOVEL PETERSBURG

The paper attempts to prove that mathematics is not just one of the keys to comprehending the conceptosphere of A. Bely’s novel Petersburg, but the basis of his poetics, including the character sphere. It is the mathematical approach to the ways of grouping characters, as well as to the types of psychological analysis, that ensures the compositional unity of the chaotic, ‘unassembled’, at fi rst sight, text. Th e ‘brain game’ of the characters and the author-narrator, which reveals the textgenerating algorithm of the novel and provides seemingly unlimited freedom to its creator, turns out to be a carefully adjusted ‘program’ based on a system of binary oppositions. A. Bely’s Petersburg, for all its ornamental ‘motleyness’, is one of the most well-ordered texts of Russian literature: random encounters and collisions of characters, their chaotic movements around the city, strange actions, as well as ‘disorderly’ thoughts, have a strict ‘mathematical foundation’. At the same time, the principle of binary oppositions as the organizing principle of the system of characters corresponds to the logic of the neo-mythological text, which is characterized by the mutual refl ection of characters into each other, as well as the division of a single ‘pro-hero’ into novel doubles. The philosophical ideas of Vyach. Ivanov, dating back to the teachings of the Pythagoreans and their interpretation of the monad and dyad (as the essence of Apollo and Dionysus), are correlated in the novel with the scientific discoveries of Professor N.V. Bugaev (arrhythmology, the doctrine of ‘continuity’ and ‘discontinuity’). Moreover, it is emphasized that Bely’s interest in the categories of singularity and multiplicity, which sets the mathematically visual vector of perception of the text — and above all its system of characters — reveals a typological relationship with other areas of mathematical knowledge (in particular, with the Banach—Tarski paradox).
Funding and University Performance: An Econometric Analysis of the Indian Public Universities

Underpinning the aim of India’s National Education Policy, 2020 to devise a performance-based funding mechanism, it is pertinent to examine the impact of existing financial support on the university’s performance. Using a fixed panel data analysis, the present study investigates the influence of funding on the performance of Indian public universities over the period 2016-2020. The funding is considered in terms of the plan and non-plan grants received by universities from the central government, while performance is measured using performance parameters given by National Institutional Ranking Framework, 2021. Controlling the age, size and ownership type of university, findings reveal a significantly positive impact of funding on teaching performance, research performance and graduation outcomes of government-owned universities. Surprisingly, university’s outreach, inclusivity and peer perception have been observed to be indifferent towards central grants. Overall, the study provides valuable insights to university practitioners and policymakers for future development of the performance-funding mechanism.
Human Detection Based on YOLOv5 Network

With the vigorous development of the economy, the improvement of people's quality of life, the continuous increase in the ownership rate of family cars, and the increasing pressure on traffic management, people urgently hope to use new technologies to improve and optimize the past system. Therefore, the research on the modernization and intelligence of traffic control has become a research hotspot for scholars. When the crowd density is too high, all kinds of accidents are easy to happen, and it will bring difficulties to the management of order managers. Timely detection of crowd density, and when the crowd density is too high, warning information will be issued, which is conducive to the managers to evacuate the crowd in time. So, the final project is about how to recognize humans in the image, and with men and women. Today's human detection can be broadly divided into traditional image processing methods and machine learning-based methods. The traditional image processing method detects the human by preprocessing the image and then detecting the human contour. Frequent stampede events in large events at home and abroad have caused a lot of casualties. Therefore, the research on crowd counting has attracted more and more attention. If the crowd density of the current scene can be accurately estimated and the corresponding security measures can be arranged, the occurrence of such incidents can be effectively reduced or avoided. Crowd counting is widely used in video surveillance, traffic monitoring, public safety, urban planning, and the construction of intelligent shopping malls.
An Application for the Human-Computer Interaction Game Based on Yolov5

The rapid development of computers makes human-computer interaction possible, and hence more people have been attracted to this subject in several different ways. After searching online about the ways to increase the accuracy. Based on the algorithm of yolov5, this article used the characters from a popular PC game called Counter-Strike: Global Offensive (CSGO). By using the 3 characteristics from the game (Human-T, Human-CT, and Head). This article shows the results of the detection of those 3 by using different numbers of epochs and images. In the two detections, 240 images and 150 epochs, and 500 images and 450 epochs are used. The results of the two detections have a large difference. In the second detection, the result is much better than the first one by having a small change in the dataset. Meanwhile, this article will also mention other ways made to increase the accuracy of the detection, such as the way to label images, etc.
Automatic Classification for Unlabeled Email Messages into Folders

Imagine returning from an excused absence because of Covid-19 or any force majeure alike, and having to immediately face 300+ unread emails; getting overwhelmed by emails has become part of office workers’ daily routine. Numerous pieces of research have shown effective methods to categorize email messages, detect potential harassment, and even automatically send a reply. But still, email is an interesting type of text to analyze and gives rise to many challenges. First discussing the challenge in the problem, this paper aims to research, study, and propose a method that can deal with a specific challenge: making folders out of income email messages and then classifying emails automatically. By cooperating basic methods, techniques, and algorithms, an intuitive program is developed that can perform the task with the given public email dataset. The method is then expected to raise prospects for future investigations and improvements in performance and robustness.
The Improvement of Federated Learning in communication efficiency

In recent years, the need to protect user privacy and data security as well as multi-party data cooperation has become increasingly strong. Privacy computing is considered a fundamental solution that is growing rapidly and gaining unprecedented popularity. Currently, federated learning is the most popular product in privacy computing. With its lightweight technology route and deployment, it has become a mainstream solution and product of choice for many privacy computing applications, such as providing advanced medical image collection segmentation technology in the medical field to protect patient privacy, where it can play a unique role. But Federated learning faces a need to improve communication efficiency. This paper introduces FedAvg, the most basic algorithm for federated learning, including its basic process, advantages and disadvantages. We analyze algorithms that optimize federated learning by adding local computation, Communication Compression (including model Compression, Periodic Averaging and Quantization Compression, and gradient Compression), computation and communication overlap, and compare the strengths and weaknesses of representative algorithms for each approach. At the end of this paper, the summary and prospect are made.
Algorithm Comparison on Email Spam Filtering Task

Email has long been a major form of communication among organizations and individual users. In recent years, with the rise of internet use, email spamming has become increasingly common. Spamming has raised security concerns as it causes potential loss to the users with fake advertisements, invalid information, undetected virus, and other harmful information. Various techniques have been developed to facilitate spam filtering, using classifying algorithms to characterize emails into different categories. This article presents an investigation of how machine-learning-based algorithms are used in email spam filtering by providing some previous researches that have shown to be successful. The algorithms are in the range from supervised learning, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) to unsupervised learning such as artificial neural networks (ANN) and partitional clustering. This article also presents an experiment that compares different implementations of such algorithms, including the SVM, NB, and K-Nearest Neighbor (KNN). The results reflect that NB gave the highest accuracy. A second attempt of the same experiment was conducted, with an improved data cleaning procedure and larger testing sets. The data collected from the second attempt again show that the NB implementation gave the highest accuracy in detecting spam emails.
The Application of Human Detection Based on YOLOv5

Human detection has several possible applications. A well-known example is autonomous driving technology, which incorporates gender recognition and fall detection for the elderly. This study will focus on the principles of these applications, which consist primarily of locating the positions of persons inside an image. It is a crucial piece of technology that protects individuals from danger. To fix this problem, this paper employed the deep learning algorithm, mainly supervised learning, which demands a significant quantity of labeled data. Fortunately, these data sets may be collected via security and in-vehicle cameras. YOLOv5 is a famous object detection algorithm, it can train the detector to operate faster and more precisely, enabling the system to identify any potential dangers instantly, which is considered in this study. The experimental results demonstrated that the employed YOLOv5 method can detect the location of human effectively, which proved the feasibility of the method used in the study.
Composition analysis and identification of ancient glass products based on population forest-KNN algorithm

This paper is based on the KNN and Random Forest algorithms in machine learning to analyze the differences and correlations in the compositions of ancient glass objects, determine their classification and identify the unknown categories of artifacts. The classification rules of different types of glass are analyzed; for each category, appropriate sub-classification processes are performed according to their chemical composition, specific classification methods and processing results are given, and sensitivity and rationality analyses are performed on the results. The chemical composition of glass artifacts of unknown categories is analyzed, and then their categories are identified and sensitivity analysis is performed on their classification results. Samples of glass artifacts from different categories and analyze the relationships between their chemical compositions, and compare the differences that exist in the relationships between the chemical compositions of different categories.
Cooperative Area Search Algorithm for Multi-UAVs in Uncertain Battlefield Environment

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperative area search in uncertain battlefield environment, a multi-UAVs cooperative area search algorithm based on co-evolutionary genetic algorithm (CEGA) is proposed. Firstly, the raster Map and extended Search Probability Map (SPM) model of the task area are constructed, the UAV state model and sensor detection model are established, and the search environment updating method based on the Bayesian criterion is presented. Then, considering the cooperative elements of multi-UAVs, the cooperative area search planning model is established, and based on the rolling optimization idea, the co-evolutionary genetic algorithm with elite retention strategy is used to optimize the solution, and the multi-UAVs cooperative search paths are generated. Through simulation experiments and comparative analysis, the effectiveness of the proposed algorithm is verified.
Development of Semantic Segmentation Based on Deep Learning

In recent years, the discipline of computer vision has seen a lot of interest in the study of image semantic segmentation. Deep learning has grown in popularity, and deep learning and image segmentation have combined and improved. These technologies are now widely employed in autonomous vehicles, intelligent robots, and other devices. In the beginning, Fully Convolutional Networks (FCN) or U-net-based semantic segmentation techniques were proposed; FCN realized an end-to-end training network and effectively applied Convolutional Neural Networks (CNN) to the semantic segmentation domain. To improve outcomes in the field of semantic segmentation, the encoder-decoder structure from the FCN approach was later implemented, and the Atrous Convolution approach was also proposed. Transformer-based semantic segmentation techniques are another recent trend, in addition to CNN-based networks. The Transformer model was first proposed in 2017, and subsequent Transformer-based semantic segmentation methods have also achieved good results. In this paper, these various methods will be compared and discussed to provide a guidance for this field.
Comparisons of Stock Prediction Methods Based on Recurrent Neural Networks

These instructions give you guidelines for preparing papers for DRP. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at DRP. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd-Fe-B"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field, but are not required. Put a space between authors’ initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstract must be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must be between 100 - 300 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstract must be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.
Comparison of different Convolutional Neural Network models on Fruit 360 Dataset

Numerous Convolutional Neural Networks emerged in the past decade, each varies in accuracy, speed, and architecture. From AlexNet to ResNet, CNN models have been developing rapidly, and the architecture of the models become more complicated. These models are known for their accuracy on ImageNet, so the topic of this research is to explore how CNN models can perform differently on the Fruit 360 dataset. A model constructed specifically in this research and three significant models developed in the past decade are applied to the Fruit 360 dataset for result comparison: VGG-16, ResNet-50, MobileNet, and SC-3. The models are selected to be different in architecture for comparison. The models are modified in similar ways and are trained on the same device under the same circumstances. The research found that ResNet-50 obtains the best prediction accuracy. MobileNet takes the least time to train and predict. The ResNet-50 and MobileNet models can be considered for real-world application, whereas VGG-16 and SC-3 are either inefficient or fails to generalize to complicated situations.
Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script.During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute.Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment.The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased.In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters.The performance of the ensemble mechanism is significantly better than the single CNN model.This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches.The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers.
Scale and View Invariant Informative Joint Descriptor (SVI2JD) for Human Action Recognition from Skeleton Data

One of the biggest challenges in the Human Action Recognition is View-point variations as the actions are captured under multiple views in real time.Furthermore, in the Skelton based action representation, for HAR, only few joints are informative and remaining joints constitutes redundancy.To sort out these problems, this paper proposes a new Action descriptor called as Scale and View Invariant Informative Joint Descriptor (SVI 2 JD).SVI 2 JD is a combination of three descriptors; they are namely Self-Similarity Joint Descriptor (SSJD), Informative Joint Descriptor (IJD) and Spherical Joint Descriptor (SJD).SSJD concentrates on the view invariance and employs a Self-Similarity Matrix (S 3 M) which computes pair wise distance between joints in each frame of action sequence.Next, SJD aims at describing the action through restricted movements of joints because they can't move beyond particular angle and distance from origin of body.IJD removes the redundant joints those have less contribution towards the action.Further, a 2D Convolution Neural Network Model is proposed for feature extraction and classification.Different fusion rules are employed to fuse the individual results.The Effectiveness of proposed model is demonstrated through its simulation on two challenging datasets; NTU RGB+D dataset and Northwestern UCLA dataset.