Reviewerly/exHarmony huggingface dataset:
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning

Keeping risk under control is often more crucial than maximizing expected reward in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is variance, while it penalizes the upside volatility as much as the downside part. Instead, the (downside) semivariance, which captures the negative deviation of a random variable under its mean, is more suitable for risk-averse proposes. This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady rewards. Since semivariance is time-inconsistent and does not satisfy the standard Bellman equation, the traditional dynamic programming methods are inapplicable to MSV problems directly. To tackle this challenge, we resort to the Perturbation Analysis (PA) theory and establish the performance difference formula for MSV. We reveal that the MSV problem can be solved by iteratively solving a sequence of RL problems with a policy-dependent reward function. Further, we propose two on-policy algorithms based on the policy gradient theory and the trust region method. Finally, we conduct diverse experiments from simple bandit problems to continuous control tasks in MuJoCo, which demonstrate the effectiveness of our proposed methods.
Overview of Neural Network

Neural network is a machine learning method evolved from the idea of having look of the human brain, a data processing system which includes large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Here are some of the examples human handwriting, reading typewritten text, compensating for alignment errors in robots, modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and briefly describes several applications.
Studying the Impact of Health Education on Student Knowledge and Behavior through Big Data and Cloud Computing

Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. The application of artificial intelligence and big data to the mental health education practice of college students has a very positive effect on accurately discovering and scientifically solving the mental health problems of college students. In order to combine big data and cloud computing platform organically, this paper introduces an intelligent algorithm based on multi-output support vector regression (MSVR) model and immune clone selection algorithm (ICSA). At the same time, we couple the two to obtain a new intelligent algorithm, namely, immune multiple output support vector regression (ICSA-MSVR) algorithm. Based on the prediction results of health education on students' knowledge and behavior by cloud computing platform, the necessary conditions for three intelligent algorithms to complete the task are summarized. Numerical experimental results show that ICSA-MSVR plays a role in both local search and global search, and is more effective in large-scale cloud computing task scheduling. In addition, in task scheduling, when the task completion time is short, ICSA-MSVR has a lower load imbalance than ICSA and MSVR, which can achieve better load balancing, and the load between virtual machines is closer. Finally, combined with the problems and the needs of students’ health education, suggestions are put forward to deepen the application of technology in students’ mental health education. This approach can provide corresponding ideas and reference methods for improving the scientificity, pertinence, and effectiveness of mental health education.
Implementation of Interactive Multimedia for the First-grade Beginning Readers of Elementary School

This study aims to improve students reading skills using interactive multimedia for the first grade of elementary school. This study is used classroom action research (CAR) by Kemmis &amp; Taggart model design. The research participants were 18 first-grade students of elementary school. Data collection was used observation and performance assessments in the form of preliminary reading practice. The findings demonstrated that interactive media might help first-grade elementary school enhance their early reading abilities. The average value before being given action of 59.7%, which is an increase in early reading skills, can be noted. The average value before being given action was 59.7%, which is in the sufficient group; then climbed in the first cycle to 73.6 percent, which is in the good category; then increased again in the second cycle to 93.1 percent, which is in the very excellent category. As a result, this study may be considered a success because the success markers established in cycle II were satisfied.
Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier.
Subminimal Negation on the Australian Plan

Abstract Frame semantics for negation on the Australian Plan accommodates many different negations, but it falls short on accommodating subminimal negation when the language contains conjunction and disjunction. In this paper, I will present a multi-relational frame semantics –multi-incompatibility frame semantics– that can accommodate subminimal negation. I will first argue that multi-incompatibility frames are in accordance with the philosophical motivations behind negation on the Australian Plan, namely its modal and exclusion-expressing nature. Then, I will prove the soundness and completeness results of a subminimal logic that consists of the multi-incompatibility semantics and a proof system with operational rules that characterize subminimal negation, conjunction and disjunction. Lastly, I will prove some key correspondence theorems that relate frame conditions to certain principles that are associated with stronger negations, which will give rise to a new kite of negations that includes subminimal negation.
Here and There, Now and Then: Creativity, Design and Instruction for Hybrid Environments

Hybrid Environments – spaces where physical and digital elements are blended in-situ – have been growing and expanding over the past few decades. As these technologies become woven into our everyday lives, new challenges and opportunities emerge as they transform our experiences in our homes, workplaces, museums, and cities. Designing Hybrid Environments requires training in both the design of physical environments and interactive technologies. Cross-trained students in higher education need to be able to creatively respond and adapt to these emerging landscapes. Students must bridge fundamental knowledge in design, architecture, art, interactivity, and computation. This is a complex pedagogical practice. How are educators meeting this emergent need? The workshop’s primary goals are to (i) form a collective understanding of the current technological and pedagogical challenges to teaching the design and understanding of Hybrid Environments; (ii) bring together educators and practitioners interested in the future of Hybrid Environments, (iii) discuss creativity support tools as well as frameworks, methods, and approaches for teaching emerging technologies, (iv) form a multidisciplinary community to shape the future of pedagogical inquiry and implementation of Hybrid Environments.
MODEL OF DISTRIBUTION OF OUTDOOR CLIMATE PARAMETERS

The paper considers a method for compiling a model of the distribution of the duration of standing of the parameters of the outdoor climate during the average statistical year
Research-Based Learning Activity Framework with STEM Approach: Implementing Strong Dominating Set Technique in Solving Highway CCTV Placement to Enhance Students’ Metaliteracy

One needed in disruptive technology is metaliteracy. It refers to literacy covering any literacy achievements especially technology and information. Its main indicators are the abilities to produce, incorporate, use, share and collaborate. Thus, higher order thinking skill is required to access these indicators by implementing the research-based learning integration with STEM approach. This research was intended to develop the research-based learning activities framework with STEM approach, namely implementing strong dominating set technique in solving the highway CCTV placement to enhance the students’ metaliteracy. The method used was narrative qualitative on developing the research-based learning activities framework integrated with STEM approach. The results were listed on a table of stages 1-6 at which each stage unraveled the learning activities.
Application of Video Mapping Technique in Kotak Band Video Music

In the current development of visual videography technology, especially in the creation of music video content, new media are developing very rapidly, one of which is video projection (Projection Mapping). apart from the screen. Video Mapping is one of the newest video projection techniques used to convert almost any surface into a dynamic video display. The purpose of video mapping is to create the physical illusion of an image by combining visual elements. Most of the mapping projects are used in fashion shows, corporate events, concerts, and theater performances and can be an asset in artistic music videos. This research explains how to implement video projection into an artistic asset. Then displayed in public spaces to reach a wider audience. This paper will focus on the aspect of video mapping with the technical aspects of developing the concept of the Musik Video Kotak Band project entitled Wings of Garuda by using visual elements of applying visual video mapping in music videos to convey visual messages in video projections on the faces of the band members of the Kotak band. visual delivery in projection mapping creates artistic innovation in music video visuals to minimize the number of production teams in music video projects and messages in video projection visuals can convey their messages, the visuals will be perfected in the editing process to achieve the desired contrast so that video projections on the face more visible detail.
Cultural Approach as a Modeling Tool of Education Environment in Pre-School Educational Organizations

The article features the cultural approach as a tool for building up integral education environment. It systematizes various culturological approaches as a multidimensional phenomenon based on certain value universals. The culturological approach acts as an important, essential, and multifaceted modeling tool that provides effective solutions. Education environment of a preschool educational organization should be value-based. The article introduces a model for mastering cultural universals by preschoolers in preschool education environment. Cultural approach is a tool for modeling education environment. It is a method for developing cultural, material, and spiritual values by immersing the children into the cultural space and adapting them to the technical, informational, didactic, and aesthetic resources of the pre-school organization. The content of the model components in a systemic construction should be based on the ideological relationship between various values. The authors believe that the culturological approach is an effective tool for organizing the cultural environment of an educational organization that ensures the development of values and personality.
On the strange kinetic aesthetic of rectangular shape partitions

Abstract In this paper, we focus on shape partitions. We show that for any fixed k , one can symbolically characterize the shape partition on a k × n rectangular grid by a context-free grammar. We explicitly give this grammar for k = 2 and k = 3 (for k = 1, this corresponds to compositions of integers). From these grammars, we deduce the number of shape partitions for the k × n rectangular grids for k ∈ {1, 2, 3} and every n , as well as the limiting Gaussian distribution of the number of connected components. This also enables us to randomly and uniformly generate shape partitions of large size.
College English Teaching Reform based on CBI Teaching Philosophy and Thematic Teaching Model

The content-based instruction abbreviated as CBI was introduced into China in the 1990s, CBI is a more scientific, advanced foreign language teaching concept. This paper introduces the CBI teaching concept and its model, and discusses the advantages of this concept and its theme model. Based on the current situation of college English teaching in China, some suggestions are put forward for the reform of college English teaching based on CBI teaching concept and theme teaching mode.
Video Transcript Summarization in Marathi

This paper's purpose is a summarization of the video in Regional Languages. During the procedure, we used methodology NLP, LSA, and MoviePy. This paper aims to produce a Short video of long video without missing any point. The technique first short video of any downloaded video. A web application that takes an input of the video and accuracy of the video, then we get this summaries video into text and this text converted into any Regional language. This paper is going to represent an Extraordinary NLP application. This application benefits Students, and teachers by saving time.
An Image Synthesis Method Generating Underwater Images

The objective of this study is to convert normal aerial images into underwater images based on attenuation values for different water types by utilizing the image formation model (IFM) with Jerlov water types. Firstly, the depth values are derived from RGB-D images. If the depth information is not available, the values between 0.5 m to 10 m are chosen, and the transmission map is estimated by these values. Secondly, the statistical average background light values of Br = 0.6240, Bg = 0.805, and Bb = 0.7651 have been derived by analyzing 890 images using two methods, namely quad-tree decomposition and four-block division. Finally, the conversion of aerial-to-underwater images is done using the derived values, and the images are verified by computer simulation using MATLAB software. The result indicates that this method can easily generate underwater images from aerial images and makes it easier for the availability of ground truth.
Virtual assets through the prism of domestic practice: risk-orientedness and implementation

Introduction. Nowadays digital technologies are generating new phenomena among which virtual assets in the diversity of their species and habitats are developing. Problem Statement. The analysis of modern scientific publications and research on the monitoring of virtual assets shows that at the theoretical level there is no single approach to the use of the conceptual apparatus on this issue. Purpose. The aim of the article is to deepen theoretical knowledge and practical skills of using virtual assets through the prism of domestic experience, as well as outlining the place, role, tasks, mechanism of operation, risk orientation and prospects of digital assets implementation as an innovative institutional form of money circulation in the conditions of globalization. Materials and Methods. To achieve this goal, methods and techniques of cognitive theory are used, such as observation, comparison, abstraction, historical and logical approach, analysis and synthesis, induction and deduction, establishing causal links between the development of processes and phenomena, tabular and graphical methods. Results. The current state and prospects of development of the mechanism of operations with digital assets are diagnosed. It is theoretically substantiated that virtual values are objects of legal regulation. The legislative acts which are called to regulate the virtual assets market are analyzed. Conclusions. It is stated that the vector dominants of the impact concerning virtual assets are aimed at the control and transparency of transactions with them, cybersecurity and user verification. The nature of modern money is observed in virtual currency – not material , but absolutely conditional, based on the mechanism of accounting of settlement obligations recognized by all participants. The main innovations and opportunities for implementing positive international practice of state regulation of the cryptocurrency market to Ukrainian realities are outlined.
Keyless or Clueless? Managing Runaway Software Ambition

Sometimes in the pursuit of features, improved sales, and the need to find technological solutions when no underlying problems are known to exist, software engineers produce some really ill-conceived designs. Of course, software engineers have been providing solutions for nonexistent problems for a long time; it is all part of the fun of developing. However, these can, at times, take a dark turn.
Mask Wearing Detection Based on Inceptionv4 and Multi-Scale Retinex Image Enhancement Algorithm

Abstract The novel coronavirus is a contagious virus with a high mortality rate, and the international health emergency due to COVID-19 has never stopped since the outbreak, due to this situation, wearing masks has become a basic recommended public epidemic prevention approach for many countries. In the current shortage of medical human resources, we urgently need a non-artificial mask-wearing detection method. In this paper, the multi-scale Retinex algorithm is involved as the preprocessing step of the input image. The mask-wearing detection is based on the InceptionV4 convolutional neural network model. During the experiment, we compared and verified the superiority of the Inception part of the InceptionV4 model compared with the Stem structure of the GoogLenet model, and from LFW (Labeled Faces in the Wild), RMFD (Real-World-Masked-Face-Dataset) more than 10,000 samples were selected from the public datasets for model training. Finally, the precise rate reaches 97.3 %.
Comparison among Four Deep Learning Image Classification Algorithms in AI-based Diatom Test.

To select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine.The "diatom" and "background" small sample size data set (20 000 images) of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network (CNN) models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve (ROC) of subjects and confusion matrixes were drawn, recall rate, precision rate, specificity, accuracy rate and F1 score were calculated, and the performance of each model was systematically evaluated.The InceptionV3 model achieved much better results than the other three models with a balanced recall rate of 89.80%, a precision rate of 92.58%. The VGG16 and Inception-ResNet-V2 had similar diatom recognition performance. Although the performance of diatom recall and precision detection could not be balanced, the recognition ability was acceptable. ResNet50 had the lowest diatom recognition performance, with a recall rate of 55.35%. In terms of feature extraction, the four models all extracted the features of diatom and background and mainly focused on diatom region as the main identification basis.Including the Inception-dependent model, which has stronger directivity and targeting in feature extraction of diatom. The InceptionV3 achieved the best performance on diatom identification and feature extraction compared to the other three models. The InceptionV3 is more suitable for daily forensic diatom examination.目的: 选择深度学习图像分类算法中复杂性和准确性较为平衡的4种算法进行硅藻的自动识别，探究最适用于硅藻识别的分类算法，为法医学自动化硅藻检验研究提供数据参考。方法: 建立真实水中尸体肺组织消化液涂片的“硅藻”“背景”小样本量数据集（20 000张），用于4种算法（VGG16、ResNet50、InceptionV3和Inception-ResNet-V2）模型的训练、验证和测试。绘制受试者工作特征曲线、混淆矩阵并计算召回率、查准率、特异性、准确率及F1分数，对各模型性能进行系统性评估。结果: InceptionV3的硅藻识别性能明显优于其他3种算法，具有更为均衡的硅藻查全（89.80%）与查准（92.58%）性能；VGG16和Inception-ResNet-V2的硅藻识别性能相当，虽无法做到硅藻查全与查准的性能均衡，但其识别能力尚可接受；ResNet50的硅藻识别性能最低，其召回率仅为55.35%。在特征提取上，4种模型均提取到了硅藻和背景的特征，且都以硅藻区域为主要识别依据。结论: 包含Inception结构的模型，在硅藻特征提取方面具有更强的指向性和靶向性。其中，InceptionV3算法能够更为准确、靶向地提取到硅藻特征，具有最优的硅藻识别性能，更适合应用于日常法医学硅藻检验。.