Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Twitter Data Using Machine Learning

Sentiment analysis is a machine learning approach where machines analyze and classify the human’s emotions, opinions and sentiments about any topic which are expressed in a way of either speech or text. The textual data which is available on the web is increasing day to day. Twitter is one among widely used social platforms for posting comments on different topics and represented as short status messages. The difficulty of sentiment analysis lies in identifying the emotions expressed in these texts. This survey is done on Google co lab to study the problem of sentiment analysis; the prediction is done by people’s expression in their views about the human which helps to analyze the neutral, positive and negative comments. The negative, positive or neutral tweets are analyzed based on keywords. The output will be represented in graphical form which is done by logistic regression. It is analyzed to find the results of sentiment analysis and how accurate the results would be. In this project, the tweets are analyzed and generate word cloud displaying the positive, negative and neutral words used in the tweets. A graph is displayed for positive and hashtags displaying which of the hashtags in twitter are more used to less used.
A Systematic Review on Load Balancing Tools and Techniques in Cloud Computing

Nowadays, cloud computing attracted wide attention as it can deliver IT services and resources on a demand basis over the Internet. Load balancing is a key challenge in cloud computing. Due to the complex structure of cloud computing, it is difficult and costly to evaluate the behaviour of load balancing techniques on different cloud resources based on QoS parameters in a real cloud environment. Therefore, to overcome this situation, cloud computing tools are used for simulation to test the behaviour of load balancing techniques in the cloud system under different conditions in a repeated manner by changing various parameters. There are many tools available today such as CloudSim, WorkflowSim, CloudSim4DWf, GreenCloud, and CloudAnalyst, and every tool varies in characteristic, architecture, parameters, result assessment. So, it is very important to choose an efficient load balancing tool which fulfils required QoS requirements. This work focusses on important cloud load balancing tools and provides a comparative study on important existing as well as newly proposed load balancing tools. Also, we will discuss load balancing techniques divided on the basis of three approaches.
Deep Convolutional Neural Network-Based Bangla Sign Language Detection on a Novel Dataset

Bangla Sign Language (BdSL) is the communication medium used by the deaf and mute people of Bangladesh. As they have speaking and hearing disabilities, they communicate with each other and with the rest of the world through sign language. It works as a communication medium and decreases the communication gap with normal people. In this research, we have worked with Bangla Sign Language detection for digit and letter signs. We have concentrated on building a complete BdSL dataset and applying various classifiers to our novel dataset. We have mostly focused on building convolutional neural networks to classify image data in this article. In addition, we have applied K-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and decision tree (DT) models to check the performance of each model on our dataset. On average, we have observed the highest accuracy of 95% for digit recognition and 91.5% for letter recognition on the CNN model. We have used 1500 images for digit and 4320 images for the letter dataset. We believe that our dataset will help researchers to carry out more in-depth studies as we have plans to make it openly accessible. Therefore, it will reduce the scarcity of the Bangla sign language dataset and expand research in this area.
Avyanna: A Website for Women’s Safety

Indian women—the better half of our Indian Society—are becoming the most vulnerable segment as per the safety, and security of women is concerned. Everyday there are many cases related to violence against women including rape cases, trafficking, sexual assaults, domestic violence, and many more. Thus, woman safety has become a major concern in India. But not many of the women know about their rights or whom to approach at the time of scarcity and problem. According to a report of the National Crime Records Bureau, the reported incidents related to women have increased by 44% as compared to last year. In this paper, the authors have proposed a website named ‘Avyanna’ that provides online legal support for those women who are in need. It is a one-stop solution for women where they can consult advocates, get details of cases and Indian Penal Code (IPC) sections related to women, and also get safety instructions for women to stay alert and accordingly take precautions.
Review: <i>The Transcendentalists and Their World</i>, by Robert A. Gross

Book Review| August 01 2022 Review: The Transcendentalists and Their World, by Robert A. Gross The Transcendentalists and Their World. By Robert A. Gross. Farrar, Straus and Giroux, 2021. 880 pages. $40.00 hardcover; $22.00 softcover; ebook available. Brian C. Wilson Brian C. Wilson Western Michigan University Search for other works by this author on: This Site PubMed Google Scholar Nova Religio (2022) 26 (1): 125–127. https://doi.org/10.1525/nr.2022.26.1.125 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Brian C. Wilson; Review: The Transcendentalists and Their World, by Robert A. Gross. Nova Religio 1 August 2022; 26 (1): 125–127. doi: https://doi.org/10.1525/nr.2022.26.1.125 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentNova Religio Search Robert A. Gross’ The Transcendentalists and Their World is a massively detailed social history of the town of Concord, Massachusetts, from the time of the Revolutionary War to roughly the 1850s. To a lesser extent, it is an intellectual history of the rise of Transcendentalism, specifically as formulated in the works of Ralph Waldo Emerson and Henry David Thoreau. As Gross states in his preface, the book is a continuation of his earlier work, the immensely popular The Minutemen and Their World (1976). That book used the social history of Concord in the early eighteenth century to answer the question of why this small New England town became the flashpoint for the American Revolution. According to Gross, Revolutionary Concord represented the triumph of “civic republicanism merged with Puritan traditions to emphasize the interdependence of individuals and families within a common way of life” (xv). By the 1830s, however, this ideology... You do not currently have access to this content.
Self-piloting path planning method based on improved RTT* algorithm

The path planning algorithm is important for the safety and stability of self-piloting. In this paper, target bidirectionalRRT*(TB-RRT*) path planning algorithm based on target gravity and improved metric function is proposed to address the problems of low search efficiency, high randomness, slow convergence, and unsmooth path of the rapidly-exploring random tree star (RRT*) algorithm. Firstly, the algorithm introduces the target gravity and dynamically adjusts the sampling step to improve the search efficiency of the algorithm and reduce the randomness of the search tree growth; through the bidirectional tree growth strategy, the convergence speed of the algorithm is improved. Secondly, the smoothness of the planned paths is improved by considering both Euclidean distance and pinch angle effects on path planning with an improved metric function. Finally, the smoothing path with optimal path cost is obtained by the path selection method. Simulation results show that the improved TB-RRT* algorithm reduces the path length by 12.8%, the running time by 56.9%, and the number of sampled nodes by 43.8%.
Improved RRT algorithm for path planning in field environment based on target bias probability

With the advancement of unmanned driving technology, unmanned vehicles' application environment has gradually moved from the structured road surface to the unstructured and complex field environment. The traditional path planning approaches cannot match the path planning needs of unmanned vehicles in the field environment due to the complicated and changing terrain. In this paper, a fast random tree search algorithm is used to find a viable path in an environmental map quickly. By introducing the target probability bias value and the path post-processing method based on the maximum turning radius constraint of the unmanned vehicle into the basic RRT algorithm, and then simulating and comparing the simulation analysis on the 3D raster map of the constructed field environment. The applicability of the RRT algorithm based on the target probability in the field environment is verified, and the algorithm has higher search efficiency, smoother paths planned by the algorithm, and more goal-oriented path generation.
Improvement of Bayesian knowledge tracking behavior model

Bayesian knowledge tracking model is used to track learners' knowledge state and predict their mastery level and future performance in intelligent teaching system. The original BKT model assumes that learners do not forget any knowledge after learning. This assumption will lead to the deviation between the predicted results of the model and the actual situation. In order to deal with above situations, this paper proposes a Bayesian knowledge tracking model based on learner behavior and forgetting factors. By using the decision tree algorithm to obtain the behavior node data information, and then initialize the forgetting parameters and assign values to update the algorithm of learners' knowledge mastery level.
Transfer learning via Laplacian feature embedding

In the real applications, labeled data is often in short supply, which make is difficult to train a reliable classifier. Transfer learning theory enables model to be trained by exploiting the auxiliary data from out domains. For transfer learning, it’s crucial to find a new feature representation where traditional classifier can be applied. In this paper, we propose a novel transfer algorithm by constructing a Laplacian feature embedding where data property is preserved and domain discrepancy is reduced by MMD principle. Furthermore, we exploit the label constraint during the embedding procedure which minimizes the empirical error on the labeled data. Extensive experiments are conducted to evaluate the effectiveness on two textual datasets, and the study shows the proposed method can significantly improve the classification accuracy over several state-of-the-art algorithms.
UAV path planning with kinematic constraints based on deep reinforcement learning

With the rapid development of UAVs, the application of UAVs in civil and military fields has greatly been expanded. UAVs are often used to perform tasks in hazardous areas because of its flexibility. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or ignore nonlinear kinematic characteristics of UAVs, which is impractical. In this paper, a Deep Reinforcement Learning (DRL) based path planning algorithm with the nonlinear kinematic constraints is proposed. For static obstacles and task points, the reward function is designed and DRL is used to generate the flight path for multiple task points. When UAV detects a dynamic obstacle, we assume that it transmits coordinates of the obstacle to a computer center to regenerate the remaining path. Wilson Confidence Interval (WCI) is then used to predict time taken by computer center to recreate the new path. Simulation results validate the efficiency of the proposed algorithm.
Single-neuron proportion integral differential control of brushless direct current motor

In today's increasingly stringent emission requirements, the requirements for aftertreatment are also increasing. Controlling the motor in the post-processing system plays a key role. In this paper, the method of controlling the motor will be analyzed. When analyzing the traditional PID controller, it is found insufficient, so a new single neuron PID controller is used, which can better control the DC brushless motor than the former; the latter structure has better adaptability. The advantages and disadvantages of the two controllers are compared in the Simulink simulation of MATLAB, and it is concluded that the latter has a faster response speed and stronger adaptability.
A Constitutional Study on the Party Nomination System in Local Government Elections: Focusing on Critical Review and Alternatives

1. Local governments shall deal with administrative matters pertaining to the welfare of local residents, manage properties, and may enact provisions relating to local autonomy, within the limit of Acts and subordinate statutes. And the organization and powers of local councils, and the election of members; election procedures for heads of local governments; and other matters pertaining to the organization and operation of local governments shall be determined by Act. In this regard, Art. 47 (1) of Public Official Election Act stipulates Party Nomination System(“A political party may nominate its member as a candidate within the limits of the fixed number to be elected in each constituency in an election”). 2. The Constitutional Court did not decide that Art. 47 (1) of Public Official Election Act is unconstitutional. Although the Party Nomination System in Local Government Elections cannot be said to be unconstitutional, it is not a system conducive to the realization of constitutional values. 3. The Party Nomination System in Local Government Elections does not conform to the spirit of Local Autonomy under the Constitution, and it is also heterogeneous with the Party System under the Constitution. 4. Therefore, it is necessary to abolish the Party Nomination System in Local Government Elections. And in the area of Local Autonomy, it is necessary to activate Residents Autonomy Association that can replace the Party and introduce Residents Autonomy Association Nomination System in Local Government Elections. 5. It is not rational to introduce Local Political Party as a means to solve the problem of the Party Nomination System in a Local Government Elections. This is because a Party is an organization that participates in the formation of the national's political will, not the residents, and a Local Political Party is also a Party.
The Correlation Between Students Achievement In Vocabulary And Speaking Ability at Grade 8 Junior High School Students

ABSTRACT&#x0D; This research aimed to find out the correlation between students’ vocabulary mastery and speaking ability at grade 8 of SMP Negeri. This research was quantitative research which used correlational technique design. The sample of the research was 30 students of grade eight selected by clustering sampling. The instruments used in the research were vocabulary test and speaking test. Vocabulary test consisted of 50 questions of multiple choices, and speaking test in the form of role play. The research data were in the form of interpretation of students’ vocabulary and speaking test results, and their correlation. Based on data analysis, it was found that students’ vocabulary mastery and speaking ability were fair. They were proved by the mean score 65 for vocabulary mastery and 68 for speaking ability. Moreover, there was strong correlation between students’ vocabulary mastery and speaking ability. It was shown by the rvalue = 0,703 .&#x0D; &#x0D; Keywords:Students’ Achievement, Vocabulary, Speaking
The Optimizing Digital Evidence: Perspective of the Criminal Procedural Law System

Proof in the concept of procedural law is a very important condition, evidence to clarify a case a quo.However, the existence of a portrait of law-making which always limps along with the times, forces the law to be able to find its existence in the legal state of society.This also includes the state of society 4.0, which is of course proven that a case that is regulated is still conventional.digital evidence must have a certain position.This paper raises a view of the evidentiary system in procedural law, especially including in the perspective of the electoral procedural law system.Through the legal approach, case approach, library approach and comparison approach.This paper focuses on the issue of optimizing digital, especially in the view of the Criminal Procedure Law System.
KREDİ KARTI, BANKA KARTI İLE ENFLASYON ORANLARI ARASINDAKİ İLİŞKİ: TÜRKİYE ÜZERİNE BİR İNCELEME

The reforms to be made regarding the determination of the relationship between inflation rates and debit and credit card expenditures are highly effective in the success of anti-inflation programs. In this context, the main purpose of this study is to examine the interaction between the consumer price index (CPI) and the use of credit and debit cards for the period 2014m04 to 2022m05 in Turkey. In this study, impulse-response functions and extrinsic Granger causality analyzes were tested on vector autoregressive models. According to the Granger causality test, there is causality from inflation to credit-debit card usage volume and from credit-debit card usage volume to inflation at the same time. According to the impulse-response functions, a standard deviation shock to the bank card had a positive effect on inflation. However, a one-unit shock on inflation did not have a statistically significant effect on the volume of credit-debit card usage
Factors for forming value orientations of children's houses as an axiological problem

This article deals with the questions of the value orientation of the pupils of the houses of mercy as an axiological problem. Value orientation factors are classified by type into macro factors, mesofactors and micro factors. The factors of value formation are characterized in accordance with the components of the axiological worldview.
The Principle of Global Axiology: ’’The Shepherd’s Rod’’ for the Whole Human Name

We want peace and not war is the calling of the whole world. It is our daily calling. How do we pedagogues of all levels respond to the call of immortality? This is the Ion Druță’s calling to which we align by thinking and acting in the context of our being, directly of the being of the whole world, the Principle of Global Axiology - "The shepherd’s rod" of the whole human race. It is appropriate to emphasize the achievements of the creation of the entire human race in cultural, economic and social diversity. This is the case of our everyday occupation in which, in the format of set theory, the accents of complementary meaning the theory of multiple graces / TGM (L. Botezatu) - the theory of multiple intelligences / TIM (H. Gardner) guarantees its philosophical-centric positioning. It is the fact that the Paideia art reaches the peaks of the first signs of the markers in accessing "to be here". It is the day-to-day operation of "being beyond", vocally engaged in the work of responsibility for value resignation. Know how to choose the most efficient epistemic configurations; to order them and subject them to the laws of the mechanics of action by connecting the PAG/ Principle of Global Axiology from the TMSC/ Theory and Special Methodologu of Creativity (vis-à-vis the specific concerns of the embraced field) to the TMGC/ Theory and General Methodoogy of Creativity means to diligently replace yourself on the scale of meritological practices; return to essence - ascension through Spirituality into the Great Spirituality.
Supervised Learning-Based PV Output Current Modeling: A South Africa Case Study

Photovoltaic (PV) plants utilization for green solar energy is growing exponentially in demand as industries committed to move away from carbon energy sources such as coals, oil, or gas. However, for efficient green solar energy utilization, a precise prediction method is required to minimize design composition wastage. The measured output current determined by empirical method will be compared with the predicted current obtained from the proposed neural network (ANN) and random forest (RF) methods. The comparative analysis of the measured and the proposed models is evaluated by using the minimum root means square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE). The obtained results suggest the superiority of RF over the ANN with improvement performance metrics values of 173% for RMSE, 39% for MAPE, and 188% for MBE.
Robotics

Recent progress in musical generation and classification has largely been achieved by deep learning. While many of these systems represent improved results in comparison to older methods, they have significant broader ethical implications that have not been as widely examined from a creative perspective. These costs are beginning to be explored in broader functional AI research, but we believe that they have not been adequately addressed in creative work. They include the prohibitive training requirements and subsequent environmental impacts, as well as the exclusion of artists without adequate computational resources. Other challenges include the ownership of material and the data that has been trained on, often coming from external sources with no input. For the research process itself, these networks include the lack of transparency in system design – emphasised through the removal of the need for domain expertise – which itself increases the lack of transparency and interpretability. As a counter system, we present a human-first deep learning design allowing agency over interaction and generation. The system is simple, easy to train, and allows effective, interactive real-time system for generating musical improvisations in performance with human musicians. The system is comprised of a generative convolutional neural network and uses a novel data format that appears to allow improved learning of nonlocal dependencies and repetitive structure across beats within musical phrases. We have observed that the system is able to learn to generate convincing and coherent improvisations from relatively small amounts of data. It can run effectively with limited computational resources and produces pleasing musical interactions in a live performance setting. We discuss our proposed data format, the system design, and the affordances of the system.
Obscenity, Nonconsensual Pornography, and Cyberbullying

The rise of the Internet brought about new challenges in the regulation of undesirable sexually explicit content in the United States. At the core of these challenges is our nation’s fundamental belief in the protection of expression under the First Amendment. Increased use of electronic communication devices and social media, however, transformed these challenges into everyday concerns for employers, parents, and communicators – looking to limit the exchange and receipt of sexual photos, text messages, and other content and prevent themselves and others from becoming a pornographic deepfake. This chapter examines the legal implications of instantaneous communication in this context, including cyberbullying, nonconsensual, and deepfake pornography, and the exchange of indecent and obscene images and texts.