Reviewerly/exHarmony huggingface dataset:
An Effective Sentiment Analysis of Social Media Data Using Deep Recurrent Neural Network Models

Today we can do a lot of analytics and statistics using social media data. The world had been exposed to the COVID-19 pandemic recently. With the rapid increase in epidemics and deaths, people have different feelings about the disease. Collecting and reading boring tweets creates real emotions in these difficult times. The purpose of this study is to provide a specific perspective for understanding the emotions people around the world experience in relation to this condition. To do this, various Tweets related to a specific domain are received through the Twitter platform. When Tweets are collected, they are categorized and read to effectively reflect the Tweet's true sentiment for COVID-19.Natural Language Processing(NLP) is used to analyze the review comments from social media and Recurrent Neural Network (RNN) for sentiment classification .The model identifies the emotional polarity of "conversations" often work with ambiguous tweets, reduced accuracy, and reduced tolerances. Also the RNN model defines the expression of emotions occurring in a subject at any given time.
Synthesis of glioma histopathology images using generative adversarial networks

Deep learning, a subset of artificial intelligence, has shown great potential in several recent applications to pathology. These have mainly involved the use of classifiers to diagnose disease, while generative modelling techniques have been less frequently used. Generative adversarial networks (GANs) are a type of deep learning model that has been used to synthesize realistic images in a range of domains, both general purpose and medical. In the GAN framework, a generator network is trained to synthesize fake images, while a dueling discriminator network aims to distinguish between the fake images and a set of real training images. As GAN training progresses, the generator network ideally learns the important features of a dataset, allowing it to create images that the discriminator cannot distinguish from the real ones. We report on our use of GANs to synthesize high resolution, realistic histopathology images of gliomas. The well- known Progressive GAN framework was trained on a set of image patches extracted from digital slides in the Cancer Genome Atlas repository, and was able to generate fake images that were visually indistinguishable from the real training images. Generative modelling in pathology has numerous potential applications, including dataset augmentation for training deep learning classifiers, image processing, and expanding educational material. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Explain basic principles of generative modelling in deep learning. 2. Discuss applications of deep learning to neuropathology image synthesis.
Professional personal development in the educational space

This article reveals the essence of issues such as professional development of the individual in the educational environment, approaches to personal development, abilities, special and general abilities, talents. Inclinations are natural prerequisites that are a condition for the development of abilities, not only in the sense that they give originality to the process of their development, but also in the sense that, within certain limits, they can determine the content side and influence the level of achievement.
Reconsidering reading span as the sole measure of working memory in speech recognition research

Although it is generally accepted that speech recognition depends on working memory, it can be difficult to delineate the roles of different aspects of working memory in speech recognition. A limitation of many studies relating speech recognition and working memory is the reliance on a single measure of working memory, often reading span. While reading span is a valuable assessment of working memory, its association with several aspects of cognition makes its connection to speech recognition difficult to interpret when used in isolation. Here, we present the benefits of using a combination of different stimuli, listening conditions, and tasks to assess working memory. By varying the stimuli and listening conditions in a serial recall task we isolated serial recall ability from difficulty identifying words in adverse listening conditions. Separating these aspects allowed us to characterize their relationship with vocoded sentence recognition. We also found that recognition of vocoded speech in the presence of two-talker competing speech was precited to the same extent by serial recall, free recall, and reading span tasks, which indicates that alternative tasks are viable for assessing the aspects of working memory which support speech recognition. We encourage assessing working memory ability with multiple short memory tasks.
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning

In parametric Bayesian learning, a prior is assumed on the parameter $W$ which determines the distribution of samples. In this setting, Minimum Excess Risk (MER) is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if $W$ was observed. In this paper, we build upon and extend the recent results of (Xu & Raginsky, 2020) to analyze the MER in Bayesian learning and derive information-theoretic bounds on it. We formulate the problem as a (constrained) rate-distortion optimization and show how the solution can be bounded above and below by two other rate-distortion functions that are easier to study. The lower bound represents the minimum possible excess risk achievable by any process using $R$ bits of information from the parameter $W$. For the upper bound, the optimization is further constrained to use $R$ bits from the training set, a setting which relates MER to information-theoretic bounds on the generalization gap in frequentist learning. We derive information-theoretic bounds on the difference between these upper and lower bounds and show that they can provide order-wise tight rates for MER under certain conditions. This analysis gives more insight into the information-theoretic nature of Bayesian learning as well as providing novel bounds.
Interactive Learning from Activity Description

We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Unlike imitation learning (IL), our protocol allows the teaching agent to provide feedback in a language that is most appropriate for them. Compared with reward in reinforcement learning (RL), the description feedback is richer and allows for improved sample complexity. We develop a probabilistic framework and an algorithm that practically implements our protocol. Empirical results in two challenging request-fulfilling problems demonstrate the strengths of our approach: compared with RL baselines, it is more sample-efficient; compared with IL baselines, it achieves competitive success rates without requiring the teaching agent to be able to demonstrate the desired behavior using the learning agent's actions. Apart from empirical evaluation, we also provide theoretical guarantees for our algorithm under certain assumptions about the teacher and the environment.
Assessing Recommercialisation Potential at the Mary Kathleen mine, Queensland

The Mary Kathleen U-REE deposit (9.48 Mt @ 0.131% U3O8) is a uraninite-allanite-garnet dominated hydrothermal deposit hosted within a garnet-pyroxene skarn. The ore is enriched in LREE, mainly lanthanum and cerium which comprise about 85% of the total REE content with minor neodymium, praseodymium and samarium. Uranium was extracted but the REE remain as a significant resource in the tailings. Previous investigations on the tailings suggest highest grades of La and Ce are confined to finer tailings size fractions, which will impact on recovery if the site considers an economic rehabilitation pathway. This study focuses on examining reprocessing potential and, using geophysical and hyperspectral data, makes broad deposit-scale interpretations regarding the remaining resource at the site.
Design of Three Directional Dumping Trailers

By observing the older dumper or tipper trailers, themajordifficulty is, unloading the constructionmaterials. Ourresearch in the several building sites, uncovered the fact that thematerialswereunloadedfromthetrailerinthemostdifficultwaypossible. Traditional trailers dump materials just one way that isat the posterior of the vehicle. It is hard to empty the materials inlittle minimized roads and little streets. In our task, these arecorrected to empty the trailer on the whole three sides by usinghydraulics.Thehydraulicpumpisconnectedtointernalcombustion engine, with the help of engine power, the workingfluid were pressurized. This compressed fluid is utilized to enactthe chamber, when the valve is turned on. By this project work, itissimpleforthedriver toemptythedumping trailerand itdecreasestheenergyandtime.
Digital Literacy Skills of Secondary Level Students: An Assessment

With the emergence of digitalization, the students, particularly at the secondary level of education, have to acquire skills to understand, find, utilize, and create information from various sources on the internet. These skills are popularly known as digital literacy skills. Here, an important question arises in our minds that what is the level of digital literacy skills of secondary level students. This research attempted to answer this question by selecting a sample of 100 secondary level students through random sampling. The digital literacy skills of chosen students were assessed by administering the Digital Literacy Assessment Test (DLAT) in online mode. The scores of the students served as the basis for the assessment of their digital literacy skills. The obtained results are further used to provide some useful recommendations to improve teacher preparation programmes to prepare a new generation of teachers ready to utilize secondary level school students' digital skills for educational and social betterment.
Notary Accounting Software

A notary public is a person in a place of force that is entrusted with checking the legitimacy of significant authoritative archives, desk work, and exchanges. Their essential job is fundamentally to direct the marking of significant desk work and authoritative records, and to guarantee that individuals that marked are who they say they are, and that they have marked where they should, when they should. Normally a legal official public is a public authority that has been named by a state government as an enemy of deceitful technique. Legal official publics are not appointed to give legitimate counsel, nor are they qualified to do as such. They are as a rule there, fairly, to guarantee that reports are endorsed by individuals who should sign them, and when. Regularly, you'll likewise track down that the assent given by a legal official public is the last advance in things, for example, opening retirement accounts, in setting up force of lawyer, or in finishing on house. The system that is built is easy accounting software for notaries and signing agents. It makes pretty much all that you do simpler - sending invoices, following mileage, dealing with your schedule, following your costs, and assisting you with your charges. This system will be built using modern technologies such as Laravel is a free, open-source PHP web framework intended for the development of web application, Restful API Representational state transfer is a software architectural style which uses a subset of the http protocol. It is generally used to make intelligent applications that utilization Web administration.
Iot Aware Margin Truncative Support Vector Regressed Random Forest Bagging Classification for Frequent Pattern Mining

World Wide Web (WWW) comprises an enormous amount of web pages and links that offer huge information for internet users. The rising trend of the websites has become more complexity and the size of web content is also high. Web usage mining is a significant process for extracting the user behavior. The behavioral pattern notices the set of objects having similar activities. The conventional mining techniques find the user behavior but it still faces more complexity while considering the large volume of weblogs. To improve the user behavior pattern prediction accuracy, an IoT Aware Margin Truncative Support Vector Regressed Random Forest Bagging Classification (IoT-MTSVRRFBC) Technique is introduced. The major aim of the IoT-MTSVRRFBC technique is to perform the web user behavior pattern mining with higher accuracy and lesser time consumption. In the IoT-MTSVRRFBC technique, an IoT device is used to collect the data (i.e., weblog files). The IoT-MTSVRRFBC technique comprises the two major processes, namely preprocessing and classification for web user behavior analysis. At first, Margin Truncative Support Vector Regressed Preprocessing (MTSVRP) is applied for preprocessing the weblogs to remove the unnecessary patterns for reducing the time consumption.   After that, Random Decision Forest Bagging Classification (RDFBC) is applied to identify web user behavior by analyzing the frequently accessed web patterns. RDFBC is an ensemble learning technique that comprises ‘n’ regression trees with the necessary web patterns. After that, the votes are generated for each regression tree. The votes of all regression trees are combined to identify the majority vote of patterns for web user behavior analysis. In this way, web user behavior analysis is accurately performed with big data with minimal time consumption. Experimental evaluation of the IoT-MTSVRRFBC technique is conducted with weblog dataset from Kaggle on factors such as prediction accuracy, error rate, time, and space complexity with respect to the number of web patterns. Results demonstrate that our IoT-MTSVRRFBC techniques are time and memory-efficient with higher accuracy as well as lesser error rate in finding the frequent pattern than the existing algorithms
Action Recognition Using Deep Learning And Cnn

Automated action recognition using Deep learning and CNN is playing a vital role in today‘s day to day society, it may be video action recognitions through cctv, or it may be the smart homes. Now day’s human actions are used in many devices to control them like HoloLens VR, for that recognition of action is important that why video recognition. This Paper represents practical, reliable, and generic systems for video-based human action recognition, technology of CNN network is used to recognize different layers of the video images features. These features are obtained by extracting the features from different layers that are through the CNN (Convolutional Neural Network).
Scan This to See a Cat: A Pilot for Digital Image Collections Outreach around Campus [poster]

Poster presented at the 2021 ARLIS-NA Virtual Conference May 11, 2021. For many art information professionals, COVID-19 has impacted not just physical library operations but also opportunities for instruction and outreach to new and returning art and design students. This poster will look at both the programmatic and technical elements of a planned pilot to highlight the Libraries' digital art and design image resources to these students at specific locations on the UNC Greensboro campus through the use of memetic signage, QR codes, and custom JavaScript. With this pilot initiative, we hope that a simple entreaty to scan a sign in order to see a smile, a horse, a rainbow (and so on) will lead to an increase in overall usage of our image collections, while also raising art and design student awareness about library resources and services (including their liaison librarian). The poster will include a sign prototype as well as share sample code that can be adapted by other art librarians in their own work.
Education in Seonggyungwan, Township School and Family: The Relationship between Imperial Examination and Self-Cultivation of Yeongnam Family Precepts in Joseon Dynasty

為學不必以出仕為目的, 但若然士人棄絕科場, 獨處修身之地, 亦難以實踐儒家「內聖外王」之道。《小學》東傳, 帶動朝鮮全國上下通過科舉制度、鄉校學令和家訓教育方面, 重新思考科舉與修身之間的微妙關係。本文比較朝鮮王朝成均館、鄉校和嶺南家訓的教育內容, 探討以下問題 : 一、朝鮮太宗增設科舉「錄名」制度,藉此勸勉士人誦讀《小學》, 注重修身工夫, 其效用如何? 二、館學、鄉學和家學的教育目的各有側重, 它們在科舉與修身的關係上, 如何取捨? 三、嶺南士人尊崇程朱性理學, 致力傳播《小學》, 他們對嶺南家訓帶來了甚麼影響? 四、嶺南家訓在強調修身的同時, 又如何維護家族聲譽, 勸勉士人應試科場, 光耀門楣?
FACE MASK DETECTION USING DEEP LEARNING

According to WHO info, the global pandemic of COVID-19 has wreaked havoc on the planet, infecting morethan eight million citizens. Face masks and healthy social distance are two additional protective measures thatmust be implemented in public places to discourage the infection from spreading. We suggest a computervision-based solution that focuses on real-time automated surveillance of individuals to identify both securesocial distancing and face masks in public areas, by implementing the model on a Raspberry Pi4 to observebehaviour and detect violations through camera. The Raspberry Pi 4 sends an alert to the state policeheadquarters' management centre when it detects a breach, as well as sounding an alarm to the general public.Modern deep learning algorithms are combined with geometric techniques in this proposed framework to createa strong modal that addresses detection, monitoring, and confirmation are three facets of the detection process.As a result, by saving time and reducing the transmission of the corona virus, the proposed solution supportssociety. It is often used successfully in the current scenario, as lockdown is eased to appear at public events,shopping centres, and other locations. Automated inspection saves time and money for the general public, and itcan be used everywhere.
Comparative Study of Techniques used for Word and Sentence Similarity

This study is intended to analyze the methods used to test resemblance of sentences. For many Natural Language Processing applications such as text grouping, information recovery, brief reaction reviewing, machine learning, passage summary and text categorization, measuring resemblance between sentences is a vital activity. In this paper, we classify the approaches to measuring the resemblance of sentences based on the methods implemented into three groups. The most frequently used methods to finding phrase resemblance are word-to-word based, structure-based, and vector-based. Centered on a particular viewpoint, each approach tests the interaction between short texts. Furthermore, to provide a full view of this problem, datasets that are often used as benchmarks for testing techniques in this field are added. Better outcomes are obtained through methods that incorporate more than one viewpoint. In addition, resemblance of sentences is based on the correspondence of their meanings that tests the semantic resemblance between two concepts, words or sentences needs further research.
Optimization of garbage dumping mechanism of intelligent sanitation vehicle based on particle swarm algorithm

Abstract Aiming at the requirements of low power consumption and light weight of the intelligent garbage collection and transportation equipment of sanitation vehicles, this paper proposes an optimization method based on the particle swarm algorithm. In this paper, the kinematic equation and dynamic equation of the mechanical claw in the dumping action are derived by using the complex interpolation approach. The optimization objectives are the minimum maximum power, the minimum average power, the minimum maximum mutation of the instantaneous driving force and the minimum time-consuming of the dumping action. The garbage dumping angle is selected as constraints, and the arc radius of the dumping track, the centre distance of the mechanical claw’s groove wheels and the time-consuming of the dumping action are optimized. The optimization results show that the optimization objectives have been well optimized except for the increased time-consuming dumping action. And the driving force should act on the axis of the lower groove wheels.
Analisis Kesalahan Sintaksis Pada Karangan Deskripsi Bahasa Jerman

This study aims to determine the types of syntactic errors in the essay descriptions of the German language of SMA NEGERI 9 Makassar students. This type of research is descriptive qualitative. The analytical method used is the method proposed by Ellis. The research data is in the form of descriptive essays written by students. The data source of this research is the students of class XII Mia 8 SMA Negeri 9 Makassar, amounting to 31 students. The data collection technique was carried out through a German writing test. The results of the data analysis show that the syntactic errors that appear are phrase errors, including 4 types of errors in noun phrases, 8 types of errors in pronouns, 7 types of errors in prepositional phrases, 4 types of errors in adjective phrases, and 4 types of verb phrases.
Vibro-acoustics of Central Africa harps

Central Africa harps are string instruments, often anthropomorphic, whose soundbox is built from a hollowed out tree trunk. There strings, usually 8 in number using nowadays fishing line, are wrapped to wooden tuning pegs on the neck and attached to a tailpiece placed under animal skin used as soundboard. Each instrument-making element can vary according to ethnic groups and material availability. This study aims at understanding the vibro-acoustic behavior of these instruments in order to determine relevant acoustic descriptors linked to there building process. Hence, a numerical model is developed based on the Udwadia-Kalaba modal formulation, relying on substructuring concepts. Each subsystem's dynamics is described by its modal parameters in terms of unconstrained modes and the coupling is enforced by boundary conditions. Strings displacement is described in two polarizations including geometrical nonlinear effects. Modal parameters are experimentally identified, on severa harps collected from different ethnic groups, and turn out to be discriminating descriptors. The reliability of the model is validated against laboratory measurements. Finally, sound syntheses based on the proposed approach are compared to experimental data, showing good agreement. [This work, part of the project NGombi, was funded by the Agence Nationale de la Recherche (French National research agency), Grant No. ANR-19-CE27-0013-01.]
Evaluating the Effects of Visual Fidelity and Magnified View on User Experience in Virtual Reality Games

JVRB, (16)2019, no. 1. - Virtual reality has been becoming more affordable in recent years. This led to more content specifically developed for this medium. Training with virtual reality is one of the promising areas in terms of the benefits. Virtual reality properties may affect user performance. This study aims at exploring effects of visual fidelity (high and low) and view zoom (normal and magnified) on task performance in virtual reality. Effects of visual fidelity have previously been explored but yielded different results based on the task design. Effects of view zoom on task performance haven’t been explored yet. An inspection task in virtual reality was developed and a user study was performed with 15 participants. Results indicated that low visual fidelity led to better task performance whereas view zoom did not have an effect on the performance.
Keynote: Training with imperfect and weak labels

The talk will focus on the task of training machine learning models with data with imperfect and weak labelling in scenarios where annotations consist of subsets of categories that may contain the true class and possibly several noisy classes. First, he will describe a general approach to such problems to then dive into specific applications, including learning from crowds or learning from label proportions. Finally, he will discuss strategies to measure label quality and how to learn in situations that involve combining annotations of different quality levels.