Reviewerly/exHarmony huggingface dataset:
How Classical, Super and Quantum Computers Work

Computers are a big part of our daily lives. From school to offices to-home, we are all using computers. You probably have a desktop or laptop computer in your house. You also probably have a cellphone in your house. Believe it or not it's a computer. However, you don’t have a super computer and definitely not a quantum computer.In this presentation I'll explain how Classical computers, Super computers and Quantum computers work and compare them to one another.Bibliography[1] “Computer Basics: What Is a Computer?” GCFGlobal.org, edu.gcfglobal.org/en/computerbasics/what-is-a-computer/1/. [2] “Computers for /The Basics.” Computers for Beginners/The Basics - Wikibooks, Open Books for an Open World, en.wikibooks.org/wiki/Computers_for_Beginners/The_Basics[3] Fisher, Chris, and Eric Abenojar. “IBM: What Is Quantum Computing?” IBM Quantum, 2 Apr. 2009, www.ibm.com/quantum-computing/learn/what-is-quantum-computing/. [4] Gunter, Elsa L. "Computer." World Book Student, World Book, 2020, worldbookonline.com/student-new/#/article/home/ar127860/Cpu[5] Chubb, Jennifer C. "Quantum computing." World Book Student, World Book, 2020, worldbookonline.com/student-new/#/article/home/ar757070/quantum%20computers[6] Australian Research Council Gate based Quantum Computers. University of Bristol (Quantum in the Cloud Documentation} Center for Quantum Computing and Communication Technology University of New South Wales (http://www.cqc2t.org/)[7] University of Science and Technology (China) China hits milestone in developing quantum computer[8] Author’s in-person interviews with three IBM researchers at IBM Watson Quantum computers research center, Yorktwon Heights, NY, USA
Accelerating Exemplar-based Image Inpainting with GPU and CUDA

Compute Unified Device Architecture (CUDA), a parallel computing platform and programming model architecture developed by NVIDIA, has grown to become the defacto standard for using Graphic Computing Units (GPUs) to solve the problem of High-Performance Computing (HPC) at the moment. This paper presents a benchmarking to show CUDA and GPU's advantage in accelerating computing performance in image processing. We take the Exemplar-based inpainting algorithm of Criminisi et al. to be the case study for the research. This paper optimizes the Exemplar-based inpainting algorithm (EBII) using GPU instead of CPU like other implementations before. We justify that the GPU can improve the performance of the algorithm to state-of-the-art level. We compare the algorithm's velocity between multiple implementations: one public Matlab implementation, one public C++ implementation, and our version using GPUs.
Time–space Distribution Characteristics of the Circum-Pacific Seismic Belt Based on Multifractal Analysis

A complete scientific system of earthquake prediction has yet to be developed, and most studies on the time–space sequence of seismic activity analysis are based on existing seismic models. By applying fractal theory with the two aspects of magnitude and intensity, a new dimension is added. In this study, we applied multifractal theory to analyse data of the Circum-Pacific seismic belt, which contains multifractal spectrums such as the relation, relation, and relation. The results suggest that earthquakes in the area which we studied contain multifractal features. The study also shows that the time–space propagation characteristics of the earthquakes are affected by the internal geological structures of the region and the adjacent area.
The e-Library of the National Library of Sakha Republic (Yakutia): The general approaches

The upgrade project for the Electronic Library of the National Library of the Republic of Sakha (Yakutia) is discussed. The goal is to provide remote access to its digital content, to expand and develop library information services in the digital environment. The e-library enables to improve library services of the republican population and to raise the profile of the library in the modern technological world. The elibrary’s new services emerge at the synthesis of new information technologies (implementation tool) and new approaches to traditional library and bibliographic processes. The user-group oriented segments were designed ("School", "Reading since very birth", "Science in Yakutia", "Electronic publishing house") which enables to meet information demands to the fullest extent possible. The options for using personal accounts, statistical performance indicators, e-library document collection, resources selection and systematization, and grouping methods are characterized in brief. The search instrument makes an important performance indicator; it can be used for every segment and collection and is locked to descriptive metadata fields for all types of resources (formats). The concept of creating Single Library and Information Environment based on the National Library of the Republic of Sakha (Yakutia) provides the organizational and technological prospects for the e-Library.
BIDGAN: Blind Image Deblurring with Improved CycleGAN and Frequency Filtering

The details of images can be severely impaired when they are captured in motion . To overcome such a blind image deblurring problem, we propose a deblurring method based on improved CycleGAN. This method can remarkably improve the definition of unknown blur kernel images and provide instant higher quality images for the application of many image processing technologies concerned with object detection. Unpaired samples are adopted for training due to the limited sample size. We use random rotation to amplify training effect on distorted images, and it indeed improves the accuracy of irregular image recognition. To minimize the problems of pattern collapse and gradient vanishing, the network adopts Wasserstein distance instead of Jensen-Shannon divergence. Switchable Normalization is also employed because it is more robust to mint-batch size than the former Instance Normalization. The test results show that the deblurring model has achieved better performance in terms of image quality on several popular benchmarks, such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Kernel MMD; running time has been reduced by 80% compared with conventional deblurring methods based on CNN; mAP on GOPRO Large street view has been improved within the framework of YOLOv3 to overcome the object detection challenge of blurred images. Ultimately, the bright spots have been filtered in the frequency domain and the problem of fake recognition has been solved, so we can retain more image details to provide better pre-processing quality for later object detection.
Comparative Analysis Of Different Speaker Recognition Algorithms

Speaker Recognition is the process of identifying a human based on their speech for biometrics, authentication and other purposes. With the advancement of computing and rise of Machine Learning, various algorithms and techniques have been put forth for the purpose of speaker recognition. In this paper we discuss the entire algorithmic flow of the process, use the toolset of algorithms already deployed for the same and use their benchmarks to compare the performance using various metrics.
An Open Domain Question Answering System Trained by Reinforcement Learning

Recently, Reinforcement learning has shown his ability as an effective approach for improving deep learning algorithms performance, moreover question answering problem are leveraged nowadays as competitive deep learning tasks. In this paper, we tried to realize a robust question answering model, by integrating a reinforcement learning such as combining a self-critical policy gradient for improving the seq2seq training, by the setup of a reward and baseline reward on the top of the model.
Evaluating Semantic Co-creation by Using a Marker as a Linguistic Constraint Tool in Shared Cognitive Representation Models

Abstract Semantic co-creation occurs in the process of communication between two or more people, where human cognitive representation models of the topic of discussion converge. The use of linguistic constraint tools (for example a shared marker) enable participants to focus on communication, improving communicative success. Recent results state that the best communicative success can be achieved if two users can interact in a restricted way, so called team focused interaction hypothesis. Even though the advantage of team focused interaction sounds plausible, it needs to be noted that previous studies enforce the constraint usage. Our study aims at investigating the advantage of using shared markers as a linguistic constraint tool in semantic co-creation, while moving them becomes optional. In our experimental task, based on a shared geographic map as a cognitive representation model, the two participants have to identify a target location, which is only known to a third participant. We assess two main factors, the teams’ use of a shared marker and the two complexity levels of the cognitive representation model. We had hypothesized that sharing a marker should improve communicative success, as communication is more focused. However, our results indicated no general benefit by using a marker as well as team interaction, itself. Our results suggest that the use of a shared marker is an efficient linguistic constraint at higher levels of complexity of the cognitive representation than those tested in our study. Based on this consideration, the team focused interaction hypothesis should be further developed to include a control parameter for the perceived decision complexity of the cognitive representation model.
Hand-Crafted and Learned Spatiotemporal Filters to Inform and Track Visual Saliency

This paper describes an event-tracking algorithm based on an unsupervised learning method to follow salient features. By learning spatiotemporal filters using computationally inexpensive distance metrics such as determinant comparisons, we show that salient features are captured by the learned activation prototypes, known as spatiotemporal templates. First, we discuss previous hand-crafted filter methods to capture spike-based data. While spatial and temporal filters are easily crafted for obvious features, hand-crafted filters are not robust and exhaustive templates for detecting events that may not be so obvious. It becomes clear that learning filters is a more diverse, rectifying method in identifying important features while remaining independent from human observations. We then show how spatiotemporal filters are learned through a series of prototype clustering. In order to handle information over time, we propose a series of decision trees in the form of a random forest inspired by lifelong learning. Finally, we conclude promising results on feature tracking, as well as the need for a ground-truth spike-based data-set to validate saliency algorithms.
Face detection in the darkness using infrared imaging: a deep-learning-based study

Face detection is one of the most important research topics in the field of computer vision, and it is also the premise and an essential part of face recognition. With the advent of deep learning-based techniques, the performance of face detection has been largely improved and more and more daily applications have been witnessed. However, face detection is greatly affected by environmental illumination. Most of existing face detection algorithms neglect harsh illumination conditions such as nighttime condition where lighting is insufficient or it is totally dark. These conditions are often encountered in real-world scenarios, e.g., nighttime surveillance in law enforcement or civil settings. How to overcome the problem of face detection in the darkness becomes a critical and urgent demand. We thus in this paper study face detection in the darkness using infrared (IR) imaging. We build an IR face detection dataset and design a deep learning-based model to study the face detection performance. Specifically, the deep learning model is a Single Stage Detector which has the advantage of fast speed and lower computation cost compared with other face detectors that consists of multiple stages. In the experiment, we also compare the performance of our deep learning model with that of a well-known traditional face detection algorithm, AdaBoost. In terms of True Positive Rate (TPR), our model significantly outperforms AdaBoost by 5% -- a dramatic boost from 87% to 92%, which suggests our deep learning-based method with IR imaging can indeed meet the requirement of real-world nighttime face detection applications.
Research on hand key point recognition and localization method based on SSD network

Abstract With the development of the times and the progress of society, multimedia presentation has become a necessary skill for people. In the field of education, the application of multimedia presentations for teaching activities has become a necessary and common way. In this paper, we propose to use camera capture for image analysis and wireless data transmission technology for information interaction with computers. Among them, SwiftShow performs image analysis by capturing the gestures of the presenter using the camera. The device provides personalization, and users can choose the mode that suits them through the interaction interface, and even customize the gestures. The device can be controlled by the simple hand gesture recognition overhead, which not only can realize the basic functions such as ppt play, pause, page turn, and focus, but also includes many practical and unique presentation functions, the research aims to completely free the presenter from the computer.
Markov Chain and Adaboost Image Saliency Detection Algorithm Based on Conditional Random Field

The traditional salient object detection algorithms are used to apply the underlying features and prior knowledge of the images. Based on conditional random field Markov chain and Adaboost image saliency detection technology, a saliency detection method is proposed to effectively reduce the error caused by the target approaching the edge, which mainly includes the use of absorption Markov chain model to generate the initial saliency map. In this model, the transition probability of each node is defined by the difference of color and texture between each super pixel, and the absorption time of the transition node is calculated as the significant value of each super pixel. A strong classifier optimization model based on Adaboost iterative algorithm is designed.The initial saliency map is processed by the classifier to obtain an optimized saliency map, which highlights the global contrast. In order to extract the saliency region of the final saliency map, a method using conditional random field is designed to segment and extract the saliency region. The results show that the saliency area detected by this method is prominent, the overall contour is clear and has high resolution. At the same time, this method has better performance in accuracy recall curve and histogram.
Fabrication, Testing and Statistical Analysis of a Project-Based Single-Screw Filament Extruder

The Additive Manufacturing industry and its continuous rise are both promising and problematic at the same time because of the waste produced. In this study, the researchers fabricated a modified filament extruder based on the "Precious Plastic" project by Dave Hakkens. This study serves two purposes, to lessen the costs involved with procuring 3-D printing filament to use and to reduce the environmental impact of plastic waste such as failed 3-D prints. The testing involves the variation of nozzles, extrusion temperatures, and motor speed with key results: 190°C and 10rpm setup yielded the filament closest to the targeted value by having a mean diameter of 1.7490mm; motor speed and its interaction with temperature are significant to the determination of filament diameter; and in terms of tolerance, the filaments produced from testing is within the ± 0.05 range with 95% confidence level. For future testing, the inclusion of physical properties such as strength and flexibility can provide a concrete basis for selecting optimal settings and determining the quality of filament produced.
Selected poster presentations from the American Association of Applied Linguistics conference 2021

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Phase matching between energy-time entanglement and two photon absorption processes

Energy-time entanglement can enhance two photon absorption by combining a precise two photon resonance with temporal correlations between the arrival times of the two photons. In general, the optimal timing of photon arrival is determined by the dynamics of the system between the initial and the final excitations. In this presentation, we show that the optimal timing of photon arrival corresponds to a specific phase dispersion in the frequency difference of the two photons. The absorption cross-section of energy-time entangled photons can be enhanced beyond the rate observed for optimal photon coincidence by adjusting the phase dispersion of one of the photons to match the optimal characteristics. We determine the maximal enhancement factor as a function of the bandwidth of the intermediate states and their detuning relative to the average photon frequency. Significant additional enhancements can be achieved when the bandwidth of the intermediate states is very narrow.
Peran Pembina Pramuka dalam Menanamkan Kepedulian Sosial Siswa di MTsN 2 Bondowoso Tahun 2019

Instilling social awareness is an important thing to be instilled in students to foster feelings and behavior of caring for others. Instilling social awareness through scout extracurricular activities at MTsN 2 Bondowoso has an important role in developing positive attitudes, leadership characters and caring for others. This research is a qualitative research and the type of research is descriptive. Data collection techniques using non-participant observation techniques, interviews and documentation. Analysis of the data using the interactive model of Miles and Huberman. The validity of using triangulation of sources and techniques. The results of this study are: 1). inculcating student social awareness; (a) through habituation such as greeting each other, say hello, pay infaq, do charity work and pray for a sick friend; (b) training activities such as, work together and help each other in pioneering, setting up a tent and simulating First Aid In Accident; (c) work programs such as: social services, sowing flowers, raising funds for victims of natural disasters and distributing 1000 zakat; 2) supporting factors for inculcating student social awareness, that is: (a) the existence of scout coaches who can guide and teach about manners to students with mutual respect and help each other; (b) the presence of a teacher, who provides direction and motivation to students; (c) the role of parents who give permission to their children in participating in scouting activities and participating in material matters
Multi-layer Classification and Segmentation using Ensemble Learning for Opinion Mining

In recent years, social media has become the prime place for advertisements, activities, campaigns, protests etc. It provides a platform for the people to express their views and beliefs to the masses. The user beliefs, practices and interests are of great importance to organizations and provide insight into the minds of users. Data Mining is one such tool that enables these organizations to extract relevant information from user data, which can be analyzed to create a knowledge set and determine user opinion that allows companies to create products tailored to the user. Data Mining of Twitter and other social platforms is of a great importance because, its large user base is a goldmine of public opinions and views which if analyzed properly, can potentially be used to predict campaign results and product assessments and likeability. This project proposes a classification scheme that aims to perform Segmentation based Twitter Opinion Mining using Ensemble Learning. The proposed scheme is able to detect and filter out bots and uses text segmentation for effective text classification and part of speech tagging.
A Reasoning System for Fuzzy Distributed Knowledge Representation in Multi-Agent Systems

Knowledge does not necessarily exist only within a single agent; it may exist collectively within a number of agents combined together (cf. the wisdom of the crowd), and this sort of knowledge is called distributed knowledge. In light of bounded rationality and uncertainty, distributed knowledge can be incomplete, and there may be gradations in the certainty of distributed knowledge, that is, knowledge may be distributed within a system of agents up to some degree of certainty only. Fagin, Halpern, Moses, and Vardi gave an axiomatic system for reasoning about distributed knowledge, and developed a special technique to prove its fundamental properties such as completeness. Here we extend their classic results so as to incorporate fuzzy distributed knowledge; in addition we prove several other properties of fuzzy modal systems such as the finite model property and Gödel-style translation theorems.
Lived experience of kindergarten parents in modular distance learning

This study was conducted in order to determine the lived experience of kindergarten parents in modular distance learning.The researcher believed that there were many ways for the betterment of the learning of the child while they were at home.The researcher's main objective was to help the parents solve the challenges faced in the new normal set up of education.As an output of the study, the researcher produced a localized video to enhance the learning of parents in guiding their kindergarten children at home.The research design used in this study was qualitative with phenomenological analysis as approach with the use of purposeful sampling.The respondents of the study were fifteen ( 15) kindergarten parents in selected public schools in the East district of Division of Calamba.The researcher used thematic analysis as a means of analyzing the data collected from the responses of the participants and generated ten (10) superordinate themes which were Continuity of Learning Despite the Pandemic, Typical Day to Extraordinary Day as a Parent-Teacher, Varied Parents Responses on Distance Learning, Embracing New Responsibilities, Parents Challenging Aspects for Distance Learning, Key to Conquer Challenges in Education, Factors that Affect the Child's Learning Focus, Holistic Preparation for Child's Education, Varied Techniques towards the Child's Effective Learning and Using Various Means to Understand the Pandemic's Effect on Education.The findings revealed that the parents faced different challenges in guiding their child in modular distance learning and these challenges were time management, organizing daily routines, distractions, new responsibility, understanding lesson and quality education.The researcher reflected that with the help of the teachers and the localized video, the mentioned challenges could be able to surpass by the parents.
Errata to “Functional Models for Time-Varying Random Objects”

We discovered that in Dubey and Müller (2020) the rate of convergence of the Fréchet mean function μ ̂ ⊕ ( · ) in Theorem 3 was incorrectly stated as O P log n / n 1 / β 2 . The corrected Theorem 3 and rate is as follows. Theorem 3.Under assumptions (A3)–(A4) and (A8)–(A11), The corrected proof, which also involves modifications to the proof of Lemma 1, is provided in Lemmas 4 and 5 in Section A.3 of the supplement of Dubey and Müller (2021), which in Section A.1 of the supplement also includes an updated overview of the theoretical results. This change also impacts the rate of convergence of the Fréchet scores in Corollary 2 of Dubey and Müller (2020). The correct version of Corollary 2 is as follows. Corollary 2.Under assumptions (A3)–(A5) and (A8)–(A11),