Reviewerly/exHarmony huggingface dataset:
WG-WaveNet: On-Demand Speech Synthesis on CPU

This paper proposes WG-WaveNet, a generative model for compact, accurate, and quick, waveform generation model.It comprises a lightweight flow-based model with an additional post-filter.The proposed process involved joint training of the two components by maximising the conformity to the training data and refining the loss functions tuned to their frequency.The proposed model demands significantly fewer computations against various other generative models on both training and testing, as it's designed as a heavily compressed flow-based model.Despite the heavy compression of the model, the quality of the generated waveform is maintained by the post-filter.On an NVIDIA 1080 Ti GPU, the PyTorch based implementation can allow training with under 8 GB of GPU memory and produces audio waveforms at above 960 kHz.Additionally, it has been demonstrated that the suggested approach can generate 44.1 kHz speech waveforms 20% quicker as opposed to real-time even when synthesised on CPUs.
Online Quiz Website

Online quiz is a web software for efficiently taking online quizzes and checking papers. Online quizzes examine candidates completely through a fully automated approach that saves time and offers speedy results. This is utilised in schools and businesses. Web-based programme can be utilised anywhere, anytime. Administrators can edit and delete accounts. Teachers can build assessments and situations. Students can register, login, take the test, and check the results. It provides a competitive environment where students can test and enhance their knowledge. This app's administrator has full permissions. Adding questions and answers, marks for each area, setting a timer for each quiz, and generating a report with quiz scores.
Monitoring and Managing a Hybrid Environment

SQL Server 2022 has functionality, which helps you to manage your SQL Server estate in a hybrid environment, meaning that you can centralize administration and gain some of the benefits of cloud, even for your on-premises SQL Server estate.
Phase-reference polarization attack on continuous-variable quantum key distribution with a real local oscillator

The trusted phase noise model for continuous-variable quantum key distribution protocol with a real local oscillator (LLO CVQKD) has been established recently, which can lead to a better quantum key distribution (QKD) performance by moving part of the phase noise from the untrusted channel-added noise to the trusted detector-added noise. However, the calibration of the trusted phase noise is related to the intensity of the phase-reference pulse, which can be used by the eavesdropper to hack the QKD system. Here, we present a polarization attack scheme against the phase-reference pulse. In practical LLO CVQKD systems, only a part of the phase-reference pulses are used to measure and compensate for the polarization drift of the signal pulses due to the limitation of polarization measurement. We show that Eve can manipulate the polarization of the unmeasured part of the phase-reference pulses to control the trusted phase noise. Simulations show that improving the polarization measurement ratio to 100% or monitoring the phase-reference pulse intensity in real time is necessary to guarantee the security of the practical LLO CVQKD system.
A feasible two-step optimization method for continuous-variable quantum key distribution

Continuous-variable quantum key distribution (CV-QKD) offers the advantages of high secret key rates in metropolitan areas. Optimization of modulation variance is an efficient method to improve the secret key rate of CVQKD system. However, in practical CV-QKD system, inevitable slight parameter fluctuation could occur after the modification of modulation variance, and controlling the modulation variance with arbitrary accuracy is also difficult. In this paper, we propose a two-step optimization for practical CV-QKD. The first step is to determine the optimal working state by combining the modulation variance optimization with error correction matrix optimization. The second step is to optimize the rate-adaptive reconciliation parameters to compensate the loss of secret key rate caused by inaccuracy modulation variance. Our results show that the secret key rate can be improved by 17.8% in comparison to one-step optimization method. Our method can be conveniently applied to CV-QKD protocol with homodyne and heterodyne detection, which will pave the way to the deployment of high stable and high performance for CV-QKD.
Online-based English Listening Skill Learning Model

This article is part of a research entitled "Online-based Student English Learning Models in Facing Curriculum Changes in the New Normal Era". This research was conducted to see and identify online-based listening learning models for students. The research method used is a descriptive qualitative method using a questionnaire and the final assignment of the Project course (Lesson Plan and Instruction). The number of samples used was 52 students who took the Project course (Lesson Plan and Instruction). From the 52 students, 6 students chose to teach the listening skill. Data analysis includes observation, open coding, and axial coding. The results reveal that the media used by students in learning listening skills is divided into online and offline media each 50% (3 people. Applications used by students in learning listening skill are divided into the use of British Council Web (16.67%), YouTube (16.67%), Google Meet (16.67%) and offline learning/Face-to-Face (50%). The learning methods that students apply in learning listening are Audio-Lingual Method (66.67%), Scientific Approach Techniques (16.67%), and Community Language Learning (16.67%). The topics presented included: Listening, Procedure Text, Songs, Job Application Letter, and Listening to a short cut of film.
IMPACT OF DIGITAL COLLABORATIVE TOOLS ON THE DEVELOPMENT OF COMMUNICATION IN ENGLISH

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 7849-7856ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.2017Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
EDUCATION AND SCIENCE COMMUNICATION TRAINING: A STUDY WITHIN A RESEARCH FELLOWS NUCLEUS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 4572-4580ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1099Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
INTELLIGENT TUTORS DESIGNED TO IMPROVE STUDENT LEARNING IN PHYSICS

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 8506 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.2239Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Cloud computing data security access control model using improved CP-ABE algorithm

The current cloud computing data security access control model reduces the risk of data being maliciously modified, but it can’t change the local attributes, and lacks some flexibility in the dynamic update of permissions. An improved CP-ABE algorithm is applied to design a cloud computing data security access control model. Between the cloud computing server and data users, a collaborative private key management mode is established based on the single attribute authority architecture. Each user has the attribute set and the corresponding private key. The CP-ABE algorithm cannot revoke the authority of a single user. A revocation scheme is added to the CP-ABE algorithm. The data belongs to the master, changes the hidden cloud key and authorized user attributes, and updates the private key with new random parameters. The improved CP-ABE algorithm is applied to establish the access control model. After the user accesses, the public key is obtained, and the input public and private keys are converted by the conversion algorithm. The test results show that the design model can reduce the time of key encryption and decryption when increasing the number of user attributes, complete data security access with less energy consumption, and is conducive to the expansion of access attributes.
Enhancing the Digital Literacy Experience of Teachers to Bolster Learning in the 21st Century

The interconnected constructs of education in the 21st century cannot be divorced from teacher training. This article reports on the digital literacy experience of teachers amidst the worldwide recommendations of using distance learning and open educational applications to address education during the COVID-19 pandemic lockdown. Altogether, 840 participants were purposively selected from urban, semi-urban, and rural areas in South Africa; 217 completed an online qualitative survey, while 23 took part in three virtual focus-group discussions. Van Dijk’s Resources and Appropriation Theory guided the study. Data analysis involved descriptive statistics and thematic analysis. The findings revealed the gaps between what is expected of teachers and their own realities, besides teachers’ eagerness to be involved in digital literacy. Recommendations include the need to continually align the teacher education curriculum with the country’s skills need and to provide teachers with the required support. Associated directions for further research are suggested.
INVESTIGATION OF POLYPS IN ENDOSCOPY IMAGES BY USING DEEP LEARNING ALGORITHM

Recent advances in machine learning, particularly with regard to deep learning, help to recognize and classify objects in medical images. In this study, endoscopy images were examined and deep learning method was used to classify healthy and polyp cells. For the proposed system, a database was created from the archives of General Surgery Department Endoscopy Unit in Kutahya Evliya Celebi Training and Research Hospital. The database contains 93 polyps and 216 normal images from 54 archive records. For image multiplexing, a total of 1236 images were obtained by rotating each image 90 degrees around its axis. While 2/3 of the randomly selected data from this obtained data was used for training the model, the rest of the data was reserved for testing. K-fold Cross Validation method was used to reduce the variability of performance results. In this study, 48 different models were created by using different activation and optimization functions to find the best classification model in deep learning. According to the experimental results, it was observed that accuracy of the models depends on the selected parameters; the best model with the accuracy rate of 91% was obtained with 64 neurons in the hidden layer, ReLU activation function and RmsProp optimization method whereas the worst model with the accuracy rate of 76% was obtained with 32 neurons in the hidden layer, Tanh activation and PmsProp optimization functions. Accordingly, classification performance of polyp images can be optimized by utilizing different activation and optimization methods during the design of deep learning models.
A Boolean model for conflict-freeness in argumentation frameworks

&lt;abstract&gt;&lt;p&gt;The Boolean models of argumentation semantics have been established in various ways. These models commonly translate the conditions of extension-based semantics into some constraints of the models. The goal of this work is to explore a simple method to build Boolean models for argumentation. In this paper, the attack relation is treated as an operator, and its value is calculated by the values of its target and source arguments. By examining the values of the attacks, a Boolean model of conflict-free sets is introduced. This novel method simplifies the existing ways by eliminating the various constraints. The conflict-free sets can be calculated by simply checking the values of the attacks.&lt;/p&gt;&lt;/abstract&gt;
Efficient Extraction of Pathologies from C-Spine Radiology Reports Using Multi-task Learning

Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. Generally, if one has multiple tasks on a given dataset, one may finetune different models or use task specific adapters. In this work, we show that a multi-task model can beat or achieve the performance of multiple BERT-based models finetuned on various tasks and various task specific adapter augmented BERT-based models. We validate our method on our internal radiologist’s report dataset on cervical spine. We hypothesize that the tasks are semantically close and related and thus multitask learners are powerful classifiers. Our work opens the scope of using our method to radiologist’s reports on various body parts.
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge

Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases. However, reasoning with numerical values remains challenging for phenotyping in clinical text, for example, temperature 102F representing Fever. Current state-of-the-art phenotyping models are able to detect general phenotypes, but perform poorly when they detect phenotypes requiring numerical reasoning. We present a novel unsupervised methodology leveraging external knowledge and contextualized word embeddings from ClinicalBERT for numerical reasoning in a variety of phenotypic contexts. Comparing against unsupervised benchmarks, it shows a substantial performance improvement with absolute gains on generalized Recall and F1 scores up to 79% and 71%, respectively. In the supervised setting, it also surpasses the performance of alternative approaches with absolute gains on generalized Recall and F1 scores up to 70% and 44%, respectively.
Jonathan Nathan OAM DSc

"Jonathan Nathan OAM DS." Clinical and Experimental Optometry, ahead-of-print(ahead-of-print), pp. 1–2
DOE Office of Legacy Management Geospatial Data Preservation Strategy Overview.

ongoing changes in scale, technology, and standards. Key enabling factors are intended to highlight critical data management responsibilities that must be addressed by LM to meet its preservation and curation objectives. A summary of best practices for geospatial data preservation is provided as part of the strategy. This page left blank
Reflection on Slide Rules, Calculators, Log Tables, and Computers

As I approach my 70th year in physics, let me tell my younger colleagues about my adventures with computation. When I was 14, my father gave me a slide rule because I appeared to be headed toward a career in physics. Since then, I have had experience with many other calculating devices.
Deep Learning-Based Matching of Sar Images with Optical Images

There are essential differences between optical and SAR remote sensing imaging in terms of mechanism, so the geometric and physical properties of optical and SAR images are extremely different, which makes it more difficult to match images. We designed and improved a deep learning network, firstly, we extracted the high-dimensional features of the image using convolutional neural network, then we used nearest neighbor search for feature matching, in order to reduce the false matching points, we introduced MAGSAC + + to eliminate the outlier points, finally, we compared with SIFT_KNN_BBS and orb_feature_matching matching algorithms. The results show that the matching of heterogenous remote sensing images using deep learning approach has high accuracy and robustness.
Optimization Landscape of Neural Networks

This chapter summarizes recent advances on the analysis of the optimization landscape of neural network training. We first review classical results for linear networks trained with a squared loss and without regularization. Such results show that under certain conditions on the input-output data spurious local minima are guaranteed not to exist, i.e. critical points are either saddle points or global minima. Moreover, the globally optimal weights can be found by factorizing certain matrices obtained from the input-output covariance matrices.We then review recent results for deep networks with parallel structure, positively homogeneous network mapping and regularization, and trained with a convex loss. Such results show that the non-convex objective on theweights can be lower-bounded by a convex objective on the network mapping. Moreover, when the network is sufficiently wide, local minima of the non-convex objective that satisfy a certain condition yield global minima of both the non-convex and convex objectives, and that there is always a non-increasing path to a global minimizer from any initialization.
Preface

HEP 2022 - 39 th Conference on Recent Developments in High Energy Physics and Cosmology was successfully held online between the 15 th to the 18 th of June 2022, in Thessaloniki, Greece. The conference was jointly organized and sponsored by the Hellenic Society for the Study of High Energy Physics, the Aristotle University of Thessaloniki and the Research Committee of the Aristotle University. The conference invited scholars and experts in the fields of High Energy Physics, involved both in experimental and theoretical research, from various universities and institutions. The conference focuses on the more recent advancements in topics such as ‘LHC experimental results’, ‘Astroparticle Physics’, ‘Cosmology’, ‘String Theory’, ‘Dark Matter’ and others. The main goal of the conference is to present the related research work of the Greek scientific community, to promote the cooperation of experts and scholars in the field, to share the research achievements both on a national and international level. All accepted speakers were eligible to contribute to the Conference Proceedings on a voluntary basis. This collection of Proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the Editorial Committee. The HEP2022 Conference venue took place at Aristotle University’s Research Dissemination Center (KEDEA Building), at Aristotle University’s campus between the Student Club and the University Gymnasium. The opening ceremony was held in the Amphitheatre I of KEDEA Building and a welcome speech was delivered by the chair of the Conference, Professor Kostas Kordas. Each author delivered and oral talk of 35 minutes accompanied with the slides material, along with 5 minutes extra time, allocated for questions and discussion. A dedicated convener chaired each plenary session. The organizing and editorial committee of the HEP2022 hopes you enjoy the reading of this volume of the JPCS. Additionally, we want to thank all authors and participants for providing their valuable contributions for this proceeding as well as the reviewers for their constructive recommendations and feedback aiding to improve the presented articles. List of Organizing Committee is available in this Pdf.
The Implementation Of Ananda's Ringing Program Based On Linking Books To Improve Grade 1 Learning Discipline

This research is motivated by the lack of learning discipline of students at SDIT Ar Ruhul Jadid Jombang. Student learning discipline is formed from a good behavior that must be gradually and continuously applied. One of the implementations is to implement a link book-based ananda ring program. The objectives to be achieved in this research are to improve students' learning discipline through ananda ringing program based on connecting books, describe the form of student learning phenomena and describe how to grow student discipline at SDIT Ar Ruhul Jadid Jombang. This study uses a qualitative approach with a qualitative descriptive type of research. The subjects of this study were parents, classroom teachers and students. The data collection method used is the method of observation, interviews and documentation. The validity of the data was obtained by using source triangulation, time triangulation and technical triangulation. The results showed that; 1) Ananda ringing program based on the link book is able to improve the learning discipline of SDIT Ar Ruhul Jadid Jombang students. 2) Efforts to foster student learning discipline at SDIT Ar Ruhul Jadid is to use the Ananda ringing program which is supported by forming a TPDS (School Discipline Enforcement Team). 3) The form of student learning discipline can be seen from the increase in 3 aspects, namely; a) aspects of worship, b) social aspects, and c) aspects of independence.
Credit Card Fraud Detection Using Ml Algorithms

Bank credit card default rates are (as predicted by S&amp;P dow Jones index) at all time high for the past few months. This possess a real challenge for banks and other lending financial institutions, as they are now more than ever are in need of a robust risk prediction model to generalize the economic behavior of their current and potential future clients. In real world massive stream of payment requests are coming in every second which is huge challenge to monitor. Machine Learning algorithms help in solving these huge challenges to an extent. These predictive models would not only benefit the lending institutions but also the customer as it would make them more aware of their potential defaulting rate. The goal of this project is to resolve this problem by building and comparing predictive risk models using supervised learning algorithms. We use python libraries to perform various Machine learning algorithms on the credit card dataset and data diagrams to have better visualization of data. Here the credit card fraud detection is done on the basis of some attributes in the dataset like the time factor and the amount factor. The accuracy of the algorithms is also being determined and the most efficient one is said with the highest accuracy obtained..
Make Your Own Sprites

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https://github.com/WuZongWei6/Pixelization.
A survey on adoption of blockchain in healthcare

In this technology and automation era, blockchain technology travels in the direction of consistent studies and adoption in different sectors. Blockchain technology with a chain of the block provides security and establishes a trusted environment between individuals. In the past couple of years, blockchain technology attracted many research scholars, industrialists to study, analyse and apply the technology in their own application needs. The major advantage of blockchain technology is the security, user privacy preserved, transparency. The purpose of this proposed paper is to provide a survey on blockchain scope in healthcare providing high security of patient health information's during sharing and their impact to reduce the operational and capital investments. Also, this paper briefs on the new business opportunities in the health sector integrating blockchain technology.