Reviewerly/exHarmony huggingface dataset:
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning

Non-i.i.d data distribution and Differential privacy(DP) protections are two open problems in Federated Learning(FL). We address these two problems by proposing the first noise intrinsic FL training algorithms. In our proposed algorithm, we incorporate a stochastic gradient Langevin dynamices(SGLD) oracle in local node's parameter update phase. Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections. We theoretically analyze our algorithm by formulating a min-max objective functions and connects its upper bound with global loss function in FL. The convergence of our algorithm on non-convex function is also given as contraction and coupling rate of two random process defined by stochastic differential equations(SDE) We would provide DP analysis for our proposed training algorithm and provide more experiment results soon.
LOAD BALANCING IN CLOUD COMPUTING: THE ONLINE TRAFFIC MANAGEMENT

Cloud computing is a kind of Internet-based computing. Clouds are highly configured infrastructure, which allow the users to make the payment only for the services procured. Cloud computing is basically delivery of different type of services to an organization’s computers and devices over the Internet. These resources can include tools and applications like data storage, servers, databases, networking, and software. As opposed to keeping records on a selective hard drive or other nearby storage devices, cloud-based storage makes it conceivable to spare them to a remote database or server farms in a protected and versatile manner. The electronic device just needs to access the web, using which it gets to access the data and the software programs to run it. Cloud computing is now days becoming a popular option for people and businesses for a number of reasons including cost savings, increased productivity, speed, efficiency, performance and security. For the most part the cloud is placed on data centers which are amazing to deal with countless clients. Load balancing mechanism comes into role to manage the workloads, which further defines the efficiency of the cloud. The basic aim behind balancing the load over the machines is to distribute workloads and then computing resources across one or more servers by reducing the energy consumption and providing maximum resource utilization. This paper gives concise information about load balancing, necessity to maintain the load and basic algorithm used to maintain the load over the cloud. In addition to this Static and dynamic load balancing algorithms have also been compared.
Multivariate Analysis of Psychometric Variables Affecting Job Satisfaction Among School Teachers: A Case Study at Primary and Secondary Schools in Shashemene Town, Ethiopia

This study has dealt with factors affecting the level of job satisfaction among school teachers at Shashemene town in 2010/11 academic year.Data were obtained from primary and secondary sources.A cross-sectional survey was conducted on a total of 405 teachers from different government and non-government schools using stratified random sampling technique.In the present study, the relationships between psychometric items (55-items) were examined by the combination of factor analysis and Bayesian logistic regression analysis.Firstly, factor analysis was used to group the items of the same characters as factors or explanatory variables of job satisfaction among teachers.Then, ten factors, having eigen-values greater than 1, were selected as independent variables and the factor scores coefficients were used in the Bayesian logistic regression analysis.It was found that eight unobserved variables had significant effects on job satisfaction.
TOPICALISSUES IN THE DEVELOPMENT OF THE PEDAGOGIC EDUCATION IN THE REPUBLIC OF KARELIA: EXPERIENCE, PROBLEMS, PROSPECTS

The article presents the experience of the Institute of Pedagogy and Psychology of Petrozavodsk State University in creating the regional system of the continuous pedagogical education in the Republic of Karelia in conditions of the current socio-economic challenges to education, and the need to perform the tasks of the National project “Education”.The article reveals the problems of functioning and development of the system of professional pedagogical education, also describes the efficient mechanisms and directions of development. The key program activities and organizational conditions for improving the quality of the continuous pedagogical education in the Republic of Karelia have been identified.
Playing Nondeterministic Games through Planning with a Learned Model

The MuZero algorithm is known for achieving high-level performance on traditional zero-sum two-player games of perfect information such as chess, Go, and shogi, as well as visual, non-zero sum, single-player environments such as the Atari suite. Despite lacking a perfect simulator and employing a learned model of environmental dynamics, MuZero produces game-playing agents comparable to its predecessor AlphaZero. However, the current implementation of MuZero is restricted only to deterministic environments. This paper presents Nondeterministic MuZero (NDMZ), an extension of MuZero for nondeterministic, two-player, zero-sum games of perfect information. Borrowing from Nondeterministic Monte Carlo Tree Search and the theory of extensive-form games, NDMZ formalizes chance as a player in the game and incorporates it into the MuZero network architecture and tree search. Experiments show that NDMZ is capable of learning effective strategies and an accurate model of the game.
Cybersecurity Analysis in the Context of Digital Wallets

A Recent study by industry body ASSOCHAM suggests that the e-Commerce market in India might attain $38 billion in 2016. In February 2016; a report by Counterpoint Research stated that India had achieved the second largest smartphone market in the world with 220 million smartphone users. Nowadays, digital wallet and mobile banking apps which allow fast transaction anywhere any time play a significant role in the digital transactions. This brings into focus the security issues related to these apps. Keeping these facts in view in the present study we investigate the security constraints of some digital wallet and mobile payment apps in India.
Pengenalan Simbol Matematika dengan Metode Convolutional Neural Network (CNN)

Handwriting recognition of mathematical symbol has problem in the field of pattern recognition and that make more difficult to detect than only handwriting. The complex structure of writing and diverse mathematical symbol makes handwriting of mathematical expressions very difficult to process the segmentation, symbol recognition, and structure analysis. The process is used to convert handwritten mathematical expressions into digital text format such as LaTeX or MathML. In this case, the process of symbol recognition becomes the focus of research, by comparing models contained in the Convolutional Neural Network method and find the greatest accuracy. Convolutional Neural Network (CNN) is one of the methods that can be used to recognize handwriting very accurately. In this research CNN will be used to recognize symbols in mathematical expressions and will be tested with CNN models (ResNet34 and DenseNet121). This research also explains how the deep learning approaches, such as CNN that can distinguish an object in an image, and after the two models have been tested we learn that both models have different performance and architecture, the DenseNet121 model becomes a better model when compared to ResNet34 in accuracy.
Operation Situation Analysis and Improvement Measure Study for Reactive Voltage Control System in Wind Farm

The control strategy of reactive power and voltage, communication between systems and the performance of the single device will affect the performance of the reactive and voltage control of the field station. On the basis of one million kilowatts of wind power base in the past three years the actual operation data and field test data, firstly analyzes the status of the wind farm without the system voltage and reactive power control system, introduces the current voltage and reactive power control system architecture and common control strategy; secondly from the station of reactive power and voltage control of the process, clarify the key factor effect of terminal voltage and reactive power control performance; the final form of the promotion of wind farm reactive voltage control performance and operation level of the measures, which is verified.
Suspicion Map: an unsupervised middleware for analyzing digital mammograms

Identifying “suspicious” regions is an essential process for clinical assessment of digital mammograms in breast cancer screening. Traditional solutions attempt to model malignant lesions directly, necessitating segmentations/annotations for training machine learning models. In this paper, we present a novel approach to identify a suspicion map – a middleware preserving only the suspicious regions in digital mammograms to effectively narrow down the image search space. Our unsupervised method is implemented by modeling normal breast tissue and subsequently identifying tissue abnormal to the model as suspicious. Our method consists of three main components: superpixel-based breast tissue patch generation, deep learning-based feature extraction from normal tissue patches, and breast density-guided one-class modeling of normal patches using the extracted features. Our machine learning approach is able to safely eliminate normal regions of tissue in a digital mammogram. Our normal tissue models were learned from 2,602 normal mammogram images and tested on 180 images (including 90 normal screening mammogram images and an independent set of 90 mammogram images with breast cancer diagnoses). Initial experiments showed that our proposed method can eliminate 97% of normal regions in the normal testing mammograms and 96% of normal regions in the malignant testing mammograms. Our method, based on modeling normal breast tissue, provides a novel and unsupervised scheme to more effectively analyze digital mammogram images towards identifying suspicious regions, and has the potential to benefit a variety of downstream applications for computeraided detection, diagnosis, and triage of breast cancer in mammogram images.
Role of Electronic Resources (e-Resources) in Research amidst COVID-19 Pandemic: An Analysis in the Context of Assam

The sudden outbreak of the COVID-19 pandemic has led to certain circumstances that affected the normal lives of the people. The only alternative left to stop the spread was to impose nationwide lockdown. Like other spheres of life, this pandemic has also brought changes to the teaching learning process.  A radical transformation was seen from offline mode of teaching to online mode; both the teachers and the students have been facing problems adapting to the new change. Besides psychological trauma, researchers had to face other problems due to the sudden lockdown of the country. They could not access the library service which plays a very crucial role in any successful research work. Researchers from Assam have also been facing difficulties in their research work. However, it seems that various e-resources appeared to be of great help for the researchers. This paper is an endeavour to understand the role of e-resources in research work in Assam during the crisis.
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN DENGAN KETERAMPILAN MENULIS TEKS BERITA SISWA KELAS VIII SMP NEGERI 7 PADANG

ABSTRACT There are three aims of this study. First, describe the skills of reading comprehention to the news of VIII grade students at SMP Negeri 7 Padang. Second, describe the writing text skills of VIII grade students at SMP Negeri 7 Padang. Third, describe the correlation of reading comprehention to the news with the writing text skills of VIII grade students at SMP Negeri 7 Padang. The type of this research is quantitative in terms of descriptive methods. The design used in this study is correlational design. The population of this study was the eighth grade students of the SMP Negeri 7 Padang which was registered in the 2018/2019 school year, it was 254 students. The sample in this study was determined by proportional random sampling of 15% of the population, namely 40 students. The research data is in the form of scores on the skills test results of reading comprehention to the news and scores on the results of the test writing news text skills. The instrument of this study is an objective test to measure news reading comprehention skills and performance tests to measure news text writing skills. The results of this study are three. First, the skills to write news texts of VIII grade students of the SMP Negeri 7 Padang is in accordance with the Good qualifications (B) with an averange value 81,25. Second, the skill of reading comprehention to the news text of VIII grade students of the SMP Negeri 7 Padang in accordance with Good qualifications (B) with an averange value 81,37. Third, there is a significant correlation betwen reading comprehention skills and news text writing skills of VIII grade students of the SMP Negeri 7 Padang on n-1 freedom degrees and 95% significant level. H0 is reject and H1is accepted because the tcount is greater than ttable (4,49&gt;2,68). Kata Kunci: korelasi, keterampilan membaca pemahaman, keterampilan menul teks berita
UPAYA MENUMBUHKAN MINAT BACA ANAK MELALUI PERMAINAN ABACA FLASH CARD DI RA AISYIAH KECAMATAN KRUI SELATAN KABUPATEN PESISIR BARAT

This study aims to increase children's reading interest through abaca flashcard games for students at the Raudhatul Athfal (RA) level. The subjects in this study were students of group B with a total of 20 students, consisting of 6 female students and 14 male students. The research was carried out in March-April 2020 at RA Aisyiah Pekon Letakan in group B students. The object of this study is a beginner's reading interest which includes pleasure, interest, initiative, and attention to the abaca flashcard game. The research instrument used observation sheets and documentation. The data analysis technique used quantitative descriptive with percentage technique. The results showed that the beginner reading interest in group B children at RA Aisyiah Letakan could be increased through playing abaca flash cards.
A distributed system improves inter-observer and AI concordance in annotating interstitial fibrosis and tubular atrophy

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc. (Clifton Park, NY). Twenty-five whole slide images (WSIs) were classified into a training set of 21 and a validation set of four. The training set was composed of seven unique subsets, each provided to an individual pathologist along with four common WSIs from the validation set. In CS 1, all pathologists individually annotated IFTA in their respective slides. These annotations were then used to train a deep learning algorithm to computationally segment IFTA. In CS 2, manual and computational annotations from CS 1 were first reviewed by the annotators to improve concordance of IFTA annotation. Both the manual and computational annotation processes were then repeated as in CS1. The inter-observer concordance in the validation set was measured by Krippendorff's alpha (KA). The KA for the seven pathologists in CS1 was 0.62 with CI [0.57, 0.67], and after reviewing each other's annotations in CS2, 0.66 with CI [0.60, 0.72]. The respective CS1 and CS2 KA were 0.58 with CI [0.52, 0.64] and 0.63 with CI [0.56, 0.69] when including the deep learner as an eighth annotator. These results suggest that our designed annotation framework refines agreement of spatial annotation of IFTA and demonstrates a human-AI approach to significantly improve the development of computational models.
HISTOGRAM PATTERN AND KALMAN FILTER APPROACH BASED REAL TIME OBJECT RECOGNITION AND TRACKING SYSTEM

Human tracking is implemented on a video sequence, which involves the analysis of the presence, size, shape and position of human beings. Human tracking is generally used in applications like video surveillance, crowd monitoring. Videos are series of images, called frames, which are displayed in faster frequency, such that human eyes will not be able to percept the continuous contents. Usually, the information of the contents present in two consecutive frames will be closely related. This paper presents a new approach for recognition of human use Histogram Specification with Kalman filtering approach. The main aim of this work is provide accurate recognition of human and view of their location from an unknown scene, Finally Kalman filter approach is apply the different positions of humans are identified based on the computation of Euclidian distance by using correlation matrix method. In this work results are drawn by using MATLAB tool by in view of the input video dataset taken from different sources and extract the frames from the input video for the detection and the efficiency of the proposed techniques will be calculated.
When User Research Leads Design Astray

User research in all its forms—from early ethnographic studies to late stage usability validation studies— undoubtedly can add value to any product design and development process. At its best, user research is tightly integrated with the design process, and designers and other team members eagerly seek out research findings to guide design decisions. At its worst, user research can hinder rather than help the efforts of the larger product design and development team—answering the wrong questions, providing misleading information, and focusing attention on issues that are not critical to product success. This can lead to friction between researchers and designers and other members of the product development team. This panel addresses the challenges of integrating user research into the product development process in a way that truly adds value, while suggesting ways to avoid common pitfalls that can result in user research leading designers astray.
Method and Algorithm for Implementation of Decoding Operation in the Threshold Cryptomodule of Secret Separation Using a Minimally Redundant Modular Number System

The article presents a new development of method and algorithm for performing secret separation in a threshold cryptomodule with masking transformation of the decoding operation. To solve this problem a recursive binary exponent division scheme and computational technology on the ranges of large numbers of the table-adder type, based on minimally redundant modular arithmetic (MRMA) are applied. A distinctive feature of the developed approach is usage the secret-original domain of finite residue rings for modules that have the form of powers of the number 2. This significantly reduces the complexity of the resulting decoding MRMA-procedure. Decomposition of scalable residues into large modules allows you to efficiently map the computational process being implemented to sets of easily implemented data extraction operations from table memory and their summation, providing a high level of performance, uniformity, and unification of basic structures. In terms of speed, the created MIMA decoding algorithm surpasses non-redundant analogues by at least l(19l-3)/(22l-6) times (l is the number of subscribers restoring the secret original). When l = 7...40 a (6.15...34.65)-fold increase in productivity is achieved.
Sistem Pakar Diagnosa Kerusakan Mesin Jahit Janome Menggunakan Metode Variable Centered Intelligent Rule System (VCIRS) dan Naïve Bayes

The Janome sewing machine is one of the portable machine products that can be used for sewing and embroidery clothes with maximum results, but the number of technicians who are experts or experts in diagnosing damage to Janome sewing machines is currently still low. Therefore, researchers designed a system that can be used to help experts or experts diagnose the damage that occurred to a Janome sewing machine using Microsoft Visual Studio 2008. This system is designed as an expert or expert can diagnose the damage that occurs to a Janome sewing machine based on symptoms. the symptoms on the machine. The methods applied to this system are the Variable Centered Intelligent Rule System (VCIRS) and the Naive Bayes method. The results of this study resulted in an expert system that can diagnose the Janome sewing machine malfunction to operate on a personal computer.
Density peaks clustering based on geodetic distance and dynamic neighbourhood

Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilising this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighbourhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbours K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other five clustering algorithms on six synthetic datasets and ten real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets.
Merging Well-Trained Deep CNN Models for Efficient Inference

In signal processing applications, more than one tasks often have to be integrated into a system. Deep learning models (such as convolutional neural networks) of multiple purposes have to be executed simultaneously. When deploying multiple well-trained models to an application system, running them simultaneously is inefficient due to the collective loads of computation. Hence, merging the models into a more compact one is often required, so that they can be executed more efficiently on resource-limited devices. When deploying two or more well-trained deep neural-network models in the inference stage, we introduce an approach that fuses the models into a condensed model. The proposed approach consists of three phases: Filter Alignment, Shared-weight Initialization, and Model Calibration. It can merge well-trained feed-forward neural networks of the same architecture into a single network to reduce online storage and inference time. Experimental results show that our approach can improve both the run-time memory compression ratio and increase the computational speed in the execution.
ЛИРИКА КАК АКТ ПОЗНАНИЯ: К ПРОБЛЕМЕ ПОЛНОЦЕННОГО ВОСПРИЯТИЯ ЛИРИЧЕСКОГО ТЕКСТА В ПРАКТИКЕ ШКОЛЬНОГО И ВУЗОВСКОГО ОБРАЗОВАНИЯ

For the formation of a full-fledged perception of lyric works, an algorithm for teaching the reading of lyrics based on the principle of procedurality and a logical-conceptual approach to the figurative structure of poems is presented. The algorithm reproduces the stages of the reader's cognitive activity as an active speech interpretive activity of a schoolchild and a student.