Reviewerly/exHarmony huggingface dataset:
Online Social Welfare Maximization with Spatio-Temporal Resource Mesh  for Serverless

Serverless computing is leading the way to a simplified and general purpose programming model for the cloud. A key enabler behind serverless is efficient load balancing, which routes continuous workloads to appropriate backend resources. However, current load balancing algorithms implemented in Kubernetes native serverless platforms are simple heuristics without performance guarantee. Although policies such as Pod or JFIQ yield asymptotically optimal mean response time, the information they depend on are usually unavailable. In addition, dispatching jobs with strict deadlines, fractional workloads, and maximum parallelism bound to limited resources online is difficult because the resource allocation decisions for jobs are intertwined. To design an online load balancing algorithm without assumptions on distributions while maximizing the social welfare, we construct several pseudo-social welfare functions and cost functions, where the latter is to estimate the marginal cost for provisioning services to every newly arrived job based on present resource surplus. The proposed algorithm, named OnSocMax, works by following the solutions of several convex pseudo-social welfare maximization problems. It is proved to be $\alpha$-competitive for some $\alpha$ at least 2. We also validate OnSocMax with simulations and the results show that it distinctly outperforms several handcrafted benchmarks.
Assessing a Single Image in Reference-Guided Image Synthesis

Assessing the performance of Generative Adversarial Networks (GANs) has been an important topic due to its practical significance. Although several evaluation metrics have been proposed, they generally assess the quality of the whole generated image distribution. For Reference-guided Image Synthesis (RIS) tasks, i.e., rendering a source image in the style of another reference image, where assessing the quality of a single generated image is crucial, these metrics are not applicable. In this paper, we propose a general learning-based framework, Reference-guided Image Synthesis Assessment (RISA) to quantitatively evaluate the quality of a single generated image. Notably, the training of RISA does not require human annotations. In specific, the training data for RISA are acquired by the intermediate models from the training procedure in RIS, and weakly annotated by the number of models' iterations, based on the positive correlation between image quality and iterations. As this annotation is too coarse as a supervision signal, we introduce two techniques: 1) a pixel-wise interpolation scheme to refine the coarse labels, and 2) multiple binary classifiers to replace a naïve regressor. In addition, an unsupervised contrastive loss is introduced to effectively capture the style similarity between a generated image and its reference image. Empirical results on various datasets demonstrate that RISA is highly consistent with human preference and transfers well across models.
Vanilla Probabilistic Autoencoder

The autoencoder, a well-known neural network model, is usually fitted using a mean squared error loss or a cross-entropy loss. Both losses have a probabilistic interpretation: they are equivalent to maximizing the likelihood of the dataset when one uses a normal distribution or a categorical distribution respectively. We trained autoencoders on image datasets using different distributions and noticed the differences from the initial autoen- coder: if a mixture of distributions is used the quality of the reconstructed images may increase and the dataset can be augmented; one can often visualize the reconstructed im- age along with the variances corresponding to each pixel. The code which implements this method can be found at https://github.com/aciobanusebi/vanilla-probabilistic-ae.
INTERVIEW BETWEEN IRINA N. SEDOVA — HEAD OF SCULPTURE DEPARTMENT AT THE STATE TRETYAKOV GALLERY, AND ALEXANDER N. BURGANOV — AN OUTSTANDING RUSSIAN SCULPTOR, NATIONAL ARTIST OF RUSSIA, THE ACADEMICIAN OF THE RUSSIAN ACADEMY OF ARTS, DOCTOR OF ART HISTORY, PROFESSOR, DIRECTOR OF THE MOSCOW STATE MUSEUM BURGANOV HOUSE

The journal traditionally opens with an academic interview. In this issue, we present an interview between Irina N. Sedova — head of Sculpture department at The State Tretyakov Gallery, and Alexander N. Burganov — an outstanding Russian sculptor, National Artist of Russia, the academician of the Russian Academy of Arts, doctor of art history, professor, director of the Moscow State Museum Burganov House. This dialogue became part of the sculptor’s creative evening at the State Tretyakov Gallery, which combined a personal exhibition, a screening of an author’s film and a dialogue with the audience in the format of an interactive interview.
Deadline-Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

Cloud computing environment is a conglomeration of computing resources that are maintained to provide a plethora of services to users as utility on a pay-per-use basis. When users request for services, a cloud provider has to allocate relevant computing resources not only to complete a task but also to satisfy the user requirements such as deadlines. This necessitates the design of suitable scheduling algorithm for effective utilization of available resources at the provider’s end. While doing so one of the concerns of the provider is to balance the load uniformly across resources. In this paper, the Max-Min algorithm has been extended by incorporating the concept of free time in order to effectively utilize the cloud resources through load balancing and at the same time meet the deadlines of individual tasks.
New Tools for Increasing the Practice-Orientedness of Russian Universities: The Market for Checklists

The article deals with the problem of weak integration of Russian universities into the real economy. This is one of the most pressing problems of the Russian university system. The purpose of the article is to develop a specialized pilot checklist "Towards business: a model for integrating higher education and the real sector of the economy" and to demonstrate the prospects for its use in regional universities. The general hypothesis of the research is to test the feasibility of creating and using such checklists to help Russian universities in restructuring their organizational models in the direction of strengthening practice orientation. It is shown that at present the market for a new information and analytical product – checklists – is in its infancy and is evolving in several directions. Firstly, it becomes more complicated: the number of constituent documents increases, the number of questions increases, etc. Secondly, the forms of their implementation are being improved: chargeable, bonus, free, by subscription, with an additional package, etc. Thirdly, different algorithms for their application appear: straightforward replication of a standard document, provision of a checklist with subsequent in-depth research and recommendations in relation to a specific customer, the possibility of choosing an additional service for expert assessment, creation and implementation of mobile applications for prompt updating of checklists, and etc. All of this allows the new methodological tool to find application in the higher education system. The article examines some typical examples of the new trend emerging in Spain, Croatia, Great Britain, the USA and Russia, as well as pan-European and Latin American initiatives. The methodology of the author's checklist "Towards business" is based on taking into account the world experience of integrating universities and enterprises in a compact form: an instruction document and a cover note. There have not yet been effective prototypes of the developed checklist in Russia. Cases of using the pilot checklist for reorganizing the model of work of universities in the Kamchatka Territory, the Republic of Dagestan and the Lipetsk Region confirm the general hypothesis of the article.
Drone technology is flying to new heights

There could hardly be a soul alive today, not even in the most remote corner of the world, who has not marveled at the sight and sound of a little quadrotor buzzing past overhead. They are a modern phenomenon, a long dreamt-of capability, that seemingly all of a sudden burst onto the scene in the early 2000s and whose uses continue to evolve and multiply.
Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.
Well-Defined Morphology is Sentence-Level Morphology

Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context. However, the lack of a clear linguistic definition for words destines the annotative work to be incomplete and mired in inconsistencies, especially cross-linguistically. In this work we expand morphological inflection of words to inflection of sentences to provide true universality disconnected from orthographic traditions of white-space usage. To allow annotation for sentence-inflection we define a morphological annotation scheme by a fixed set of inflectional features. We present a small cross-linguistic dataset including semi-manually generated simple sentences in 4 typologically diverse languages annotated according to our suggested scheme, and show that the task of reinflection gets substantially more difficult but that the change of scope from words to well-defined sentences allows interface with contextualized language models.
HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data.
An Analysis of Data Processing for Big Data Analytics

The need for high-performance Data Mining (DM) algorithms is being driven by the exponentially increasing data availability such as images, audio and video from a variety of domains, including social networks and the Internet of Things (IoT). Deep learning is an emerging field of pattern recognition and Machine Learning (ML) study right now. It offers computer simulations of numerous nonlinear processing layers of neurons that may be used to learn and interpret data at higher degrees of abstractions. Deep learning models, which may be used in cloud technology and huge computational systems, can inherently capture complex structures of large data sets. Heterogeneousness is one of the most prominent characteristics of large data sets, and Heterogeneous Computing (HC) causes issues with system integration and Advanced Analytics. This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies. The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted. Data Analytics and coping with a wide range of data are discussed.
Probabilistic Modeling of Histogram Based Fuzzy Image Enhancement Algorithm

Probabilistic model checking is frequently used to examine stochastic behavior. Model-checking is important for examining before extensive simulations, in differing fields, including computer communications, networks, security, and biology. In this work performance of histogram-based fuzzy image enhancement (HFIE Algorithm). The fuzzy contrast enhancement algorithm based on histograms is used to improve low contrast colored photographs. K is the contrast intensification parameter calculated from the histogram. The RGB image is transformed into HSV to preserve the chromatic information in the original image. To improve the image, only the V component is stretched using the M and K parameters. HFIE Algorithm is analyzed by developing its Discrete-time Markov Chain (DTMC) model in the Probabilistic Symbolic Model Checker (PRISM). First, a labeled transition diagram showing the functionality of HFIE Algorithm is constructed, then the HFIE Algorithm model using the PRISM tool is developed. The expected time to convert RGB image to HSV, probability of achieving enhanced RGB image, and expected SNR are measured with the help of properties.
Effectiveness of VAR (Virtual Augmented Reality)-Based Educational Games in Trigonometry Learning in University

The development of technology today is very demanding of a teacher to be able to develop effective media in the learning process.Trigonometry learning is one of the courses in higher education that requires a virtual understanding of concepts.Therefore, it is very important to have fun virtual media through games in understanding the concept of Trigonometry.The purpose of this study was to determine the effectiveness of educational games based on VAR (Virtual Augmented Reality) in learning trigonometry in university.The research method uses the ADDIE model (Analysis, Design, Development, Implementation, Evaluation).However, this article contains the implementation stages.The data analysis techniques of this research are the Normality Test, Homogeneity Test, T-test.From the results of the study, it can be concluded that learning trigonometry using educational games based on Virtual Augmented Reality meets the effective criteria because the experimental class learning outcomes are better than the control class and the results of the student response questionnaire to the VAR-based educational game media showed that the media was very effective in learning trigonometry in university.
Potential factors influencing the rhetorical patterns of research article discussion sections

The present study reports potential factors influencing the rhetorical patterns of research articles (RA) discussion sections. The study was conducted by utilizing descriptive qualitative research. The researcher purposefully focused on investigating 10 bilingual writers who wrote both one English and one Indonesian research article. The selected writers were those who had an educational background in language and language teaching. The interviews covered the interviewees’ background information, current activities, writing activities, and their rhetorical patterns of discussion sections. The interviews were conducted by utilizing the snowball technique to search for more information. The interview data were analyzed into some steps namely, transcribing the interview data, organizing data, summarizing data, and interpreting data. All data transcription was then categorized and coded. Research findings revealed that the writers’ choice of move structure could be as a result of learning from other people’s rhetorical patterns, believing themselves, having high self-confidence, having high writing frequency, and having high awareness in the micro and macrostructure of writing discussion sections. The Indonesian writers have opened their minds to learn and read other researchers’ articles and then determine whether the patterns are suitable for them or not. The writers’ starting point of experiencing to have their RA published made them believe in themselves and felt self-confident. Thus, the more they wanted to write RA, the higher they had writing frequency and awareness in the micro and macrostructure of writing discussion sections.
Knowledge extract and ontology construction method of assembly process text

The actual product assembly process mainly relies on manual assembly by workers, and the personal experience of workers is difficult to effectively reuse. Ontology as a knowledge management and expression tool is gradually applied in the field of assembly. However, the manual construction of the ontology is time-consuming and labor-intensive, and the automatic construction of the ontology requires a large number of corpora for training, both of which are difficult to obtain a good assembly case ontology. This paper proposes a method in which automatically extracts relevant knowledge from case assembly process files to generates case database and integrates ontology framework of assembly domain to construct ontology. It shows that the accuracy can be guaranteed on the basis of the rapid construction of case ontology. The feasibility of this method is proved by a practical case.
Job Standard Parameters from Online Job Vacancy

The internet has provided an efficient and economical way to provide job vacancy information to applicants in a way that is far more dynamic and consistent than happened in the past. This is directly proportional to the number of companies exploiting online technology (job portals, company websites, etc.) to make job advertisements reach a growing audience. To further optimize the selection process concerning processing time and accuracy, researchers have begun developing a sophisticated search engine to automatically sort resumes based on job offer requirements. Overcoming this problem requires the Information Extraction (IE) process. Research on IE regarding vacancies actually already exists and has been applied. Research on IE regarding vacancies actually already exists and has been applied In addition, we want to develop previous research involving all job vacancies websites to a wider extent. We realize that this limitation makes the extraction process difficult. we try to define job problems more easily, such as identifying various JSPs on job vacancy websites in Indonesia as a first step in the extraction process. Our findings in the survey of 14 job vacancy websites are 26 job standard parameters including structure collection and extraction methods. This study provides a detailed description of each component of information extraction on the job vacancy website in Indonesia. Starting from identifying the type of Structured Extraction to the output of extraction. This study also developed JSP from previous research.
Quantum Searching on Markov Chains - The Complete Graph

We will give an introduction into the quantum search algorithms on the Markov chains introduced by Szegedy and recent modifications based on partially absorbing Markov chains due to Krovi et al.Algorithmica 74, 851 (2016) It has been shown that a quantum search can find a set of marked vertices quadratically faster (in units of the hitting time) for any reversible Markov chain.The proofs are based on certain properties of the stationary state of the partially absorbing walk and rely on quantum phase estimation techniques.We will offer an alternative view of the underlying mechanism of the quantum search based on spectral properties of the quantum walk operator.By considering the complete graph as an example, we identify the relevant two-level quantum subspace leading to a Grover-like rotation in the underlying vector space.
A method of searching and marking artifacts in images applying detection and segmentation algorithms

The paper explores the possibility of using neural networks to single out target artifacts on different types of documents. Numerous types of neural networks are often used for document processing, from text analysis to the allocation of certain areas where the desired information may be contained. However, to date, there are no perfect document processing systems that can work autonomously, compensating for human errors that may appear in the process of work due to stress, fatigue and many other reasons. In this work, the emphasis is on the search and selection of target artifacts in drawings, in conditions of a small amount of initial data. The proposed method of searching and highlighting artifacts in the image consists of two main parts, detection and semantic segmentation of the detected area. The method is based on training with a teacher on marked-up data for two convolutional neural networks. The first convolutional network is used to detect an area with an artifact, in this example YoloV4 was taken as the basis. For semantic segmentation, the U-Net architecture is used, where the basis is the pre-trained Efficientnetb0. By combining these neural networks, good results were achieved, even for the selection of certain handwritten texts, without using the specifics of building neural network models for text recognition. This method can be used to search for and highlight artifacts in large datasets, while the artifacts themselves may be different in shape, color and type, and they may be located in different places of the image, have or not have intersection with other objects.