Reviewerly/exHarmony huggingface dataset:
Exploring Resources in Word Sense Disambiguation for Marathi Language

Word Sense Disambiguation (WSD) is one of the most challenging problems in the research area of natural language processing.To find the correct sense of the word in a particular context is called Word Sense Disambiguation.As a human, we can get a correct sense of the word given in the sentence because of word knowledge of that particular natural language, but it is not an easy task for the machine to disambiguate the word.Developing any WSD system, it required sense repository and sense dictionary.It is very costly and time-consuming to build these resources.Many foreign languages have available these resources, that is why most of the foreign languages like English, German, Spanish etc lot of work is done in these Natural languages.When we look for Indian languages like Hindi, Marathi, Bengali etc. very less work is done.The reason behind this is resource-scarcity.In this paper, we majorly focus on Marathi Language Word Sense Disambiguation because of very less work is done in the Marathi Language as compared to Hindi and other Indian Languages.Our main objective is to provide information about various resources available for the Marathi language which will be helpful for researchers who wants to do work for Marathi WSD.This paper also gives a review on work done for Marathi Language WSD and its challenges and problems.
Ensemble Squared: A Meta AutoML System

There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an off-the-shelf system can take care of many of the steps for end-users without the need for expertise in machine learning. This paper presents Ensemble Squared (Ensemble$^2$), an AutoML system that ensembles the results of state-of-the-art open-source AutoML systems. Ensemble$^2$ exploits the diversity of existing AutoML systems by leveraging the differences in their model search space and heuristics. Empirically, we show that diversity of each AutoML system is sufficient to justify ensembling at the AutoML system level. In demonstrating this, we also establish new state-of-the-art AutoML results on the OpenML tabular classification benchmark.
The Military Scientific Library of Mikhailovskaya Military Artillery Academy. On the occasion of the 200th anniversary

For the first time, the findings of the integrated study of the history of the Military Scientific Library of the Mikhailovskaya Military Artillery Academy are presented. The study covers its development since 1820 when it was founded as the library of the Artillery School up to the period of 1863-1917 when it became the specialized library of the Mikhail Artillery Academy and School. Printed catalogs of books of the Military Scientific Library of the Academy and School 1871 and 1895 are characterized. For the first time, other library’s catalogs are analyzed to be introduced into scientific use. The author characterizes and compares book catalogs of other universal military scientific libraries (those of General Staff, General Staff Nikolaevskaya Academy) with the catalogs of Mikhailovskaya Academy Military Scientific Library) and concludes that the latter has acquired the most complete book collection on artillery in the country. The collection matches science potential of this educational institution. Many prominent researchers well-known in Russia and abroad have taught at the Academy and the School. Their works are available not only from the Academy and School Library, but also can be found in the book collections of many libraries subordinate to the Military Ministry.This study is to expand the idea of military scientific libraries of the country, the knowledge of their history, collection development principles. Its findings will be useful to librarians of military educational institutions and academic libraries in the aspect of materials selection and efficient collection development.
PRE-SERVICE TEACHERS PERFORMANCE IN IMPLEMENTING TEACHING METHODS AT TEFL CLASS

This study aimed to describe the implementation of microteaching activity by pre-service teachers in TEFL 2 class as well as to get an overview of the problems faced during microteaching so that they are able to improve the competence of teaching English later in both schools and other educational institutions. In this study, the method used was descriptive qualitative. The subject of the study were taken purposively by considering the achievement gained in previous subject namely in TEFL I class they reached the average value of 90 out of 100 with the number of subjects four (4) pre-service teachers. Data were collected through observation using video recording and field note as well as semi structured-interview. Aspects to be analyzed were methods used from opening, main, and closing activities by considering the characteristics of the method applied. The results of this study showed that Grammar Translation Method (GTM) and Audio-Lingual Method (ALM) became a method preference during the microteaching. Pre-service teachers showed no hesitation and well prepared in applying the methods. Translation activities, dialogue memorization, repetition technique, word pair technique, and game grammar technique were occurred. Problems faced were the gap between the theories and the practice of teaching methods such as lack of skills in facial and body expression, classroom management, and stresses with noises from classmates as the students
The Creative Abilities’ Development as the Component of the Process of Formation Soft Skills with the Help of Distant Technologies

The article is dedicated to the problem of the development of the creative abilities of students as the component of soft skills.The conditions for the formation of the creative professional potential of future professionals are considered.There is determined that the necessary condition for the formation of the creative professional potential of the future specialists is the motivation on the student's creative self-actualization.During the process of the formation of the creative professional potential of future specialists, the leading role is given to the informational cognitive program and subject-genesis of the professional reflection.It is proposed the model of formation of the creative professional potential of future specialists.To describe the conceptual model, the methodology of functional modeling within the graphical notation IDEF0 is used.There is determined that for the formation of the creative abilities during distant learning it is advisable to use systems of education's management, to use cloud-technologies, systems of videoconferences' organization, messengers.There is described the experience of the formation of the creative professional potential of students of Berdiansk State Pedagogical University with the help of distance learning.
A review of on-device fully neural end-to-end automatic speech recognition algorithms

In this paper, we review various end-to-end automatic speech recognition algorithms and their optimization techniques for on-device applications. Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on. To obtain sufficiently high speech recognition accuracy with such conventional speech recognition systems, a very large language model (up to 100 GB) is usually needed. Hence, the corresponding WFST size becomes enormous, which prohibits their on-device implementation. Recently, fully neural network end-to-end speech recognition algorithms have been proposed. Examples include speech recognition systems based on Connectionist Temporal Classification (CTC), Recurrent Neural Network Transducer (RNN-T), Attention-based Encoder-Decoder models (AED), Monotonic Chunk-wise Attention (MoChA), transformer-based speech recognition systems, and so on. These fully neural network-based systems require much smaller memory footprints compared to conventional algorithms, therefore their on-device implementation has become feasible. In this paper, we review such end-to-end speech recognition models. We extensively discuss their structures, performance, and advantages compared to conventional algorithms.
Educating tolerance in a multicultural environment: opportunities and boundaries

The article reveals the social situations that determine the need to educate a tolerant person, which is considered as a social phenomenon; presented an analysis of approaches to the design of effective pedagogical conditions for the education of a tolerant person in the university; noted boundaries and risks in the education of a tolerant person in the multicultural educational environment of the university.
What Meaning-Form Correlation Has to Compose With

Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed by measuring meaning-form correlation. We analyze meaning-form correlation on three sets of languages: (i) artificial toy languages tailored to be compositional, (ii) a set of English dictionary definitions, and (iii) a set of English sentences drawn from literature. We find that linguistic phenomena such as synonymy and ungrounded stop-words weigh on MFC measurements, and that straightforward methods to mitigate their effects have widely varying results depending on the dataset they are applied to. Data and code are made publicly available.
Strategies of positive evaluation in computer-mediated communication

The aim of this paper is to present the results of our research into the politeness strategy of complimenting or giving positive evaluations in English on the Internet with a specific focus on comments responding to selected YouTube music videos.Compliments in face-to-face interactions have been widely studied for decades, with a new tendency emerging: the occurrence of compliments and compliment responses in digital contexts (Placencia and Lower 2016).Although there is no obvious formula for expressing compliments, research shows that there seems to be a limited number of patterns used for their construction (Válková 2012).On the corpus of complimentary evaluations in online comments on music (modern and classical), we aim to show to what extent the variety of forms conforms to the types presented in the available material and whether there are any essential differences in the characteristic lexical and syntactic repertoire with respect to the selected music genres.
Hybrid Quantum Computing -- Tabu Search Algorithm for Partitioning Problems: preliminary study on the Traveling Salesman Problem

Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations, which have motivated the carrying out of this paper. In this paper, we introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. To assess the quality of our method, we have used 7 different Traveling Salesman Problem instances as benchmarking set. The obtained outcomes support the preliminary conclusion that our algorithm is an approach which offers promising results for solving partitioning problems while it drastically reduces the access to quantum computing resources. We also contribute to the field of Transfer Optimization by developing an evolutionary multiform multitasking algorithm as initialization method.
Machine-Learning Arithmetic Curves

We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around one hundred thousand, we demonstrate the utility of machine-learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998.
EPIDEM: A Mobile Application to Contain Coronavirus Disease 2019

With the spread of COVID-19 across the globe, almost 33 million people became sick and almost 1 million have died (WHO).The government of the Philippines, along with the government of other countries declared movement restrictions or lockdown in the attempt to contain the virus.This resulted in loss of job and economic breakdown.The Philippines, in particular, experienced the longest and strictest lockdown.As a down side, a lot of people have lost their jobs and the country is currently facing economic recession.With the aim of containing the virus to reopen the economy, a team of experts developed a mobile application called EPIDEM.The application is available for both Android and Apple smartphones.With the use of EPIDEM, lay people can monitor their infection risk for COVID-19 and isolate themselves or get themselves tested when infection risk is high.Users can also monitor the health risk of other people that they come in contact with, so they can decide if meeting them is safe or risky.Further, the application provides a monitoring map that shows the area that is safe from COVID-19 and area where a possible outbreak might occur.The monitoring map will be helpful to both lay people, so they would know the places to avoid and to government officials, so they can quickly respond to outbreak.The teams of developers are recommending the nationwide implementation of EPIDEM to help contain the COVID-19 outbreak in the country.The application is not a new strategy of containment.Instead, it's a better wheel that utilizes advanced technology to execute the manual containment strategies that are already being implemented in the country.
A Secure Algorithm for Rounded Gaussian Sampling

Presented in this paper is a new Gaussian sampler targeted at lattice-based signatures. It is the first secure algorithm for implementing the Box-Muller Gaussian sampling algorithm, which produces continuous Gaussian samples. The samples can be made discrete by rounding and this method has recently been shown to suffice for the purpose of discrete Gaussian sampling for lattice-based signatures. Rounded Gaussians allow quick transformations from samples of low standard deviation to samples with a high standard deviation. This makes them well-suited to producing the wide distributions needed for the target primitives. Current state-of-the-art methods sample wide distributions with multiple samples from a narrow distribution, joined by a convolution algorithm, for each single sample. The number of required samples per output sample grows with the width of the distribution. The rounded Gaussian method allows sampling wide distributions with complexity which is constant with increasing standard deviation. The main contribution of the work is a novel, low-memory algorithm, based on the CORDIC family of algorithms, for the fixed-point and secure evaluation of the elementary functions necessary for the Box-Muller continuous sampling method. It is the first secure, continuous sampler for the production of rounded Gaussian distributions. A proof-of-concept implementation of the algorithm is used to demonstrate that Box-Muller sampling is a competitive alternative to sampling the discrete Gaussian distribution, for lattice-based signatures.
Rethinking supervised learning: insights from biological learning and from calling it by its name

The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave rise to a hype loaded with ambitious promises and overstatements. Soon the community realised that the success owed much to the availability of thousands of labelled examples and supervised learning went, for many, from glory to shame: Some criticised deep learning as a whole and others proclaimed that the way forward had to be alternatives to supervised learning: predictive, unsupervised, semi-supervised and, more recently, self-supervised learning. However, all these seem brand names, rather than actual categories of a theoretically grounded taxonomy. Moreover, the call to banish supervised learning was motivated by the questionable claim that humans learn with little or no supervision and are capable of robust out-of-distribution generalisation. Here, we review insights about learning and supervision in nature, revisit the notion that learning and generalisation are not possible without supervision or inductive biases and argue that we will make better progress if we just call it by its name.
Rethinking movie genre classification with fine-grained semantic clustering

Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal content of movies. By leveraging pre-trained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. Using a contrastive loss, we continue to fine-tune this 'coarse' genre classification network to identify high-level intertextual similarities between the movies across all genre labels. This leads to a more 'fine-grained' and detailed clustering, based on semantic similarities while still retaining some genre information. Our approach is demonstrated on a newly introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes pre-computed audio, location, motion, and image embeddings.
Cross-Site Search Attacks: Unauthorized Queries over Private Data

Cross-site search attacks allow a rogue website to expose private, sensitive user-information from web applications. The attacker exploits timing and other side channels to extract the information, using cleverly-designed cross-site queries. In this work, we present a systematic approach to the study of cross-site search attacks. We begin with a comprehensive taxonomy, clarifying the relationships between different types of cross-site search attacks, as well as relationships to other attacks. We then present, analyze, and compare cross-site search attacks; We present new attacks that have improved efficiency and can circumvent browser defenses, and compare to already-published attacks. We developed and present a reproducibility framework, which allows study and evaluation of different cross-site attacks and defenses. We also discuss defenses against cross-site search attacks, for both browsers and servers. We argue that server-based defenses are essential, including restricting cross-site search requests.
Feature fusion quality assessment model for DASH video streaming

Dynamic Adaptive Streaming over HTTP (DASH) employs the flexible rate adaptation scheme to combat with time-varying channel conditions. In addition to compression impairment, DASH video streaming suffers from transmission impairment, such as rate switches and stalling events. Both of them severely degrade users' Quality of Experience (QoE). Herein, an assessment model is established for DASH video streaming by directly jointing multiple QoE influential factors, which quantify impairments resulting from the compression and transmission. To demonstrate the influence of video content characteristics on users' QoE, spatio-temporal content perceptual features are employed to represent the compression impairment. When reflecting temporal characteristics, a novel motion vector padding method is proposed to quantify the influence of intra macroblock on the human visual system. The proposed model is evaluated on a newly public Waterloo SQoE-III database, which is available for DASH video streaming. Experimental results demonstrate that the authors' model outperforms the comparative models and owns the strong generalization ability to different video contents. Moreover, the proposed model is statistically superior to the existing models.
Methodological aspects of the implementation of additional education in robotics

The purpose of the study is actualized by the fact that progress in the field of computer technology and robotics allows radically changing the idea of teaching computer science to schoolchildren, or rather programming in a modern secondary school, realizing one of the most important tasks facing the education system today. The scientific novelty of the study lies in the fact that these achievements allow updating traditional teaching methods, making them much more useful and modern. Nowadays, students have the opportunity to study various programming languages at school and at the same time, using the developed resources, observe the process of their execution by specially created robots, which can develop the student's desire to study further and give motivation to study the academic discipline. The practical significance of the work lies in the fact that one of such projects containing educational resources and allowing a different look at modern computer science lessons is LEGO and ROBBO. The advantages and disadvantages of traditional methods of teaching programming languages in high school are analyzed in the study. Also, the advantages of learning programming with LEGO and ROBBO are revealed, methodological recommendations for using the LEGO and ROBBO programming environment are presented, the capabilities of the LEGO project and ROBBO are described, and their main elements are characterized.
An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

Image segmentation is a crucial role towards clinical diagnosis and therapy planning due to the existence of abundant noise, blurry boundaries and heterogeneity. In this work, a novel matrix factorization based approach with the ability of edge preservation is presented. Firstly, to obtain more comprehensive feature description, we use the local spectral histograms to describe the local structures formed by feature values. Secondly, the energy function is established via matrix factorization theory, which makes each pixel fall into the sub-region with the largest coverage area in its neighborhood. Then, the edge preservation is used to obtain a smoother and more accurate object boundary. Finally, a number of synthetic and natural images are performed for verification. Experiments demonstrated that our approach achieves satisfactory results and has more robust against the complex background than other methods.