Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF THE ELECTRONIC TEXTBOOK FOR THE HIGHER COURSE OF MATHEMATICS ACCORDING TO THE PROGRAM MACROMEDIA FLASH 8

This article discusses the ways of organizing the educational process for the course of higher mathematics at a university independently or jointly with a teacher through an electronic textbook, the use of effective methods and techniques used in the preparation of the program and training.When compiling an electronic textbook, a model was developed using the Macromedia Flash 8 program. When choosing a design with an interface from individual methods, the social method was used, and when it was introduced into the educational process, control methods were used. As a result of the research, a large-scale electronic textbook was compiled using various methods. During the research work, several possibilities of an electronic textbook were identified, designed to facilitate the teacher's lesson progress, and the student to comprehend the material provided as effectively as possible.During the preparation of the program, ways of further integration into Windows were developed.All formulas used in the course of higher mathematics have been saved as a separate reference.The first scheme of the program with a total volume of 8043 kilobytes is fully working. The developed electronic textbook can be used as a methodological guide when studying a course of higher mathematics, as a theoretical basis for creating an electronic textbook for future teachers of mathematics.
EFFECTIVENESS OF USING DIGITAL EDUCATIONAL RESOURCES IN TEACHING ELEMENTS OF MATHEMATICS

This article considers the use of interactive teaching methods based on the effective organization of teaching the elements of mathematics in the context of digitalization of education. One of the most pressing issues in education is to improve its quality. The solution of this problem depends on improving the content of education, modernization of technologies and methods of organizing the educational process. The introduction of state educational standards highlighted the importance of using digital educational resources on the basis of interactive teaching methods in the learning process on the basis of a competency-based approach. In order to demonstrate the effectiveness of the use of digital educational resources on the basis of interactive teaching methods in the teaching of high school mathematics, the study used methods of observation, pedagogical experiment, questionnaire. Brainstorming and Case Study techniques were also used to increase students' interest. The study proved that the use of digital educational resources in the teaching of mathematics can significantly increase the speaking experience of each student in the classroom, achieve mastery of the learning material for all students, as well as solve various developmental and educational problems. The results of the study can be used in the future as a guide for school teachers and university students in the teaching of elements of mathematics. The use of digital educational resources in the teaching of mathematics in schoolchildren, along with interactive teaching methods, has increased the effectiveness and quality of teaching, the positive dynamics of the formation of universal cognitive and regulatory learning activities of students.
A Tale of EDA's Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation

Long-tailed distribution is a common and critical issue in the field of machine learning. While prior work addressed data imbalance in several tasks in electronic design automation (EDA), insufficient attention has been paid to the long-tailed distribution in real-world EDA problems. In this paper, we argue that conventional performance metrics can be misleading, especially in EDA contexts. Through two public EDA problems using convolutional neural networks and graph neural networks, we demonstrate that simple yet effective model-agnostic methods can alleviate the issue induced by long-tailed distribution when applying machine learning algorithms in EDA.
Rcall: Calling R from Matlab

Summary: R and Matlab are two high-level scientific programming languages which are frequently applied in computational biology. To extend the wide variety of available and approved implementations, we present the Rcall interface which runs in MATLAB and provides direct access to methods and software packages implemented in R. Rcall involves passing the relevant data to R, executing the specified R commands and forwarding the results to MATLAB for further use. The evaluation and conversion of the basic data types in R and MATLAB are provided. Due to the easy embedding of R facilities, Rcall greatly extends the functionality of the MATLAB programming language. Availability: Source code is freely available at https://github.com/kreutz-lab/Rcall, implemented in MATLAB and supported on Linux, MS Windows and Mac OS X.
VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living

Many attempts have been made towards combining RGB and 3D poses for the recognition of Activities of Daily Living (ADL). ADL may look very similar and often necessitate to model fine-grained details to distinguish them. Because the recent 3D ConvNets are too rigid to capture the subtle visual patterns across an action, this research direction is dominated by methods combining RGB and 3D Poses. But the cost of computing 3D poses from RGB stream is high in the absence of appropriate sensors. This limits the usage of aforementioned approaches in real-world applications requiring low latency. Then, how to best take advantage of 3D Poses for recognizing ADL? To this end, we propose an extension of a pose driven attention mechanism: Video-Pose Network (VPN), exploring two distinct directions. One is to transfer the Pose knowledge into RGB through a feature-level distillation and the other towards mimicking pose driven attention through an attention-level distillation. Finally, these two approaches are integrated into a single model, we call VPN++. We show that VPN++ is not only effective but also provides a high speed up and high resilience to noisy Poses. VPN++, with or without 3D Poses, outperforms the representative baselines on 4 public datasets. Code is available at https://github.com/srijandas07/vpnplusplus.
Deep-Lock: Secure Authorization for Deep Neural Networks

Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models. Prevention of IP theft and unauthorized usage of such DNN models has been raised as of significant concern by industry. In this paper, we address the problem of preventing unauthorized usage of DNN models by proposing a generic and lightweight key-based model-locking scheme, which ensures that a locked model functions correctly only upon applying the correct secret key. The proposed scheme, known as Deep-Lock, utilizes S-Boxes with good security properties to encrypt each parameter of a trained DNN model with secret keys generated from a master key via a key scheduling algorithm. The resulting dense network of encrypted weights is found robust against model fine-tuning attacks. Finally, Deep-Lock does not require any intervention in the structure and training of the DNN models, making it applicable for all existing software and hardware implementations of DNN.
Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling

In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts. English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments. With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs. We identify several systematic errors in the original data, and propose to also propagate adjuncts. We observe high inter-annotator agreement for this semantic annotation task. Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English. We show that learning propagation rules is more effective than hand-designing heuristic rules. When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelinesusing a basic-layer tree parser plus converters.
Repairing Pronouns in Translation with BERT-Based Post-Editing

Pronouns are important determinants of a text's meaning but difficult to translate. This is because pronoun choice can depend on entities described in previous sentences, and in some languages pronouns may be dropped when the referent is inferrable from the context. These issues can lead Neural Machine Translation (NMT) systems to make critical errors on pronouns that impair intelligibility and even reinforce gender bias. We investigate the severity of this pronoun issue, showing that (1) in some domains, pronoun choice can account for more than half of a NMT systems' errors, and (2) pronouns have a disproportionately large impact on perceived translation quality. We then investigate a possible solution: fine-tuning BERT on a pronoun prediction task using chunks of source-side sentences, then using the resulting classifier to repair the translations of an existing NMT model. We offer an initial case study of this approach for the Japanese-English language pair, observing that a small number of translations are significantly improved according to human evaluators.
MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization

This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel. We focus on improving information sharing between agents and propose a new multi-agent actor-critic method called \textit{Multi-Agent Cooperative Recurrent Proximal Policy Optimization} (MACRPO). We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in critic's network architecture and propose a new framework to use a meta-trajectory to train the recurrent layer. This allows the network to learn the cooperation and dynamics of interactions between agents, and also handle partial observability. Second, we propose a new advantage function that incorporates other agents' rewards and value functions. We evaluate our algorithm on three challenging multi-agent environments with continuous and discrete action spaces, Deepdrive-Zero, Multi-Walker, and Particle environment. We compare the results with several ablations and state-of-the-art multi-agent algorithms such as QMIX and MADDPG and also single-agent methods with shared parameters between agents such as IMPALA and APEX. The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.
Bayesian Elicitation

We study how a decision-maker can acquire more information from an agent by reducing her own ability to observe what the agent transmits. In a large class of binary-action games, opacity design is just as good as full commitment to actions and also guarantees that ex ante information acquisition always benefits the receiver, even though without opacity design this learning might actually lower the receiver's expected payoff.
Empirical Analysis of Fund Index Volatility Based on Conditional Heteroscedasticity Model

In order to study the changes of China's fund market, this paper carries out time series modeling and fitting prediction on the series based on the monthly series data of Shanghai Securities Fund Index from January 2010 to December 2019.EGARCH (1,1) model has a good fitting effect on Shanghai Securities Fund Index series, all parameters are not 0, and the residual series of the model is tested to obey the standard normal distribution.Finally, the fitted model is used to predict the Shanghai Securities Fund Index from January to May 2020, and compare it with the real value to test the accuracy of the model.The results show that the actual values are within the prediction interval of 95% confidence coefficient, and the fitting effect of the model is superior.
Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF

The article introduces a new set of Polish word embeddings, built using KGR10 corpus, which contains more than 4 billion words. These embeddings are evaluated in the problem of recognition of temporal expressions (timexes) for the Polish language. We described the process of KGR10 corpus creation and a new approach to the recognition problem using Bidirectional Long-Short Term Memory (BiLSTM) network with additional CRF layer, where specific embeddings are essential. We presented experiments and conclusions drawn from them.
Learning Free-Form Deformation for 3D Face Reconstruction from In-The-Wild Images

The 3D Morphable Model (3DMM), which is a Principal Component Analysis (PCA) based statistical model that represents a 3D face using linear basis functions, has shown promising results for reconstructing 3D faces from single-view in-the-wild images. However, 3DMM has restricted representation power due to the limited number of 3D scans and the global linear basis. To address the limitations of 3DMM, we propose a straightforward learning-based method that reconstructs a 3D face mesh through Free-Form Deformation (FFD) for the first time. FFD is a geometric modeling method that embeds a reference mesh within a parallelepiped grid and deforms the mesh by moving the sparse control points of the grid. As FFD is based on mathematically defined basis functions, it has no limitation in representation power. Thus, we can recover accurate 3D face meshes by estimating appropriate deviation of control points as deformation parameters. Although both 3DMM and FFD are parametric models, it is difficult to predict the effect of the 3DMM parameters on the face shape, while the deformation parameters of FFD are interpretable in terms of their effect on the final shape of the mesh. This practical advantage of FFD allows the resulting mesh and control points to serve as a good starting point for 3D face modeling, in that ordinary users can fine-tune the mesh by using widely available 3D software tools. Experiments on multiple datasets demonstrate how our method successfully estimates the 3D face geometry and facial expressions from 2D face images, achieving comparable performance to the state-of-the-art methods.
Improving readout in quantum simulations with repetition codes

Near term quantum computers suffer from the presence of different noise sources. In order to mitigate for this effect and acquire results with significantly better accuracy, there is the urge of designing efficient error correction or error mitigation schemes. The cost of such techniques is usually high in terms of resource requirements, either in hardware or at the algorithmic level. In this work, we follow a pragmatic approach and we use repetition codes as scalable schemes with the potential to provide more accurate solutions to problems of interest in quantum chemistry and physics. We investigate different repetition code layouts and we propose a circular repetition scheme with connectivity requirements that are native on IBM Quantum hardware. We showcase our approach in multiple IBM Quantum devices and validate our results using a simplified theoretical noise model. We highlight the effect of using the proposed scheme in an electronic structure VQE calculation and in the simulation of time evolution for a quantum Ising model.
Entropic Regularisation of Robust Optimal Transport

Grogan et al [11,12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al's formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals.
Looking beyond the horizon: Evaluation of four compact visualization techniques for time series in a spatial context

Visualizing time series in a dense spatial context such as a geographical map is a challenging task, which requires careful balance between the amount of depicted data and perceptual precision. Horizon graphs are a well-known technique for compactly representing time series data. They provide fine details while simultaneously giving an overview of the data where extrema are emphasized. Horizon graphs compress the vertical resolution of the individual line graphs, but they do not affect the horizontal resolution. We present two variations of a new visualization technique called collapsed horizon graphs which extend the idea of horizon graphs to two dimensions. Our main contribution is a quantitative evaluation that experimentally compares four visualization techniques with high visual information resolution (compact boxplots, horizon graphs, collapsed horizon graphs, and braided collapsed horizon graphs). The experiment investigates the performance of these techniques across tasks addressing both individual graphs as well as groups of adjacent graphs. Compact boxplots consistently provide good results for all tasks, horizon graphs excel, for instance, in maximum tasks but underperform in trend detection. Collapsed horizon graphs shine in certain tasks in which an increased horizontal resolution is beneficial. Moreover, our results indicate that the visual complexity of the techniques highly affects users' confidence and perceived task difficulty.
Stone Tools Illustrations with Vector Art: The &amp;#39;STIVA&amp;#39; Method v2

Lithic illustrations are often used in scientific publications to efficiently communicate the technological and morphological characteristics of stone tools. They offer invaluable information and insights not only on how stone raw materials were transformed into their final form, but also on the individuals that made them. Here, the “Stone Tools Illustrations with Vector Art” (STIVA) Method is presented, which involves the illustration of lithic artefacts using vectorial graphics software (Adobe® Illustrator®). This protocol follows an optimised step-by-step method, presenting ten major sections that constitute the creation of a lithic illustration: photography, vectorial software configuration, scale, outline, scar borders, ripples, cortex, symbols, composition, and export. This method has been developed to allow researchers, students and educators to create clear and competent illustrations for any application, from scientific publications to public outreach. (The last step contains a supplemental video with extra context and tips, as part of the protocols.io Spotlight series, featuring conversations with protocol authors.)
Impartial games with entailing moves

Combinatorial Game Theory has also been called `additive game theory', whenever the analysis involves sums of independent game components. Such {\em disjunctive sums} invoke comparison between games, which allows abstract values to be assigned to them. However, there are rulesets with {\em entailing moves} that break the alternating play axiom and/or restrict the other player's options within the disjunctive sum components. These situations are exemplified in the literature by a ruleset such as {\sc nimstring}, a normal play variation of the classical children's game {\sc dots \& boxes}, and {\sc top~entails}, an elegant ruleset introduced in the classical work Winning Ways, by Berlekamp Conway and Guy. Such rulesets fall outside the scope of the established normal play theory. Here, we axiomatize normal play via two new terminating games, $\infty$ (Left wins) and $\overline\infty$ (Right wins), and a more general theory is achieved. We define {\em affine impartial}, which extends classical impartial games, and we analyze their algebra by extending the established Sprague-Grundy theory, with an accompanying minimum excluded rule. Solutions of {\sc nimstring} and {\sc top~entails} are given to illustrate the theory.
EMERGING CLOUD COMPUTING SECURITY ISSUES

As cloud computing continues to grow in IT industry, additional challenges and issues are being responsible by users.Deploying cloud computing in corporation brings important security considerations.Sure-fire execution of cloud computing in an organization needs correct designing and understanding of risk, rising threats and countermeasures.These forms of concerns arise from the actual fact that the data is kept over a collection of network resources, permitting the information to be accessed through virtual machines.Security is one all the foremost debated problems in cloud computing filed.Plenty of organizations scrutinize cloud computing hesitatingly thanks to security risk.As there's increasing use of cloud computing, security becomes an issue.During the analysis of this paper we aim to grasp rising threats in cloud computing.This paper introduces the concepts, architecture, varied security threats and to produce a higher understanding of challenges in cloud computing and propose available/offered solutions to them.
DETECTION OF RECORDS OF WEAK LOCAL EARTHQUAKES USING MACHINE LEARNING

In this paper, a detection method is proposed that is able to outperform deterministic methods in detecting events on seismograms, successfully detecting the same or more events with fewer false detections.
JURNAL TEORI BERMAIN DAN PERMAINAN ALAT MUSIK ANAK USIA DINI

The problem of this research was children low language ability development. The objective of this research was to find out the correlation between role playing and children language ability in group B. Research used quantitative correlational methode. This research was conducted in Al-Hidayah kindergarten school in Kalirejo Lampung with population of 81 children. The 30 respondent were taken by using purposive sampling technique. Data were collected by observations and documentations and analyzed by using Spearman’s Rank correlation technique. The result showed that there was a correlation between role playing and children language ability development.
The Effects of Rewards and Ice-Breaking on Students' Learning Motivation at a Rural Public Elementary School in Lampung, Indonesia

The main concern in this study is the low learning motivation of primary school children in Rejomulyo Village, which is brought on by the repetitive nature of the curriculum and the dearth of educators with a range of teaching styles. Offering incentives and icebreakers during lessons is one technique to enhance students' motivation to study. The goal of this study was to investigate whether rewarding and ice-breaking had any impact on their motivation to learn in Rejomulyo Village's public elementary school students, Lampung, Indonesia. This study was conducted in a quasi-experimental manner. A questionnaire and documentation methods were used in the data collection process. A t- test analysis was the method of data analysis employed in this study (independent sample t-test). The test's calculations reveal a strong impact of rewards and ice-breaking on the motivation to learn of Rejomulyo Village's elementary school students.