Reviewerly/exHarmony huggingface dataset:
Research on English Teaching Based on Multimedia-assisted Teaching

The multimedia-assisted college English teaching model is student-centered and effectively integrates traditional classroom teaching and network resources. It not only gives full play to the guiding role of teachers, but also greatly stimulates students' subjective initiative and improves students' English, especially listening and speaking ability has been significantly improved, which has significantly improved the teaching effect of college English. This article briefly introduces the important position of multimedia computer-assisted language teaching in the current English teaching reform from the perspective of constructivism, and conducts a preliminary discussion on the necessity and enlightenment of this teaching mode in English teaching, hoping that the multimedia teaching environment can be more effective It promotes the improvement of students' English communicative ability, so that students become autonomous learners and lifelong learners.
Stochastic alternating direction method of multipliers for Byzantine-robust distributed learning

This paper aims to solve a distributed learning problem under Byzantine attacks. In the underlying distributed master-worker architecture, there exist a number of unknown but malicious workers that can send arbitrary messages to the master to deviate the learning process, called Byzantine workers. In the literature, a total variation (TV) norm-penalized approximation formulation has been investigated to alleviate the effect of Byzantine attacks. To be specific, the TV norm penalty not only forces the local variables at the regular workers to be close, but is robust to the outliers sent by the Byzantine workers as well. For handling the separable TV norm-penalized approximation formulation, we propose a Byzantine-robust stochastic alternating direction method of multipliers (ADMM). Theoretically, we prove that the proposed method converges to a bounded neighborhood of the optimal solution at a rate of O(1/k) under mild assumptions, where k is the number of iterations and the size of neighborhood is determined by the number of Byzantine workers. Numerical experiments on the MNIST and COVERTYPE datasets further demonstrate the effectiveness of the proposed method to various Byzantine attacks.
Structural and parametric synthesis of population algorithms for global optimization

From year to year, the computational complexity and dimension of applied global optimization problems is growing rapidly. This trend requires the development of new and increased efficiency of well-known global optimization algorithms. One of the most effective classes of such algorithms are intelligent population algorithms (P-algorithms). Due to the large and ever-increasing number of such algorithms, the development of algorithmic and software for their automated synthesis is relevant. We show that P-algorithms have a similar modular structure. This makes it possible to obtain a large number of variants of algorithms by combining the operators of various algorithms. P-algorithms also contain of a large number of free parameters in them. So, its efficiency can vary significantly depending on their values, while formal rules for choosing the values of these parameters are usually absent. These circumstances are the basis for posing the problem of structural-parametric meta-optimization of P-algorithms. We present the formulation of the problem of structural-parametric synthesis of P-algorithms. We offer a methodology for solving this problem, as well as present a prototype software system that implements this technique. In addition, we show in the work some results of computational experiments. This work was supported by the RFBR grant 18-07-00341.
Academic, social, and cultural learning in the French #bac2018 Twitter hashtag

Despite the continued use of social media in educational contexts, there remains skepticism about whether platforms like Twitter can actually contribute to learning. In this paper, we argue that such skepticism is based on an overly narrow conception of learning that focuses on academic performance and disregards other manifestations. To advance this argument, we document use of the #bac2018 Twitter hashtag in the month leading up to the 2018 baccalauréat exams (the "bac"), which are significant not only for their role in the French educational system but also for their connections with broader French society and culture. We found that participants engaged in sharing notes; slacking, doubting, and fearing; requesting retweets; preferring topics; complaining; connecting with bac heritage and experience; joking; and showing awareness of time. In keeping with the significance of the bac, we found that these practices within the #bac2018 hashtag were associated with not only academic learning but also social and cultural practices that are significant despite their absence from any formal curriculum. These findings underline the complexity and richness that characterizes learning-especially in digital contexts.
Markov-switching state-space models with applications to neuroimaging

State-space models (SSM) with Markov switching offer a powerful framework for detecting multiple regimes in time series, analyzing mutual dependence and dynamics within regimes, and assessing transitions between regimes. These models however present considerable computational challenges due to the exponential number of possible regime sequences to account for. In addition, high dimensionality of time series can hinder likelihood-based inference. To address these challenges, novel statistical methods for Markov-switching SSMs are proposed using maximum likelihood estimation, Expectation-Maximization (EM), and parametric bootstrap. Solutions are developed for initializing the EM algorithm, accelerating convergence, and conducting inference. These methods, which are ideally suited to massive spatio-temporal data such as brain signals, are evaluated in simulations and applications to EEG studies of epilepsy and of motor imagery are presented.
PP-PG: Combining Parameter Perturbation with Policy Gradient Methods for Effective and Efficient Explorations in Deep Reinforcement Learning

Efficient and stable exploration remains a key challenge for deep reinforcement learning (DRL) operating in high-dimensional action and state spaces. Recently, a more promising approach by combining the exploration in the action space with the exploration in the parameters space has been proposed to get the best of both methods. In this article, we propose a new iterative and close-loop framework by combining the evolutionary algorithm (EA), which does explorations in a gradient-free manner directly in the parameters space with an actor-critic, and the deep deterministic policy gradient (DDPG) reinforcement learning algorithm, which does explorations in a gradient-based manner in the action space to make these two methods cooperate in a more balanced and efficient way. In our framework, the policies represented by the EA population (the parametric perturbation part) can evolve in a guided manner by utilizing the gradient information provided by the DDPG and the policy gradient part (DDPG) is used only as a fine-tuning tool for the best individual in the EA population to improve the sample efficiency. In particular, we propose a criterion to determine the training steps required for the DDPG to ensure that useful gradient information can be generated from the EA generated samples and the DDPG and EA part can work together in a more balanced way during each generation. Furthermore, within the DDPG part, our algorithm can flexibly switch between fine-tuning the same previous RL-Actor and fine-tuning a new one generated by the EA according to different situations to further improve the efficiency. Experiments on a range of challenging continuous control benchmarks demonstrate that our algorithm outperforms related works and offers a satisfactory trade-off between stability and sample efficiency.
Model Reuse With Reduced Kernel Mean Embedding Specification

Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">specification</i> for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification. Theoretical results and extensive experiments validate the effectiveness of our approach.
Wide-Sense Stationary Policy Optimization with Bellman Residual on Video Games

Deep Reinforcement Learning (DRL) has an increasing application in video games. However, it usually suffers from unstable training, low sampling efficiency, etc. Under the assumption that Bellman residual follows a stationary random process when the training process is convergent, we propose the Wide-sense Stationary Policy Optimization (WSPO) framework, which leverages the Wasserstein distance from the Bellman Residual Distribution (BRD) between two adjacent time steps, to stabilize the training stage and improve the sampling efficiency. We minimize the Wasserstein distance with Quantile Regression, where the specific form of BRD is not needed. Finally, we combine WSPO with Advantage Actor-Critic (A2C) algorithm and Deep Deterministic Policy Gradient (DDPG) algorithm. We evaluate WSPO on Atari 2600 video games and continuous control tasks, illustrating that WSPO compares or outperforms the state-of-the-art algorithms we tested.
A Study on Dengue Fever in Bangladesh: Predicting the Probability of Dengue Infection with External Behavior with Machine Learning

The “2019 Dengue Outbreak” was a nationwide pandemic situation in Bangladesh, particularly in Dhaka city. About 179 people died and 101,354 confirmed dengue cases were found all over the country. The developing countries like Bangladesh have some limitations in the medical sector and many people don't get proper treatment in time. Henceforth, this research work has attempted to predict the chances to get infected with dengue fever from some external behaviors, like-fever, pain, sitophobia, headache etc. This article has demonstrated a model to predict the probability of dengue fever before taking the pathological test. So, the suspective patient may get some initial diagnosis by giving their anatomical symptoms as input and further this will decrease the dependency on the pathological test for acquiring the primary treatment. Different machine learning models are used to predict the probability and an accuracy near to 100% has been achieved finally.
Applications of Association Rule Mining Algorithms in Deep Learning

This paper focuses on the different association rule mining algorithms and their applications in modern fields of deep learning and neural networks which form the pillar stones of new age problem solving. Association rule mining algorithms are categorized under "if–then" category as they have an antecedent (if) and a consequent (then). Deep learning is a machine learning technique which uses neural networks to pass the input to obtain outputs. The field of deep learning has become ubiquitous in all fields of problem solving due to its ability to accept raw inputs without feature extraction but this leads to overfitting. The algorithms Apriori and FPGrowth help in finding an association between features of the input which then can be reduced to a handful of features which then can be given to deep learning models. This paper attempts to explain these algorithms and their use in deep learning.
Towards autonomous semantic stream fusion for distributed video streams

Video streams are becoming ubiquitous in smart cities and traffic monitoring. Recent advances in computer vision with deep neural networks enable querying a rich set of visual features from these video streams. However, it is challenging to deploy these queries on edge devices due to the resource intensive nature of the computing operations of this sort. Hence, this paper will demonstrate our approach in pushing these computing operations closer to the video stream sources via autonomous stream fusion agents. These agents will facilitate an edge computing paradigm that enables edge devices to utilize its computing resources to serve federated queries over video streams. Our demonstration shows that edge devices can significantly alleviate the bottleneck of the centralized server in dealing with distributed video streams.
Policy Evaluation and Seeking for Multiagent Reinforcement Learning via Best Response

Multiagent policy evaluation and seeking are long-standing challenges in developing theories for multiagent reinforcement learning (MARL), due to multidimensional learning goals, nonstationary environment, and scalability issues in the joint policy space. This article introduces two metrics grounded on a game-theoretic solution concept called sink equilibrium, for the evaluation, ranking, and computation of policies in multiagent learning. We adopt strict best response dynamics (SBRDs) to model selfish behaviors at a meta-level for MARL. Our approach can deal with dynamical cyclical behaviors (unlike approaches based on Nash equilibria and Elo ratings), and is more compatible with single-agent reinforcement learning than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -rank, which relies on weakly better responses. We first consider settings where the difference between the largest and second largest equilibrium metric has a known lower bound. With this knowledge, we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory. We then consider settings where the lower bound for the difference is unknown. For this setting, we propose a class of perturbed SBRD such that the metrics of the policies observed with nonzero probability differ from the optimal by any given tolerance. The proposed perturbed SBRD addresses the scalability issue and opponent-induced nonstationarity by fixing the strategies of others for the learning agent, and uses empirical game-theoretic analysis to estimate payoffs for each strategy profile obtained due to the perturbation.
Development of Cryptocurrency in the Indonesian Economy

Cryptocurrency has now become a phenomenon both in the world and in Indonesia. Many investors are starting to look to invest in Cryptocurrency. However, is it safe to invest in cryptocurrency, is it promising, and what are the risks. Whether cryptocurrency has been officially recognized by the government and has been regulated in certain regulations. These questions are what we try to discuss in our study. This research is a descriptive qualitative study using primary and secondary data. Our primary data is summarized from interviews with practitioners in the cryptocurrency field and also the government regulatory body that regulates this matter. Meanwhile, we obtained secondary data from government regulations and preliminary research papers published internationally. Our research results state that cryptocurrency in Indonesia is currently still considered a commodity compared to a means of payment, even though there are government regulations regarding cryptocurrency. As a commodity, cryptocurrency has the potential to become a superior commodity like gold. However, there are also threats and challenges related to cryptocurrency, such as hacker threats and exchange rate volatility.
Detection of Blood Cancer-Leukemia using K-means Algorithm

Blood cancer (Leukemia) is one of the leading causes of death among humans. The pace of healing depends mainly on early detection and diagnosis of a disease. The main reason behind occurrence of Leukemia is when bone marrow produces a lot of abnormal white blood cells this happens. Microscopic study on images is done by Hematologists who make use of human blood samples, from which it leads to the requirement of following methods, which are microscopic color imaging, image segmentation, clustering and classification which allows easy identification of patients suffering from this disease. Microscopic imaging allows for various methods of detecting blood cancer in visible and immature white blood cells. Identifying Leukemia early and quickly greatly helps practitioners in providing appropriate treatment to patients. Initially to start with, Segmentation stage is achieved by segregating white blood cells from other blood components i.e. erythrocytes and platelets by using Statistical parameters such as mean, standard deviation. For diagnosing prediction of Leukemia, geometrical features such as area, perimeter of the white blood cell nucleuses investigated. In the proposed methodology we make use of K-means, for identifying cancerous stages and its early detection. Experimentation and results were found to be promising with the accuracy of 90% identification of the cancer cells.
Simultaneous Localization and Mapping Technique Based Autonomous Lawn Mower

The objective is to design and fabricate an autonomous lawn mower with the of simultaneous localization and mapping technique so that the lawn mower can map its location and mow the lawn without the human intervention. The heart of the mower is electric powered dc motors. The dc motors given to the wheels and blade of the mower are powered by a 12v battery. Using Kinect module the concept of slam has been introduced in the lawn mower. In addition to the slam, the lawn mower can also avoid obstacles with the help of ultrasonic sensor. The microcontroller used here is the raspberry pi which along with some motor drivers to limit the speed of the dc motors and also in controlling the mower to avoid obstacles with the feedback from the ultrasonic sensor. By comparing the average human walking speed the speed of the motors for the wheels have been fixed. As the mower is complete electrical there is no risk of pollution as like in engine driven mowers.
Where is our data? A Blockchain-based Information Chain of Custody Model for Privacy Improvement

The advancement of Information and Communication Technologies has brought numerous facilities and benefits to society. In this environment, surrounded by technologies, data, and personal information, have become an essential and coveted tool for many sectors. In this scenario, where a large amount of data has been collected, stored, and shared, privacy concerns arise, especially when dealing with sensitive data such as health data. The information owner generally has no control over his information, which can bring serious consequences such as increases in health insurance prices or put the individual in an uncomfortable situation with disclosing his physical or mental health. While privacy regulations, like the General Data Protection Regulation (GDPR), make it clear that the information owner must have full control and management over their data, disparities have been observed in most systems and platforms. Therefore, they are often not able to give consent or have control and management over their data. For the users to exercise their right to privacy and have sufficient control over their data, they must know everything that happens to them, where their data is, and where they have been. It is necessary that the entire life cycle, from generation to deletion of data, is managed by its owner. To this end, this article presents an Information Chain of Custody Model based on Blockchain technology, which allows from the traceability of information to the offer of tools that will enable the effective management of data, offering total control to its owner. The result showed that the prototype was very useful in the traceability of the information. With that it became clear the technical feasibility of this research.
Speckle pattern creation methods for two‐dimensional digital image correlation strain measurements applied to mechanical tensile tests up to 700°C

Abstract The purpose of this study is to develop novel speckle pattern techniques for digital image correlation (DIC) kinematic measurements of mechanical tests at high temperatures, typically from 400 to 700°C. In this context, the speckle pattern should not only meet morphological criteria (size, density, distance) in order to improve spatial resolution, but it should also present a high contrast and resist high temperature and strain levels. To find a speckle pattern matching these specifications, a comparison was performed on six types of speckle made using different techniques. First, a computer‐generated speckle pattern that meets DIC criteria was numerically designed to produce six types of speckle pattern. Next, the speckle patterns produced using these six techniques were compared in terms of speckle morphology, image quality and adherence to titanium alloy TA6V material at high temperatures. From 25 to 600°C, the speckle pattern made by the technique combining anodisation and laser engraving named M5 technique gave the best contrast (highest value of mean intensity gradient [MIG] and Shannon entropy value) and the adherence of 200% of strain measurements to the TA6V material. At 700°C, speckle image quality is considerably reduced due to oxidation of the titanium alloy, and this may not be suitable for DIC measurements. Only the speckles produced by painting in which the paint plays a protective role provide with a better speckle contrast compared with other techniques. However, these speckle patterns enable only a strain measurement of 22% by the DIC method. This article concludes with guidelines for producing a speckle pattern suitable for high‐temperature mechanical tests.
Ternary and binary representation of coordinate and momentum in quantum mechanics

To simulate a quantum system with continuous degrees of freedom on a quantum computer based on quantum digits, it is necessary to reduce continuous observables (primarily coordinates and momenta) to discrete observables. We consider this problem based on expanding quantum observables in series in powers of two and three analogous to the binary and ternary representations of real numbers. The coefficients of the series ("digits") a re, t herefore, Hermitian o perators. We investigate the corresponding quantum mechanical operators and the relations between them and show that the binary and ternary expansions of quantum observables automatically lead to renormalization of some divergent integrals and series (giving them finite values).
A Blockchain-Based Distributed Storage Network to Manage Growing Data Storage Needs

IoT industry has come to the fore in this modern techno-frenzy age. This has caused digital data explosion and has kept the data storage industry on its toes. The paper proposes a novel blockchain-based data storage model where users contribute the storage space of their personal electronic devices to meet the growing data storage needs. The paper dives into a decentralized system design, along with an equitable compensation model for all the storage space contributing users. The decentralized framework provides a common platform for interaction between the storage space contributors and buyers. The proposed solution provides an alternative to cloud storage which relies heavily on servers.