Reviewerly/exHarmony huggingface dataset:
Using of Open-Source Technologies for the Design and Development of a Speech Processing System Based on Stemming Methods

This article discusses the idea of developing an intelligent and customizable automated system for real-time text and voice dialogs with the user. This system can be used for almost any subject area, for example, to create an automated robot - a call center operator or smart chat bots, assistants, and so on. This article presents the developed flexible architecture of the proposed system. The system has many independent submodules. These modules work as interacting microservices and use several speech recognition schemes, including a decision support submodule, third-party speech recognition systems and a post-processing subsystem. In this paper, the post-processing module of the recognized text is presented in detail on the example of Russian and English dictionary models. The proposed submodule also uses several processing steps, including the use of various stemming methods, the use of word stop-lists or other lexical structures, the use of stochastic keyword ranking using a weight table, etc.
SolarNet: A Deep Convolutional Neural Network for Solar Forecasting via Sky Images

The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, especially short-term solar forecasting (STSF), has been adopted to assist power system operations. The STSF takes inputs from various sources, among which sky image-based STSF is not yet well-studied. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the 10-min ahead GHI by only using sky images without numerical measurements and extra feature engineering. The SolarNet, composed of 21 convolutional, max-pooling, and fully-connected layers, learns latent patterns between sky images and global horizontal irradiance (GHI) in an end-to-end manner. Numerical results based on ten years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models by up to 11.88% under various weather conditions.
A mathematical approach for creative graphics design

Mathematics is most beautiful thing in our daily life but when it comes to equations and formulae people seems to be unpleasant. This equations and formulae exhibits interactive properties and relation in our daily life. It is sure that the almost all beautiful thing in our nature even Universe unveils mathematical properties. These properties produce many patterns and shape and is directly related to geometric algebra as we can see in nature for example, fluorescent green beetle and colourful galaxies. These gorgeous properties of nature can be transformed into equations and logics. Therefore, the main aim of this paper is to generate creative graphics using mathematical equations such as number theory, position vectors and trigonometry functions. The sectional organization of this paper is as follows: section 1 gives the introduction of interactive graphic design and related work. Mathematical approach for graphic design has been briefed in section 2. In section 2 adopted algorithms and Graphical visualization of design is given. Graphic designs are applied to Indian ethnic wear and T-shirts. Last Section gives the conclusions and further scope of this work.
An instructional design for vocabulary acquisition with a hidden disability of dyslexia

The aim of the study is to design an effective instruction build on the Dick and Carey Model and game-based learning that enables an undiagnosed language learner of dyslexia, facing problems in vocabulary acquisition to acquire a pre-defined number of vocabularies in a given time. The method of the curriculum development study is based on a case study in a real-life context with quantitative and qualitative evidence that relies on multiple data collection tools such as checklist, interviews, questionnaire and report cards of the learner. The results of the study demonstrated that the capability of the dyslexic learner can be increased to acquire 55 vocabularies in a week, the same number expected from their peers. The performance increase of the learner can be attributed to a new method of learning English vocabulary through game-based learning supported with spaced repetition.&#x0D; Keywords: Instructional design, learning disability, dyslexic learners, game-based learning.
Off-Policy Recommendation System Without Exploration

Recommendation System (RS) can be treated as an intelligent agent which aims to generate policy maximizing customers’ long term satisfaction. Off-policy reinforcement learning methods based on Q-learning and actor-critic methods are commonly used to train RS. Though these methods can leverage previously collected dataset for sampling efficient training, they are sensitive to the distribution of off-policy data and make limited progress unless more on-policy data are collected. However, allowing a badly-trained RS to interact with customers can result in unpredictable loss. Therefore, it is highly desirable that the off-policy method can stably train an RS when the off-policy data is fixed and there is no further interaction with the environment. To fulfill these requirements, we devise a novel method name Generator Constrained Q-learning (GCQ). GCQ additionally trains an action generator via supervised learning. The generator is used to mimic data distribution and stabilize the performance of recommendation policy. Empirical studies show that the proposed method outperforms state-of-the-art techniques on both offline and simulated online environments.
Weighted Scoring in Geometric Space for Decision Tree Ensemble

In order to improve the classification performance of a single classification model, Multiple Classifier Systems (MCS) are used. One of the most common techniques utilizing multiple decision trees is the random forest, where diversity between base classifiers is obtained by bagging the training dataset. In this paper, we propose the algorithm that uses horizontal partitioning the learning set and uses decision trees as base models to obtain decision regions. In the proposed approach feature space is divided into disjoint subspace. Additionally, the location of the subspace centroids, as well as the size and location of decision regions, are used in order to determine the weights needed in the last process of creating MCS, i.e. in the integration phase. The proposed algorithm was evaluated employing multiple open-source benchmarking datasets, compared using accuracy and Matthews correlation coefficient performance measures with two existing MCS methods - random forest and majority voting. The statistical analysis confirms an improvement in recognition compared to the random forest. In addition, we proved that for infinitely dense space division proposed algorithm is equivalent to majority voting.
Guide to image editing and production of figures for scientific publications with an emphasis on taxonomy Image editing for scientific publications

Figures for scientific publications go through various stages from the planning, to the capturing of images, to the production of finished figures for publication. This guide is meant to familiarise the reader with the main image-editing software used by professional photographers. The guide’s focus is on digital photo editing and the production of figures using Adobe Photoshop to produce publication-quality figures for scientific publications. This guide will be of fundamental use for the academic public, especially taxonomists and others who work with images. Besides, it should be useful for anyone interested in becoming familiar with the basic tools of image editing.
FAKTOR-FAKTOR DETERMINAN YANG MEMPENGARUHI PRESTASI BELAJAR SISWA

The purpose of this study was to determine the determinants that influence student learning achievement. The population in this study was class XII students, totaling 521 people. Sampling in this study was random sampling from 52 students. This research uses quantitative methods. Data collection is done by questionnaire, and documentation. Data were analyzed using inferential statistics, hypothesis testing with multiple linear regression, t test and f test. From the results of this study it can be concluded that 1) there is an influence of learning motivation on learning achievement, 2) there is an influence of learning interest on learning achievement, 3) there is no influence on the economic condition of the family on learning. achievement, 4) there is an influence on the level of parental education on learning achievement, 5) there is an influence on learning motivation, learning interest, family economic situation, parental education level on learning achievement.The implication for educators is to always motivate, and develop students' interest in learning and achievement.
Analyzing the Surprising Variability in Word Embedding Stability Across Languages

Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages.To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages.We discuss linguistic properties that are related to stability, drawing out insights about correlations with affixing, language gender systems, and other features.This has implications for embedding use, particularly in research that uses them to study language trends.
Data Hiding using Meaningful Encryption Algorithm to Enhance Data Security

Encryption allows you to securely ensure confidentiality.The secrecy of embedded data is essential in the field of data security and privacy.Hence, the data should be hidden from the ones who intend to corrupt it or use it in unauthorized manner.That's why it is important to enhance the secrecy of the encrypted data, hide the existence of confidential data and ensure data security while device to device data transfers.Only the owner should have the access to the information.If we are ever being watched, inadvertently or not, here we hide our data by applying symmetric encryption in such a manner that encrypted data is often represented in non-understandable language and easily identified as an encrypted form.The scheme makes use of global database system for information hiding and also overcome limitation of Steganography .This paper discuss about enhancement of communication security by embedding secret messages into an inconspicuous carrier and thereby transmitting them to receivers.
InformationBased Hierarchical Brain Organization/ Evolution from the Perspective of the Informational Model of Consciousness

Hypertension is one of the components of non-communicable diseases (NCDs). It is the primary risk factor for cardiovascular disease, including stroke, heart failure, heart attack and aneurysm. The causes of hypertension are diabetes, kidney diseases, nerve damage, overactive thyroid gland, sleep apnoea, pregnancy, obesity, etc. The problem of hypertension is more prevalent among elderly people worldwide and by 2025 the majority of the elderly people will reside in developing countries [1-3]. The disease is rapidly emerging in developing countries [4,5].
Perceived View of Teachers Towards Pentaksiran Tingkatan Tiga (PT3) (Form Three Assessment) English Language: A Case Study

The Standard-Based Curriculum for Secondary Schools (KSSM) was introduced in secondary schools in 2017. Parallel to the changes in the curriculum, Malaysian ESL (English as a Second Language) was revised to align with the Common European Framework of References (CEFR). Similarly, there is a change in the assessment of form three students. Therefore, this study aims to identify the perceived view of teachers towards ‘Pentaksiran Tingkatan Tiga’ (PT3) (Form Three Assessment) English Language. A qualitative research method using snowball sampling was employed in obtaining the data. This study is designed based on a case study approach. Five TESL (Teaching English as Second Language) teachers were interviewed by using open-ended questions to ascertain their perceived view on the PT3 English language. The findings show that the teachers were generally positive on the CEFR-aligned PT3 English language. However, the teachers indicated that the teaching of CEFR-aligned English language in an ESL classroom must be tailored by the teachers according to the proficiency levels of the students. This study also illustrated that teachers faced challenges of facilities and relevant materials in assessing the students. Further research should employ a larger sample size to obtain a broader perspective on the perceived view of teachers towards PT3 English Language.&#x0D; &#x0D; KEYWORDS: Form Three Assessment (PT3), Pentaksiran Tingkatan Tiga (PT3), Perception of teachers.
DiaNet: An Efficient Multi-Grained Re-configurable Neural Network in Silicon

A hardware friendly topology of neural network is proposed in this work. Instead of full connections between neighbor layers, the bisection-propagation from “parents” to “twins” is performed to retrieve the behaviors of conventional neural network. In this manner, the conventional dense-butshallow topology is organized in sparse-but-deep fashion. A large scale of synapses and neurons array is symmetrically designed with VLSI circuits on-chip. According to specific application demands, the entire array is cut into arbitrary diamond-shape pieces without redundant synapses. Each diamond-cut behaves as an independent neural network for corresponding tasks in fully parallel. Namely, the proposed network-on-chip is multigrained re-configurable by configuring synapse and neuron behavior (fine-grained), reshaping the diamond-cut (mediumgrained), and organizing multiple DiaNets (coarse-grained). To carry out the synapse and neuron computations, a set of analog calculation circuits is designed with 80 MOS transistors for one processing unit including two synapses and one neuron in dual activation-modes of sigmoid and rectified linear function. For proof-of-concept, several case studies of regression tasks with one-, two, and nine-variables are implemented by the proposed network. From the circuit simulation results, all the demonstrated regressions are executed by the compact hardware resource of 720 MOS transistors with the maximum power consumption of 19:4%W. The regression error is about 4:2%, 4:3%, and 1:2% for one-, two-, and nine-variable examples, respectively.
S-Blocks: Lightweight and Trusted Virtual Security Function With SGX

Despite the advantages of scalability and flexibility, Security Function Virtualization (SFV) raises concerns about its own security. To enhance the security of SFV, a promising approach is to run critical components of off-the-shelf security software inside Software Guard Extensions (SGX) enclaves. This idea, however, is hardly practical due to the difficulty of detaching components from the monolithic security function and the unacceptable cost of executing them inside enclaves. In this article, we propose S-Blocks, an architecture to modularize virtual security functions (VSFs) and protect crucial modules with SGX in an efficient manner. S-Blocks decomposes VSFs into trusted and untrusted modules and provides dedicated APIs systematically. Only crucial VSF modules are hardened with enclaves. Furthermore, aiming at addressing state consistency and secure migration issues of security function scaling, we design a fine-grained state synchronization and migration mechanism to ensure loss-free, order-preserving, and state security for VSFs. To demonstrate the effectiveness of our approach, we prototype S-Blocks using Fast-Click on a real Skylake platform and implement three critical types of virtual security functions based on the S-Blocks architecture. Our evaluation results show that S-Blocks only imposes a manageable performance overhead, and low latency and resource consumption when protecting VSFs.
Multi-Classifier Approaches for Supporting Clinical Decision Making

Diagnosis is one of the most important processes in the medical field. Since the knowledge domains of clinical specialties are expanding rapidly in terms of complexity and volume of data, clinicians have, in many cases, difficulties to make an accurate diagnosis. Therefore, intelligent and quantitative support for diagnostic tasks can be effectively exploited for improving the effectiveness of the process and reduce misdiagnosis. In this respect, Multi-Classifier Systems represent one of the most promising approaches within Machine Learning methodologies. This paper proposes a Multi-Classifier Systems framework for supporting diagnostic activities with the aim of improving diagnostic accuracy. The framework uses and combines several classification algorithms by dynamically selecting the most competent classifier according to the test sample and its location in the feature space. Here, we extend our previous research. The new experimental results, compared with several multi classifier techniques, based on dynamic classifier selection, on classification datasets, show that the performance of the proposed framework exceeds the state-of-the-art dynamic classifier selection techniques.
Research on Teacher's Role of Mobile Pedagogy Guided by the Zone of Proximal Development

Mobile pedagogy has been growing rapidly worldwide with the growth of mobile learning. Many countries and higher education institutions promote innovative pedagogical practice with mobile technology set for open online and offline courses. Teacher's role becomes diverse and flexible given by virtual teaching environments and learner-centered mobile learning. Social constructivism provides theoretical support for teachers in the role of scaffolding knowledge to learners in a higher level beyond learners' ability. This paper describes the Zone of Proximal Development, then modified framework for the digital education is presented to illustrate the relationship between teachers in mobile pedagogy and learners in mobile learning. With comparison, teacher's role is clarified in a series of visualized figures. Implication is given on teacher's improvement for innovative pedagogy in the digital era.
Research on Extended Image Data Set Based on Deep Convolution Generative Adversarial Network

The Deep Convolution Generative Adversarial Network (DCGAN) adds the structure of Generative Adversarial Network (GAN) on the basis of the generation countermeasure network, and specially generates image samples. In this paper, DCGAN is used to generate the image which does not belong to MNIST data set, and then, a new data set is obtained. Finally, Convolutional Neural Networks (CNN) [1] is used to test the new data set. We need define an initializer to make the GAN converge better, and use the standard LEAKYRELU function to activate the GAN. The generator is defined by a fully connected layer with an input size of 128. The noise is Gaussian white noise, which uses RELU as the activation function. The discriminator uses two convolution layers, the first one uses RELU as the activation function, and the second layer uses sigmoid function. The results show that the accuracy of the new data set is the same as that of the original data set when tested on CNN, and the method of expanding MNIST data set by using deep convolution is effective.
Deep Learning based Intelligent Surveillance System

In the field of developing innovation, pictures are assuming as an important entity.Almost in all fields, picture base data is considered very beneficial, like in the field of security, facial acknowledgment, or therapeutic imaging, pictures make the existence simple for people.In this paper, an approach for both human detection and classification of single human activity recognition is proposed.We implement the pre-processing technique which is the fusion of the different methods.In the first step, we select the channel, apply the top hat filter, adjust the intensity values, and contrast stretching by threshold values applied to enhance the quality of the image.After pre-processing a weight-based segmentation approach is implemented to detect and compute the frame difference using cumulative mean.A hybrid feature extraction technique is used for the recognition of human action.The extracted features are fused based on serialbased fusion and later on fused features are utilized for classification.To validate the proposed algorithm 4 datasets as HOLLYWOOD, UCF101, HMDB51, and WEIZMANN are used for action recognition.The proposed technique performs better than the existing one.
Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.
Mathematical Model of Formation of a Unified Digital Platform of Scientific and Educational Resources

The article considers a mathematical model that proves the potential possibility of creating a single information Internet space of scientific and educational resources on the basis of a single digital platform, the need for which is due to the rapid increase in the volume of information arrays in educational and scientific activities, the advent of modern digital technologies, and the need for these resources of all layers users.The work analyzes the simulation results and experience in the formation of such a space.The proposed project is the implementation of the ideas of A. Kitov.and academician Glushkov V.M. on the development of the National Automated System (NAS) for the collection and processing of information for accounting, planning and managing the national economy in the USSR in the 60s of the twentieth century in the field of science and education.Such a space should also become an instrument for improving the quality of human capital, its assessment, and the impact on the socio-economic situation in the regions and the country.