Reviewerly/exHarmony huggingface dataset:
Effective Ways to Overcome Classification Limitations for Activities of Daily Livings (ADLs)

Development of reliable and precise methods of Human Activity Recognition (HAR) are highly important, since wrong or inaccurate recognition can cause harmful consequences for human health. Scientists working in the field try to find the ways to enhance achievements for recognition accuracy. Taking this into account, it is vital to choose classifiers which make classification of activities with reliable rates. However, limitations of the currently existing algorithms and inherent lack of precision level can put their applicability to the field under the question. In particular, Hidden Markov Model has certain restrictions, which is caused by the principle of random selection of parameters and it is problematic to discriminate between the classes with high accuracy. Other well-known methods also have some drawbacks due to their nature. With the aim to solve these problems and to ensure the required results, we propose a hybrid complex of classifiers that provide adequate models of the study area. This work will deal with the classification of daily living human activities using wearable inertial sensors. Walking, Lying, Standing up, Stair Ascent, Stair Descent, Sitting on the Ground, etc. are examples of these activities. In this study, a dataset including twelve activities is created using three inertial sensors. Two hybrids of classifiers that combines Bayesian Networks with distance based classifier, namely with k Nearest Neighbor and Neural Network with Hidden Markov Model will be presented in the study. The achieved results will be compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features will be used separately as inputs of the classifier. The inertial sensor units worn by different healthy subjects are placed at key points of upper/lower body limbs (chest, right thigh and left ankle). In this study, only acceleration data is used, as a modality for estimating the activities.
A legal framework for blockchain technology in Brazil

Is it possible to draft legislation for blockchain technology? From this reflection, the research proposes an approach to the regulatory design of blockchain technology. The Federative Republic of Brazil is the paradigm adopted because it is the first time that, in the last twenty years, the government's digital agenda is proving to be efficient and with positive results2. Among the implemented technologies, blockchain technology has some problems related to data integrity and information security in the Brazilian electronic government. However, the research understands that there are records of blockchain technology projects in federal government agencies making each architecture-independent. May lead to possible legal uncertainty among public agencies, citizens, and the private sector. In this case, the biggest challenge is the regulation of blockchain technology in e-government to ensure the legal certainty of processes in public administration. Despite the varied literature on blockchain technology, few studies are focusing on regulation, especially in Brazil. The purpose of this particular initiative is to contribute to the research and practice of the Brazilian e-government in building a legal draft for blockchain technology. As part of the solution, the research finds answers in the computational structure of blockchain technology and the qualitative experience of the Brazilian Civil Rights Framework for the Internet, which is considered an essential regulatory framework for the country and the international community.
Data Visualization Association Algorithm Based on Spatio-Temporal Association Big Data

This paper mainly studies and realizes the spatio-temporal multidimensional visualization under the big data environment. By analyzing the spatiotemporal correlation of wireless sensor networks under the random large-scale dense deployment of sensor nodes, ensure that there is no monitoring blind spot in the area, the accuracy of data sampling and the reliability of communication. By studying the Markov chain’s spatial correlation data prediction algorithm, it solves the problem that nodes need to store a large amount of historical data during prediction, and the requirements for node storage space are relatively high; At the same time, the spatial correlation of the data in the network is analyzed for data prediction, which solves the problem of low prediction accuracy using the time-based correlation prediction method in the case of irregular data fluctuations.
Literature Review of Various Nature-Inspired Optimization Algorithms Used for Digital Watermarking

Today, a tremendous amount of data is transferred online, so there is a need to secure this data. Digital watermarking is a process of embedding some presumed content image or data in any cover data so that the quality of the content should not degrade and it should not be visible to human eyes. This paper describes various characteristics required by any watermarking algorithm and explains some of the optimization algorithms. DWT, DCT and SVD alone are not sufficient for achieving the required robustness, imperceptibility and security of the digital content; some of the optimization algorithms are required to achieve these, so this paper reviews various nature-inspired optimization algorithms used for optimizing the process of digital watermarking and shows a comparative study of these techniques in tabular form.
Bad data identification method of power system based on <scp>co‐evolutionary</scp> symmetric loss function

With the rapid development of power system, the structure and operation mode of power grid are more and more complex, and the identification of bad data in power system is an important basis to judge the operation status and fault of power system. Therefore, an evolutionary symmetric loss function based identification method for power system bad data is proposed. The Karl Pearson coefficient of variation is used to represent the Poisson distribution of power system data. Based on the prior distribution parameters of any symmetric loss function as posterior mean, the coefficient of variation of power system is estimated. Considering coevolutionary coevolution model of coef power system & variational coefficient method, the data based on population density is constructed, and the disturbance is adjusted - the optimization term is added to the model. Based on the optimized coevolutionary algorithm and framework, combined with local evolution adaptive mutation strategy, the population evolution is completed, and the output solution is the result of identification of bad data in power system. The experimental results show that the method has high recognition rate, known bad data and unknown bad data, low packet loss rate, high false alarm rate, and alarm rate in the process of identifying bad data in power system. It shows that the method has small error and high recognition rate.
A camera-based solution for customer behavior identification

Nowaday, automatic or semi-automatic sale becomes the competitive advantage of retail or producer. In particular, understanding customer behaviors can help improve any vending system. In this work, we focus on finding a solution supporting automatic sales from shelves, that can detect what customers want. We try to set up a hypothetical system and propose a camera-based solution that helps identify customer activity. First, the layout of shelf is determined. Then, our system detects and tracks customer hands. While tracking, hand status (holding or non-holding) are also verified to see if the customer take something. Finally, based on hand's status changes, the system know if the customer takes or returns a product. Further, with a camera-based solution, we would like the proposed solution can run on a machine with a modest configure. In our solution, we choose tiny-YoloV3 for hand detection and MobileNetV2 for hand status classification. We evaluated each sub-module on available datasets. The overall performance of the system is manually evaluated on a set of sessions from different customers. We obtained promising results (84% in terms of accuracy) on the test set.
Improved Number Detection For Low Resolution Image Using the Canny Algorithm

Improved number detection at low resolutions using the Canny algorithm in this paper. Data from low resolution numbers are randomly selected for this algorithm's approval. There are a number of problems, image detection rates at low resolutions are needed Methods that can improve edge detection images to get better. In finding the right way to change numbers at low resolution using an algorithm that compares like the Sobel operator, Laplacian operator, and Canny operator. From the experimental results 2 numbers in low resolution are read and clear detection using the Canny operator algorithm as the following images are MSE 3,371 and PSNR 42,852, image 2 is worth MSE 30,060 and PSNR 33,350 with these other algorithms namely Sobel operator and Laplacian operator.
Efficiency Analysis of Collaborative Based Recommendation System

Abstract Over the recent times, there has been great enhancement towards online shopping and platforms that provide commerce. Hence, great research and work has been done and is being done in field of recommendation systems. With this great development, there has been an exponential increase in online inventory due to the great number of users excessing these online platforms for buying and selling purposes and companies are often looking for advanced recommendation systems to provide their customers with the best online experience in respect towards each individual customer. It is believed that recent advancements in Deep Learning may provide an optimal solution for better recommendation systems, but it requires validation. The main aim of this paper is to follow through different research and investigate whether modern Deep Learning algorithms live up to the expectations and demands. Different reviews have been given in support with experiments. This literature review provides an analysis of different practices, state of the industrial methodologies and current research.
Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations

In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVAEs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0, I). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover’s distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.
Study of exploring industrial interaction by using augmented reality as the new a learning method

Due to the rapid changes of technologies, it is also important to deal with new approaches in the field of learning. This study will use the augmented reality technology and interactive book to identify the benefits of augmented reality in learning enhancement and to measure the comprehension level of students to determine whether augmented reality is a powerful tool to enhance the learning process in Iraqi colleges.
Low Dimensional Manifold Regularization Based Blind Image Inpainting and Non-Uniform Impulse Noise Recovery

Blind image inpainting is a challenging task in image processing.Motivated by the excellent performance of low dimensional manifold model (LDMM) in image inpainting for large-scale pixels missing, we introduce a novel blind inpainting model to repair images with missing pixels or damaged with impulse noise, in spite of the unknown locations of the corrupted pixels.We applied logarithmic transformation to separate the image and binary mask.LDMM regularization and l 0 norm regularization were applied respectively to simultaneously estimate the image and the mask.The resulted minimization problem was then solved by the split Bregman algorithm.The simulation results showed that the proposed model, compared with the existing ones, can effectively restore the image with large uniform and non-uniform missing rate.
Integration of Big Data and College English Education

Abstract With the arrival of the era of big data, the traditional English education has been difficult to adapt to the needs of social development. Under the background of big data, college English teaching needs to keep pace with the times. By analyzing the impact of big data on college English education, this paper discusses the innovative strategies of college English teaching. Only by making full use of high-quality resources in the era of big data, changing teachers’ traditional ideas, using data platform or learning software reasonably, and promoting the integration and development of various teaching modes, can the quality of college English education be improved.
Research on the Application of Digital Audio Media Based on Computer in English Teaching

Abstract Based on the wide application and good effect of computer multimedia technology in various teaching activities, this paper studies the application of digital audio media in English teaching. Firstly, the concept of digital audio media is explained. Then the application dilemma and the necessity of further application are pointed out. Finally, the application advantages, application approaches and application results are discussed.
Design of Question Answering System for Railway Electricity Accidents

Based on multi-source data of railway electrical accident, an intelligent Q&A(question answering) system based on knowledge graph of railway electrical accident is realized in this paper, which can improve the intelligence and convenience of accident information retrieval. First of all, in order to complete the construction of the railway electrical accident knowledge graph, the conceptual structure design, knowledge extraction, and knowledge storage technology are used in this paper. In addition, the overall architecture of the railway electrical accident intelligent Q&A system is designed, and question preprocessing, question classification, and answer retrieval techniques are used to achieve the Q&A system. Finally, in order to achieve the functions and visualizations of Q&A system, this system is built based on Flask-framework, combining Echarts module, data tables and Neo4j-knowlegde-graph. Therefore, correctness, scientificalness and visual effects are guaranteed.
Laplace Naive Bayes classifier in the classification of text in machine learning

The purpose of this publication is to compare the accuracy of a new algorithm based on the Naive Bayesian classifier using the Laplace distribution and named the Laplace Naive Bayes classifier with the Gaussian Naive Bayesian classifier. The t-pairs test statistically proves that the observed difference in the accuracy of the two algorithms on a selected database of English film reviews is not the result of a random order of examples used for training and testing, but of the different classification behaviour of the algorithms.
The Security Enhancement of Symmetric Key Crypto Mechanism based on Double Stage Secret Model

All smart services in cloud computing platforms have a data-sharing process to execute the task. Moreover, several crypto techniques were implemented to provide efficient and secure data transmission channel. However, it has been suffered with a number of issues because of several harmful attacks and unauthenticated key retrieval. Thus in the wireless medium, transferring the data amid two users are challengeable task. To overcome this issue, a novel Double-Stage Secret (DSS) cryptosystem is proposed to prevent the attacks in the communication channel, here the encryption and decryption function is processed based on some specific condition. If the DSS condition is satisfied then the client or user is requested to decrypt the message. To validate the secure and success rate of the proposed model, an efficient novel back sniff is developed and launched in the data transmission channel and its secure range is evaluated. In addition, the simulation of this research is done by Java, running on net beans IDE 8.2 in windows 10 platform and the results illustrated that the proposed model has attained high performance by gaining a high secure rate and less processing time of encryption and decryption than the existing algorithms.
Graphic models as a means of visualization text tasks in elementary school

The paper substantiates the use of the method of graphic modeling, as one of the effective means of visualizing the solution of a text problem in accordance with the requirements of the federal state educational standards elementary general educationin the field of the formation of cognitive educational skills among students. Based on the analysis of educational and methodological complexes in mathematics for elementary school, a classification of graphic models used in solving text problems is compiled. The article also provides examples of solving tasks using the compilation of a graphical model "block diagram" and discusses the methodological features of working with a text task on a model of this type.
An Emperical Framework of Idioms Translator From Bengali to English: Rule Based Approach

Idioms are taking a vital part in effective communication as well as a crucial part of cultural inheritance. It represents the group of words together have the meaning which is different from an individual word meaning, for this metaphorical behavior idioms arise difficulties in the general machine translation system. In this paper, we have proposed a framework for translating Bengali to English. Context sensitive grammar rules are created for parsing. The top-down algorithm is used for parsing the sentences. We have proposed an algorithm for translating idioms in sentences. The proposed system is implemented and tested with about 15000 sentences. The performance analysis of the system gives 85.33% accuracy, which is quite satisfactory.
Independence in Infinite Probabilistic Databases

Probabilistic databases (PDBs) model uncertainty in data. The current standard is to view PDBs as finite probability spaces over relational database instances. Since many attributes in typical databases have infinite domains, such as integers, strings, or real numbers, it is often more natural to view PDBs as infinite probability spaces over database instances. In this article, we lay the mathematical foundations of infinite probabilistic databases. Our focus then is on independence assumptions. Tuple-independent PDBs play a central role in theory and practice of PDBs. Here we study infinite tuple-independent PDBs as well as related models such as infinite block-independent disjoint PDBs. While the standard model of PDBs focuses on a set-based semantics, we also study tuple-independent PDBs with a bag semantics and independence in PDBs over uncountable fact spaces. We also propose a new approach to PDBs with an open-world assumption, addressing issues raised by Ceylan et al. (Proc. KR 2016) and generalizing their work, which is still rooted in finite tuple-independent PDBs. Moreover, for countable PDBs we propose an approximate query answering algorithm.
Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data

Driverless vehicles are more and more becoming a reality. However, people still have some concerns in using them, the main concern is fear, hence the importance of creating a surveillance system inside those vehicles. For the detection and classification of human movements to be possible it is necessary to train the system with data representative enough for all kinds of possibilities. Although the production of large quantities of data becomes an expensive process and adds the problem of data protection, the use of synthetic data once they are artificially generated allows lower costs and eliminates the problem of data protection. A bibliographic study was carried out in this paper with articles from 2017 or later on the use of synthetic data. In these studies, it is noted that synthetic data is widely used with good results. As far as image capture is concerned, they show that 3D cameras have better results, but they are more expensive, so 2D cameras are more often used with later conversion to 3D images. The stitched puppet (SP) model is capable of adapting to the most difficult poses having obtained good results in its application in the FAUST dataset.
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks

Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs. Following an ablation study on six datasets, we measure the performance of GNNs on the original graphs, and the change in performance in the presence and the absence of community structure. Our results suggest that communities typically have a major impact on the learning process and classification performance. For example, in cases where the majority of nodes from one community share a single classification label, breaking up community structure results in a significant performance drop. On the other hand, for cases where labels show low correlation with communities, we find that the graph structure is rather irrelevant to the learning process, and a feature-only baseline becomes hard to beat. With our work, we provide deeper insights in the abilities and limitations of GNNs, including a set of general guidelines for model selection based on the graph structure.