Reviewerly/exHarmony huggingface dataset:
The Development of Two Wheel Mobil Robot: Generated Path Using Simulation and Actual Path of Mobile Robot Are Compared

This paper shows a mobile robot movement whose is gotten from the best predetermined path is derived from simulation. The robot movement is programmed on a simulated bordered floor, and the trajectory is optimized before the program is downloaded to the mobile robot. This paper describes the development of a two wheel mobile robot. The kinematics and dynamic model of the mobile robot are created and simulated using MATLAB. The experiment is conducted to compare the differences between the simulated path and the actual path. The calculations and the strategies in the simulation, the path is almost similar compared to the actual mobile robot path. The program helps the user to start the programming and optimize it to obtain the best trajectory and position of a mobile robot.
Assessing the Symbiotic Organism Search Variants using Standard Benchmark Functions

Abstract Symbiotic Organism Search (SOS) is one of the latest meta-heuristic algorithms created to solve optimization problems. Combining the fact that this new algorithm is parameter-less (no need for tuning) and having a superior performance compared with other meta-heuristic algorithms, the interest to investigate and enhanced this algorithm had emerged. In this paper, we present a new version of SOS by looping the current algorithm rather than doing it one after the other. The target of this paper is to find the effect of changing the structure of algorithm from the original SOS using selected benchmark functions. Our findings indicate that using a loop structure can improve the solution in some of the benchmarks functions as compared to the original SOS.
Applied, type and rational-using of code switching in Insecurity novel

Background: People often used the code in their daily conversation, spoken or written language, formal or informal. When people do code switching, it can make them become bilingual. Purpose: The objective of this paper is to find out how many code switching are applied, types of code switching in dialog between the characters in Insecurity novel of Angelina Johnson included the reasons for using the code switching. Design and methods: The method used in this analysis is qualitative descriptive method with the library and internet research. This analysis focused on analyzing the types of code switching based on the theory of Poplack and reasons for using code switching based on Hoffman theory. Results: The results of analysis found the total in dialog that code switching occurs 141 data from chapter 1 to 19. Total 3 types of code switching, that most widely used is intra-sentential occurs 122 times, then inter-sentential 13 times, while tag switching occurs 6 times. And also found that only six reasons based on Hoffman theory by talking about particular topic found 47 times, quoting somebody else found 3 times, being empathic found 14 times, interjection found 6 times, repetition used for clarification found only once, while intention of clarifying found 14 times.
A Systematic Mapping Study on Blockchain Technology for Digital Protection of Communication in Manufacturing

In the next few years, Blockchain will playa central role in IoT as a technology. It enables the traceability of processes between multiple parties independent of a central instance. Blockchain allows to make the processes more transparent, cheaper, and safer. This research paper was conducted as systematic literature search. Our aim is to understand current state of implementation in context of Blockchain Technology for digital protection of communication in industrial cyber-physical systems. We have extracted 28 primary papers from scientific databases and classified into different categories using visualizations. The results show that the focus in around 14% papers is on solution proposal and implementation of use cases Secure transfer of order data using Ethereum Blockchain, 7% papers applying Hyperledger Fabric and Multichain. The majority of research (around 43%) is focusing on solution development for supply chain and process traceability.
Research on Super-Resolution Enhancement Algorithm Based on Skip Residual Dense Network

In this paper, a super-resolution enhancement model based on skip residual dense net(SRDN) is proposed. We design a model with a two-channel skip residual dense nets to extract deeper feature information. The two channels have the same network structure and are connected by skip connections. The model first divide the input image into two components by a guided filtering. The features of two components is learned by one convolution layer and a three layers double-channel SRDN network respectively. Then model uses the concatenation operation to combine two channels’ feature. Finally, the super-resolution image is obtained by the up-sampling network and one convolution operation. Different to the classical loss function, we define a joint loss functions for training, which is consisted of content loss, perceptual loss and color discrimination loss. The experimental results show that the proposed algorithm achieves better super-resolution visual results ans objective evaluation indicators.
Standards Developments in Scholarly Publishing

Publication is an essential step in research and is the responsibility of all scientists. Scholarly publications should provide a comprehensive and detailed record of scientific discoveries. They affect not only the research community, but society at large. Scientists have a responsibility to ensure that their publications are complete, detailed, clear, honest, balanced, and avoid misleading, selective, or ambiguous statements. Journal editors are also responsible for ensuring the integrity of the research literature. Publishers are adapting their roles in response both to changing needs and to these new competing services that can include researchers, research institutes, universities. Aside from their traditional roles in supporting quality assurance and peer review, publishers participate in many initiatives and develop services, often in partnership with universities and other organisations, to support communication and develops standards. This chapter shows the importance of standards and presents some concrete examples of standards organizations and perpetual evolution.
ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.
Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.
Image Retrieval Method Based on Improved Local Binary Pattern

Local binary pattern (LBP) is an important image texture feature extraction method. This paper proposes a combination of adaptive threshold and directional local image feature representation, and to realize the image retrieval. This local combination feature is composed of two parts: 1)The standard deviation of the gray value of all pixels in the local neighborhood of the image and the gray value of the center pixel are used as the comparison threshold for encoding. This threshold is adaptively changed following the grayscale changes in the neighborhood, and can indicate the severity of the gray scale change; 2)directional local pattern: by comparing the grayscale changes of local neighborhood pixels in different directions, discrimination and binary encoding are performed to indicate the local texture features directionality. Finally, the two features are combined to form the local features of the image, and the features are used for image retrieval. Experiments on the Corel-1k database show that this method has a better image retrieval effect than existing local feature representation methods.
Depth Estimation for Monocular Image Based on Convolutional Neural Networks

Perceiving the three-dimensional structure of the surrounding environment and analyzing it for autonomous movement is an indispensable element for robots to operate in scenes. Recovering depth information and the three-dimensional spatial structure from monocular images is a basic mission of computer vision. For the objects in the image, there are many scenes that may produce it. This paper proposes to use a supervised end-to-end network to perform depth estimation without relying on any subsequent processing operations, such as probabilistic graphic models and other extra fine steps. This paper uses an encoder-decoder structure with feature pyramid to complete the prediction of dense depth maps. The encoder adopts ResNeXt-50 network to achieve main features from the original image. The feature pyramid structure can merge high and low level information with each other, and the feature information is not lost. The decoder utilizes the transposed convolutional and the convolutional layer to connect as an up-sampling structure to expand the resolution of the output. The structure adopted in this paper is applied to the indoor dataset NYU Depth v2 to obtain better prediction results than other methods. The experimental results show that on the NYU Depth v2 dataset, our method achieves the best results on 5 indicators and the sub-optimal results on 1 indicator.
Legal Dilemma and Outlet of Privacy Protection in the Era of Big Data

The development of the Internet has promoted the development of big data, and the quantification of information in the era of big data has caused an upsurge of personal privacy protection. To solve the problem of privacy protection of personal privacy in the era of big data, this article uses the literature analysis method to refer to the research experience of many previous scholars, and synthesize your own understanding, use cryptography to encrypt personal privacy in the era of big data, and implement encryption and decryption operations of the data processing process through the ESF algorithm to ensure the security of personal privacy. And comparatively analyzed the degree of importance my country and the United Kingdom, France and India attach to privacy-related laws, and the privacy leaks in four countries from 2015 to 2019. The final result shows that, before the right to privacy was widespread, China and India had 239 cases and 347 cases of privacy leakage, while Britain and France had 144 cases and 131 cases of privacy leakage because they had already implemented privacy laws. With the improvement of privacy-related laws, there are fewer and fewer privacy leaks, but the country still needs to pay attention to privacy-related laws and restrict and sanction offenders through relevant laws to reduce or avoid personal privacy leakage.
Modeling and inference for mixtures of simple symmetric exponential families of ‐dimensional distributions for vectors with binary coordinates

Abstract We propose tractable symmetric exponential families of distributions for multivariate vectors of 0's and 1's in dimensions, or what are referred to in this paper as binary vectors, that allow for nontrivial amounts of variation around some central value . We note that more or less standard asymptotics provides likelihood‐based inference in the one‐sample problem. We then consider mixture models where component distributions are of this form. Bayes analysis based on Dirichlet processes and Jeffreys priors for the exponential family parameters prove tractable and informative in problems where relevant distributions for a vector of binary variables are clearly not symmetric. We also extend our proposed Bayesian mixture model analysis to datasets with missing entries. Performance is illustrated through simulation studies and application to real datasets.
Analyzing and Predicting The Success of Box Office Collection of a Movie Using Machine Learning

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies are available over the internet. The framework will foresee an estimated achievement pace of a film dependent on its productivity by dissecting verifiable information from various sources like IMDB, Rotten Tomato, Box Office Mojo and Metacritic. Utilizing distinctive AI calculations, Machine Learning Tools, and different procedures the framework will foresee a film box office benefits depending on certain highlights like caste, genre, budget, actors, and many more features. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. Thus, predicting the box-office will help this growing industry experts to imply some important business decisions in order to make the upcoming movie more successful.
The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?

The chapter considers the recent but already classic theoretical result called No Free Lunch Theorem in the context of optimization practice. The No Free Lunch Theorem is probably the fundamental theoretical result of the Machine Learning field but its practical meaning and implication for practitioners facing “real life” industrial and design optimization problems are rarely addressed in the technical literature. This discussion is intended for a broad audience of mathematicians, engineers, and computer scientists and presents a probabilistic understanding of the theorem that can shed light to its meaning and impact in the industrial optimization practice.
An Exploration of Entropy Techniques for Envisioning Announcement Period of Open Source Software

Through the rising intricacies of the software, the quantity of probable bugs is furthermore growing promptly. These bugs hamper the prompt software improvement series. Bugs, if deferred unanswered, may initiate complications in the elongated track. Moreover, with no former information around the position and the quantity of bugs, administrators might not be competent to assign supplies in a beneficial way. In order to affect this trouble, investigators have formulated abundant bug estimation methods till now. These source encryptions practice periodic variations in order to encounter the novel characteristic introduction, characteristic improvement, and faults fix. A significant part of concern for OSS is when to announce a latest edition. In this paper, a method by assuming the quantity of faults documented in numerous announcements of Bugzilla software has been established and distinctive degrees of entropy specifically, Shannon entropy and Kapur entropy aimed at variations in several software revisions during interval periods have been computed. A simple linear regression is employed initially to forecast the faults that are still impending. By means of these anticipated faults and entropy degrees in multiple linear regression, the announcement period of the software has been forecasted. Data visualization using Python has been elucidated. The outcomes are significantly effective for the software administrators to announce the edition on that interval. The outcomes of projected versions through the prevailing in the texts are evaluated and discovered that the projected simulations are beneficial fault forecaster since they have exhibited substantial enhancement in their operations.
IR-FF-kNN: Image Retrieval Using Feature Fusion with k-Nearest Neighbour Classifier

Presence of inconsistency in the visual appearance of image tends to degrade the retrieval process performance. Increasing image data across several domains encourages to explore visual information of image representation to simplify the interpretation and concentrate on discriminative features of images so as to use them for retrieving relevant images for increasing machine learning model performance. Thus, features fusion and k-Nearest Neighbours (IR-FF-kNN); an Image Retrieval framework is proposed to increase retrieval performance. The Histogram of oriented Gradients (HoG), Color Moments (CM) and Center Symmetric Local Binary Pattern (CSLBP) descriptors are used to obtain multiple features of images in the features extraction phase and in similarity computation phase, the kNN classifier is used. The proposed framework is tested on MIR Flickr dataset and provides mean average precision of 85% compared to the state-of-the-arts.
Optimization of Merge Hypothesis for Automatic Image Caption Generation

Image captioning is the process of automatically generating appropriate image descriptions of the given images without human intervention and using appropriate algorithms. These descriptions should be in proper sentences with the natural language; hence, the algorithms used to require the fusion of computer vision techniques with those of natural language processing. The rise of social media and cybercrimes has substantially increased the amount of research done in this area. The objective is to generate image captions that are semantically closer to those generated by humans. This paper focuses on the merge architecture used in image captioning process and briefly compares it with the inject architecture also used to find image captions. Optimization of the general algorithm is done by using the vector embeddings of the words obtained from the dataset itself and using concatenation in the merging process. Finally, the performance of the proposed method is compared using a similarity measure and BLEU score.
New Pythagoreanism, Complexity, Artificial Intelligence

Leibniz defined mathematics as the “science of possible worlds.” But what worlds were assumed as the possible ones by thinkers of different eras? It is this question that is considered in this article. According to Einstein, development of science requires “external justification” (observations and experiments that should be explained or predicted) and internal perfection (following the internal logic of this discipline). Mathematics has no “external justification” and experiment cannot refute mathematical theory. In this sense, mathematics is closer to creativity, to art than to natural science. Its connection with culture turns out to be more complex and mediated. To explain this connection, Daniel Bell, within the framework of his theory of social development, puts forward the “axial principle,” according to which the role of science is considered as the main characteristic of society. From this point of view, we single out traditional, industrial and post-industrial societies. Each of these phases has its own ideals, norms, and types of mathematical creativity. In traditional society, following the Pythagorean tradition, mathematics is focused on finding harmony in nature, on identifying unity on the basis of universal relationships determined by the numerical characteristics of the objects under study. As the industrial era approaches, the constructivist, or “project-based,” approach becomes increasingly important. And unity emerges at a higher “meta-level.” The forerunner of this direction is Descartes, who raised the question of finding a single, universal method for solving all mathematical problems. The work traces the change in the formulation of a number of “invariant,” “eternal” mathematical problems as well as the evolution of the concept of “complexity” in the historical retrospective. Close attention is paid to the post-industrial phase of civilizational development and to “computer mathematics,” which has become the basis for formation of virtual reality, which in many respects changes the very direction of progress. As a result of this, the “extraverted orientation” of humanity, the course toward new horizons was replaced by the “introvert” one, which prioritizes tasks associated with comfort, convenience, and consumption. The “change of milestones” that has taken place is traced in a comparison of “big projects” related to mathematics that were put forward in the 1960s, and those that are considered as priorities at present. In fact, we are faced with a “crisis of expectations.” We see a way out of this crisis in a revival of the “Pythagorean tradition,” at a new level. But if in a traditional society the goal of the development of mathematics in alliance with other sciences and arts was to reveal harmony in the natural world, then at the post-industrial phase, the priorities are different. They are associated with computer modeling, understanding, and identifying the foundations of harmony in the human world.