Reviewerly/exHarmony huggingface dataset:
A Fast and High-Precision Correction Method of Time Uniform System Deviation Based on Double Quadratic Polynomial Fittings

When the measurement and control equipment is evaluated for accuracy in the outer field, it is necessary to track and measure the target flying along the precise route. When we compare standard coordinate data with measured coordinate data, the time of the two data needs to be precisely aligned first. Based on the analysis of the straight-line path tracking parameters with the minimum radial range point [1], and based on quadratic polynomial fitting, a fast and high-precision correction method of time uniform system deviations designed based on double quadratic polynomial fittings. Simulation and comparison of the calculation time and accuracy of the one-time fitting method show that the method can provide strong support for rapid correction of time series deviations.
Design of Text Resources and Tools

This chapter guides the reader through the key stages of creating language resources. After explaining the difference between linguistic corpora and other text collections, the authors briefly introduce the typology of corpora created by corpus linguists and the concept of corpus annotation. Basic terminology from natural language processing (NLP) and corpus linguistics is introduced, alongside an explanation of the main components of an NLP pipeline and tools, including pre-processing, part-of-speech tagging, lemmatization, and entity extraction.
Implementation of Efficient Modulo 2<sup>n</sup>Adders for Cryptographic Applications

In this brief, modulo 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> adders are presented based on different parallel prefix adders for 16, 24, 32, 48 and 64 bits. AT and AT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are the two different performance parameters considered to evaluate area-delay trade-offs. VLSI implementations shows that modulo adder based on Kogge-Stone tree has delay improvement in the range of (8.8-87) % than other adders at a cost of increased hardware by (26-68) %. Han-Carlson adder achieves better compromise between chip-area and computational delay. It is also shown that large fanout significantly effects the delay performance.
The autocorrelation function of the logistic map chaotic signal in relation with the statistical independence issue

The paper contributes to the complex theoretical characterization of a chaotic system. Thus, it brings together experimentally the following theoretical aspects: the statistical autocorrelation function of the chaotic system, the independence distance for extracting independent samples and the sensitivity of the chaotic system to the control parameter. The study is illustrated on the logistic map, one of the simplest chaotic systems also used in cryptography. It is presented a mean test which is applied on the autocorrelation function of the logistic map, test designed in a previous study for the tent map chaotic system. A Monte Carlo analysis is performed to highlight the results obtained.
Analysis of Software Design Modeling Ideas of Intermediate-Controller Plane Interface Based on SDOTN-ACTN

The industry standard definition of “multi-domain and multi-vendor management and control service orchestrator” is an essential part of SDOTN architecture and the key to realizing service automation. Based on the engineering practice of SDOTN project, this paper focuses on the MPI (MDSC-PNC interface) modeling idea of ACTN model and the design of engineering implementation. This interface model based on IETF protocol has been applied in the SDOTN project of China Unicom.
Nonparametric Estimation of Estimable Parameter

Nonparametric Bayes estimate of estimable parameter is obtained using the Dirichlet process as a priori. By deleting the effect of the prior, we obtain a limit of Bayes estimate. As estimators of estimable parameter, U-statistics and V-statistics are well known. These three statistics are written as a convex combination of U-statistics. For the convex combination, we give H-decomposition. Using this, we give the Berry–Esseen bound of the convex combination in the case of non-degenerate estimable parameter. In the case of degenerate parameter, we give asymptotic distribution of the convex combination. We also give a jackknife statistic of the convex combination of U-statistics.
Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

Due to the stochastic characteristics of bio-inspired optimization algorithms, several executions are often required; then a suitable infrastructure must be available to run these algorithms. This paper reviews a virtualized distributed processing scheme to establish an adequate infrastructure for the execution of bio-inspired algorithms. In order to test the virtualized distributed system, the well known versions of genetic algorithms, differential evolution and particle swarm optimization, are used. The results show that the revised distributed virtualized schema allows speeding up the execution of the algorithms without altering their result in the objective function.
Research on Modal Analysis of Qin Structure Based on Finite Element Technology

Abstract Qin is one of the traditional plucked musical instruments in China and has a history of more than 3,000 years. Qin has deep historical heritage and rich cultural connotation. It is often used for solo, but also for accompaniment. The timbre of Qin is quiet and the aftertaste is long. It is often used to express a person’s interest and purity. It is a traditional musical instrument which puts emotions on it. Qin is loved by the public. Based on the finite element analysis method, this paper makes use of the Hypermesh finite element calculation and analysis software to obtain the finite element model of the Qin and analyze the modal of the Qin. [1] Through modal analysis, the effectiveness of the computer simulation method using in the production of Qin has been verified, which supplies data sustain and theoretical sustain for the study of the structure of the Qin and the other traditional Chinese national musical instruments.
Toward Universal Data Interoperability in Networked Belief Models

An increasing number of applications in the sensor fusion, internet of things, knowledge processing, graph database, and networked information fields (e.g. Markov models, Bayesian networks, semantic webs) require (1) integrating confidence levels with probability processing and/or (2) belief representations other than the dominant probabilistic approach. Although alternatives such as fuzzy sets and Dempster-Shafer theory exist, the techniques to update belief levels, combine beliefs, and losslessly convert one form of belief into others are currently fragmented and inadequate. Our goal is to develop the capabilities to (1) translate among multiple belief representations and (2) update beliefs and confidence levels in a consistent manner across representations. The proposed solution utilizes an integrated belief meta-structure into which, and from which, all types of belief models can be converted. This method tracks the conceptually distinct components separately, but fosters their interaction where appropriate. In this way, even measures which are conceptually incommensurable will become interoperable in practice. We first describe the various types of belief representations divided into measures of credence and measures of confidence, then we discuss the challenges of translating and combining them, and finally outline the belief meta-structure used for interoperability.
A Signature Best feature selection matching using FAST and genetic algorithm

Abstract Verification for human is an important method for identifying persons. One of the most important biometric verification is signature which is used to ensure human privacy, both in banking and electronic business signature is the main authentication step for verify the user identity. In this paper system be tested by genetic algorithm to choose the best feature to applied in training and testing which will reduce the require time needed for matching and training and to obtain better matching by checking only the strong features. The feature selection algorithm allows us to reduce the process time and have more accurate results. The GA diagram was acquainted and applied with an element Issue choosing a subset to approve the mark The calculation demonstrated striking execution in all tests led System split mainly to many stages which is mainly (pre-processing, feature extraction using FAST (Fast Affine Invariant Image Matching), feature reduction (best feature selection) using genetic algorithm and matching process using neural network) The system was tested with 240 signature images with accuracy of 87% with the matching process. The system is simulated and tested using MATLAB.
Color Image Watermarking in Encryption Domain

Image watermarking is one of the techniques used for copyright protection of digital images. In this paper, copyright protection of color images using watermarking is presented with the help of data encryption. The main contribution in this algorithm is that the color image converts into an encrypted image using compressive sensing (CS) based encryption and then watermark logo is inserted into the encrypted image to get the watermarked encrypted image. After that, CS based decryption is applied to the watermarked version of an encrypted image to get a watermarked color image. Experimental results of this algorithm show that this technique effectively works for copyright protection of color images and provide better robustness compared to existing algorithms available in the literature.
An improved fast segmentation algorithm for text and graphics data

The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method.
Study on Optimal Convolutional Neural Networks Architecture for Traffic Sign Classification Using Augmented Dataset

Internet of Vehicles has become a very popular paradigm in the last years. Due to the technological progress and improved computing power, Deep Learning techniques are becoming a valid solution for multiple domains, including automotive. One area where Deep Learning algorithms are very useful is the traffic sign classification. In the last years, several solutions were proposed for this task, each of them having some downsides, usually related to the dataset quality or quantity. This paper presents a study on several architectures with a different number of layers, feature maps and input sizes with the goal of finding the best tradeoff for classifying traffic signs. Beside comparing architectures, this paper also presents a dataset augmentation method in order to avoid known dataset problems such as quality or quantity. The result of this study will be used in a second phase of real-time traffic objects detection.
Parallel Computation Of Deep Neural Network Optimized Based On ML-Metis

In recent years, accelerated computation of deep neural networks(DNN) has become a hot topic. Using Metis, a graph partitioning algorithm, to divide the DNN data flow graph, and then perform model parallelization on DNN is a common method to improve the speed of its calculation. However, because Metis cannot capture all the characteristics of a data flow graph, it is often inefficient. This paper proposes an improved graph partitioning algorithm, ML-Metis, which can take into account the memory reuse and intermediate data of the data flow graph during partitioning, and provide an efficient parallel strategy for DNN.
Loco-Store: Locality-based Oblivious Data Storage

With the growing popularity of cloud storage, how to prevent information leakage from cloud access patterns attracts great attention. Oblivious RAM is proposed for this purpose. It is designed for the memory system, and most existing work focused on improving performance in the main memory. Recently, ORAM has been extended to the cloud environment, and it is called Oblivious Data Storage. TaoStore, the state-of-the-art oblivious data storage system, integrates the ORAM technology with synchronous I/O technology to reduce the mean response time. As we observed, there is a strong locality existing in user accesses. However, existing Oblivious Storage research did not consider this. In this article, we propose Loco-Store, an oblivious data storage. In Loco-Store, we design a novel stash controller scheme that can dynamically group relevant blocks during the oblivious I/O processes. We also propose a locality-based eviction algorithm to keep the security guarantee. The theoretical proof proves that our scheme keeps the security definition of ORAM. Finally, we implement a prototype and conduct extensive experiments on real-world datasets. The results show that Loco-Store can save the network bandwidth consumption up to 39.19 percent, and reduce the overall access time by 26.17 percent
A Novel Algorithm for Quality Evaluation Metrics of Fused Live Video Frames

Image fusion technique is playing important role in image processing. The main objective of image fusion is to integrate the information from several input images into a single image. The consequence of fused image consists of more precise information when compared to any of the input images. Image fusion plays pivotal role in image reconstruction. We have developed novel algorithm and implemented to evaluate quality metrics of fused video frames. This algorithm determines the quality performance between the fused video frame and unprocessed input video frame. The focal point of this paper is evaluating quality of fused video frame using structural similarity index (SSIM) and visual information fidelity (VIF) assessment methods.
Development of Web-based Research and Community Service Database at Universitas Negeri Surabaya

Abstract One of the data used as a benchmark for determining the progress of a university can be seen from the productivity of research and community service. The research objectives are to (1) analyze Unesa lecturer research based on the distribution of faculties/departments/study programs; (2) analyzing trends in the research focus of Unesa lecturers; (3) analyzing the achievement of Unesa lecturer research outcomes compared with the target research outcomes; and (4) analyzing the profile of the service to the Unesa lecturer community based on the distribution of faculties/departments/study programs. The reference used in analyzing is from 2015-2019. The development model used is the Waterfall model with a linear development system. The research subject used was the procedure of developing Sistem Informasi Riset dan Pengabdian Kepada Masyarakat (SIRIP) in Higher Education. Data analysis techniques were carried out in three stages, namely pre-development, development, and post-development. Based on the results and discussion, the conclusions in this study are as follows. The results of the trial of Sistem Informasi Riset dan Pengabdian Kepada Masyarakat (SIRIP) using the black box testing method in accordance with 18 functional requirements of the system can run successfully. The profile of research in Unesa from 2015-2019 experienced a significant increase and decrease (fluctuation). The number of research titles funded from 2015-2019 was as many as 250 titles, 585 titles, 700 titles, 480 titles, and 447 titles. Meanwhile, the number of community service titles funded from 2015-2019 was 46 titles, 51 titles, 29 titles, 22 titles, and 338 titles, respectively.
Naïve Bayes Method to Determine Learning Specialization for New Students

Abstract In life, a person is often confronted with several choices and is required to be able to choose from those choices. One example is the selection of Major’s specialization. There are several campuses or universities in Indonesia that require students to choose the type of specialization or concentration before starting their first lecture, such as the Postgraduate Program at Universitas Atma Jaya Yogyakarta. Naive Bayes method is one of the probability methods used to predict the likelihood that will occur in the future by using previous data. The purpose of this study is to use the Naive Bayes method to make a recommendation based on the attributes that have been determined. The method used in this research is: data collection using qualitative methods and distributing questionnaires to parties related to this research. As for the points of the questions in the questionnaire are (1) the previous department, (2) the final score of the cumulative achievement index, (3) interest in the field, (4) individual ability, (5) certain subject values, (6) concentration chosen, and (7) the level of suitability of concentration selection. This study uses a sample data of 25 Postgraduate students of Magister Teknik Informatika at Universitas Atma Jaya Yogyakarta. The results of this study are a recommendation for the selection of majors at the Postgraduate Department of Universitas Atma Jaya Yogyakarta.
Administration of Mega and Open Universities With Technological Singularity Beyond Master-Human

The aim of this study is to foresee a futuristic view of how open universities can achieve their sustainability in the context of technological singularity. Technological singularity predicts that artificial intelligence will prevent human intelligence in the future. Not only can artificial intelligence radically change human habits, but it can also alter learning practices. The foundation of a revolutionary transformation on humanity learning will be established for both the open universities and for the technological singularity beyond master-human. Thus, open universities are not only sustainable, but, at the same time, they can be transformed into ecological learning environments. The framework of the internalizations and predictions of the study participants on open and distance learning environments will help us save open universities in the future.
An Integrated and Robust Evoting Application Using Private Blockchain

Building a protected electronic voting system that offers the decency and security of current voting plans, while giving the straightforwardness and adaptability offered by electronic systems has been a test for quite a while. Right now, progress paper, a use of block chain as a help to actualize disseminated electronic voting systems is accessed. In the present paper and polling form voting a ballot system, the checking of vote's takes hours and some of the time days and hardly any occasions mess up the outcomes by virtue of human or machine blunder, which obviously brings about the procedure taking considerably more. The block chain innovation proposes a fact wherein that individual flaw is removed from the condition and votes are checked right away. The two voters and political decision executives can get the significant advantages from the e-voting a ballot programming application. And yet, e-voting a ballot presents broad dangers to political race security and honesty and furthermore principally changes the idea of political decision straightforwardness and investigation. E-Voting has a Relative bit of leeway; at the end of the day, Relative Advantage is degree when a development is viewed as superior to the past one; Since, e-Voting is superior to the manual voting system.
Estimating Levels of Learning Outcomes Acquirement Based on Fuzzy Sets, Relations, and Their Compositions

New curriculum designers should prepare the set of learning outcomes that will be studied by students, and their acquirement will be verified by teachers. It is not easy to estimate whether students achieved the required learning outcomes even in the range of knowledge and skills, but when the competencies are considered, it becomes harder. Because of the convenience of using the linguistic terms (excellent, poor, good, and so on), it is better to apply fuzzy relations (type 1 if the numbers are chosen or type 2 if the sets are used), which can handle with the linguistic terms better than classical relations. The estimation of the learning outcome's acquirement can be conducted on the bases of a test or tests. For calculating the levels of learning outcome's acquirement, two fuzzy input relations are designed. Experts build one based on their knowledge, and the second one is based on the test results. The output relation, showing levels of learning outcome/outcomes' acquirement, is built with the application of the S-T composition of the first and second relations.
Constructing authorial pseudonyms and authorial identity in online fanfiction communities

Abstract This article undertakes the first linguistic analysis of the innovative online pseudonyms (e.g., &lt;smollouisandtolharry&gt; , &lt;00Q007Narry&gt; , &lt;b0yfriendsinl0ve&gt; ) used by fanfiction authors. Specifically, this research explores the most frequent lexical formation processes employed when creating pseudonyms, why these processes are used, and what they reveal about authorial identity in fanfiction communities. The most common formation methods identified across the 600 names analysed are compounding, blending, and variant spellings. All three of these processes allow authors to create memorable and unique names which distinguish their work from that of other writers in their community. Indeed, despite their use of pseudonyms, these authors are still highlighting their individual authorial identities, and they do this by turning the process of creating a pseudonym into a ludic experiment in linguistic innovation. Consequently, future studies should further explore the relationship between self-selected names and the articulation of identity.
Designing an Effective User Interface for Analyzing Software Repositories

The introduction of Git 1 , a version control system, has radically changed the software development process. Currently, Git is considered an essential skill for software engineering developers and is necessary for collaboration between developers [1] . This reliance on Git means that software development creates a vast pool of publicly available data. This data provides an opportunity for software engineering researchers interested in analyzing the software development process. Such analysis could facilitate numerous tools for developers that substantially increase developer productivity.