Reviewerly/exHarmony huggingface dataset:
PRACTICAL RESULTS FROM BIBLIOGRAPHY RESEARCH OF MEDIA LITERACY AND COPYRIGHT

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5172-5177ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1230Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
TRANSFORMATION OF TEACHERS' CAPABILITIES IN THE IMPLEMENTATION OF PROFESSIONAL ACTIVITIES IN CONDITIONS OF UNCERTAINTY

TRANSFORMATION OF TEACHERS' CAPABILITIES IN THE IMPLEMENTATION OF PROFESSIONAL ACTIVITIES IN CONDITIONS OF UNCERTAINTY
Towards Configurable Conformance Checks of PLC Software with Company-specific Guidelines

In machine and plant engineering, ensuring a short time-to-market to stay competitive in the global market requires planned reuse of control software. Modularity is a key enabler for the reuse of software parts in a different application, i.e., within a different project. Thereby, dependencies between modules play an essential role in software modularity and, thus, its reusability. Especially data exchange between modules via global variables (referred to as indirect data exchange) leads to hidden dependencies potentially hindering planned reuse. Currently, there are only a few general best practices for control software design available. Thus, companies define their own programming guidelines tailored to their application domain, the used development platforms and the software development team structure. However, means for checking the software's conformance to these programming guidelines are limited and violations might decrease reusability. Therefore, this paper presents a rule-based, configurable analysis concept for indirect data exchange, which considers company-specific programming guidelines. The analysis concept is prototypically implemented and successfully evaluated with a demonstrator's control software and an industrial software project. Moreover, the concept's industrial applicability is confirmed in an expert interview.
High-capacity Reversible Data Hiding for Encrypted Videos Based on Multi-domain Embedding

Aiming at the problem that the embedding capacity of reversible data hiding in encrypted videos is not high, a high-capacity reversible data hiding scheme in encrypted videos based on multi-domain embedding is proposed. First, the flow cipher is used to encrypt the signs of the quantize discrete cosine transforms coefficient and the motion vector difference to form two ciphertext fields. Then the information is embedded in QDCT and MVD, respectively, using different histogram panning methods. Experimental results show that the peak signal-to-noise ratio(PSNR) of encrypted video is not more than 15dB, and the PSNR of stego video is maintained at more than 30dB, and the embedding capacity is increased by up to 24.3%.
A Novel Generative Adversarial Network simulating the complementary structure of DNA genetic information

To solve the problems of mode collapse and training instability in generative adversarial networks (GANs), a framework simulating the complementary structure of DNA is proposed, in which a complementary unit and a generalization unit are added. Four latent vectors representing four bases of A, T,C and G are obtained from the complementary unit. Through the combination of latent vectors, the generalization unit avoids the fitting of high-dimensional data distribution and obtains a more comprehensive vector space. Experimental results show that the problems of model collapse and training instability are effectively solved, compared with state-of-the-art VAE-GAN, the FID score increases 52.2%, indicating that the quality and diversity of images generated by the model are improved.
A preliminary study on the assignment of GitHub issues to issue commenters and the relationship with social smells

Background: GitHub is the world's largest software hosting platform. Its features affect millions of developers. Investigating the impact of GitHub features on software teams is essential to gain insights into features' usefulness.
Approximate Algorithm and Empirical Study of L1 Principal Component Analysis based on Weighted Least Squares

The traditional PCA uses the L2 norm, while the L1-PCA uses the L1 norm to measure the error. In this paper, matlab is used to reproduce two algorithms with the maximum projected variance as the objective function and three algorithms with the minimum reconstruction error as the objective function., and carried out experiments with different proportions and different degrees of outliers on the UCI data set, and carried out face reconstruction experiments on the Yale data set. The calculation experiments show that: First, the awPCAs algorithm has a good effect on outliers in low dimensions., and it also has a good effect on Yale, and then PCA-L1 has better performance on the Yale dataset, while the improved PCA-Non-greed algorithm based on PCA-L1 does not perform well on the Yale dataset.
Image Radiation Compensation Method Based on Two-dimensional Attention Module

With the diversification of projection scene requirements and the continuous development of projection technology, various scenes containing color textures are used as projection surfaces in daily life. These projection surfaces lead to color distortion of projection image affected by color texture and affect projection effect. The radiation compensation of the projector is to counteract the impact of the textured surface on the projected image by processing the input image of the projector. Aiming at this problem, this paper proposes a color compensation network model for projected images based on a two-dimensional attention module. The model uses a convolutional neural network to learn a complex mapping between the projector input image and the image captured by the camera. An attention module is added to the feature extraction network to generate attention map refinement adaptive features. Finally, a combination of loss functions is set for the specific task of color compensation for network optimization. The experimental results show that the method proposed in this paper effectively solves the problem of color distortion of projected images caused by colored textured surfaces and improves the quality of the projection.
IoT Engineering Nanomaterial's Approach To Sustainable Advance Crop Production Management

As we all know that internet of things has now become a vital part of our generation and it has a rapid growth in the field many people see this as the next big thing but they are very much unaware of its norms and benefits it has been estimated that by the end of 2022 to there may be about 50to 60 billion internets of things are going to be installed in our rapidly growing world there are numerous benefits of the internet of things in today's fast-growing and running world but there are numerous backward points are too which make the internet of things that should be handled with care in the right hands basically what are internet of things or the thing we call as IOT so these are the touchable objects which are attached in with sensors, processing abilities, software and other technologies that connect and exchange and process data. automation in manufacturing, farming techniques, surveillance equipment, traceability, decision - making process, and prediction have the potential to transform agricultural production and the food business. Nanotechnology leads to more efficient pesticide use and more advanced agricultural production processes. More efficient, sustainable, and precise agricultural operations, as well as enhanced food processing, will result in increased output and profitability while using less raw and non-renewable resources.
Research on Web Big Data Migration Algorithm based on Association Rule Mining Model

With the popularization of the Internet and the rapid development of information technology, big data on the Web has spread across all fields of life and has become an important part of people's lives in the cloud computing era. However, the scale of Web big data is so huge that users cannot directly obtain the information they need from it. Data migration is a key part of data system integration to ensure smooth system upgrades and updates, and it also plays an important role in cloud computing. The quality of data migration is not only an important prerequisite for the new system to be put into use, but also a strong guarantee for stable operation in the future. When cloud computing lays out data sets, because it mainly considers the load in the data, it will generally give priority to distributing the data sets in the data with lower load. Such a layout method makes it possible to perform tasks of these two types of applications. When the number of network visits increases, problems such as increasing the number of network visits will be prevented. For these two special applications, this paper designs an efficient and dynamic data migration algorithm based on the association rule mining model to optimize the migration performance.
Application of the Refactoring to the Understandability and the Cognitive Complexity of a Software

Cognitive complexity of a software determines the methodology of comprehending the internal logic of a given software by an individual, quantitatively. The procedure of handling a software by different users is different, which results the cognitive complexity as a subjective measurement. The quantification of the cognitive complexity is still not standardized due to the varied number of factors affected for the cognitive complexity determination and its nature of the subjectivity. This paper evaluates the relationship between the cognitive complexity and understandability as one of the qualitative factors to determine the cognitive complexity and the usage of refactoring techniques to reduce the cognitive complexity without refraining its calculation process with respective to the internal logic of the software as in other standard software complexity metrics perform.
Length Controllable Literature Summarization Using Transformers

This paper defines an approach to implement an output length controllable text summarizer that would help readers and learners understand large chunks of text quickly and efficiently and develop a tool to do the same. It would help and play an important role in revolutionizing today’s education. The approach defined here is a way to better leverage the current state-of-the-art (SOTA) transformer-based models to help achieve length-controllable text summaries from text corpora. The paper introduces an algorithm that, when used in unison with existing transformer-based models, enables the user to specify the required extent of summarization to generate the output summary of the desired length. The approach has been tested on a widely accepted text summarization dataset and gives acceptable ROUGE(45.31) and METEOR(22.16) scores when compared to current state-of-the-art models.
A Pseudorandom Bit Generator Based on Hyperbolic Sine Function

In the literature, little attention is paid to devising and analyzing novel one dimensional chaotic mappings. In our previous efforts, we have tried fold, translation &amp; scale on arctangent function &amp; sigmoid function respectively, which brings good results. In this paper, we do the same to obtain a variant of Hyperbolic Sine Function. Both Bifurcation Diagram &amp; Lyapunov Exponent Spectrum manifest that the new mapping possesses wonderful chaotic properties. Then, a pseudorandom bit generator is designed based on it. Pseudorandom tests demonstrate that the generator is much better than our previous ones. It owns great application prospect.
A Combined Algorithm for Imbalanced Classification Based on Dual Distribution Representation Learning and Classifier Decoupling Learning

Existing classification algorithms for imbalanced datasets adopt data resampling, classes reweighting and other class balancing strategies to strengthen representation ability for minority classes and adjust the classification interface. However, these algorithms weaken the network&#x2019;s representation ability for majority classes. Therefore, a combined algorithm is proposed based on dual distribution representation learning (DDRL) and classifier decoupling learning (CDL). Here, DDRL preserves the original distribution and samples the balanced distribution from it to guide the learning of dual distribution representation, which enhances minority classes' feature representation ability and retains it for majority classes. CDL decouples the classifier from feature representation network, and trains an MLP classifier with a balanced subset, aiming at adjusting the classification deviation caused by weak features of minority classes. Experimental results show that the proposed algorithm can improve the classification accuracy on class imbalanced datasets effectively.
The Method of Reconstructing Three-Dimensional Human Posture from Two-Dimensional Images in Film and Television Production

With the development of computer software and hardware technology, film and television production has made a breakthrough. More and more film and television works have changed from the traditional plane two-dimensional space projection to three-dimensional space projection. Three-dimensional works give the audience an immersive feeling. Taking human posture, one of the most important components in film and television works, as an example, this paper discusses the method of reconstructing three-dimensional human posture from two-dimensional images in film and television production. Starting from the research significance of two-dimensional image reconstruction of three-dimensional human posture in film and television production, the existing three-dimensional human posture in film and television production is analyzed. The audience can watch the film and television works in an all-round way to keep the film and television works fresh to the greatest extent. This paper provides a certain reference value for 3D stereoscopic projection.
RESTful Web Services

A web service can be described as a client and server application that communicates over HTTP, which provides a standard means for communication and interoperability between different applications. There are many different web service implementations available across different programming platforms. A web service is made accessible via an endpoint implementation. Entire applications can be implemented using web services that transmit messages and data to and from each other. Jakarta RESTful Web Services (formerly known as JAX-RS) play a vital role in enterprise application development.
End-to-end Model for Estimating Heat loss of Building envelope using Deep learning with Infrared image

Estimating heat loss on the envelope of buildings is important in efficient management of energy within buildings. To determine the degree of heat loss, various methods which use infrared images as input data have been suggested. However, the studies have limitations; they require consideration of environmental conditions and lots of additional metadata inputs to describe them. This paper presents a novel method structured as an endto-end process using a convolutional neural network (CNN) to estimate heat loss, only with infrared images of the building envelope. The process is divided into three steps: CNN-based object detection to identify building components from infrared images, edge detection to calculate actual area, and estimation of relative heat loss among objects with identical material. The results of our process are estimated heat loss types of the objects with their classes and locations. We first generate a temperature distribution using a histogram of pixel colors of object areas, decide a threshold to determine whether the image shows heat loss or not and assign heat loss types based on the threshold. We experimented our method for windows and showed its usability. In the future, we will generalize our method by applying it to other parts of building envelope.
Authors: Improve Your Bibliometrics with Digital Enhancements

It has been hypothesized that articles that practice open science have out-sized impact in their communities.We have analyzed four years of citation data of the Astrophysical Journal, the Astrophysical Journal Letters, the Astrophysical Journal Supplement, and the Astronomical Journal to investigate if including data and enhanced figure displays increases citations.Articles with these enhanced digital assets have equal or higher citations than articles without these products.The implication is that authors that take the time to include or link to underlying tabular and figure data or utilize enhanced graphics, i.e., animations or interactive figures, reap bibliometric benefits.
Research on Text Classification based on Deep Learning

Text classification is a fundamental task in natural language processing, and machine learning and deep learning have seen more research and great progress in this task. In this paper, we introduce the current state of research and analyse the application of deep learning networks to text classification, based on the concept of text classification, feature extraction, text representation and other related techniques and text classification methods.
Quantum processes without a causal order

Less than ten years ago it was realized that quantum theory permits the existence of processes which do not have a defined causal order. To date, several experiments have confirmed the existence of one such process, called the quantum SWITCH, in which it is impossible to say which of two parties acts first. This has since spawned a new field of research on quantum processes with an indefinite causal order. Building on a mathematical analogy to entanglement certification, various methods to quantify the lack of a causal order have been proposed and demonstrated. These characterization techniques range from playing specially designed games to the measurement of a so-called causal witness. Despite these promising strategies, there has not yet been a complete characterization of the quantum SWITCH and no measurement of its process fidelity. Here we present a new protocol to perform causal tomography which we carry out using a novel implementation of the quantum SWITCH, based on time-bin qubits in optical fiber. Our new implementation is readily scalable to more than two parties and, hence, could be used to observe the scaling advantages of certain quantum games. Using this platform, we perform the first measurement of the process fidelity of the quantum SWITCH.
How can shared reading be used to develop community connectivity in the contemporary church?

Abstract This paper reports on a project to use, within church communities, previous experience in universities and schools of shared reading for the purposes of outreach, widening participation, and community inclusion. It outlines and explores the experience of selecting a book, and its subsequent discussion among a group of parishioners, to promote a sense of connectedness and belonging. The outcomes have implications for creating and strengthening links within existing church communities, reaching out to those who live locally but are not regular church attenders, and drawing others towards the church from the wider, not necessarily geographically local, community.
Parallel Lines for Calibration of Non-Central Conical Catadioptric Cameras

In this paper we propose a new calibration method for non-central catadioptric cameras that use a conical mirror. This method consists of using parallel lines, extracted from a single omnidirectional image, instead of using the typical checkerboard to obtain the calibration parameters of the system.
Simultaneously Sorting Overlapping Quantum States of Light

We use a multi-plane light converter (MPLC) to sort non-orthogonal states of light encoded in dimensions ranging from d=3 to d=7 using unambiguous state discrimination. We also use this framework to accurately classify overlapping images.
Pengembangan Prototipe Bahan Ajar Puisi Berbasis Multimedia 3D Flipbook (Prototype Development of 3D Multimedia-Based Poetry Teaching Materials Flipbook)

This study aims to develop a prototype of poetry teaching materials based on 3D Flipbook multimedia. Through this research, it is hoped that there will be more varied teaching materials as well as learning media than the previous ones. The research design is a procedural descriptive research model, which is guided by the ADDIE model development research procedure initiated by Lee &amp; Owens. Broadly speaking, this development model consists of 4 stages, namely: 1) analysis, 2) design, 3) development and implementation, and 4) evaluation. Thus, this research is a category of development research. The result of this research is poetry teaching materials in the form of 3D Flipbook multimedia. The results showed that according to 30 respondents, the 3D Flipbook multimedia display aspect was very feasible, the 3D Flipbook multimedia material aspect was feasible, and the 3D Flipbook multimedia learning aspect was very feasible. In conclusion, according to respondents, 3D multimedia flipbooks can be used as a very feasible alternative for learning poetry. Penelitian ini bertujuan mengembangkan prototipe bahan ajar Puisi berbasis multimedia 3D Flipbook. Melalui penelitian ini diharapkan tersedia bahan ajar sekaligus media pembelajaran yang lebih variatif dari yang sebelumnya. Penelitian pengembangan ini berpedoman pada model ADDIE yang terdiri atas empat tahap yaitu: 1) analisis, 2) desain, 3) pengembangan dan implementasi, dan 4) evaluasi. Hasil penelitian ini adalah bahan ajar puisi berupa multimedia 3D Flipbook . Berdasarkan respons dari informan dapat diketahui bahwa aspek tampilan dan pembelajaran multimedia 3D Flipbook sangat layak, serta aspek materi multimedia 3D Flipbook layak. Multimedia 3D Flipbook bisa dijadikan alternatif dalam pembelajaran puisi.
DIGITAL EDUCATIONAL ENVIRONMENT AS A PHENOMENON OF UNCERTAINTY IN PEDAGOGICAL PRACTICE

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 2786-2790ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0716Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
A Novel Robust Variable Selection Algorithm for Multilayer Perceptron

In the study, a robust embedded variable selection algorithm for nonlinear modeling of datasets with outliers is proposed. The proposed algorithm, named LAD-LASSO-MLP, is an iterative two-step approach. Firstly, a multilayer perceptron (MLP) is trained with the initial dataset. Secondly, the least absolute shrinkage and selection operator (LASSO) is introduced to shrink input weights of the trained MLP and select the significant variables, in which the residual of LASSO is robustified by the least absolute deviations (LAD). Determination of the shrinkage parameter is carrying out via cross-validation. Finally, a couple of compared artificial dataset simulation are provided to verify the effectiveness of the proposed algorithm. Simulation results demonstrate that such estimation has strong resistance to outliers of response variable.
Loop Closure Detection with Incremental Proximity Graph and Multi-words Quantization

This paper studies the visual loop closure detection problem. The online loop closure detection algorithm is proposed embedded with an incremental visual words codebook. The main novelty of the work lies: 1) To mitigate the coherent quantization error of feature words, a multi-words quantization approach is designed. 2) A hierarchical navigable small world graph is utilized as the codebook structure to alleviate the deficiency of high dimensional search in space partition methods. 3) A complete loop closure detection framework based on tracked word elements is tested and analyzed on several public image datasets. The results validate the computational efficiency of this algorithm.