Reviewerly/exHarmony huggingface dataset:
Self-adversarial Network Based on MADDPG for Emergency Power Dispatch

A self-adversarial network based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed for emergency power dispatch. This paper proposed two methods to improve the performance of MADDPG in mixed cooperative-competitive environments. The self-adversarial mechanism sat an adversarial network to improve the convergence and training speed. For the relations of cooperation, a self-attention layer is set for improving communication efficiency. Experiment results on the MPE environment show that the proposed model method has better performance and greater learning speed than MADDPG.
Quality Assessment Criteria and Methods for 3D Digital Replica of Historical Printing Woodblocks

With the available 3D scanning technologies and the special historical characteristics of printing woodblocks, it is necessary to have concrete methods to assess the visual quality of 3D digital replica for preservation and promotion purposes. In this paper, we present our study for assessing the visual quality of 3D digital replica of historical printing woodblocks. In our study, first, assessment criteria are formed to meet the preservation requirements by consulting with printing woodblock conservationists and assessment methods are developed to assess the quality of 3D digital woodblock replica regarding those criteria. Then, a suitable 3D scanning method is applied to a typical set of printing woodblocks to obtain their 3D digital replicas which are used to evaluate the proposed assessment methods. The experimental results reveal that our proposed assessment criteria and methods can be used to evaluate visual quality of 3D digital woodblock replicas regarding the requirements of preservation and promotion of woodblocks.
Research on Named Entity Recognition in Fault Text of Railway Signal Equipment

The unstructured text of railway signal equipment failure records important information such as the failure cause and failure phenomenon of the signal equipment. Most of them are stored in Word, Excel, etc. The traditional technology cannot explore the important value contained in the text data. In order to convert the analysis of the fault causes of the signal equipment recorded in the text into knowledge that can serve fault diagnosis, this paper uses the BiLSTM+CRF model to realize named entity recognition and analyzes 638 fault texts of railway signal equipment in a railway field from 2021 to 2022. The accuracy of the model reaches 83.38%, which shows that the named entity recognition model of railway signal fault equipment has a high evaluation standard and can be applied to the extraction of signal equipment fault entities based on text mining.
Memory Protection with Dynamic Authentication Trees

As embedded devices increase in use and handle more critical information and functionalities, the importance of security grows even greater. Defense against bus attacks such as spoofing, splicing, and replay attacks is of particular concern. Traditional memory authentication techniques, such as hashes and message authentication codes, require significant amounts of on-chip memory and introduce a large performance impact when protecting off-chip memory during run-time. Balanced authentication trees such as the well-known Merkle tree or TEC-Tree can be used to reduce this cost. This work proposes a new method of dynamic authentication trees, which updates a tree structure based on a processor's memory access pattern. An HDL model for use in an FPGA has been developed as a transparent and highly customizable AXI-4 memory controller. The performance of our tree design is comparable to that of the TEC- Tree in several memory access patterns. Speedup over the TEC- Tree is possible to achieve when applied in scenarios that frequently access previously processed data.
Adoption of Cloud Computing to Accounting: Benefits and Challenges

Cloud computing is the most notable notion that has changed the traditional approach of offering accounting solutions. The bookkeeping industry is moving towards online goods. The numerous benefits of cloud services have been established that a technology revolution is unquestionably required in the accounting department of businesses. Cloud-based solutions have accelerated the pace of change in the accounting industry. Cloud computing services assist businesses in developing an inclusive strategy and prioritizing real-time and practical financial advice for clients. Cloud service providers have developed cloud-based accounting systems that offer significant benefits. Accounting on the cloud, enabled by cloud computing technology, is becoming a new commercial reality. Cloud accounting gives every company that uses it a lot of flexibility, allowing any company, big or little, to customize cloud software to match their individual needs. The risk of data imperfectness and incompatibleness that were present in prior techniques are reduced by using cloud based accounting solution. The main purpose of this article is to investigate the significance of cloud computing and its influence on accounting, as well as the factors to consider when using cloud computing in accounting and the benefits of cloud accounting. The study is done based on the existing literature related to cloud computing and its use in accounting.
Generating a Visual-Inertial Odometry Dataset based on a Helmet Prototype for Recognizing Human Activities

Human activity recognition (HAR) is an important area for elderly care. Because with an effective HAR clinical management authorities can monitor movement, abnormality, human behavior, chronicle diseases, and suddenly fall remotely. HAR may also reduce the workload of a caregiver. Our research mainly focuses on HAR for sudden fall detection and prediction. Usually, raw signals or features extracted from raw signals are used in HAR developmental works, which can increase false alarm rates (FAR). Besides, it is hard to differentiate various human activities through the illustration of this time-series signal. If these activities can be patterned in regular shape and can be expressed with a simple mathematical equation, then the recognition algorithm can not only detect daily activities but also predict them. Therefore, we will present a new and much effective technical way by using visual-inertial odometry (VIO) for human activity recognition in this paper. We consider walking, running and jumping activities to show our claims. From the results, we can see that considered human activities are easy to differentiate. &#x0027;Goodness of fit&#x2019; of these activities will show how we could model mathematically them.
A hybrid of local and global atmospheric scattering model for depth prediction via cross Bayesian model

Monocular depth estimation is a fascinating and challenging problem in virtual vision. However, the training of networks based on deep learning largely depends on the training data. This paper proposes a depth prediction method based on the depth cue: atmospheric light scattering, which can effectively predict the depth in different atmospheric light scenarios. But the assumption of global atmospheric light constancy can produce the unavoidable error. Especially for complex scenes, the complex reflected light of the scene leads to uneven distribution of atmospheric light. This paper proposes a new local atmospheric light estimation method, which can simulate the real distribution of atmospheric light scattering in the air more effectively. And the experiments found that the two models are complementary. In order to fuse the intrinsic real information of the two models, this paper adopts the fusion strategy based on the Bayesian model, and edge-preserving filtering is used to preserve the detailed information.
Stratigraphic surfaces

This chapter explains the difference between sequence stratigraphic surfaces and other types of stratigraphic contacts, and provides the criteria that afford the identification of all surfaces with relevance to sequence stratigraphy. A sequence stratigraphic surface is a type of stratigraphic contact that invariably serves, at least in part, as a systems tract boundary. As systems tract boundaries, sequence stratigraphic surfaces mark changes in stacking patterns between the strata below and above the contact. This defining attribute separates a sequence stratigraphic surface from any other type of stratigraphic contact, and provides the basis for sequence stratigraphic correlation. Sequence stratigraphic surfaces define the framework of sequences and component systems tracts. Within this framework, lithological changes inside systems tracts define “within-trend” facies contacts, which have physical expression (i.e., they are surfaces of lithostratigraphy or allostratigraphy) but do not mark a change in stratal stacking pattern. All types of stratigraphic surfaces are important in stratigraphic studies. However, the workflow of sequence stratigraphy requires the identification of sequence stratigraphic surfaces first, followed by the placement and rationalization of facies contacts in the context of systems tracts. The same types of stratigraphic surfaces can be observed at different stratigraphic scales (hierarchical levels). The physical expression of all types of sequence stratigraphic surfaces and within-trend facies contacts is exemplified in this chapter with outcrop images and subsurface (borehole and seismic) data.
PENGEMBANGAN MEDIA PEMBELAJARAN SEPAKTAKRAW BERBASIS ANDROID BAGI PESERTA DIDIK KELAS X SMA NEGERI 1 DEMAK

Technology is developing rapidly, the use of model and media learning must also keep up with technological developments. Matter of learning which of printed books are still used in the school, but on this current development, it is necessary to conduct learning that utilizes other media such as computers / laptops, even smartphones. This research aims to develop media learning based on android Sepaktakraw which is applied to class X students of Senior High School 1 Demak. The method used in this research is Research and Development (R&amp;D). In this research, the steps was taken included potential analysis and problem, data collection, product design, design validation for media and material experts, design revisions, product testing, and product revisions. Does android applications using Android Studio software with 3 tests, namely functionality testing with black box, validation of designs by media experts and material experts, and student acceptance by class X Senior High School Negeri 1 Demak. The results of this research indicate that the application of Android-based Sepaktakraw learning media is in the category of feasible use in learning based on functionality testing and expert validation. From the analysis of functionality testing (the black-box test gets 100% results), the media test gets 100% results and the material tests get 100% results. The results of the analysis of student acceptance of the media learning based on android Sepaktakraw application showed very positive with an average percentage per aspect of 83,20%.
OPTIMIZING VIDEO TUTORIALS FOR SOFTWARE TRAINING THROUGH CUEING

Video tutorials substantially support demonstration-based training where the main goal is to enhance procedural knowledge by observing various understandable examples of performing a task.Although video tutorials are broadly popular nowadays, little attention is given to the design features of an instructional tutorial.The aim of this study is to investigate the use of cueing on video tutorials for software training.Task performance, mental effort, and self-efficacy were included as dependent variables to explain possible effects mechanisms.The experiment included 118 undergraduate students with ICT experience from a Computer Science department in Greece.All subjects viewed three video tutorials on video editing software followed by practice.The participants achieved significant learning gains, reaching moderate to high levels of success on task performance.No cueing effect was found.The discussion proposes several alternatives for improving the effectiveness of video tutorials.
Modernize the teaching of mathematics at the secondary level through R programming

Teaching mathematics using programming has cognitive benefits, improves practical skills, and provides powerful independent thinking and problem-solving strategies. R is a free software environment and one of the most popular programming tools, which allows for solving various mathematical problems through coding. In this work, we will use the R language to solve some problems posed in complex numbers and cover some exercises from the third-grade mathematics textbook in Algeria.
Study on the Orientation, Path and Measure of Computer Course Teaching in Higher Vocational Education

In order to successfully complete the teaching task and promote the improvement of teaching quality, it is necessary to correctly set the teaching goal, pay attention to the basic knowledge, teach computer application skills and encourage students to innovate.And how to improve teaching facilities, improve the quality of teachers, stimulate learning motivation are important issues to be addressed.Scientific and effective methods shall be used to strengthen the teaching of basic knowledge, professional skills training, innovative ability of students, and innovative teaching methods.This method will not only innovate teaching methods, effectively guide the teaching, but also ensure the effectiveness of computer teaching and personnel training quality.
<scp>XRS</scp> reviewer award editorial 2022

Every year, X-Ray Spectrometry receives hundreds of manuscripts for regular and special issues. Each of these submissions is screened by no less than two independent referees to insure a maximum of fairness and impartiality. Despite the Covid-19 pandemic more than 200 reviews were done for X-Ray Spectrometry in 2021. The review process aims at promoting the quality of the journal and the work represents a huge effort by knowledgeable experts, who are not paid for this activity and who are usually very busy scientists. To compensate a little, Wiley has a “Best Referee of X-Ray Spectrometry” award. This award consists of a certificate and a cash gift. The award is given to the referee(s) who have performed a significant volume of thorough, impartial, and timely reviews. The editorial board members (who normally do quite a bit of reviewing for the journal) and their near co-workers are not considered for this award. For the calendar year 2021, the highest scores for reviewing for X-Ray Spectrometry went to Dr Mateusz Czyzycki, from the AGH University of Science and Technology in Krakow, Poland. Dr Czyzycki did several reviews in 2021, with useful comments to the authors, usually returning his review within a few weeks. The award was presented at the conference dinner of the 2022 EXRS Conference in Bruges, Belgium, on June 30. Best referee winner Dr Mateusz Czyzycki [Color figure can be viewed at wileyonlinelibrary.com] I was always curious about the composition of materials. XRF is an ideal method to get that. So, I decided to specialize in XRF spectrometry. Reference-free XRF spectrometry becomes more and more robust and attractive to the XRF community. If editors ask me to review a submitted manuscript, they trust in my competences. I want to show they did a right choice by appointing me as a reviewer and I do my best to prepare the most honest and solid review that I can. Opera, classical music of the Strauss dynasty. Of course, numerous other referees did excellent jobs, but they cannot all be rewarded unfortunately. We would like to take this opportunity to thank Dr Czyzycki and all the other anonymous referees for all their hard and important review work for X-Ray Spectrometry. A journal cannot survive, at a high-quality level, without the peer review system.
Cost Effective Image Classification Using Distributions of Multiple Features

Our work addresses the issues associated with usage of the semantic features by Bag of Words model, which requires construction of the dictionary.Extracting the relevant features and clustering them into code book or dictionary is computationally intensive and requires large storage area.Hence we propose to use a simple distribution of multiple shape based features, which is a mixture of gradients, radius and slope angles requiring very less computational cost and storage requirements but can serve as an equivalent image representative.The experimental work conducted on PASCAL VOC 2007 dataset exhibits marginally closer performance in terms of accuracy with the Bag of Word model using Self Organizing Map for clustering and very significant computational gain.
Multi-resolution Fusion Network for Human Pose Estimation in Low-resolution Images

2D human pose estimation still faces difficulty in low-resolution images.Most existing topdown approaches scale up the target human bonding box images to the large size and insert the scaled image into the network.Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions.To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation.Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network.Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image.We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.
Representative Batch Normalization for Scene Text Recognition

Scene text recognition has important application value and attracted the interest of plenty of researchers.At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level.They have a good effect on reading regular scene texts.However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur.This exacerbates the difficulty of feature extraction because the image quality is uneven.In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods.In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement.In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet.It reduces the dependence of the model on training data and improves the feature representation ability of different instances.In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information.Enhanced feature representation capability helps to improve the recognition performance of the model.We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts.The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP .
Design of a high-speed data acquisition card for continuous-variable quantum key distribution

The Continuous-variable quantum key distribution (CVQKD) technique is one of the practical uses of quantum information technology. CVQKD system is implemented based on existing optical networks and standard telecommunication technologies. CVQKD can be put into use of the information transmission in city-based areas, within the extent of absolute security and without any restrictions of security. To reduce the experimental system to a commercial prototype, a data acquisition (DAQ) card can be used to replace the DAQ equipment in the laboratory. It can save the cost of the system experiment and commercial application. In this paper, through the high-speed DAQ card, we implement the collection and uploading of the electrical signal, when the light passes through the continuous optical coherent state detector. The whole experiment test system consists of a field-programmable gate array (FPGA), an analog-to-digital converter (ADC) chip, and optical components. This paper provides a method of gain adjustment of the balanced homodyne detector and a way to test the &pi; phase voltage of the optical modulator. The results show that the DAQ card can be used in the development of the CVQKD prototype.
A novel technique of sign language recognition system using machine learning for differently abled person

Sign language is a language used by deaf and dumb people to communicate through hand gestures or facial expressions combined with non-manual elements. Various automotive tools and software have been developed by many developers, but they require hardware and an Internet connection, which adds to the cost of the software. In this paper, the presented software captures the hand gesture, and with the help of various machine learning optimization algorithms such as Stochastic Gradient Descent (SGD) and Adam (optimizer), the accuracy will be determined to give predictive value. With the help of computers, it could be a new way of learning for deaf and dumb people. During such a pandemic, various group learning apps and software have been developed for the purpose of conducting online classes, but they are useful for ordinary people. Using this technique will help deaf and dumb people with online learning.
Learn to Ask What You Don’t Know

Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, question generation is a challenging topic in Natural Language Understanding. In this work, we propose a task called “question generation with masked target answer,” which emphasizes asking questions from text passages without providing a target answer. Compared to other related question generation tasks, our task demands rigorous language comprehension and closely resembles the question asking ability of humans. We then propose various sequence to sequence-based models leveraging additional information about the text, such as its part of speech and named entity recognition(NER) tags. Results show that the proposed models perform on par with other related question generation tasks, despite lacking the key answer phrase.