Reviewerly/exHarmony huggingface dataset:
Garbage Recognition Algorithm Based on Self-attention Mechanism and Deep Sorting

Garbage sorting plays a very important role in ensuring a life safety. Aiming at the problems of poor real-time detection and low recognition accuracy of current garbage classification, an improved multi-objective real-time garbage classification recognition algorithm based on YOLOv5s is proposed. By combining the Coordinate Attention (CA) self-attention module and the neck part of YOLOv5s, the defect of insufficient receptive field is reduced to improve the detection accuracy, and the DeepSort algorithm is introduced to optimize the multi-target garbage feature recognition and strengthen the real-time detection ability. The experimental results show that the improved YOLOv5s garbage classification detection model can effectively identify 44 different types of garbage. Compared with the original YOLOv5s algorithm, the detection mAP value is 76.56%, an increase of 11.3%, and the precision is 85.63%, an increase of 12.38%. While ensuring the multi-target real-time detection efficiency, the improved algorithm can have better detection accuracy Rate.
High-Speed Anonymous Device Authentication Without Asymmetric Cryptography in the Internet-of-Things

A large portion of end-point and edge devices in the Internet-of-Things (IoT) are constrained in computational power and bandwidth, which means they cannot easily afford the resource-consuming asymmetric cryptography such as Diffie-Hellman key exchange and RSA-2048 digital signatures. On the other hand, these devices are still confronted with similar threats against conventional devices in authenticity and privacy. In this paper, we present a high-speed authentication protocol for resource-constrained devices in IoT, which provides both authenticity and anonymity without any use of asymmetric cryptography. Moreover, we show how our protocol can be extended for devices that can execute symmetric encryption for secure data transmission. The security and anonymity of the new protocols have been analyzed comprehensively, and the evaluation demonstrates their efficiency.
Russian hackers-for-hire pose multiple risks

Headline INT: Russian hackers-for-hire pose multiple risks
Blind denoising of dental X-ray images

The paper considers a problem of automatic analysis and noise suppression in dental X-Ray images, e.g., in images acquired by dental Morita system. Such images contain spatially correlated noise with unknown spectrum and with standard deviation that varies for different image regions. In the paper, we propose two deep convolutional neural networks. The first network estimates the spectrum and level of noise for each pixel of a noisy image, predicting maps of noise standard deviation for three image scales. The second network uses the maps as inputs to suppress noise in the image. It is shown, using modelled and real-life images, that the proposed networks provide PSNR for dental X-Ray images by 2.7 dB better than other modern denoising methods.
Towards an image-computable visual text quality metric using deep neural networks

Image quality metrics have become invaluable tools for image processing and display system development. These metrics are typically developed for and tested on images and videos of natural content. Text, on the other hand, has unique features and supports a distinct visual function: reading. It is therefore not clear if these image quality metrics are efficient or optimal as measures of text quality. Here, we developed a domain-specific image quality metric for text and compared its performance against quality metrics developed for natural images. To develop our metric, we first trained a deep neural network to perform text classification on a data set of distorted letter images. We then compute the responses of internal layers of the network to uncorrupted and corrupted images of text, respectively. We used the cosine dissimilarity between the responses as a measure of text quality. Preliminary results indicate that both our model and more established quality metrics (e.g., SSIM) are able to predict general trends in participants&#x2019; text quality ratings. In some cases, our model is able to outperform SSIM. We further developed our model to predict response data in a two-alternative forced choice experiment, on which only our model achieved very high accuracy.
A Novel Strategy to Achieve Video Transcoding Using Cloud Computing

One of the fundamental challenges faced in deploying multimedia systems is delivering smooth and uninterrupted audio-visual information anywhere and anytime. In such systems, multimedia content is compressed within a certain format, this requires format conversion for various devices. Thus, a transcoding mechanism is required to make the content adaptive for various devices in the network. Video transcoding converts one digitally encoded format into another, this involves translating any file format containing video and audio at the same time. This is an essential feature for devices that do not support a specific format of media or have limited storage that requires a reduced file size. Through this paper, we provide a novel way of transcoding the block-based video coding schemes using cloud architecture by establishing a video pipelining architecture. The solution discussed in this paper would enable the end users to extract videos in any format and resolution seamlessly, combined with the scalability, reliability, and cost-effectiveness of the cloud. The proposed idea would be lucrative for all the video streaming applications that are currently relying on their legacy infrastructure for video transcoding.
Multi-label Classification Performance using Deep Learning

Understanding and using extensive, elevated, and heterogeneous biological data continues to be a major obstacle in the transformation of medical services. Digital health records, neuroimaging, sensor readings, and literature, which are all complicated, heterogeneous, inadequately labelled, and frequently unorganized, are all growing in contemporary biology and medicine. Prior to building prediction or sorting designs in front of the attributes, conventional information retrieval and statistical modelling predicates need to do data augmentation to extract useful and more durable features from the information. In the case of complex material and inadequate technical understanding, a variety of problems along both phases. The most recent convolutional technological advancements offer new, efficient frameworks to create end-to-end teaching methods from massive information. Therefore, in paper, we examine the most recent research on using deep techniques to improve the medical field. We propose that deeper learning technologies may be the means of converting large-scale physiological data into enhancing human ability based on the reviewed studies. We additionally draw attention to some drawbacks and the requirement for better technique design and application, particularly in terms of simplicity of comprehension for subject matter experts and social researchers. In order to bridge deeper learning models with natural interpretability, we examine these problems and recommend creating comprehensive and meaningful decipherable architectures.
Joint structured constraint discriminant analysis dictionary learning for pattern classification

Analysis dictionary learning (DL) has been successfully applied to the field of pattern classification. However, it is still a challenge to utilize the local structure information and the class information of samples to improve the discrimination capability of analysis dictionary. We proposed a joint structured constraint discriminant analysis DL (ADL) method (JSCDADL) to learn a structured discriminant analysis dictionary by combining the local structure information and the structured information of samples. Specifically, we first designed an adaptive local structure preserving term (ALSPT) to improve the discrimination capability of analysis dictionary. It adaptively transmits the local structure information of samples to analysis dictionary, which ensures that the same class of samples has similar sparse codes under the action of the analysis dictionary. Then, we designed a discriminative sparse coding error term that forces the coding coefficient matrix to have the desired block diagonal structure. To further enhance the discrimination capability of analysis dictionary, we designed an analysis dictionary combination term by constantly approximating the two analysis dictionaries learned to obtain an analysis dictionary with the local structure information and the structured information of samples. Moreover, we designed an effective iterative algorithm to solve the optimization problem of JSCDADL. Extensive experimental results on six datasets demonstrate that JSCDADL can achieve satisfactory classification performance compared with some state-of-the-art methods.
Implementasi Sistem Informasi Geografis Dengan Metode Poligon Tertutup Untuk Menentukan Area Genangan Rob

Pekalongan City is located on the north coast of Java Island and is in a lowland so it is prone to tidal disasters. Rob happened a few years ago, the longer it turns out that the more frequent and higher it is, this happens because of the massive land subsidence due to global warming so that sea levels rise. Land subsidence that occurs in Pekalongan, especially North Pekalongan, is classified as high per year around 10-34 cm. To solve the Rob problem The government built a giant embankment on the seashore that is expected to inhibit or reduce the rob. Areas that were originally affected by the tidal with the presence of this giant embankment can be reduced, including bandengan, Pasirsari, Customs, Panjang Baru, Panjang Wetan, Pasir Kramat Kraton and Slamaran villages. The problem in this study is that so far there has been no digital map that depicts the rob area and changes in the location of the rob area in the city of Pekalongan. This research was carried out using the Research and Development (R&amp;D) method, a system development method using Waterfall. To describe the rob area using polygons and to depict the map using google earth, as for determining the coordinates of the location using Latitude and Longitude. Data collection using Observations, Interviews and Questionnaires. The purpose of this study is to make a Geographic Information System application mapping rob areas and their changes based on year criteria with Web-based multi-layer in Pekalongan City. The results of this polygon depiction are stored in a KML file and are expected to be a solution for structuring the area, especially the coastal part and for the community is expected to be better prepared to face disasters
Research on Robustness of Financial Accounting Intelligent System Based on Offline Network Data Protection Algorithm

In cloud computing, user data storage and computing process are all carried out in the cloud, and the homomorphic encryption technology is used to realize end-to-end aggregation encryption. The objective requirements and development trends of the construction of financial and accounting information systems of banks in my country are discussed. Transaction and accounting Separated Yinqiao financial accounting information system. The primary task of BPM is to ensure that the business process of the enterprise should be oriented to customers and market demands, and then it studies the robustness of financial information systems, saving energy and bandwidth. At the same time, the track graph topology is constructed so that each node has multiple parent nodes, and when the link between the node and the main parent node fails, other parent nodes can repair the aggregated value.
Research and Practice on the Training of Accounting Talents Based on Information Technology and Internet

At present, education reform is in the stage of deepening development, and the development of education informatization is the key factor to support education reform. In the process of the development of information technology and the profound reform of accounting education, college teachers must actively respond to these challenges and make a difference in the educational reform. Based on this, the practice of financial accounting teaching reform and talent cultivation based on information technology and the Internet in the financial accounting specialty is carried out, and the reform approach of financial accounting teaching mode integrating information technology and the Internet is mainly discussed. Under the influence of "Internet plus", the financial accounting specialty must clarify the goal of talent cultivation, combine the advantages of the Internet, provide rich resources for teachers and students, and achieve interaction and exchange between teachers and students.
Exploring the educational paradigm shift to create a dynamic relationship between artificial intelligence and human beings: Focusing on artificial intelligence secondary textbook analysis

Objectives This study aims to analyze the bias problem of modern science, such as technological determinism contained in artificial intelligence secondary education textbooks, through the theoretical concept of Science and Technology Studies (STS).&#x0D; Methods This study was largely conducted by (1) the survey of public perception of artificial intelligence education and (2) the analysis of contents of current high school artificial intelligence textbooks. A survey method was applied for public perception, and a qualitative analysis was applied for textbook analysis.&#x0D; Results Through the analysis of artificial intelligence textbooks, it was possible to examine (1) technological determinism, (2) gender bias and labor bias, and (3) ethically dangerous contents in artificial intelligence textbooks. These biases and risks divide the relationship between artificial intelligence and human actors in a dichotomous way and limit it to economic aspects, and in the end will prevent a dynamic relationship of both and lead to a rigid or hate.&#x0D; Conclusions Our society tends to limit science &amp; technology textbooks to vocational education use only. Future generations will face unexpected situations while living in conditions where information/data/artificial intelligence are generalized. The main goal of AI curricula should be for civic education, not just only for job-seeking. Citizens in the era of artificial intelligence should be dynamic and participatory users (subjects), not simply passive labors or consumers. In line with this, additional diagnosis and improvement of current artificial intelligence textbooks and curricula are needed.
An Analysis on the Excellence Course in KMOOC: Based on Educational Scalability Analysis Instrument

Objectives The purpose of this study is to analyze design component and an aspect of interaction for verifying how it was designed and managed in K-MOOC excellence courses, which need not additional time investment and endeavor of faculty in spite of a large-scale.&#x0D; Methods 10 excellence courses in the casebook published in 2022 by National Institute for Lifelong Education were analyzed an aspect and level of interaction with ESAI and drawn common element in design among lectures during course period. We analyzed two dimensions, which stand for design and interaction: one from the side of course design is implemented character including learning week, the length of video and the other one, which is related to learning activity, is interaction’s type such as T-S, S-S, C-S and feed-back level of them.&#x0D; Results The result of this study shows that 8 out of 10 lectures consisted of 15 weeks like one semester in university as common design component, and modules and units with variable subtopic followed each week learning content. Learning activities were mainly video, quiz, discussion and homework, a running time of video is around 10-30 min. Interactions like S-C, S-T, S-S were executed evenly among the lectures and the level of feedback was simply responded to learner’s activity.&#x0D; Conclusions K-MOOC course components such as course segmented content, various learning activities, and S-C interaction should be prepared at the phase of course design, not the phase of running course. Also both design element and the strategy of interaction in 10 excellence lectures presented difference from general online course, therefore it needs to be developed more effective and efficient strategy to conjugate K-MOOC.
The Effect of Early Childhood Education Institution's Professional Support Environment on Early Childhood Teachers' Digital Play Support Competencies

Objectives The effect of the professional support environment of early childhood education institutions on the digital play support competency of early childhood teachers was analyzed.&#x0D; Methods A survey was conducted on 844 teachers working at daycare centers and kindergartens in early childhood educational institutions in Seoul and Gyeonggi Province, and SPSS 23.0 and AMOS 23.0 were used to analyze descriptive statistics, reliability and confirmatory factor analysis, and structural equation models.&#x0D; Results First, it was found that there was a positive (+) correlation between the professional support environment of early childhood education institutions, early childhood teachers' perception of play rights, perceived usefulness of digital play, and digital play support competency. In addition, it was found that the professional support environment of early childhood education institutions recognized by early childhood teachers had a statistically significant positive (+) effect on the perceived usefulness of digital play. Second, the mediating effect of perception of play rights and perceived usefulness of digital play was proved in the relationship between professionalism support environment and digital play support competency.&#x0D; Conclusions As a result of the study, the perceived usefulness of digital play as a mediator to increase digital play support competency was found to have a large effect. Therefore, it is necessary to seek ways to increase the perceived usefulness of digital play by early childhood teachers and the positive perception of the right to play.
Does COVID-19 Pandemic Bring the Changes in Schooling Satisfaction of Elementary School Students?

Objectives The purpose of this study is to analyze the factors affecting school life satisfaction of elementary school students before and after the COVID-19 pandemic, and to identify influence factors affecting School Life Satisfaction.&#x0D; Methods On school life satisfaction in K province from 2019 to 2022, a survey measuring democratic school culture, learning-centered education, and communication and caring community as influencing factors for school life satisfaction as a dependent variable targeting 5th and 6th graders in elementary school was conducted, and the final 4,843 response data were analyzed. The collected data were analyzed using the SPSS WIN 22.0 program.&#x0D; Results It is estimated that elementary school students’ satisfaction with school life did not change at the level of general concern before and after COVID-19. Also, it can be seen that it increases, and among the sub-influencing factors(democratic school culture, learning-centered education, and communication and caring community), it can be seen that the democratic school culture factor had the greatest impact on elementary school students' satisfaction with school life.&#x0D; Conclusions There is a research significance in providing policy implications and basic data for school innovation in order to improve the quality of life and happiness of students through school education.
High-speed optical imaging and spatio-temporal analysis of sound sources of edge tone phenomena

Aerodynamic sounds are one of the noises of high-speed trains, automobiles, and wind turbines. To understand the characteristics of those noises, measuring sound sources is important. In general, microphones are used for measuring aerodynamic sounds. However, measuring the sound fields inside flow fields is difficult for microphones because they disturb flows. Thus, optical measurement methods have been applied to visualize aerodynamic sounds. The optical method can measure the sound fields without installing devices inside measurement fields. Therefore, it can capture the sound around sources. In this paper, we performed visualization and spatio-temporal analysis of sound sources of edge tones using parallel phase-shifting interferometry (PPSI). We experimentally confirmed the difference in pressure fluctuations near the sound source depending on the frequency of the edge tones.
Mapping Maintenance Related Information Using the MIMOSA CRIS Standard: A Case Study Within Gravel Road Maintenance

The efficient maintenance of gravel roads depends on digital tools and data as well as information systems adapted for specific needs while conforming with current standards for better interconnectivity. Within gravel road maintenance, cloud-based solutions are a key alternative. This because necessary information, such as road condition data, could be shared throughout the network and provide the opportunity to coordinate maintenance activities between stakeholders. Effective standards are essential since they permit various services with the ability to work together while at the same time supporting differences that ease competition and innovation. Among several maintenance standards, the OSA-CBM and MIMOSA CRIS database schema are intended for handling information exchange and communication within and between systems supporting condition-based maintenance. This paper tests the usability of these standards for translating context-dependent information into a standardized data set. The domain studied is gravel road maintenance. Critical data was gathered and elicited from different stakeholders and represented in the form of a conceptual information model. This paper exemplifies how the conceptual model could be mapped into a logical model with respect to the CRIS information model. The main results are in the form of a conceptual and logical information model for purposes of gravel road maintenance, as well as a procedure suggested for mapping the information model following specific steps.
Characteristic Functions

This chapter is devoted to a few facts, several of which I learned from an interesting set of notes by Bryc [3], about and related to what probabilists call a characteristic function.
Blockchain phenomenon: a penchant to reshape developing economies across Sub-Saharan Africa (SSA)

The enabled-applications of blockchain technology have advanced beyond crypto-currencies to potential transformations in the activities of individuals, businesses and economies. Blockchain technology computationally revamps many sets of processes in finance, manufacturing, healthcare and governance without third-party interventions. From a phenomenon-based approach, we address the potential of blockchain technology enabled-applications like Internet-of-Things (IoT), Smart Contracts and Distributed Ledger Technologies (DLT) to reshaping economic sectors. We also present three main blockchain-enabled models with the cases of Agriculture & Agro-Processing, Mining & Minerals Processing and Finance sectors in developing economies across Sub-Saharan Africa especially Ghana, on management objectives of cost-effectiveness, efficiency, reliability and risk reduction. Blockchain-enabled applications can reshape the supply chains of Agriculture & Agro-Processing to be less costly, Mining & Minerals Processing sector more corrupt-free and efficient with a reduced level of risks, offering efficient banking systems, reduction in privacy risks and operational complexities to the Finance sector.