Reviewerly/exHarmony huggingface dataset:
Ontological analysis of the advanced engineering school project

The project of organizing an advanced engineering school is described based on the analysis of the main entities that make up the structure of this project. It is emphasized that the position of an advanced school is ensured by the choice of a frontier task, and the engineering orientation is associated with production activities in the framework of the creation of innovative products. To achieve this goal, the school forms a symbiosis between an educational institution and a high-tech industrial enterprise. Finally, the main essence of the school involves the training of qualified personnel, and, consequently, educational activities. The innovative nature of the school is also linked to the selection of qualified personnel from the educational institution and experienced production workers from the industrial partner to provide mentoring. A feature of this technology is a reduction in the volume of training, with an increase in time for considera-tion of problem resolution procedures; the time of experimentation, analysis, comparison increases; the usual tasks for independent work are replaced by instructions to sort out the issues, while the time for the solution and the place are not specified in any way; classroom lessons are replaced by individual consultations; instead of general programs, individual and group plans are developed; traditional training is carried out by the very forms of organizing cooperation; the educational and developmental work of the teacher is replaced by his participation in solving common problems; internal regulations are adopted by the students themselves; discipline is ensured by the requirements of one's own and collective security and joint development; there are no groups in a general sense, but a community of like-minded peo-ple.
Building Business Intelligence Data Extractor using NLP and Python

Abstract: The goal of the Business Intelligence data extractor (BID- Extractor) tool is to offer high-quality, usable data that is freely available to the public. To assist companies across all industries in achieving their objectives, we prefer to use cuttingedge, business-focused web scraping solutions. The World wide web contains all kinds of information of different origins; some of those are social, financial, security, and academic. Most people access information through the internet for educational purposes. Information on the web is available in different formats and through different access interfaces. Therefore, indexing or semantic processing of the data through websites could be cumbersome. Web Scraping/Data extracting is the technique that aims to address this issue. Web scraping is used to transform unstructured data on the web into structured data that can be stored and analyzed in a central local database or spreadsheet. There are various web scraping techniques including Traditional copy-and-paste, Text capturing and regular expression matching, HTTP programming, HTML parsing, DOM parsing, Vertical aggregation platforms, Semantic annotation recognition, and Computer vision webpage analyzers. Traditional copy and paste is the basic and tiresome web scraping technique where people need to scrap lots of datasets. Web scraping software is the easiest scraping technique since all the other techniques except traditional copy and pastes require some form of technical expertise. Even though there are many webs scraping software available today, most of them are designed to serve one specific purpose. Businesses cannot decide using the data. This research focused on building web scraping software using Python and NLP. Convert the unstructured data to structured data using NLP. We can also train the NLP NER model. The study's findings provide a way to effectively gauge business impact
Using Open Access Research Discovery Solutions, Dimensions, to Increase Research Outputs &amp;amp; Visibility for Zambia Academic Community

Training Centre in Communication (TCC Africa) was honored to host the Zambia Academic Community in a workshop on using open access research discovery solutions to Increase their research output and visibility.
Load-Balancing Implementation With An Algorithm Of Matrix Based On The Entire Jobs

Cloud computing is the on-demand delivery of IT resources over the internet with pay-as-you-go pricing. Instead of purchasing, retaining, and managing personal data centers and servers, you can access technology services, such as computing capability, storage, and databases, on as-necessary support from a cloud provider. Cloud computing is internet-based computing. There are a more significant number of servers associated with the system to provide many kinds of network services to give cloud clients. Deficient quantities of servers related to the cloud have to produce extra per task at a time. So, it is a complex issue to perform all tasks at a peculiar time. Some systems carry out all tasks, so there is a need to readjust all amount of loads at a time. Load balancing adjusts the closure time and presents all tasks, respectively. There is not available always to remain a corresponding amount of servers to carry out the same tasks. Tasks to be dealt with in cloud computing would be higher than the linked servers. Inadequate servers have to go on a million volumes of tasks at a time. We propose algorithms where some machines 110perform the jobs here. The jobs are higher than the machines and balance all machines to exploit the quality of services in cloud computing.
Clarifying the Positive Thoughts Structure of Sport Societal Values by Neural Network Approaches in COVID-19 Pandemic

Purpose of this study was to clarifying the relationship structures with social values of complex sports and positive thought under today's COVID-19 social anxiety by neural networks and network-centric analysis.The Value items were the terminal ones (benevolence, equality, accomplishment, global stability, hope for future, proud country, family bond etc.) and the instrumental ones (consistent, creative, light hearted, honest, ambition, creative, polite, etc.).The subjects (university students; n = 221) received an explanation of the study's structure and of various sports-related benevolent activities such as donations, charity matches and voluntary disaster relief activities.Next, they were asked, 'What types of values do you recognize in benevolent sports activities in world-wide health anxiety under COVID-19 pandemic?' Neural network model, random forest model and correspondence analysis were executed and discussed some relation structure of societal human values of sport with the positive thoughts under COVID-19 pandemic.Current study shows the possibility of supporting the understanding of the complex, gradual, transitive, and central structures of diverse sport values by approaching network analysis.As a result, it was clarified that the social wisdom of these sports would be a desire to support the survival of the physical society more creatively.Under the social unrest in the advanced information society, the importance of tactile communities such as sports was confirmed.
The Relationship Between Auditory Memory Digit And Receptive Language Ability Of Preschool Age Child In Surakarta

Background: Receptive language is the ability to understand what is seen and heard to help children develop listening skills, identify concepts through understanding the labeling of words, and improve the ability to respond to any communication. Auditory memory involves the ability to retrieve information presented orally. This study analyzed the relationship between auditory memory digit and preschool-age child receptive language ability.&#x0D; Methods: This study was quantitative research. The research design used in this study was an observational correlation with a cross-sectional approach. The research sample consisted of 58 students in three Private Kindergartens existing in the Ministry of Education and Culture of Surakarta. Auditory memory testability of sample using hearing forward test and receptive language ability of sample using Assessment of Children's Language Comprehension form. Hypothesis testing using the Chi-Square hypothesis.&#x0D; Results: The result it can be concluded that auditory memory digit does not have a relationship with receptive language ability in preschool-age children because significance value more than 0.05 (ρ&gt;0.05) with an OR value of 0.857, this means that the auditory memory digit is above average has a meager chance of influencing receptive language ability above average, which is only 0.857.&#x0D; Conclusion: The dominant factor influencing the receptive language skills of children aged 3-6 years is communication during interactions or activities with parents, such as reading fairy tales or telling stories. Receptive language skills predicted symbolic understanding as reflected in picture comprehension and how language skills are interrelated with social skills.
Coherent light absorption and photon number discrimination (Conference Presentation)

Interference on thin-film and metamaterial absorbers enables coherent control and processing of quantum light. Recently, this phenomenon was used to demonstrate deterministic control of photon absorption probability, quantum states filtering, anti-Hong-Ou-Mandel interference, and application of geometric (Berry) phase for remote control of light dissipation. Here, we expand these ideas by introducing the regime of distributed coherent absorption where light quanta are absorbed within spatially separated active layers. We show that this scheme allows photon number discriminating detection free from the limitations of conventional temporal and spatial multiplication approaches. Free space and integrated designs are discussed.
Quantum nonlocality in an urban network combining SPDC- and quantum dot-based photonic hardware (Conference Presentation)

The prospect of quantum networks is pushing technical advances in entangled photon generation, and different solutions have now the potential to coexist. After reviewing the development that led to implement entanglement-based quantum key distribution using a quantum dot—a technology motivated by the goal of on-demand operation—in an urban free-space optical link, we present its extension to a three-node quantum network including a source based on spontaneous parametric down conversion. Using separable measurements, we combine intrinsically independent sources to demonstrate a significant violation of a Bell-like inequality associated to nonlocal correlations in a tripartite hybrid network.
AO-SF: Alignment Optimization and Sample Fusion for Unsupervised Nighttime Segmentation

Among the existing nighttime domain adaptation methods, the semantic consistency of the same features is largely ignored in the process of semantic alignment, which leads to negative shift and distribution discrepancy between the source and target domains. To address this problem, we propose an unsupervised nighttime semantic segmentation network based on semantic alignment optimization and sample fusion (AO-SF), which can make a contribution to the segmentation performance of our nighttime model. Specially, the AO-SF constructs a nighttime generator with mutually exclusive classifiers in order to enhance the alignment of each feature. Moreover, we further design an image fusion model to leverage shared information between day-night image pairs, which can boost the quality of relighting network. The experimental results on Dark Zurich and Nighttime Driving show that the segmentation performance of our method is superior to that of other methods in unsupervised nighttime semantic segmentation tasks. The mIoU reaches 45.2% and 46.5%, respectively.
PAHD-YOLOv5:Parallel Attention and Hybrid Dilated Convolution for Autonomous Driving Object Detection

Object detection in autonomous driving scenarios is a complicated task. As onboard cameras always move, the object scale varies violently, which makes object distinction and localization difficult. To solve the issues mentioned above, we propose PAHD-YOLOv5 by improving YOLOv5. Firstly, the backbone is reconstructed using hybrid dilated convolution combined with a cross-stage partial network, whose role is to extract the contour information of objects and maintain the resolution of feature maps. Secondly, we give the same priority to channel attention and spatial attention, causing the network to modulate the channel and space weights simultaneously, whose role is to focus on the interest region of feature maps. Our proposed PAHD-YOLOv5 improves YOLOv5s by 2.3 mAP and 2.89 mAP on KITTI and BSTLD benchmarks, and the inference speed has reached 159FPS and 124.9FPS. PAHD-YOLOv5 shows its outstanding advantages in speed and accuracy compared with other advanced detectors.
The Small Target Recognition Method of Neural Network Based on Spatial and Temporal Information

In the aerospace applications, due to the long observation distance, the target to be recognized often exists in the form of a point target in the image. The recognition of small targets has become an urgent problem to be solved. The grayscale, texture, shape and other characteristics of the small target cannot be directly used for recognition. Due to the characteristics of the small targets are very limited, the traditional target recognition methods cannot obtain better recognition accuracy. In order to solve the above problems, this paper proposes a dual-stream small target recognition network. The network includes two branches, one of which is a traditional convolutional neural network (CNN) to extract spatial information, and each image is extracted through the network in turn. The other branch uses the Temporal convolutional network (TCN) to extract temporal information for consecutive images. The proposed method not only uses the spatial information of the image itself, but also combines the temporal information of consecutive images to complete the recognition task, which obtains better target recognition accuracy. Experimental results also verify the effectiveness of the proposed method.
Comparative Analysis of Customer’s Face to Face Interaction using Linear Regression over Random Forest Algorithm with Increase in Accuracy of Prediction

To Perform the Comparative analysis of Customers face to face interaction by comparing Linear regression algorithm and Random forest algorithm. Material and Methods: The pattern size for this innovative telecommunication undertaking was 60 samples (Group 1=30, Group 2=30). For the telecom interaction the available techniques are quick, efficient and more accurate. The main problem in this problem is to derive the data from the different fields which are dependable to predict the accuracy. To analyze using Random forest algorithm technique. It allows you to find the element among the subset that brings to improve a wide variety of ranges to develop the trees. Results: The Random forest set of rules gives higher overall performance with advanced accuracy (97%) than the Linear regression algorithm accuracy (79%). There is no statistical significance between Random forest and linear regression (p=0.767). Conclusion: Random forest algorithm shows better performance based accuracy compared with the Linear regression.
Application of DIAdem in while-drilling vibration data analysis

While-drilling vibration data acquisition module has the characteristics of collecting more data and high acquisition frequency, which leads to the problems of long processing time, low efficiency, and poor visualization in traditional vibration data analysis methods. In order to solve these problems, this paper proposes the application of DIAdem in the analysis of while-drilling vibration data. It introduces some powerful functions of DIAdem software in detail. Based on DIAdem software to process while-drilling vibration data, data correction, time-frequency domain analysis, automatic operation can be completed. The processing time is short, high efficiency, and has excellent visualization.
PEMBUATAN SISTEM PERHITUNGAN HPP DAN PERSEDIAAN BANK SAMPAH INDUK CERIA DI DESA PEGANDEN KECAMATAN MANYAR KABUPATEN GRESIK

Peganden Village is one of the villages in Manyar District, Gresik Regency, East Java Province. The large number of people who live there makes a lot of household waste generated by the community. The Waste Bank is an alternative to manage and organize the collected waste. In its management, the Waste Bank also makes Financial Reports. Therefore, the undergraduate students of the Accounting Study Program of the Regular Blended KKN Group 11 Muhammadiyah University of Gresik in Peganden Village made the HPP system and the Inventory of the Cheerful Main Garbage Bank which aims to make it easier to manage Financial Reports systematically. The socialization was carried out using an understanding method to the participating partners, namely the Treasurer of the Ceria Induk Waste Bank. The success of the implementation of the socialization can be seen from the application of the HPP Calculation System and the Inventory of the Ceria Main Waste Bank by the participating partners.
Pembelajaran Struktur Data Berbasis Visualisasi dengan Menggunakan Visualgo.Net

Abstract: Purpose: The purpose of the activity include preparing Prambanan Sleman High School students who will take part in the National Science Competition, especially in the field of Informatics. One of the preparations made is assistance on data structure material. Data structure is an important material in learning programming. Method: The activity method is provided by providing assistance in the process of providing data structure material that is adjusted to the syllabus including primitive data types, arrays, strings, stacks, queues, binary heaps, disjoint sets and segment trees. The material is packed with visualization-based explanations. Visualization learning is given using the visualgo.net application. Result: By using visualization, students who take part in the mentoring have experience of the process and logic in creating and understanding data structures. The results of the implementation, students get data structure material based on visualgo.net and explained how the data structure algorithm process is related to visualization-based. Conclusion: Data structure learning is given by using the visualgo.net application. Visualgo.net can help students in the process of studying data structure material. Visualization-based learning in studying data structure material is very helpful in the process of learning programming material. In this activity, students get an explanation of the algorithm used in the data structure, both with theory and with visualization-based explanations. Keywords: 1. Competition 2. computer 3. data structure 4. visualization 5. students
A study of ‘nari’・‘tari’ on glossed reading of Lunyu (the Analects) in Middle Ages of Japan

This paper focuses on ‘nari’ and ‘tari’, which were used as assertive auxiliary verbs and ending of a nominal adjective in the diacritical material of Mubatsu-bon Lunyu Jijie vol. 1, which indicated the standard glossed reading of Lunyu in the Middle Ages of Japan.BR There are 75 examples of ‘nari’ used as an assertive auxiliary verb. Among these cases, 47 examples are used in the end form, which is most frequently used. Of these 47 examples of the end form, 27 examples do not show an empty word that performs assertion in the original Chinese sentences.BR Word classes that precede the assertive auxiliary verb ‘nari’ are nouns, verbs, conjugated words with attributive form, and adverbs. In the case of ‘nari’ connected with the attributive form of conjugated words, some are semantically similar with ‘noda’ in present Japanese as pointed out in previous studies, but it is also confirmed that some show a function to make a copula sentence with noun predicative complement.BR There are 15 examples where ‘tari’ is used as the assertive auxiliary verb and is attached to nouns. Among these, 8 examples are glossed on ‘爲’, which has a similar function with ‘tari’, in the original Chinese sentences. Further, there are 2 examples where ‘爲’ may be read as the verb‘naru’, but is read as ‘tari’.BR There are 66 examples of ‘nari’ that is used as an ending of a nominal adjective. Among these cases, Chinese characters in the stem are read in Kundoku (Japanese reading) in 20 examples and are read in Ondoku (Sino-Japanese reading) in 46 examples. In the case where the same Chinese character is used in stem, it was revealed that there is a difference in meaning between ‘Ondoku+nari’ and ‘Kundoku+nari’, and this is related to contexts of annotation of Lunyu Jijie.BR There are 50 examples of ‘tari’ used as an ending of a nominal adjective. It was revealed that ‘tari’ as an ending of a nominal adjective is glossed on the Chinese character for onomatopoeia representing a situation or state in classical Chinese. Further, all Chinese characters for stem are glossed with Sino-Japanese readings.
Constructing a natural language database in the new media era: Focusing on the KWIC Video Automatic Generation Technique

Collection of examples is an important part that determines the basis of language research on actually used language data. The conventional collection method of usage examples can be primarily divided into two cases: (i) A case where a researcher personally collects examples from media, such as newspapers or novels, and (ii) a case where a researcher uses a public corpus constructed based on these media. However, the distinction between such cases became diluted with the emergence of new media. This is because of the shift that occurred from a print-based to a digital-based era, allowing anyone, including individual researchers, to collect examples and build a corpus easily. The software and online homepage introduced in this study are typical examples of easy establishment of a “multimedia corpus equipped with ‘keyword in context’ (KWIC) video automatic generation technique” based on user videos. Further studies need to be conducted to examine the characteristics of language research in an era where the boundary between collection of examples and the corpus construction is blurred.
Computer Graphics and VR/AR: From Clouds, to Cartoons, to a 3-D Virtual Museum

Associate Editor-in-Chief Richard Zhang outlines published articles in this regular queue issue.
Helmet Wearing Detection Algorithm Based on Improved YOLOX

Aiming at the importance of helmet detection in the current construction industry, an improved YOLOX helmet wearing detection algorithm is proposed. The MobileNetv3 network after optimizing the attention module is used to replace the backbone feature extraction network of YOLOX-s, so as to optimize the convergence ability of the model and greatly reduce the amount of network parameters; Then fusion factor in FPN is introduced to improve the detection accuracy of small targets. The experimental results on the helmet wearing dataset SHWD (safety helmet wearing dataset) show that the mAP value of FF-YOLO algorithm proposed in this paper reaches 91.84%, which is 1.21% higher than that of YOLOX-s model, and the parameters of this model are 37.8% lower than that of YOLOX-s model.
Unsupervised Specific Emitter Identification Based on Feature Parameter Fusion and Adaptive Clustering

In recent years, satellite communication technology has achieved great development and the security threat becomes more severe. The identification of legal satellite emitters is important to enhance the security of satellite communication systems. In this paper, we propose an unsupervised specific emitter identification (SEI) method based on feature parameter fusion and adaptive clustering. We designed a feature extractor which fuses diverse feature parameter extraction methods. Then, an adaptive clustering algorithm is introduced to achieve higher accuracy and efficiency in non-cooperative communication scenarios. Experimental results show that the proposed method outperforms existing unsupervised SEI in terms of recognition accuracy.