Reviewerly/exHarmony huggingface dataset:
On the Complexity of Lombardi Graph Drawing

In a Lombardi drawing of a graph the vertices are drawn as points and the edges are drawn as circular arcs connecting their respective endpoints. Additionally, all vertices have perfect angular resolution, i.e., all angles incident to a vertex $v$ have size $2\pi/\mathrm{deg}(v)$. We prove that it is $\exists\mathbb{R}$-complete to determine whether a given graph admits a Lombardi drawing respecting a fixed cyclic ordering of the incident edges around each vertex. In particular, this implies NP-hardness. While most previous work studied the (non-)existence of Lombardi drawings for different graph classes, our result is the first on the computational complexity of finding Lombardi drawings of general graphs.
Minimum Uncertainty as Bayesian Network Model Selection Principle

In this paper study, we develop a Bayesian Network model selection principle that address addresses the incommensurability of network features obtained from incongruous datasets and overcomes performance irregularities of the Minimum Description Length model selection principle. This is achieved (i) by approaching model evaluation as a classification problem, (ii) by estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) by utilizing this error estimate to penalize uncertainty in the Minimum Uncertainty (MU) model selection principle. We validate our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on a tRNA structural biology example.
Vehicle Detection Using YOLOv5

Object detection is a computer vision method that allows for the identification and localization of specific classes of objects in images or videos. It goes beyond simple object classification and helps provide a better understanding of the object in question. Object detection has numerous applications, including automating business processes like inventory management in retail. It can detect objects that occupy between 2% and 60% of an image's area and clusters of objects as a single entity. Additionally, it can localize objects at high speeds, typically greater than 15 frames per second. Vehicle detection is a crucial component in the development of autonomous vehicles, enabling them to identify and perceive objects in their environment. It involves identifying and locating vehicles in image or video frames and has various applications in surveillance and security systems. There are different techniques and models for object detection, including traditional image processing methods and modern deep learning networks. Traditional methods like Viola- Jones, SIFT, and histogram of oriented gradients do not require historical data for training and are unsupervised. Popular image processing tools like OpenCV can be used for these techniques. On the other hand, modern deep learning networks like CNN, RCNN, YOLO, ResNet, RetinaNet, and MANet are supervised and efficient for object detection.
A class of LCD BCH codes of length $n=\frac{q^{m}+1}{\lambda}$

Abstract LCD BCH codes are an important class of cyclic codes which have efficient encoding and decoding algorithms, but their parameters are difficult to determine. The objective of this paper is to study the LCD BCH codes of length n = (qm +1) / λ , where λ | (q + 1) is an integer. Several types of LCD BCH codes with good parameters are presented, and many optimal linear codes are settled. Moreover, we present the first few largest coset leaders modulo n = qm + 1 , (qm +1) / 2 , (3 m + 1) / 4, and partially solve two conjectures about BCH codes.
The proposal of new Ethereum request for comments for supporting fractional ownership of non-fungible tokens

During the last couple of years, non-fungible tokens became the most prominent implementation of blockchain technology apart from cryptocurrencies. This is mainly due to their recent association with digital art, but the application of non-fungible tokens has been in the focus of researchers since the appearance of Blockchain 2.0. It was usually tightly coupled with the research on possible applications of blockchain technology in some real-life applications, such as land administration, healthcare, or supply chain management. Since the initial release of the Ethereum blockchain in 2015, until 2022, more than 44 million smart contracts have been created, and out of those that are still active, more than 70% are based on some prominent templates. In the Ethereum blockchain, the creation of nonfungible tokens is usually based on Ethereum Request for Comments 721. In this paper, the authors are proposing the creation of a new standard that would support fractional ownership of non-fungible tokens. Fractional ownership is necessary so non-fungible tokens and blockchain technology could be applied to an even wider number of use cases. This paper also presents an example of a possible implementation of the newly proposed standard in the Solidity programming language.
Two-path Multi-behavior Sequence Modeling

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.
Development of Digital Worksheet Based on Ngerti, Ngrasa, Nglakoni to Improve College Student Lifeskill

The application of technology in education in the 4.0 era can make it easier for students to build their knowledge. The model of this research is the Research and Development of Borg and Gall which was modified by Sukmadinata (2006) conducted to describe the development of a Tringo-based digital worksheet that is suitable for use in lectures on Natural Sciences III to increase students' lifeskill. This article discusses the Develop phase. Data collection techniques using a questionnaire technique. From the results of the study it can be concluded that the Digital Worksheet is appropriate to be used in Science III lectures with the average results of expert validator ratings on each aspect as follows: content worthiness of 32.25 (Very Good), Conformity of presentation with learning approaches of 36 (Very Good), Conformity with didactic requirements is 18.5 (Very Good), Conformity with construction requirements (linguistic) is 20 (Good) and Conformity with technical requirements (graphic) is 26.75 (Very Good). The validation results of tringo-based Digital worksheets are declared to meet valid criteria by the validator with an average total validity of 4.0 (valid). The use of digital worksheets was responded positively by students at 80.4% and Lecturers of Natural Sciences III at 84.4%.
Otsu and Double-Dimensional Clustering With Wingsuit Flying Search Algorithm (WFSA) For Multi-Level Thresholding In Image Segmentation

In recent years image segmentation plays very vital and important role in branch of computer vision.This process of segmentation is divides pixels of particular image into the groups related to image.here in this proposed algorithm we use another segmentation method which based on Otsu and Double-Dimensional Clustering with Wingsuit Flying Search Algorithm (WFSA) for multi-level thresholding in image segmentation (ODDC-WFSA-MTIS) is proposed.this algorithm having fast multilevel segmentation method as compared to other.Segmentation needs threshold level to separate the object from the background to read the image properly and identify meaningful content of an image.In this era it has found more attention of researchers.Thresholding is a process, used to split the image pixels in to different group/cluster by comparing the intensity value of image pixels with reference threshold level.The threshold value split the image pixels in a way that pixels having lower intensity-value than fixed threshold level belongs to one cluster while pixels whose intensityvalue is more than fixed threshold level belongs to another cluster.Image segmentation method is useul for face recognition, medical image processing image analysis, object detection.In our proposed algorithm we have study about the thresholding technique of image segmentation and implemented in Wingsuit Flying Search Algorithm (WFSA) is used to optimize threshold, and then uses this thresholding value to segment the image.
From trust to truth: Advancements in mitigating the Blockchain Oracle problem

Smart contracts gave a new dimension to Blockchain applications. Subsequently, Blockchains evolved from decentralized cryptocurrency ledgers to platforms for developing decentralized applications. A decentralized application needs a variety of data from the real world that cannot be directly provided by smart contracts. Oracles provide a mechanism to input data to the smart contracts for decentralized applications in a trusted manner. Oracles are the entities that collect data from data sources, verify it and then transmit it to the Blockchain. The introduction of Oracles has brought back the issues of centralization, single point of failure and reliance on a trusted third party in the Blockchain ecosystem. Oracles have adopted different means to reduce centralization and achieve some trustless operating mechanisms involving decentralized algorithms based on voting or reputation. In this paper, we analyse the concept of the Blockchain Oracle problem, its effects on various Blockchain applications, various Oracle types and trust mechanisms. We also analyse some of the significant Oracles (like chainlink, provable), and their underlying mechanisms and compare them. In the end, we carry out a gap analysis and discuss some open research issues that must be addressed to achieve robust and secure decentralization.
Application of Microsoft Teams Applications in History Learning

The Microsoft Teams application is one of the learning applications that can be applied to online distance learning during the covid-19 pandemic, with the application of the Microsoft Teams learning application, teachers can increase students' interest/motivation in learning while they are at home. This study aims to analyze the implementation of history learning, the application of the Microsoft Teams application, and the obstacles encountered by history teachers using the Microsoft Teams application in learning history. This type of research uses qualitative research with a case study approach. The subjects in this study were 1 history teacher and 5 students in class XII IPS 5 whose learning was done through the Microsoft Teams application. The trial of this research was in class XII IPS 5 with 35 students. Data collection methods used interviews, observations and take notes. Analysis of the data used is the Analysis Interactive model of Miles and Huberman. The results showed that learning history during distance learning using the Microsoft Teams application can still run smoothly, but this condition can also affect the interaction of teachers and students in learning. The teacher has been able to apply the Microsoft Teams application in learning because there are media that support the teacher during online learning, the learning media in question are the meter, quiz, padlet and group room. The obstacles encountered by the teacher in learning history by implementing the Microsoft Teams application were mainly on the internet network so that students were quite passive in carrying out history learning.
A historical comparison of social media influencer’s effect on purchase intention of health related products: A longitudinal pre-post Covid 19 study using an Artificial Neural Network (ANN) and Structural Equation Modeling (SEM)

Purpose: This research examines the connection between social media influencers and consumer buying intent in a longitudinal study studying the historical difference pre and post covid 19. This article utilizes the concepts of source reliability and TRA to determine what aspects of influencers—such as Trustworthiness and credibility influence consumers’ purchase decisions of health related products. Methodology: This study’s data collection used a Google Form survey of 28 predetermined questions. In total, 324 valid questionnaires were submitted by the respondents. The vast majority of these respondents were students. With the help of the Smart PLS 4 program, the study performed a partial least squares structural equation modeling (PLS-SEM) analysis of the data. The result was late cross-validated using Artificial Neural Network ANN. Findings: The data shows a positive correlation between an Influencer’s credibility and consumer decisions. The influencers’ credibility is the most critical factor in determining whether they will purchase. In particular, credibility is the most crucial factor influencing a customer’s purchase. This research shows that people’s (students’) reactions to influential figures have evolved. The Trustworthiness of influencers is a bigger draw for them. Originality: This research studied the influence of social media influencers before covid and after covid by collecting data before covid (a general study), then collecting data after covid and then comparing the result using Multi-Group Analysis (MGA) in smart PLS 4. Practical Implications: Successful social media influencer marketing is attributed to the expertise and credibility of the influencer, the product’s match within the influencer’s specialty, and the engagement of the target audience. Using this information, marketers can identify relevant influencers for their products and develop campaigns that appeal to the requirements and desires of their target demographic.
SOUND – MUSIC – CULTURE: SPACE-TIME REFLECTION

The article presents reflections on the metaphysics of music, first of all, by posing the eternal question, which is difficult to answer: "What is music?"; Why is it, along with literature, painting, religious creativity, always at the boundary of acute socio-cultural discussions, becoming a stumbling block in endless disputes about the meaning of being? In this series, the definitions of music are clearly read by thinkers and musicians. The methodology of the “sphere of soundmusic” concept is essentially as universal as the sphere of culture and religion; that is why scientists and theosophists of antiquity and the Middle Ages often used music as a phenomenon to explain the world order and the complex mechanisms of the human soul. Music in all its aspects, therefore, has already been recognized for many centuries as something exceptionally suitable for building models that help to understand areas no less material than itself. The author comes to the conclusion that in people gifted with exceptional creative impulsiveness, inquisitiveness, volitional struggle means one of the important properties of his work. Pulsating feelings and thought of the creator, passion, emotionality, volitional aspirations give rise to a certain style of musical thinking, individual in its manifestations, but based on the main spiritual and creative foundations of the time. Not an individual, but rather a confessional, intimate-personal aspect of the confessional expresses a special symbolic dominant of culture and is especially clearly manifested in music.
Machine Learning and Image Processing for Character Recognition: An Application in Handwriting Instruction

This research paper presents an innovative application of machine learning and image processing techniques for teaching handwriting skills.The proposed system utilizes a convolutional neural network (CNN) to accurately recognize characters written by students and provides valuable feedback on their handwriting.To enhance the input images' quality, an image processing algorithm incorporating edge detection, binarization, and noise reduction is employed.The system's potential applications extend to educational settings, where it can offer personalized feedback on students' handwriting skills, and to individuals seeking to improve their handwriting abilities through systematic practice.Feedback is provided on crucial aspects such as letter formation, size, spacing, and slant, enabling users to address specific areas for improvement.Experimental results demonstrate that the proposed system achieves high recognition accuracy for both uppercase and lowercase letters.Additionally, the system successfully identifies errors in input images, enabling targeted feedback to highlight areas requiring improvement.Leveraging recent technological advances, this research emphasizes the use of tablets as a tool to foster independent learning while respecting the perspectives, knowledge, and preferences of teachers.
An anchor-free pedestrian detection algorithm based on visible-thermal feature fusion

Visible and thermal modalities are strongly complementary in object signal representation. Using the two modalities simultaneously is beneficial to reduce the impact of illumination variation on pedestrian detection. To effectively utilize multimodal information, this paper proposes an anchor-free multimodal pedestrian algorithm. First, a modal feature fusion module is proposed, which executes modal fusion by decaying dense connections and combines convolution with the self-attention mechanism to account for local and global information between the modalities. Secondly, through the multiwindow global context module and the pyramid feature fusion module, a new feature pyramid network enhanced by global context information is proposed. On the visible-thermal pedestrian detection datasets KAIST, CVC-14 and LLVIP, the proposed method achieves 5.67%, 20.51% and 2.21% average miss rate respectively, which is better than the mainstream algorithms.
Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine)

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".
Estimasi Data Insight Social Media Ads Menggunakan Neural Network, Linear Regression dan Deep Learning

PT IlmuKomputerCom Braindevs is a professional training company that sells products in the form of training services (courses). In service companies such as PT IlmuKomputerCom Braindevs Sistema, holding courses with high demand is the key to increasing company profits. The marketing division is a very important division in the context of holding a course/training. To manage the target participants needed in organizing training. In addition, PT IlmuKomputerCom Braindevs also needs to estimate advertising costs and ad duration in the training promotions that will be held. To analyze the marketing division using data mining techniques and the Cross-industry standard process for data mining (CRISP-DM) method to obtain the desired estimate. So to get the final result of the participant's estimated value, ad duration and ad cost, an algorithm that has the most accurate accuracy is needed according to the reference from the results of the comparison of algorithms by looking at the value of RMSE (Root Mean Square Error). The closer the resulting value is to 0, the better the estimated accuracy of the RMSE (Root Mean Square Error) estimate will be.
BIOECOLOGICAL RESEARCH PROJECTS OF SCHOOLCHILDREN (METHODOLOGICAL TRAINING OF STUDENTS DURING PEDAGOGICAL PRACTICE)

The methodology and content of the project activities of students in biology is relevant in a modern school and should be applied in educational activities. Project activities have a fruitful effect on the personality of students, include them in social and research work. This work provides for the implementation of a system of sequential actions, makes it possible to express one's opinion and realize individual goals and objectives, to realize ideas, forms competencies aimed at searching and analyzing information, collecting data, observing and experimenting, helps develop communication skills and contribute to the product by group or individual work. Also, students most often acquire competence in the use of ICT. Each student acts simultaneously as an organizer, executor, expert of activity. Since the biology curriculum includes knowledge from many academic disciplines, the project activity clearly implements interdisciplinary connections. The knowledge acquired by students in the course of project activities is more solid. The analysis of work on project activities shows that students increase the level of formation of key competencies (problem setting, goal setting, planning and evaluation of the result, search and processing of information, written communication, oral presentation, group work), which is recommended in the Federal State Educational Standard.Based on the data of various events for students in the Samara region, an analysis of the topics, content and quality of projects in biology and ecology was carried out, the main shortcomings were identified, and recommendations were given for their elimination. Developed, implemented and presented as examples of projects in grades 5-9 on the general topic «Medicinal plants». The proposed study is of practical importance, since the materials and conclusions can be used in the organization of educational and scientific and educational work of schoolchildren in the process of bioecological education and methodological training of students in the course of industrial pedagogical practices on the basis of Samara State University of Social Sciences and Education.