Reviewerly/exHarmony huggingface dataset:
Recognition Algorithm of Transaction Risk Events in Automobile Financial Market Based on Deep Learning

With the rapid development of economy, people’s material living standard has obviously improved, and people’s demand for material has also become greater. At the same time, a series of traffic environmental problems and technical problems of traffic management have gradually become prominent. In many aspects, the usual traffic management methods are not enough to meet these outstanding problems. In order to solve these problems, the design of intelligent transportation system becomes more and more important. In this paper, the vehicle recognition method based on deep learning is studied. The depth residual network is selected as the basic classification network and improved, and its second convolution block is introduced into dense connection to strengthen the feature flow of different depth convolution layers in the network, so as to improve the feature expression ability of the network. At the same time, random weight averaging and label smoothing are introduced into the residual network. On the one hand, the convergence of the network is stable and the oscillation amplitude is smaller, and the problem of weight oscillation is solved. On the other hand, the generalization ability of the model is strengthened to ensure the accuracy of fine-grained classification. In this paper, through the method of transfer learning, using the knowledge learned from a large number of data in ImageNet database to optimize network parameters, the Stanford CMS Kramp-Karrenbauer 196 data set is marked with fine granularity, divided into training set and test set for model training and learning, and finally effective features are extracted for vehicle classification. The accuracy of the two improved networks can reach 87.4% respectively. The experimental results show that the accuracy of the proposed method is high, which verifies the effectiveness of the method.
Service Robot Active Object Detection based on Spatial Exploration using Deep Recurrent Q-learning Network

Active object detection (AOD) allows robot to approach target object smoothly, but previous studies have assumed that the robot can always see the object from the start. This study introduces a new AOD model that can learn to spatial explore from any position to complete AOD task using a deep recurrent Q-learning network (DRQN). The model combines historical with current state information to accurately predict the action that should be performed at the current moment. This study also introduces a new reward function and termination condition for AOD task to ensure task completion rates and efficiency. This approach has been validated on an active vision dataset (AVD), demonstrating that service robot can actively complete AOD task from random positions through spatial exploration. Our approach has also been found to be superior to other methods.
The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps

Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools like the Google Vision APIs for automatically transcribing text from these maps into machine-readable formats, they do not work well with large-sized images (e.g., high-resolution scanned documents), cannot infer the relation between the recognized text and other datasets, and are challenging to integrate with post-processing tools. This paper introduces the mapKurator system, an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans. The output data, comprising bounding polygons and recognized text, is in the standard GeoJSON format, making it easily modifiable within Geographic Information Systems (GIS). The proposed system allows users to quickly generate valuable data from large numbers of historical maps for in-depth analysis of the map content and, in turn, encourages map findability, accessibility, interoperability, and reusability (FAIR principles). We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection. We also demonstrated a seamless integration of mapKurator with a collaborative web platform to enable accessing automated approaches for extracting and linking text labels from historical map scans and collective work to improve the results.
Exploring the Use of Blockchain Technology in IoT Applications

The integration of blockchain and IoT presents tremendous potential for unlocking new opportunities and capabilities. With additive decentralized features, businesses and individuals can benefit from increased security, transparency, and efficiency in various applications. This chapter first presents the technical aspects of this integration, including the role of smart contracts in decentralized IoT systems and how blockchain enhances the security, stability, and transparency of IoT networks. Then, a step-by-step tutorial for developing smart contracts and ledger on Ethereum blockchain is presented, particularly from the perspective of IoT nodes. The considered scenario is for an IoT device that writes/retrieves data from the blockchain; however, the presented methodology can easily be extended for different use cases.
A Study of the Values of Student Programmers in the Context of the Ideas of V.P. Tugarinov (to the 300th Anniversary of Saint Petersburg State University)

I n tr oduc ti on. The article deals with the conceptual ideas of V.P. Tugarinov about the unity of knowledge and values, which reflect the integrity of knowledge – evaluation – practice. Within the framework of these theoretical guidelines, the results of an empirical study of the human capital of students are presented. Me t hodology and sources. The methodological foundations of the study are formed as a synthesis of ideas around the essential characteristics of values expressed by V.P. Tugarinov, provisions of the theory of human capital by G. Becker and methodological guidelines for the strategies of the sociological study of values by V.A. Yadov. Resul t s and discussion. The first result is that students recognize a significant connection between the knowledge, skills and abilities acquired at the university with their system of values, which confirms the hypothesis about the relationship between human capital and personality values. The second result is that students-programmers mainly fix the role and importance of knowledge gained in the study of subjects of the socio-humanitarian cycle, which, in their opinion, play the role of a leading principle in the formation of a system of values. Conclusion. The conducted research shows the relevance of the modern reading of the conceptual ideas of V.P. Tugarinov regarding the connections of knowledge with values and further with productive activity. In the study, such connections are identified and characterized in a number of areas and content elements, it becomes obvious that any cognitive activity stimulates and develops the value system of students.
THE POSSIBILITIES OF DEVELOPMENT OF THE CHILD'S CREATIVITY AND ENTREPRENEURSHIP IN A PRE-SCHOOL

Today's rapidly changing era and the resulting demands bring into focus aspects related to children's education already from preschool, as this is the age when the basic qualities are formed and the foundations are laid for skills that are important for future development and self-fulfillment. The creativity and entrepreneurship of a preschool child is the ability to create new ideas and look for alternative solutions for their implementation, the readiness to act and finish what has been started, overcoming obstacles and showing initiative. The aim of the paper is to analyze the possibilities of development and assessment of the child's creativity and entrepreneurship in a preschool educational institution. The paper identifies the criteria and indicators for the assessment of the child's creativity and entrepreneurship, offers a model for the development of this skill in a preschool educational institution, and analyses the results of its approbation.
On the Characteristic Functions in Listing Stable Arguments

An abstract argumentation framework (af) is viewed as a directed graph such that graph vertices represent abstract arguments while graph edges denote attacks between these arguments. We say that a set, S, of arguments, are conflict-free if and only if for every $$(x,y) \in S \times S$$ , x does not attack y. A set, S, of arguments of a given af, is called a stable extension in af if S is conflict-free and such that every argument outside S is attacked by an argument inside S. To the best of our knowledge, a thorough mathematical analysis of the truth of what so-called characteristic functions (which are an essential component in generating all stable extensions of a given af) is not previously addressed in the literature. We fill this gap; we rigorously analyze the verity of characteristic functions employed in listing all stable extensions in a given af.
DRAMA FOR THE DEVELOPMENT OF COMMUNICATIVE COMPETENCE IN PRESCHOOL

Rapid changes in society force people to face increasingly complex life challenges that require the ability to adapt to changes and function effectively in a social context. The issue of communicative competence, on the level of which the quality of this adaptation and functioning depends to a large extent, is becoming more relevant. The development of communicative competence begins already in preschool. It is necessary to create an appropriate and supportive environment in which the development of verbal and non-verbal aspects of communication, including vocabulary, would be promoted in various ways according to the child's educational needs.The aim of the paper is to analyze the possibilities of using drama for the development of communicative competence in preschool. The results obtained show the usefulness of drama as a method for the development of communicative competence, as well as differences depending on the gender of a child: boys need more pedagogical support for the development of communicative competence than girls.
MONOLOGIC AND DIALOGIC METHOD IN THE PROCESS OF SOCIAL SCIENCE STUDIES

Higher education, faced with many of today's global challenges, focuses on innovative approaches to the organization of the study process, which will be able to professionally prepare and develop students' thinking. It is important for social science students, especially future social science professionals, to develop a holistic experience. Therefore, the purpose of the article is to analyze monological and dialogic methods in the study process. Studies based on verbal text knowledge transfer develop the working memory of a social science professional, but the ability to navigate and work in real professional situations remains very weak. It is hoped that students will acquire the necessary skills in practice, although in reality the acquired theoretical knowledge exists separately from the specific case and the ability to work with this case. The conclusions show, that it is necessary not only to teach students to work with a large amount of information, but also to highlight the importance of how to apply it in practice and whether it is possible at all. The most important conclusions show, that by using the dialogic method, students delve into various events, they develop an "image" of the topic, a context is formed for knowledge, an understanding of the studied phenomena is gained, and experience is formed. In order to implement the dialogic method, the teacher places significant emphasis on group relations and processes. Ideally, all group processes become study processes. The method of the article is the analysis of theoretical literature.
Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.
Salient Object Detection Network with Selection Mechanism for Consumer Electronics Imaging Systems

Salient object detection aims to identify the most attentive regions of an image, and as an image processing technology, it is widely used in imaging systems of many consumer electronics. With the development of deep learning, a series of notable results has achieved in salient object detection field. However, there are two intractable challenges still remain to be studied: 1) the issue of model lightweight; 2) model robustness. To address these two problems, we propose a feature filtering and fusion module based on a novel selection mechanism (SM), which can adaptively discover and fuse multi-scale features. Compared to other methods, it reduces the number of features that need to be processed, greatly reducing the complexity of the salient object detection model. We simultaneously explored the role of global contextual information and global multi-scale features in saliency detection task and validated them. Based on the SM block, we propose a lightweight salient object detection network named SMNet. Extensive experimental results on six datasets show that our model achieves satisfactory results, which can greatly save storage space and improve data transmission speed of image acquisition equipment.
A Structured Cyber Attack Representation Model based on the Diamond Model and Adversary States

Threat modeling is essential for selecting effective cybersecurity measures to secure industrial control systems (ICS). However, in-depth cybersecurity knowledge is required to perform the process. The results can vary from one security analyst to the next, particularly concerning prioritizing cyber attack scenarios. Cyber attack scenarios should be represented using a structured model to reduce the dependency on analysts. This study proposes a new structured model that expresses cyber attack scenarios for ICSs. The suggested model is based on the Diamond model and can express adversarial behavior against a targeted system in a structured manner. As a result, it can represent structured cyber attack scenarios and reduce the dependency on analysts in threat modeling. This paper describes the details of the proposed model to eliminate personal judgment in threat modeling and presents the evaluation of the validity and effects of the model.
Penerapan Teknologi Video Pembelajaran Jarak Jauh Mengenai Kewirausahaan dan Pemanfaatannya Sebagai Media Edukasi bagi Remaja SMAN 3 Batusangkar, Sumatera Barat

This paper discusses the assistance of implementing long-distance video learning technology in entrepreneurship and its utilization as an educational tool for teenagers at SMAN 3 Batusangkar, West Sumatra. Entrepreneurship education is becoming increasingly crucial in globalization and information technology development. However, access to relevant and high-quality learning materials is often limited in remote areas. This activity aims to evaluate the effectiveness of using long-distance video learning technology as a supportive tool in entrepreneurship education in a high school setting. The activity started with data collection through interviews and observations of students and teachers involved in the learning process. The results indicated that long-distance video learning technology positively impacts students' understanding of entrepreneurship concepts. Students responded positively to the more interactive and engaging learning approach provided by video learning. In addition, teachers reported increased student motivation and participation in entrepreneurship education. In the context of SMAN 3 Batusangkar, long-distance video learning technology provides an opportunity to overcome resource limitations and access high-quality learning materials. The study also notes some challenges, such as varying internet connection quality and the need for further teacher training to integrate this technology into the curriculum. This community service highlights the importance of using technology as an innovative and effective learning tool to teach entrepreneurship concepts to the younger generation.
Peningkatan Keterampilan Menulis Hasil Wawancara Peserta Didik Melalui Metode Outdoor Study

The objective of this study was to enhance students' proficiency in documenting interview outcomes through the application of outdoor study methods. The participants were 10 students selected from an elementary school in Kapanewon Samigaluh, Kulon Progo Regency, comprising 6 boys and 4 girls. The research utilized a classroom action research approach. The findings indicated a progressive improvement in students' ability to write down interview results in each cycle. During the initial cycle, students achieved an average score of 11.1 with a success rate of 50%, which falls within the category of "good" (category B). In the second cycle, the score increased to 13 with a success rate of 60%, still classifying as "good" (category B). The third cycle saw a further surge, with the average score reaching 15.7 and a success rate of 80%, placing it in the "very good" (SB) category. These results lead to the conclusion that employing outdoor study techniques contributes to the enhancement of students' writing skills in documenting interview results.
IMAGE RECOGNITION AND IDENTIFICATION USING MACHINE LEARNING

Face recognition has been a rapidly growing and intriguing region progressively applications. A huge number of face recognition calculation have been produced in a long time ago. In this paper, for face detection we are using HOG (Histogram of oriented Gradient) based face detector which gives more accurate results rather than other machine learning algorithms like Haar Cascade. In recognition process we are using CLAHE (Contrast Limited Adaptive Histogram equalization) for pre-processing than we are using HOG which is a standard technique for features extraction. HOG features are extracted for the test image and also for the training images. And finally for classification we are using SVM (support vector machine). SVM will classify the HOG features. Pre-processing technique is use to remove the noise, contrast enhancement, and illumination equalization. The result of this paper show the liability and productiveness in better face recognition performance. Key Words: Face detection, Face recognition, Machine Learning, Support Vector Machine, CLAHE.
American Sign Language (ASL) Detection System using Machine Learning

One of the main challenges of communicating with people who have hearing disabilities is to understand their sign language. This paper presents a dedicated research project to investigate the difficulties involved in recognizing characters in American Sign Language (ASL), which is the most widely used sign language in the world. Sign language is essential for communication among people with hearing or speech impairments, but it can be hard for those who are not familiar with it, as the signs made by people with disabilities may look complicated or messy. Effective communication requires a two-way exchange. This paper proposes a Sign Language detection system that uses American Sign Language, where users can take pictures of hand gestures through a web camera and get them analyzed. The system aims to predict and show the name that matches the picture. The proposed research proposes a method for detecting sign language that uses the collaborative features of the OpenCV and MediaPipe frameworks. The Convolutional Neural Network (CNN) is used to train the model and identify the pictures. The proposed methodshows a high detection rate and excellent accuracy. Keywords: American Sign Language, OpenCV, MediaPipe, Convolutional Neural Network
Incremental Learning Based on Dual-Branch Network

Incremental learning aims to overcome catastrophic forgetting. When the model learns multiple tasks sequentially, due to the imbalance of new and old classes numbers, the knowledge of old classes stored in the model is destroyed by large number of new classes. The existing single-backbone model is difficult to avoid catastrophic forgetting. In this paper, we proposes to use the dual-branch network model to learn new tasks to alleviate catastrophic forgetting. Different from previous dual-branch models that learn tasks in parallel, we propose to use dual-branch network to learn tasks serially. The model creates a new backbone for learning the remaining tasks, and freezes the previous backbone. In this way, the model can reduce damage to the previous backbone parameters used to learn old tasks. The model uses knowledge distillation to preserve the information of old tasks when the model learns new tasks. We also analyze different distillation methods for the dual-branch network model. In this paper we mainly focuses on the more challenging class incremental learning. We use common incremental learning setting on the ImageNet-100 dataset. The experimental results show that the accuracy can be improved by using the dual-branch network.
Plugging Stylized Controls in Open-Stylized Image Captioning

Image captioning is a classical multi-modal task for vision-language understanding. In recent years, researchers have begun to focus on generating captions with personalized styles, but the range of available styles is often fixed. The existing methods for Stylized Image Captioning Generation are mainly done by reinforcement learning or contrastive learning. Even with the assistance of large models such as CLIP and GPT, previous methods still require fine-tuning to generate targeted style captions and these methods necessitate a certain amount of computational resources and training costs. In this paper, we design a Plug-in Stylized Controls Module (PSCM), which can be directly inserted into the text-generation procedure of a well-trained model to generate open-stylized captions. Specifically, PSCM uses the style factor and fluency factor to guide the text-generation decoder. The style factor helps to generate text in a specified style, while the fluency factor helps to improve the fluency of generated texts. PSCM is a straightforward yet effective plug-and-play module that can readily produce open-stylized captions for each image without fine-tuning of backbones and text-image paired training data in target style. We add PSCM to two existing Stylized Image Captioning models and conduct experiments on four datasets to demonstrate the effectiveness of this module.