Reviewerly/exHarmony huggingface dataset:
Systemicity as a Characteristic of Society in the Interpretation of Yelena Yakuba: Heuristic Possibilities for Contemporary Sociology

The article analyzes systemicity as a metaphilosophical and world view characteristic for society in the sociological conception of Kharkiv sociological school’ founder Yelena Alexandrovna Yakuba. Carrying out an epistemological analysis of systemicity in the concept of Y. Yakuba, the author demonstrates various aspects and manifestations of this property, and also analyzes the actual and potential connections with the general theoretical sociological and philosophical heritage. The features of the interpretation of society as a system by E. Yakuba are studied, a comparison with other interpretations of the system and systemicity is made. It is emphasized that ignoring the ontological systemic nature of society (which is not identical with the genetic systemicity) in modern sociology leads to a number of epistemological and methodological effects. Y. Yakuba's arguments regarding systemicity as a property of society are given and analyzed. The author singles out a number of such arguments, carrying out a detailed analysis of each of them separately. Special emphasis is placed on the analysis of how systemicity manifests itself, what it consists of, what is its phenomenality and epiphenomenality. It is shown that in the concept of Y. Yakuba, society is a real phenomenon, it is connected with the individual not directly, but through a whole network of elements of the social structure. At the same time, personality as a social product manifests itself in any person, the question is only in the degree of manifestation. The measure of this manifestation is the status, and an indicator of the status is the role as a set of actions that the individual occupying this position should perform. Statuses and roles are studied as ones integrating various human acts into the social order. It is the point, from which, as the author shows, the problem of social activity arises. Here, the most important plot is the connection of systemicity as a property of society with activity and subjectivity as a property of the action of its members. A number of conclusions are formulated regarding systemicity as a category, its place in the sociological concept of Y. Yakuba, as well as its heuristic potential in modern sociology.
A Study on Improving the Performance of Gastroscopy Image Classification CADx System with SAM Optimizer

Gastric cancer has a high incidence in East Asians, and the risk increases over time. Often, gastric cancer presents no early symptoms, leading to missed treatments. Consequently, in Korea, support is provided to individuals over 40 years of age who undergo gastroscopy. However, as the number of gastroscopy patients increases, doctors
Data-Driven Modeling and Analysis for Solar Generation Considering the Post-Snow Day Meteorology Factors

As the global trend towards decarbonization and decentralization of power systems accelerates, the adoption of solar photovoltaic (PV) systems has seen a significant increase at both utility and residential levels. However, the reliability of power grids in colder regions, particularly those with heavy snowfall, could be at risk due to substantial reductions in solar power generation caused by snow cover. The performance of PV systems can be adversely affected by snow for prolonged periods, and the rate of recovery is primarily dependent on post-snow climatic conditions. This research aims to quantify the impact of snow on PV generation considering meteorological factors in the days following a snow event. A data-driven analysis is presented, with empirical data employed to evaluate the influence of snowfall on PV system performance. Additionally, a multilinear regression model is proposed to predict PV solar power generation under snow-affected conditions. This study not only provides a better understanding of the interaction between snowfall and PV systems but also offers a novel forecasting tool that aids in enhancing the resilience and reliability of power grids in cold regions.
Sistem Informasi Akuntansi Koperasi Berbasis Web Pada KPN “Sehati” RSUD Kota Sungai Penuh

The role of information technology in the business world is very important. Especially helping business people in managing their business well. One aspect that really needs the role of information technology is the financial aspect. Because in this aspect errors often occur if business actors still manage finances manually. This is what is being experienced by KPN "Sehati" RSUD Kota Sungai Penuh at this time. It is not surprising that errors often occur when processing data, considering that the data processed by KPN "Sehati" RSUD Kota Sungai Penuh is quite large. To help overcome these problems, a system design in the form of a prototype is needed which can later help work to be more transparent and accountable in compiling financial reports if the system design has been implemented properly into a complete system. The method used to design this system using the prototyping method. The purpose of this system design is as the initial foundation for business actors in planning the development of existing systems into a more computerized and well-integrated form. So the hope is that later with the implementation of this prototype system, it will help answer the problems experienced before.
Using Riddles Technique to Develop Students’ Vocabulary Mastery

The educational value of applying the riddles technique is to develop students’ vocabulary mastery at SMP Negeri 1 Sausu. Students’ lack of vocabulary, especially about countable nouns and students’ participation throughout the procedure of instruction in the class are the reasons for carrying out this research. The researchers applied a quasi-experimental research design, includes experimental and control class. In the academic year 2022-2023, students in first grade at SMP Negeri 1 Sausu made up the population, includes the classes VIIA and VIIB. The total number of students were 77 students. The sample class was determined using a cluster sampling technique. The instrument in this research was tests, pre-test and post-test. The researchers come to the conclusion that using riddles technique can develop students’ vocabulary mastery in the first grade of SMP Negeri 1 Sausu. It was evidenced by the experimental class’s post-test results (79.29), that was higher than the control class’s post-test (68.40). Also, the t-counted score (8.52) was exceeds than the t-table value (1.674).
Investigating Children’s Experiences for Smart Wearables: Implications on Design for Additive Manufacturing

Abstract Additive manufacturing (AM) is becoming more widely used to develop consumer products, from functional devices to static artefacts. Nowadays due to the competitive market consumer products go beyond satisfying the users’ needs, by generating meaningful and relevant experiences. This is the scope of User eXperience (UX) design, to create more user-friendly, easy-to-use, useful and enjoyable products. Previous literature has emphasised the importance of considering UX design during the development of smart wearables to decrease their rejection rate and achieve more user-adherent wearables. When producing these types of devices, the impact of AM on the user-product interaction is however overlooked. Within this context, this paper presents a study conducted with children to identify what characteristics of AM influence their experience when interacting with smart wearables. The feedback from these users can help depict a clearer understanding of what aspects of both opportunistic and restrictive Design for Additive Manufacturing (DfAM) elements can help achieve a positive UX, which can ultimately increase the acceptance rate and lifespan of the smart wearables. The outcomes obtained are discussed in light of their impact on sub-elements of UX. Additionally, future work is proposed to develop an ontology to aid design engineers during the development of smart wearables for children, adding to the existing body of knowledge of DfAM.
Automated Evaluation of Written Assignments in Engineering Using Task-Independent Features and Rubric-Guided Features

Abstract The increasing student enrollment in engineering and the growing importance of written communication motivate us to implement automated evaluation of written technical documents. This study aims to enhance our understanding of the automatic grading of written assignments in engineering and provides insights into effectively evaluating technical communication. We collected a graded dataset from a mechanical engineering course and manually selected features based on its grading rubric. Manually selected features include both task-independent features and task-dependent features. We evaluated the performance of the selected features on a classification model using grades provided by human graders. The combination of task-independent features and rubric-guided topic features outperformed any other single features or their combinations and reached a moderate agreement with human graders on 3 out of 5 datasets. The results suggest a new way to evaluate students’ assignments based on pre-defined rubrics and provide a significant first step toward building an automated evaluation framework that can provide detailed feedback on students’ complex assignments.
ANALYZING MANADO YOUTH PREFERENCES ON ONLINE MOVIE STREAMING

Nowadays, young people dominate the use of the internet. One thing that we can do with the internet is watch a movie. Currently, there are many online movie streaming platforms available, including Viu, Disney+ Hotstar, and Netflix. Based on the problem's background, this research aims to determine which movie streaming platforms are most demanded by young people in Manado. This research uses quantitative methods to measure data. The method used is the Analytical Hierarchy Process (AHP) method. The AHP method is for rating alternatives to decisions and selecting the best multiple criteria, allowing users to assess the relative weights of some criteria given intuitively. The study shows that young people in Manado prefer the Disney+ Hotstar streaming application over other alternatives. Ease of Use is the essential criterion for young people in Manado in deciding which movie streaming application they choose. Keywords: customer preferences, analytical hierarchy process
Research on Automatic Generation and Verification of Test Sequence for New Train Control System

Aiming at efficiency and correctness of test sequences generation, we propose a method to automatically generate test sequences and verify their correctness. First, we design an automatic test sequence generation method based on the BERT model, which is used to learn the context between test cases to automatically generate test sequences. Second, we design a method to judge the correctness of test sequences based on the rule engine. The Named Entity Recognition (NER) is used to extract information from domain documents. We design a rule extraction algorithm to realize the automatic construction of the Rule Base. Then, we complete the correctness verification of test sequences based on Drools rule engine. Experimental results show that this method can automatically generate high-quality test sequences, and effectively verify the correctness of test sequences.
3D Point Cloud-Based Hand Gesture Recognition

The main focus of this paper is to estimate the class of 3D point cloud gestures, using some statistical descriptors. To check the performance of proposed descriptors, we have evaluate it on a public 3D hand gesture dataset. We used several classification techniques with the different feature combinations and find that the random forest classifier provides the best performance. It achieves an accuracy of 95.20% on this public dataset.
Motion-Based Representations for Trajectory-Based Hand Gestures: A Brief Overview

Action/gesture representation especially modeling of actions/gestures has a special role in the recognition process. Here, in this paper, we would primarily look for motion-based hand gesture representations which are widely used but less talked about topics. Model-based and appearance-based methods are the two primary techniques for hand gesture representation. Apart from these two, motion-based approaches have gained quite impressive performance in various applications. Many researchers generally include motion-based methods in appearance-based methods. But here we want to discuss the motion-based methods separately with special attention representing hand gestures. Most of the representations generally depend on the shape, size, and color of the body/body part. But these may vary depending on many factors, e.g., illumination variation, image resolution, skin color, clothing, etc. But motion estimation should be independent of these factors. Optical flow and motion templates are the two major motion-based representation schemes that can be used directly to describe human gestures/actions. The main benefits of these techniques are basically their simplicity, ease of implementation, competitive performance, and efficiency.
Automated Cervical Dysplasia Detection: A Multi-resolution Transform-Based Approach

Pattern detection and classification of cervical cell dysplasia is an important ongoing study. The primary goal of this study is to create a complete model for real-world application for cervical dysplasia that has the highest degree of accuracy and the least computation time. The study initially builds the model that will be used to train and evaluate the system to classify the dysplasia. Three different color models, three transformations, each with a different filter, two feature representation schemes, and two well-known classification approaches are used in conjunction to determine the optimal combination of ‘transform (filter) $$\Rightarrow $$ color model $$\Rightarrow $$ feature representation $$\Rightarrow $$ classifier’. Extensive studies on two datasets, one indigenous and the other public, demonstrated that the NSCT-based classification performs well. When compared to two approaches, the model proposed yields the most satisfying results for both the datasets, with an accuracy of (98–99.50)%.
A Pre-processing-Aided Deep Transfer Learning Model for Human Object Detection in Crowd Scenarios

Human object detection in crowd scenarios is crucial for crime prevention and overall monitoring. However, this is a very challenging task. Typically, a wide range of surveillance systems operates in real-time applications. The quality of such captured real-time footage of the cameras is not comparable in terms of resolution, size, and orientation. Thus, improving the quality of those videos/images is necessary to detect objects accurately. In this work, we employed a pre-processing-aided deep learning technique for detecting human objects in the image of crowd scenarios. First, we enhanced the image quality using a variety of pre-processing approaches. Then, objects are detected in the pre-processed images of crowed scenarios using several pre-trained deep learning (DL) models. The efficacy of the proposed framework has been analyzed on a standard WIDER-FACE dataset concerning various performance measures, both with and without pre-processing of images. From the experimental results, it is found that the use of pre-processing at the top of deep learning models performs significantly better than that of without pre-processing.
Application of Tool Empowerment in the Teaching Process of Thermal Power Plants in the Era of Artificial Intelligence

The Thermal Power Plant course involves a wide range of energy sources, including the utilization of coal, gas, nuclear energy, and other energy sources, and introduces the theoretical and practical knowledge of using various energy sources to generate high-temperature and high-pressure steam to drive steam turbines for power generation. The course design can not only help students better understand and master the design principles of thermal power generation systems, but also cultivates their ability to analyze, design, and solve problems. Traditional teaching methods cannot stimulate students' interest in learning and help them better understand principles and methods. In the era of artificial intelligence, various intelligent tools can be widely applied in the teaching process of courses to enhance students' understanding of knowledge, interest in learning, mastery of technology and comprehensive problem-solving ability.
Integrative Study of Continuation Task in High School based on Themetic Progression

The writing component of college entrance exams now includes a brand-new form of test called a continuation task. In high school, a well-defined form of thematic growth is a midwife to improving its coherence, articulation, and logic. The narrative essay's mode of thematic progression in this study is examined using the examples “My Amazing Naadam Experience” from Unit 1 Festival and Celebration, “Li Lan’s Travel Journal” from Unit 3 Diverse Culture, in PEP Compulsory Three, and “Seeing the True North via Rail: Vancouver and the Heart of Canada” from Unit 4 Journey Across, in Selective Compulsory Two. The concept and tips for the test question “Continuation Task” from the New Curriculum II Paper 2022 were examined in this study, using it as an example. The “six elements and four patterns” technique is offered, along with the evaluation criteria, hoping that this can give front-line teachers a reference for teaching the continuing assignment.
Mechanism Designs of a Robot for Charging Multiple Electric Vehicles

Abstract Automobile industry is experiencing a major transformation into the era of electric vehicles. General Motors Global Research and Development Center developed a concept robotic system to charge multiple electric vehicles. This work demonstrated the process of designing the optimal robot mechanisms that can fulfill the functionalities of this system. Due to the application specific constraints, this system demands a robot that can cover a very large workspace, enter narrow gaps between parked vehicles, and reach vehicle inlets at a large height span at the same time. To navigate among a large number of potential mechanism options and to identify the optimal designs without the overuse of actuators, a Key DOF was proposed for this application and the design process was unfolded based on the different realizations of the Key DOF. Our results showed that the system might have limited applications when using a 5 degree-of-freedom robot. Redundancies were evaluated for their benefits in this application. The first redundant joint could be used to achieve the long-range travel in the parking lot drive aisle direction. This redundancy enabled many designs and two were recommended for the application towards different volumed products. Additional redundancy in the robot mechanism didn’t bring in enough benefits for the application to justify the increase in cost and complexity.
AAS-enabled Engineering Using MTP

This article shows a practical implementation of how the technologies MTP (Module Type Package) and AAS (Asset Administration Shell) can be combined in a typical engineering workflow. For the practical implementation, the selection of one of the current Software Development Kits (SDKs) is explained, which makes it easier for the developer to generate Asset Administration Shells and to connect to a corresponding server. The corresponding AAS submodel of the MTP is shown and typical use cases between the engineering tool and the AAS server are explained.
Enhancing the Understanding of Distance Related Uncertainties of Vocal Navigational Commands Using Fusion of Hand Gesture Information

Itis a prevalent trend nowadays in most places that the elderly population keeps increasing. Also, there is a community that has physical disabilities, especially mobility. Due to the lack of trustworthy caretakers, the busy lives of family members, and psychological issues attributed to loneliness, intelligent service robotic devices have been developed. Vocal navigational commands consist of uncertain terms. There are methods introduced to enhance these uncertainties using spatial parameters such as obstacle distances, previous robot movements, robot localization, etc. However, they have only considered the vocal information provided by the user and deduced the rest of the information from external spatial information. In reality, users include other partial information from complementary modalities such as hand gestures and there is a significant probability that the partial information carried by the hand gestures might change the interpretation completely. Therefore, this paper presents an intelligent system that would enhance the understanding of distance-related uncertainties of vocal navigational commands using multimodal fusion of hand gesture information. Partial gesture information extracted from hand gestures has been used to interpret the distance-related uncertainties using a fuzzy logic-based approach. Experiments were conducted to validate the intelligent system and the user ratings given were used to validate the system.
Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets' information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
Active Learning—Validation

This chapter provides an overview of the datasets utilized, outlines the employed evaluation methods encompassing classification metrics and clustering indices, and elucidates the validation technique implemented.
Active Learning—What, When, and Where to Deploy?

The journey of human learning is characterized by its gradual and extended nature, involving years of education in schools and universities to attain expertise in specific domains.
Underground Water Resources Management for Flooding Prevention

Technogenic factors that affect urban area flooding development, forecasts of improving groundwater level changes, and comprehensive management of urban groundwater must be taken into account and controlled. A three-dimensional mathematical model of groundwater level changes in cities has been developed, which takes into account atmospheric water infiltration, additional groundwater replenishment, transpiration, evaporation, evapotranspiration, and groundwater intake.