Reviewerly/exHarmony huggingface dataset:
Optimized Design of Micro-Joint Motors Using MPSO With Embedded ELM Indirect Surrogate

This paper develops a modified particle swarm optimization algorithm with an embedded extreme learning machine (ELM-MPSO), improving accuracy and efficiency for global optimization of a micro-jointed motor with multiple modes. Different from conventional optimization, ELM acts as an indirect surrogate model (indirect-SM) to drive the MPSO algorithm online. To enhance the generalization ability of ELM, a fitness grading strategy is formulated. By just training optimized samples, indirect-SM can be built into the algorithm to form an online interaction mechanism. With this mechanism, optimized particles are pre-filtered and pre-supplemented online according to grade, thereby enhancing their quality. Sobol sampling and double updating techniques are introduced to ensure particle global and local characteristics. In addition, fitness is evaluated according to its complexity during optimization, which reduces function calls. Optimization evaluation results show that the ELM-MPSO algorithm impro1ves convergence and diversity for global optimization. An optimal candidate design is obtained by applying the proposed algorithm to representative points (RPs) optimization of a micro-joint motor. Finally, the prototype experiments demonstrate that the entire optimization strategy is effective.
A Discussion on the “Charging Model” of the Collaborative Mechanism Between Teachers and Artificial Intelligence Based on Satir's Iceberg Theory

In recent years, artificial intelligence has become a new direction of scientific and technological development. Machine learning, as the most core and hot technology in the field of artificial intelligence, provides the possibility to meet the needs of intelligent education and personalized learning. Through the desktop research method and literature research method, this paper sorts out and analyzes the EAI and Satir's iceberg theory and finds a new educational model that can be integrated with each other -- the “charging model”. On the basis of EAI's ability to improve classroom efficiency, this paper proposes a “charging model” to explain and analyze its feasibility. It also uses Satir's iceberg theory to deepen humanistic concern and pay attention to classroom humanistic management to help students realize the third birth and fulfill the historical burden of teachers' “teaching and educating”. Meanwhile, it promotes deep learning of artificial intelligence and improves affective computing.
Machine Learning-Based Smart Waste Management in Urban Area

The population of our country continues to grow, and the need for a good lifestyle is most visible in the urban region. Traditional waste management systems have failed, necessitating the development of a new strategy to address this problem. Traditional waste management systems have failed, necessitating the development of a new strategy to address this problem. Because we are unable to limit the amount of waste generated by citizens, a proper technique for managing this massive waste is essential. The proposed model is mainly focused on the emptying dustbin from time to time and recycling of the bio-wastages. Here we will install MACHINE LEARNING and IoT to provide a smart management system for the waste. Ultrasonic sensor, load measuring sensor, and microcontroller is used to make a smart dustbin so that when it is filled with enough waste, the municipality gets a message to clean it. Using IoT this smart dustbin is wirelessly connected with the base station. In machine learning CNN (Convolutional Neural Network) model algorithms are used to identify the digestive and indigestive wasted so that we can easily sort them and recycle the waste. Image processing methods of ML are used to detect the bio and non-bio wastes. CNN has some hidden convolutional layers with a particular no. of filters in each layer. These filters detect the pattern of an image and then compare it to the pre-trained image of the ML algorithm. It’s a smart way to monitor the waste and clear remediation of the wastage problem in big cities, organizations, etc. also this project makes us a step forward to the green environment. The efficiency and accuracy of the predicted model are evaluated by machine learning algorithms so changes of model failure will be less in percentage.
Measuring Learning Progress Based on Cognitive Operations

Measuring students’ growth and change is considered one of the main ways for evidence-based development of educational systems. However, it is a non-trivial methodological task, despite the numerous approaches available for its conceptualization and statistical realization. In this article, we describe the main features of measuring students' growth and change using Item Response Theory (IRT) in detail. We then expand this approach to allow for the modeling of cognitive operations with the Linear Logistic Test Model (LLTM). We show that the synthesis of traditional IRT models for measuring growth and change with LLTM significantly enriches the interpretability of ability estimates while preserving the advantages of the traditional approach. To illustrate this approach, we use a set of monitoring tests to measure educational progress in mathematics in secondary school.
COOPERATIVE LEARNING FOR ENHANCED READING COMPREHENSION: A STUDY WITH FIRST-YEAR STUDENTS

This research aimed to determine the effectiveness of teaching reading comprehension using a cooperative learning model for first-year students at Universitas Muhammadiyah Bone. The study employed a quasi-experimental design, involving two groups: an experimental group (30 students) and a control group (30 students). Both groups underwent pre-test assessments, material presentations, and post-test evaluations. The data, collected through multiple-choice items, were analyzed using mean scores and the t-test formula. The research revealed that reading comprehension improved significantly in the experimental group (mean score 83.11) compared to the control group (mean score 61.56). The t-test also indicated a significant difference between the two groups, favoring the cooperative learning model. In conclusion, the use of the cooperative learning model effectively enhances students' reading comprehension.
Conclusion

This chapter provides a summary of Part I ”Experience Reports” and conclusions to be drawn from the single chapters together.
Web3 enhanced Insurance System using Ethereum Smart Contract

Abstract:-The traditional insurance industry has long struggled with inefficiencies, too much paperwork, and complex processes for handling policies and claims. This often means waiting a long time for claims to be sorted out because of all the go-betweens involved. To solve these issues, we're putting forward a new web3 solution that uses Blockchain technology. Our plan uses smart contracts to make managing policies and processing claims simpler, and we don't need all those middlemen. This change has lots of good points like stopping fraud, making everything more transparent and trustworthy, and making things happen faster. Instead of sticking with the old ways of doing insurance, we're bringing Blockchain into the mix and keeping track of policies and transactions using smart contracts. This shift means fewer middlemen and a much more efficient system overall. We've tested our idea with an experimental model in the web3 framework, and it's shown that our plan works and is cost-effective and quick. Keywords: Insurance Blockchain · Smart Contract · web3 Insurance System· Quick Claims Processing · Cost Effective web3 Insurance
CHINESE CHILDREN'S ILLUSTRATED BOOKS: HISTORY AND MO-DERNITY

An «illustrated book», as the name implies, is a book containing images. This is a book for reading, which focuses on pictures supplemented with words, and which is mainly intended for children. This article examines the process of evolution of Chinese children's illustrated books, as well as the features of the work of illus-trators of the past and present. The main conclusion we come to is that the study of the origin and evolution of a children's picture book plays an important role in its creation and use today.
Using Fractional Super-Resolution to Improve Lossy Compression of Point Cloud Geometry

We present a method for post-processing point clouds’ geometric information by applying a previously proposed fractional super-resolution technique to clouds compressed and decoded with MPEG’s G-PCC codec. In some sense, this is a continuation of that previous work, which requires only a downscaled point cloud and a scaling factor, both of which are provided by the G-PCC codec. For non-solid point clouds, an a priori down-scaling is required for improved efficiency. The method is compared to the GPCC itself, as well as machine-learning-based techniques. Results show a great improvement in quality over GPCC and comparable performance to the latter techniques, with the advantage of not needing any kind of previous training.
Privacy Preserving Function Evaluation using Lookup Tables with Word-Wise FHE

Homomorphic encryption (HE) is a promising approach for privacy-preserving applications, enabling a third party to assess functions on encrypted data. However, problems persist in implementing privacy-preserving applications through HE, including 1) long function evaluation latency and 2) limited HE primitives only allowing us to perform additions and multiplications. A homomorphic lookup-table (LUT) method has emerged to solve the above problems and enhance function evaluation efficiency. By leveraging homomorphic LUTs, intricate operations can be substituted. Previously proposed LUTs use bit-wise HE, such as TFHE, to evaluate single-input functions. However, the latency increases with the bit-length of the function's input(s) and output. Additionally, an efficient implementation of multi-input functions remains an open question. This paper proposes a novel LUT-based privacy-preserving function evaluation method to handle multi-input functions while reducing the latency by adopting word-wise HE. Our optimization strategy adjusts table sizes to minimize the latency while preserving function output accuracy, especially for common machine-learning functions. Through our experimental evaluation utilizing the BFV scheme of the Microsoft SEAL library, we confirmed the runtime of arbitrary functions whose LUTs consist of all input-output combinations represented by given input bits: 1) single-input 12-bit functions in 0.14 s, 2) single-input 18-bit functions in 2.53 s, 3) two-input 6-bit functions in 0.17 s, and 4) three-input 4-bit functions in 0.20 s, employing four threads. Besides, we confirmed that our proposed table size optimization strategy worked well, achieving 1.2 times speed up with the same absolute error of order 10-4 for Swish and 1.9 times speed up for ReLU while decreasing the absolute error from order 10-2 to 10-4 compared to the baseline, i.e., polynomial approximation.
Reading Comprehension Level and Academic Performance of College Students in Their Mathematics Course

Reading is regarded as a vital skill in attaining proficiency and widening knowledge in different subjects. The research aimed to establish the relationship of reading comprehension to academic performance in Mathematics. The respondents were the freshmen students in specified Math in the Modern World course. This study employed descriptive quantitative design. A researcher-made instrument which underwent experts’ validation and reliability test was used in determining the comprehension level in reading of the respondents. The results specified that positive correlation of reading comprehension and academic performance exists. Based on the results, there exists a highly significant difference between the reading comprehension level of the participants and their academic performance in their math courses. Moreover, with the generated value of .298, it can be inferred that that there is a positive relationship between the variables. This means if one variable increases the other variable moderately increases.
Critical Reading Skills in English of Incoming Grade 10 Learners: An Analysis and Interpretation

Critical reading encourages students to think more deeply and actively to understand a reading material. It is a manner of broadening one's mind and enhancing their thought process. The study analysed the critical reading skills of Grade 9 students and assessed the difficulties they encountered to shed light and provide valuable solutions in the successful development and improvement of the students critical reading skills. The study utilized a qualitative research design involving Grade 9 learners and English teachers who are employed in a public high school in the Philippines. Data were gathered through the use of semi-structured survey questionnaires, both for Grade 9 learners and teachers. In this study, the researcher used a purposive sampling technique to select the three Grade 9 students, and three English teachers who became part of the study. The findings revealed that students' perception of their reading ability is below average. The majority of the respondents have poor reading skills. In addition to these, students have limited vocabulary and often disassociate themselves to reading. The different strategies used in motivating students to read were presented. The majority of the teacher respondents stated that critical reading skills are part of higher-order thinking skills. Moreover, a shortage in teaching facilities and resources in the classroom are the major factors why students have poor reading ability. Findings revealed that the majority of the teacher respondents claimed that improving critical reading skills of the students can also enhance their reading comprehension skills.
Tiny Object Tracking With Proposal Position Enhancement

Tiny object tracking is challenging due to the target's weak appearance and features. The current state-of-theart approach for this task uses probabilistic regression based on discriminative correlation filters (DCF) to predict regression scores. However, the probabilistic regression equally relies on the position and size of the proposal box. In this letter, we point out that the position is of greater importance relative to the size when regressing the tiny object. To this end, we introduce the receptive field distance to define the quality of the proposal box, which places more emphasis on the position. By incorporating quality as an additional important factor for Monte Carlo sampling, the number of high quality proposals can be effectively increased, leading to regression optimization. Moreover, we propose a network to generate quality scores for proposals. The combination of probability and quality scores serves as a selection criterion for the optimal proposal, and can boost the tiny object tracking performance. Extensive experiments demonstrate the effectiveness of the proposed method. The code and model will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jankin987/track-mcsq</uri>
How Useful TutorBot+ is for Teaching and Learning in Programming Courses: a Preliminary Study

Objective: The objective of this paper is to present preliminary work on the development of an EduChatBot tool and the measurement of the effects of its use aimed at providing effective feedback to programming course students. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT3.5 and is tasked with assisting and providing timely positive feedback to students in computer science programming courses at UCSC. Methods/Analysis: The proposed method consists of four stages: (1) Immersion in the feedback and Large Language Models (LLMs) topic; (2) Development of tutorBot+ prototypes in both non-conversational and conversational versions; (3) Experiment design; and (4) Intervention and evaluation. The first stage involves a literature review on feedback and learning, the use of intelligent tutors in the educational context, as well as the topics of LLMs and chatGPT. The second and third stages detail the development of tutorBot+ in its two versions, and the final stage lays the foundation for a quasi-experimental study involving students in the curriculum activities of Programming Workshop and Database Workshop, focusing on learning outcomes related to the development of computational thinking skills, and facilitating the use and measurement of the tool’s effects. Findings: The preliminary results of this work are promising, as two functional prototypes of tutorBot+ have been developed for both the non-conversational and conversational versions. Additionally, there is ongoing exploration into the possibility of creating a domain-specific model based on pretrained models for programming, integrating tutorBot+ with other platforms, and designing an experiment to measure student performance, motivation, and the tool’s effectiveness.
A parallel approach to text data augmentation for sentiment analysis using the PoS Wise Synonym Substitution algorithm

Over the last decade, the use of social media as a massive communication medium has given people a tool to express their opinions. In it, people write their thoughts and feelings about plenty of topics generating large amount of data that can be analyzed by companies and researchers. Being tasks of the Natural Language Processing, Emotion analysis focuses on extracting the underlying emotions in text, meanwhile, Sentiment Analysis focuses on extracting the polarity of it. To accomplish this two tasks, Traditional Machine Learning and Deep Learning techniques are used. However, to reach good generalization performance, these techniques require large datasets of labeled data for training. For researchers this is an issue because in languages like Spanish the labeled datasets are sparse. To solve this, Data Augmentation techniques are used to generate wider datasets of labeled data from a small, labeled dataset. This work presents an OpenMP version for shared memory systems of a Data Augmentation technique called PoS Wise Synonym Substitution that replaces some of the words of a sentence with their synonyms extracted from Wordnet to create new sentences. With the parallel approach we reduced the execution time reasonably compared to the original version reaching a speedup of up to 17.5x.
Characterizing Team Cognition Within Software Engineering Teams in an Undergraduate Course

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> The study characterizes aspects of cognitive and metacognitive dimensions of team cognition of software development teams in educational settings. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> The software development industry requires software engineers and developers to work in teams; for this, there is substantial research on teamwork in the context of the organization. However, little is known about it in the context of educational settings, where there is scant research on teamwork in engineering and computing projects. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Questions:</i> How do students enact teamwork cognitive engagement and metacognitive regulation in the context of a systems analysis and design course? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> The participants were 127 undergraduate students in a systems development course organized into 26 teams. Qualitative categories were derived from a content analysis based on recorded teamwork sessions, which were then quantized and visualized for pattern identification. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> Results provide each team’s overall cognitive engagement coefficient and metacognitive regulation coefficient. The findings also describe three clusters, each with a description of a selected team to provide further insights into the identified patterns.
Dysarthric Speech Recognition using Depthwise Separable Convolutions: Preliminary Study

As a neurological disability that affects muscles involved in articulation, dysarthria is a speech impairment that leads to reduced speech intelligibility. In severe cases, these individuals could also be handicapped and unable to interact with digital devices. For such individuals, Automatic Speech Recognition (ASR) technologies could be life changing by enabling them to communicate with others as well as computing devices via voice commands. Nonetheless, ASR systems designed to recognize healthy speech have shown very poor performance to transcribe dysarthric speech, signaling the need to design ASR specifically tailored for dysarthria. Dysarthric Speech Recognition (DRS) research has progressed gradually because of the challenges the research community faces such as the scarcity of dysarthric speech that does not allow the researchers to design deeper acoustic models needed to better learn dysarthric speech variations. In this paper we report on our preliminary findings to improve our previous DSR called Speech Vision and study the effects of Separable Convolutional neurons to improve its acoustic model. Speech Vision is a novel Dysarthric Speech Recognition system that learns to recognize the shape of the words uttered by dysarthric speakers instead of recognizing phone sequences and then mapping them to words. Experiments conducted on the utterances provided by all UA-Speech dysarthric speakers indicate the proposed Depthwise separable architecture provided better word recognition accuracies compared to the original Speech Vision’s architecture across all dysarthric speech intelligibility classes.
Pengembangan Media Pembelajaran Powerpoint Interaktif Materi Jenis-Jenis Pekerjaan di SD Kelas IV

The lack of use of learning media can be an inhibiting factor in the teaching and learning process as a result of which learning is not optimal. In order to overcome this, it is necessary to develop learning media. One of the learning media that can overcome this is powerpoint media, powerpoint media is a learning media that was developed to increase students' understanding and interest in social studies subjects. Therefore the authors conducted research. This research is seen as development research which aims to develop powerpoint learning media as social studies learning media for class IV at SD Negeri 1 Parakannyasag. The type of research used in this research is a qualitative model. For data collection techniques used are validity and observation. Powerpoint media was developed by implementing it directly based on the Analyze, Design, Development, Implementation, Evaluation (ADDIE) procedure. The results of the validity of the material experts and media experts after implementing them at SD Negeri 1 Parakannyasag obtained a percentage of 95% for material experts and 89.2% for media experts so that they can be categorized as valid. Based on the results of research and development, it can be concluded that powerpoint media in social studies class IV material for types of work in elementary schools is appropriate for use in learning.