Reviewerly/exHarmony huggingface dataset:
Metacognitive Effort Regulation across Cultures

Success in cognitive tasks is associated with effort regulation and motivation. We employed the meta-reasoning approach to investigate metacognitive monitoring accuracy and effort regulation in problem solving across cultures. Adults from China, from Israel, and from Europe and North America (for simplicity: "Western countries") solved nonverbal problems and rated their confidence in their answers. The task involved identifying geometric shapes within silhouettes and, thus, required overcoming interference from holistic processing. The Western group displayed the worst monitoring accuracy, with both the highest overconfidence and poorest resolution (discrimination in confidence between the correct and wrong solutions). The Israeli group resembled the Western group in many respects but exhibited better monitoring accuracy. The Chinese group invested the most time and achieved the best success rates, demonstrating exceptional motivation and determination to succeed. However, their efficiency suffered as they correctly solved the fewest problems per minute of work. Effort regulation analysis based on the Diminishing Criterion Model revealed distinct patterns: the Western participants invested the least amount of time regardless of item difficulty and the Israelis invested more time only when addressing the hardest items. The Chinese group allocated more time throughout but particularly in moderate to difficult items, hinting at their strategic determination to overcome the challenge. Understanding cultural differences in metacognitive processes carries implications for theory (e.g., motivational factors) and practice (e.g., international teams, education). The present findings can serve as a foundation for future research in these and other domains.
SUGAN: A Stable U-Net Based Generative Adversarial Network

As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based GAN (U-Net GAN), a recently developed approach, can generate high-quality synthetic images by using a U-Net architecture for the discriminator. However, this model may suffer from severe mode collapse. In this study, a stable U-Net GAN (SUGAN) is proposed to mainly solve this problem. First, a gradient normalization module is introduced to the discriminator of U-Net GAN. This module effectively reduces gradient magnitudes, thereby greatly alleviating the problems of gradient instability and overfitting. As a result, the training stability of the GAN model is improved. Additionally, in order to solve the problem of blurred edges of the generated images, a modified residual network is used in the generator. This modification enhances its ability to capture image details, leading to higher-definition generated images. Extensive experiments conducted on several datasets show that the proposed SUGAN significantly improves over the Inception Score (IS) and Fréchet Inception Distance (FID) metrics compared with several state-of-the-art and classic GANs. The training process of our SUGAN is stable, and the quality and diversity of the generated samples are higher. This clearly demonstrates the effectiveness of our approach for image generation tasks. The source code and trained model of our SUGAN have been publicly released.
Conformer-Based Human Activity Recognition Using Inertial Measurement Units

Human activity recognition (HAR) using inertial measurement units (IMUs) is gaining popularity due to its ease of use, accurate and reliable measurements of motion and orientation, and its suitability for real-time IoT applications such as healthcare monitoring, sports and fitness tracking, video surveillance and security, smart homes and assistive technologies, human–computer interaction, workplace safety, and rehabilitation and physical therapy. IMUs are widely used as they provide precise and consistent measurements of motion and orientation, making them an ideal choice for HAR. This paper proposes a Conformer-based HAR model that employs attention mechanisms to better capture the temporal dynamics of human movement and improve the recognition accuracy. The proposed model consists of convolutional layers, multiple Conformer blocks with self-attention and residual connections, and classification layers. Experimental results show that the proposed model outperforms existing models such as CNN, LSTM, and GRU. The attention mechanisms in the Conformer blocks have residual connections, which can prevent vanishing gradients and improve convergence. The model was evaluated using two publicly available datasets, WISDM and USCHAD, and achieved accuracy of 98.1% and 96%, respectively. These results suggest that Conformer-based models can offer a promising approach for HAR using IMU.
Historic Algorithms Help Unlock Shortest-Path Problem Breakthrough

By revisiting key algorithms from computing, a team unlocked hidden efficiency in a long-standing computer science problem.
IMPLEMENTASI MEDIA OBJEK LANGSUNG DALAM PEMBELAJARAN MENULIS PUISI

Implementation of Direct Object Media in Poetry Writing Learning. This study aims to determine the implementation of direct media objects in learning to write poetry in class VIII of SMP Islam Al Falah Jambi. The researcher used a qualitative approach, with data collection methods in the form of observation, interviews with class VIII Indonesian teachers, and backups. Data analysis techniques were carried out by collecting, reducing, presenting, and concluding. The results of the research show that the medial implementation of direct objects is carried out effectively. Stages of learning through direct object media, namely the planning stage of preparing direct object media and preparing learning materials. Then comes the implementation stage of learning with medial objects Students directly observe objects outside the classroom and are divided into several groups, Students begin to observe predetermined objects and start writing poetry, and then the results of their writing are collected. Finally, the teacher's evaluation stage assesses the results of the students' poetry.
Would you rather come to a tango concert in theater or in VR? Aesthetic emotions &amp; social presence in musical experiences, either live, 2D or 3D

This work shows the preliminary results of a pioneering project aimed at comparing the aesthetic experience of a musical concert experienced in different contexts for which the audience's perceived presence is modulated in a continuum ranging from a live concert to a music video, passing through immersive artificial environments. In contrast to previous qualitative investigations of various immersive contexts, our study is unique in both the use of validated scales and the structured comparison of four experimental conditions: 1.live concert (LC), 2.the same concert through a traditional non-immersive music video (MV, analogous to fruition on YouTube), and finally in a virtual reality environment (VR), provided by two different devices, 3. a google cardboard (CVR) and 4. an HTC vive (HVR), allowing respectively for a basic and easily accessible experience, or for a less affordable but more immersive one. Through these manipulations we presumably affected not just the subjective aesthetic experience, but also the perceived presence of the Other/s. Consistently we measured both through the administration of the Aesthetic Emotions Scale (Aesthemos) and the Networked Minds Measure of Social Presence (NMMSP). The NMMSP showed no notable differences between conditions, which instead emerged from the analyses on the Aesthemos. The most liked experience was the Live one. Results also showed that LC experience had a stronger emotional impact only when compared to MV and CVR, but not to HTC since this last manipulation was the one eliciting the greatest interest. Theoretical implications are critically discussed, suggesting novel applications of the proposed approach.
GRAMMATICAL COLLOCATION OF IMPERATIVES IN PRODUCT USER GUIDE

This research was conducted to find out what kind of English grammatical collocation used in Imperatives in Product User Guide. The research applied a qualitative descriptive method, the data from which were collected, analyzed and concluded without drawing a general conclusion. The data of this study are grammatical collocations in imperatives found in KRISBOW Impact Drill 500W User Guide. The analysis of the 69 collected data shows there are as many as 1 type GB data (1.4%), 2 GD type data (2.9%), 12 type Gd data (17.4%), 1 type GE data (1.4%), 2 type GH data (2.9%), 1 type GI data (1.4%), 8 type GL data (11.6%), 8 type GN data (11.6%), 29 type GP data (42.02%), 3 type GQ data (4.3%), 2 type Gs data (2.9%). In conclusion, type GP (Verb + (O) + Adverbial) is the most frequently used grammatical collocation in the form of imperatives in the User Guide.
Sentiment Analysis: Sekolah Tatap Muka in the New Normal Era

Through the Circular of the Minister of Education, Culture, Research, and Technology (Mendikbudristek) Number 3 of 2022, face-to-face learning is limited to educational units following the provisions stipulated in the Joint Decree of the Four Ministers. In addition, it is explained that parents/guardians of students are given the choice to permit their children to take part in Limited face-to-face learning or distance learning. This study aimed to determine public sentiment regarding Sekolah Tatap Muka. To achieve the research objectives, sentiment analysis is used using the Drone Emprit application. Data collection was obtained from the Twitter social media application, the data obtained were posts from February 1, 2022, to March 31, 2022. The results showed that there were 898 tweets or 29.23 percent of positive sentiments about sekolah tatap muka, and there were 1,954 tweets or 63.61 percent of negative sentiments about sekolah tatap muka, and there were 220 tweets or 7.16 percent of neutral sentiments about sekolah tatap muka.
Zur Bedeutungskonstitution der Partikel tanto im gesprochenen Italienisch

The paper deals with the use of the particle tanto and its semantic structure in spoken Italian from an interactional perspective. The analysis shows that there are at least three different uses of tanto as a particle. All three uses are characterized by a particular sequential and syntactic position, a specific prosodic realization, different contextual restrictions, and a specific semantic structure. The second part of the paper takes a diachronic perspective and analyzes the question of how the particle tanto could have developed out of the original use of tanto as a quantifier. It discusses the hypothesis that the development of tanto was triggered by a rhetorical use at earlier stages and can be related to processes of subjectification.
Buddy System Based Alpha Numeric Weight Based Clustering Algorithm with User Threshold

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.
Šolar, the developmental corpus of Slovene

Abstract The paper presents the Šolar developmental corpus of Slovene, comprising the written language production of students in Slovene elementary and secondary schools, along with teacher feedback. The corpus consists of 5,485 texts (1,635,407 words) and features linguistically categorized teacher corrections. Notably, the annotation of language problems is based on actual teacher corrections, making the corpus unique in reflecting authentic classroom correction practices. The paper addresses the corpus compilation, content and format, annotation, availability, and its applicative value. While learner corpora are abundant, developmental corpora are less common. The paper bridges the gap by introducing the evolution from Šolar 1.0 to 3.0, emphasizing improvements in text collection, error and correction annotation, and categorization methodology. It also underlines the challenges and unresolved issues of compiling developmental corpora, most notably the lack of openly available tools and standards for different steps of the compilation process. Overall, the Šolar corpus offers valuable insights into language learning and teaching, contributing to teacher training, empirical studies in applied linguistics, and natural language processing tasks.
Information Retrieval Performance in Text Generation using Knowledge from Generative Pre-trained Transformer (GPT-3)

The rise of advanced language models like GPT-3 and text generation has witnessed remarkable progress. However, leveraging the vast amount of knowledge within these models to enhance information retrieval performance remains an area that needs to be explored. This research used Artificial Intelligence, specifically the OpenAI GPT-3 language model, to create an application to help make written content. This research investigates the impact of incorporating GPT-3's knowledge into text generation processes and evaluates its influence on information retrieval tasks. Several features in text generation generate text that requires exact information, such as specifications for a product and accurate descriptions of a job or product, which are included in the concept of information retrieval in text creation by language models. The research used the few-shot learning method in the GPT-3 language model. The generated responses are then evaluated using established information retrieval metrics such as precision, recall, and F1-score. The findings of this research reveal the effectiveness of utilizing GPT-3's knowledge in enhancing information retrieval performance. The generated responses demonstrate improved relevance to user queries, resulting in the same performance precision and recall scores compared to other paid text generator websites. Application results are testing in capabilities of retrieving some information. Application capabilities tested on other commercial text generator engines. The test results obtained BERTscore 86\% (precision), 88\% (recall), and 87\% (F1-Score).
IT Tools Applications in Audit Profession: The CPA’s Perspective

Purpose: The purpose of this research is to investigate the extent of the application of information technology tools in the field of auditing in Jordan. Specifically, the study aims to determine the importance and the degree of use of information technology in audit control, planning, and documentation. Theoretical Framework: The theoretical framework of this research revolves around the utilization of information technology in auditing. It seeks to understand how information technology tools are perceived and utilized by auditors in Jordan and how these tools impact audit control, planning, and documentation processes. Findings: Based on the survey of 60 auditors in Jordan, the research found the following key findings: There is a significant awareness (72.60%) among auditors about the importance of using IT tools in auditing. The use of audit applications and the perceived importance of these tools varied significantly among auditors. The degree of using information technology in planning and control tasks was reported to be at 72.13%. The application of information technology tools in documenting and recording audit work reached 78.04%. The perception of auditors regarding planning and controlling tasks assisted by IT tools is not affected by their experience. Implications of the Research: The findings of this research have several implications for the field of auditing in Jordan: The awareness of the importance of IT tools in auditing indicates the potential for further integration of technology in the profession. The variation in the use and perceived importance of audit applications calls for a deeper understanding of the factors influencing these differences. Originality and Value: The originality and value of this research lie in its focus on the application of information technology in auditing in the specific context of Jordan. As of the study's date, it provides up-to-date insights into the extent of IT adoption and its impact on audit control, planning, and documentation. The research contributes to the existing body of knowledge by shedding light on the specific challenges and opportunities faced by auditors in Jordan regarding IT adoption.
Performance Analysis of Multi-Threaded Distributed Evolutionary Algorithms for Image-Processing Applications

Evolutionary algorithms (EAs) in the repository of the evolutionary computing (EC) paradigm are simplistic and stochastic in nature.The desirable characteristic of adaptive simplicity has sustained research community's rampant curiosity in EAs.Distributed evolutionary algorithmic (DEA) framework is an instinctive extension of EAs.This paper summarizes the design and implementation of a multi-threaded DEA (MTDEA) framework.Empirical analysis between a traditional DEA framework and a simulated MTDEA is presented.The MTDEA framework was outfitted with three plug-in algorithms to handle commonly occurring faults in distributed environments.The propriety of the MTDEA framework was validated in two customary applications -Image thresholding and Image reproduction.The thresholding and reproduction problems were addressed by incorporation of the differential evolution (DE) and genetic algorithm (GA) methods respectively, into the MTDEA framework.The performances of DE and GA routines were compared with their sequential counterpart frameworks.The MTDEA framework was evaluated for reliability and scalability on a high-performance computing (HPC) system.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients

Federated learning (FL) is a distributed framework for collaborative training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalance) with poor annotation quality (label noise). The co-existence of label noise and class imbalance in FL’s small local datasets renders conventional FL methods and noisy-label learning methods both ineffective. To address the challenges, we propose FEDCNI without using an additional clean proxy dataset. It includes a noise-resilient local solver and a robust global aggregator. For the local solver, we design a more robust prototypical noise detector to distinguish noisy samples. Further to reduce the negative impact brought by the noisy samples, we devise a curriculum pseudo labeling method and a denoise Mixup training strategy. For the global aggregator, we propose a switching re-weighted aggregation method tailored to different learning periods. Extensive experiments demonstrate our method can substantially outperform state-of-the-art solutions in mix-heterogeneous FL environments.
Soliton versus single-photon quantum dynamics in arrays of superconducting qubits

Superconducting circuits constitute a promising platform for future implementation of quantum processors and simulators. Arrays of capacitively coupled transmon qubits naturally implement the Bose-Hubbard model with attractive on-site interaction. The spectrum of such many-body systems is characterized by low-energy localized states defining the lattice analog of bright solitons. Here, we demonstrate that these bright solitons can be pinned in the system, and we find that a soliton moves while maintaining its shape. Its velocity obeys a scaling law in terms of the combined interaction and number of constituent bosons. In contrast, the source-to-drain transport of photons through the array occurs through extended states that have higher energy compared to the bright soliton. For weak coupling between the source or drain and the array, the populations of the source and drain oscillate in time; for chains of even length, their population remains low at all times, while it can reach half the number of total bosons in odd chains. Implications of our results for actual experimental realizations are discussed.
Security and privacy in federated learning: A survey

Federated Learning (FL) allows multiple nodes without actually sharing data with other confidential nodes to retrain a common model. This is particularly relevant in healthcare applications, where data such as medical records are private and confidential. Although federated learning avoids the exchange of actual data, it still remains possible to fight protection on parameter values revealed in the training process or on a generated Machine Learning (ML) model. This study examines FL’s privacy and security concerns and deals with several issues related to privacy protection and safety when developing FL systems. In addition, we have detailed simulation results to illustrate the problems under discussion and potential solutions.
The Effect of Shifting to Student-Centered Learning: Implementing Student-Centered Reading

The "traditional" forms of teaching are extremely familiar to students, who frequently favor learning that is focused on receiving instruction and reproducing knowledge. This study aims to examine the effects of these traditional forms of teaching practices, such as teacher-centered learning on EFL learners' engagement in implementing student-centered reading. The study uses an observation checklist and interview as tools for collecting data from twelve EFL elementary reading classes and their teachers. The statistically analyzed data has shown that whenever reading activities demand deeper, personalized discussion, the learners engage less and rely more on their teachers for assistance. When interacting with non-communicative tasks, EFL students take full ownership of their own learning; however, when interacting with communicative tasks, they require teacher prompting. Furthermore, learners' collaboration with each other on achieving reading tasks was, in most cases, either done to some extent or in a very limited range. Thus, EFL learners maintained dependency in carrying out reading activities through their preference for non-communicative tasks over communicative tasks and their collaboration upon reading activities in a limited range. Based on this, the design of communicative reading activities demands prompting, and learners need to be encouraged and guided for engagement. Students may be more receptive and interested if they are more informed about the advantages and effectiveness of student-centered learning. In spite of the effectiveness of student-centered learning in many contexts, it needs to be redefined to fit within the culture and specific EFL learning and teaching contexts.