Reviewerly/exHarmony huggingface dataset:
Applying 3D Human Hand Pose Estimation to Teleoperation

3D human hand pose estimation from visual data has received an increasing amount of attention, and the availability of low-cost depth cameras gives a great impetus to the development of this field. Nearly all recent hand pose estimation methods are oriented towards unified evaluation criteria defined by popular public benchmark datasets: the ultimate goal is to reduce the estimation error. However, the fact is that there exists a gap between human hand pose estimation and its applications. It is unclear how to recover global and local degrees of freedom from a set of structural hand joints, which is a necessary condition to apply human hand pose estimation to teleoperation, i.e., mapping estimated human hand poses at the master side to robotic hand poses at the slave side. Conventional teleoperation systems are implemented with the aid of a data glove or essentially built on gesture recognition. These solutions are inferior to vision-based hand pose estimation in offering an easy-to-use and natural human-robot interaction interface. In this paper, we propose three methods to teleoperate robotic hands by 3D vision-based human hand pose estimation. The feasibility of the three methods is tested in a simulated environment.
GEMM-SaFIN(FRIE)++: Explainable Artificial Intelligence Visualisation System with Episodic Memory

Neuro-fuzzy network systems take advantage on functionalities of hybrid fuzzy logic and neural networks approaches. IF–THEN fuzzy rules allow good interpretability for human experts to understand the correlation between inputs and outputs. However, only designers know the mechanism and behavior of neuro-fuzzy systems. Details on how a neuro-fuzzy system derives and formulates the predictions or rules are unknown to users. It is due to the lack of transparency of the design and connections of neuro-fuzzy systems. We propose a novel explainable artificial intelligent (AI) visualization system for the neuro-fuzzy architecture, named general episodic memory mechanism (GEMM) Self Adaptive Fuzzy Inference Network with Fuzzy Rule Interpolation or Extrapolation with online learning capabilities (i.e., GEMM-SaFIN(FRIE)++). The proposed explainable AI visualization system is designed in a form of graphical user interface to assist users better understanding inner function mechanism on how rules are generated and how conclusions are drawn from the data fed into the neuro-fuzzy system. One of the challenges for neuro-fuzzy systems is making real-time predictions in the fast changing applications where data can be sparse. It may not be able to automatically detect and react to the occurrence of concept drifts, affecting the online learning capabilities. GEMM-SaFIN(FRIE)++ employs fuzzy rule interpolation and extrapolation techniques to make inference when concept drifts are detected. The GEMM mimicking human brains is employed to capture and retrieve from past events that GEMM-SaFIN(FRIE)++ learns. This is done by storing and retrieving them from an episodic memory storage during the transient event behaviors. Several experiments are conducted to evaluate the performance of the GEMM-SaFIN(FRIE)++. Firstly, three sub datasets in Nakanishi dataset including a sub dataset for daily price of stock in a stock market are utilised in the experiments. Secondly, GEMM-SaFIN(FRIE)++ is used to detect market trend reversal for two stock market indexes: S&P 500 and DJIA index, under various events through the period during COVID-19, subprime and 9–11 attack. Encouraging experiment results are observed for event detections to capture a shift in stock prices.
Segmentation Pseudo-label Generation using the Multiple Instance Learning Choquet Integral

Weakly supervised target detection and semantic segmentation (WSSS) approaches aim at learning object or pixel level classification labels from imprecise, uncertain, or ambiguous data annotations. A crucial step in WSSS is to produce pseudolabels which can be used to train a fully supervised semantic classifier. Post-hoc attention mechanisms, such as class activation mapping (CAM), are commonly used to produce these pseudolabels. While traditional CAM methods derive feature importance from heuristics on gradient information, this work alternatively investigates whether a subset of discriminative activation feature maps can be down-selected and fused to improve pseudo-label accuracy. More specifically, the Multiple Instance Choquet Integral (MICI) [1] was explored as a method for discriminative feature selection and fusion. Experimental results on both synthetic and real-world datasets indicate the utility of the MICI in downselecting class-discriminative activation feature maps. Fusion of the MICI-selected sources was competitive to CAM methods for generating pseudo-labels.
In conversation, answers are remembered better than the questions themselves.

Language is used in communicative contexts to identify and successfully transmit new information that should be later remembered. In three studies, we used question-answer pairs, a naturalistic device for focusing information, to examine how properties of conversations inform later item memory. In Experiment 1, participants viewed three pictures while listening to a recorded question-answer exchange between two people about the locations of two of the displayed pictures. In a memory recognition test conducted online a day later, participants recognized the names of pictures that served as answers more accurately than the names of pictures that appeared as questions. This suggests that this type of focus indeed boosts memory. In Experiment 2, participants listened to the same items embedded in declarative sentences. There was a reduced memory benefit for the second item, confirming the role of linguistic focus on later memory beyond a simple serial-position effect. In Experiment 3, two participants asked and answered the same questions about objects in a dialogue. Here, answers continued to receive a memory benefit, and this focus effect was accentuated by language production such that information-seekers remembered the answers to their questions better than information-givers remembered the questions they had been asked. Combined, these studies show how people's memory for conversation is modulated by the referential status of the items mentioned and by the speaker's roles of the conversation participants. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
High-Machting and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.
Archive Management and Archive Arrangement in the Finance Division (Education Office of Medan City Government)

This study aims to determine the management of incomingand outgoing letters and arrangements, especially in the Medan City Education Office. The results showed that the management of incoming and outgoing letters at the Medan City Government Education Office, especially in the financial room, was still not carried out optimally, which was caused by: 1) limited equipment for storing letters or documents, especially filing cabinets; 2) limited letter storage space so that the continuous increase in the volume of letters resulted in the available storage space and equipment unable to accommodate letters.
Investigating the Effect of Using English Islamic Pop Songs on Students’ Vocabulary Size

English is a foreign language in Indonesia. Students need to know more about its vocabulary to use English to communicate. One of the strategies to improve students’ vocabulary size is through English Pop songs. This study investigated the effect of English Islamic Pop songs on students’ vocabulary size. The method employed was experimental research with a Pre-Experimental design. The population was the first-year students of an Islamic senior high school, and the samples selected were 20 students using a simple random technique. This study used two vocabulary tests to collect data: pre and post-test and Vocabulary Level Test (VLT). Data gained were analyzed using data analysis techniques through SPSS Statistical V22.0. The result indicated that using English Islamic Pop songs improved students’ vocabulary size; however, this was not followed by the level improvement. Students’ vocabulary size level remained at 2000 words. Various songs by different singers will be needed for further research. Therefore, using Islamic Pop songs in teaching vocabulary is suggested to introduce Islamic English words and phrases to students.
Exploring Students’ Informal Digital Learning of English (IDLE) and Self-Regulated Language Learning from a Sociocultural Perspective

This study investigates the phenomenon of informal digital learning of English (IDLE) among students engaged in independent online learning environments. Focusing on practices for students’ language development, this study explores how students autonomously acquire English language skills through informal digital means and examines the implications. It employs a mixed-methods approach, combining surveys and interviews to collect and analyse data from a diverse group of independent online learners of English (N= 182) from 23 different Indonesian universities. Quantitative data reveals that approximately 45.1% of the variation in self-regulated language learning can be explained with informal digital learning of English identified as one of the predictors. On the other hand, qualitative data findings indicate that students actively engage in IDLE through a variety of digital channels, including social media, online forums, language learning apps, and YouTube tutorials. These informal learning activities encompass a wide range of language skills, from vocabulary acquisition and pronunciation improvement to cultural understanding and conversational proficiency. Implications of this research point to the importance of recognising and harnessing students’ IDLE practices in formal educational settings. Educators and institutions can leverage these insights to design more effective online language learning programmes that incorporate elements of informal digital learning.
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction

Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning, a distributed collaborative training scheme, can utilize multi-center data without the need to transfer data between institutions. However, existing federated learning MR image reconstruction methods rely on manually designed models which have extensive parameters and suffer from performance degradation when facing heterogeneous data distributions. To this end, this paper proposes a novel FederAted neUral archiTecture search approach fOr MR Image reconstruction (FedAutoMRI). The proposed method utilizes differentiable architecture search to automatically find the optimal network architecture. In addition, an exponential moving average method is introduced to improve the robustness of the client model to address the data heterogeneity issue. To the best of our knowledge, this is the first work to use federated neural architecture search for MR image reconstruction. Experimental results demonstrate that our proposed FedAutoMRI can achieve promising performances while utilizing a lightweight model with only a small number of model parameters compared to the classical federated learning methods.
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization

Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which may negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to estimate style-mixed versions of image features. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring lower computation. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.
EXPLORING THE NARRATIVES SURROUNDING GENERATIVE ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF COMPUTER SCIENCE EDUCATION

EXPLORING THE NARRATIVES SURROUNDING GENERATIVE ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF COMPUTER SCIENCE EDUCATION
DIGITAL TUTORING AND MENTORING PLATFORM: A PATHWAY TO ENHANCE STUDENT RETENTION IN HIGHER EDUCATION

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 8890-8895ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2265Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
SUPPORT FOR CONCEPT-CENTRIC AND TOOL-INDEPENDENT SOFTWARE MODELING TEACHING THROUGH SOFTWARE MODEL CONVERTERS

SUPPORT FOR CONCEPT-CENTRIC AND TOOL-INDEPENDENT SOFTWARE MODELING TEACHING THROUGH SOFTWARE MODEL CONVERTERS
THE PERSPECTIVE OF TEACHERS IN THREE TEACHER TRAINING INSTITUTIONS REGARDING THE IMPLEMENTATION OF A NEW CURRICULUM FOR BASIC EDUCATION

THE PERSPECTIVE OF TEACHERS IN THREE TEACHER TRAINING INSTITUTIONS REGARDING THE IMPLEMENTATION OF A NEW CURRICULUM FOR BASIC EDUCATION
PREPARING PRESERVICE TEACHERS TO TEACH CYBER SECURITY EDUCATION: EXAMINING THEORETICAL APPROACHES

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 2215-2221ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0616Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
ARTIFICIAL INTELLIGENCE FOR EDUCATIONAL ASSESSMENT

1 Regional Center of Education and Training Professionsة, Ministry of National Education (MOROCCO)2 Regional Academy of Education and Training, Ministry of National Education (MOROCCO)3 Ecole Normale Supérieure, Abdelmalek essaadi University (MOROCCO)
CREATIVE COMPUTATIONAL PROBLEM-SOLVING IN PRESERVICE TEACHER COMPUTER SCIENCE INSTRUCTION: ASSESSING LEARNER PERCEPTIONS

CREATIVE COMPUTATIONAL PROBLEM-SOLVING IN PRESERVICE TEACHER COMPUTER SCIENCE INSTRUCTION: ASSESSING LEARNER PERCEPTIONS
SEQUENTIAL IMPLEMENTATION OF INNOVATIVE METHODOLOGIES BASED ON THE COMBINATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES AND FLIPPED CLASSROOM IN THE DEGREE IN CHEMICAL ENGINEERING

SEQUENTIAL IMPLEMENTATION OF INNOVATIVE METHODOLOGIES BASED ON THE COMBINATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES AND FLIPPED CLASSROOM IN THE DEGREE IN CHEMICAL ENGINEERING
THE USE OF SOCIAL NETWORKS BY HIGH SCHOOL AND UNIVERSITY STUDENTS DURING THEIR SELF-STUDY

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 5452-5458ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1361Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
PENGEMBANGAN MEDIA PELAYANAN INFORMASI BERBASIS PREZI UNTUK KETERAMPILAN PENGAMBILAN KEPUTUSAN KARIER SISWA KELAS XII SMK NEGERI 1 CIBINONG

This study aims to determine the feasibility of Prezi-based information services in students' career decision-making skills, in connection with Vocational High School (SMK) graduates who are prepared to become "BMW" namely work, continue education and entrepreneurship for students, then SMK students start to take career decisions seriously. This research uses research and development (R and D) methods. The subjects in this study were 10 class XII students of SMK Negeri 1 Cibinong. Product validity tests were carried out by product experts, material experts, practitioners in schools and student responses to Prezi-based information service media. The results showed that the development of Prezi-based career information service media products obtained 96% results, which means the research product has a feasibility in the very good category to be used in providing career information services.
The Use of Digital Tools for Mastering Practical Disciplines in the Distance Format of Training Bachelors of Preschool Education

The article examines the peculiarities of the use of digital tools (DT) in the education of students of the specialty 012 Preschool Education in the discipline «Art needlework». The authors identified the aspects of the study of the problem of using digital tools in the professional training of future teachers, carried out by modern scientists. The relevance of the application of DT for mastering practical disciplines by bachelors in the process of distance learning is determined. It was established: the lack of development of future bachelors in the field of preschool education competence regarding the application of DT for the organization of artistic and productive activities of preschool children. A model of the use of these digital tools in the process of teaching the discipline «Art needlework» has been developed. The experience of using various digital tools of visualization, collective interaction, game services, augmented reality in mastering disciplines is described, such as: Genially, Jamboard, Conceptboard, Kahoot, H5P, Craiyon, Deepdreamgenerator, Dreamstudio, Canva, Fotor, LightShot, Fanny Pho.to, Blippbuilder. The effectiveness of the use of digital tools for mastering practical disciplines in the distance format of the training of bachelors of preschool education has been proven.
Effectiveness of Using Assemblr Edu Learning Media to Help Student Learning at School

Assemblr Edu is an application that provides interactive 3D augmented reality-based learning media for students. research was conducted on the effectiveness of the application as a learning medium for teaching and learning activities of the Assemblr Edu application. In the application, various learning topics are presented that can be studied by students from elementary to high school levelsThis research uses a descriptive quantitative research method by utilizing google Forms in disturbing questionnaires in form of questionnaires. The result of the research is: The Assemblr Edu learning application can be a more interesting and modern learning solution to be applied in schools; The use of the Assemblr Edu application which is quite easy can provide opportunities for more effective learning with the features of learning topics that are already complete enough to be learned; the lack of knowledge of students about Augmented Reality technology has an impact on the Assemblr Edu application that is rarely used by schools or students independently; and support from hardware such as smartphones is very important with specifications that are large enough in terms of memory storage, to run applications better and efficiently and connection stability also affects the use of this learning application.
Making Android-Based Augmented Reality in Buffer Solution Practicum to Improve Students Multiple Representation Ability

This study aimed to develop android-based augmented reality multiple representation in buffer solution practicum. The research wants to describe the appearance, analyze results of the validation test and feasibility test of android-based augmented reality in buffer solution practicum, which is named Augmented Reality of Buffer Experiment (ARBE). The research was conducted using the Design Based Research (DBR) method which consisted of the analysis and development stages. The subjects of this research were two lectures of chemistry education, one lecture of informatics engineering, one teacher of chemistry, for validation test and 31 high school students of eleventh grade of nature science program for feasibility test. This research use a validation test and feasibility test instruments. The display on ARBE present a practicum menu and also five other menus completes the practicum menu. The results of the validation tests obtained an average value of rcount 0.85 and was declared valid, while the feasibility test obtained an average percentage of 82.14% indicating that android-based augmented reality in the buffer solution practicum to improve student’s multiple representation abilities is very feasible to use as a learning medium for buffer solution material.
Do They Mind Their Ps and Qs: Politeness Strategies in the Movie, Joy

This study was an interpersonal communication study on the politeness methods used by the main characters in the film Joy in the setting mostly of a family cum workplace communication. The purpose of this study was to describe the major characters' politeness tactics. The data for this descriptive qualitative study, the dialogues uttered by all of the movie's principal characters, were scrutinized using Brown and Levinson's (1987) paradigm. The investigation studied how the characters used politeness strategies such as positive politeness, Bald-on -Record, negative politeness and off-record techniques in their utterances. The data came from the script of Joy, and it was identified that 833 politeness strategies were employed. Bald on Record (8%), Positive Politeness (51%), Negative Politeness (35%), and Off Record (6%). The findings revealed that positive politeness strategies were the most frequently used in this film. The results indicated that the main characters had a tendency to use positive politeness to show their respect and also to maintain a harmonious relationship in the family and workplace.
Developing the Complexity and Security of the Twofish Algorithm Through a New Key Scheduling Design

The Twofish cipher is a very powerful algorithm with a fairly complex structure that permeates most data parsing and switching and can be easily implemented. The keys of the Twofish algorithm are of variable length (128, 192, or 256 bits), and the key schedule is generated once and repeated in encrypting all message blocks, whatever their number, and this reduces the confidentiality of encryption. This article discusses the process of generating cipher keys for each block. This concept is new and unknown in all common block cipher algorithms. It is based on the permanent generation of sub keys for all blocks and the key generation process, each according to its work. The Geffe's Generator is used to generate subkeys to make each explicit block a new key that differs from block to block, gaining protection against attacks. Finally, this algorithm works almost like a One-Time Pad.