Reviewerly/exHarmony huggingface dataset:
A PEPS Plugin for TNQVM

This work introduces an extension to the Tensor Network Quantum Virtual Machine (TNQVM) tool, enhancing the existing stack of ExaScale Tensor Network (ExaTN), ExaScale Accelerator (XACC), and TNQVM. It features a new plugin that enables efficient simulation of a Projected Entangled Pair State (PEPS), a 2D tensor network. To improve simulation efficiency for PEPS, we have implemented the snake boundary contraction algorithm. By integrating this capability into the existing stack, we enhance the overall functionality and versatility of the framework. We tested this new PEPS topology for a simple GHZ bell-pair generation quantum circuit and saw that its runtime is very close to that of the MPS topology. We estimate that the real potential of the PEPS topology becomes discernible when quantum circuits with multidimensional entanglement are simulated using tensor networks. In such cases, 1D tensor networks fail to represent or contract them efficiently.
A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images

Deep learning-based segmentation has emerged as a powerful and effective technique for addressing diverse medical imaging tasks. Particularly, in mammography image analysis, segmenting the anatomical structures plays a significant role in computer-aided diagnosis assistance and positioning assessment. However, accurately identifying the pectoral muscle in the craniocaudal view presents challenges even for experienced radiologists due to its variable size, potential absence, and fibroglandular tissue overlaps. These challenges are further amplified when dealing with error-prone annotations, where mislabeled or inaccurately labeled data can lead to training the model on incorrect information. Consequently, this can cause the model to learn from these errors and produce underperforming or suboptimal results during inference. To address this, we propose a two-stage data-centric approach to enhance the accuracy of the deep-learning-based mammography segmentation model. In the first stage, we introduce a shape-based label analysis to automatically identify pectoral muscle labels with possible inconsistencies for a posterior manual review and correction. Then, in the second stage, we downsample the training dataset by removing outlier images with dubious annotations. The experimental results show the effectiveness of prioritizing training data quality and reliability. This approach significantly improved the model’s ability to detect and accurately segment the pectoral muscle.
A MODEL FOR TEACHING MASTER STUDENTS ENGLISH FOR SPECIFIC PURPOSES AT HIGHER SCHOOL

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 9458-9461ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2436Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
WHAT TO TEACH TO FUTURE INTERPRETERS TODAY

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 7252-7255ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1804Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
ATTITUDES OF EARLY CHILDHOOD EDUCATION TEACHERS TOWARDS ACQUIRING NEW DIGITAL LITERACY SKILLS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 4303-4309ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1079Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
NAMING, FRAMING, AND STAGING LIMINALITY: A TRAINING DESIGN FOR UNCERTAINTY

1 Norwegian University of Science and Technology (NORWAY)2 The Norwegian Police University College (NORWAY)
Exploring Hand Tracking and Controller-Based Interactions in a VR Object Manipulation Task

As the uses of virtual reality (VR) continue to expand, more systematic research on interaction design is needed to improve user performance and experience. Although hand tracking technology holds promise for being a more natural and intuitive interface, previous research has consistently shown that hand tracking has been rated as less usable and led to worse performance than controller-based interactions. In this experiment, we sought to explore why this might be the case, focusing on feelings of agency and perceived naturalness of the interaction. Specifically, twenty-one participants used two different interaction types that included a hand tracking condition and a controller-based condition with four variations that manipulated what participants saw (i.e., controllers, virtual hands, both, and animated virtual hands) in a ball sorting task. We found that hand tracking led to the lowest ratings of perceived naturalness and feelings of agency and the worst performance overall. Participants showed little preference among the controller conditions on overall experience or performance, with the exception of the animated hands condition, which was rated lower on some items than the other controller conditions. Participants overwhelmingly rated the hand tracking condition as their least favorite and many described tracking accuracy issues as a cause. Interestingly, they also described a lack of feedback and having nothing physical to touch as other reasons for disliking this condition. Implications for interaction design are discussed.
Hubungan Lingkungan Sekolah Dan Motivasi Belajar Dengan Keterampilan Sosial Siswa Sekolah Dasar

This research aims to determine the relationship between the school environment and learning motivation with the social skills of third-grade students at SD Negeri 5 Tilamuta, Boalemo Regency. The objective is to investigate the relationship between learning motivation and students' social skills at Negeri 5 Elementary School in Tilamuta, Boalemo Regency. The research method used in this study is correlational. A sample of 33 students was selected using purposive sampling from a total student population of 187. The results obtained in this study are as follows: There is a correlation between the school environment and children's social skills at Negeri 5 Tilamuta Elementary School, Boalemo Regency, with a correlation coefficient of 42.9%. There is a relationship between learning motivation and children's social skills at Negeri 5 Tilamuta Elementary School, Boalemo Regency, with a correlation coefficient of 19.17%. There is a combined relationship between the school environment, learning motivation, and children's social skills at Negeri 5 Elementary School in Tilamuta, Boalemo Regency, with a correlation coefficient of 68%.
Mediating Role of Training Engagement Between Service Quality, Interface Quality, and Employee Creativity in Workforce of Ministry of Interior Abu Dhabi, UAE

Purpose: Employee creativity is vital for the success of any organization. Organizations conduct trainings and engagement of employees in the trainings helps organizations to get successful results. Theoretical framework: Employee creativity is highly dependent on service quality and interface quality. Therefore, this study is tailored to identify the mediating role of training engagement between service quality, interface quality and employee creativity. In order to test the framework developed on the basis of new website quality assessment model by the supporting role of engagement theory and investment theory of creativity. Design/methodology/approach: For testing the model, adopted questionnaires have been used and structural equation modeling has been applied. The findings revealed that training engagement significantly mediate the relationship between service quality, interface quality and employee creativity. Findings: The findings are significant for the policy makers to stress on engagement of employees in the training activities and by improving the service quality and interface quality to develop creativity among employees to get fruitful results. Research, Practical &amp; Social implications: provides basics for understanding effectiveness e-learning, training engagement over employee creativity. As a result, it is revealed that training engagement in the presence of information qualities, system qualities, service qualities, interface qualities, and emotional qualities have a significant effect on employee creativity. Originality/value: the current research focuses on the importance of service quality and interface quality over training engagement and its interaction together over employee creativity.
On the Issue of Multiple Conceptualizations of the Historical Quantorium

The article examines the possibilities of conceptual substantiation of the historical quantorium as a digital edu-cational platform within the framework of studying the history of the native land. The authors’ assumption re-garding the possible mitigation of the risks of digital education “Generation Z” (as well as the ability to over-come the “digital divide” at the level of semantic and ideological transformation of traditional practices through the justification and implementation of the digital historical quantorium) is explicated by a synthesis of various models and their own proposals. The multivalence of conceptual approaches to quantoriums, associated with their habitual consideration as natural science platforms, is rethought in the perspective of the development of digital historical education. The key characteristics of the digital historical quantorium are highlighted, the main directions and types of activity of the participants are determined, and possible options for methodological equipment of the educational platform are proposed (projects with elements of augmented reality, interactive maps, interactive desktops, video games).
E-BOOK MEDIA TRENDS IN THE LEARNING PROCESS AT SCHOOL

E-books are digital teaching materials that can be integrated with various multimedia, including images, videos, animations or manipulative media. For the next e-book development process, data and information on what kind of e-book and what media have been integrated are needed. For this reason, a Systemic Literature Review (SLR) was conducted which examined what and how e-books were researched. This research uses PRISMA (Preferred Reporting Items for Systemic and Meta-Analysis) guidelines, by searching for scopus indexed articles in the 2019-2023 range involving a total of 193 articles. After the screening process, 14 articles were obtained which were then analyzed in accordance with the Research Question (RQ) that had been formulated. From the results of the study based on two RQs, it was found that (1) the most research design that examines e-books is design/development research; (2) the least integrated aspect in e-books is animation/manipulative media. These results have limitations in terms of the scope of data sources and the depth of study on the material discussed. So this research provides opportunities for further researchers both in development design and SLR.
TEACH2 TOMORROW – HYDROGEN AS AN ALTERNATIVE ENERGY CARRIER – A TEACHING SERIES IN THE CONTEXT OF ENERGY TRANSITION USING SELF-CREATED LEARNING VIDEOS AND CONCEPT MAPPING AS AN EVALUATION TOOL

TEACH2 TOMORROW – HYDROGEN AS AN ALTERNATIVE ENERGY CARRIER – A TEACHING SERIES IN THE CONTEXT OF ENERGY TRANSITION USING SELF-CREATED LEARNING VIDEOS AND CONCEPT MAPPING AS AN EVALUATION TOOL
Implementation of Backpropagation Artificial Neural Network on Forecasting Export of Palm Oil in Indonesia

Export activities are one of the largest revenues in Indonesia with the largest contributor to export is being palm oil. Increasing volume of palm oil exports, it will be able to spur economic growth in Indonesia. In this research, palm oil export forecasting in Indonesia is carried out based on the main destination countries using the Artificial Neural Network (ANN) method with the Backpropagation algorithm. The data used is palm oil export data for 2012-2022 obtained from the Central Statistics Agency (BPS) website. From the data used, the optimal architecture model is 10-1-3-3-1 with a MAPE of 9.68%, which means that this architecture uses 10 input data, 3 hidden layers with the number of each input neuron (1,3,3), and there is 1 output output. From this study, it is estimated that 90% of the results of palm oil export forecasting using the ANN method are close to the actual value.
Self-Supervised Query Reformulation for Code Search

Automatic query reformulation is a widely utilized technology for enriching user requirements and enhancing the outcomes of code search. It can be conceptualized as a machine translation task, wherein the objective is to rephrase a given query into a more comprehensive alternative. While showing promising results, training such a model typically requires a large parallel corpus of query pairs (i.e., the original query and a reformulated query) that are confidential and unpublished by online code search engines. This restricts its practicality in software development processes. In this paper, we propose SSQR, a self-supervised query reformulation method that does not rely on any parallel query corpus. Inspired by pre-trained models, SSQR treats query reformulation as a masked language modeling task conducted on an extensive unannotated corpus of queries. SSQR extends T5 (a sequence-to-sequence model based on Transformer) with a new pre-training objective named corrupted query completion (CQC), which randomly masks words within a complete query and trains T5 to predict the masked content. Subsequently, for a given query to be reformulated, SSQR identifies potential locations for expansion and leverages the pre-trained T5 model to generate appropriate content to fill these gaps. The selection of expansions is then based on the information gain associated with each candidate. Evaluation results demonstrate that SSQR outperforms unsupervised baselines significantly and achieves competitive performance compared to supervised methods.
A TEXTUAL CONVERSATIONAL AGENT AS A VIRTUAL ASSISTANT TO STUDENTS IN THE MOODLE LMS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 5046-5054ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1266Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
VoXED: an online XED file extractor

Kinect devices are used up to this day for image capturing and video recording in many activities. These devices make it possible to capture color and depth data, crucial to many research works. Despite that, these data are stored in a xed file, in the case of the Kinect V1, that cannot be opened directly. Existing tools to extract data from these files include the Kinect Studio application and the xed extractor developed by Daniel Jackson. Although valuable tools, they require a higher computational knowledge from the user since they require either the compilation of a source code or to develop an application that uses the stored data like it was received from a Kinect device while a Kinect device is physically connected to the user’s computer. In this paper, we propose a web service that allows users to retrieve color data from xed files in an easier way than the existing tools, by retrieving a compressed file with the images stored on xed files. The obtained results show that we successfully retrieved the images stored on these files, making it possible to reuse old xed files containing important information such as body posture datasets.
Aplikasi Absensi Non ASN Berbasis Web

Presence is crucial in a government institution. With an effective attendance system, it is hoped that it can assist in controlling the task completion process. The Attendance Recording System used in the Public Section applies fingerprint technology. Fingerprint is one of the information technology innovations that makes it easier to record employee activities every day. At this time employees do not take attendance using a fingerprint machine and return to using a manual system. The system is done manually, namely by signing the attendance book that has been provided by the staffing department. The purpose of developing this application is to facilitate, and speed up, employees in taking attendance and managing attendance outside the office. The method used in developing web-based non-ASN attendance applications is the waterfall model. as a structured software approach, starting from the stages of analysis, design, coding, testing, to the support stage. The PHP programming language, Laravel Framework and Database Management System (DBMS) use MySQL. The result of this research is the development of a web-based non-asn attendance application for non-asn employees in the general section of the regional secretariat of Bekasi district
Analysis of ZIS Fund Management in the Madrasa Teacher Care Program for Minority Regions of North Sumatra in Baznas, North Sumatra Province

This study aims to find out and analyze how ZIS funds are managed in the Madrasah Teacher Care Program in Minority Areas of North Sumatra at BAZNAS, North Sumatra Province. This study uses a qualitative method where this research method is to understand social or human events by creating a comprehensive and complex picture that can be presented in words, reporting detailed data from informant sources. The result of this study is to find out how to manage ZIS funds in the minority madrasa teacher care program in North Sumatra Province. With the aim that if this program is successful, it will become a strong and good foundation for us to help the people.
Rule-Based Testing of Neural Networks

Adequate testing of deep neural networks (DNNs) is challenging due to lack of formal requirements and specifications of functionality. In this work, we aim to improve DNN testing by addressing this central challenge. The core idea is to drive testing of DNNs from rules abstracting the network behavior. These rules are automatically extracted from a trained model based on monitoring its neuron values when running on a set of labeled data, and are validated on a separate test set. We show how these rules can be leveraged to improve fundamental testing activities, such as generating test oracles and supporting testing coverage with semantic meaning.
A Fishing Detector with Attentional Mechanisms

The threat posed by phishing scams to Ethereum’s security has grown significantly with the advancement of blockchain technology. As a result, the detection of phishing scams has emerged as one of the most prominent research areas in the field of blockchain. Most existing studies represent transaction information as a static subgraph and employ random walks to extract potential user features. However, real-world graphs often exhibit dynamic behavior and evolve over time. To address these challenges, we introduce a novel approach called Dynamic Weighted Node Classification (DWNC). In this approach, we partition transaction records into multiple temporal snapshots based on time. We then capture the structural and temporal characteristics of the nodes using the structural aggregation module and the time aggregation module, respectively. Finally, we leverage the learned features for classification purposes. The proposed DWNC method demonstrates superior performance in classification, as evidenced by its evaluation on nine benchmark and Ethereum datasets.
Multi-Modal Sarcasm Detection via Cross-Modal Attention Mechanism

With the popularity of social media, the sarcasm figure of speech has become a common phenomenon on social media platforms, and many studies have utilized text and visual information for multimodal sarcasm detection. This paper use a method based on cross modal attention mechanism. Specifically, the paper extract multimodal features firstly, use attention mechanism to focus on the inconsistent information between modalities, and then use the inconsistent information for prediction. The experimental results show that the performance of this paper is improved on the Twitters sarcasm dataset.
Task Allocation Mechanism of UAV

In the past few decades, as a prominent representative of the third generation of intelligent robots, drones have taken the lead in developing from single task to multi machine collaborative work. Research has shown that using multiple drones to search for target areas can expand the search field, improve the efficiency of drones in completing tasks, reduce energy consumption during flight, and reduce environmental instability. Thus, high-precision positioning can be further carried out to improve the target hit rate. However, clustered drones also face some new challenges, such as the robustness of multi-agent topology, real-time performance of online perception mechanisms, reliability of information exchange, and loose coupling of collaborative systems. The method proposed in this paper improves the problem that the traditional Ant colony optimization algorithms is easy to fall into the local optimum and the Rate of convergence is slow. The obstacle avoidance factor is added to the calculation formula of the state transition probability, and an improved Pheromone volatilization coefficient based on the Gaussian distribution is given, so that the Pheromone volatilization factor changes from a fixed value to an adaptive value that changes with time, which makes the path obtained by the algorithm better and greatly speeds up the Rate of convergence of the algorithm. In this system, all UAVs can share their original data through direct communication between them. Even if one UAV fails, the whole task will not be affected.
PENINGKATAN KEMAMPUAN MENGENAL HURUF PADA TEMA TANAMAN DI KELOMPOK B TK MARAQITTA’LIMAT TEMBENG PUTIK

This research aims to determine the increase in the ability to recognize letters on the theme of plants in group B of Maraqitta'limat Tembeng Putik Kindergarten by using letter card games. This research is classroom action research consisting of two cycles. Research stages include planning, implementation, observation and reflection. The research subjects were group B, totaling 15 children, consisting of 10 boys and 5 girls. Data collection methods through interviews, observation and documentation. The instrument used in this research was an observation sheet on the ability to recognize letters which consisted of four indicators, namely naming letter symbols, matching letter symbols, composing words, and showing letters. Data were analyzed using a percentage formula to determine classical completeness in each cycle. The results of the research show that letter card games can improve children's ability to recognize letters. This increase can be seen from the children's classical completeness in cycle I, namely 60%, then increasing to 86% in cycle II. This is because children are very enthusiastic and motivated in learning. Through playing letter cards, children become more responsive so that their ability to recognize letters increases.
Kinerja Dinas Pendidikan dan Olahraga Dalam Peningkatan Literasi Digital Melalui Wajib Pojok Baca di Kabupaten Badung

Reading is a mandatory activity carried out by students as an effort to support the smooth running of teaching and learning activities at school. However, in reality it can be said that the reading ability and interest of students, especially elementary school students, is still very low. One of the reasons for this is the lack of attention from schools or the government in providing facilities to support reading activities that can be carried out at school. One of the government's efforts to increase students' interest in reading is to require every school to create a reading corner where the books used are books that suit the needs of children at school according to their respective levels. So, from this reading corner, students are expected to be able to build enthusiasm and rebuild interest in reading in Badung Regency. This research was carried out at SDN 1 Sempidi using a descriptive qualitative method where data collection used interview techniques with sources who were believed to be able to provide information about the problem.