Reviewerly/exHarmony huggingface dataset:
THE INFLUENCE OF ONLINE LEARNING VIA WHATSAPP ON STUDENTS’ LEARNING MOTIVATION IN PMKR SUBJECT

Online learning as a concept certainly has several variables, one of which is student learning motivation. This study aims to determine how much influence caused by online learning via WhatsApp on students' learning motivation and to find out whether online learning has a significant effect and has a positive or negative relationship on students' learning motivation. The author raised the topic of this study after conducting a survey to 15 students and seeing the facts in the form of learning outcomes which indicated a lack of student motivation in online learning. The design of this study used a mix method approach. Data collection techniques use a questionnaire, observation, interview and data analysis using simple linear regression analysis. The results showed that online learning via WhatsApp had a significant influence and had a positive relationship on students' learning motivation, as well as an influence of 55,7% on students' learning motivation, 60.6% on students' desire to carry out learning activities, 30.4% on students' interest in learning activities, 25.7% on encouragement of students to carry out learning activities, 15.2% of the needs of students to carry out learning activities, 8.2% of the students' expectations to achieve their goals, 17.6% of students' desire to achieve their goals, 28.8% of self respect, 18.7% to the attractiveness of learning activities, by 28.3% to the conduciveness of the learning environment.
THE DEVELOPMENT OF PTC CREO PARAMETRIC WEBSITE MODULE FOR BASIC PARAMETRIC MODELLING WORK

The development of the PTC Creo Parametic website-based learning module for basic modeling work aims to facilitate DPTM UPI students in developing basic parametric modeling skills. The method used is Research Development (RD) with a qualitative approach. In order for the development flow to be directed, the process of developing this learning module uses the Hannafin and Peck development model. The research instrument for module testing used questionnaires, interviews, and observations to find out how the DPTM UPI students responded. This research produced a website-based PTC Creo Parametric learning module for basic parametric modeling work that can facilitate DPTM UPI students and get positive responses from DPTM UPI students. In addition, learning modules provide benefits for users because they are supported by a website-based multitopic scheme, so users can easily access learning materials.
Abnormal event detection based on masked reconstruction and dual-channel adversarial prediction

Abnormal event detection in computer vision addresses the task of identifying events that deviate from expected behavior in video scenes. Issues, such as occlusion in crowded scenes, the powerful generalization capabilities of deep neural networks, and the heavy reliance on contextual information, make this task particularly challenging. To address these issues, we propose a cascaded form of abnormal detection framework that combines the paradigms of reconstruction and prediction in this paper. First, stochastic masking techniques are employed for image reconstruction to alleviate the overgeneralization of neural networks under abnormal conditions. Second, an innovative motion characterization of frame-difference streak streams is introduced to better characterize the motion of video frames in crowded scenes. Finally, a dual-channel autoencoder-based prediction network is introduced to jointly learn appearance and motion features. This network captures contextual information to better generate predictive features. Meanwhile, adversarial learning is introduced for abnormal inference to improve the detection performance. Experimental results on several benchmark datasets validate the effectiveness of our approach.
Sign Language Recognition with Multimodal Sensors and Deep Learning Methods

Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues. In this research, we aim to improve accuracy through data fusion of 2-axis bending sensors and computer vision. We obtain the hand key point information of sign language movements captured by a monocular RGB camera and use key points to calculate hand joint angles. The system achieves higher recognition accuracy by fusing multimodal data of the skeleton, joint angles, and finger curvature. In order to effectively fuse data, we spliced multimodal data and used CNN-BiLSTM to extract effective features for sign language recognition. CNN is a method that can learn spatial information, and BiLSTM can learn time series data. We built a data collection system with bending sensor data gloves and cameras. A dataset was collected that contains 32 Japanese sign language movements of seven people, including 27 static movements and 5 dynamic movements. Each movement is repeated 10 times, totaling about 112 min. In particular, we obtained data containing occlusions. Experimental results show that our system can fuse multimodal information and perform better than using only skeletal information, with the accuracy increasing from 68.34% to 84.13%.
Unsupervised hypergraph convolutional clustering networks

Deep clustering algorithms based on graph convolutional networks are widely used due to their strong ability to mine network structure. However, the construction of neighborhood graphs may introduce noise and affect the clustering results. Meanwhile, focusing on ordinary topology alone ignores the higher-order connections between data about attributes. To address the above problems, an unsupervised hypergraph convolutional clustering network (UHCCN) is proposed in this paper. We construct hypergraph structures using attributes and incorporate higher-order information encoding into representation learning through hypergraph convolution. Using an attribute encoder will extract node features and fuse it into the hypergraph convolution. Finally, representation learning and clustering are optimized jointly. The experiments validate the effectiveness and superiority of UHCCN.
Impact of ChatGPT in Higher Education Learning

The existence of technology in the world of education is a very important need. As time goes by, technology is developing rapidly along with the intelligence and creativity of the generation that will be the successor of the nation in the future. Therefore, students must also be able to develop technology in learning such as ChatGPT. The purpose of this study was to determine the impact of ChatGPT in learning in college as a medium that can help lectures. The method used in this research is quantitative method, data obtained through online questionnaire distribution. The results found that there are various impacts such as ChatGPT can make it easier for students to do some of their assignments, however, socially the interaction and communication between students and lecturers will be reduced, because in the world of education what is needed is not only grades, but also the process that makes it clear that ChatGPT can be beneficial because it can help with assignments, but on the other hand, there is a lack of communication between lecturers and students. The limitation of this study is that researchers only conducted research on some students in higher education so that the data obtained is less relevant. Penliti hopes that future researchers can conduct more in-depth research. This research also recommends ChatGPT in college.
Problem-Based Learning and Project-Based Learning in STEM

The time and context in which today's students live and learn are complex and dynamic. Pedagogical methods and approaches that gave good results yesterday are insufficiently effective today. It is imperative to combine working models with innovative educational technologies based on the integral and constructivist approach, to upgrade and enrich pedagogical practice with educational methods, approaches and tools adequate to the requirements of the time. This article examines the role of project-based learning and problem-based learning in STEM subjects for the formation of the key competencies and soft skills necessary for successful personal, social and professional realization. The holding of a Science Fair in the Second English Language School is brought out as a good practice and its role is emphasized in increasing the motivation of students for achievements in the field of natural sciences, as well as for their professional orientation in this direction.
Digitalization of the Learning Process with Cross-curricular Links (Mathematics, Physics and Information Technology) and Artificial Intelligence

In modern life, cross-curricular connections, which include mathematics, physics and information technology, play an important role in improving education and student preparation. Artificial intelligence technologies can be used to improve the educational process, to develop analytical thinking in students, and to apply their knowledge in real situations. The digitalization of the learning process and the use of information technology and artificial intelligence in connection with the interdisciplinary connections between mathematics, physics and information technology offer new opportunities for learning and development of students. This helps students improve not only technical skills, but also critical thinking, problem solving and creative thinking – all key competencies needed to successfully cope in today's world.
The Competency-based Learning Approach in the 21st Century Classroom – Interdisciplinary Learning and Teaching

The aim of the article is to present one of the best learning and teaching practices for utilising the competency-based learning approach in uppersecondary education through interdisciplinary learning and teaching. Based on the curricula for the 11th grade in academic profiles Bulgarian Language and Literature and English Language and Literature, and the curriculum for general education in Civic Education, as well as on The European Reference Framework for Key Competences for Lifelong Learning and Reference Framework of Competences for Democratic Culture (RFCDC) a series of lessons in the abovementioned subjects is carried out at “Thomas Jefferson” English Language School, Sofia, on the topic of “Shared Values. Human Dignity”. The final lesson is conducted simultaneously in Bulgarian and English by four teachers with four groups of students from different academic profiles integrating new technologies in the learning and teaching process – a shared virtual Google Classroom. The teachers’ goal is to develop students’ civic competencies as well as their cultural awareness and expression competencies based on their acquired multilingual competencies.
Corrigendum: The changing nature of basic skills in numeracy

CORRECTION article Front. Educ., 29 November 2023Sec. Teacher Education Volume 8 - 2023 | https://doi.org/10.3389/feduc.2023.1333190
Smart Society Development through the Initiation of New Library Infrastructure Development in Gowa Regency

This research discusses the Gowa Regency Government's initiative to build a new library infrastructure as a step towards developing a smart society. In this ever-evolving information age, libraries are places to read books and knowledge centers that support digital literacy and intellectual growth. Data was collected through interviews, observation, and documentation. The analytical tool that is maximized is Nvivo 12 Plus. The research results show that constructing a new library infrastructure has increased people's access to digital knowledge and literacy. Inclusive design has enabled the participation of all walks of life, while digital library services have expanded the range of educational resources. Government coordination has also supported efficiency in project implementation. However, low digital literacy and data privacy protection require further attention. This research provides an in-depth look at how library infrastructure development can contribute to developing a smart society, with particular attention to the still-needed improvements.
An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

The equilibrium optimizer (EO) represents a new, physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium. Despite its innovative foundation, the EO exhibits certain limitations, including imbalances between exploration and exploitation, the tendency to local optima, and the susceptibility to loss of population diversity. To alleviate these drawbacks, this paper introduces an improved EO that adopts three strategies: adaptive inertia weight, Cauchy mutation, and adaptive sine cosine mechanism, called SCEO. Firstly, a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation. Next, an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity. Finally, the Cauchy mutation is utilized to prevent the loss of population diversity during searching. To validate the efficacy of the proposed SCEO, a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite. The outcomes are subsequently benchmarked against both the conventional EO, its variants, and other cutting-edge metaheuristic techniques. The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors. In addition, the developed SCEO is implemented to deal with a mobile robot path planning (MRPP) task, and compared to some classical metaheuristic approaches. The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.
ISO 27001 Information Security Survey of Medical Service Organizations

The differences between medical institutions in the security management of information systems were investigated by comparing the differences and the means used by personnel in different units in public and private hospitals. Personnel responsible for information security require the protocol of relevant units to solve information security issues. Based on ISO 27001 as a reference standard, a questionnaire survey was conducted to investigate the need for information security management in medical institutions. The information system security in each unit of medical institutions needs to pursue the goal of more perfection for a fully optimized information system. To help medical institution personnel understand the importance of information security and allow appropriate decision making, the results of this study can be used as a reference.
Model Budidaya Kepiting Soka Skala Rumah Tangga Sistem Apartemen Sebagai Sarana Edukasi Masyarakat Pulau Bangka

Mangrove crab is one of the important and potential economic commodities on Bangka Island. One of the products is soka crab. Opportunities and prospects for soft crab cultivation have led to the emergence of aquaculture ponds. Cultivation of this horizontal system requires high costs and is less applicable for household scale. Bangka Belitung University students build a fish farming business unit with a vertical apartment system. The apartment uses plastic boxes arranged vertically equipped with water re-circulation. The duration of crabs reached molting at 27 -30 days rearing with 100% survival. In addition to being a student entrepreneurship lesson, this business unit is an educational model for the people of Bangka Island. The local government also sent envoys to study the production of this soft crab. This business provides opportunities for new entrepreneurs from the academic community on campus, becomes the choice of the local government to move the regional economy, as well as an option for the people of Bangka who are trying to escape from dependence on tin mining
Autonomous systems: Challenges and opportunities

The dawn of Autonomous Systems has marked a pivotal transformation in the realm of technology, establishing new paradigms in various industries, from healthcare to transportation. These systems, characterized by their ability to operate without human intervention, promise enhanced efficiency, precision, and adaptability in tasks otherwise prone to human error or constraints. However, as with all revolutionary innovations, they bring forth a plethora of challenges. This paper aims to provide a comprehensive exploration of Autonomous Systems, discussing their architectures, applications, and the intricacies of their operation. We delve into the pressing challenges, both ethical and technical, and speculate on the potential future trajectories of this transformative technology. By amalgamating insights from diverse disciplines, this paper offers a holistic perspective on Autonomous Systems, setting the stage for informed discourse and future research endeavors.
Comparative Assessment of NeRF and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction

While Neural Radiance Fields (NeRF) are gaining increasing interest in various domains as innovative methods for novel view synthesis and image-based reconstruction, their potential application in the realm of Cultural Heritage remains unexplored. Purpose of this paper is to assess the effectiveness of applying NeRF to sets of images of digital heritage objects and sites. The study’s findings demonstrate that NeRF could be valuable when used in combination with or as a comparison to other well-established techniques such as photogrammetry, to expand the possibilities of preserving and presenting heritage assets with enhanced visual fidelity and accuracy. Particularly, NeRF show promising results in improving the rendering of translucent and reflective surfaces, objects with homogeneous textures, and elements with intricate details. In addition, we demonstrate that, when considering the same set of input images (with known camera poses), reducing the image quality or the number of images results in significantly less information loss with NeRF compared to photogrammetry. This suggests that NeRF is preferentially suited for scenarios involving sparse information or low-quality photos or videos, which could be especially valuable in risky or challenging situations.
Influence Scores at Scale for Efficient Language Data Sampling

Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic. Understanding \textit{which} examples are important to the performance of a learning algorithm is crucial for efficient model training. Recently, a growing body of literature has given rise to various "influence scores," which use training artifacts such as model confidence or checkpointed gradients to identify important subsets of data. However, these methods have primarily been developed in computer vision settings, and it remains unclear how well they generalize to language-based tasks using pretrained models. In this paper, we explore the applicability of influence scores in language classification tasks. We evaluate a diverse subset of these scores on the SNLI dataset by quantifying accuracy changes in response to pruning training data through random and influence-score-based sampling. We then stress-test one of the scores -- "variance of gradients" (VoG) from Agarwal et al. (2022) -- in an NLU model stack that was exposed to dynamic user speech patterns in a voice assistant type of setting. Our experiments demonstrate that in many cases, encoder-based language models can be finetuned on roughly 50% of the original data without degradation in performance metrics. Along the way, we summarize lessons learned from applying out-of-the-box implementations of influence scores, quantify the effects of noisy and class-imbalanced data, and offer recommendations on score-based sampling for better accuracy and training efficiency.
Understanding the Topics and Challenges of GPU Programming by Classifying and Analyzing Stack Overflow Posts

GPUs have cemented their position in computer systems, not restricted to graphics but also extensively used for general-purpose computing. With this comes a rapidly expanding population of developers using GPUs for programming. However, programming with GPUs is notoriously difficult due to their unique architecture and constant evolution. A large number of developers have encountered problems of one kind or another, and many of them have turned to Q&A sites for help. Unfortunately, there has been no prior work to comprehensively study the topics discussed and challenges encountered by developers in GPU programming. To fill this knowledge gap, we conduct a comprehensive study to understand the topics and challenges of GPU programming using Stack Overflow. We collect 25,269 relevant posts from Stack Overflow, propose a novel approach that combines automatic techniques and manual thematic analysis to extract topics, and build a taxonomy of topics with detailed discussions of the popularity, difficulty, and changing trends of these topics. In addition, we analyzed relevant posts through extensive manual efforts to understand the challenges of each topic and to summarize them for future research.
Short-Depth Circuits and Error Mitigation for Large-Scale GHZ-State Preparation, and Benchmarking on IBM's 127-Qubit System

This paper conducts an evaluation of two IBM quantum systems: Quantum Eagle r3 (Sherbrooke, 127 qubits) and Falcon r8 (Peekskill, 27 qubits), with an emphasis on benchmarking these systems and their differing approaches to generating Greenberger-Horne-Zeilinger (GHZ) states, a specific type of multi-partite entangled quantum state. Our primary objective is to augment quantum fidelity via depth-reduction circuit designs. Sherbrooke's larger qubit capacity presents significant opportunities for implementing more complex algorithms, thus benefiting quantum cryptography [4], measurement-based quantum computing (MBQC) [5] and quantum simulation [6]. We introduce the Tree-based and Centred-tree-based approaches, enabling the exploitation of entangled states. Our strategies demonstrate promising potential for increasing quantum fidelity and broadening quantum applications. This work lays a firm foundation for subsequent advancements in quantum computing, highlighting the potential for heightened efficiency and versatility in future quantum systems.
A Simulator of Atom-Atom Entanglement with Atomic Ensembles and Quantum Optics

One fundamental goal of quantum networks is to provide node-to-node entanglement distribution. In this work, we develop a simulator, called A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Tango, for entanglement generation between two remote atom-ensemble nodes in a quantum network following Briegel, Dur, Cirac and Zoller (BDCZ) protocol. We encode quantum information to the two spatial modes of local atomic-ensemble spin waves and polarization states of single photons. The basic operations include atom-photon entanglement generation, quantum memory write-read operations, two-photon Bell-state measurement, and quantum state tomography. We model multi-photon events during the local excitation and propagation to account for their induced error in entanglement generation and distribution. We investigate the entanglement generation rate and fidelity as functions of the parameters which are realizable in experiments. Our work improves the open-sourced SeQUeNCe simulator and inspires the development of future quantum networks.