Pelatihan Penggunaan Aplikasi Publish Or Perish (POP) Untuk Mencari Referensi Jurnal Pada Mahasiswa Universitas Muhammadiyah Jambi

Abstract: Every student has the ability to write scientific papers, but in reality there are still many students who have difficulty. This is due to the limited ability of students to access references, as experienced by students at Muhammadiyah University in Jambi. Publish or Perish (PoP) is an application that can help students find references. Therefore, this service activity is carried out with the aim of providing training to students to improve students' abilities in writing scientific papers through Publish or Perish (PoP). The training activity began with the delivery of material on the importance of using Publish of Perish (PoP) in writing scientific papers. Introduction to the Publish or Perish (PoP) application. Delivery of material with lectures, questions and answers. Then carry out practice in the form of downloading the Publish or Perish (PoP) application on the desktop, installing the Publish or Perish (PoP) application. Operate the Publish or Perish (PoP) application. The conclusion from this PKM activity is that the majority of activity participants who are students at Muhammadiyah University of Jambi are able to use the Publish or Perish (PoP) application well.


Analisis Prinsip Kesantunan Dalam Teks Anekdot Pada Buku Cerdas Cergas Berbahasa Dan Bersastra Indonesia Kelas X

Abstract: Oral and written communication is broad and free. However, the content in it must comply with applicable rules, one of which is the principle of politeness contained in three anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil”. Criticism in it is conveyed by paying attention to the principles of politeness. However, not all speech contained in the text meets the principles of politeness. Therefore, the author was moved to carry out an analysis of the principles of politeness in the anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil” contained in the Cerdas Cergas Berbahasa dan Bersastra Indonesia SMA/SMK Kelas X. This analysis aims to discover and examine further the principles of language politeness by the discipline of pragmatics. The approach used in this research is a qualitative descriptive approach. The results of data analysis were collected using reading and note-taking techniques. The results of the analysis in this research found 19 violations of politeness principles, dominated by violations of the thimble agreement. Apart from that, there is compliance with the principles of politeness, totaling 4 utterances, namely 2 on the thimble of agreement, and the other 2 on the thimble of humility and the thimble of feelings. With this research, it is hoped that it will be useful for providing understanding and as additional reference material for further research regarding the principles of language politeness in communication.


Peningkatan Keterampilan Membaca Pemahaman Isi Cerita Anak Melalui Media Handout Buku Bacaan Siswa Kelas V UPT SPF SD Inpres Bontomanai Kota Makassar

Abstract: The problem in this research is: How do reading comprehension skills improve in concluding the contents of children's stories through developing reading book handouts for class V UPT SPF SD Inpres Bontomanai Makassar City students? The objectives of this research are; to determine the increase in learning outcomes in reading comprehension skills in concluding the content of children's stories through the development of reading book handouts for class V UPT SPF SD Inpres Bontomanai Makassar City students. The type of research used in this research is Classroom Action Research which consists of two cycles where each cycle is carried out in two meetings. The research subjects were teachers and students of class V UPT SPF SD Inpres Bontomanai, Makassar City, totaling 32 students consisting of 17 male students and 15 female students. The use of handout media can improve reading skills and understanding of the contents of children's stories in reading books for class V UPT SPF SD Inpres Bontomanai Makassar City. This can be seen from the results of the analysis of teacher activity observations in cycle I, namely 79.16% and increased in cycle II to 95.83%. Student activity also increased from 78% in cycle I to 95% in cycle II. Meanwhile, the results of the mastery test results in student learning outcomes in reading comprehension skills in cycle I were 37.5%, increasing to 87.5% in cycle II. Thus, it can be concluded that the use of handout media can improve reading skills, understanding the contents of children's stories, reading books for class V UPT SPF SD Inpres Bontomanai, Makassar City.


Relationship of Parenting Styles with Decision-Making and Self-concept Among Adolescents

Abstract: The current research aimed to examine the relationship of parenting styles with decision-making and self-concept among adolescents (N=400). The sample comprised Multan and Vehari college students and government and private colleges. 200 male and 200 female college students were recruited for the present study. Data were collected through the Parental Authority Questionnaire (Buri, 1989), the Self-Concept Questionnaire (Robson, 1989), and the Melbourne Decision Making Questionnaire (Mann et al., 1997). Results show that self-concept has a significant negative correlation with authoritarian fathers, authoritarian mothers, permissive fathers, and permissive mothers. In contrast, a significant positive correlation is between authoritative fathers and authoritative mothers. Findings revealed that gender moderates the relationship between authoritarian parenting styles and hypervigilance, as well as procrastination. Moreover, gender also moderates the relationship between authoritarian parenting and self-concept. Studies demonstrate that parenting styles, especially authoritarian parenting styles, play a distinctive role in determining adolescents' adaptive decision-making and firm self-concept.


Dolomitization of the Lower Part of the Sargelu Formation, Northeastern Iraq

Abstract: The prominent characteristic of the lower part of the Sargelu Formation of the Middle Jurassic is the occurrence of destructive dolomitization. This dolomitized unit, cropped out in the high folded and Imbricated zones in Northern Iraq, is situated in the Gotnia Basin and has been distinctly identified and are documented in previous studies. Dolomite crystals are euhedral in shape; hence, they are classified as saccharoidal dolomite, resulting in the elimination of all biocontent and the destruction of the original rock fabrics. Despite the abundance of intercrystalline pores in this unit and their role in the quality of reservoirs in the oil fields, the origin or mechanism of dolomitization is not well understood yet. The aim of this investigation is to find out a suitable model for the dolomitization and to propose possible mechanisms that led to the dolomitization. At the top of the Lower Jurassic Sehkaniyan Formation, an extensive mass of destructive dolomitization has also occurred. The dolomitic unit of the Sargelu Formation, which extends for hundred kilometers constantly. Stratigraphic correlation reveals that the Sargelu Formation is juxtaposed with different successions, such as the Alan and Sehkaniyan formations, that show evidence about peritidal and sabkha environments, where the dolomitization units were associated with solution collapse breccia and microbial stromatolite. This study proposes that the reflux and burial dolomitization models for the lower part of the Sargelu Formation by comparing the paleoenvironmental conditions of the late Lower Jurassic and early Middle Jurassic in the Gotnia basin with a modern analogue model.


The Effectiveness of two Dimensional Haar Wavelet Image De-noising performance using Soft or Hard Thresholding Approach

Abstract: Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement ..etc. this work is aimed at developing effective algorithm for denoising image using new strategy algorithm of wavelet techniques ,by applying two dimensions wavelet transform using Haar wavelet. Wavelets are hierarchically decomposing mathematical tools. A noisy picture is sent to the Haar wavelet transform to create four decomposed bands(for each level), and the noise is then removed by applying a threshold to the resultant image band. Then, an inverse transform is used to provide a noise-reduced picture. Performance characteristics are improved by the suggested design in contrast to those of the current approaches.


Research on low-light image enhancement algorithm based on filtering and data-driven composite

Abstract: Low-light images have low visibility, poor contrast, and weakened details, which pose significant obstacles for subsequent computer vision tasks. When enhancing low-light images, multiple factors such as brightness, contrast, artifacts, and noise need to be considered, making this problem challenging. Low-light enhancement algorithms based on the Retinex theory are important methods in the field of image enhancement, with a wide range of applications and practicality. In this paper, starting from the low-light image imaging model, we address the issues of color distortion and limited brightness improvement exhibited by traditional Retinex-based algorithms and deep learning algorithms in actual low-light scenarios. We propose a filtering and data-driven composite enhancement algorithm (FADDC) that performs weighted fusion on output and intermediate quantities before outputting them. Experimental results combining SCI-Net with traditional algorithms SSR and MSR demonstrate that this fusion method can effectively enhance image brightness without color distortion, achieving better enhancement results.


<i>Is Gravity a Force or Not? A Tale of Scientific Models</i>

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Is Gravity a Force or Not? A Tale of Scientific Models. Phys. Teach. 1 February 2024; 62 (2): 158. https://doi.org/10.1119/10.0024604 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search


Digitalisation of Banking as a Catalyst for Inter-Country Money Transfers in Africa

Abstract: The purpose of this research was to examine how banking digitalisation acts as a catalyst for money transfers and economic development between countries of origin and country of labour migration in Africa. This chapter used desktop research to understand how improvements in banking digitalisation using different platforms is transforming African movement of money and saving money senders a lot of money and time in inter account transfers. M-PESA; Mukuru, supermarket money market transfers, bank transfers have been dominant FinTech innovations transforming and improving quality of life and fostering financial inclusion within the African continent and diaspora. This chapter aids in understanding how digitalisation assists ordinary citizens, especially the poor who need to save every rand or dollar that they can save in order to make meaningful change in their lives and the lives of their loved ones anywhere in Africa and the world under these trying post-COVID 19 economic conditions.


To the 75th Anniversary of Academician of the Russian Academy of Sciences Valery Ivanovich Sergienko

Abstract: January 5, 2024 marked the 75th anniversary of the birth of Doctor of Medical Sciences, Professor, Academician of the Russian Academy of Sciences Valery Ivanovich Sergienko, a leading Russian specialist in the field of physico-chemical medicine.


A study on the Regional regeneration and youth cultural activities in the island fishing village of Gukhwado

Abstract: Korea is facing a declining population and an aging population, which is leading to a rise in sprawl. Various policies and measures are being explored at the national level to solve this problem, but they are not having much effect. The biggest problem with regional decline is the disappearance of young people and the aging population, which is caused by the influx of young people into large cities such as the Seoul metropolitan area and the natural marginalization of rural areas. This problem is more severe in island fishing villages than in general rural areas. The Gyeonggi-do Fishermen’s and Fisherwomen’s Support Center conducted capacity-building projects for fishermen and cultural activities for youth centered on Pungdo Island in 2020 and Gukhwado in 2021, and achieved significant results. In particular, the center conducted unconventional youth cultural activities by analyzing problems from the perspective of young people and exploring alternatives for promoting Gukhwado and revitalizing tourism. The youth, mainly university students, analyzed the strengths and weaknesses of Gukhwado and came up with revitalization plans based on them. For example, they pointed out problems in marginalized areas, such as signage problems, marine garbage, and problems receiving electronic boards. In order to resolve these inconveniences and promote the hardware-oriented development of the Fishing Village New Deal 300, the government proposed a plan to implement the ‘Friendly Chrysanthemum Seed’ program, install digital tourist information signs, campaigns and educational activities, Smart Island Ecotourism, establish smart city services, install public wireless internet (Wi-Fi), and provide convenience for tourists. It is of great significance that it solved the problem of Gukhwado through youth cultural activities and presented a new solution to solve the disappearance of island fishing villages. The government is carrying out the Fishing Village New Deal 300, and while such hardware-centered development is important, it is also necessary to transform the island fishing village into a livable island through cultural activities and establish it as a prosperous island. Gukhwado, which was presented as an example, is also a representative area where young people are disappearing, and by pointing out the problem and deriving solutions from the perspective of young people, we are diagnosing the fundamental problem of “why” young people are turning away from island fishing villages. In this respect, Gukhwado can be seen as a novel youth cultural activity success story.


COMPARISON OF MANUAL AND AUTOMATIC METHODS IN DETERMINATION OF HEMATOCRIT AND HEMOGLOBIN LEVELS IN GREEN SEA TURTLE

Abstract: Sea turtles as protected animals in Indonesia need to recover and increase their population through comprehensive rehabilitation efforts. Examination of hematocrit and hemoglobin in green sea turtles (Chelonia mydas) at the rehabilitation center can function to determine the condition of green sea turtles because arriving turtles have different conditions. Dehydration and anemia in green sea turtles can occur due to transportation of turtles and unsuitable environmental conditions before the turtles were rescued. Examination of hematocrit and hemoglobin can be done manually or using a hematology analyzer. However, automated hematological analysis used for mammals generally not applicable for reptiles including green sea turtles due to the presence of nucleated erythrocytes. This study aims to compare the results of hematocrit and hemoglobin examination in green turtles using manual and automatic methods. This study used blood samples from 10 green turtles at the Turtle Conservation and Education Center (TCEC), Serangan Bali. Then a manual check was carried out at TCEC, Serangan, Bali and automatically using the Vet AutoRead IDEXX tool. Manual hematocrit examination uses the microhematocrit method and manual hemoglobin examination uses the Sahli method. The results were then analyzed using the Statistical Product and Service Solution (SPSS) application. Analysis of the significance of manual and automatic calculations of hematocrit and hemoglobin using paired sample t-test. The results of hematocrit (p=0.972) and hemoglobin (p=0.841) showed no significant difference (p>0.05). It can be concluded that the examination of hematocrit and hemoglobin in green turtles is not different so that it can be done manually and automatically.


Super Neurons

Abstract: Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localized (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the “non-localized kernel operations” for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">randomly localized kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deformable convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden. PyTorch implementation of Self-ONNs with super-neurons is now publically shared.


Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

Abstract: Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.


Empirical Study of Agile Software Development Methodologies: A Comparative Analysis

Abstract: The comparative analysis of software development models, also called the Software Development Life Cycle (SDLC), is an everyday discourse among software engineers, reflecting the dynamic nature of the field. Within this realm, various software development methodologies, such as prototyping, spiral development, and Rapid Action Development, have been established and recognised for their unique approaches to software creation. In recent years, Agile methodologies have emerged as prominent contenders in software development, offering flexibility, adaptability, and efficiency in delivering high-quality software within designated timeframes. Among the array of Agile methodologies, including Dynamic System Development Method (DSDM), Scrum, Feature-Driven Development (FDD), Extreme Programming (XP), Kanban, Adaptive Software Development (ASD), Mendix, Lean, and Crystal, several have garnered significant attention in the software development community. Specifically, ASD, DSDM, XP, FDD, Kanban, and Scrum have emerged as prominent choices among Agile methods utilised by software developers. This study conducts a comprehensive examination and comparison of these six Agile software models, aiming to elucidate their functionalities, strengths, and weaknesses. The findings of this comparative analysis seek to provide valuable insights for software industries, enabling informed decision-making when selecting software development models for upcoming projects. By understanding each Agile methodology's nuanced differences and capabilities, software developers and industry stakeholders can align their project requirements with the most suitable software development approach, ultimately optimising project outcomes and software quality.


From Clustering to Cluster Explanations via Neural Networks

Abstract: A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks—or “neuralized.” Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations.


Transformer-based visual object tracking via fine–coarse concatenated attention and cross concatenated MLP

Abstract: Transformer-based trackers have demonstrated promising performance in visual object tracking tasks. Nevertheless, two drawbacks limited the potential performance improvement of transformer-based trackers. Firstly, the static receptive field of the tokens within one attention layer of the original self-attention learning neglects the multi-scale nature in the object tracking task. Secondly, the learning procedure of the multi-layer perception (MLP) in the feed forward network (FFN) is lack of local interaction information among samples. To address the above issues, a new self-attention learning method, fine–coarse concatenated attention (FCA), is proposed to learn self-attention with fine and coarse granularity information. Moreover, the cross-concatenation MLP (CC-MLP) is developed to capture local interaction information across samples. Based on the two proposed modules, a novel encoder and decoder are constructed, and augmented in an all-attention tracking algorithm, FCAT. Comprehensive experiments on popular tracking datasets, OTB2015, LaSOT, GOT-10K and TrackingNet, reveal the effectiveness of FCA and CC-MLP, and FCAT achieves the state-of-art on the datasets.


Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Abstract: Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided.


High-order relational generative adversarial network for video super-resolution

Abstract: Video super-resolution can reconstruct a sequence of high-resolution frames with temporally consistent contents from their corresponding low-resolution sequences. The key challenge for this task is how to effectively utilize both inter-frame temporal relations and intra-frame spatial relations. The existing methods for super-resolving the videos commonly estimate optical flows to align the features of multiple frames based on temporal correlations. However, motion estimation is often error-prone and hence largely hinders the recovery of plausible details. Moreover, high-order contextual dependencies in the feature space are rarely exploited for further enhancing the spatio-temporal information fusion. To this end, we propose a novel generative adversarial network to super-resolve low-resolution videos, which makes full use of patch embeddings and is effective in exploring high-order spatio-temporal relations of the feature patches. Specifically, a motion-aware relation module is designed to handle the alignment between neighboring frames and reference ones. Depending on a patch-matching strategy for adaptive selection of multiple most similar patches, the cross-scale graph is constructed to reliably aggregate these patches using a feature pyramid. Based on the structure of multi-scale graph, a context-aware relation module is developed to capture high-order dependencies among resulting warped patches for better leveraging long-range complementary contexts. To further enhance reconstruction ability, the temporal position information of video sequences is also encoded into this module. Dual discriminators with cycle consistent constraints are adopted to provide more informative feedback to the generator while maintaining the global coherence. Extensive experiments have demonstrated the effectiveness of the proposed method in terms of quantitative and qualitative evaluation metrics.


Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Abstract: Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are characterized by a computationally-intensive training procedure that badly matches the fast-paced release of new (mobile) applications, resulting in significantly limited efficiency of model updates. To address this shortcoming, in this work we systematically explore Class Incremental Learning (CIL) techniques, aimed at adding new apps/services to pre-existing DL-based traffic classifiers without a full retraining, hence speeding up the model’s updates cycle. We investigate a large corpus of state-of-the-art CIL approaches for the DL-based TC task, and delve into their working principles to highlight relevant insight, aiming to understand if there is a case for CIL in TC. We evaluate and discuss their performance varying the number of incremental learning episodes, and the number of new apps added for each episode. Our evaluation is based on the publicly available MIRAGE19 dataset comprising traffic of 40 popular Android applications, fostering reproducibility. Despite our analysis reveals their infancy, CIL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.


Topic Particles, Agreement, and Movement in an Arabic Dialect

Abstract: The dialect of North Hail, a variety of Najdi Arabic, has a set of sentence-initial particles marking topics of various kinds that correspond to those argued by Frascarelli and Hinterhölzl (2007) to be characteristic of Italian and German: Shift-Topic, Contrastive Topic, and Familiar Topic. In their work, as in much other work in the cartographic tradition, a hierarchy of abstract topic heads is postulated in the C-domain, which host the topical phrases as specifiers. In North Hail Arabic, the topic heads are overt, realized as particles. Some of the topic heads mark topics by attraction to the C-domain; other particles mark topics by ϕ-feature agreement. The particles in the C-domain agree in ϕ-features with a TP-internal DP, subject or object. Analyzed in terms of Agree (Chomsky 2001, 2008), arguments and adverbials are assigned particular topic values either by agreement or by movement. The particles thus provide evidence that topicality can be a syntactic feature, inherent in lexical items (the particles) and assigned to constituents by operations familiar from standard syntactic relations like subject agreement and case. The theory articulated observes the Inclusiveness Condition, known to be a problem for the cartographic theory of topic and focus.


Validating spatio-temporal environmental machine learning models: Simpson’s paradox and data splits

Abstract: Abstract Machine learning has revolutionized environmental sciences by estimating scarce environmental data, such as air quality, land cover type, wildlife population counts, and disease risk. However, current methods for validating these models often ignore the spatial or temporal structure commonly found in environmental data, leading to inaccurate evaluations of model quality. This paper outlines the problems that can arise from such validation methods and describes how to avoid erroneous assumptions about training data structure. In an example on air quality estimation, we show that a poor model with an r2 of 0.09 can falsely appear to achieve an r2 value of 0.73 by failing to account for Simpson’s paradox. This same model’s r2 can further inflate to 0.82 when improperly splitting data. To ensure high-quality synthetic data for research in environmental science, justice, and health, researchers must use validation procedures that reflect the structure of their training data.


A Comparison of Uncertainty Quantification of Deep Learning models for Time Series

Abstract: With the advancement of artificial intelligence, machine learning, and deep learning, their applications in various industries, particularly finance, have increased. However, interpreting predictions from deep learning models poses challenges, especially in finance where result interpretation is important. This study aims to determine the uncertainty of stable deep learning models, despite changes in the model like dropout, to establish standards for reliable models and identify those detecting anomal data through model uncertainty. In the experiment, the traditional statistical model ARIMA and deep learning models mainly used for time series analysis, CNN, LSTM, MLP, and CNN-LSTM. Uncertainty was measured from a Bayesian perspective using MC Dropout. The experimental results confirmed that deep learning models performed better than statistical models for various patterns of time series data. It was observed that, even if the performance was not the best, LSTM based models exhibited low uncertainty, indicating stability. Consequently, this study highlights the importance of considering uncertainty along with accuracy in model selection. Moreover, it was confirmed that models with higher uncertainty are suitable for anomaly detection, making CNN based models particularly fitting for this purpose.


Anomaly Trajectory Detection Model Using LSTM Auto Encoder

Abstract: With the rise in aviation demand and the emergence of Urban Air Mobility, developing a safe aviation system in urban areas is becoming increasingly important. This study addresses the challenge of detecting anomalous flight trajectories, which can be influenced by environmental factors. We propose a novel Long Short-Term Memory-Auto Encoder (LSTM-AE) model that processes both environmental and trajectory data but only reconstructs trajectory data in its output. This approach was validated by assessing the average reconstruction error for specific trajectories. Additionally, the model's ability to identify anomalies was confirmed by evaluating the Area under the ROC curve (AUC) for typical anomalous trajectories, such as go-around maneuvers. Our findings indicate that the proposed LSTM-AE model effectively learns trajectory patterns in relation to environmental variables and shows enhanced anomaly detection capabilities compared to traditional AE and LSTM-AE models. These results contribute to the development of advanced models that incorporate a wider range of environmental factors, enhancing safety in urban air travel.


Legislative review of the use and regulation of generative artificial intelligence ChatGPT: Focusing on copyright law and personal information protection legislation

Abstract:
With the advent of ChatGPT, the boundary between humans and machines has disappeared, making it increasingly difficult for people to discern whether the person they are dealing with is a machine or a human. Nevertheless, ChatGPT is bringing about changes in the way we work, communicate, and play in the digital world by bringing about a shift in information search and access methods and creating products such as books and art.
 ChatGPT optimizes conversations using Reinforcement Learning from Human Feedback (RLHF). Reinforcement Learning is a type of machine learning technique that learns how to perform a task through repeated trial and error interactions. A person scores ChatGPT's responses and reflects them in the reinforcement learning rewards algorithm to learn ChatGPT's reward model. It is learned that the more a human user judges a given query to be a good response, the greater the reward, and the more harmful text is generated, the less reward it receives. It is thanks to this RLHF technology that when talking with ChatGPT, you feel like you are talking with a logical and fluent person rather than a machine.
 ChatGPT will enable the commercialization of a generative AI service that enables interactive conversation rather than focusing on one-sided commands, and since it is a very large-scale model, versatility is its core competitiveness. In other words, ChatGPT allows you to exchange conversations and finds or converts results such as writing, composing, coding, or drawing with a single text input. As ChatGPT significantly lowers the threshold of time and limitations associated with content production, it could gain attention in a variety of fields including advertising, publishing, and gaming. Additionally, ChatGPT has great potential for growth in the field of education as it can unravel and explain complex concepts and correct errors through conversation.
 This study reviewed the technological development process and general-purpose use of generative artificial intelligence (ChatGPT) and the legal regulations of ChatGPT, focusing on the legal issues surrounding the emergence of generative artificial intelligence (ChatGPT) in the AI era.


Elastic moduli prediction of a short fiber composite with interface debonding at fiber ends

Abstract: This work deals with the fiber ends debonding-induced elastic moduli degradation of a short fiber reinforced composite (SFRC). The strain fields of the matrix in a representative volume element (RVE) of the SFRC are determined from an imaginary fiber technique. By using the debonding boundary conditions, the debonded modulus of the composite is derived, which is affected by not only the far-field but also the transient solutions for the longitudinal strain of the matrix. Particularly, the imaginary fiber technique provides unbounded solution for a moderately large fiber aspect ratio. This is resolved by separating the RVE along the fiber direction into two domains. Combining the longitudinal strains in both domains, the longitudinal modulus is determined, and the longitudinal bridging tensor element in the micromechanics Bridging Model is amended. Five elastic moduli of an SFRC after the fiber end debonding are obtained on the amended Bridging Model. The effects of the fiber volume fraction, fiber aspect ratio, and fiber-to-matrix modulus ratio on the end-debonded longitudinal modulus, transverse modulus, and longitudinal Poisson’s ratio are investigated.


Variational quantum algorithm for experimental photonic multiparameter estimation

Abstract: Abstract Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter estimation problems that often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a quantum multiphase sensor. Leveraging the reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances. The quantum circuit evaluations are used to compute the system partial derivatives by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a classical learning algorithm run to optimize the measurement settings. Our experimental results showcase significant improvements in estimation accuracy and noise robustness, highlighting the potential of variational techniques for practical applications in quantum sensing and more generally in quantum information processing using photonic circuits.


Real-time displacement monitoring using camera video records with camera motion correction

Abstract: Structure from Motion (SfM) method can reconstruct the story drift ratio, but the camera motion produced by earthquakes reduces the measuring accuracy. This paper improves the applicability of the traditional method by correcting camera motion according to the identification of translation and rotation of camera. Experiments at different levels are designed to prove the proposed method. First, one set of experiments proves the motion correction method by the Single-degree-of-freedom (SDOF) system. The error of maximum response is 4.5% for the case with less rotation. As for the camera motion with larger rotation, the average error increases to 7.9%, which still meets the practical utilization. After that, the accuracy of using SfM method is confirmed by the Multi-degree-of-freedom (MDOF) system with the average error of 4.9%. This paper is expected to extend approaches for the application of the SfM method in the case of huge earthquakes.


Image captioning: Semantic selection unit with stacked residual attention

Abstract: Semantic information and attention mechanism play important roles in the task of image captioning. Semantic information can strengthen the relationship between images and languages, while attention operation can steer the relevant regions spatially in the image. However, in most current works, semantic attributes are always confined to be learned from pairs of images and sentences, which ignore to fully utilize more semantic attributes and the structure information of sentences, thus limit the variety of sentences to be generated. Meanwhile, current attention models usually lack the ability to learn the positional information in an explicit way during attention generation, and have the problem of vanishing gradient in the training process. This paper proposes a Semantic Selection Unit (SSU) and a Stacked Residual Attention (SRA) to remedy these drawbacks. Specifically, the SSU is designed to capture selectively semantic information from expanding attributes or guidance sentences. With the help of expanding vocabulary and the structure information in sentences, the SSU can improve the quality of the generated sentences. The SRA is constructed to solve the problem of positional information missing and vanishing gradient problem during attention generation. Architecturally, the SSU and SRA work together in a jointed framework with end-to-end learning for image captioning. Extensive experiments have been conducted on the public dataset of the MS COCO, achieving 139.7 CIDEr score on the test set.


MENGISI WAKTU LUANG DENGAN MENUMBUHKAN DAN MENINGKATKAN BUDAYA LITERASI MELALUI SI PITUNG DI DESA SIRNAGALIH KECAMATAN CILAKU KABUPATEN CIANJUR

Abstract: This activity was motivated by the low reading interest of children in Sirnagalih Village, Cilaku District, Cianjur Regency. Apart from that, children spend most of their free time playing gadgets. Gadget addiction will have an impact on children's cognitive, motoric and social development. Thus, one of the KKN work programs is by holding an integrated and superior science literacy training center or abbreviated as "Si Pitung". The purpose of carrying out this activity is to fill free time while at the same time driving children's literacy using the Fun Learning method in Sirnagalih village. The results of this activity affect the community's awareness of the importance of literacy, strengthen ties among the community and form a cohesive community so as to facilitate village community access to provide information related to Sirnagalih village.


The Effects of Learner Characteristics on Learning Flow and Learning Satisfaction in the Flipped Learning of Chinese Beauty Major College Students

Abstract: The advancement of information and communication technology, a central element in the Fourth Industrial Revolution, is influencing the way people think and act with the introduction of artificial intelligence, augmented reality, and the Internet of Things. In education, there's a notable shift from a teacher-centric approach to a learner-centric environment, emphasizing the flipped learning teaching method that facilitates self-directed learning. This study examined how learner characteristics influence learning flow (classroom class and online class) and learning satisfaction in order to establish a foundation for the effective implementation of beauty education in a flipped learning environment. An empirical study was conducted from July 10 to August 10, 2023, involving 166 surveys administered to beauty-related students in China. The data were analyzed using SPSS 25.0, employing techniques such as frequency analysis, factor analysis, reliability analysis, descriptive statistics, correlation analysis, and multiple regression analysis. The research findings indicated high reliability scores for learner characteristics (3.98), learning flow (3.75), and learning satisfaction (3.77) within the flipped learning environment. Correlation analyses between learner characteristics, learning flow, and learning satisfaction revealed statistically significant correlation, especially in terms of the correlation between learning attachment of learner characteristics and learning self-efficacy in learning satisfaction. In particular, a higher level of learning attachment in learner characteristics was associated with a statistically significant positive effect on learning satisfaction, whether in online or classroom learning contexts. The research findings suggest that flipped learning classes can improve students' satisfaction by boosting their learning attachment, leading to increased learning self-efficacy. This study highlights the importance of a flipped learning instructional approach when designing courses to enhance learning flow and attachment, addressing academic self-regulation and self-determination motivation.


Effects of Classroom Atmosphere on the Quality of Learning (QoL) for Students Learning

Abstract: Learning is the most important issue in teaching and regarding class atmosphere is one of the main issues that has special place in partial quality of learning. Teacher creates attractive class by applying proper management skills that makes positive and active relationships between him/her and his pupils. This study was performed to investigate the relationship of Atmosphere on the Quality of Learning (QoL). This study was done in Guilan University of Guilan that about 0.5 of students were selected through cluster sampling method with regard to the size of population (7200 students) and through systematic random sampling. 360 persons were selected and 357 questionnaires were filled and returned among 360 distributed questionnaires in various disciplines. Also 35 academic staffs (about 0.1) were participated in this research for conducting interview. Instruments for collecting the information of questionnaire have been researcher-made and interview. Interview with dear faculty members was about the impact of mental state of on quality of learning. Descriptive- analytic method has been used in this research. In all cases null hypotheses rejected and research hypotheses were confirmed. Also in analysis of interview with academic staffs that questions were related to research main hypotheses, much emphasis has been put on the effect of factors influencing on class atmosphere and quality of teaching and learning. It should be stated that there is a positive and direct relation- ship between class atmosphere and its factors (order and organization in class, clear- ness of rules in class academic staff control over the class, academic staffs and student mutual respect) and quality of learning. Clearness of rules of class results in relaxation in professor and student and in fact helps both of them to move toward goals of class and qualitative improvement of learning.


Perceptual Quality-Oriented Rate Allocation via Distillation from End-to-End Image Compression

Abstract: Mainstream image/video coding standards, exemplified by the state-of-the-art H.266/VVC, AVS3, and AV1, follow the block-based hybrid coding framework. Due to the block-based framework, encoders designed for these standards are easily optimized for peak signal-to-noise ratio (PSNR) but have difficulties optimizing for the metrics more aligned to perceptual quality, e.g. multi-scale structural similarity (MS-SSIM), since these metrics cannot be accurately evaluated at the small block level. We address this problem by leveraging inspiration from the end-to-end image compression built on deep networks, which is easily optimized through network training for any metric as long as the metric is differentiable. We compared the trained models using the same network structure but different metrics and observed that the models allocate rates in different ratios. We then propose a distillation method to obtain the rate allocation rule from end-to-end image compression models with different metrics and to utilize such a rule in the block-based encoders. We implement the proposed method on the VVC reference software – VTM and the AVS3 reference software – HPM, focusing on intraframe coding. Experimental results show that the proposed method on top of VTM achieves more than 10% BD-rate reduction than the anchor when evaluated with MS-SSIM or LPIPS, which leads to concrete perceptual quality improvement.


A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

Abstract: The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.


Kinemon: An inductively shunted transmon artificial atom

Abstract: We experimentally investigate inductively shunted transmon-type artificial atoms as an alternative to address the challenges of low anharmonicity and the need for strong charge dispersion in superconducting quantum systems. We characterize several devices with varying geometries and parameters (Josephson energies and capacitances), and find a good agreement with calculations. Our approach allows us to retain the benefits of transmon qubit engineering and fabrication technology and high coherence, while potentially increasing anharmonicity. The approach offers an alternative platform for the development of scalable multiqubit systems in quantum computing.


Perceived Body Image, Anti-fat Attitudes and Healthy Lifestyle Practices among Nursing Students towards Obesity Prevention

Abstract: Obesity rates will continue to grow worldwide for the foreseeable future. Obesity is preventable. To improve the notion of health, everyone in society must practice a healthy lifestyle to prevent obesity and overweight. Recently, Malaysians who practice healthy lifestyles have decreased and the rate of overweight or obesity is increasing. Nurses and nursing students have a significant role and should become role models with good body image in educating and promoting healthy lifestyles to the public. However, a lot of studies show that nurses and nursing students fail to engage in healthy lifestyles. Therefore, the main objective of this study is to determine perceived body image, anti-fat attitudes and healthy lifestyle practices among nursing students at private healthcare university colleges in Malaysia. Methods: Data was collected using valid and reliable questionnaires to determine Perceived Body image, Anti-fat Attitude and Healthy Lifestyle behaviour. Dependent variables were correlated with independent variables. The results of this study showed that nursing students are satisfied with their body image (61.2%), most nursing students engage in unhealthy lifestyles (48.6%) and the majority of nursing students have strong anti-fat attitudes (58.6%). There is no significant relationship between perceived body image, anti-fat attitude and healthy lifestyles. Conclusions: This result could conclude that it confirms that there is no direct link between Perceived body image, anti-fat attitudes, and Healthy lifestyle behaviours, as suggested in the Theory of Planned Behaviour (TPB). There are intermediary factors such as health motivation and perceived control as determining factors to contribute to behaviour prediction. This evidence can help nursing educators improve motivation factors in the nursing curriculum to encourage nursing students to practice healthy lifestyles.


Study of Voice Generation Method Suitable for Characters based on Human Cognitive Characteristics

Abstract: Previous studies have attempted to estimate existing voices from images of animated characters as a way to generate voices suitable for animated characters, but without good results. Therefore, in this study, to link the voice characteristics to match the animation character with the image, we devised a method to analyze the voice’s tendency to not be uncomfortable and then establish the ratio of voice learning data based on the analyzed tendency data. Specifically, this study prepares multiple voices for one illustration of an anime character, asks subjects to evalu- ate the voices, and calculates an evaluation based on the evaluation values. In experiments, we conducted an evaluation experiment using the one-pair comparison method, calculated the distri- bution of learning data based on the evaluation values obtained, and prepared for the subsequent learning process.


PENERAPAN MODEL PROBLEM BASED LEARNING (PBL) BERBANTUAN LKPD QR-CODE UNTUK MENINGKATKAN HASIL BELAJAR SISWA MATA PELAJARAN IPAS KELAS V SD NEGERI 1 LALANGLINGGAH

Abstract: This research is an implementation of the use of the Problem Based Learning (PBL) Learning Model assisted by LKPD QR Code as a form of innovation in the learning and teaching process. The aim of this research is to determine the improvement in student learning outcomes in science subjects using the PBL learning model assisted by LKPD QR Code. This research was carried out for 1 month starting from 26 July to 25 August 2023, on 19 class V students of SD Negeri 1 Lalalinggah. This research procedure was carried out in 2 cycles with research methods using quantitative methods. In the initial research, it was found that 13 students out of 19 students scored less than 65. However, in subsequent learning activities through the PBL learning model assisted by LKPD QR Code, student learning outcomes could be improved. This can be seen from the average score before treatment which was 57.63. Meanwhile in cycle I the average score of students increased to 62.00 and in cycle II the average score of students' abilities increased to 72.55.


Classification the Characteristics of Traffic Accident Victims in Pariaman Using the Chi-square Automatic Interaction Detection Algorithm

Abstract: Traffic accidents are incidents that occur when motor vehicles collide on the road, resulting in damage to vehicles and road infrastructure, as well as the potential for material losses, injuries, physical damage, and even death for those involved. Data from the Indonesian National Police show that the number of traffic accident victims between 2010 and 2020 ranged from 147.798 to 197.560 people, with fatalities predominantly occurring among individuals aged 15-34. The high number of traffic accident victims has negative impacts on various aspects of life, ranging from material losses to physical damage to the victims. Classification is a technique used to group objects or data into pre-defined classes or categories based on their attributes or features. One method in the field of classification is Chi-Square Automatic Interaction Detection (CHAID). The results of the classification using this method indicate that the age of the victims and the type of accident are the most significant variables influencing the condition of traffic accident victims. The evaluation of the model using a confusion matrix yielded an accuracy rate of 92%. This indicates that the model performs well in overall data classification.


A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier

Abstract: An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.


EVALUATION OF THE APPLICATION OF THE ASSURE MODEL BASED ON THE COUNTENANCE STAKE MODEL ON RUKHSAH MATERIAL IN BUILDING EFFECTIVE LEARNING

Abstract: The learning process determines student success in learning, so teachers need to build effective learning that supports these achievement goals. This study aims to analyze the evaluation of the application of the ASSURE model based on the Countenance Stake model on rukhsah material in building effective learning at SMP Hikmah Teladan. This research uses a qualitative approach using a descriptive analysis method. The results and conclusions of this study show that the application of the ASSURE model to rukhsah material in SMP Hikmah is exemplary based on the results of evaluation using the countenance stake model has been applied very well and supports building effective learning; the average good student learning outcomes show this.


Negation Detection Techniques in Sentiment Analysis: A Survey

Abstract: Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field.


Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain

Abstract: DANA is a digital payment platform that provides various features to make it easier for users to make payments, transfers, and balance replenishment online. DANA application users provide a variety of reviews that include both constructive and critical opinions, which can be valuable input for DANA application developers. The purpose of this research is to evaluate the results of sentiment classification of DANA application user reviews on the Google Play Store service using the Naïve Bayes Classifier method and Information Gain feature selection. In addition, this study aims to assess the effect of applying IG feature selection on the performance of the resulting model. In this study, reviews are divided into two categories, namely positive and negative based on lexicon-based labeling. Furthermore, data weighting, feature selection, and data division are carried out with a proportion of 80% train data and 20% test data before model building. There are two models, namely a model without feature selection (NBC model) and a model with feature selection (NBC-IG model). The evaluation results showed that the NBC model with 1106 features performed well, with 82.91% accuracy, 83.96% precision, and 90.23% recall. Meanwhile, the NBC-IG model with 536 features showed higher performance, with 85.09% accuracy, 85.79% precision, and 92.09% recall. The application of IG feature selection with the IG value limit parameter > 0.01 in the NBC model successfully reduced the number of features by 570, and improved model performance with an increase in accuracy by 2.18%, precision by 1.83%, and recall by 1.86%.


Application Analysis of Nursing Students' Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

Abstract: By analyzing the correlation between courses in students’ grades, we can provide a decision-making basis forthe revision of courses and syllabi, rationally optimize courses, and further improve teaching effects. With the help of IBM SPSS Modeler data mining software, this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine, and to explore the correlation between professional basic courses and professional core courses. Lastly, according to the detailed analysis of the mining results, valuable curriculum information will be found from the actual teaching data.


Is There A Relationship Between Motivational Components in Foreign Language Learning?

Abstract: Motivation is the main determining factor to ensure successful achievement in foreign language acquisition. Ever since influential Canadian Psychologists, Gardner and Lambert introduced the term motivation in foreign and second language learning in 1959, numerous researchers investigated the nature of motivation, however, the area on relationships between motivational components is still understudied until now, especially in foreign language acquisition area. A random sampling of 131 participants from a public university in Malaysia responded to the survey. This quantitative study attempted to explore the relationships between motivational components, which have been identified as value components, expectancy components and affective components. Motivational scale by Pintrich & De Groot (1990) is used to compose the questionnaire, which examined the students’ motives in learning foreign language. The learners answered four sections, consist of Demographic Profile, Value Components, Expectancy Components and Affective Components by using a 5-point Likert scale survey. Analysis by using SPSS has been done to discover results in the form of mean scores and correlations scores. The findings revealed that the three motivational components have strong relations with students’ motivation. Additionally, the correlation analyses revealed interesting discoveries; Value and expectancy components showed favourable correlations, whereas there were negative correlations between expectancy and affective components and also between value and affective components. These findings are useful for teachers and curriculum writers since contribution of this study will clearly provide teachers and curriculum writers the foundation ideas to design and produce authentic lesson plans. The implementation of ideas from this study will motivate the students to learn foreign language skills based on value and expectancy components.


Mitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization

Abstract: Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.


Analysis of Students' Critical Thinking Ability Profile Using HOTS-Based Questions

Abstract: Critical thinking includes higher processes, such as analysis, synthesis, evaluation, conclusion, and reflection. This research aims to determine the profile level of students' critical thinking abilities. The research sample consisted of 28 students taken by random sampling consisting of 2 classes. Data was obtained through test and non-test techniques which included a critical thinking ability test using 20 questions. Meanwhile, the non-test uses an observation sheet on students' critical thinking abilities. Analysis of data obtained from the results of students' critical thinking skills tests was then analyzed using analysis items with the Rasch model. The results of the profile analysis of students' critical thinking skills were classified as moderate. This is proven by test results and observations in basic biology courses. Based on the results of tests on students' critical thinking skills, it is known that there are 16 students who have high levels of critical thinking skills, 5 students with moderate critical thinking skills, and 6 students with weak critical thinking skills. Meanwhile, the results of observations of the critical thinking abilities of Bima Muhammadiyah University students in basic biology courses are in the moderate critical thinking ability category with a category score of around 68.06%.


A Study on Teaching Competency of Teacher Educators in Colleges of Education

Abstract: The research is in the form of a Descriptive survey method. The purpose of this study is to determine the Teaching Competency of Teacher Educators in Colleges of Education. It also contrasts Teaching Competency of Teacher Educators with two types of background variables, such as Gender and Locality. The study's findings reveal that the Male and Female Teacher Educators have similar mean scores of Teaching Competency. As well as Rural and Urban Teacher Educators, have similar mean scores of Teaching Competency.


A Case Study of Teacher’s Practices in Village-Linked Curriculum Implementation- from the Perspective of Community Sustainability -

Abstract: This study analyzed teacher practices in village-linked curriculum implementation from the perspective of sustainability of communities. A village-linked curriculum is a school curriculum that aims to realize the vision of a village education community and is based on the virtuous circle perspective that local capacity can be mobilized to improve the quality of education, which in turn can improve the community. This study analyzed teacher practices from the perspective of sustainability to provide an in-depth understanding of how a village-linked curriculum enhances local sustainability and to suggest policy measures to enhance teacher practices. For this purpose, an analytical case study was conducted to analyze the experiences of a typical teacher who had been implementing a village-linked curriculum for more than six years. Various materials, such as literature, interviews, photos, and videos, were analyzed; supplementary interviews were conducted; and validity was ensured through member checks and peer verification. The study found that in terms of the education system, the teacher aimed to improve the welfare system, engage in governance activities and build social capital, and create a virtuous cycle of education and society. In terms of curriculum implementation, the teacher provided lessons that were designed to foster their students’ sense of local identity and community belonging, as well as the students’ agency for local development. The policy recommendations for enhancing teacher practices include building a school-centered welfare system, improving the system for educational autonomy, building a local education―career―vocation linkage system, budgeting for school equality, and building an educational system focused on the lesson.


Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Abstract: At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.


Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases

Abstract: Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.


Study of Web Security Using Blockchain Technology and Its Application in Education

Abstract: Present paper discussed about Blockchain Technology and its application in Education. Cybersecurity offers protection from cyberattacks for protected data on internet-connected devices as well as for hardware and software. A set of regulations, guidelines, and laws that apply to hardware, software, programmers, and secure information provide defense against dangers from without, such as destruction, theft, manipulation, and unauthorized access [1]. The words "cyber" and "security" are combined to form the compound word "cybersecurity." The network of protected devices, services, networks, and data is referred to as "cyber". On the other hand, security is concerned with safeguarding data, networks, applications, and systems. The application of blockchain technology to improve data security, dependability, and device security has increased significantly in a number of industries in recent years [3]. A number of cryptographically chain blocks can be eagerly decoded from the blockchain technology.


Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning

Abstract: Abstract Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and unseen classes accurately. In the absence of target samples, many GZSL models may overfit training data and are inclined to predict individuals as categories that have been seen in training. To alleviate this problem, we develop a parameter-wise adversarial training process that promotes robust recognition of seen classes while designing during the test a novel model perturbation mechanism to ensure sufficient sensitivity to unseen classes. Concretely, adversarial perturbation is conducted on the model to obtain instance-specific parameters so that predictions can be biased to unseen classes in the test. Meanwhile, the robust training encourages the model robustness, leading to nearly unaffected prediction for seen classes. Moreover, perturbations in the parameter space, computed from multiple individuals simultaneously, can be used to avoid the effect of perturbations that are too extreme and ruin the predictions. Comparison results on four benchmark ZSL data sets show the effective improvement that the proposed framework made on zero-shot methods with learned metrics.


Mergers and Acquisition of Deposit Money Banks in Nigeria: Empirical Analysis of Contribution to the Economic Growth of Nigeria (2006-2021)

Abstract: Despite impressive results declared regularly by banks in Nigeria, their contribution to the economic growth of Nigeria has been a subject of mixed results as economic growth has been inconsistent for some time and historically been characterised by oscillations in both positive and negative directions. The Central Bank of Nigeria embarked on a major Mergers and Acquisition of Banks in 2005. The study evaluated the effect of Mergers and Acquisition of Deposit Money Banks’ contribution to Economic Growth of Nigeria. The study adopted ex-post facto research design. Total yearly data for Economic Growth, Total Commercial Bank Lending and Total Commercial Bank Assets were extracted from the Central Bank Statistical Bulletin for the period 2006 to 2021 and they are considered valid and reliable. The study adopted descriptive and inferential statistics and the Auto-Regressive-Distributed Lag-ARDL model was adopted for data analysis.The elasticity of Total Bank Lending- upsurges pertaining to Gross Domestic Product (GDP)- is 0.9951, indicating that ceteris paribus, a 1% rise in is expected to increase (Economic Growth ) by 0.995. The positive nexus was statistically significant at 5% (p = 0.009), showing that Total Bank Lending ( ) impact economic growth positively ( ) in the long run. Furthermore, the study revealed that the Total Bank Assets- depicted a positive nexus with GDP- . The elasticity of pertaining to is 0.5773, indicating that ceteris paribus, a 1% rise in is expected to increase Economic Growth- by 0.5773%. The positive nexus was statistically significant at 0.05 (p = 0.018), hence denoting that Total Bank Assets have a significant individual impact on Economic Growth ( ) in Nigeria in the long run. The study concluded that Total Commercial Bank Lending and Total Commercial bank Assets jointly influenced Economic Growth positively. It is recommended that the policy makers should pay attention to Mergers and Acquisition of Banks and continue to ensure that banks are strong enough to support Economic Growth.


Investigation of Causal Linkages Between Financial Development and Economic Growth: Evidence from Pakistan

Abstract: Financial development is a driving factor for economic growth and is important in fostering growth. An efficient and effective financial system contributes to economic growth and fosters the performance of other economic measures. This study aims to investigate the influence of financial innovation on economic expansion to understand the correspondence between financial development and economic growth in Pakistan. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) are used for stationarity. Auto Regressive Distributed Lag (ARDL) and Error Correction Model are employed to analyze the correlation between economic growth and financial development. The finding shows that financial development stimulates economic growth. The research explains that foreign direct investment and personal remittances adversely affect GDP growth. These understandings recommend that although financial development generally promotes, its results can vary extensively and depend on specific context-dependent conditions. Furthermore, applying CUSUM AND CUSUM of square tests to measure model stability convincingly confirms a long-term relation between financial development and economic expansion, disproving the null hypothesis of no co-integration and reinforcing that financial development is essential to domestic economies. Policymakers need to practice these understandings in actionable plans by highlighting financial innovations that advance access to credit and efficiently allocate resources so investment in education and agriculture can maximize economic growth.


The formation of project thinking in training specialists in working with youth: Interaction with NPOs and educational institutions

Abstract: Modern socio-economic conditions necessitate the active involvement of the population and, first of all, youth in the activities of civil society institutions, including the work of non-profit organizations that can become a driving force for the integration and mobilization of society. At the same time, in most sectors of human activity in recent years, a project approach has been actively introduced, in the implementation of which NPOs have significant experience. The development and implementation of a project requires a specialist possessing to have such a non-standard competence as design thinking. It is this personal and professional skill that can help a young person quickly adapt to changing conditions and can provide the necessary competitive advantage in employment and career advancement. Specialists in working with youth are able to become conductors of project thinking in the youth sphere; their activities can have a significant impact on the dissemination of the project approach among representatives of the new generation. In the course the sociological study, it was found that NPOs are a significant partner of educational organizations in the formation of project thinking of future specialists in working with youth. The existing practices of interaction between universities and NPOs in these matters serve as the basis for further expansion of cooperation and improvement of the educational process.


Features of agenda setting in Telegram

Abstract: This article focuses on the aspects of agenda formation in Russian-language Telegram messenger channels. Within the framework of the classical model of M. McCombs and D. Shaw, the main principles of agenda setting by traditional media are analyzed. Building upon the findings of more recent research in the field of agenda setting in the digital space, the authors highlight the advantages of social media as communication platforms and potential reasons for their increasing popularity among both individuals and political actors. Furthermore, the relevant features of the Telegram messenger in the context of political communication are systematically described, such as the lack of strict content moderation, emphasis on the speed of information exchange, user privacy, and the need for an individualized approach to shaping the information field for each user. Based on the platform’s characteristics, a model for agenda formation is proposed, which involves categorizing channels into two levels: content information producers and news aggregators/interpreters. The hypothesis is also put forward that channels that aggregate and interpret information through an extensive network of links have the greatest potential for setting the agenda for their audience. To test the model, a pilot study was conducted using the Tgstat service, where the top 10 most cited channels from the “Media and News” and “Politics” categories for October 2023 were selected. Subsequently, no more than 50 channels were included in the analysis, those which were most frequently mentioned by the popular authors. The researchers conclude that multiple agendas can coexist within the messenger within various thematic blocks. These thematic blocks are formed through the network of links to channels that the most popular platforms on the messenger feature in their news feeds.


Online Teacher Professional Development (oTPD) for Digital Competencies in Higher Education: A Systematic Review of the Literature

Abstract: The need for professional development for digital, subject-specific competencies is increasing among educators in higher education. With online teacher professional development (oTPD), this demand can be accommodated flexibly. The article presents a systematic review of the literature and evidence on oTPD for digital competencies in higher education. The purpose of the review is to identify factors that influence the effect of an oTPD programme. A total of 661 peer-reviewed articles that included empirical studies and literature reviews related to oTPD from 2010 and onwards were identified using the EBSCO and ERIC databases and screened. 57 of these were coded and included in the analysis, and 604 articles were excluded due to wrong focus, educational level, publication type, or year. The analysis identifies sixteen underlying factors that influence the effect of oTPD related to either the institutional aspect, the design of the programme, and/or the delivery of the programme. Furthermore, the review identifies political implications for research on oTPD—the institutional context including technical solutions, the general pedagogical approach, and educator support—and methodological limitations related to the ambiguity of the oTPD concepts and the transferability of case studies, which call for more research.


Full Online Teacher Training Service Scheme on Improving Pedagogical and Professional Competencies

Abstract: Implementing teacher teaching training services for Islamic Religious Education and Islamic School teachers in the Indonesian Ministry of Religious Affairs ranks in 2021 was carried out with a complete online scheme. Implementing in-service teaching training aims to produce professional educators with academic, professional, personal, and social competencies. The difference in participants' conditions, such as different areas, technology tools mastery, and infrastructure availability, became a challenge for LPTK organizers. The article aims to measure the effectiveness of implementing teacher teaching training services in a whole online scheme at LPTK of the State Islamic University of Bukittinggi in 2023. This evaluation research was conducted in LPTK of the State Islamic University of Bukittinggi in 2023. The sample was selected by purposive technique with the number of samples 416 teachers, consisting of 266 Islamic Religious Education teachers and 150 Islamic School teachers. The data was collected by distributing questionnaires and analyzed using descriptive statistics. The results showed that the average paedagogic and professional competencies achievement score was 86.90 points, with a high interpretation of achievements. The implementation of teacher teaching training service in an entire online scheme at LPTK of the State Islamic University of Bukittinggi in 2023 has increased effectiveness.


Design of QR Code Technology in MC Swimming Pool Website Design to Improve User Experience and Operational Efficiency

Abstract: QR Code which is an abbreviation of Quick Response Code, QR Code is in the form of a square box, similar to a barcode, but with a more detailed appearance. The use of QR Code Technology in this research serves to facilitate identification and confirmation of entrance tickets for swimming pool visitors increase the speed and easy to buy a ticket. The research methodology used in system design is waterfall method. The waterfall method displays simple processes that do not overlap with the SDLC (system development life cycle) model.The programming language used is PHP, with the implementation of the CodeIgniter 3 Framework, and MySQL as the Database management system.The problem that often occurs is when the queue of visitors enters on holidays or days when there are lots of visitors causing long queues and less efficiency in ticket sales operations and the process of entering visitors to the swimming pool.This research aims to improve the user's operational system in ordering tickets which must initially be done come directly to the swimming pool location and in the new system visitors can purchase tickets online and operational efficiency of the MC Swimming Pool through the application of QR Code technology on the website, it makes the process of checking ticket data easier so that visitors can enter the swimming pool area The result of this research is to create an information system plan that allows users to order tickets online and visitors can enter the swimming pool area quickly and easily using a QR code as a visitor entry ticket.


Path Planning Design for Avoiding Obstacles of Moving Objects Using Digital Twin Modeling

Abstract: In order for autonomous driving to be effective, sufficient prior information and stability about the area of use must be guaranteed. To do this, it is necessary to ensure stability by avoiding obstacles that were not initially identified and regenerating the path. In this study, we design a DEVS-based moving path movement plan and conduct simulations in a digital twin environment. It also implements communication of moving objects to transmit and receive information about unidentified obstacles that are not within the detection range of moving objects. As a result, if an unidentified obstacle appeared, an avoidance path could be created and driven to the destination. Through the above experiment, we expect that the results of route movement in the digital twin environment will be pre-tested and used in reality.


Learned Mesh-Based Object Global Coordinate Estimation for Vehicle Tracking in Close Surveillance Environments Using Fisheye Cameras

Abstract: Artificial intelligence-based methods for detecting and tracking objects in videos, typically represented by bounding boxes, are well established. However, accurately determining the global coordinates of these bounding boxes, especially in environments monitored by fisheye cameras, presents unique challenges. These challenges stem from significant variations in the bounding box


Typographic Analysis of Mapping Table for Similar Font Matching Between Hangul and Thai Fonts

Abstract: When exporting or importing global content, it is essential to identify fonts with similar shapes, in addition to translating text. In our previous research, we developed a font-matching algorithm using deep learning that works across multiple languages. In this paper, we present the process of creating a pivot font mapping table, which serves as an example of matching actual Hangul and Thai fonts for similar font matching. We conducted a survey among experts to establish the pivot font mapping and confirmed its significance by analyzing typography. Using the mapping table generated through this process, we successfully matched similar fonts between Hangul and Thai, as well as Thai and Hangul. Our results demonstrate that our font matching system effectively identifies similar-looking fonts in each language.


A Case Study of Cyber Class Production Using a Multimedia Generation Tool Applied with Edutech and Analysis of Results on the Basic Survey to Identify Learners’ Characteristics and Course Evaluations

Abstract: This paper is a case study in which classes were operated by creating contents using multimedia generation tools applied with Edutech for more effective cyber class operation, reinforcing the sense of reality, feedback, and interactions, and improving class satisfaction. To this end, I first planned and prepared suitable lecture contents and materials that could enhance learners


A Study on Technological Perception Analysis of LLMs through Big Data Analysis of News Articles

Abstract: This study analyzed the technological perception of the LLMs through the analysis of big data in media articles and examined the changes, focusing on before and after the advent of ChatGPT. The analysis revealed that before the advent of ChatGPT, words related to chatbots and education were predominant in media reports. After the emergence of ChatGPT, keywords shifted to business, various technology sectors, and corporate-related terms. N-gram analysis showed that in 2022, articles were primarily focused on chatbots and virtual assistants, also mentioning education and content creation. However, in 2023, LLMs gained attention in diverse fields like blockchain, security, and data utilization. Topic modeling results indicated that while 2022 emphasized the use of chatbot technology, 2023 saw an expansion in the use of LLMs across various technological sectors including finance, data technology, chatbots, and semiconductors. In conclusion, the advent of ChatGPT marked significant developments in the application of generative AI across various sectors like healthcare, finance, education, and content creation, showcasing the potential of LLMs.


A Study on the Development of major competencies Diagnostic Tools for Children’s English Education: Focusing on K Women’s University

Abstract: The purpose of this study is to develop the major competencies of the G department. It will do this by linking the core competencies of K Women's University with the major competencies of major subjects. Additionally, this study aims to conduct a self-diagnosis of the major competencies of students. A survey was carried out on 31 first and second-year students in Department G. The collected data underwent a data coding process. Descriptive statistics and independent sample testing using the SPSS v.21.0 statistical program were performed to conduct Levene's equal variance test F and significance probability analysis. The average achievement of the three significant competencies of Department G was analyzed to be 3.89. Among the major competencies, the creative convergence competency was found to be the highest with an average of 4.24. In addition, within the detailed competency analysis of major competencies, the highest performances were shown to be understanding multiculturalism in the development of convergence English curriculum, application of early English education in creative English education implementation, and interpersonal competency in creative convergence competency. This tool can be used as an example to evaluate the competency level of both the department's major field and individual students. Providing feedback on the current students using this tool is expected to aid in curriculum refonn and development. Additionally, it can be used as a benchmark to identify systematic improvements in major education and help create annual plans for the department. (Kwangju Women's University)


Mitigating land fire disasters: Implementation of agricultural land fire risk management information system in Aunupe Village, South Konawe Regency

Abstract: Climate change, which causes increased temperatures, prolonged droughts, and dry conditions, heightens the risk of land fires. A similar scenario unfolded in Aunupe Village, where several fires occurred on agricultural land over the past three years, resulting in crop failure and degraded soil quality, leading to financial losses for land-owning farmers. To mitigate the risk of land fire disasters, an agricultural land fire disaster risk management information system is necessary in Aunupe Village, South Konawe Regency. The objective of this community-based empowerment program is to mitigate the risk of land fire disasters. To achieve the desired outcomes, initial coordination with farmer group partners is conducted, followed by the design and installation of fire sensor equipment sets in collaboration with farmer groups. Furthermore, focus group discussions (FGD) are conducted with farmer group partners to ensure their understanding and cooperation. The activities result in the development of an agricultural land fire risk management information system, facilitating the prompt detection, monitoring, and response to fire incidents in the agricultural land of the Lembah Harapan Farmers Group. Additionally, farmer group partners acquire knowledge regarding the utilization of fire mitigation systems.


International Comparative Analysis of Advanced Diagnostic Imaging Equipment Demand Characteristics Using the Bass Diffusion Model

Abstract: Objectives: This paper proposed the direction of future supply and demand management policies through demand prediction and demand characteristics analysis of advanced diagnostic imaging equipment using new medical technology.Methods: This study used the Bass Diffusion Model to analyze demand characteristics for advanced diagnostic imaging equipment. Derived diffusion coefficients were applied for medium to long-term demand predictions (2021-2050). Parameter estimation employed non-linear regression analysis (SPSS 27.0). Demand characteristics were further analyzed using the ratio of the Bass model’s innovation to imitation coefficients for each country and device.Results: Comparing the imitation coefficients (q/p ratio) for computed tomography (CT) and magnetic resonance imaging (MRI) among South Korea, the United States, and Japan, South Korea has the highest q/p ratio for both CT and MRI. This implies that the influence of opinion leaders and group pressure is relatively more significant in South Korea, particularly in CT purchases. In the demand prediction results, South Korea’s q/p ratio for CT and MRI is notably larger, suggesting that institutional pressures and collective decision-making, rather than individual clinical decisions, play a significant role in the purchase of advanced diagnostic imaging Equipment. The q/p ratio for advanced diagnostic imaging Equipment in South Korea is higher than in other countries, reflecting a large power distance in the adoption of these technologies.Conclusions: This study utilizes the Bass Diffusion Model to predict the demand for advanced diagnostic imaging equipment in South Korea, Japan, and the United States. Findings highlight that imitation, not innovation, is the main driver of diffusion, with South Korea showing a faster adoption rate for CT and MRI. The study identifies factors influencing diffusion in South Korea, noting the impact of the quantity system on CT and the absence of influence on MRI. CT diffusion is positively linked to the quantity-based payment system, while MRI remains unaffected.


A Survey on the Utilization of Big Data in the Healthcare Sector and the Demand for Integrated Data

Abstract: Objectives: To investigate the status of secondary data and big data platform utilization and the demand for integrated data in the health care sector.Methods: An online survey was conducted using a structured questionnaire targeting members of six academic societies in the healthcare field. A total of 220 researchers responded to the survey.Results: 88.2% of respondents said they had experience using secondary data sources in the healthcare field, and 19.0% responded that they had experience linking data using the healthcare big data platform. Among the data in the health and medical field, the most used dataset was health insurance claims data, and if linking between dataset, the most desired linked data type is ‘National Health and Nutrition Examination Survey-Health Insurance Claims Data–National Health Examination-Death Information’. The advantages of linked data on big data platforms were ‘large volume of big data or representative’, ‘diversity of data’, and ‘long-term follow-up’. The major items requiring improvement were ‘delay in time to provide data’, ‘limitation of data provision range’, and ‘low linkage rate between dataset’.Conclusions: To promote the use of linked data in the health care field, comprehensive efforts are needed, including rapid data provision, improvement of the combination rate, standardization of data, and establishment of ethical guidelines.