Reviewerly/exHarmony huggingface dataset:
Penerapan Metode Brainstorming dalam Pembelajaran Teks Pawarta Siswa Kelas X.4 SMAN 1 Kedungwuni

This study aims to describe the results of the application of the Brainstorming method in learning to write news texts for class X.1 students of SMAN 1 Kedungwuni. This is influenced by the lack of student interest in learning Javanese, especially in the material for writing news or news texts. These factors can also cause students to have difficulty understanding the material and lack understanding of vocabulary and language in Javanese subjects. The design used in this study was a pre-experimental design in the form of a one-shot case study involving only one group as a sample without a control group using a purposive sample sampling technique. The data obtained from this study are data collected through a posttest and a questionnaire (questionnaire).After conducting the research, the following results were obtained: 1) in the knowledge aspect competency test, a percentage of 92% was obtained or a total of 32 students received a complete score above the KKM, and there were only 2 students who had not yet reached the KKM. From these acquisitions, the final average value was 89 on the knowledge aspect. 2) in the skills aspect competence test, a percentage of 89% or 31 students whose grades have exceeded the KKM is obtained and 11% or 4 students whose scores have not reached the KKM. From this acquisition, a final average value of 79 was obtained in the skill aspect. Based on the analysis of the data obtained, learning to write an announcement text using the brainstorming method can have an influence on the learning activities of writing an essay text for class X.1 sman 1 Kedungwuni.
Identification and Disruptors of the Digital Era

Identity and identification are being impacted by disruptors of the digital era. Disruptors such as working from home, online platforms, choosing and using avatars, operating in the metaverse… all of this is reshaping our social and professional identities and facilitating the emergence of new identities. The purpose of this panel symposium is to engage a group of panelists in a formal, moderated, interactive discussion of (1) the disruptions technologies of digital era are causing to professional identities; (2) the panelists’ understanding of them; (3) the newly emerging professional identities; (4) the implications of work environment on workers; and (5) research opportunities.
Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.
A comparative analysis of neural network models for hand gesture recognition methods

In recent years, gesture recognition methods have undergone major changes. Because the very demand for it has reached a different level. Humans have increasingly begun using various areas of human activity. The purpose of gesture recognition is to record gestures formed in a certain way and then tracked by a device such as a camera. Hand gestures can be used as a form of communication within many different applications. It can be used by people with various disabilities, including those with hearing and speech impairments, to communicate and interact socially with others. Our research demonstrates various methods for implementing hand gesture recognition based on Hidden Markov Model (HMM), Convolutional Neural Network (CNN), Diffractive Deep Neural Network (D2NN) and other neural networks. This research reviews previous approaches and results of hand gesture recognition methods, hypotheses, diagrams, as well as a comparative review between various gesture recognition methods are given in this paper.
Colour image cross-modal retrieval method based on multi-modal visual data fusion

Because the traditional colour image cross-modal retrieval methods have the problems of low retrieval accuracy and recall, and long retrieval time, a colour image cross-modal retrieval method based on multi-modal visual data fusion is proposed. First, multimodal visual colour images are collected, and then bilateral filtering method is used to filter the collected images to enhance colour images. Then, the enhanced multimodal visual data is coded, the image modal features and colour modal features through cross-layer fusion encoder are decomposed, and finally the decoded two modal features are fused. According to the multimodal visual data fusion results, cross-modal retrieval of colour images is performed by using two scale similarity measurement. The simulation results show that the proposed method has higher precision and recall rate for colour image cross-modal retrieval, and shorter retrieval time.
Image based Model Predictive Controller for Autonomous Driving

With cameras being one of the most vital sensors for perception and planning it is more intuitive to design controllers that are able to operate directly on the camera data. In this work we present two approaches for designing a model predictive controller (MPC) that is able to directly operate in the perspective coordinates and show their equivalence to the standard MPC formulation. Consequently, it eliminates the need for dedicated modules for converting the output of the planner and estimating the state of system in the 3D coordinates, thereby enabling a lean design of the system architecture. We apply this method for the task of automated lane following and lane changing, a common use case arising in autonomous driving, and demonstrate its effectiveness.
Comprehensive Survey on Privacy-Preserving Spatial Data Query in Transportation Systems

With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.
Sistem Informasi Feeder Data Kependudukan berbasis Mobile

The uneven internet network in East Nusa Tenggara Province has resulted in a web-based population administration system application becoming less effective in the process of directly filling in population data in people's homes. The purpose of this research is to develop a system that can fill in population data offline and synchronize it with the server periodically. The method used in the development process of this system is extreme programming, with stages consisting of planning, design, coding, and testing. In the planning stage, interviews are conducted to gather user stories. Prioritization of user stories is arranged in the design stage. In addition, the system architecture design is also created at this stage. In the coding stage, the application is developed, and unit tests are made to reduce defects in the application. In the testing stage, a synchronization simulation is performed between the application and the server using dummy data up to 5000 records per transaction. The results of this study show that the application can fill in data offline and synchronize data with the server with a success rate of 100%. This mobile-based population data feeder application can assist local and provincial governments in obtaining valid information about the population in their areas, thus becoming a decision-support tool for various things, including social assistance and temporary voter list data for the upcoming regional, legislative, and presidential elections.
PERAMALAN NILAI TUKAR PETANI SUBSEKTOR TANAMAN PANGAN PROVINSI BALI MENGGUNAKAN METODE FUZZY TIME SERIES CHEN

Forecasting is a way to predict future events.One of the methods for forecasting is to use fuzzy time series Chen method.Fuzzy time series Chen is a development of fuzzy time series Song and Chissom method with more simplified arithmetic operations.In this study, the forecasting for the NTP especially in food crops sub-sector of Bali was done by using first-order and high-order fuzzy time series methods to predict next period.The results show that the most appropriate forecasting method is the secondorder fuzzy time series Chen with the result for June 2022 are 90.95,July 2022 are 91.95, and August 2022 are 92.45 with MSE value of 0.4563 and MAPE value of 0.2824%.
Can Large Language Model Surprisal Capture the Informativity Bias in Human Language Processing?

Surprisal as a general theory of human sentence processing proposes that the processing difficulty of a word is inversely proportional to its contextual probability. However, it is not always the case that more predictable material is easier to process. Recent studies suggest that humans also have an informativity bias, whereby they prefer more informative or newsworthy contents. If a word is too predictable given the context, it provides virtually no new information and may cause extra processing difficulty. Despite many existing studies showing that the surprisal value obtained from large language models (LLMs) is a good fit for human reading time data, it has not been investigated whether they can also explain the informativity bias in humans, or they will only favor more probable information. We answer this question by comparing the results of several state-of-the-art autoregressive LLMs and two recent human reading experiments, and find that contra humans, LLMs do not show a bias towards more informative material. We discuss the implications of the current study on Natural Language Processing and human linguistic cognition.
Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara

PT. Perkebunan Nusantara 1 is a company that works in palm oil mills with a total land area of 1,144 Ha in Aceh Utara. This research aimed to determine the cluster of the productive palm oil production's target. The expected results of palm oil production are for the following year so that it can be used as a recommendation for the managers to maximize performance. Research data are taken from PTPTN 1 PKS Cot Girek consisting of plantation and oil palm production data. The results of PKS Cot Girek palm oil production data for 2019-2022 from January to December were 1,365,530, while in 2022, it reached 1,768,720. The overall value obtained is 4,431,180 production data. The results of a land area of 1,144 Ha got 800.4 Ha of productive land and 343.6 Ha of less effective land. The test result in the first iteration of the C-Means process is 1.87, the second iteration is 3.87, the first iteration of the K-Means is 2.27, and the seventh iteration is 4.165 with an accuracy of 0.46 and 0.295. Meanwhile, the prediction model results have an accuracy rate of 90.77%. As a comparison, the fuzzy time series' accuracy level is 81.27%.
Kore: A Friend in Web Development

Abstract: The technological breakthroughs and innovations that are taking on across the world today are endless. Things are getting indisputably simple in all imaginable ways. Technologies simplify people’s lives, as they perfectly fill the void and propel people towards a solution. Nowadays, people tend to endorse the technical advancements that ease their life. Anything that bridges the gap between people's issues and solutions gets highly upvoted. But these technological advancements bring the necessity of extreme technical abilities that might be a hindrance for a few people to get a hand on them. In today's modern era, web applications have an extraordinary impact on people's lives. They play a significant role in individuals' lives, as well as in enterprises. However, developing one is tiresome. A few open-source softwares exist that expedite the web application generation process. "JHipster" is one such software that assists people in generating a web application. However, working with JHipster is a bit challenging as people need insights into the technical aspects. To address this issue, we propose a solution, "Kore". Kore is a creative approach to minimising a person's effort to generate a web application. This paper discusses the implementation of Kore and its role in solving the above-discussed issues. Kore can demolish the impediments and assists people wanting to generate a web application effortlessly and lets them experience the impact of these technological advancements
Retracted: Feasibility Analysis and Discrete Dynamic Modeling of Physical Education Teaching Strategy Based on Intelligent Computing

[This retracts the article DOI: 10.1155/2022/4093924.].
Retracted: The Use of Internet of Things and Cloud Computing Technology in the Performance Appraisal Management of Innovation Capability of University Scientific Research Team

[This retracts the article DOI: 10.1155/2022/9423718.].
Attention Based Cross-Domain Synthesis and Segmentation From Unpaired Medical Images

Medical images from different modalities, contrast sequences, and settings can provide diverse information in clinical applications and medical analysis. However, some modalities or contrast sequences may be missing or degraded due to strict timing and artifacts during acquisition, leaving many unpaired data. Therefore, it is meaningful to synthesize realistic medical images with unpaired data. This article proposed a general multi-task method for end-to-end cross-domain synthesis and segmentation network, named SSA-Net, based on cycle generative adversarial network (CycleGAN), using unpaired data for training. A gradient-consistency loss is introduced to supervise the synthesis around the contour, refining the boundaries in synthesized images. And a special shape-consistency term is designed to constrain the anatomical structure in synthesized images, guiding segmentation without target labels. Besides, we introduce the attention mechanism into the generators to focus on some hard-to-learn regions in the images. The FC-DenseNet is employed as a segmentation network to enhance segmentation. Our results demonstrate that the proposed SSA-Net can achieve an S-score of 0.895 on CT images of the CHAOS dataset and a DSC of 0.838 for segmenting the liver, which is a significant increase compared to baseline CycleGAN. Experiment results on four datasets demonstrate the effectiveness of the proposed cross-domain synthesis and segmentation framework.
The Research Trends on Parenting Program

본 연구는 국내에서 이루어진 양육 프로그램 관련 논문을 분석함으로써, 양육 프로그램의 연구동향을 파악하고, 양육 프로그램의 발전방향을 제시하고자 한다. 이를 위해 RISS, DBpia, Earticle, Kiss, newnonmun, scholar 및 기타 연구정보 사이트에 ‘양육’ and ‘프로그램’을 검색하여 2002년부터 2023년 5월까지 이루어진 논문을 수집하여 총 74편의 논문을 수집하였다. 수집된 74편을 연도별, 논문유형, 연구주제, 연구대상 및 참여대상, 양육대상, 대상 수, 회기 수, 전공특성 등으로 분류하고, SPSS WIN 27.0을 통하여 분석하였다. 연구결과 2016년과 2019년에 가장 많은 연구가 이루어졌으며, 연구주제는 양육역량강화가 가장 높게 나타났다. 또한 연구대상 및 참여대상의 경우 어머니가 가장 많았으며, 양육 프로그램의 대상을 생애주기별로 나누어 조사한 결과 복합이 가장 많았으며, 다음으로 유아, 영아 등의 순으로 나타났다. 프로그램의 평균 대상 수는 26.1명 회기 수는 9.61회로 나타났다. 양육 프로그램의 전공은 교육학이 가장 많은 것으로 나타났다. 이러한 결과를 바탕으로 양육 프로그램 연구의 향후 연구방향을 제시하였다.This study aims to find out research trends and suggest the direction of development of parenting programs by analyzing the research papers in Korea. For this purpose, 74 research papers were collected by searching for ‘parenting' and 'program' on RISS, DBpia, Earticle, Kiss, newnonmun, scholar and collecting articles from 2002 to 2023. The collected 74 research papers were classified into research year, research paper type, research topic, number of participants and session, major, and analyzed through SPSS WIN 27.0. The research results are as follows. Most research was conducted in 2016 and 2019, and the research topic was parenting capacity building. In the case of research subjects and participants, mothers were the most common. As a result of dividing the subjects of parenting programs by life cycle, the complex period was the most common, followed by infants and toddlers. The average number of participants in the programs was 26.1 and the number of sessions was 9.61. Education was the most common major in parenting programs. Based on these results, future research directions for the study of parenting programs were suggested.
Intention-aware Sequential Recommendation with Structured Intent Transition : (Extended Abstract)

Human behaviors in recommendation systems are driven by many high-level, complex, and evolving intentions behind their decision making processes. In order to achieve better performance, it is important for recommendation systems to be aware of user intentions besides considering the historical interaction behaviors. However, user intentions are seldom fully or easily observed in practice, so that the existing works are incapable of fully tracking and modeling user intentions, not to mention using them effectively into recommendation. In this paper, we present the Intention-Aware Sequential Recommendation (ISRec) method, for capturing the underlying intentions of each user that may lead to her next consumption behavior and improving recommendation performance. Specifically, we first extract the intentions of the target user from sequential contexts, then take complex intent transition into account through the message-passing mechanism on an intention graph, and finally obtain the future intentions of this target user from inference on the intention graph. The sequential recommendation for a user will be made based on the predicted user intentions, offering more transparent and explainable intermediate results for each recommendation. Extensive experiments on various real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines in sequential recommendation in terms of different metrics.
ENLD: Efficient Noisy Label Detection for Incremental Datasets in Data Lake

Due to the difficulty of obtaining high-quality data in real-world scenarios, datasets inevitably contain noisy labeled data, leading to inefficient data usage and poor model performance. Thus, noisy label detection is an important research topic. Previous efforts mainly focus on noisy label detection on specific datasets that have been collected. Some works select clean samples based on relations between representations during the training process; some works utilize confidence outputs of a pre-trained model for noisy label detection. However, how to perform efficient and fine-grained noisy label detection on constantly arriving datasets in a data lake with a large amount of inventory data has not been explored. The rapidly growing volume and changing distribution of data make conventional methods either incur large computation overhead due to repeated training or become increasingly ineffective on newly arriving data. To address these challenges, in this work, we propose a novel approach ENLD to perform efficient and accurate noisy label detection on incremental datasets. Our extensive experiments demonstrate that ENLD outperforms the next best method in both efficiency and accuracy, which achieves 3.65 ×-4.97× detection speedup and higher average f1 scores with various noise rate settings.
INSTRUMENT VALIDATION OF WEBSITE QUALITY TO ATTRACT DONATION

Background and Purpose: With fund scarcity eminent in Malaysia, soliciting donations effectively is essential to any non-profit organisation (NPO). Many NPOs struggle to raise funds to execute a beneficial project for society. The dimensions to identify relevant online factors influencing the NPO website user decision-making process slowly evolved in a local context due to limited interest in exploring this field. This research was conducted to identify relevant variables related to NPO website user interest to donate. However, a proper instrument must be developed to meet the research aim. Thus, this research was conducted to identify relevant variables and items in the research instrument.&#x0D; &#x0D; Methodology: The study collected 269 responses from ten popular NPO website users. Their responses were recorded using the five-point Likert Scale questionnaire ranging from strongly agree to strongly disagree. At first, the study conducted a content validity test and a pre-test. The sample size is determined based on the SEM requirement. Finally, the responses are analysed using pooled confirmatory factor analysis to determine the instrument reliability and validity.&#x0D; &#x0D; Findings: Only seven variables are retained after the validity and reliability analysis, with 43 items out of 74 items. The result indicated only information, system, service quality, perceived ease of use, and trust are relevant for the NPO to meet website user satisfaction and influence their decision to donate.&#x0D; &#x0D; Contributions: The finding is essential as a guideline to develop a website that meets user preferences. The result also contributed to the website quality literature for the non-profit sector.&#x0D; &#x0D; Keywords: Website quality, trust, satisfaction, non-profit organisation, technology acceptance model.&#x0D; Cite as: Ibrahim, M. S. (2023). Instrument validation of website quality to attract donation. Journal of Nusantara Studies, 8(1), 446-468. http://dx.doi.org/10.24200/jonus.vol8iss1pp446-468