Reviewerly/exHarmony huggingface dataset:
The impact of college students majoring in non-commissioned officer majors on their career decision-making level.

College students majoring in non-commissioned officers are equipped with the military knowledge and skills necessary for the military, and are commissioned as non-commissioned officers or transferred to one of the Korea Army Academy at Yeongcheon to choose a career as an officer and grow into key talents who will lead the military of the future.&#x0D; In general, college students choose their major by collecting various information, taking into account their aptitude, interests, popularity, and social awareness of the major. However, in the case of students majoring in non-commissioned officer majors, they choose their major based on external factors, such as parental recommendation and job stability, rather than their own, and as a result, they are unable to adapt to college life or give up midway.&#x0D; Therefore, in order to solve these problems, students need to clearly set goals for their future career. It is most important to understand the direction of college education and major design that can provide military expertise, and to establish a career plan for the future.&#x0D; For the purpose of this research direction, we would like to conduct an empirical analysis of the impact of college students majoring in non-commissioned officer majors' motivation for choosing a major on their career decision level.&#x0D; For the purpose of this research direction, we would like to conduct an empirical analysis of the impact of college students majoring in non-commissioned officer majors' motivation for choosing a major on their career decision level.
Penerapan Metode Demonstrasi Berbantuan Media Kartu Bergambar untuk Meningkatkan Hasil Belajar

This study aims to determine the Application of the Illustrated Card-Assisted Demonstration Method to Improve Science Learning Outcomes in Class IV SD Negeri 2 Tegalrejo. This research method is pre-experimental, with the design used is one-group pretest-posttest design. The population in this study were all fourth grade students at SD Negeri 2 Tegalrejo, namely class IV totaling 20 students. Data collection techniques using multiple choice tests. Data analysis technique with steps: normality test and Z test. Based on the results of the Z-test analysis with the level of confidence obtained α = 0.05 data zcount = 3.13 and Ztable = 1.64, the data shows that the Zcount value &gt; Ztable. The students' pretest average score was 61.33 and the posttest average score was 79.00, so it can be said that H0 was rejected and Ha was accepted. Thus it can be concluded that the Application of the Demonstration Method Assisted by Picture Card Media to Improve Science Learning Outcomes for Grade IV Students at SD Negeri 2 Tegalrejo is significantly complete.&#x0D; Keywords: Application, Demonstration, Picture Cards, Learning Results
Penerapan Strategi Card Sort Terhadap Hasil Belajar IPA

This study aims to determine whether the application of the card sort strategy can complete the science learning outcomes of grade V students of SD Negeri 22 Lubuklinggau. The research method used is quasi-experimental. Data collection techniques in research using essay tests. Data analysis techniques with steps: determining the mean and standard deviation, normality test and one-sample test type t test calculated using SPSS 24 calculation. The results of the analysis using the one-sample test t test at a significant level ∝ = 0.05 and dk = 24 showed a p-value value of &lt; 0.05 (0.003 &lt; 0.05) so that it can be concluded that Hₒ was rejected and Hₐ was accepted, meaning that the average science learning outcomes of grade V students of SD Negeri 22 Lubuklinggau after the application of the significant card sort strategy were completed with categories more or equal to 75 (μₒ≥ 75). So it can be concluded that the results of science learning for grade V students of SD Negeri 22 Lubuklinggau after the implementation of the card sort strategy are significantly complete.&#x0D; &#x0D; Keyword: Applicability, Card Sort, Learning Outcomes, Socil Education.
Short-Term Photovoltaic Power Forecasting Using Convolution Neural Network

This work presents the four types of short-term photovoltaic (PV) power forecasting models using a 1D convolution neural network based on 4 types of data handling. Multi-Timestamp Multi-Feature-2 (MTMF-2) forecasting model gives satisfactory results compared to Single-Timestamp Multi-Feature (STMF) forecasting, Multi-Timestamp Multi-Feature-1 (MTMF-1) forecasting, and Multi-Timestamp Single-Feature (MTSF) forecasting. Historical data taken from one of the PV stations in China is used as input to these forecasting models. Data contains all 6 features which are Power, Irradiance, Temperature, Pressure, Wind Direction, and Wind Speed for every 15-minute timestamp. TensorFlow libraries are used for model building, and the results are taken from the VS code IDE.
Long Short-Term Memory Decomposition Model for Forecasting Solar Irradiance Photovoltaic Household Scale on Grid

A proposed study using the development of the Long Short Term Memory Decomposition hybrid method for maximalization forecasting of solar irradiance (SI) based on meteorology data using a development decomposition and long short-term memory model. This combination modeling is a development decomposition model and an LSTM model. The combination development decomposition long short-term memory (DD-LSTM) modified hybrid model is designed to predict solar irradiance photovoltaic household scale on grid 1 hour ahead based on weather data for solar irradiance household scale on grid photovoltaic station. The proposed research of this hybrid model is taken into calculation based on meteorology data. The method development decomposition is a process of training and testing to the Long Short Term Memory model. In the error statistical calculation index of the DD-LSTM model, the mean absolute percentage error (MAPE) is 1 %. The hybrid combination model forecasting is then compared to actual data, development decomposition, and training results indicate that the DD-LSTM model explained in the study can calculate hourly solar irradiance with optimal accuracy.
An Oversampling Method Based on KL-Divergence for Imbalanced Datasets and Credit Risk Assessment

Despite more than three decades of vigorous development, imbalanced learning is still attracted growing attention from both academic and finance. Skewed distributions for imbalanced datasets make classifiers biased towards the majority group. However, the opposite, the minority group, is more valuable in real world datasets. In this paper, we proposed an oversampling method E-SMOTE based on entropy theory to synthesis minority samples with designed weight over minority dangerous set. Firstly, positive dangerous samples are collected adaptively based on a division, where each minority sample is recognized as inner, danger or noise according to the distribution information of the nearest neighbors. Secondly, a sampling weight is designed based on KL-divergence of two distributions for one positive dangerous sample leaving or not. In other words, the sampling weight quantifies the importance of the positive dangerous sample. Finally, SMOTE synthesizes minority samples between positive dangerous sample and its nearest minority neighbors. In experiments, the proposed method is applied on 16 benchmark imbalanced datasets and the risk assessment problem of personal credit loan.
A Multi-Strategy Improved Butterfly Optimization Algorithm

Butterfly optimization algorithm (BOA) is an excellent swarm intelligence algorithm that simulates the foraging behavior of butterfly populations, but it has such defects as poor accuracy, easy to stagnate at the local optimum, insufficient convergence and no balance between exploitation and exploration. Hence this paper proposes a multi-strategy improved butterfly optimization algorithm (MIBOA). By introducing four optimization strategies, including Bernoulli chaos map, dynamic switch probability, adaptive inertia weight and opposite-based learning, the above problems are effectively improved. And MIBOA is compared with other swarm intelligence algorithms on eight classical benchmark functions. The results demonstrate its excellent optimization performance and robustness.
Image Dehazing Method Based on Enhancement and Fusion

Due to incomplete dehazing, low contrast, and poor visibility in traditional image dehazing algorithms, we propose a dehazing method that combines several image enhancement approaches. The adaptive Gamma correction is utilized to improve the clarity of the hazy image, and the adaptive histogram equalization method with limited contrast is used to enhance the contrast and brightness. Then, the dehazed image is obtained by the multi-exposure fusion method. The experimental results show that the proposed method has better clarity and brightness on the dehazed image. Compared with the classical image dehazing algorithm, this method not only can retain more detailed information, but also the dehazed images are more natural, and dehazing effect is remarkable.
Calico Salmon Migration Algorithm: A Novel Meta-Heuristic Optimization Algorithm

A novel population-based optimization method is proposed in this paper, the Calico Salmon Migration Algorithm (CSMA), which is inspired by the natural behavior of calico salmon during their migration for mating. The CSMA optimization process comprises four stages: selecting the search space by swimming into the river, expanding the search space from the river into the ocean, performing precise search during the migrating process, and breeding new subspecies by the remaining calico salmon population. To evaluate the effectiveness of the new optimizer, we conducted a series of experiments using different optimization problems and compared the results with various optimization algorithms in the literature. The numerical experimental results for benchmark functions demonstrate that the proposed CSMA outperforms other competing optimization algorithms in terms of convergence speed, accuracy, and stability. Furthermore, the Friedman ranking test shows that the CSMA is ranked first among similar algorithms.
Parameterized Inverse Eigenvalue Problem for Quantum Sensing

A system of tunnel-coupled quantum dots is considered in the presence of an applied electric field. Given the measurements of differences between ground state and excited state energy levels as the electric field is varied, we seek to recover the quantum Hamiltonians that describe this system. We formulate this as a parameterized inverse eigenvalue problem and develop algebraic and computational methods for solving for parameters to represent these Hamiltonians. The results demonstrate that this approach is highly precise even when there is error present within the measurements. This theory could aid in the design of high resolution tunable quantum sensors.
Selection of a Suitable Curriculum to Study the Career of Electronic Engineering in Northern Lima Using AHP

The need for professionals who are able to develop industries through technology in Northern Lima (NL) has increased the offering in electronic engineering career (EEC) in that area, but the educational quality and its results for future students are still uncertain. For this reason, this paper selects an academic curriculum (AC) of this career that is more suitable for future students of NL, and that maximizes their opportunities. As alternatives, the three available ACs to study this career at present in private universities in NL were presented. The analytical hierarchical process (AHP) method was applied for the selection, considering four criteria: employability, research, technology and infrastructure, and tuition fees, which were ordered hierarchically by a group of four experts in the pertinent fields. The result is that the most suitable AC for studying the EEC in NL is the third one, closely followed by the first one, and then, far away from the other two, the second one, with the specific details of the universities that offers them being provided in this article.
Development of Booklet Learning Media and Its Role on Learning Interest of Islamic Elementary School Student

This study aims to develop a Learning Media Booklet with the theme of Diversity of Professions and Their Roles, and to examine students' learning interest when using the booklet media. This research uses the Research and Development (R&amp;D) Model developed by Brog &amp; Gall, simplified into 7 stages, including (1) pre-survey, (2) initial planning, (3) development of initial product, (4) initial product expert testing, (5) revision of initial product, (6) main field trial, and (7) evaluation of final product. The instructional media trial was conducted at Ma'arif Bulusari Islamic Elementary School, Bumi Ratu Nuban Subdistrict, Central Lampung Regency, Lampung Province. The average ratings given by content experts, media experts, and language experts are 3.77, 3.66, and 3.85, respectively, which are categorized as very good, appropriate, and highly appropriate, in that order. The results of the student's learning interest test indicate a significant difference between using the instructional media booklet and not using the booklet, with an increase of 86.43% when using the instructional media booklet. Therefore, it can be concluded that the Diversity of Professions and Their Roles Learning Booklet meets the criteria for usability and can be tested in the third-grade class of Ma'arif Bulusari Islamic Elementary School.
Multilingual Transliteration based Urdu Chatbot using Rasa Framework

Chatbots, which emulate human conversation, are meeting the rising demand for efficient customer service by reducing the need for manual intervention and alleviating the challenges of timeconsuming human interactions. This paper introduces Ubot, a domain-specific chatbot fluent in both Urdu and English transliteration, designed to reduce the need for human intervention and lower service costs. We address the challenge of low-resource languages by creating a dataset comprising 750 utterances, 28 intents, and 45 entities, validated by 36 participants. Ubot, powered by the Rasa framework, achieves remarkable accuracy in intent detection 94.1% and entity extraction 95.4%, offering efficient multilingual conversational AI solutions. Our approach holds promise for similar script-sharing languages such as Persian and Arabic, contributing to advanced language applications and intelligent virtual assistants.
Experimental Corroboration of Trained Classification Performance Predictions

A recent paper established asymptotic expressions for classification performance appropriate when the decision is based on training data. These expressions are asymptotically rigorous, and aspirational in the sense that they show what could be done with the best use made of available tools; and they are exponential with rate that - remarkably - scales with the number of relevant data in the test observation (e.g., number of independent observations, or the size of a target to be detected within an image, irrespective of the total image size). The paper also showed a close approximation of this performance that seems to give high accuracy even in non-asymptotic situations. In this paper we briefly present these results, but the main contribution is to demonstrate their validity and general applicability in the standard MNIST digit classification set.
Parallel Frequent Itemset Mining Algorithm and Optimization Based on Spark

Frequent item set mining is an important task in data mining, which can help us find the variable combinations that occur frequently in the data set. In order to solve the problem of frequent itemset mining in large-scale high-dimensional data, we propose a parallel frequent itemset mining algorithm based on Spark, OSFPG. The algorithm is optimized on the basis of the traditional FP-growth algorithm, and the number of candidate item sets is reduced by constructing a special data structure called FP tree, thus improving the efficiency of frequent item set mining. The algorithm mainly uses the idea of load balancing to optimize the grouping strategy. It also considers two factors of partition computation and FP-Tree size to ensure that the total load between each group is approximately equal. On the basis of Spark based SFPG algorithm, we implement this optimized algorithm. Based on the SFPG algorithm, it is improved to dynamically allocate computing resources and carry out distributed computing, so that the intermediate results generated in the operation process are saved in memory, thus effectively reducing the data consumption. In order to verify the performance of OSFPG algorithm, we apply it to a real data set for experiments. Experimental results show that compared with the traditional FP-Growth algorithm, the proposed algorithm has higher efficiency and lower time complexity when processing large-scale high-dimensional data. In addition, the algorithm can deal with dynamically updated data effectively and has strong practicability.
Energetic Content based Image Retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique

One popular approach to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIR. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer-accessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and permanent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIR system. We introduce LAIRS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For a transparent evaluation of the proposed scheme’s efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth observational experiments for various CBIR tasks employing an image database have shown encouraging results, teaching us important lessons for making CBIR system more efficient. With the use of CBIR structures, a different collection can find similar pictures to the query image. There is little doubt that Google’s search per photo tool is perhaps the most widely used CBIR approach.
EFEKTIVITAS PENGGUNAAN LKPD UNTUK MENINGKATKAN LITERASI NUMERASI PESERTA DIDIK

The aim of this research is to determine the effectiveness of using LKPD in increasing the literacy and numeracy of class VIII students of SMP Negeri 4 Yogyakarta. The material contained in the LKPD is the System of Linear Equations in Two Variables (SPLDV). This research is an experimental type with a pretest-posttes control group design. The results show that the use of LKPD is effective in increasing literacy-numeracy compared to learning without using LKPD. This is supported by the Wilcoxon test which show the Asymp. Sig. (2-tailed) of 0.000 which is less than 0.050, meaning that there is an increase in literacy-numeracy in the control class after implementing learning without using LKPD. Apart from that, the results of the Mann-Whitney test also show that the Asymp. Sig. (2-tailed) is 0.039 which is less than 0.050, meaning that learning using LKPD is more effective than learning without using LKPD. The average test results show that the average increase in the experimental class the average control class . Based on these results, it can be recommended to use LKPD to increase the literacy-numeracy of students at SMP Negeri 4 Yogyakarta.
A DC Algorithm Scheme For Generalized Eigenvalue Classifiers

Generalized eigenvalue classffiers handle the classification problem very well. However, the major disadvantages of generalized eigenvalue based method lie in that they may be time-consuming and fail to get a stable or optimal solution due to the matrix singularity. This paper proposes a new method using a DC Algorithm scheme to solve a fractional optimization models which ensure the optimality of the solution. Numerical experiments show that the proposed method can get better results for public data sets.
Reliability Analysis of the Gamma Knife Source Change Process Based on Spatial Radiation Dose Distribution

In order to solve the problems of inefficiency, poor economy and low safety of manual-based Gamma Knife radiation source change work, the use of robots to replace manual source change has become an important development trend. To achieve an automated source change, it is necessary to design source shielding devices with excellent weight and shielding performance, as well as a highly reliable source change workflow. In this paper, the spatial dose distribution after source shielding and protection during source change is modelled and analysed, and the source shielding device is designed, as well as the process for robots to complete the source change operation. The stability and rationality of the source change workflow are verified by simulation experiments. With the designed shielding device, the maximum leakage of the spatial radiation dose distribution is 8 mGy/h, and the robot can work stably and complete the source change operation several times.
Low-Illumination Image Enhancement Based on End-to-End Network Using Attention Module

Images are always susceptible to variations in light which makes the low-illumination image enhancement an important task. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness and contrast, while suppressing image noise simultaneously. Recently, the deep learning-based methods have also been applied to image enhancement. However, the restoration of the original brightness and detailed textures in dark images remains challenging. In this paper, an end-to-end neural network is proposed. The coordinate attention (CA) module and the squeeze excitation(SE) module are introduced to refme and highlight key features. A perceptual loss function is also proposed to enhance the texture of the details and restore the visual distortion. The effectiveness of the proposed network is demonstrated in experiments on popular datasets.
Dual Adversarial Contrastive Learning for Underwater Image Enhancement

Underwater images are highly distorted, which makes high-level computer vision tasks difficult. Existing underwater image enhancement algorithms mainly focus on restoring the appearance of images. As a result, enhanced images may not be useful for high-level computer vision tasks. The lack of label images also makes most supervised learning networks impractical. In this paper, a dual adversarial contrastive learning enhancement network is proposed, which is both visually friendly and task-oriented. A circular network is proposed to achieve self-supervised learning between unpaired images. We also introduce a contrastive prior between the enhanced and degraded results in feature space to ensure the good visual appearance of the enhanced results. Furthermore, the high-level detection task is also used to constrain the enhanced results. The experiments were carried out on a popular underwater dataset, the enhanced images of the proposed method showed better visual quality and improve tracking performance as well.