Reviewerly/exHarmony huggingface dataset:
Latent Profile Analysis of Core Competencies: Focusing on the Scholarship Support for Underprivileged Students

본 연구는 네 가지 핵심 인재 역량 – 지속도전, 능동학습, 진로개척, 사회기여 –의 응답 패턴에 따라 잠재집단을 유형화하고 분류된 잠재집단에 영향을 미치는 다양한 요인들을 탐색하였다. 자료 분석을 위해 삼성꿈장학재단의 배움터 교육지원사업 2020년도 데이터를 활용하였으며, 잠재프로파일 분석과 다항 로지스틱 회귀분석을 수행하였다. 첫째, 잠재프로파일 분석 결과, 핵심 인재 역량의 응답 패턴에 따라 4개의 잠재집단이 분류되었다. 둘째, 다항 로지스틱 회귀분석을 통해 잠재집단 별 관련 변인들의 영향력을 확인하였다. 분류된 잠재집단 유형에 프로그램 만족도, 교육포부, 경제상황, 거주지역, 가정구조 변수가 유의미한 영향을 미쳤다. 프로그램 만족도의 경우, 만족도가 높은 학생일수록 차상위 역량 집단에 속할 확률이 높았고, 교육포부의 경우, 교육 포부가 높은 학생일수록 최상위 역량 집단에 포함될 가능성이 높았다. 경제상황 변수에서는, 기초수급대상에 속하는 학생들이 일반 저소득층에 해당하는 학생들에 비하여 최상위 역량 집단에 포함될 확률이 높았으며, 중소도시에 사는 학생들이 대도시에 사는 학생과 비교했을 때, 중간 역량 집단에 포함될 가능성이 높았다. 마지막으로 한부모, 새터민, 시설, 조손가정에 속하는 학생들은 일반 가정에 속하는 학생들에 비해 중간 역량 집단에 속할 확률이 높았다. 본 연구의 결과는 네 가지 핵심 인재 역량 응답 양상에 따라 산출된 각각의 잠재집단을 위한 차별화된 교육 프로그램 및 지원 사업의 계발 및 보급 뿐 아니라, 다각도의 관점에서 교육 소외 학생들의 핵심 인재 역량 향상을 위한 체계적 지원의 필요성을 시사한다.This study classified potential latent groups according to core competency – sustainable challenge, active learning, career development, and social contribution. The potential latent groups were explored based on the responses for sub-scales of core competency, and various factors affecting the classified potential latent groups were investigated. For analysis, the data from the 2020 learning center project by the Samsung Dream Scholarship Foundation was used to conduct the latent profile analysis and multinomial logistic regression. First, four potential latent groups were detected according to the response patterns on core competency as a result of latent profile analysis. Second, the influence of related variables for each latent group was confirmed through multinomial logistic regression analysis. According to the multinomial logistic regression, program satisfaction, educational aspiration, economic situation, residential area, and family structure had a significant impact on latent group classification. For program satisfaction, the higher the satisfaction level students had, the higher probability of belonging to the second competency group. For educational aspiration, the higher educational aspiration the student had, the higher probability of belonging to the highest competency group. In the case of economic situation, the students, basic pension recipients, were more likely to be included in the highest competency group compared to the students in the low-income group. In addition, students living in small and medium-sized cities were more likely to be included in the medium competency group compared to the students living in large cities. Finally, students in groups, living with single parents, North Korean defectors, living in facilities, or living with grandparents were more likely to be included in the medium competency group than students living in a typical family. The results of this study suggested the development and dissemination of differentiated educational programs for each potential group according to the four types of core competency. For a long-term perspective, this study also suggested the need for systematic support to improve the core capacity of underprivileged students.
COMPARATIVE STUDY OF MACHINE TRANSLATION TECHNIQUES

Machine translation (MT) is an approach to give knowledge and train computer to translate the sentence from one language to another language with the same fluent and meaning. MT is evaluated by the techniques that are giving the best translating accuracy. The four major techniques are Statistical Machine Translation, Rule-based Machine Translation, Example-based Machine Translation and Neural Machine Translation. We will see comparison and evaluation between these techniques in this particular paper with the examples and BLEU scores.
Interactive Visualization With Plotly Express

Data visualisation is the process of visualizing data for the purpose of analyzing and correlating data to confirm complicated ideas and to spot visual patterns after an investigation. Data visualization allows data scientists to quickly identify visual patterns and trends that may not be immediately apparent from raw data. Visualization also enables us to build models that are easier for stakeholders to understand and apply. Data visualization is an essential tool for exploratory data analysis (EDA). Plotly Express is a terse, high-level Python visualization library. It is a wrapper module for Plotly library that provides simple interface for visualizing data in the form of various plots, maps, graph objects, layouts, traces, figures, etc. Plotly Express is a user-friendly, high-level interface to Plotly that generates easily styleable, interactive, beautiful, and informative figures visualizing various types of data. Plotly Express provides interactive visualizations.
A New Optimal Design of Stable Feedback Control of Two-Wheel System Based on Reinforcement Learning

&lt;div&gt;The two-wheel system design is widely used in various mobile tools, such as remote-control vehicles and robots, due to its simplicity and stability. However, the specific wheel and body models in the real world can be complex, and the control accuracy of existing algorithms may not meet practical requirements. To address this issue, we propose a double inverted pendulum on mobile device (DIPM) model to improve control performances and reduce calculations. The model is based on the kinetic and potential energy of the DIPM system, known as the Euler-Lagrange equation, and is composed of three second-order nonlinear differential equations derived by specifying Lagrange. We also propose a stable feedback control method for mobile device drive systems. Our experiments compare several mainstream reinforcement learning (RL) methods, including linear quadratic regulator (LQR) and iterative linear quadratic regulator (ILQR), as well as Q-learning, SARSA, DQN (Deep Q Network), and AC. The simulation results demonstrate that the DQN and AC methods are superior to ILQR in our designed nonlinear system. In all aspects of the test, the performance of Q-learning and SARSA is comparable to that of ILQR, with some slight improvements. However, ILQR shows its advantages at 10 deg and 20 deg. In the small deflection (between 5 and 10 deg), the DQN and AC methods perform 2% better than the traditional ILQR, and in the large deflection (10–30 deg), the DQN and AC methods perform 15% better than the traditional ILQR. Overall, RL not only has the advantages of strong versatility, wide application range, and parameter customization but also greatly reduces the difficulty of control system design and human investment, making it a promising field for future research.&lt;/div&gt;
Three Stage Weighted-Mean Filter Using Euclidean Distance

This paper presents a three-stage weighted-mean (TSWM) filter to restore digital images corrupted by high noise density impulsive noise. The proposed filter evaluates the weighted mean of non-noisy pixels (NFP) in $$3\times 3$$ or $$5\times 5$$ grid where the respective weights taken are proportional to their euclidean distance from the center pixel. The first step functions as a pre-processing step where the noisy pixels with the highest information or least uncertainty are denoised first using the two adjacent pixels in a straight line. And in the third stage, edge cases such as corners and boundaries are handled. The performance comparison of this algorithm is done using a $$512\times 512$$ grey-scale Lena image. The experiment results show on an average increment of 2.54 dB and 0.15 dB of Peak signal to noise ratio for high (10% to 90%) and extremely high (90% to 98%) noise densities over popular denoising filters.
Automatic Chest Radiology Report Generation Using Reinforcement Learning

Radiology report generation is the manual task assigned to radiologists which analyze the given chest image (x-ray) of patient and generates the report based on that analysis. Many of the research is undergoing in this field to make this process automatic. However, there are a large number of difficulties which have been faced by the researchers while developing the efficient automatic report generation model. The main difficulties include the generation of clinically inaccurate reports when analyzed with the reports of the expert radiologist. The introduction of deep learning based approaches achieved tremendous advancement in these areas and can produce chest X-ray reports in a more accurate manner. BY further optimizing the deep learning based approaches, we proposed a reinforcement learning-based model that generates automated X-ray reports based on input chest X-ray images. We also developed a lookahead inference mechanism to overcome the limitation of beam search, which selects words based on policy and value networks at each particular step of time. The proposed model achieved 0.454, 0.546, 0.466, 0.410, and 0.424 for the Bleu-1, Bleu-2, Bleu-3, Bleu-4, and Rouge evaluation metrics, respectively, surpassing all available state-of-the-art algorithms.
Cross-Lingual Reverse Dictionary Using Multilingual mBART and mT5 Models

The task of a cross-lingual reverse dictionary can be considered a combination of conventional text summarization and language translation. The description needs to be summarized into target words and further translated into different languages. The problem is considered complex because it requires identifying the correct target word from colloquial descriptions. The description of the target word ranges from a dictionary definition to a conversational description. A regular dictionary maps words to their respective definitions, whereas reverse dictionaries attempt to map the descriptions to words. In this work, different models, named auto encoder-decoder and sequence-to-sequence models based on Translation Language Modeling (TLM) are explored for solving the limitations of current models, and presents two state-of-the-art cross-lingual reverse dictionary models using multilingual BART and T5 transformers. The experiment is performed on the trilingual dataset Wikt3l, and the performance of the proposed models are evaluated in terms of Accuracy @1, Accuracy @10, and Mean Reciprocal Rank is used as the performance measures. The results showed that the proposed models achieved higher accuracy as compared to the existing Masked Language Modeling (MLM) based models.
Investigating the impact of a mobile learner-generated-content tool on pupils' after-school English vocabulary behavioural learning patterns, learning performance and motivation: a case study

This paper reports on a case study of investigating the effect of a mobile learner-generated content (m-LGC) tool on pupils' after-class English as a second language (ESL) vocabulary behavioural learning patterns, learning performance and motivation. Participants were 34 students in grade 4 from two classes in a primary school in Hong Kong. Data collection included students' learning logs on the m-LGC tool, pre-and post-vocabulary tests and pre-and post-questionnaires. Both qualitative and quantitative data analysis methods were adopted. An active group (AG) and a passive group (PG) were categorised based on visualisation and the number of student created learning logs. The results show that: 1) student behavioural learning patterns varied across AG and PG; 2) students in the AG made significant improvement in their learning performance, but those in the PG did not; 3) student learning motivation in the AG was improved, but that in the PG dropped significantly.
A Study on University Professors' Perception of Convergence Education

The purpose of this study is to prepare a plan to revitalize convergence education through the current status of convergence education awareness among teachers at University A. To this end, a survey was conducted on teachers at University A, and interviews were conducted with those with experience in convergence education to confirm how they perceive convergence education in detail. University A teachers recognize that the most necessary thing for convergence education is the will and effort of professors, and that collaboration between professors is not easy. It is said that a lot of time and effort are required for convergence education, and it is difficult to design and organize convergence education, so institutional and financial support from the school is needed. Therefore, it is necessary to establish a dedicated organization for convergence education, raise awareness of convergence education, and establish a framework for stable support, and provide step-by-step support to strengthen teachers' convergence education capabilities.
A Preliminary Study on the Prediction Method for the Probability of Drone Threats Using Markov Chain Model

본 연구에서는 드론 위협의 발생확률을 예측하는 모델로서 마르코프 체인 모델의 적용 가능성을 분석하고자 하였다. 이를 위해 우선적으로 국내외 드론 위협 사례에 대한 자료를 조사 · 분석하였다. 그 결과를 마르코프 체인 모델에 적용하여 향후 드론 위협에 대해 예측하였으며, 이를 비교하기 위하여 회귀분석을 활용하였다. 결과적으로, 2022년 1월 발생 가능한 드론 위협으로 마르코프 체인 모델은 0.3~1.02건의 범위로 예측하였으며, 회귀분석은 17건으로 예측하였다. 실제로 발생한 드론 위협은 스웨덴 Forsmark의 원전 상공에서 대형드론이 비행하는 것을 발견한 1건으로 마르코프 체인 모델 예측치가 관찰된 값에 근접하였다. 이를 통해 마르코프 체인 모델을 이용한다면 드론 위협 발생 가능성을 예측할 수 있음을 확인하였다. 향후 원자력발전소 인근 드론의 불법비행 자료가 구축된다면 위협을 사전에 보다 정확하게 예측할 수 있을 것이다. 결과적으로 본 연구결과는 드론 위협 발생확률을 예측함으로써 선제적으로 물리적방호 체계 수준 및 탐지장비 성능수준 등을 결정하는데 기초자료로 활용할 수 있을 것으로 기대한다. The purpose of this study is to analyze whether the probability of drone threat can be predicted using markov chain model. For this study, we investigated international and domestic drone threat data. The results were applied to the markov chain model to predict drone threats and regression analysis was used to verify this. As a result, the markov chain model predicted the probability of drone threats in the range of 0.3∼1.02 cases, whereas the regression analysis predicted 17 cases in January 2022. Actually, one drone threat occurred, and the markov chain model prediction was close to the observed values. Through this, it was confirmed that the probability of drone threat could be predicted by using markov chain model. If illegal drone flight data near nuclear power plants are established in the future, threats can be predicted more accurately in advance. Therefore, the results of this study could contribute to enhancing the level of physical protection of nuclear power plants.
Moving Object Detection and Recognition Algorithm Based on Deep Learning

Aiming at the problem that the deep learning detection framework based on video moving object detection cannot make full use of the motion information of the upper and lower frames, and the computational complexity is high, an improved SSD detection algorithm is proposed. The algorithm uses the lightweight network MobileNet as the basic network in the SSD model to reduce the time and computation spent in extracting image features, and establish the correlation between frames. Based on the Tensorflow deep learning framework, training is carried out on COCO (Common Objects in Context) dataset, and comparative experiments on moving object recognition accuracy and recognition speed are carried out. Experimental results show that the algorithm has good performance in terms of detection accuracy and speed.
Research Status and Improvement Methods of Manta Ray Foraging Optimization Algorithm

The manta ray foraging optimization algorithm is a bionic optimization algorithm that imitates the manta ray foraging behavior. This article introduces the basic concept and operation process of the MRFO algorithm. By summarizing the research status of the manta ray foraging algorithm and several improved methods for the manta ray foraging algorithm, the performance differences and their respective advantages and problems are analyzed and compared. Finally, the future development and improvement direction of the manta ray foraging optimization algorithm are prospected.
Research on the profit mechanism of data elements of platform economy in the era of big data

In today’s era of big data, the data itself gradually established as the factors of production, platform economy is actually the data as the core of the economic model, platform economic data elements profit mechanism as data elements supply the size of the size of the market and gradually development andprosperity, this paper through the analysis of data elements profit mechanism, establish data elements market scale expansion coefficient formula model, the research platform of the value of data value, shows that the data supply, value is farfrom fully realized.
MDFN:Multi-scale Dense Fusion Network for RGB-D Salient Object Detection

In RGB-D salient object detection, the low quality of the salient maps produced after detection is caused by the unclear contents of some RGB-D images and the poor utilization of image information by some methods. To solve the above problems, in this paper, we propose a Multi-scale Dense Fusion Network (MDFN) to improve RGB-D image understanding and enhance the performance of salient object detection. First, in the design of the encoder, we chose the Pyramid Pooling Transformer (P2T) as the backbone network of the encoder, and take advantage of the strong image understanding capability of the P2T to prevent the loss of important information when extracting RGB-D features. For the limitations such as less depth map information and noise interference, we propose the Depth Attention Enhancement Module (DAEM) to enhance the depth feature representation, and design the RGB-Depth Fusion Module (RDFM) to achieve RGB-D information matching. In the decoder design, to improve the utilization of image information, we propose the Dual-dense Fusion Module (DFM) to fully integrate the features of each layer and achieve multiple applications of the object information. Compared with 7 SOTA methods on 3 public datasets, MDFN achieves excellent performance on 4 evaluation metrics $(S_{\alpha}, F_{\beta}, E_{\xi}, M)$ and saliency maps, which proves the effectiveness of our method.
Principal Component-Based Semi-Supervised Extreme Learning Machine for Soft Sensing

Soft sensing technique has been extensively used to predict key quality variables in industrial systems. However, due to the difficulty of quality variable acquisition, only limited labeled data samples are available, and a large number of unlabeled ones are discarded. This raises a big challenge to build a high-quality soft sensor model. In order to furthest exploit information contained in both the labeled and unlabeled data, this paper proposes a principal component-based semi-supervised extreme learning machine (referred to as PCSELM) model. Through this model, extracting latent features and learning nonlinear input-output relationship can be simultaneously performed. In this way, unlabeled samples are utilized efficiently for feature representation and model accuracy improvement. Moreover, mixed regularizations are employed to work in conjunction with the PCSELM to obtain high generality and flexibility. We also derive an efficient parameter learning algorithm with theoretically guaranteed convergence. Comprehensive experiments are conducted via an industrial process. Comparison results illustrate that the proposed PCSELM outperforms other representative semi-supervised algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes in general incorporate unlabeled samples which are ubiquitous in real world applications. The focus of this paper is to develop a semi-supervised soft sensor model (PCSELM) that is capable to learn the nonlinear features and regression relationship efficiently with both the labeled and unlabeled samples. The proposed model can automatically implement the feature representation and the input-output relationship description. In addition, we introduce mixed norms for the model objective function to improve the final prediction performance and generalization. A feasible model optimization technique with proved convergence is also derived. Experimental results based on a real industrial dataset manifest that PCSELM achieves better prediction accuracy than its peers.
Evidence Reasoning and Curriculum Learning for Document-level Relation Extraction

Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. Compared with the sentence-level counterpart, it has raised two significant challenges: a) In most cases, a relational fact can be adequately expressed via a small subset of sentences from the document, namely evidence. But the traditional method cannot model such strong semantic correlations between evidence sentences that collaborate to describe a specific relation; b) The data of this task is extremely long-tail in terms of too many NA instances and imbalanced relational types. Such data can mislead the tail prediction bias to the head categories in the RE model. In this paper, we present a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> vidence reasoning and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> urriculum learning method for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> oc <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</b> (DRE-EC) to address these challenges. Particularly, we first formulate evidence extraction as a sequential decision problem through a crafted reinforcement learning mechanism with an efficient path searching strategy to reduce the action space. Providing the evidence for each entity pair as a customized-filtered document in advance helps infer the relations better. To address the long-tail issue, we further develop a hybrid curriculum learning method at the NA-level (NC) and relation-level (RC) with our customized difficulty measure score. In NC, the NA samples are scheduled in an easy-to-hard scheme and gradually added, resulting in the data distribution from ideal and balanced to real and unbalanced. In RC, the scheme is switched into hard-to-easy to enhance the hard and tail samples. In addition, we propose a new Equalization adaptive Focal Loss(EFLoss) that can adjust to the changing data distribution and focus more on the tail categories. We conduct various experiments on two document-level RE benchmarks and achieve a remarkable improvement over previous competitive baselines. Furthermore, we provide detailed analyses of the advantages and effectiveness of our method.
An Exemplary Case of Industrial Metaverse: Engineering Product Demonstration Using Extended Reality Technologies

Metaverse is a new medium that effectively fuses digital and physical worlds into a seamless experience. Metaverse platforms are limited in their current scope of applications and require further exploration to expand their capabilities beyond social networking. In this paper, we present a system framework that aims to realize the concept of industrial Metaverse for engineering product demonstration using extended reality (XR) technologies. The framework incrementally integrates virtual and real environments throughout three phases with a focus on transitioning from business to engineering aspects. A virtual reality (VR) exhibition hall, an augmented reality (AR) catalog, and a mixed reality (MR) on-site assessment tool have been developed to support technical marketing in each phase, respectively. Test results show that these XR applications enable real-time interaction for potential customers to evaluate industrial coolers without geographic and temporal restrictions. The presented framework may work as a foundation for the implementation of industrial Metaverse in the post-pandemic era.
On the Spaces of Interval Valued Cesaro Convergent Sequence Spaces

The concept of quasilinear space is a field that needs to be matured, the foundations of which were laid by S. M. Aseev's published work in 1986. The simplest nonlinear quasi linear space example is the set P which is the class of closed intervals of real numbers. It was given an interval-valued sequence space using the Cesaro limitation method's matrix domain. Also, its quasilinear space structure, some topological characteristics, and some inclusion relations were examined.