Reviewerly/exHarmony huggingface dataset:
A Case Study on the Online Learning Experience of Elementary School Students in the Initial Stage of COVID-19

이 연구는 코로나19 초기, 국가 재난 상황에서 취약 대상인 초등학생이 느끼는 혼란과 어려움에 대한 문제인식에서 시작하여, 초등학생이 가정 안에서 겪는 온라인학습 경험을 탐색하는 것을 목적으로 하였다. 연구참여자는 수도권에 거주하는 초등학생 3인을 대상으로 하였다. 연구방법으로 참여관찰과 심층면담을 중심으로 질적 사례연구를 수행하였으며, 자료의 분석은 Strauss & Corbin(1998)이 제시한 3단계 분석 절차에 따라 수행하였다. 분석결과 상위주제 2개와 10개의 하위주제로 분석되었으며 연구 결과는 다음과 같다. 첫 번째, 주제는 코로나19 온라인 개학 상황에서 초등학생은 사회활동과 소통 정도에 따른 온라인 학습 적응 차이를 경험하였다. 하위주제로는 진도율 중심 온라인 학습에 대한 압박과 스트레스, 빈번한 온라인 학습 시스템 장애와 오류, 온라인 학습활동 지시사항 이해의 어려움, 어머니에 대한 학습의존, 단절된 학급 친구와의 관계, 긴장감 없는 지루한 일방향 수업, 시간 관리의 어려움으로 인한 학습시간의 지연을 경험하는 것으로 나타났다. 두 번째, 주제는 가정 배경에 따른 불평등한 온라인 학습 여건의 차이를 경험하고 있었다. 하위주제로, 가정환경 여건에 따른 온라인 학습공간 및 지원, 동시학습 형제자매의 유무 등 가족 수에 따른 한정된 학습자원, 어머니의 학습관여 정도에 따른 학습 결과물의 차이를 경험하고 있었다. 이 연구는 코로나19 초기 상황에서 실제 가정 안에서 이루어지고 있는 초등학생의 온라인 학습경험 탐색을 통해, 코로나 이후 시대를 대비하여 온라인 학습체제를 구축하는 데 학습자의 이해를 위한 기초자료를 제공하였다는 점에서 의의가 있다.This study explored the online learning experiences of elementary school students at home during the initial stages of COVID-19. Participants were 3elementary school students living in a metropolitan area. The research method consisted of participatory observation and in-depth interviews based on a qualitative case study. Data analysis was performed according to the three-step analysis procedure proposed by Strauss and Corbin (1998). Two top themes and 10 subtopics were identified through the analysis. The first theme was differences in online learning adaptation according to the degree of social activity and communication. The subtopics for this theme were pressure and stress related to progress-based online learning, frequent online learning system failures and errors, difficulties in understanding instructions of online learning activities, learning dependence on mothers, disconnected relationships with classmates, one-way classes without tension, and delayed learning time due to time management difficulties. The second theme was differences in online learning conditions according to home environment. The subtopics online learning space and support according to home environment conditions, limited learning resources according to the number of family members such as presence or absence of siblings studying simultaneously and learning outcomes according to the degree of mothers’ involvement in learning were identified. This study is innovative in that it records and analyzes the learning situation at home of learners in the initial stage of COVID-19 and provides basic data of elementary school learners’ understanding of online learning in a national disaster situation.
A Study on Process and Types of Novice Teachers’ Induction in Elementary Schools

본 연구는 질적 연구의 전략 중 하나인 근거이론을 통해 우리나라 초등 신규교사의 교직적응 과정을 도출하고 그 유형을 탐색적으로 이론화하였다. 연구 참여자는 교직경력 3년 이내의 초등 정규 교사로 한정하고 전국단위로 총 25명을 선정하였다. 학교 소재지의 규모와 단위학교의 풍토 등을 고려하여 특별시 1개, 광역시 2개, 중소도시 2개, 읍면 지역 1개의 6개 집단으로 구성하고 초점집단면담(FGI)을 실시하여 질적 자료를 수집하였다. 연구의 주요 결과는 다음과 같다. 첫째, 초등 신규교사들은 학교 현장에서 학생들과 직접 부딪히면서 교직 수행과 관련된 다양한 방법을 연구하고 적용하는 반복적 과정을 통해 성공과 실패를 경험하고 자신만의 교육실천 방향을 정립해 간다. 둘째, 신규교사들은 부장교사, 동료교사 등 인적자원을 적극 활용하여 학교에서 마주치는 문제들을 해결하고자 노력하였고, 문제 상황을 차단하고 자기합리화하기도 하였으며, 자체적으로 자기만의 생존전략을 찾는 방식 등으로 교직 수행의 어려움을 극복하면서 점차 적응해 가는 모습을 보였다. 이러한 전략 활용의 과정에서 순차적으로 ‘불안’, ‘탐색’, ‘조정’, ‘정립’이라는 교직적응의 네 단계를 거치는 것으로 나타났다. 셋째, 이 연구의 중심 주제인 신규교사의 교직적응은 ‘교직 수행의 어려움 극복 과정에서 실천 방향을 정립하여 교육전문가로 발돋움하기’로 집약되었으며, 구체적인 교직적응의 양상은 ‘고군분투형’, ‘내적고심형’, ‘무난순탄형’의 세 유형으로 최종 도출되었다. 마지막으로 전술한 본 연구의 주요 결과가 기존 신규교사 교직적응의 학술적ㆍ실천적 논의에 어떠한 시사점을 가지는지 제시하였다.This study explores the types of novice teachers’ induction process in elementary schools based on the grounded theory. Main participants are 25 elementary school teachers whose working experiences are less than three years. Focus group interviews are conducted in a total of 6 groups based on school locations for collecting qualitative data. Main findings are as follows. First, novice teachers experience educational success and failure in an iterative cycle while working with students at schools. Second, they have induced to teaching profession, when they tried to solve problems with a help from colleagues, rationalize their own decisions, and/or search for survival strategies at schools. This explains four steps of anxiety, searching, adjustment and establishment. Third, the novice teacher’s induction is defined as a process of leaping forward and establishing teaching profession as their identities. Three types of fighting alone, inner consideration, easy and smooth are concluded. At last, this study discusses theoretical and practical meaning of its findings for investigating novice teacher’s induction.
An Analysis of Verb Errors Made by Japanese Learners of Korean

The purpose of this study is to analyze the contrastive errors made by Japanese learners of Korean verbs, including “verbs,” “adjectives,” “auxiliary verbs,” and “determiners,” and to provide suggestions for vocabulary education for Japanese learners based on the results. To achieve this, the National Institute of Korean Language’s learner error annotated corpus was used to extract errors made by Japanese learners of Korean verbs. The results were classified into three categories: “interlanguage errors,” “intralanguage errors,” and “errors caused by the interaction of interlanguage and intralanguage factors.” Errors caused by Japanese interference were further subdivided into “errors caused by differences in vocabulary correspondence between Korean and Japanese” and “errors related to word formation.” Finally, based on the analysis results, suggestions for vocabulary education for Japanese learners were provided, which include presenting vocabulary through word lists, and cautions for teachers such as differences in honorific language between Japan and Korea. The findings of this study can serve as a fundamental resource for developing effective vocabulary instruction for Japanese learners.
An Educational Study on the order of Korean Alphabet

이 연구는 한글 자모의 체계와 특성에 대하여 개괄한 후 학습 대상에 따른 자모의 제시 순서를 분석하여 효과적으로 한글 자모 교육이 이루어질 수 있도록 교육적 관점에서 고찰하는 데 목적이 있다. 주지하는 바와 같이 『훈민정음』에서는 한글 자모의 순서를 별도로 정하지 않고 있는데, ‘ㄱ, ㄴ, ㄷ...’, ‘ㅏ, ㅑ, ㅓ, ㅕ, ㅗ, ㅛ, ㅜ, ㅠ’와 같은 순서는 『훈몽자회』 범례(凡例)에서 사용된 것이 현재까지 이어진 것으로 볼 수 있다. 현행 초등 국어과 교과서에서는 자음을 먼저 제시하고, 모음을 제시하고 있는데 대체로 ‘기본 자모’, ‘복잡한 자모’의 순서로 되어 있다. 한국어 교육을 위한 교재에서는 모음을 먼저 제시한 후 자음을 제시하였는데 자음의 경우 다양한 순서로 제시되어 있다. 이처럼 국어·한국어 교재에서 한글 자모의 순서를 다양하게 제시하고 있는 것을 고려하면 교육적으로 유의미한 순서를 위한 원리가 필요하다는 것을 알 수 있다. 이에 따라 ‘전통성’, ‘체계성’, ‘용이성’이라는 한글 자모의 제시 순서를 위한 원리를 설정하여 이에 따라 학습 순서를 정할 수 있다. 자음자의 경우 전통성(ㄱ, ㄴ, ㄷ, ㄹ, ㅁ, ㅂ, ㅅ, ㅇ, ㅈ, ㅊ, ㅋ, ㅌ, ㅍ, ㅎ, ㄲ, ㄸ, ㅃ, ㅆ, ㅉ), 체계성(/ㄱ, ㅋ, ㄲ/, /ㄴ, ㄷ, ㅌ, ㄸ/, /ㅁ, ㅂ, ㅍ, ㅃ/, /ㅅ, ㅆ/, /ㅈ, ㅊ, ㅉ/, /ㅇ, ㅎ/), 용이성(/ㅂ, ㅍ, ㅃ/, /ㄷ, ㅌ, ㄸ/, /ㅅ, ㅆ/, /ㅈ, ㅊ, ㅉ/, /ㄱ, ㅋ, ㄲ/, /ㅁ, ㄴ, ㅇ/, /ㄹ/, /ㅎ/), 용이성(/ㅂ, ㄷ, ㄱ/, /ㅅ, ㅈ/, /ㅍ, ㅌ, ㅋ, ㅊ, ㅎ/, /ㅃ, ㄸ, ㄲ, ㅆ, ㅉ/, /ㅁ, ㄴ, ㅇ/, /ㄹ/)으로 제시할 수 있고, 모음자의 경우 전통성(ㅏ, ㅑ, ㅓ, ㅕ, ㅗ, ㅛ, ㅜ, ㅠ, ㅡ, ㅣ → 가획모음), 체계성(ㅣ, ㅔ, ㅐ, ㅡ, ㅓ, ㅏ, ㅜ, ㅗ, ㅟ, ㅚ → 이중모음), 용이성(/ㅣ/, /ㅓ, ㅏ/, /ㅕ, ㅑ/, /ㅡ/, /ㅗ, ㅜ/, /ㅛ, ㅠ/, 복잡한 모음 )으로 제시할 수 있다.The purpose of this study is to examine the characteristics of Hangeul and to propose an effective teaching order. 『훈민정음』 does not set the educational order of Korean alphabet. ‘ㄱ, ㄴ, ㄷ...’, ‘ㅏ, ㅑ, ㅓ, ㅕ, ㅗ, ㅛ, ㅜ, ㅠ’ are the order derived from 『훈몽자회』. In elementary school Korean language textbooks, consonants are presented before vowels. The order of consonants and vowels can also be subdivided. Consonants are presented in the order of basic consonants to complex consonants, and vowels in the same way. In KSL and KFL textbooks, vowels were usually presented first, followed by consonants. However, the order within consonants and vowels varied widely. Under this circumstances, formulating educationally meaningful principles and orders is required. Accordingly, the principle for the presentation order of Korean letters such as ‘traditionality', ‘systematicity', and ‘usability' can be established. Based on the principle above, it can be arranged as follows. Consonant; ‘traditionality' (ㄱ, ㄴ, ㄷ, ㄹ, ㅁ, ㅂ, ㅅ, ㅇ, ㅈ, ㅊ, ㅋ, ㅌ, ㅍ, ㅎ, ㄲ, ㄸ, ㅃ, ㅆ, ㅉ), ‘systematicity'(/ㄱ, ㅋ, ㄲ/, /ㄴ, ㄷ, ㅌ, ㄸ/, /ㅁ, ㅂ, ㅍ, ㅃ/, /ㅅ, ㅆ/, /ㅈ, ㅊ, ㅉ/, /ㅇ, ㅎ/), ‘usability'(/ㅂ, ㅍ, ㅃ/, /ㄷ, ㅌ, ㄸ/, /ㅅ, ㅆ/, /ㅈ, ㅊ, ㅉ/, /ㄱ, ㅋ, ㄲ/, /ㅁ, ㄴ, ㅇ/, /ㄹ/, /ㅎ/). Vowel; ‘traditionality'(ㅏ, ㅑ, ㅓ, ㅕ, ㅗ, ㅛ, ㅜ, ㅠ, ㅡ, ㅣ), ‘systematicity'(ㅣ, ㅔ, ㅐ, ㅡ, ㅓ, ㅏ, ㅜ, ㅗ, ㅟ, ㅚ), ‘usability'(/ㅣ/, /ㅓ, ㅏ/, /ㅕ, ㅑ/, /ㅡ/, /ㅗ, ㅜ/, /ㅛ, ㅠ/)
Lifting Uniform Learners via Distributional Decomposition

We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution ‍D. The efficiency of our transformation scales with the inherent complexity of ‍D, running in (n, (md)d) time for distributions over n whose pmfs are computed by depth-d decision trees, where m is the sample complexity of the original algorithm. For monotone distributions our transformation uses only samples from ‍D, and for general ones it uses subcube conditioning samples.
Dual-domain fusion deep convolutional neural network for low-dose CT denoising

BACKGROUND: In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) has shown excellent performance in removing low-dose CT noise. However, previous work mainly focused on deepening and feature extraction work on CNN without considering fusion of features from frequency domain and image domain. OBJECTIVE: To address this issue, we propose to develop and test a new LDCT image denoising method based on a dual-domain fusion deep convolutional neural network (DFCNN). METHODS: This method deals with two domains, namely, the DCT domain and the image domain. In the DCT domain, we design a new residual CBAM network to enhance the internal and external relations of different channels while reducing noise to promote richer image structure information. For the image domain, we propose a top-down multi-scale codec network as a denoising network to obtain more acceptable edges and textures while obtaining multi-scale information. Then, the feature images of the two domains are fused by a combination network. RESULTS: The proposed method was validated on the Mayo dataset and the Piglet dataset. The denoising algorithm is optimal in both subjective and objective evaluation indexes as compared to other state-of-the-art methods reported in previous studies. CONCLUSIONS: The study results demonstrate that by applying the new fusion model denoising, denoising results in both image domain and DCT domain are better than other models developed using features extracted in the single image domain.
Few-Shot Learning for Multi-POSE Face Recognition via Hypergraph De-Deflection and Multi-Task Collaborative Optimization

Few-shot, multi-pose face recognition has always been an interesting yet difficult subject in the field of pattern recognition. Researchers have come up with a variety of workarounds; however, these methods make it either difficult to extract effective features that are robust to poses or difficult to obtain globally optimal solutions. In this paper, we propose a few-shot, multi-pose face recognition method based on hypergraph de-deflection and multi-task collaborative optimization (HDMCO). In HDMCO, the hypergraph is embedded in a non-negative image decomposition to obtain images without pose deflection. Furthermore, a feature encoding method is proposed by considering the importance of samples and combining support vector data description, triangle coding, etc. This feature encoding method is used to extract features from pose-free images. Last but not the least, multi-tasks such as feature extraction and feature recognition are jointly optimized to obtain a solution closer to the global optimal solution. Comprehensive experimental results show that the proposed HDMCO achieves better recognition performance.
N°386 – Seizure detection using personalized machine learning methods based on wearable ECG

It is previously discovered that evacuees’ mental state involves two key elements: the awareness of the situation, namely the clarity; and the intensity of the stimulation (Deng et al., 2022). In this paper, we aim to construct a clarity-intensity model for the delay time and the speed preference through a VR experiment, and to compare the model with real-world cases. 15 disaster stimuli are designed to build a clarity-intensity plane, and are applied in the virtual environment (VE). The VE is made walkable with an omni-directional treadmill, and the behaviours are videotaped for later analysis. The analysed behaviours are quadratically regressed on the clarity-intensity plane, and it is discovered 1) that the delay time is effectively reduced with high clarity, and appears an inverted U-shape on the intensity dimension, and 2) that the stride frequency ratio, which represents the speed preference, rises with both clarity and intensity. The results on the delay time are further compared with a dormitory building fire case, and consistency is concluded.
Politeness in the Classrooms of the Visually Impaired Students: A Case Study

The present study focuses on the use of politeness in teacher-student interaction in a Special Education institute in Pakistan. Using the concepts of Brown and Levinson (1987) and Leech (2014), visually impaired students in grade 8th were observed using or flouting politeness strategies and maxims in the classroom. The results show that the students used both positive and negative politeness strategies. The students repeatedly used negative politeness strategies in order to maintain social distance and to avoid imposition on the teacher. Also, they used maxims of modesty and obligation more frequently than the other maxims. Moreover, it has been observed that these students usually remained quiet and asked fewer questions during class. This research is significant in providing future researchers with a framework to work specifically in the domain of Special Education not only in Pakistan but also globally and will facilitate the development of pedagogical methods for inclusive education.
Existing Decentralized Storage Blocks the Future of Blockchain Technology: What Will Come as Replacement

This study examines why no existing decentralized storage is suitable for storing personal data, from a legal and ethical point of view, and what negative consequences the development of blockchain technologies may have for society without taking this crucial factor into account.We take a closer look at various aspects of this problem:sociallegislativetechnical and technological.Followed by an in-depth comparison of existing decentralized storage projects.We list technical requirements for new solutions that will replace the current ones.
Research and Implementation of Multi-feature Tracking Algorithms

A single feature cannot adapt to the dynamic changes of the scene during video target tracking. This paper, to address this issue, first studies the tracking algorithm of multi-feature fusion, which uses the complementarity between different features to better adapt to the scene changes. On this basis, the APCE anti-occlusion criterion is added to enable the algorithm to resist the influence of target occlusion on tracking to a certain extent. The experimental results show that the average tracking accuracy of the proposed algorithm is about 0.779, which is about 2% higher than that of the SAMF algorithm, and the tracking success rate can be as high as 72%.
Automatic Exposure Control of Line Structured Light Sensor Based on Fuzzy Logic

Exposure time is one of the important reasons that affect the accuracy of 3D vision measurement. Different exposure times need to be set to ensure the measurement accuracy for parcel volume measurement in different lighting environments. The existing exposure time setting is mainly based on manual experience and lacks scientific basis. For this reason, this paper proposes an algorithm for automatic camera exposure adjustment based on fuzzy rules. The exposure time is coarsely adjusted first and then finely adjusted to obtain a more accurate exposure time. Experiments show that the proposed camera automatic exposure algorithm using fuzzy rules is fast and reliable.
Automatic Optimization of Variational Quantum Algorithm-Based Classifiers

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.
Image Encryption Algorithm Based on Latin Squares and Adaptive Z-Diffusion

This paper proposes a chaotic encryption algorithm based on Latin squares and adaptive Z-diffusion. First, in order to improve the defects of the traditional Sine system, two-dimensional enhance Sine chaotic system (2D-ESCS) is designed. In terms of bifurcation diagram, Lyapunov exponent and NIST, we can observe that 2D-ESCS have continuous and large chaotic ranges. Second, the generation of Latin squares through pseudorandom sequences generated by 2D-ESCS and then perform scrambling operation with the image. Third, adaptive Z-diffusion depends on the location of the pixels. the cipher image is calculated by different combinations of pseudorandom numbers, plain images pixel values and intermediate cipher image pixel values. Finally, simulation experiments and security analysis show that the proposed algorithm has a high security level to resist various cryptanalytic attacks and a high execution efficiency.
Effects of PM2.5 on the Detection Performance of Quantum Interference Radar

In order to study the influence of PM2.5 particles on the detection performance of quantum interference radar, this article analyzes the relationship between the concentration of PM2.5 particles and the extinction coefficient under different particle sizes based on the spectral distribution function of PM2.5 particles and the Mie scattering theory. Then establish the influence model of PM2.5 particles on the detection distance and maximum detection error probability of quantum interference radar. The simulation results show that as the concentration of PM2.5 particles increases, the extinction coefficient of PM2.5 particles shows a gradually increasing trend; the energy of the detected photons is attenuated, resulting in a decrease in the transmission distance of the photons; when the energy of the emitted photons remains unchanged, The maximum detection error probability of quantum interference radar increases with the increase of PM2.5 particle concentration; when the PM2.5 particle concentration remains unchanged, the maximum detection error probability decreases gradually with the increase of the emitted photon energy. Therefore, the average number of emitted photons should be appropriately adjusted according to PM2.5 pollution in order to reduce the impact of PM2.5 atmospheric pollution on the detection performance of quantum interference radar.
No-reference Omnidirectional Image Quality Assessment via Equirectangular Convolution and Hierarchically Fusion

The number of omnidirectional images and videos, which have gained much attention due to their large viewing angles and interactive viewing experience, has grown significantly. Thus omnidirectional image quality assessment (OIQA) has become more critical. In this paper, we propose an Equi-StairNet, an end-to-end no-reference (NR) image quality assessment method that fuses high and low-level visual features and predicts directly on the equirectangular projection (ERP). The Equirectangular convolution kernels (EquiConvs) were adopted, which take into account the geometric deformations of the ERP. An Equirectangular convolution kernel samples the points on the spherical surface by mapping the points of the spherical points to the ERP. Through depth-wise and point-wise convolutions, a staircase structure network is adopted to hierarchically fuse feature mappings from the stage. Experimental results show that the proposed method outperforms the state-of-the-art OIQA methods.
Traffic Road Detection Based on Dynamic Anchor Frame

In recent years, deep convolution neural networks have made great progress in object detection tasks. Generally speaking, the bounding box and the type of bounding box play a very important role in object detection. However, it is not easy for convolution neural networks to directly generate disordered bounding boxes. A widely used solution is to adopt the idea of divide and conquer and introduce the concept of anchor box. At present, anchor frame mechanism has been widely used in top-level object detection framework, and has achieved good results on common datasets. The innovation of this paper is that a novel anchor frame generation method is proposed, which can generate error frames with various aspect ratios for object detection frames. Different from the previous method of generating the anchor box in a predefined way, the anchor box in this method is dynamically generated by the anchor box generator. The feature is that the anchor box generator is not fixed, but learns from anchor boxes defined by fixed rules, which means that the anchor box generator can be adapted to a variety of scenarios. In this paper, the dynamic anchor frame method is used to detect the traffic road. In addition, the weights of the anchor box generator are predicted by a small network whose inputs are predefined anchor boxes. Compared with the traditional anchor frame generation methods, the proposed anchor frame generator has the following innovations: (1) it adaptive adjusts the size and aspect ratio of the anchor frame to improve the quality of the anchor frame. (2) The adaptive IOU country value is used to balance the number of positive samples of the size target. Finally, good efficiency and results are obtained.
A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification

The lack of labeled data is the bottleneck restricting deep text classification algorithm. State-of-the-art for most existing deep text classification methods follow the two-step transfer learning paradigm: pre-training a large model on an auxiliary task, and then fine-tuning the model on a labeled data. Their shortcoming is the high cost of training. To reduce training costs as well as alleviate the need for labeled data, we present a novel simple Semi-Supervised Joint Learning (SSJL) framework for few-shot text classification that captures the rich text semantics from large user-tagged data (referred to as weakly-labeled data) with noisy labels while also learning correct category distributions in small labeled data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-labeled setting. Besides, an appropriate temperature hyper-parameter can improve model robustness under label noise. The experimental results on four real-world datasets show that our approach outperforms the other baseline methods. Moreover, SSJL significantly boosts the deep models’ performance with only 0.5% (i.e. 32 samples) of the labeled data, showing its robustness in the data sparsity scenario.