Reviewerly/exHarmony huggingface dataset:
Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network

The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature’s semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods.
Towards Frugal Unsupervised Detection of Subtle Abnormalities in Medical Imaging

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-off between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design effort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and efficient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.
A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

Introduction Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion. Methods The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance. Results Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost. Discussion Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.
Improving Students' Interest in Learning and Language Literacy through Creating Reading Corners for Fifth Grade Students at GMIT Fatumnasi Elementary School

This study discusses the efforts of teachers in managing reading corners for students fostering interest in reading for fifth grade students at GMIT Fatumnasi Elementary School. This study uses a qualitative research design, namely where the researcher plays a direct role as planner, implementation, data collection, analyzing data and at the same time reporting data. Data collection is done by observation, direct interview techniques and documentation. Researchers found that the teacher's efforts in utilizing reading corners to foster interest read students namely, (1). Encourage children to tell about what happened he read, (2). Exchange books with friends, (3). Provide interesting books read students, (4). Provide reading time, and (5). Give gifts. The obstacle in fostering students' interest in reading is the minimum classrooms, lack of variety of books or limited books and there are students who don't like to read. As for overcoming these obstacles is to make neat and beautiful reading corner decoration, suggesting that the school raise it library budget, and provide motivation and support. Results of research this means that the existence of a reading corner makes students feel happy and more active in reading.
Hybrid Model to Predict Leaf Disease Prediction Using Ensembling Machine Learning Approach

In this work, the use of machine learning to forecast instances of leaf disease is advocated. The automated leaf disease detection system used in precision agriculture makes use of camera input, computer vision processing, picture segmentation, feature extraction, and machine learning. A rapid and precise diagnosis of plant disease is made possible by an automated disease detection system for the benefit of the farmer. In order to speed up crop diagnosis, it is crucial to use an automated method for detecting plant leaf diseases.
A Study on Priority Selection Criteria of Risks for Focused Management Considering the Goal Achievement Planning Period of Business Continuity

In accordance with the standard for disaster management of the Act on assistance to the autonomous activities of enterprises for disaster mitigation, a BCMS(business continuity management system) is established to maintain the continuity of major functions and critical actiyities of enterprises. The BCMS is an approach to the management system of disaster management, and it is a process of deriving the critical actiyities of business impact analysis and the risks subject to intensive management through priority. For RA(risk assessment), the risk subject to focus management is selected through priority analysis of the probability and impact of occurrence of possible risks. However, in selecting priorities, the possibility of probabilistic occurrence of risk factors for the actual risk occurrence period is overlooked due to the selection without considering the target achievement plan period of the business continuity. Therefore, this study compares and analyzes the results of the RA currently and according to the priority of the residual impact for risk factors that may occur within the target achievement plan period, and suggests effective criteria for selecting risks for priority management.
The Effects of Flipped Learning in Core Nursing Skill Education in the COVID-19

The purpose of this study was to examine the effects of on-tact FLPE(Flipped Learning Practical Education) on SM(Satisfaction with Major), CS(Core nursing Skills), ASE(Academic Self-Efficacy), and CP(Clinical Performance). The research design was one group pre-posttest and participants were 65 fourth-year nursing students. Data were collected from 25/02 to 03/04 2022. The data were analyzed by paired t-test, pearson’s correlation coefficient method using SPSS 25.0 program. After participating in FLPE, SM (t=.3.40, p&lt;.001), CS (t=-13.93, p&lt;.001), and CP (t=-7.99, p&lt;.001) were significantly improved. There was a static correlation between SM and CS (p=.016), SM and CP (p&lt;.001), and CS and CP (p&lt;.001). The results suggested that FLPE was an effective education method to improve SM, CS and CP for nursing students during the COVID-19 pandemic period. Therefore, it is essential to actively apply FLPE with regard to nursing education.
Analysis of Job Perception of Youtuber Perceived by Adolescents

본 연구는 청소년들이 가장 많이 사용하고 꿈꾸는 유튜버라는 직업을 어떻게 인식하고 있는지 살펴보기 위해 척도를 개발하고, 초, 중학생의 직업이미지 차이를 살펴보는 것을 목적으로 한다. 이를 위해 초․중학생 12명에게 인터뷰를 실시하고, 전문가 평정 이후 최종 완성된 설문지를 초․중학교 학생 400명에게 실시하였다. 불성실한 응답을 제외하고 총 320부를 분석에 사용하였다. 연구는 예비연구와 본연구로 진행되었으며, 예비연구는 진로전문가들이 함께 초점질문을 개발하고 초,중학생 7명을 인터뷰하여 직업인으로 유튜버에 대한 아이디어 산출문을 작성하였고, 이를 토대로 예비연구와 전문가 평정과정, 요인분석, 타당도 검증을 한 결과 최종 15문항의 유튜버 직업이미지 인식척도가 완성되었다. 최종 추출된 유튜버직업 이미지는 직업가치, 직업특성, 직무환경, 진입용이성의 총 4개의 요인으로 구성되었고, 초․중학생의 성별, 현재 유튜브 사용여부, 유튜버를 직업으로 생각해본 적이 있는지, 향후 유튜버를 직업으로 고려해볼 의향이 있는지 여부에 따라 추출요인들의 평균차이가 있는지 알아보았고, 요인별 배경변인 간 차이를 확인하였다. 연구결과 직업가치요인과 직업특성요인은 유튜버를 직업으로 생각해보거나 고려하고 있는 정도의 차이에 따라 차이를 보였고, 직무환경은 초․중학생의 인식에 차이를 나타냈다. 한편 진입용이성은 유튜브 사용여부에 따라 차이를 보였다. 이러한 결과를 토대로 초기 청소년의 진로상담을 담당하는 교사 및 상담자들에게 필요한 정보제공 및 유튜버에 대한 인식의 선 이해를 가능하게 했다는데 의의가 있다.The purpose of this study is to develop a scale to examine how adolescents recognize the job of Youtubers, which they use and dream of the most, and to examine the difference in job image between elementary and middle school students. To this end, 12 elementary and middle school students were interviewed, and after expert evaluation, the completed questionnaire was administered to 400 elementary and middle school students. A total of 320 copies were used for analysis, excluding insincere responses. The study was conducted through a preliminary study and a main study. In the preliminary study, a focus question was developed with career experts and seven elementary and middle school students were interviewed to derive an idea production statement for YouTubers as professionals. After that, in the main study, the final 15 questions of YouTuber job image recognition scale were completed through the expert evaluation process, factor analysis, and validity verification. The final extracted Youtuber job image consisted of four sub-factors: job value, job characteristics, job environment, and ease of entry. It was investigated whether there was a difference in the average of extraction factors depending on whether the respondent was willing to consider YouTubers as a job, and the difference between background variables for each factor was confirmed. As a result of the study, job value factors and job characteristics factors showed differences according to the degree to which Youtubers are considered or considered as a job, and work environment showed differences in the perceptions of elementary and middle school students Subsequently, On the other hand, the ease of entry showed a difference depending on whether Youtube was used or not. Based on these results, it is meaningful that teachers and counselors who are in charge of career counseling for early adolescent have provided necessary information and made it possible to understand the perception of Youtubers in advance.
Comparative Analysis on Unreal Engine 5 VS Unity

Unreal Engine 5 and Unity are two popular game development engines widely used in the industry. This research paper presents a detailed comparative analysis of Unreal Engine 5 and Unity, aiming to provide comprehensive insights into their features, performance capabilities, and suitability for game development. Through an in-depth examination of these engines, this study highlights their similarities, differences, and factors that influence their adoption in the game development landscape.
DCA-Based Weighted Bagging: A New Ensemble Learning Approach

Ensemble learning is a highly efficient method that combines multiple machine learning models to improve the accuracy and robustness of predictions. Bagging is a popular approach of ensemble learning that involves training a group of independent classifiers on various subsets of the training data and combining their predictions using a voting or average technique. In this study, we present a new weighted bagging approach based on difference of convex functions algorithm (DCA), called BaggingDCA. The proposed algorithm combines multiple base models trained on different subsets of the training data and assigns a weight to each model based on its performance. The weights of the ensemble model are determined thanks to LS-DC, a unified approach for various loss functions in machine learning, both convex and non-convex. We evaluated our proposed algorithm on several benchmark datasets and compared its performance to existing bagging methods. We show that the proposed algorithm using both convex and nonconvex losses improves upon standard bagging, and also outperforms dynamic weighting bagging in terms of prediction accuracy.
Neural Machine Translation with Diversity-Enabled Translation Memory

Neural machine translation (NMT) using translation memory (TM) has been introduced as an emergent technique for improving machine translation systems (MTS). In this study, we propose an end-to-end NMT model with TM by exploiting the diversity of the retrieval-augmented phase using maximal marginal relevance (MMR). In particular, the proposed model is designed with monolingual TM, which is able to support low-resource scenarios. Furthermore, the memory retriever and translation models are jointly trained to improve translation performance. For the experiment, we use IWSLT15 (En $$\longleftrightarrow $$ Vi) as a benchmark dataset to evaluate the performance of the proposed method. Accordingly, the experiential results show the effectiveness of the proposed method compared with strong baselines in this research field.
NegT5: A Cross-Task Text-to-Text Framework for Negation in Question Answering

Negation is a fundamental grammatical construct that plays a crucial role in understanding QA tasks. It has been revealed that models trained with SQuAD1 still produce original responses when presented with negated sentences. To mitigate this issue, SQuAD2.0 incorporates a plethora of unanswerable questions to enable pre-trained models to distinguish negative inquiries. In this study, we assess the performance of the model on answerable and unanswerable questions that incorporate negative words and find out that the model’s performance on unanswerable negative questions surpasses the baseline. However, the model’s performance on answerable negative questions falls short of the baseline. This outcome prompts us to surmise that SQuAD2.0 includes a substantial number of unanswerable questions, but the pattern of these questions is typically limited to the addition of negative adverbs such as “never” and “not”. As a result, the trained model tends to produce “unanswerable” responses when confronted with questions that contain negative expressions. To address this issue, we propose a novel framework, called NegT5, which adopts the text-to-text multi-task fine-tuning principle introduced in T5 for making the model able to deal with negation in QA.
A Novel Question-Context Interaction Method for Machine Reading Comprehension

Machine reading comprehension (MRC) is a challenging NLP task that requires machines to model the complex interactions between questions and specific contexts. In Question-Answering (QA) tasks, most existing works rely on the powerful encoder of pre-trained language models (PrLM) in order to represent word/subword embeddings for extracting the answer. In this study, we present a novel method for enriching the context representation by exploiting the question-context interaction at the sentence level. In particular, we introduce the sentence-based question-context interaction (S-QCI) block, which combines two main layers such as the question-aware layer and the cross-sentence layer, to represent the sentence embedding of the context. The sentence information is then used to enrich question information for the context representation at the word level. The main idea is that the word units in the sentence, which have a high attention score of question-sentence interaction, can be enriched with more question information for the final output of the extractive-span MRC task. The experiment on NewsQA, a benchmark dataset in this research field, indicates that the proposed method has significant improvements compared with the baselines using PrLM and achieves new state-of-the-art results.
Deep Learning Models for Future Occupancy Prediction in Residential Buildings

This paper contributes to the occupancy prediction problem by developing state-of-the-art deep learning models. The occupancy prediction problem is addressed from two different viewpoints: multi-label classification and a sequence-to-sequence time-series analysis using encoder-decoder architectures. The following deep learning algorithms are employed in this study to construct occupancy models: multi-layer perceptron (MLP), recurrent neural networks, long-short term memory (LSTM), gated recurrent units (GRU), and bidirectional LSTMs. The performance of these models is evaluated and compared in terms of accuracy and computational speed. The results demonstrate that addressing this problem using MLP models provides the best performance for short-term predictions, while for predictions more than 90 min ahead, GRU results in the highest accuracy. It is also demonstrated that the accuracy of the deep learning models can be approximated as a function of the occupancy index with an MAE of 0.014.
A funny thing happened on the way to the Shiny Digital Future

Picture is unrelated. Seriously. I’m just allergic to posts with no visuals. Stand by for more random brachiosaurs. Here’s something I’ve been meaning to post for a while, about my changing ideas about scholarly publishing. On one hand, it’s hard to believe now that the Academic Spring was almost a decade ago.
ETL Last

What matters for application of knowledge – what substantiates it? I think two things do: shared conceptualizations, and use cases.
ISOMORPH: an efficient application on GPU for detecting graph isomorphism

Purpose The authors will review the main concepts of graphs, present the implemented algorithm, as well as explain the different techniques applied to the graph, to achieve an efficient execution of the algorithm, both in terms of the use of multiple cores that the authors have available today, and the use of massive data parallelism through the parallelization of the algorithm, bringing the graph closer to the execution through CUDA on GPUs. Design/methodology/approach In this work, the authors approach the graphs isomorphism problem, approaching this problem from a point of view very little worked during all this time, the application of parallelism and the high-performance computing (HPC) techniques to the detection of isomorphism between graphs. Findings Results obtained give compelling reasons to ensure that more in-depth studies on the HPC techniques should be applied in these fields, since gains of up to 722x speedup are achieved in the most favorable scenarios, maintaining an average performance speedup of 454x. Originality/value The paper is new and original.
Molearn: a Python package streamlining the design of generative models of biomolecular dynamics

Musson et al., (2023). Molearn: a Python package streamlining the design of generative models of biomolecular dynamics. Journal of Open Source Software, 8(89), 5523, https://doi.org/10.21105/joss.05523
Strategic planning in the sphere of education in regions

The article is devoted to problems of strategic planning in education. Special attention is paid to the formation of regional education development programs. The article considers the main provisions of the strategic program for the development of the education system at the municipal level as a key position for the formation of a unified educational space. The authors’ analysis of such programs has shown that such programs are often devoid of consistency and are not consistent with the development programs of the leading branches of knowledge and the economy of the region. Based on the legal framework, scientific articles, and the experience of the regions the authors propose a model for the formation of regional strategic planning documents in the field of education, based on the principle of a triple helix of interaction between the state, educational organizations and business. The educational space planned according to this model will differ not only in a new organizational format designed to coordinate the path of education and training, but will also serve as a consolidation of resources from all three sources.