Reviewerly/exHarmony huggingface dataset:
Satellite-Based Regionalization of Solar Irradiation in Vietnam by k-Means Clustering

Abstract This study presents the application of k -means clustering to satellite-based solar irradiation in different regions of Vietnam. The solar irradiation products derived from the Himawari-8 satellite, named AMATERASS by the solar radiation consortium under the Japan Science and Technology Agency (JST), are validated with observations recorded at five stations in the period from October 2017 to September 2018 before their use for clustering. High correlations among them enable the use of satellite-based daily global horizontal irradiation for spatial variability analysis and regionalization. With respect to the climate regime in Vietnam, the defined 6-cluster groups demonstrate better agreement with the conventionally classified seven climatic zones rather than the four climatic zones of the Köppen classification. The spatial distribution and seasonal variation in the regionalized solar irradiation reflect interchangeable influences of large-scale atmospheric circulation in terms of the East Asian winter monsoon and the South Asian summer monsoon as well as the effect of local topography. Higher daily averaged solar radiation and its weaker seasonal variation were found in two clusters in the southern region where the South Asian summer monsoon dominates in the rainy season. Pronounced seasonal variability in solar irradiation in four clusters in the northern region is associated with the influence of the East Asian monsoon, resulting in its clear reduction during the winter months.
Three‐dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre‐operative multimodal MR images

Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel three-dimensional (3D) multipath DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multimodal pre-operative MR images. We hypothesized that the multipath architecture could achieve more accurate segmentation than a singlepath architecture.Two hundred and fifty-eight GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multipath DenseNet that could be trained to achieve an end-to-end mapping from four MR images to the corresponding GBM tumor contour. A 3D singlepath DenseNet was also built for comparison. Both DenseNets were based on the encoder-decoder architecture. All four images were concatenated and fed into a single encoder path in the singlepath DenseNet, while each input image had its own encoder path in the multipath DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95% ). Wilcoxon signed-rank tests were conducted to assess statistical significances.The singlepath DenseNet achieved the DSC of 0.911 ± 0.060, ASD of 1.3 ± 0.7 mm, and HD95% of 5.2 ± 7.1 mm, while the multipath DenseNet achieved the DSC of 0.922 ± 0.041, ASD of 1.1 ± 0.5 mm, and HD95% of 3.9 ± 3.3 mm. The P-values of all Wilcoxon signed-rank tests were less than 0.05.Both DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multimodal MR images. The multipath DenseNet achieved more accurate tumor segmentation than the singlepath DenseNet. Here presented the 3D multipath DenseNet that demonstrated an improved accuracy over comparable algorithms in the clinical task of GBM tumor segmentation.
RETRACTED: Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.
Characterizing signal propagation to close the performance gap in unnormalized ResNets

Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training examples within a batch, can incur compute and memory overhead, and often results in unexpected bugs. Building on recent theoretical analyses of deep ResNets at initialization, we propose a simple set of analysis tools to characterize signal propagation on the forward pass, and leverage these tools to design highly performant ResNets without activation normalization layers. Crucial to our success is an adapted version of the recently proposed Weight Standardization. Our analysis tools show how this technique preserves the signal in ReLU networks by ensuring that the per-channel activation means do not grow with depth. Across a range of FLOP budgets, our networks attain performance competitive with state-of-the-art EfficientNets on ImageNet.
Coronavirus infections: health care planning based on Orem's Nursing Theory

to report the experience of professors and students of a graduate course on nursing care in coping with the new coronavirus (COVID-19) based on Self-Care Theory.the active methodologies used were a literature search and seminar presentations, with an understanding of Orem's theoretical concepts: health; man; self-care; universal, developmental and health deviation requirements; self-care activities; self-care deficits; the required therapeutic demand; nursing systems. The pandemic was considered a health deviation that requires critical thinking and nursing care planning. Methodological frameworks to classify nursing diagnoses, interventions, and outcomes were used.for each health deviation, nursing systems were identified; self-care deficits, diagnoses; actions, interventions; and the form of assessment, outcomes.theoretical-practical reflections of the academic context support nursing care planning.
Detection-Aware Trajectory Generation for a Drone Cinematographer

This work investigates an efficient trajectory generation for chasing a dynamic target, which incorporates the detectability objective. The proposed method actively guides the motion of a cinematographer drone so that the color of a target is well-distinguished against the colors of the background in the view of the drone. For the objective, we define a measure of color detectability given a chasing path. After computing a discrete path optimized for the metric, we generate a dynamically feasible trajectory. The whole pipeline can be updated on-the- fly to respond to the motion of the target. For the efficient discrete path generation, we construct a directed acyclic graph (DAG) for which a topological sorting can be determined analytically without the depth-first search. The smooth path is obtained in quadratic programming (QP) framework. We validate the enhanced performance of state-of-the-art object detection and tracking algorithms when the camera drone executes the trajectory obtained from the proposed method.
Designing supply chain models with blockchain technology in the fishing industry in Indonesia

Abstract The fishing industry contributes to the national economy and has a high risk of being traded because it has a long shipping time, limited time to consume, and the amount traded varies depending on the season. This risk can reduce the potential profit of players in the fishing industry. To reduce this risk and maximize added value, blockchain technology is used to change the supply chain of the fishing industry. Blockchain technology is a ledger database that is distributed to record transactions among parties that can be verified and permanently. This paper proposes a blockchain technology framework in the fishing industry. This paper will explain the definition and benefits of blockchain, current applications, and design a new model of the supply chain. The potential benefits of the blockchain are transparency and traceability, reducing costs and time, and preventing illegal or unethical fishery products from entering the supply chain. Moreover, this paper will also explore how to build this blockchain technology in the supply chain.
A medical decision support system for predicting the severity level of COVID-19

The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms' impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.
Research data services from the perspective of academic librarians

Purpose This study aims to investigate the perceptions of academic librarians regarding research data services (RDS) in academic library environments. This study also examines a range of challenges in RDS from the perspectives of academic librarians. Design/methodology/approach A nationwide online survey was administered to academic librarians engaged in data services at research universities around the USA. The collected survey responses were analyzed quantitatively using descriptive statistics, hierarchical clustering and multidimensional scaling. Findings Academic librarians perceived that consultation services would be more valuable to users than technical services in offering RDS. Accordingly, skills associated with consultation services such as instructional skills and data management planning were perceived by participants to be more important. The results revealed that academic libraries would need to seek collaboration opportunities with other units on campus to develop and offer RDS, especially technical services. Originality/value This study contributes to the existing body of research on the topic of data services in research universities. The study investigated various types of specific professional competencies and used clustering analysis to identify closely associated groups of service types. In addition, this study comprehensively examined both relevant resources for and barriers to RDS.
Improving the discretely modulated underwater continuous-variable quantum key distribution with heralded hybrid linear amplifier

Abstract The current investigation on continuous-variable quantum key distribution (CV-QKD) is mainly based on the optical fibre or the free space atmosphere channel, while the ocean, which covers most of the Earth, has not been well utilized. In this paper, we analyze the influence of depth and wavelength on the attenuation effect of seawater on light by using a model based on the concentration of chlorophyll-a, and propose an improved four-state underwater CV-QKD protocol with a heralded hybrid linear amplifier (HLA). Both homodyne detection with a phase-sensitive amplifier and heterodyne detection with a phase-insensitive amplifier are considered. Simulation results show that the introduction of the heralded HLA can effectively extend the secure transmission distance of the key. Besides, when the transmission distance is relatively long, the secret key rate of the protocol is higher than that of the protocol without HLA.
Topic-aware Neural Linguistic Steganography Based on Knowledge Graphs

The core challenge of steganography is always how to improve the hidden capacity and the concealment. Most current generation-based linguistic steganography methods only consider the probability distribution between text characters, and the emotion and topic of the generated steganographic text are uncontrollable. Especially for long texts, generating several sentences related to a topic and displaying overall coherence and discourse-relatedness can ensure better concealment. In this article, we address the problem of generating coherent multi-sentence texts for better concealment, and a topic-aware neural linguistic steganography method that can generate a steganographic paragraph with a specific topic is present. We achieve a topic-controllable steganographic long text generation by encoding the related entities and their relationships from Knowledge Graphs. Experimental results illustrate that the proposed method can guarantee both the quality of the generated steganographic text and its relevance to a specific topic. The proposed model can be widely used in covert communication, privacy protection, and many other areas of information security.
Application of a Contextual Approach in Indonesian Language Learning to Improve the Ability to Write Poetry

nProblem Background Learning to write free poetry in grade V SDN is very low. Students' competence in writing, especially in writing free poetry, can be improved if they use appropriate methods, techniques and learning approaches in learning to write free poetry. The research objectives that the researchers achieved through this study wanted to know: 1) Describe the ability to write free poetry of fifthn of SDN before learning Indonesian using a contextual approach. 2) Describe student learning outcomes in learning to write free poetry after participating in learning by applying a contextual approach. 3) To describe the response of student interest in learning to write free poetry using a contextual approach. 4) Knowing the obstacles faced by the teacher in learning to write free poetry. This study uses a classroom action research method . The choice of this method is based on the writer's desire to increase the student's ability to write free poetry. This study consists of four components,n planning, action, observation, and reflection. The results of the research and discussion are as follows based on the data analysis of the results of the research on the free poetry writing skills of fifth grade elementary school students in the pre-test and in the final test, it is seen that there is an increase in learning outcomes and student interest responses using a contextual approach. Conclusion, the application of a contextual approach to learning Indonesian to write free poetry in fifth grade elementary school students is very good and useful
The Use of Collaborative Reading Strategies in Fiction Reading Learning

This classroom action research is motivated by the existence of problems in learning Indonesian in elementary schools, especially in grade V. Learning Indonesian in elementary schools is still not good, and the learning tends to be conventional. This research was conducted to determine and increase the activity and ability of students to read in a better direction. The data collection technique was carried out using instruments: observation sheets, interviews, and field notes, test sheets, and documentation. Data analysis was carried out using qualitative and quantitative approaches. From the resulting data, it shows an increase in reading comprehension activity of fiction. The increase is calculated from the average difference between cycle I and cycle III. To be more detailed, in cycle I the assessment of reading and cycle activities increased by 21%, and in cycle II and cycle III it increased by 9%. Meanwhile, from the final evaluation, there was an increase in the average score of students in cycle and cycle II getting an average -The average was 9%, and in cycle II and cycle III students experienced an increase of 12%. From the descriptive analysis it was found that there was a change in reading learning activities. Learning activities became more active, creative, and fun. Thus, from the two data analyzes shows that the Shared Reading strategy has a positive influence on, namely, it can increase reading activity and reading comprehension skills.
The Application of Paired Story Type Learning Models to Improve Reading Ability in Indonesian Lessons

Creating learning activities that are active, innovative, creative, effective, and fun is an obligation that must be done by an educator. At public elementary schools, teachers as educators still use conventional methods in learning activities. This fact is a problem that must be solved. Because this will have an impact on the low value of learning outcomes. Therefore, the researcher made a new idea to use the paired story type learning method in carrying out Indonesian language learning activities. The method used in this research is classroom action research. Researchers used 3 cycles as a comparison of the results of the study. In the activity cycle 1 the average student learning outcomes showed a value of 60. This score is still not satisfactory because only a few students have achieved the KKM score. In cycle 2 the average learning outcomes showed an increase of 75.56. The average value of cycle 2 is better than the learning activities of cycle 1. Whereas in the learning activities of cycle 3 the value of student learning outcomes has increased quite satisfactorily, where the student scores show an average of 89.63 and all students have been able to achieve the KKM score. Seeing the results of this study, the authors conclude that Indonesian language learning activities will be more appropriate if the teacher uses the pair story method. This is evidenced by the results of the research which are felt to be quite good in overcoming the problems that exist in Indonesian language learners.
Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.
Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning

Social media is a vital source to produce textual data, further utilized in various research fields. It has been considered an essential foundation for organizations to get valuable data to assess the users’ thoughts and opinions on a specific topic. Text classification is a procedure to assign tags to predefined classes automatically based on their contents. The aspect-based sentiment analysis to classify the text is challenging. Every work related to sentiment analysis approached this issue as the current research usually discusses the document-level and overall sentence-level analysis rather than the particularities of the sentiments. This research aims to use Twitter data to perform a finer-grained sentiment analysis at aspect-level by considering explicit and implicit aspects. This study proposes a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach by embedding a feature ranking process with an amendment of feature selection method for Twitter and sentiment classification comprising of Artificial Neural Network; Multi-Layer Perceptron (MLP) is used to attain improved results. In this study, different machine learning classification methods were also implemented, including Random Forest (RF), Support Vector Classifier (SVC), and seven more classifiers to compare with the proposed classification method. The implementation of the proposed hybrid method has shown better performance and the efficiency of the proposed system was validated on multiple Twitter datasets to manifest different domains. We achieved better results for all Twitter datasets used for the validation purpose of the proposed method with an accuracy of 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%, respectively, compared to the baseline approaches. The proposed approach revealed that the new hybrid aspect-based text classification functionality is enhanced, and it outperformed the existing baseline methods for sentiment classification.