Reviewerly/exHarmony huggingface dataset:
Evaluation of Duhat (Syzygium cumini) Leaves as Cytological Stain

Context: Staining is a vital histology process and synthetic stains, notably Hematoxylin and Eosin, are widely used in identifying tissues in the Histopathology section. Despite their remarkable efficiency in staining, these synthetic dyes pose a health hazard to laboratory workers, especially with frequent exposure. Hence, creating an alternative natural stain is imperative and has become of great interest in the research field. Objectives: This study aimed to develop a natural stain from the leaf extract of Syzigium cumini. Furthermore, to determine if the extract can be an alternative to Eosin as a stain for cytological specimens like urine and buccal samples, along with efficiency in staining, the study intends to determine the stain’s viability when stored over time at different storage conditions. Data sources: The ethics committee approved the research before starting and followed specific protocols for the participants’ data and safety. 180 slides were prepared from the participants’ buccal and urine specimens collected. The slides were then stained using the stain produced from the S. cumini extract and then viewed and graded under the microscope by four registered Medical Technologists. Results: The results demonstrated that distilled water and acetone, leaves extract, and acetone extract showed no significant difference compared to Eosin’s staining efficiency. On the other hand, distilled water leaf extract can also stain; however, it is inferior to Eosin.
Networking, For Skeptics

(in the spirit of Dale Carnegie and post-rat etiquette guides) Scientists, engineers, and other technical people often make fun of networking.
Automated calibration of consensus weighted distance-based clustering approaches using sharp

Abstract Motivation In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms. Results We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score compared to existing calibration scores and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes. Availability and implementation The R package sharp (version ≥1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.
OpenCitations needs you: support the change in research practices

In OpenCitations, we like to define our infrastructure organization as “community-based” and “community-driven”, and we really mean it. The support coming from the number of academic libraries and consortia coming after OpenCitations’ involvement in the 2nd SCOSS funding cycle has made it possible, starting from 2020, to make OpenCitations develop from a small university project based on time-limited grant incomes to being an
FAIR Data Digest #19

on a fast Python clustering algorithm for bibliographic data and a talk about Belgian journalism at the DH virtual discussion group
Development of Innovative Approaches to the Popularization of Science and Technology for the Development of Interaction Between Science and Society

The purpose of the study is to develop a concept of a virtual space for the promotion and popularization of science and technologies for communication between scientists, society and state; promoting the inclusive development of science and technology in Ukraine; the formation of a scientific worldview and the innovative culture of society. Research methods: methods of analysis, synthesis and comparison; hermeneutic, contextual, sociological methods; systematic, transdisciplinary, prognostic-analytical, historical approaches. Results. Among benefits of the study there is the expected outcome of the research: to formulate proposals for the development of scientific communications in society, dialogue and cooperation of scientists and all age and professional strata of population; the formation of an innovative culture, critical thinking and a scientific worldview; increasing the attractiveness of the profession of a scientist, the social status of a scientist. Conclusions. The authors of the article propose the measures which should be implemented that in order to contribute to the high-quality modern training of future specialists, oriented to participate in the innovative reconstruction of Ukraine based on the latest achievements of science and high technologies, and will ensure a worthy replenishment of the personnel potential of Ukrainian science.
Faktor Penyebab Rendahnya Motivasi Belajar Matematika Siswa Kelas IV Sekolah Dasar Negeri 12 Andaleh Baruh Bukit

The low motivation to learn mathematics students is a problem that must be solved. This study aims to describe the factors causing the low motivation to learn mathematics for grade IV students of SDN 12 Andaleh Baruh Bukit. Researchers use qualitative descriptive research designs with case studies. The informants of this study are teachers and students of grade IV. Samples were selected by purposive sampling technique. The instrument of this study is the researcher himself, in the implementation assisted by interview guidelines, observations, and recording devices. Data collection techniques use interviews, observations, and documentation. Data analysis uses a Miles and Huberman model consisting of data reduction, data display, and inference. The validity of the data is carried out by triangulation techniques. The results of this study show that the factors causing the low motivation to learn students in the mathematics learning process in grade IV are: 1) students' ability to memorize multiplication and most students who forget multiplication of 10 students; and 2) teachers as educators use special techniques to raise enthusiasm for learning. The efforts made by grade IV teachers to increase student learning motivation are to use finger techniques.
On Using a Microearthquake Recognition System for an Early Warning System at Cotopaxi Volcano

Volcanic activity has been increasing throughout the world, posing a significant threat to populations in the event of eruptions. Ecuador, which hosts several active volcanoes, requires robust methods for identifying potential eruptions and issuing reliable alerts to protect lives and minimize damages. This paper presents the development of an automatic microseism recognition system integrated with the Early Warning Broadcast System (EWBS). Using models such as k-Nearest Neighbors, Support Vector Machine, and Decision Trees, along with frequency-based features extracted from seismic data provided by the Instituto Geofísico de la Escuela Politécnica Nacional, the recognition system aims to accurately detect and classify microeartquakes associated with the Cotopaxi volcano during 2012. During the detection stage, the system achieves an impressive Balanced Error Rate (BER) of 0.01, indicating its effectiveness in identifying events. In the subsequent classification stage, the system achieves a BER of 0.11, demonstrating its ability to classify events accurately. The classifiers were further evaluated using 82 microearthquakes, comprising 41 LP events and 41 VT events, resulting in an accuracy of 85% and a BER of 0.15. In addition, a larger data set of 563 earthquakes, consisting of 522 LP events and 41 VT events, was used to assess the performance of the classifiers. The results showed a 7% increase in accuracy compared to the previous test, demonstrating improved performance. However, 11 earthquakes were still misclassified. Integration of these classifiers with a voting system improves their performance. The selected set of 50 features plays a crucial role in achieving accurate results. The recognition system seamlessly interfaces with the EWBS, ensuring a 30 s delay before launching an early warning. This delay provides valuable time for preparation and response measures. In conclusion, the developed microearthquake recognition system, combined with the EWBS, demonstrates its effectiveness in detecting and classifying events, thereby enhancing the ability to issue timely and reliable alerts for volcanic activity. The findings contribute to improved volcano monitoring and risk mitigation strategies.
Learning Spatio-Temporal Sharpness Map for Video Deblurring

Video deblurring is a challenging task because only input blurry sequences are available. To further constrain the optimization process, existing methods explore various additional information, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., events, depth and sharpness prior. However, they consume large computing costs or generate unpleasant visual results due to the insufficient exploitation of spatio-temporal information. In this work, we develop a novel spatio-temporal sharpness map learned by a prior-based generation network implicitly. The proposed generation network blends both spatial and temporal sharpness priors in a blurry sequence, while few extra parameters are added. We show that the proposed map has better spatial continuity and guidance for video deblurring than the previous method. Furthermore, different from the simply concatenation in the previous work, we allow the sharpness map to accommodate to more effective video deblurring via a dual-stream network. Specifically, the network is decomposed by two branches, namely inter-frame and intra-frame reconstructions. The inter-frame reconstruction obtains the sharp patches of cecutive frames from the sharpness map to restore textures well. Meanwhile, the other intra-frame branch is responsible for recovering structures of the latent frame, where a novel histogram statistical method is developed to quantify and count textures in the feature under the modulation of the sharpness map. Quantitative and qualitative experiments successfully validate the effectiveness of our proposed method.
Knowledge Graph Alignment Under Scarce Supervision: A General Framework With Active Cross-View Contrastive Learning

Over recent years, a number of knowledge graphs (KGs) have emerged. Nevertheless, a KG can never reach full completeness. A viable approach to increase the coverage of a KG is KG alignment (KGA). The majority of previous efforts merely focus on the matching between entities, while largely neglect relations. Besides, they heavily rely on labeled data, which are difficult to obtain in practice. To address these issues, in this work, we put forward a general framework to simultaneously align entities and relations under scarce supervision. Our proposal consists of two main components, relation-enhanced active instance selection (RAS), and cross-view contrastive learning (CCL). RAS aims to select the most valuable instances to be labeled with the guidance of relations, while CCL contrasts cross-view representations to augment scarce supervision signals. Our proposal is agnostic to the underlying entity and relation alignment models, and can be used to improve their performance under limited supervision. We conduct experiments on a wide range of popular KG pairs, and the results demonstrate that our proposed model and its components can consistently boost the alignment performance under scarce supervision.
Application Research of Collaborative Filtering Algorithm in Catering Recommendation System

Collaborative filtering algorithm is commonly used in recommendation systems. Based on the traditional collaborative filtering algorithm, this paper proposes an improvement scheme. When calculating similarity, an improved cosine similarity formula is first used, followed by adding time threshold parameters for weighting. Furthermore, the similarity results are re-weighted based on the number of common scoring items among users, and finally normalized.
DEVELOPMENT OF INFORMATION MANAGEMENT SYSTEM FOR AKADEMI KOMUNITAS PRESIDENT

&lt;p&gt;The Akademi Komunitas President is an educational&lt;br /&gt;institution under the auspices of the president university, the&lt;br /&gt;President Community Academy provides education with diplomas&lt;br /&gt;2 and 3 and there are also LPK programs or job training&lt;br /&gt;institutions for people outside the president Community Academy&lt;br /&gt;who wish to receive job training courses. However, the President's&lt;br /&gt;Community Academy does not have an information management&lt;br /&gt;system that supports administrative, academic and information&lt;br /&gt;sharing activities, this causes the workload to be high and work&lt;br /&gt;efficiency to be low. To solve this problem, I propose this final&lt;br /&gt;project to create an information management system for the&lt;br /&gt;President's Community Academy.&lt;/p&gt;
A Blockchain Model for Sharing Information in Criminal Justice Systems

Criminal justice systems around the world encounter missing case dockets and digital evidence. Problems are also posed by the mechanisms used to share criminal case data, especially email and paper documents that provide exposure to illegal data alteration. This chapter describes a blockchain model for sharing criminal case data securely and efficiently with authorized criminal justice system entities. The model is implemented using Hyperledger Fabric and promising results were obtained during the simulation experiments. The model enables entities to access criminal case data in real time, which helps speed up the delivery of justice. Moreover, the model improves collaboration among the various entities, especially when it comes to joint operations and investigations involving law enforcement and prosecutors. The model also stores credible evidence because the underlying data is immutable and cannot be deleted.
Creation of a SaaS-System for Image Analysis in Agriculture Using Artificial Intelligence Methods

In various branches of agricultural production, including agriculture, some problems require the use of an intellectual approach. The authors develop a classification neural network, develop the server part of the application, as well as develop the user part of the application. The initial data set consists of 250 images, which is insufficient for high-quality training of a neural network. The main result of the created service is the ability, within the framework of the website, to receive an image that represents the original snapshot of the field and the layer mask superimposed on it. The use of the ANN classification type has shown that neural networks can reliably solve the problems of reclamation recognition of the state of agricultural fields. Advanced information technologies allow transferring resource-intensive calculations to the computing cloud. Computing in the cloud will help more convenient scaling of computing power when changing the formulation of the problem of intelligent classification. It is economically feasible to monetize a cloud service as a SaaS system.
Automatic correction of performance drift under acquisition shift in medical image classification

Abstract Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.
Exploratory Data Analysis of the Groundwater Contaminants at the Former Panna Maria Uranium Recovery Facility

The former Panna Maria mill was a uranium recovery facility that operated from 1979 to December 1992. Sulfuric acid leach was used to process several tons of ore daily. Therefore, the groundwater was sampled at the site to monitor the concentration of background chemical pollutants. The purpose of this study was to perform exploratory data analysis to evaluate whether the arsenic, nickel, selenium, sulfate, and uranium concentrations were in accord with state-regulated groundwater drinking standards at the site. To accomplish this, a substitution method was used to replace concentration values that were below measurable ranges of well monitoring units. Additionally, random forest regression was employed to account for missing not-at-random values in the dataset. Groundwater samples collected in wells before the facility began operations were used to determine an upper bound on background pollutants consistent with US Environmental Protection Agency Standards. The upper tolerance limit comprising 90% of the groundwater sample at a confidence coefficient of 95% was used to establish alternate concentration limits for each chemical based on well data from the National Uranium Resource Hydrogeochemical and Stream Sediment Reconnaissance program. Results obtained in this analysis establish a baseline on the chemical concentrations in the background groundwater at the former Panna Maria mill.
Online Sparse Super-Resolution Method for Radar Forward-Looking Imaging Using Majorize-Minimization

Recently, super-resolution techniques have been widely used in real aperture radar super-resolution imaging. And the majorize-minimization(MM) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this paper, we strive to alleviate this problem, deriving an online MM algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The proposed method is a regularized extension of the current MM implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current MM algorithm. Our experimental assessment, conducted using simulated data, demonstrates the advantage of the online MM(OMM) algorithm in the task of sparse reconstruction for scanning radar.
Aligned Feature for Vector-Based Rotated Object Detection

Angle-based methods have become mainstream in rotated object detection, while the vector-based method has shown advantages in solving angular periodicity. However, the vector-based method uses basic CenterNet structure, where the feature misalignment and top-feature weakening problem exist, limiting the detection performance. In this paper, we explore the structure of vector-based method and integrate feature aggregation and feature alignment into the detector, promoting final detection performance. To be specific, Semantic Feedback Feature Pyramid Network (SFFPN) and Attention-based Deformable Convolution Network (ADCN) are designed accordingly, and these two parts of sub-networks are finely embedded in the detector. We hope that our discovery and designs can make vector-based a common rotation detection method.
Research of Data Mining C4.5 Algorithm in Construction Engineering Costing

The article first introduces the principle of the C4.5 algorithm in detail and introduces it to the construction cost industry. Based on the C4.5 algorithm, it models and analyzes the bill of quantities cost of a case construction project, extracts the classification rules, and then randomly selects cases to verify the universal applicability of the classification rules. The results show that the classification rule is universally applicable, which can help practitioners of engineering cost to make rapid predictions and improve the efficiency of decision-making analysis.
Applying the sidekick principles to the prototyping of human-machine teaming in collection planning

Collection planning for multi-domain operations is a complex task that is heavily manual, resource-intensive, and sub-optimally setup for collaboration. To solve these gaps and leverage advances in Artificial Intelligence and Machine Learning, which suggest that those are resolvable, our team employed the sidekick principles for human-machine teaming to design and prototype COGSWORTH, a system for Collection Optimization and Generation Synchronized with Optempo Reasoning Tailored to Humans. COGSWORTH is envisioned as a cognitive assistant to help collection planners. We report here on (1) our application of the sidekick principles to the prototyping of COGSWORTH, (2) how the sidekick principles are tangibly reflected in prototype artifacts, and (3) lessons learned and practical takeaways that may inform other researchers and practitioners in the human-machine teaming field.
A Hybrid Named Entity Recognition System for Aviation Text

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that aims to identify and categorize named entities in text. While NER has been well-studied in various domains, it remains a challenging task in new domains where annotated data is limited. In this paper, we propose an NER system for the aviation domain that addresses this challenge. Our system combines rule-based and supervised methods to develop a model with little to no manual annotation work.We evaluate our system on a benchmark dataset and it outperforms baseline scores and achieves competitive results. To the best of our knowledge, this is the first study to develop an NER system that specifically targets aviation entities. Our findings highlight the potential of our proposed system for NER in aviation and pave the way for future research in this area.