Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

“We Don’t Have Those American Problems”: Anti-Black Practices in Canada’s Rap Music Marketplace, 1985–2020

Beginning in the early to mid 1980s, Hip Hop culture appeared on Canadian stages and in homes, even as it was limited in supply on commercial radio and television. Unlike their American counterparts, mainstream Canadian emcees (many of whom were racialized as Black and identified with the city of Toronto) were notably dependent upon personal finances, under-resourced independent record labels, distribution deals, and state and not-for-profit grant monies to subsidize the conceptualization, production, and promotion of their art. Labelled “urban music” in an attempt to spatialize and covertly reference Blackness, Hip Hop in Canada, from the outset, was mapped against, in conflict with, and outside of the national imaginary. While building local scenes, an independent label system, and a cross-Canada college radio, television, and live music infrastructure and audience, Hip Hop artists developed spaces of resistance, circumvented industry-generated obstacles, and defined success on their own terms — all of which suggested that they were not solely at the will of the dominant white music industry. And yet artists simultaneously encountered anti-Black practices that constrained the creation and sustenance of a nationwide Hip Hop infrastructure and denoted an inequitable structuring of support for the arts in Canada. By examining the interface of Blackness, art, and the racial economy of Canada’s creative industries, this article will outline instances of Canada’s anti-Black racism as well as the challenges Hip Hop artists and industry professionals have faced in the areas of recording and label relations, music sales, broadcasting regulations, and the accolade system. These social relations — many of which are rooted in longer histories of race relations and anti-Blackness in Canada — resulted in industry-wide policies, practices, norms, and ideologies that unfairly disadvantaged Black artists and undermined the realization and marketplace potential of a Hip Hop infrastructure within and beyond Canada.

Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.

Teaching Decision-Making Based on Online Learning Big Data of Tobacco Courses: The Perspective of Student Portraits

The high-quality development and technological upgrading of the tobacco industry put forward higher requirements for the overall quality of talents. In the context of the increasing popularity of blended teaching, in order to help teachers, major in tobacco, tomake better teaching decisions in the teaching process, guide college students majoring in tobacco to better complete their studies and provide timely warnings for students’ unhealthy conditions, this article proposes a method to assist teachers in teaching decision-making based on student portraits constructed based on online learning big data. First, collect basic student information and student learning information from the online learning platform. Secondly, preprocess of the data, delete data and normalize dense data. Then, collect and classify student information to form a portrait of basic student information, a portrait of learning achievements, a portrait of learning active level and a portrait of learning status. Analyze the portrait to guide and assist students in their learning and to give early warning of bad learning conditions. At last, analyze the student portraits according to different rules and put forward corresponding suggestions according to the characteristics of different groups of college students. According to the learning situation of learners majoring in tobacco, the article constructs the student portrait label system and portrait model. According to the constructed student portrait, it puts forward learning suggestions for individual students and student groups respectively. In the field of tobacco teaching, it has certain reference significance and application value in providing decision-making reference for differentiated and individualized teaching and assisting teaching decision-making.

Discovering Known Biodiversity: Digital accessible knowledge — Getting the community involved

Plazi is a non-profit organization focused on the liberation of data from taxonomic publications. As one of Plazi’s goals of promoting the accessibility of taxonomic data, our team has developed different ways of getting the outside community involved. The Plazi community on GitHub encourages the scientific community and other contributors to post GGI-related (Golden Gate Imagine document editor) questions, requirements, ideas, and/or suggestions, including bug reports and feature requests. One can contact us via this GitHub community by creating either an Issue (to report problems on our data or related systems) or a Discussion (to post questions, ideas, or suggestions). We use Github's built-in label system to actively curate the content posted in this repository in order to facilitate further interaction, including filtering and searching before creating new entries. In the plazi/community repository, there is a Q&A (question & answer) section with selected questions and answers that might help solving the encountered problems. Aiming at increasing external participation in the task of liberating taxonomic data, we are developing training courses with independent learning modules that can be combined in different ways to target different audiences (e.g., undergraduates, researchers, developers) in various formats. This material will include text, print-screens, slides, screencasts, and, eventually to a minor extent, online teaching. Each topic within a module will have one or more ‘inline tests', which will be HTML form-based with hard-coded answers to directly assess progress regarding the subject being covered in that particular topic. At the end of each module, we will have a capstone (form-based test asking questions about the topics covered in the respective module) which the user can access whenever needed. As examples of our independent learning modules we can cite Modules I, II and III and their respective topics. Module I (Biodiversity Taxonomy Basis) includes introductory topics (e.g., Topic I — Why do we classify living things; Topic II — Linnaean binomial; Topic III — How is taxonomic information displayed in the literature) aimed at those who don't have a biology/taxonomy background. Module II (The Plazi way) topics (Topic I — Plazi mission; Topic II — Taxomic treatments; Topic III — FAIR taxonomic treatments) are designed in a way that course takers can learn about Plazi processes. Module III (The Golden Gate Imagine) includes topics (Topic I — Introduction to GGI; Topic II — Other User Interface-based alternatives to annotate documents) about the document editor for marking up documents in XML. Other modules include subjects such as individual extractions, material and treatment citations, data quality control, and others. On completion of a module, the user will be awarded a certificate. The combination of these certificates will grant badges that will translate into server permissions that will allow the user to upload new liberated taxonomic treatments and edit treatments already in the system, for instance. Taxonomic treaments are any piece of information about a given taxon concept that involves, includes, or results from an interpretation of the concept of that given taxon. Additionally, Plazi TreatmentBank APIs (Application Programming Interface) are currently being expanded and redesigned and the documentation for these long-waited endpoints will be displayed, for the first time, in this talk.

Using Artificial Intelligence Reading Label System in Diabetic Retinopathy Grading Training of Junior Ophthalmology Residents and Medical Students

Purpose Evaluate the efficiency of using artificial intelligence reading label system in diabetic retinopathy grading training of junior ophthalmology resident doctors and medical students. Methods Loading 520 diabetic retinopathy patients’ color fundus images in the artificial intelligence reading label system. 13 participants (including 6 junior ophthalmology residents and 7 medical students) read the images randomly for 8 rounds. They evaluated the grading of images and labeled the typical lesions. The sensitivity, specificity and kappa score were determined by comparison with the participants’ results and expert golden standards. Results Through 8 round reading, average kappa score was elevated from 0.67 to 0.81. Average kappa score of round 1 to 4 was 0.77, and average kappa score of round 5 to 8 was 0.81. The participant was divided into two groups. Participants in group 1 were junior ophthalmology resident students and participants in group 2 were medical doctors. Average kappa score of group 1 was elevated from 0.71 to 0.76. Average kappa score of group 2 was elevated from 0.63 to 0.84. Conclusion The artificial intelligence reading label system was a useful tool in training resident doctors and medical students in doing diabetic retinopathy grading.

Application of SNAP-Tag in Expansion Super-Resolution Microscopy Using DNA Oligostrands

Expansion super-resolution technology is a new technology developed in recent years. It anchors the dye on the hydrogel and the dye expands with the expansion of the hydrogel so that a super-resolution map can be obtained under an ordinary microscope. However, by labeling the target protein with a first antibody and secondary antibody, the distance between the fluorescent group and the actual target protein is greatly increased. Although fluorescent proteins can also be used for expansion super-resolution to reduce this effect, the fluorescent protein is often destroyed during sample preparation. To solve this problem, we developed a novel label system for expansion microscopy, based on a DNA oligostrand linked with a fluorescent dye, acrylamide group (linker), and benzoylguanine (BG, a small substrate molecule for SNAP-tag). This protocol greatly reduced the error between the position of fluorescent group and the actual target protein, and also reduced loss of the fluorescent group during sample preparation.