Tailings Settlement Velocity Identification Based on Unsupervised Learning

In order to reasonably and accurately acquire the settlement interface and velocity of tailings, an identification model of tailing settlement velocity, based on gray images of the settlement process and unsupervised learning, is constructed. Unsupervised learning is used to classify stabilized tailing mortar, and the gray value range of overflow water is determined. Through the identification of overflow water in the settlement process, the interface can be determined, and the settlement velocity of tailings can be calculated. Taking the tailings from a copper mine as an example, the identification of tailings settling velocity was determined. The results show that the identification model of tailing settlement speed based on unsupervised learning can identify the settlement interface, which cannot be manually determined in the initial stage of settlement, effectively avoiding the subjectivity and randomness of manual identification, and provide a more scientific and accurate judgment. For interfaces that can be manually recognized, the model has high recognition accuracy, has a rapid and efficient recognition process, and the relative error can be controlled within 3%. It can be used as a new technology for measuring the settling velocity of tailings.

Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial Network

Activity recognition is a fundamental and crucial task in computer vision. Impressive results have been achieved for activity recognition in high-resolution videos, but for extreme low-resolution videos, which capture the action information at a distance and are vital for preserving privacy, the performance of activity recognition algorithms is far from satisfactory. The reason is that extreme low-resolution (e.g., 12 × 16 pixels) images lack adequate scene and appearance information, which is needed for efficient recognition. To address this problem, we propose a super-resolution-driven generative adversarial network for activity recognition. To fully take advantage of the latent information in low-resolution images, a powerful network module is employed to super-resolve the extremely low-resolution images with a large scale factor. Then, a general activity recognition network is applied to analyze the super-resolved video clips. Extensive experiments on two public benchmarks were conducted to evaluate the effectiveness of our proposed method. The results demonstrate that our method outperforms several state-of-the-art low-resolution activity recognition approaches.

Geo-Landmark Recognition and Detection

The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural Networks (CNN) based efficient recognition system that serves in navigation, to organize photo collections, identify fake reports and unlabeled landmarks from historical data. It identifies landmarks correctly from a variety of images taken at different viewpoints as well as distances. An appropriate CNN architecture helps to provide the best solution for the currently selected dataset.

A single m6A modification in U6 snRNA diversifies exon sequence at the 5’ splice site

N6-methyladenosine (m6A) is a modification that plays pivotal roles in RNA metabolism and function, although its functions in spliceosomal U6 snRNA remain unknown. To elucidate its role, we conduct a large-scale transcriptome analysis of a Schizosaccharomyces pombe strain lacking this modification and found a global change of pre-mRNA splicing. The most significantly impacted introns are enriched for adenosine at the fourth position pairing the m6A in U6 snRNA, and exon sequences weakly recognized by U5 snRNA. This suggests cooperative recognition of 5’ splice site by U6 and U5 snRNPs, and also a role of m6A facilitating efficient recognition of the splice sites weakly interacting with U5 snRNA, indicating that U6 snRNA m6A relaxes the 5’ exon constraint and allows protein sequence diversity along with explosively increasing number of introns over the course of eukaryotic evolution.

45 Easier Recognition and Better Communication of The Surgical Team Across the Hospital

Introduction Recognition & communication of a surgical team is of paramount significance especially in catastrophic scenarios- like a crowded trauma call in ED, and ward rounds as surgical patients are dispersed across the hospital. Hence implementing posters across all key wards and special ID could play a key role. Method Aim was to improve the recognition & communication of the surgical team by means of posters of surgical members(Trainees/ Trust Grades/ Associates)including their name, designation & bleep; and color coded ID/Lanyard for the surgical team. An online survey was conducted for the surgical team(N = 25) and separately for the nursing staff(N = 40) across the hospital to understand how well the surgical team is recognised across the hospital and how changes would bring about a difference in communication and recognition of the surgical team. Results Surgical team was poorly recognised across the hospital(12.5%), and most of the surgical & nursing staff thought implementing the changes would play a key role. Conclusions It is vital to have posters of surgical team (trainees/ trust grades) across all key wards and in Emergency, along with unique ID for efficient recognition of Surgical team in urgent scenarios & to develop an effective communication with the nursing staff across the hospital.

A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.