Reviewerly/exHarmony huggingface dataset:
Foreground Detection Algorithm via Fusing Confidence by Weight and Visual Attention

In the foreground detection of video, the result of pixel-level background subtraction is clear and flexible. However, the pixel-level classification method based on sample consistency can't make full use of the pixel information effectively and get full foreground mask when meeting the complex situation of color camouflage and static object. In order to solve this problem effectively, we proposed an algorithm based on fusing confidences with weight and visual attention. Firstly, the foreground is determined by the sum of fusing with color confidence and texture confidence based on weight, then the confidence and weight of the samples are updated adaptively; the static foreground is determined by constructing the visual attention mechanism in the subsequence, and finally the background model is dynamically updated with the strategy of updating the minimum confidence samples. The method proposed in this paper is tested on the open dataset. It is found that the proposed algorithm is superior to the contrast algorithms, and get the accuracy and recall rate of foreground detection in the complex situation of color camouflage and still objects, and get better detection effects.
WITHDRAWN: Implementation of MLDB as high performance machine learning database for high performance applications

Machine learning and predictive analysis are the core areas for studies in many fields including bioinformatics, computer anatomy, natural language processing, speed recognition. Software Engineering, Robotics, Information Retrieval, Marketing, Computer Vision, Fraud Detection, Sentiment Analysis, User Belief Mining, Time Series Forecasting, Recommendation Systems, Economics and many others. Machine Learning is used for teaching and/or setting up programmes for Software or hardware focused on fundamental rules models of mining, forecasting and discovery. In order to obtain precision and performance from raw data sets, data researchers and studies take multiple tracked or unattended approaches. This research manuscript is presenting the adoption patterns and implementations towards the machine learning base database application of MLDB towards the effectual scenarios of predictions.
Self-Explainable Robots in Remote Environments

As robots and autonomous systems become more adept at handling complex scenarios, their underlying mechanisms also become increasingly complex and opaque. This lack of transparency can give rise to unverifiable behaviours, limiting the use of robots in a number of applications including high-stakes scenarios, e.g. self-driving cars or first responders. In this paper and accompanying video, we present a system that learns from demonstrations to inspect areas in a remote environment and to explain robot behaviour. Using semi-supervised learning, the robot is able to inspect an offshore platform autonomously, whilst explaining its decision process both through both image-based and natural language-based interfaces.
Spatial and Channel-wise Attention in Multimodality Feature Fusion for Multispectral Pedestrian Detection

Multispectral pedestrian detection with feature fusion of thermal and visible images has achieved great success in all-day scenarios, but few attentions have been paid to the quality of feature fusion. In this paper, we have proposed an improved attention aware dual-stream Faster R-CNN algorithm to ameliorate the feature quality. Quantitative analysis of three different feature fusion methods demonstrate that the performance of detector is highly related to the quality of feature fusion. Therefore, an improved multi-step spatial and channel-wise attention method is proposed to improve the feature fusion quality step by step based on the dual-stream Faster R-CNN framework. With the aid of attention mechanism, the model learning is optimized in a more smooth way, which can effectively enhance the response of fused feature map in each stage. Experiments based on the KAIST and CVC-14 dataset demonstrate that the proposed method has achieved better performance compare to thebaseline method with a decrease of 4% and 8% in AMR respectively.
Interaction (f)or Recall? Evaluating Recall on Large Screens

Remembering content (recall) is relevant for information and educational applications as well as advertisement. Previous research showed that the recall on large screens is increased when interacting with body gestures. Based on this finding we conducted a lab study evaluating the influence of interaction (no interaction, touch, voice) on recall. We found no significant effect, but participants subjectively favored interacting with touch, and had the impression their recall was highest using touch interaction. Thus, we recommend using interaction for drawing attention to the large screen and creating a joyful experience, but relying on other methods for increasing the recall.
End-to-End Elimination of Traffic Elimination Using Self-Adaptive Prediction Model in Cloud

In recent advancement in cloud computing, Traffic Redundancy Elimination (TRE) offers an effective solution to reduce the bandwidth cost. It is found that both short-term and long term data redundancy tends to appear in the network traffic and the TRE - trace driven approach captures both the data traffic redundancy. In this paper, we hence improve the design of a cooperative end-to-end TRE solution in order to improve the process of detection and removal of data redundancy between multiple layers, where the operations between them is in cooperative manner. The proposed method uses a self-adaptive prediction algorithm to increase the efficiency of TRE in multi-layer design that uses hit ratio of predictions to adjust dynamically the prediction window size. The experiment evaluation shows that proposed method reduces the operational cost in terms of reduced energy and makespan through proper scheduling.
The Distribution of Noble Metals in Metasomatites of the Kirchenovskoye Deposit (Transbaikalia)

Abstract The Kirchenovskoye gold-silver deposit is one of the fairly large deposits in the Olovyanninsky region. In addition to rich ore mineralization, the distribution of noble metals in the host rocks is of interest. For five selected rocks, the contents of noble metals (Rh, Pd, Ag, Pt, Au) were determined, the main part of which is represented by metasomatites: beresitized granite, graphite metasomatite (gneiss), graphite shale, mudstone and rocks from the weathering crust. For this, the method of mass spectrometry with inductively coupled plasma (ICP MS) was used. Promising mineralization for Au, Ag, and Pd (mudstone, graphite shales and gneisses) have been identified. The concentration of noble metals in ores and host rocks occurred due to the removal of these metals from the granitoid massif by hydrothermal solutions.
DLT-Based CO$$_{2}$$ Emission Trading System: Verifiable Emission Intensities of Imports

The European Emission Trading System is the primary tool of the European Union to reach net-zero greenhouse gas emissions by 2050. However, it faces several challenges regarding its effectiveness. To address those issues, a new mechanism based on distributed ledger technology is proposed. Previous work has shown how consumption-based emissions of European consumers can be limited by a reshaped emission trading system. An upstream approach guarantees complete coverage of carbon dioxide emissions. In order to ensure that domestic firms stay competitive, both import and export border adjustments are proposed. This principle is further investigated for import border adjustments. It is shown that a decentralized bill of material could help foreign companies to prove their carbon footprint to European authorities when importing goods into the Union. To achieve verifiable proof, the models of verifiable object identity attributes and digital object memory storing carbon footprints of importing goods are used. It is shown how digital twins extend the concept to allow companies to increase supply chain efficiency.
Assessment of professional competencies of pedagogy students in the conditions of distance learning

The subject of the study is the creation of a system for assessing professional competencies and the formation and implementation of this system into pedagogical university students’ educational activities in the context of distance learning. The authors aim to implement the evaluation procedure in a new format due to the introduction of the High Alert State in Moscow and, consequently, remote operations. The developers are faced with the task of not only realizing the assessment of professional competencies but also maintaining the openness of the procedure, ensuring the possibility of communicative interaction between students and employers. Based on the analysis of specialized sources and as a result of discussion within the expert community, new assessment tools are proposed, and an appropriate assessment scheme is developed. The procedure is based on a competency-based approach, the development is carried out considering the requirements of the federal state educational standard, as well as the WorldSkills Russia professional skill competitions in the category “Teaching in general and secondary schools”. The procedure is tested based on the Moscow City University within the framework of the Moscow Teacher Certificate project for students. The project has been implemented since 2017, however, for the first time the project has been carried out in a remote format. The data obtained during the study make it possible to identify the possibilities of the assessment procedure in the distance learning context and the key features of students’ participation in the procedure and determine the most significant organizational aspects. Based on the results of testing, conclusions are made about the advantages and disadvantages of this procedure, both in terms of content and in organizational terms. The prospects for applying the procedure and the necessary improvements in the future are determined.
Introduction to the second Geostationary Ocean Color Imager (GOCI-II) and its ground segment

&amp;lt;p&amp;gt;As the succeeder of the Geostationay Ocean Color Imager (GOCI), the world first geostationary ocean monitoring satellite, the second GOCI (GOCI-II) will be launched in 2020. GOCI-II has 12 narrow bands of 380~865nm center wavelength for earth observation and an broadband band for star observation. The main goals of this GOCI series are to monitor ocena short-term/long-term phenomena like red-tide blooming, floating algae movements, tidal movements, low sea surface salinity variation, sea surface currents, primary productivity, etc. GOCI-II is able to obtain 10 images for the area around Korean peninsula and an image for the full-disk area in everyday during its 10 years lifetime. To handle this huge GOCI-II data, we have to develop the dedicated GOCI-II Ground Segment (G2GS) system with data acquisition antenna and GOCI-II operating infrastructure. G2GS have good performance like the data distribution output delay within 60 minutes, the 99% system operability with redundancy, etc. G2GS also generates 26 level-2 data products and provides all data with dedicated software program like GOCI-II plug-in of SNAP framework.&amp;amp;#160;&amp;lt;/p&amp;gt;
Damage and protection of EMP weapon to C4I system

This paper discusses the development of the research field of EMP damage C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> I system and computer protection, introduces the mechanism and damage effect of EMP weapons damage C4I system in recent years, especially the damage to C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> I computer system, and makes a detailed introduction to the computer anti-damage protection technology [1]. It is suggested that China should strengthen the research in relevant research fields, keep tracking and studying the advanced technology of various countries, so as to attract the attention of this research and provide support for strengthening the anti-damage technology.
Analysis on the Development of Quantum Spin

Spin, still a concept or property not fully exploited, has already impacted the world in many ways with its many applications. The history of how spin came to light and how it was interpreted and developed as investigations were conducted is of particular interest with the many odd outcomes of experiments concerning this intrinsic property. This article will sort out the historical outline of how spin was first indicated in theories and how it was eventually embraced as a fundamental property of quantum particles in an attempt to holistically review the process of recognition of this remarkable concept. Through this review, it becomes apparent how the recognition and development of a concept involves the intellect of a broad range of physicists who are willing to view things from both classical and quantum perspectives particularly when it comes to new concepts as spin. How elements like luck and chance play a role will also be revealed. As it turns out, our intuitions about the world around us may not be the ultimate truth, especially when it comes to Quantum Mechanics. Space Quantization was once thought of as no more than a theoretical outcome, however, experiments anyway impose this reality on physicists requiring them to abandon stubborn biases. Admittedly, not every speculation was correct, the analogy of “Spin” was an unfortunate one. But this article shows that this is just how understanding the mysteries of the quantum world really unfolds.
Integration of GIS for Mapping Distributed Online Learning

Abstract This study was carried out to overcome one of the main obstacles encountered, namely the lack of information on the location of student residences in private universities that provide online learning system. Quantum GIS can display the distribution of students in all regions of Indonesia. The method used in this study is the descriptive analysis method. The results of this study are presented in the form of a distribution map of online learning students across Indonesia, which shows information about student data based on a regional boundary map.
Intelligence methods for data mining task

Data mining is a multidisciplinary field in computer vision which includes the computational process of massive datasets to discover effective patterns. The advanced analysis process aims to mine information from the massive data and then transform the data into an understandable form. Also, data mining is considered as a significant field wherein intelligent techniques are applied to extracting the data patterns. Numerous intelligent methods are devised in the literature that involves machine learning technique, clustering technique, artificial intelligence, database systems, statistics, and business intelligence for analyzing the data. This chapter illustrates the intelligent data mining methods considering association rule mining (ARM) and the Apriori algorithm to mine information from the dataset. In addition, the data mining functionalities are employed to specify the type of patterns to be found using the data mining tasks. The extensive research is performed in the database community for learning rules using ARM strategies.
RFP-based Faster R-CNN in Aerial Image Detection

Aerial Image Detection is increasingly used in fields such as agriculture, natural disaster rescue, and topographic surveying. However, object detection in Aerial Image is still at a relatively low level. Generally speaking, there are the following challenges: 1) Lack of datasets; 2) A large number of targets in each image; 3) Object scales in the image are greatly different; 4) Each object category is uneven; 5) Image perspective is different (e.g., aerial view, elevation view and upward view). To improve the prediction effect of the network on such images, this paper proposes a deep learning framework for aerial static image detection based on improved Faster R-CNN-RFP-based Faster R-CNN. We add the Recursive Feature Pyramid (RFP) module to the Faster R-CNN for coping with the multi-scale small object in the image and effectively handle the occlusion. Bounded IoU Loss is used to replace the smooth LI Loss at Region of Interest (Rol) Pooling, and it avoids multiple boxes corresponding to the same Smooth LI Loss effectively. RFP-based Faster R-CNN achieves better performance. The proposed approach results show that the improved model achieves 20.7% mAP on the VisDrone2019-DET test set, which is higher than the most of classical object detection algorithms.
Geographies of Semiotics in Social Mediasphere in India

We live in a world of signs and symbols. Deep meanings are often connoted through signs and symbols. Semiotics is the study of signs and symbols and emoticons/emoji are features that are used in graphical or pictorial representations in mundane expressions. The use of emoji across the platforms is inevitable and users used emoji very purposefully to express them or it works as a bridge between senders and receivers. Emoji facilitates digital language wherein users often express themselves accordingly. Emoji/emoticons are not mere a sign or symbol rather it represents spatial connotations between senders and receivers in varied expressions when two persons exchange greetings, anguish, sorrow, melancholy, pain, happiness, etc. This chapter is about to explore the meaning, pattern and nature of semiotics in social media.
Design and Research of Video Image Segmentation Based on Fuzzy Clustering Algorithm

The histogram statistical characteristic of image is introduced into the objective function of FCM clustering algorithm. The hybrid particle swarm optimization algorithm is used to solve the global optimization of the objective function, which improves the operation speed of image segmentation.
Vibration separation of multiple mixed particle curves based on image recognition and machine learning

The motion characteristics of particulate matter are very wonderful. With the development of science and technology, the motion of granular materials has gradually become a hot topic, which has attracted the attention of many scientists and experts. The research of granular matter has gradually become specialized and systematic. With the gradual improvement of the system, a frontier research field particle physics has been formed. Under the combined action of external force and internal force, particles can reflect the properties of fluid, but in the process of flow, it will show different size separation phenomenon from the fluid. The problem of particle separation was formally introduced into the field of physics in 1987. In reality, the existence of particles is not unique. In view of this, the author makes a systematic research and Analysis on the behavior and factors of vibration.
Real-time Computation of Krawtchouk Moments on Gray Images Using Block Representation

In the field of image analysis, the discrete orthogonal moments have better image representation capability than the continuous orthogonal moments and geometric moments. Krawtchouk moments are discrete orthogonal moments able to capture the local features of an image. The disadvantage of the Krawtchouk moments is the high computational cost which is increased as higher-order moments are involved in the computations. In this paper, we propose an effective approach for the computation of Krawtchouk moments. The gray image is decomposed in a set of binary images. The most significant binary images are represented using Image Block Representation and their moments are computed fast using block techniques. The least significant binary images are substituted by a constant ideal image called "half-intensity" image, which has known Krawtchouk moment values. The proposed method has low computational error, low computational complexity and under certain conditions, it is able to achieve real-time processing rates.
Usage of Particle Swarm Optimization to Improve the Performance of Supervised Classifiers

Abstract Representing the data appropriately will have a significant effect on the outcome produced by the classifier. Transforming the feature will help to represent the data points in a more suitable way for the classifier. Particle swarm optimization belongs to the Swarm Optimization techniques category and it is generally used for solving numerical optimization problems, weight updation in neural networks, and feature selection. This research work proposes a Particle swarm optimization-based transformation technique for increasing the classification metrics of popular classification algorithms namely K-Nearest Neighbor, Stochastic Gradient Descent Classifier, Decision trees, and linear discriminant analysis classifier. Experiments are conducted using the SONAR dataset and the highest accuracy of 82.69% is attained for Stochastic Gradient Descent classifier when Particle swarm optimization is used as a transformation technique
The problem of art teaching based on interactive multimedia assisted instruction platform

When digital technology reproduces and simulates real scenes, it can realistically multi-dimensionally present and simulate real-life situations. This solves the problem that the design methods that are difficult to avoid in art design teaching are out of touch with the real space environment, and the design concept and function are out of touch. The openness of the teaching knowledge of art design in interactive teaching platform is reflected in the informationization of resources and the development of diverse knowledge. This paper proposes an architecture scheme of multimedia remote interactive teaching system composed of network multimedia teaching platform, general network teaching platform, digital resource management platform, streaming media resource recording platform and interactive distance learning platform. The system architecture has the characteristics that the underlying data are mutually integrated and the platforms complement each other. The essential characteristics of e-Learning are provided in the interaction characteristics, the dynamic resource scheduling function is enabled, and the multimedia-assisted remote interactive teaching in the future m-Learning and u-learning eras The system proposes a vision.
Pyramid Deconvolution Net: Breast Cancer Detection Using Tissue and Cell Encoding Information

Accurate diagnosis of breast cancer lesions from whole slide images (WSIs) is crucial for pathologist, since the results are associated with a certain status of breast cancer development. Until now, it is not yet possible to detect all of the cancerous areas in a WSIs. This limitation leads to mistakes or low detection precision in most of the approaches based on deep learning. In this paper, we propose an automatic cancer lesions detection approach using pyramid deconvolution network (PDN) for multi-level and multi-scale H&E stained breast pathological WSIs. Our workflow integrates tissue and cell level information for the cancerous region detection, and this is neglected by state-of-the-art methods. The high-level cancerous regions (macro) are obtained by deconvolution neuron network feature map from level 3 to level 9 in WSIs. The low-level cancer cells and region analysis pipeline is designed for detecting smaller region micro an ITCs from level 0. The results demonstrated that our workflow greatly improved the performance compared with those only using tissue level information.