Reviewerly/exHarmony huggingface dataset:
A Survey of Real-Time Big Data Processing Algorithms

Data collection and processing in real time is one of the most challenging domains for big data. The sustainable proliferation of unbounded streaming data has become arduous for data collection, data pre-process, data optimization, etc. Real-time streaming for data collection can effectively be performed by windowing mechanism. In this communication, we have discussed various windowing mechanisms such as sliding window, tumbling window, landmark window, index-based window, adaptive size tumbling window, and partitioned-based window. The reliability measure, which depends upon selection of appropriate windowing mechanism, has also been discussed. These window-based algorithms have been compared on the basis of CPU utilization, memory consumption, time efficiency, and operation compatibility. In this paper, we have surveyed various aggregation algorithms such as reactive aggregator, flatFAT, flatFIT, B-Int, DABA, and two stacks aggregator and compared them based on time complexity. Remarkably, a hybrid window mechanism has been introduced in this study which can handle the most recent data stream and variable rate of data stream by sliding window and tumbling window, respectively.
Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation

Purpose The purpose of this work is to propose quad wheel robot with path navigation using an intelligent novel algorithm named as obstacle-avoiding intelligent algorithm (OAIA). Design/methodology/approach The paper proposes OAIA algorithm, which is used to minimize the path distance and elapsed time between source and goal. Findings The hardware implementation of the Quad Wheel Robot design includes a global positioning system (GPS) module for path navigation. An ultrasonic module (HC SR04) is mainly used as the sensing unit for the system. In the proposed scheme, the GPS locator (L80) is used to obtain the current location of the robot, and the ultrasonic sensor is utilized to avoid the obstacles. An ARM processor serves as the heart of the Quad Wheel Robot. Practical implications This paper includes real-time implementation of quad wheel robot for various coordinate values, and the movement of the robot is captured and analysed. Originality/value The proposed OAIA is capable of estimating the mobile robot position exactly under ideal circumstances. Simulation and hardware implementation are carried out to evaluate the performance of the proposed system.
Career guidance competence of an informatics teacher in the conditions of professional self-determination of schoolchildren

The article discusses the search for ways of professional self-determination of schoolchildren in the field of information technology. It justifies the need for an informatics teacher to have the vocational guidance competencies necessary to familiarize schoolchildren with the professions of the future in the field of IT, as well as develop the modern ideas about the purpose of these professions. The material for competitions while an extracurricular training event for high school students "Future professions in the field of information technology" is given.
Perfil e financiamento da pesquisa em saúde desencadeada pela pandemia da COVID-19 no Brasil

Introduction: Facing COVID-19 pandemic challenges Brazil on taking actions to deal with the installed health crisis, and to choose which interventions are more efficient and effective with the problems arising from the new disease. This picture of uncertainties has led to health research financing actions, which aim at scientific and technological improvement, supporting evidence-based decision making. Objective: To map the health research funding opportunities to face the COVID-19 in Brazil, in terms of priority areas, funding sources and financial volumes. Methods: We search public sources to map documents related to the promotion of research and innovation in health, with public and private sectors. We conducted a survey directly on the websites of public research institutions and an unsystematic search to identify private financing, and categorized objects of the financing notice and calls identified in large areas and specific topics. Results: We found 23 financing opportunities focusing on the combat of COVID-19 pandemic, covering 20 different financing agents in a total amount of BRL 337,460,612.00. Five public institutions finance 75.0% of the resources. Conclusions: Despite the small amount of resources, there was an effort to promote health R&D in a timely manner, mainly by federal public agencies, with emphasis on CAPES and CNPq. The most contemplated areas were the inputs needed to face COVID-19 (diagnostic tests, medical equipment and devices, medicines, vaccines and biological products) and telecommunication and information technologies. However, the contributions covered practically all the important areas for knowledge, prevention and treatment of the disease. It is noteworthy that health R&D in Brazil has had its resources significantly reduced since 2015.
Analysis of Deep Rain Streaks Removal Convolutional Neural Network-Based Post-Processing Techniques in HEVC Encoder

This paper presents Deep Rain Streaks Removal Convolutional Neural Network (Derain SRCNN) based post-processing optimization algorithm for High-Efficiency Video Coder (HEVC). Earlier, the CNN-based denoising optimization algorithm faced overfitting issues and large convergence time when training the CNN for rain streaks affected High Definition (HD) video sequences. To address these problems, Deep rain streaks removal CNN-based post-processing block is introduced in HEVC encoder. Derain SRCNN architecture consists of a parallel two residual block layer and Dual Channel Rectification Linear Unit (DCReLU) activation function with various sizes of the convolutional layer. By reducing the validate error and training the error of CNN, the overfitting issue is solved. Also, convergence time is reduced using proper learning rate and kernel weight of optimization algorithm. The proposed network provides a higher bit rate reduction and higher convergence speed for corrupted high-definition video sequences. The experiment result shows that proposed DerainSRCNN-based post-processing filtering method achieves 6.8% and 4.1% -bit rate reduction for random access (RA) and low delay [Formula: see text] frame (LDP) configuration, respectively.
Accelerated algorithm for intelligent mining of communication data in cloud computing environment

Traditional methods have some problems such as large memory occupation and slow mining speed, so an intelligent mining acceleration algorithm based on particle swarm optimization is proposed. Based on the analysis of the communication data, the features of the communication data are selected by the acceleration strategy, and multiple feature subsets of the communication data are obtained repeatedly by using the remaining attributes. Particle swarm optimization (pso) algorithm is used to select the optimal feature subset, and average classification error is used as fitness function to complete intelligent mining of communication data. The experimental results show that the memory usage of this algorithm is between 62 and 71 GB in the experimental process, which is small and the average running time is better than the traditional algorithm. The results show that the algorithm has lower memory consumption and faster mining speed.
Analysis and visualization tool for motion and gaze

Observers’ gaze is studied as a marker of attention, and by tracking the eyes, one can obtain gaze data. Attention of an individual performing natural tasks such as making a sandwich, playing squash, or teaching a class can be studied with the help of eye-tracking. Data analysis of real world interaction is challenging and time-consuming as it consists of varying or undefined environments, massive amounts of video data and unrestricted movement. To approach these challenges, my research aims to create an interactive four-dimensional (x,y,z,t) tool for the analysis and visualization of observer motion and gaze data, of one or more observers performing natural day-to-day tasks. Three solutions are necessary to achieve this goal: simulation of the environment with the ability to vary viewpoint, gaze visualization from two-dimensional scene to three-dimensions, and tracing of the observer(s) motion. The approaches to these challenges are described in the following sections.
Automated Mucous Glands Detection and Segmentation in Colon Histology Images Using Semantic Segmentation

The most prevalent form of Colon Cancer is the Colorectal Adenocarcinoma which originates particularly in intestinal glandular structures. For its prognosis and plan of treatment, pathological tests are conducted where the morphology of intestinal glands including an architectural appearance as well as glandular formation are analyzed in samples collected during biopsies. But in modern pathology, to achieve good inter-observer along with intra-observer reproducibility of cancer grading still remains a key challenge owing to variations present in staining, sectioning and fixation procedures that also lead to artifacts introduction and variances in tissue sections and ultimately resulted in variances in gland appearances. Also, manual segmentation and classification of these glandular structures are time-consuming owing to large datasets from a single patient. Thus, in this paper semantic segmentation model for effectively detecting and segmenting the mucous glands from large size histopathological images i.e. 775 × 552 × 3 is proposed which keeps the original size at the input and is the main highlight of the model. The model is trained and tested using the dataset from GlaS@MICCAI 2015 Colon Gland Segmentation Challenge.
Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles

Adversarial examples are firstly investigated in the area of computer vision: by adding some carefully designed ''noise'' to the original input image, the perturbed image that cannot be distinguished from the original one by human, can fool a well-trained classifier easily. In recent years, researchers also demonstrated that adversarial examples can mislead deep reinforcement learning (DRL) agents on playing video games using image inputs with similar methods. However, although DRL has been more and more popular in the area of intelligent transportation systems, there is little research investigating the impacts of adversarial attacks on them, especially for algorithms that do not take images as inputs. In this work, we investigated several fast methods to generate adversarial examples to significantly degrade the performance of a well-trained DRL- based energy management system of an extended range electric delivery vehicle. The perturbed inputs are low-dimensional state representations and close to the original inputs quantified by different kinds of norms. Our work shows that, to apply DRL agents on real-world transportation systems, adversarial examples in the form of cyber-attack should be considered carefully, especially for applications that may lead to serious safety issues.
Photovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks

The main goal of this chapter is to show the set up a well-defined method to identify and properly train the hybrid artificial neural network both in terms of number of neurons, hidden layers and training set size in order to perform the day-ahead power production forecast applicable to any photovoltaic (PV) plant, accurately. Therefore, this chapter has been addressed to describe the adopted hybrid method (PHANN—Physic Hybrid Artificial Neural Network) combining both the deterministic clear sky solar radiation algorithm (CSRM) and the stochastic artificial neural network (ANN) method in order to enhance the day-ahead power forecast. In the previous works, this hybrid method had been tested on different PV plants by assessing the role of different training sets varying in the amount of data and number of trials, which should be included in the “ensemble forecast.” In this chapter, the main results obtained by applying the above-mentioned procedure specifically referred to the available data of the PV power production of a single PV module are presented.
Effect Analysis of Contrast Enhancement Techniques on Cancer Classification in Colon Histopathology Images Using Machine Learning

The glandular morphology analysis done within the colon histopathological images is a crucial step required for grade determination of colon cancer. But the manual segmentation is quite laborious as well as time-consuming. Also, it suffers from the subjectivity present among the pathologists. Thus in order to minimize these limitations, the rising computational pathology has guided the researchers towards the advancement of various unsupervised and automated computerized methods for the gland segmentation task. However, this automated gland segmentation still remains a tricky task owing to numerous aspects like the need for high-level resolution for exact delineation of glandular boundaries, etc. Thus, in order to help researchers to develop automated gland segmentation techniques, various image enhancement techniques based on contrast adjustment are explained in this paper and their enhancement results are analyzed based on both objective qualitative assessment as well as subjective assessment given in the form of scores by the pathologists. And thus based on the qualitative analysis, a new combined technique i.e. Colormap Enhanced Image Sharpening is proposed in order to get an enhanced image in which all the critical elements are easily detectable. These techniques’ effectiveness is also checked by applying them to do the image classification into two classes having benign and malignant cancer using the machine learning technique i.e. Bag of Features. The classification accuracy is examined for each enhancement techniques. These techniques’ results will thus help pathologists in better colon histopathology image analysis.
Interactive Goal Model Construction Based on a Flow of Questions

Goal modeling is a method that describes requirements structurally. Goal modeling mainly consists of two tasks: extraction of goals and organization of the extracted goals. Generally, the process of the goal modeling requires intensive manual intervention and higher modeling skills than the process of the usual requirements description. In order to mitigate this problem, we propose a method that provides systematic supports for constructing goal models. In the method, the requirement analyst answers questions and a goal model is semi-automatically constructed based on the answers made. We develop a prototype tool that implements the proposed method and apply it to two systems. The results demonstrate the feasibility of the method.
An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies

The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system.
PosePropagationNet: Towards Accurate and Efficient Pose Estimation in Videos

We rethink on the contradiction between accuracy and efficiency in the field of video pose estimation. Large networks are typically exploited in previous methods to pursue superior pose estimation results. However, those methods can hardly meet the low-latency requirement for real-time applications because of their computationally expensive nature. We present a novel architecture, PosePropagation-Net (PPN), to generate poses across video frames accurately and efficiently. Instead of extracting temporal cues or knowledge someways to enforce geometric consistency as most of the previous methods do, we explicitly propagate well-estimated pose from the preceding frame to the current frame by leveraging pose propagation mechanism, endowing lightweight networks with the capability of performing accurate pose estimation in videos. The experiments on two large-scale benchmarks for video pose estimation show that our method significantly outperforms previous state-of-the-art methods in both accuracy and efficiency. Compared with the previous best method, our two representative configurations, PPN-Stable and PPN-Swift, achieve 2.5× and 6× FLOPs reduction respectively, as well as significant accuracy improvement.
Desafios e oportunidades para telessaúde em tempos da pandemia pela COVID-19: uma reflexão sobre os espaços e iniciativas no contexto brasileiro/ Desafíos y oportunidades para la telesalud en tiempos de la pandemia por la COVID-19: una reflexión sobre los espacios e iniciativas en el contexto brasileño/ Challenges and opportunities for telehealth during the COVID-19 pandemic: ideas on spaces and initiatives in the Brazilian context

COVID-19 has created enormous challenges for health systems worldwide, with the rapidly growing number of deaths and critical patients with pneumonia requiring ventilatory support. Alternative methods to control the spread of the disease such as social isolation, extreme quarantine measures, and contact tracing have been used around the world. However, these measures may not be totally effective to fight COVID-19, in step with the necessary national preparations to meet the new patient care demands. A wide range of digital technologies can be used to enhance these public health strategies, and the pandemic has sparked increasing use of telehealth. This field has grown considerably in Brazil in recent years. Still, despite the intense proliferation of recommendations and rules, until the current pandemic the country still lacked a fully consolidated regulatory framework. The emergence of COVID-19 marks a key moment in the expansion of applications and use of telehealth for improving the health system's response to the current crisis. The article discusses telehealth's contribution to the fight against COVID-19 and the recent initiatives triggered in Brazil as opportunities for the consolidation of telemedicine and improvement of the Brazilian Unified National Health System. The authors conclude that telehealth offers capabilities for remote screening, care and treatment, and assists monitoring, surveillance, detection, prevention, and mitigation of the impacts on healthcare indirectly related to COVID-19. The initiatives triggered in this process can reshape the future space of telemedicine in health services in the territory.
Power-law growth of time and strength of squeezing near a quantum critical point

The dynamics of squeezing across a quantum phase transition in two basic models, viz., the one-axis twisting model in a transverse field and the Dicke model, is investigated using Holstein-Primakoff representation in the large spin limit. Near the phase boundary between the disordered (normal) and the ordered (superradiant) phases, the strength of spin and photon squeezing and the duration of time for which the system stays in the highly squeezed state are found to exhibit strong power-law growth with distance from the quantum critical point. The critical exponent for squeezing time is found to be 1/2 in both of the models, and for squeezing strength, it is shown to be 1/2 in the one-axis twisting model and 1 for the Dicke model, which in the limit of extreme detuning also becomes 1/2.
Deep tracking using double-correlation filters by membership weighted decision

Correlation filters are well-known for tracking robustness and accuracy while convolutional neural networks (CNN) is famous for representation learning capability. However, how to combine them to further boost tracking performance remains an open problem. In this paper, we are resolved to derive a more compact double-correlation filter and incorporate an ensemble of double-correlation filters in a membership-based decision fashion where filters are trained on features obtained from different layers of a CNN respectively. The novel double-correlation filter is constructed by maximizing the similarity between the Gaussian-shape label and the correlation of template and training samples, producing a more concise solution that means more computational efficiency. Multiple filters are learned based on multiple-layer CNN features obtained. The final tracking prediction is a membership-weighted decision where membership of each tracker, which is computed according to their performance in previous frames, shows how close a weak tracker-s result is to the truth. Hence our framework not only combines correlation filters and CNN together, but also fully utilizes both deep features providing semantic information to distinguish target from background and their shallow counterparts retaining details beneficial for precise localization. We experiment on benchmark OTB and VOT where our algorithm demonstrates competitive performance versus other state-of-the-art trackers.
Platform solutions and public spaces as fabrics of the distributed production of knowledge

This article is dedicated to examination of approaches towards production of knowledge in the conditions of development of the digital methods of communication. The development of technologies and Internet concept Web 3.0 form a request for collective, interdisciplinary and &amp;ldquo;civil&amp;rdquo; character of research activity, emergence of new practices of working with knowledge, focused on confluence of the virtual and real space, transformation of the essence and role of &amp;ldquo;information&amp;rdquo; in the context of building the &amp;ldquo;society of knowledge&amp;rdquo;. The modern scientific knowledge represents a synthesis of various aspects of science and social life. Within the framework of pilot research, the author analyzes the case of confluence of the digital and real activity in production of knowledge: &amp;ldquo;public space of collective work &amp;lsquo;the pivotal point&amp;rsquo;&amp;rdquo; and digital platform Leader-ID. The article considers the hypothesis that the modern scientific knowledge represents a synthesis of social, culturological, and technological aspects of science and social life. The &amp;ldquo;distributed knowledge&amp;rdquo;, forming as a result of cooperation of scholars and various communities, becomes common through the formation of &amp;ldquo;cluster&amp;rdquo; and crowdsourcing format of the production of knowledge. Such social interaction is based on the network platform model and generated new zones of exchange and production of knowledge: online platforms (forums, crowdsourcing, scientific network platforms such as ResearchGate, simulators, etc.) or offline platforms (public spaces) that discuss and formulate the requests of scholars to each other, formulate hypothesis, and test the studies.