Reviewerly/exHarmony huggingface dataset:
Improvement of Dark Channel Defogging Algorithm Based on Duided Filtering

Aiming at the problem that the dark channel prior defogging algorithm has the problems that the dark channel fails in large areas such as sky or white objects and the guided filtering algorithm is not completely defogging, a single image defogging improvement based on dark channel prior and guided filtering is proposed algorithm. First obtain the minimum image of each color channel of the foggy image and the rough estimate of the transmittance according to the dark channel theory, use the guide filter for the minimum image to obtain the guide image containing the edge information, and then use the guide filter again to optimize the initial transmittance. The problem of underestimation of the transmittance of the foggy sky area is to determine the range of the sky area and make certain adjustments to its transmittance, and use the quadtree search algorithm to obtain accurate atmospheric light values. Finally, the fog-free image is recovered through the atmospheric physical model, and the algorithm is compared with other defogging algorithms. The results show that the algorithm can effectively process foggy images containing sky areas, and the time complexity is low.
Determination of Optimum Dynamic Threshold for Visual Object Tracker

Mobile object tracking is one of the integral parts of computer vision study. Different algorithms have been developed and open-sourced in computer vision applications, yet, there is huge scope for improvement to fulfill real-time application. Keeping this requirement in mind a new object tracker model has been proposed in this paper which dynamically selects between the Mean-shift algorithm and Unscented Kalman Filter (UKF). The novelty of the proposed method is finding a threshold value (β), which is defined for this selection purpose whose value is computed taking into account the analytical and logical decisions to determine the selection of the above algorithms. The threshold value is calculated dynamically depending upon the environment such as nonlinear motion, non-rigid object deformation, high velocity, blurriness, etc in tracking objects for real-time application and it also helps to minimize the time delay. To improve the robustness of the tracker due to changes in the pose or the light illumination of the frame, this method updates the target model based on the current and previous frames.
DEVELOPMENT OF INNOVATIONS IN CONTINUING PROFESSIONAL EDUCATION

The paper aims to study the methodological and theoretical foundations of the development of innovations in the field of continuing professional education (CPE). The methodological foundations of the study of the phenomenon of innovation in CPE (general laws of dialectics, systemic, synergetic and acmeological approaches) are identified and characterized. The external factors of the education sector cased by globalization, internationalization, digitalization and the “force majeure” of pandemic were determined and their influence on the development of CPE in the first quarter of the XXI century was revealed. The features of the current continuing education were revealed. They were developed both under the influence of environmental factors and as a result of internal tendencies of self-organization. On the basis of a theoretical analysis of the requirements for education caused by these factors, the types of CPE problems were formulated. The necessary nature of educational innovations was substantiated. The role of innovations in resolving the problems of continuing education was described. Typical modern innovations and their role in the development of continuing education as an aspect of a person's lifelong education were characterized.
Overview of Vulnerabilities of Decision Support Interfaces Based on Virtual and Augmented Reality Technologies

The visual interfaces are the important part of almost any information system with decision support module. An operator makes his decisions based on the information he/she can get only from the visual interface of the system. The challenge here is that vulnerabilities in such interfaces can lead to the wrong decisions and, what is more important, hacked interface can even harm the operator. Even more challenging the situation becomes because of the transition from regular 2D graphical interfaces to the interfaces in the virtual and augmented reality, that are much less investigated. The paper provides an overview of existing attacks on virtual and augmented reality interfaces of security systems. The classification of these attacks is proposed and the model of vulnerabilities of the VR/AR interface is designed. Based on the study, it was concluded which classes of such attacks are the most relevant and dangerous for novel interfaces in decision support systems. The paper will be useful for developers of secure interfaces for security systems, as well as researchers involved in human-machine interaction in the field of information and computer security.
Visual Attention based Cognitive Informative Frame Extraction Method for Smart Crowd Surveillance

In a smart surveillance system, the amount of video data has increased exponentially due to the increase in the number of monitoring devices and IoT sensors. To make smart and real-time decisions without latency in communication from these voluminous data is a tedious task. In this context, selecting informative frames from the video is of great importance that helps to extract only the salient features for further processing without latency and bandwidth constraints. In this paper, we are proposing a fast and reliable method for selecting informative frames from video sequences based on the human cognition process of visual attention to preserve the Spatio-temporal properties of the video. The proposed method extracts the informative frames using the frame informative score calculated based on visual attention maps, superpixel segmentation, and temporal information. Since our purpose is for analyzing crowd behavior from video data in a smart environment, we take two publicly available crowd video datasets for our experiments. The results show that the proposed approach is successful in extracting relevant video frames in linear time by preserving their spatial and temporal properties. We also analyze the feasibility of the proposed method in a fog computing-based simulated IoT framework, and it has been verified that the proposed cognitive approach could efficiently address the concerns of latency and bandwidth in smart surveillance environments.
Personalized Scholar Recommendation Based on Multi-Dimensional Features

The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation), which improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-N scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the N value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect.
Teaching Practice of "Electrical Control and Programmable Logical Controller Technology" Course Based on Information Technology Platform

This paper analyzes the current situation of "electrical control and programmable logical controller (PLC) technology" course teaching, and discusses how to reform the course teaching mode based on the "student-centered, output oriented" engineering education certification concept. This paper introduces the teaching reform of electrical control and PLC technology in automation major of Shenyang University. Based on the requirements of students' ability training, The high-quality multimedia courseware, Chaoxing Online Teaching Management Platform and TIA PORTAL design software and PLCSIM simulation software are made full use of to reform the teaching method. Besides that, this paper focuses on the introduction of "Siemens Cup" China Intelligent Manufacturing Competition into the teaching system reform of "electrical control and PLC technology", which promotes the cultivation of students' abilities in all aspects and highlights the OBE concept of course teaching.
On the Maximum of the Weighted Binomial Sum $2^{-r}\sum_{i=0}^r\binom{m}{i}$

The weighted binomial sum $f_m(r)=2^{-r}\sum_{i=0}^r\binom{m}{i}$ arises in coding theory and information theory. We prove that, for $m\not\in\{0,3,6,9,12\}$, the maximum value of $f_m(r)$ with $0\le r\le m$ occurs when $r=\lfloor m/3\rfloor+1$. We also show this maximum value is asymptotic to $\frac{3}{\sqrt{{\pi}m}}\left(\frac{3}{2}\right)^m$ as $m\to\infty$.
A deep hashing method based on attention module for image retrieval

Hashing retrieval technique has received widespread attention since its low storage consumption and fast retrieval speed. Image retrieval based on deep hashing method uses image features with high-level semantic information and discriminative expression capabilities. However, there are still some limitations: (1) Compared with the traditional feature vector, the length of the learned hash codes is short, and each bit of the code is -1 or 1, the discrimination capability of feature representation is limited; (2) the existing deep hash algorithms cannot directly learn the discrete hash codes using sign activation function, therefore, they use relaxation-based scheme to learn the hash codes, resulting in a large quantization error. In order to solve the above problems, a deep hashing retrieval algorithm incorporate with attention model is proposed. This algorithm embeds spatial and channel attention models in the feature extraction network, and uses a novel activation function in the hash layer. In addition, a novel triple loss function is further proposed to improve the learning capability of the network. Finally, experimental results on the two benchmark databases, i.e., CIFAR-10 and MINIST datasets, show the effectiveness and superiority compared with the baselines.
Comprehensive evaluation algorithm of English online teaching quality based on Artificial Intelligence

In view of the complex changing characteristics of English teaching quality, in order to obtain high-precision English teaching quality evaluation results, an English teaching quality evaluation algorithm based on principal component analysis and artificial intelligence is designed. This paper constructs the influence index of English teaching quality, optimizes and selects the evaluation index of English teaching quality by principal component analysis, and obtains the evaluation result of English teaching quality grade by artificial intelligence. The test results of specific application examples show that the proposed evaluation algorithm can accurately evaluate the English teaching quality level, and the evaluation results can provide valuable information for improving the quality of English teachig.
FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.
Research on Computer Network Teaching Mode Based on Artificial Intelligence and Web Platform

The traditional network teaching system has some effects, such as low intelligence, weak test and evaluation function, which also lacks of dynamic interaction function and so on. According to the modern education theory, this paper adopts the existing information technology, and studies the intelligent network teaching system model with personalized teaching and collaborative learning function under the web environment. The composition and module division of intelligent network teaching system based on Web and multi-agent are provided, and the main functions of each module are analyzed. Finally, through the analysis of the effectiveness of the algorithm in the actual process, the running situation and testing effect of the agent-based network teaching system are comprehensively evaluated. It is proved that the cooperation theory based on agent can establish an effective resource distribution model, which makes the distribution of educational resources more effective.
Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Abstract Background and objective Artificial intelligence‐based analysis of medical images has recently become a trendy field of study. The most significant effect on these systems to produce reliable and high‐performance results is the amount of accessed data. Although whole‐slide images (WSI), one of the current imaging techniques used in histopathology, storage costs are very expensive. Therefore, low‐dimensional visuals representing of WSIs is an essential field of study. Methods In this study, deep auto‐encoder‐based models are designed to create low dimensional representations for WSI. The size reduction was performed for the input images in different sizes, at the ratios of 1: 3, 1: 6, and 1: 12, respectively. Results Similarity index measure values were obtained as high as 0.957 and 0.938, respectively, using 128 × 128 × 3 dimensional patches in the size reduction process of 1: 3 and 1:12 on test WSI. Conclusion The proposed model has a structure that can be applied in compression of WSIs and secure data transfer. By using these representations, both storage costs and the high‐level hardware costs required by deep learning algorithms can be significantly reduced.
Hand Gesture-Based Character Recognition Using OpenCV and Deep Learning

Fast, accurate, and user-friendly human-computer interaction (HCI) requires both processing and intelligence. Understanding signs, and symbols is already possible by computers but recognizing symbols drawn live by a human in front of a camera is still a new concept. Many attempts have been made to achieve this already by using different sensors like Time of Flight (ToF) camera, Kinect sensor, etc., by using special metric systems or by special algorithms. Our research proposes doing such work using normal cameras that almost every computer has already. In this work, we tried to approach the problem from two directions. We left the detection, tracking and drawing tasks on mathematics-based algorithms like Accumulated Weight, CSRT (The Channel and Spatial Reliability) Tracker and OpenCV (Open Computer Vision) library. The recognition relies on deep learning. Our model can classify different characters of English alphabet and numerals so that when a user draws that, it can predict that. Our deep learning model is 98.56% accurate in classifying symbols which is more accurate than previous methods while not requiring any special sensors.
On the indicators of competence achievement for the group of specialties 51.00.00 “Cultural Studies and Socio-Cultural Projects”

The subject of this research is the competencies and indicators of competence achievement in the educational standards in the sphere of culture and art. The object of this research is the group of specialties 51.00.00 &amp;ldquo;Cultural Studies and Socio-Cultural Projects&amp;rdquo;, namely the federal state educational standard of higher education &amp;ndash; Bachelor's Degree in the field 51.03.02 &amp;ldquo;Folk Art Culture&amp;rdquo;. The article reviews the competences and indicators of achieving competences in the specialty 51.03.02 &amp;ldquo;Folk Art culture&amp;rdquo;, the discipline &amp;ldquo;Management of Film, Photo, and Video Studio&amp;rdquo;. The main research method is the analysis of literature and normative legal documents, including state educational standards and basic educational curricula in the context of competency approach. The author applies the method of describing personal experience based on the practical work of the Faculty of Culture and Arts and the actual pedagogical practice of F. M. Dostoevsky Omsk State University. &amp;nbsp;The scientific novelty consists in the development of indicators of general professional competencies for the indicated group of specialties. Currently, there is no uniform understanding of the indicators of competence achievement; it is on the stage of scientific discussion. The relevance for understanding competencies and their indicators is substantiated by the fact that the new federal state educational standards of higher education do not regulate this aspect, leaving the developers certain freedom on this matter. The competencies and indicators of competencies are considered on the example of the experience of the Faculty of Culture and Arts and actual pedagogical practice of F. M. Dostoevsky Omsk State University.
Research on synergic Control Algorithm and Collision Avoidance of unmanned aerial vehicle Formation

Abstract The current study mainly researches the problem of leader-follower based formation cooperative maintenance of fixed-wing UAV. First, the fixed-wing UAV formation with the leader-follower structure is used as the research object, and the flight dynamics model is established; the formation synergic control law is designed for the follower and the leader in the formation, especially the follower’s control law is improved to realize the state tracking control of the follower by controlling the speed and roll Angle of UAV; as stated by the master-slave formation, the distributed topology is introduced to realize the information interaction between adjacent UAVs, and on this basis, a collision avoidance strategy has been added to prevent drones from colliding with each other in the formation. Finally, the formation control algorithm and collision avoidance strategy are simulated through data simulation, and the results prove that the UAV formation can effectively complete the formation task and avoid collisions within the formation.
5G-Oriented IoT Big Data Analysis Method System

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.
Say in Human-Like Way: Hierarchical Cross-modal Information Abstraction and Summarization for Controllable Captioning

Image captioning aims to generate proper textual sentences for an image. However, many existing captioning models explore information incompletely and generate coarse or even incorrect descriptions of region details. This paper proposes a controllable captioning approach called Say in Human-like Way (Shway), which exploits intra- and inter-modal information in vision and language hierarchically in a diamond shape with the control signal of image regions. Shway is divided into abstraction and summarization stages. It can adequately explore cross-modal information in the first stage and effectively summarize generated contexts with a novel fusion mechanism for making predictions in the second stage. Our experiments are conducted on COCO Entities and Flickr30k Entities. The results demonstrate that our proposed model achieves state-of-the-art performances compared with current methods in terms of controllable caption quality.
Phase Transition Adaptation

Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by projecting external inputs into high dimensional dynamical system trajectories. In this paper, we propose an extension of the original approach, a local unsupervised learning mechanism we call Phase Transition Adaptation, designed to drive the system dynamics towards the ‘edge of stability’. Here, the complex behavior exhibited by the system elicits an enhancement in its overall computational capacity. We show experimentally that our approach consistently achieves its purpose over several datasets.
Development of an Augmented Reality System to Support the Teaching-Learning Process in Automotive Mechatronics

This article details the development of an augmented reality system to support the teaching-learning process, focused on the area of automotive mechatronics for gasoline vehicles. The application facilitates the user the interaction with the parts of the internal combustion engine in order to locate them in the assembly and disassembly process; in addition, the user can visualize the simulation of the thermodynamic cycle of the engine in improvements of the learning process. The geometric model of the elements of the Otto cycle internal combustion engine is made in CAD software, the development of the application and incorporation of the models and animations is done in Unity 3D. The execution of the Augmented Reality application during the pandemic originated by COVID-19 was essential to give continuity to the professional training process in the Automotive Mechatronics career, since the development of the teaching process was carried out solely online.