Reviewerly/exHarmony huggingface dataset:
Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.
Speaker recognition method based on deep residual network and improved power normalized Cepstral coefficients features

In recent years, speaker recognition technology can achieve more than 90% accuracy in quiet environment, but it is not accurate in noise environment. In order to solve the problem of speaker recognition in noisy environment, a speaker recognition method based on deep residual network and improved Power Normalized Cepstral Coefficients (PNCC) features is proposed in the study. The PNCC features extraction algorithm is improved to be suitable for PNCC features parameter extraction in noisy environment. In order to further improve the training effect of the model, the deep residual network is adopted in this study to train the model, which effectively improves the model accuracy. In the recognition stage, in order to effectively suppress the influence of noise, this study proposes a VMD algorithm based on wavelet threshold denoising to improve the accuracy of speaker recognition. The experimental results show that compared with MFCC and PNCC features parameter extraction methods, the improved PNCC features extraction method is more conducive to feature extraction of speech signal under noise environment. Compared with other speaker recognition methods, the proposed speaker recognition method based on deep residual network and improved PNCC features has higher recognition accuracy.
Psychometric Assessment of Teacher Mindset using hybrid KNN-SVM Algorithm

With the rise of Information Technology, there has been a monumental rise in the number of jobs in the industry. To counter the huge number of applications and to ensure that candidates of proper attitudes are selected, a holistic evaluation from a psychology point of view is must. We propose a system to develop a similar platform for the evaluation of teachers, i.e., using Psychometric Games to measure and evaluate traits that are favourable for teachers to have. The statistics being captured are not shown to teachers which ensures unbiasedness to a large extent. We use hybrid of KNN and SVN algorithm and similarity index to predict the acceptance rating of the teacher on the basis of the previous results. The data set is generated by playing different games by different users and storing it in a one to many model mapping. Finally, a report is generated with summaries of all the actions and the approximate adherence to different traits being captured.
Optimized Graph Search Algorithms for Exploration with Mobile Robot

Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.
Adaptive Video Caption Detection and Extraction Method Based on Color Filtering Principle

Based on the principle of color (RGB) filtering, an improved adaptive video caption detection and extraction method is proposed. Firstly, the principle of the color filtering algorithm used in the video caption detection and extraction method is analyzed, and then the algorithm is improved adaptively according to the caption pixel size to filter the noise. Finally, experiments verify the effect of this method in extracting subtitles from video. The experimental results show that the accuracy of detecting and extracting subtitles in color video is as high as 99.3%.
A Robust Approach to Noise for Plan Recognition in RTS Games

Trying to infer the strategy of the opponent is very important in games. Especially in Real-Time Strategy Games (RTS), where you have uncertainty and thus cannot see most of the opponent’s actions, but you do not want to be unprepared for its strategy. Good human players can do it almost naturally, but it is a different story for AI players. Plan recognition is a challenging problem, especially with uncertainty and the number of possible actions and states of the world in RTS games. We address the problem of plan recognition in RTS games. We show that an approach based on plan recognition as planning and heuristic search can yield good results and be robust to noise. Furthermore, we found that such an approach has its accuracy decreasing slightly with noisy data, does not need any training beforehand, and could be easily adapted to different RTS games.Our approach allows us to infer in real-time the plan that a player might be pursuing in an RTS game, here we focus on the RTS game called StarCraft, but it could be adapted to many other RTS games.
Features of Certification of Students of Artistic and Creative Directions of Training in the Context of Digital Distance Learning

The aim of the study is to determine the main features of online certification of students in artistic and creative directions of training. The article presents the characteristic features of the process of remote verification of the knowledge quality, the principles of creating a target group of students and the development of test questionnaires. The features of students’ digital certification in disciplines of the artistic direction are given; the ways of solving the problems discovered during the study are outlined. The scientific originality of the study lies in the fact that the author for the first time carries out a comparative assessment of the effectiveness of certification forms in the theoretical and practical parts of art disciplines, implemented by the Department of Design, Art Education and Technology of Bunin Yelets State University in the context of distance learning. As a result, it has been revealed that the possibility of checking the quality of knowledge in the practical part of the disciplines is objectively lower than in the theoretical one. The most preferable forms of certification, their distinctive features, strengths and weaknesses have been determined.
Human action recognition method based on wearable inertial sensor

Aiming at the problem of low recognition accuracy caused by the small number of accurate human action recognition in the practical application of traditional mechanical human action recognition method, the research of human action recognition method based on wearable inertial sensor is carried out. A new recognition method is proposed by capturing human motion data based on wearable inertial sensor, establishing human joint and motion model, and recognizing multi class human motion based on dynamic time warping. Through experimental demonstration, it is proved that the new recognition method can realize 100% high-precision recognition of human action, and effectively solve the problem of low accuracy of human action recognition by traditional methods.
A 360° Video Editing Technique that Can Avoid Capturing the Camera Operator in Frame

In this research, we have devised a shooting and video processing method/system that uses two fisheye cameras to avoid capturing the camera operator in frame. In this paper, we propose a new idea for the system. The form of 360° video has become rapidly diverse since YouTube started to support 360° video in 2015. Many companies that seek business opportunities and step into the 360° video market. And the products related to 360° cameras have appeared one after another. On the other hand, usually, when the camera operator shoots a 360- degree video, capturing the camera operator cannot be avoided. The DHI method designed by the authors previously solved this problem. However, it cannot be said that the image quality of the video is high. In this paper, we additionally propose a new method to solve the image quality problem.
EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks

Crowdsourcing data collection from a network of mobile devices is useful in various applications. Mobile devices store a large amount of visual data that aid in different situations. Trained CNNs can be deployed on mobile devices to be used in searching for objects of interest. Querying for novel objects, for which models have not been trained, presents unique challenges. When novel objects are queried, new models must be trained and distributed to all edge devices, which can be cumbersome. In this paper we propose EDIR, an efficient method and a system that enables answering these queries while taking into account the bandwidth limitations in wireless networks, and the limited energy and computational power on mobile devices. Results show that EDIR reduces the amount of data transfer by 45%compared to other approaches while achieving a good F1 score.
Deployment of Software-Controlled Distributed Laboratory Complex for a Higher Educational Institution

Abstract The intensive development of information systems and communication infrastructures has led to the emergence of new approaches and new tools for their management. Cloud services, virtualization technologies and software-based management tools are actively used today for building modern information systems which provides additional opportunities for managing components of these systems and for expanding such systems abilities. The use of such approaches and tools can provide a solution to the complex of problems faced by education institutions today, presenting new methods of distance learning and new forms of practical training. The balanced use of virtualization technologies and software-based control tools allows building distributed laboratory complexes of a new generation which will provide new possibilities for using the equipment that is available in educational institutions. The use of such complexes in the educational process allows not only to instill in students the skills of managing the confgura-tions of communication infrastructures that are especially relevant today, but also to organize, in addition to traditional laboratory work, new forms of contactless practical classes. When conducting such classes, students get the opportunity to fully-functional remote access to the virtual and physical components of the complex, which is especially important for organizing the educational process in modern conditions associated with the COVID-19 pandemic.The article discusses modern approaches to the construction of software-controlled communication infrastructures, tools for software-based management of their components and also, an example of the implementation of these approaches and such tools usage in the construction of a distributed laboratory complex for a higher educational institution. The main advantages of the approach based on the use of hypervisors and emulators are presented, the experience of its deployment in the modernization of the laboratory complex for a IRIT-RTF UrFU is considered.
Research on Effectiveness of Contrastive Learning with Adversarial Examples for Different Downstream Tasks

Contrastive learning is a new self-supervised representation learning technique, which is considered to have great potential to improve the performance of downstream learning tasks. Recently, some researchers have conducted experiments on some datasets and found that contrastive learning based on adversarial examples can improve the performance of some image classification tasks. However, does contrastive learning with adversarial examples really benefit the performance of various downstream tasks? At present, there is still a lack of in-depth research on this issue. In this paper, we introduce a contrastive learning method with adversarial examples based on different adversarial perturbations intensities, and establish a variety of downstream image classification tasks. Experiments on different types of downstream tasks show that contrastive learning based on adversarial examples may not improve the performance of downstream tasks. We also find that the adversarial perturbation intensity and the proportion of the adversarial perturbation loss in contrastive loss are important factors affecting the above performance.
Selection of Sensors for Heliostat of Concentrated Solar Thermal Tower Power Plant

As the energy demand of the world is rising, more and more efforts are being made to harness different forms of energy available. Current pollution due to fossil fuels has directed the world to shift to cleaner renewable energies, such as solar. Photovoltaic, as well as concentrated solar technologies, are developed to harness solar energy. The concentrated solar tower power plant is an emerging technology and is under development having vast areas of improvement. The efficiency of the concentrated solar tower power plant depends upon the accuracy of the tracking system of the heliostats placed all around the central tower of the plant. A closed-loop tracking system a feedback method is a need. In addition, to check the accuracy of the system, a calibration system is required. This system uses different types of sensors. In this study, an effort is made to enlist different types of sensors available and their use in the tracking system of the solar thermal tower power plant. In addition, different sensors are suggested that are best suited for calibration and correction purposes.
GRSiamFC: Group Residual Convolutional Siamese Networks for Object Tracking

Visual tracking has seen considerable success with fully convolutional Siamese networks (SiamFC). However, the features extracted by SiamFC are redundant in practical applications. Most of the feature channels are ineffective or even serve as noise, which adversely affects tracking performance. The convolutional kernels tend to converge during the training phase of SiamFC, thereby the output features are typically redundant or irrelevant. In this paper, we propose a group residual convolutional Siamese network (GRSiamFC) to address this problem. Specifically, GRSiamFC divides the convolutional layers used for feature extraction into groups and trains each group in turn. Then the residuals between the output of previous groups and the true labels are used as losses to guide the current group to learn the residuals that were not fitted by the previous group, thereby improving the discriminative ability of the model. Extensive experiments on OTB50, OTB100, UAV123, and UAV20L show that the proposed GRSiamFC significantly improves the performance of SiamFC while keeping the complexity unchanged.
Mobile Application for Mentoring Graduation Theses

In this paper mobile version of the system for mentoring graduation theses (MAK) is presented. The goal of application is to enable the Students, Mentors and Reviewers to do their tasks inside of the MAK system. After successful authentication application sends token to user. Token and user name are used for further requests to decide if the user is a Student or Mentor. With that in mind, the user gets the appropriate interface. Inside every activity the user can do various activities in the MAK system, where users input is the input to the system. After an action, the user gets a Toast message which is used as an output from the system. The messages show the state of the procedure or an error which is related to the state of the Student’s paper, and disables the navigation to the next activity. The application lifecycle management is proposed and explained in further chapters.
Multimedia Meets Archaeology: A Novel Interdisciplinary Teaching Approach

Multimedia information processing course includes image processing, text processing, video processing, audio processing, graphics, and animation. Classical multimedia information processing course is to lecture these contents as independent course units, making the course teaching inconsistently and student's learning interest and attention lost easily. Archaeology is a cross-disciplinary field, where an archaeological research project needs to use a variety of multimedia information processing technologies. This paper introduces a novel cross-disciplinary course teaching approach to combine traditional multimedia information processing techniques with archaeological research content in the new engineering era, which increases students' attention and interest in the multimedia information processing course. Teaming up with the archaeology professor, we present two multimedia-archaeology projects: intelligent pottery fragments splicing and intelligent Oracle inscription recognition. We have addressed a few challenges in our courses: 1) how to guide students efficiently to implement different project contents, e.g., using the scanner to acquire three-dimensional porcelain fragment data skillfully. 2) How to inspire students to learn and use various multimedia processing technologies and archaeology knowledge. 3) How the teacher adapts the teaching content according to the dynamic interests of the students. To address these challenges, students are grouped into two project teams based on their strengths and interests. We introduce collaborative learning and active learning strategies to help students learn and use different knowledge and address project problems and learning problems. We also invite the archaeological professor to teach basic archaeology knowledge in the class. Furthermore, to better understand the students' learning situation, we present a weekly project progress report approach, which can also help the teacher adjust the teaching content. This teaching approach can enhance the continuity of multimedia information processing teaching and stimulate students' enthusiasm and creativity in learning. Moreover, it can deepen the cultural atmosphere of the teaching in an engineering course.
Comparative analysis of two strategies for forming regions of interest in panorama vision system

Analytical expressions for estimation of the elementary computational operations number are obtained, which are required to form personal regions of interest for users of a panoramic vision system with a distributed aperture and cameras of different spectral ranges. These expressions allow us to determine the optimal computation strategy for the forming of several personal areas of interest for the known configuration of a panoramic system.
A comprehensive Study on Credit Card Fraud Prevention and Detection

Nowadays, credit card fraud is becoming more and more challenging for financial institutions. With the era of technology and digitization, frauds are taking variety of forms remaining in perpetual growth and having a huge impact on the business gain. Financial institutions try to use standard tools and to comply with industry and payment schemes requirements. They have also started to invest fully in artificial intelligence and to integrate advanced tools such as machine learning and deep learning techniques within their system. Nevertheless, the effort remains insufficient due the various challenges of this phenomenon. In this paper, we go in details with credit card and card not present frauds, as well as standard tools and classic approaches being used to prevent against them. We also propose a recent state of art on various data mining techniques used to overcome this problem. The result of our study is a very good starting point for researchers working on the same subject since it gives a good understanding of credit card fraud phenomenon as well as presents different approaches and methodologies adopted to prevent such frauds and highlights the main challenges that were faced.
Mobile Robots’ Collision Prediction Based on Virtual Cocoons

The research work presents a collision prediction method of mobile robots. The authors of the work use so-called, virtual cocoons to evaluate the collision criteria of two robots. The idea, mathematical representation of the calculations and experimental simulations are presented in the paper work. A virtual model of the industrial process with moving mobile robots was created. Obstacle avoidance was not solved here. The authors of the article were working on collision avoidance problem solving between moving robots. Theoretical approach presents mathematical calculations and dependences of path angles of mobile robots. Experimental simulations, using the software Centaurus CPN, based on colored Petri nets, has shown, that the possible conflict can be predicted in advance using calculations of the angle of movement of two possibly colliding cocoons. Authors of the paper presents and explains experimental simulations, where robots are moving in different path angles, which intersects each other. Proposed collision avoidance algorithm solves collision avoidance task using several possibilities. Software Centaurus CPN gives possibility to stop simulation of manufacturing process in the place where it finds possible collision between two cocoons. In order to check correctness of proposed collision prediction and avoidance algorithm, further simulation is done step by step, while situation is solved. Simulated manufacturing process is continued till new possible collision is obtained.
Causal interpretation of graphical models

Shafer and Vovk have shown how to base probability theory on game theory. In this framework, we give probabilities an empirical and predictive meaning by means of a form of Cournot's principle, which says that reality will not permit a gambler to win disproportionately to the capital he risks. How does this principle apply to the causal interpretation of graphical models?