Reviewerly/exHarmony huggingface dataset:
Comfortable digital education: problems and development prospects

Currently, digitalization is rapidly encompassing all spheres of society, including education. This process received particular relevance and a powerful impetus to development in connection with the pandemic in 2020. The educational process has undergone dramatic changes, which has given rise to many problems associated with the insufficient development of the principles of digital didactics, with the unwillingness of universities to provide the necessary conditions for mass online education. The study aims to determine the set of conditions that form the basis of effective and comfortable learning in the modern digital educational environment, to determine the directions for the development of new professional competencies of the teacher. The team of authors considers the readiness of the material and digital infrastructure of the university, as well as the readiness of teachers and students to carry out their functions in the digital environment, to be the main prerequisites for organizing a comfortable and effective education in digital format. The paper discusses the prospects for the development of a multi-tier infrastructure of digital education, the problems that arise among the main actors of educational relations (teachers and students), the direction of the choice of teaching methods, and the organization of classes, taking into account the peculiarities of the distance learning process. The study used methods of comparative analysis, generalization, synthesis. The result and novelty of the research are expressed in the development of a set of necessary conditions for effective and comfortable interaction of participants in the educational process in the context of mass online learning. The results of the work can be used to organize professional development courses and retraining programs for teachers in the field of designing online courses, developing tools for a digital educational environment.
Frequency domain despeckling technique for medical ultrasound images

Abstract This work proposes a novel frequency domain despeckling technique pertaining to the enhancement of the quality of medical ultrasound images. The results of the proposed method have been validated in comparison to both the time-domain and the frequency-domain projections of the schur decomposition as well as with several other benchmark schemes such as frost, lee, probabilistic non-local means (PNLM) and total variation filtering (TVF). The proposed algorithm has shown significant improvements in edge detection and signal to noise ratio (SNR) levels when compared with the performance of the other techniques. Both real and simulated medical ultrasound images have been used to evaluate the numerical and visual effects of each algorithm used in this work.
A Maximal Margin Hypersphere SVM

In this work we propose a generalization of the Support Vector Machine (SVM) method in which the separator is a curve, but the concept of margin and maximization of the margin is still present. The idea of using different functions for the separation has been explored in particular in the scope of hyperspheres. However, most of these proposals use two spheres, using concepts different from maximal margin or one sphere but with a poor performance when data from the classes have a linear shape. In this paper we present a formulation of the linear SVM that generalizes it to a spherical separation shape, but still maximizing the margin. A linear relaxation of this quadratic formulation is also presented. The performance of these two formulations for classification purpose is tested and the results are encouraging.
Automatic removal of various annotations and marking from Maritime surveillance recorded video

One of the main challenging issues in video analysis is the recovery of the original video frames from the annotated and text marked in the guided user interface (GUI) tool, particularly in cases where the original video is not available. Removing annotation from video frames is essential for any kind of algorithm development, such as noise removal, dehazing, object detection, recognition, identification in the video and tracking specific objects in the maritime environment, and further testing process. In this research work, we developed the algorithm for the removal of all annotations from any portion of the video frame, without affecting the integrity of the original video content. Here, we present a novel technique to remove unnecessary annotations and markers using a progressive switching median filter with wavelet thresholding. Experimental studies have shown that the annotation-free images generated from the proposed method can be used for the development of any basic algorithm.
Hyper-parameter Optimization for Latent Spaces

We present an online optimization method for time-evolving data streams that can automatically adapt the hyper-parameters of an embedding model. More specifically, we employ the Nelder-Mead algorithm, which uses a set of heuristics to produce and exploit several potentially good configurations, from which the best one is selected and deployed. This step is repeated whenever the distribution of the data is changing. We evaluate our approach on streams of real-world as well as synthetic data, where the latter is generated in such way that its characteristics change over time (concept drift). Overall, we achieve good performance in terms of accuracy compared to state-of-the-art AutoML techniques.
Rotated YOLOv4 with Attention-wise Object Detectors in Aerial Images

research-article Share on Rotated YOLOv4 with Attention-wise Object Detectors in Aerial Images Authors: Zhichao Zhang National University of Defense Technology, China National University of Defense Technology, ChinaView Profile , Jinsheng Deng National University of Defense Technology, China National University of Defense Technology, ChinaView Profile , Hui Chen National University of Defense Technology, China National University of Defense Technology, ChinaView Profile , Xiaoqing Yin National University of Defense Technology, China National University of Defense Technology, ChinaView Profile Authors Info & Claims ICRSA 2021: 2021 4th International Conference on Robot Systems and ApplicationsApril 2021 Pages 1–6https://doi.org/10.1145/3467691.3467706Online:09 September 2021Publication History 0citation111DownloadsMetricsTotal Citations0Total Downloads111Last 12 Months111Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment

Video streaming services account for the majority of today's traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user's environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today's multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user's needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
Application of Deep Reinforcement Learning Methods in Debt Collection

In the last few years, there is a growing interest in offline reinforcement learning (offline RL) and in reinforcement learning (RL) in general. In this paper, we presented an example of applying some of these methods to the debt collection process. We conducted several experiments using DQN, Munchausen DQN, DRQN and CQL modification for creating an optimal agent for our problem. As a result, we showed that CQL and Munchausen DQN could be successfully used in offline RL setting for debt collection process. Moreover, these agents show performance comparable with baseline DDQN agent but have several advantages for mentioned problem. We also described some practical obstacles in the usage of RL agents in a real-life task.
Demos of Passing Turing Test Successfully

Recently, a new kind of machine intelligence was born, called as UI (Ubit intelligence). The basic difference between UI and AI is encoding; UI is based on word encoding; but AI is based on character encoding. UI machine can learn from human, remember the characters, pronunciation, and meaning of a word like human. UI machine can think among the character, pronunciation, and meaning of words like human. Turing Test is similar to a teacher testing a student; Before Test, tester must teach the content of the test questions to UI machine first; after UI machine learning, tester asks testee questions; to check testee has remembered what he taught; to check testee can think among character, pronunciation, and meaning of words. This paper demonstrates that testee can remember what testee taught; and answer all 6 questions correctly by thinking. UI machine passes Turing Test easily and successfully with score 100. Following on, the works related to this study is briefly introduced. At last, this paper concludes that UI machine is based on word encoding, can form word, form concept, can possess brain like intelligence, also can possess human like Intelligence; therefore UI machine passes Turing Test easily and successfully. On the contrary, AI machine is based on character encoding; can't form word; can't form concept, AI machine can't possess brain like intelligence, nor possess human like Intelligence. Therefore, AI machine can't pass Turing Test.
Mining Opinion Features and Sentiment Analysis with Synonymy Aspects

In present days, sentiment analysis has gained much importance in the field of natural language processing (NLP). In near future, we expect its importance will grow exponentially. In this paper, our aim is to handle the problem of synonymy aspects. With lots of popular aspect mining algorithms, we sometimes get synonymy aspects, which lead to produce improper results. An algorithm to distinguish synonymy aspects and further proceed in sentiment analysis with synonymy aspects is proposed here. For sentiment analysis, aspect level categorization and document level categorization experiments are done, where we get promising outcomes. Also, we show here sentiment-polarity and sentiment-uncertainty per review, which will help the product/business owner to get proper feedback.
An SDI Approach for Managing Earth Observation Videos

Recent development of remote sensing technologies brings Earth observation (EO) videos as a new type of satellite imagery in a spatial data infrastructure (SDI). Although SDI has been applied successfully for traditional EO data management, EO videos in SDI still face some problems including incompatible video formats of different product systems and the registration of videos with GIS (Geographic Information System) maps. The paper presents an SDI approach on how to manage EO videos, taking Jilin-1 satellite videos as an example. It suggests an open standards based approach for acquiring, storing, accessing, and processing EO videos. Some key technologies including video storage, video object extraction, and alignment of video and ground maps are highlighted.
Decision Support Proposal for Imbalanced Clinical Data

The difficult diagnosis of acute appendicitis of patients appealing to the hospital with abdominal pain often leads to unnecessary acute appendicitis operations. Accordingly, the aim of this study is to be able to provide the correct diagnosis whether the existing case indeed necessitates operation or not through machine learning algorithms based on classification. To that purpose, SMOTE, random oversampling, and random undersampling methods were proposed to reduce the negative effects of imbalanced data set problem on classification, and it was benefitted from the risk factors in relation to Alvarado Score to predict the diagnosis of acute appendicitis. Additionally, a classification model was generated by using support vector machine classification algorithm. A decision support system was developed that could contribute to the decision making by generating interface for support vector machine algorithm in which the best performance was obtained.
Handwritten Text Line Recognition Using Deep Learning

Handwritten Text Line Recognition in Machine Learning is one of the emerging fields within computer vision. There are different languages and in every language, there are several handwritings of people which need to be correctly identified. Humans identify and process the text that they see that is the same which is expected by the HTLR. Although it is difficult to recognize the text by the system. In handwritten line text recognition, there are several steps involved. Pre-Processing the input image, feature extraction of it and transcription are some of the steps involved. During these processes, our system is being trained and similarities and differences of several handwritten samples are noted. The application hence takes pictures of the transcription thereby converting it into a digital text which is the desired output.
The Serbian primary school teachers' profiles regarding the preference for a teaching model during the COVID-19 pandemics

The quality of the teaching process highly depends on teachers and their qualifications, and the teacher's role plays a very important part in the implementation of the new teaching methods. The results presented in this paper are part of the quantitative study which investigated the primary teachers' perception of the online teaching model that has been realized in Serbia during the COVID-19 pandemic, in comparison to the traditional teaching model. The paper aims to define and present the teachers' profiles, based on their preference for a certain teaching model: the profile of the teachers who prefer working in the traditional teaching model, and the profile of the teachers who prefer working in the online teaching model, or do not make any difference between the two, based on the professional data and their statements and attitudes regarding the comparison of traditional and online teaching models. It was assumed that the teachers with less work experience, more professional training and higher education level would generally prefer the online teaching model. For the research purposes, the specialized two-part questionnaire was designed. The first part included questions concerning the socio-demographic background, while the second part investigated the teachers' attitudes towards the characteristics of the teaching process in both traditional and online teaching models. The independent-sample t-test was used to estimate the influence of previous training on the choice of the preferred teaching model. The research was conducted during January and February 2021. The research sample included 609 examinees. The research results showed that the choice of the preferred teaching model depends on the self-assessment of the teachers' competencies required for the online teaching model, as well as on the duration of the training in the field of Information and Communication Technologies, as a part of the regular professional improvement training. The initial assumption was only partially confirmed by the research results.
A computational approach for progressive architecture shrinkage in action recognition

Abstract Efficiency plays a key role in video understanding modeling, and developing more efficient spatiotemporal deep networks is a key ingredient for enabling their usage in production scenarios. In this work, we propose a methodology for reducing the computational complexity of a video understanding backbone while limiting the drop in accuracy caused by architectural changes. Our approach, named, Progressive Architecture Shrinkage, applies a sequence of reduction operators to the hyperparameters of a network to reduce its computational footprint. The choice of the sequence of operations is automatically optimized in a coordinate‐descent schema, and the approach transfers knowledge from both the initial network and previous stages of the shrinking process by employing a Knowledge Distillation and an adaptive fine‐tuning strategy. As each iteration of the shrinking algorithm requires to train a large‐scale video understanding network, we perform experiments on MARCONI 100—a supercomputer equipped with an IBM Power9 architecture and Volta NVIDIA GPUs. Experimental evaluations are conducted using two backbones and three different action recognition benchmarks. We show that, through our approach, high accuracy levels can be maintained while reducing the number of multiply–adds operations by four times with respect to the original architectures. Code will be made available.
Deep Reinforcement and Imitation Learning for Self-driving Tasks

In this paper we train four different deep reinforcement and imitation learning agents on two self-driving tasks. The environment is a driving simulator in which the car is virtually equipped with a monocular RGB-D camera in the windshield, has a sensor in the speedometer and actuators in the brakes, accelerator and steering wheel. In the imitation learning framework, the human expert sees a photorealistic road and the speedometer, and acts with pedals and steering wheel. To be efficient, the state is a representation in the feature space extracted from the RGB images with a variational autoencoder, which is trained before running any simulation with a loss that attempts to reconstruct three images, the same RGB input, the depth image and the segmented image.
An illumination direction estimation algorithm of face images

Abstract Illumination is one of the important factors affecting the accuracy of face recognition. This paper proposes an illumination direction estimation algorithm of face images and it can be applied to the non-frontal face image. In order to eliminate the influence of face’s albedo, the algorithm use SfSNet to get the shading image. The algorithm divides the face region into eight regions by using five landmarks in face and estimate the illumination direction by analyzing the average brightness of each regions. Experiment on YaleB dataset shows that the algorithm can estimate the illumination direction correctly in most cases.
Improving Server and Client-Side Algorithms for Adaptive Streaming of Non-Immersive and Immersive Media

HTTP adaptive streaming is a technique widely used in the internet today to stream live and on-demand content. Server and client-side algorithms play an important role in achieving a better user experience in terms of metrics such as latency, rebufferings and rendering quality. In this doctoral study, we propose and evaluate a number of new algorithms for both non-immersive and immersive media in different settings ranging from low-latency live to on-demand streaming.
Novel Methodologies for Processing Structured Big Data Using Hadoop Framework

There are many tools and techniques to store and process data. But with big data usual traditional systems fail to handle big data. The reason is its structure, size, etc. Because of this reason many new tools have been developed and Hadoop is one of them. Hadoop is a framework that contains many tools to manage big data. Apache Hadoop has tool called Hive which can be used to process big data in structured form. There are many ways in which big data can be processed. But if the user is not well-versed with programming and knows query language like SQL, then required information can be retrieved by using Hive. Using Apache, Hive not only reduces the number of lines of coding but also saves time of programmer. This paper explains the working of Hive along with an illustration of how useful data can be retrieved by using HiveQL. This paper presents the effective way of achieving big data analytics using big data using Hadoop Hive.
Speech Dictation System Based on Character Recognition

To solve students' dictation problems, a speech dictation system based on character recognition is proposed in this paper. The system applied off-line handwritten Chinese character recognition technology, denoised the image through Gaussian filter, segmented the text through projection method, and converted the image to text through OCR technology. The straight line mark in the picture was detected by Hough transform technology, and then SKB-FSS algorithm and WST algorithm were used for speech synthesis. Experiments show that the system can effectively assist students in dictation.
Query Interface Schema Extraction for Hidden Web Resources Searching

It is an urgent task of the Web search field to satisfy people’s demand for having effective access to the high-quality Web. Instead of specifying a URL to send an HTTP request to get the static page information, accessing hidden Web resources (deep Web) need to post queries to the query interface provided by the website. The query interface is the entrance to get the Web database information. Therefore, the research on schema extraction from deep Web query interface is a key step in hidden Web resources mining. This paper presents a novel approach to extract interface schema from deep Web based on domain ontology. Besides, it also proposes a new presentation of query interface attribute, which reflects the semantic relationships between the labels, on the basis of location, label semantic relationship and string similarity. Experimental results show that our system is feasible and efficient, and achieves high precision, recall and F-Measure value across a variety of databases.
Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data

Anomaly Detection (AD) is used in many real-world applications such as cybersecurity, banking, and national intelligence. Though many AD algorithms have been proposed in the literature, their effectiveness in practical real-world problems are rather limited. It is mainly because most of them: (i) examine anomalies globally w.r.t. the entire data, but some anomalies exhibit suspicious characteristics w.r.t. their local neighbourhood (local context) only and they appear to be normal in the global context; and (ii) assume that data features are all numeric, but real-world data have numeric/quantitative and categorical/qualitative features. In this paper, we propose a simple robust solution to address the above-mentioned issues. The main idea is to partition the data space and build local models in different regions rather than building a global model for the entire data space. To cover sufficient local context around a test data instance, multiple local models from different partitions (an ensemble of local models) are used. We used classical decision trees that can handle numeric and categorical features well as local models. Our results show that an Ensemble of Local Decision Trees (ELDT) produces better and more consistent detection accuracies compared to popular state-of-the-art AD methods, particularly in datasets with mixed types of features.