Reviewerly/exHarmony huggingface dataset:
Video Features with Impact on User Quality of Experience

One of the major challenges in wireless networks is to quantify and evaluate the user quality of experience (QoE). Hence, network operators are continuously working towards identifying the factors that most affect the user quality of experience for better network resource management. This paper proposes an evaluation method for video features with impact on user quality of experience. The goal is to extract and sort the features that highly affect the user experience and satisfaction while watching a video. The paper contributes first in determining the video parameters as well as the network features affecting QoE. Second, a survey is conducted to obtain subjective assessment on user satisfaction where users give their experience score and comments while watching videos with different feature values. We conduct experimental measurements and record videos with different frame rate, video resolution, transmission data rate, packet loss, delay and codec types. After collecting the QoE assessments, the supervised training data set is developed and imported into Rapid-Miner data mining tool. The feature evaluation is performed first using forward elimination feature selection algorithm, and second using decision tree classification to extract and sort the features that highly affect the subjective QoE. The results showed that the video resolution had the highest impact on user QoE. In addition, the transmission data rate was correlated to packet loss and delay and had higher impact than video frame rate and codec type.
Subjectivity Formation of a Technical University Master’s Student: A New Challenge for Engineering Education

An important condition for the digital transformation of education is the subjectivity of students and teachers. The formation of subjectivity as the ability to set one’s own goals and change either oneself or the educational environment to achieve them raises a number of questions: what is meant by the subjectivity of a student and a teacher, what does it mean to be a subject of the educational process, which factors contribute to and which hinder the formation of subjectivity. The authors focus on the conditions that promote and prevent the formation of subjectivity in master’s education of future engineers. Based on a review of foreign and domestic approaches to understanding subjectivity and the conditions for its development, it is concluded that all researchers distinguish similar aspects and manifestations in subjectivity: motivational (intrinsic motivation), emotional-valuable (proactive and responsible attitude to life), regulatory (the ability to set goals and follow them through to the end), cognitive (desire and ability to apply knowledge) and practical (skills, experience of independent activity). It has been established that foreign studies more often consider subjectivity as a resource, which improves the quality of education, while Russian studies consider it as a goal and a result of education. The article describes a taxonomy of subjectivity, which allows to measure it as an educational result. Based on the analysis of necessary and sufficient conditions for subjectivity formation, the hypothesis is made that the sufficient condition for subjectivity formation in mastering the “pedagogics” discipline is stepwise equitable partnership between all subjects of educational process. The condition is specified as a sequence of actions of a teacher and a master’s student in the process of mastering the discipline. The article describes the results of a pedagogical experiment in a group of master’s students of PNRPU Faculty of Civil Engineering, aimed at testing the proposed conditions. The results obtained show that step-bystep organized peer-to-peer interaction enabled most students in the experimental group to achieve the subjectivity level by the end of the discipline, while in the control group most students reached only the objectivity level. The motivational and practical aspects of subjectivity changed most significantly in the experimental group.
Fast pornographic video detection using Deep Learning

The recent rapid development of internet technology and applications leads to the booming of videos uploaded to and shared on the internet. However, some of them may contain impermissible content, especially pornographic videos, for the viewers. This problem raises a vast challenge in video filtering for many input videos. Concerning this matter, we introduce our system in which a key frame extraction method will be applied to the input video at the very first step. Subsequently, the Tensorflow object detection API is in charge of detecting and cropping any existing person in these frames. A Convolutional Neural Network (CNN) model then takes the cropped images and classifies them as pornography or not. The video is finally is marked valid for publishing according if the number of adult frames is below a threshold. Our experiments show that the proposed system can process videos much faster than human do while the accuracy is around 90% which can be meaningful to assist people in the task of video filtering.
Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases

Partial periodic pattern mining is an important model in data mining with many real-world applications. However, this model’s successful industrial application was hindered by the problem of combinatorial explosion of patterns, which involves generating too many patterns, most of which might be redundant or uninteresting to the user. Furthermore, the problem of combinatorial explosion increases the memory, runtime, and the energy requirements of a mining algorithm. This paper aims to tackle this challenging problem by proposing a novel model of maximal partial periodic pattern that may exist in a database. We also present a new tree structure and a pattern-growth algorithm, called Maximal Partial Periodic Pattern-growth (max3P-growth), to find all desired patterns effectively. Experimental results demonstrate that the proposed model prunes many redundant patterns, and the max3P-growth is efficient and scalable. Finally, we show the usefulness of our model with a case study on traffic congestion analytics.
How the Far-Right Polarises Twitter: ‘Hashjacking’ as a Disinformation Strategy in Times of COVID-19

Twitter influences political debates. Phenomena like fake news and hate speech show that political discourses on social platforms can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of ‘hashjacking’(The use of someone else’s hashtag in order to promote one’s own social media agenda.). For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative für Deutschland (AfD) and their potential use of ‘hashjacking’ in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by ‘hashjacking’ COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.
Speech data collection system for Kazakh language

Speech data in most of the languages that have a low resource doesn’t even exist. Therefore, producing speech corpora is very challenging and requires tremendous amount of time. Kazakh language due to its lack of popularity considered to be low-resource language. This paper provides an overview on many data collection techniques, marking some of the issues related to a particular method. The main aim of this paper is to present crowd sourcing web-based tool called “Kazakh recorder” which accessible on the website and designed to make the collection of Kazakh speech data more conveniently and quickly. Moreover, this section provides a statistics of people (age, gender, number of sentences) who made a contribution on collecting this speech data. Using this tool, we have collected over 50 hours of speech data 65 different native speakers, each having on average 500 sentences pronounced in Kazakh language.
Kernel-Based Dynamic Ensemble Technique for Remaining Useful Life Prediction

Prognostic and health management (PHM) has been widely used in manufacturing system, particularly, for predictive maintenance (PdM). The purpose of PdM is to predict whether equipment or parts is in health. Typically, the statistical exponential models with the health index were often applied for the remaining useful life (RUL) estimation. However, due to the diverse equipment characteristics and rapid environmental change, no single prediction model can predict well for RUL based on its assumptions and limitations. This study proposes a kernel-based dynamic ensemble technique (KDET) embedded with Inference Confidence Index (ICI) to build the weight adjustment of each model and model retraining mechanism. The ICI is built to measure the belief of the prediction by evaluating the similarity of multiple prediction models, and thus guide the concept drift to update the models immediately for the incoming streamline data. Two datasets are applied to validate the proposed KDET, and the result shows that the KDET can dynamically and effectively integrate multiple models for robust RUL prediction over time and thus improve the PdM system.
Modeling the decision-making process of lenders based on blockchain technology

In this article, the credit system is finalized to ensure the implementation of the Decree of the President of the Republic of Uzbekistan dated May 12, 2020 No. 5992 «On the Strategy for Reforming the Banking System in the Republic of Uzbekistan for 2020-2025». Information security problems in the credit system and their solutions, as well as effective modeling of lender-borrower relations, model iterative calculation and quantitative analysis of the assessment of loan funds by credit institutions based on established criteria, the introduction of a two-way graphical method for assessing the decision-making process, development of decision-making models by banks and other credit organizations as an integral part of the UzBCS credit platform (Uzbekistan Blockchain Credit System), a mathematical model of an integrated decision-making process has been proposed that allows banks and other organizations to evaluate and select reliable borrowers.
Quantum-Safe Cryptography and Security

The state-of-the-art information protection is at the threat posed by Quantum computing. If we look at the current state-of-the-art data protection techniques, then we will find that they are under serious threat by Quantum computing. And we hence have two aspects. One is Quantum Cryptography, and the other is Post Quantum safe Cryptography.
Deep Reinforcement Learning for Optimization at Early Design Stages

Deep reinforcement learning is shown to improve the design cost of hardware char63software interfaces within an industrial design framework. Based on optimization preferences specified by a designer, the proposed approach generates optimized solutions.
ASTRAEA: A Fair Deep Learning Scheduler for Multi-Tenant GPU Clusters

Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple tenants concurrently. Each tenant may have varied and dynamic resource demands. Unfortunately, existing GPU schedulers fail to thoroughly consider the fairness among the tenants and jobs, which can result in unbalanced resource allocation and unfair user experience. In this article, we present an efficient solution to provide strong fairness while maintaining high scheduling effectiveness in multi-tenant GPU clusters. First, we introduce a novel Long-Term GPU-time Fairness metric, which can comprehensively evaluate the fairness at both the tenant and job levels, based on both the temporal and spatial impacts of resource allocation. Second, we design a new and practical GPU scheduler, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astraea</small> , to enforce the desired fairness among tenants and jobs. Large-scale evaluations show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astraea</small> can improve tenant fairness by up to 9.42× compared to state-of-the-art schedulers, without sacrificing the average job completion time.
Quantization in Graph Convolutional Neural Networks

By replacing classical convolutions with graph filters, graph convolutional neural networks (GNNs) have emerged as powerful tools to learn a nonlinear mapping for data defined over graphs and address a variety of tasks encountered in many applications. GNNs inherit the distributed implementation of graph filters, where local exchanges among neighbor nodes are performed. In such distributed setting, the quantization can play a fundamental role to save communication and energy resources prior to data transmission, in scenarios where nodes are resource constrained. In this paper, we propose a quantized GNN architecture based on distributed graph filters for signals defined on graphs and analyze how the quantization noise can affect its performance. We show also that the expected error due to quantization at the GNN output is upper-bounded and the use of a decreasing quantization step size leads to more accuracy. The performance of the proposed schemes is evaluated by numerical experiments for the application of source localization.
Deep Reinforcement Learning for FlipIt Security Game

Reinforcement learning has shown much success in games such as chess, backgammon and Go [21, 22, 24]. However, in most of these games, agents have full knowledge of the environment at all times. In this paper, we describe a deep learning model in which agents successfully adapt to different classes of opponents and learn the optimal counter-strategy using reinforcement learning in a game under partial observability. We apply our model to $$\mathsf {FlipIt}$$ [25], a two-player security game in which both players, the attacker and the defender, compete for ownership of a shared resource and only receive information on the current state of the game upon making a move. Our model is a deep neural network combined with Q-learning and is trained to maximize the defender’s time of ownership of the resource. Despite the noisy information, our model successfully learns a cost-effective counter-strategy outperforming its opponent’s strategies and shows the advantages of the use of deep reinforcement learning in game theoretic scenarios. We also extend $$\mathsf {FlipIt}$$ to a larger action-spaced game with the introduction of a new lower-cost move and generalize the model to n-player $$\mathsf {FlipIt}$$ .
Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

In the next generation of computing environment e-health care services depend on cloud services. The Cloud computing environment provides a real-time computing environment for e-health care applications. But these services generate a huge number of computational tasks, real-time computing and comes with a deadline, so conventional cloud optimization models cannot fulfil the task in the least time and within the deadline. To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time. In order to overcome existing issues, an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications. In this work, two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier. The predictive model enhances the quality of service using performance metrics, makespan, least average task completion time, resource usages cost and utilization of the system. From results as compared to existing algorithms the proposed ANN-WHO algorithms prove to improve the average start time by 29.3%, average finish time by 29.5% and utilization by 11%.
Research on Real-Time Disconnector State Evaluation Method Based on Multi-Source Images

Infrared imager is widely used in substation inspections for early fault warning. However, infrared images have less texture information and large imaging differences in multiple states, which may lead to false or missed detection. This article proposed a disconnector state evaluation method based on infrared and visible images. This method combines the advantages of these two data sources and realizes individual-level temperature analysis. First, an optimized model disconnector-YOLOv4 (D-YOLOv4), with fewer model parameters and better detection performance than YOLOv4, is proposed to identify and locate the disconnectors in visible images. Experiments show that mean Average Precision (mAP)&#x0040;0.5 of the optimized model is 92.86&#x0025; and frames per second (FPS) is 105. Second, the detected area is mapped to the infrared image by image registration. Here, to address the problem arising from the different imaging characteristics of visible and infrared images, edge detection and affine transformation are introduced to extract the common features and complete image transformation. Third, we fit a <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> function to convert RGB value to temperature, and then the final state evaluation is conducted depending on the required standard. Experiments prove the feasibility and superiority of the proposed algorithm, which adapts the development demand of intelligent power substations, and the tendency of predictive maintenance.
Learning Sequential Visual Appearance Transformation for Online Multi-Object Tracking

Recent online multi-object tracking approaches combine single object trackers and affinity networks with the aim of capturing object motions and associating objects by using their appearance, respectively. Those affinity networks often build on complex feature representations (re-ID embeddings) or sophisticated scoring functions, whose objective is to match current detections with previous tracklets, known as short-term appearance information. However, drastic appearance changes during the object trajectory acquired by omnidirectional cameras causes a degradation of the performance since affinity networks ignore the variation of the long-term appearance information. In this paper, we deal with the appearance changes in a coherent way by proposing a novel affinity model which is able to predict the new visual appearance of an object by considering the long-term appearance information. Our affinity model includes a convolutional LSTM encoder-decoder architecture to learn the space-time appearance transformation metric between consecutive re-ID feature representations along the object trajectory. Experimental results show that it achieves promising performance on several multi-object tracking datasets containing omnidirectional cameras.
Inception of Perception—Augmented Reality in Virtual Reality: Prototyping Human–Machine Interfaces for Automated Driving

Augmented Reality in vehicles through Head-Up or Windscreen Displays offer numerous opportunities to develop road safety or user experience applications or both. These opportunities are further expanded in the context of automated driving, where human drivers are partially freed of the safety-critical driving task to attend to other activities, so-called non-driving related tasks. This brings new human factor challenges, such as lack of situation awareness when the driver is asked to take control of the driving vehicle. There are methodological challenges when undertaking user experience design or human factors research in this domain because of the current development status of future technologies. Prototypes of windscreen displays and automated vehicles are difficult to access and expensive. Therefore, we have developed an affordable virtual reality automated driving simulator that implements real-world driving videos for interface prototyping and testing. In this chapter, we discuss two concepts (dynamic windshield tinting to communicate certainty and an Augmented Reality video screen movements to communicate intention) to extend the windshield with additional driving relevant information, which were rapidly prototyped and evaluated in our Virtual Reality simulator. Following the user experience design approach, we report on qualitative findings from a user study design iteration (N = 16) about those concepts. We reflect on the identified strengths and weaknesses of our design concepts, which were critically influenced by participants' experience and trust in vehicle automation, as well as the methodological process. Lastly, we report on valuable lessons learnt from developing, improving and implementing our prototyping, simulation and evaluation tool for Augmented Reality applications in automated driving.
Comparison of Nearest Neighbor and Probabilistic Data Association Filters for Target Tracking in Cluttered Environment

Target tracking is having of great importance as it is one of the developing areas which has various applications in the military as well as civilian applications. Detection of target and data association in presence of false alarms are the two main difficult situations faced during tracking a maneuvering target. A survey has been done on the number of algorithms developed so far to solve the difficulties in target tracking. This paper gives a brief review of the need for data association and the algorithms and techniques proposed so far to resolve the problem due to data correlation in target tracking. In this paper, a simulation has been carried out to analyze the filter performance using nearest neighbor (NN) and probabilistic data association (PDA) as data association techniques. A comparison has been done between these two algorithms based on the variation in the values of the sampling time, clutter rate, standard deviation in noise covariances. The results fosters the use of PDA as the better data association algorithm for tracking process especially in high cluttered environment.
Metaheuristics in classification, clustering, and frequent pattern mining

Classification, clustering, and frequent pattern mining are the most common tasks in data mining. Almost all of these tasks can be completed using machine learning algorithms, but sometimes even these algorithms will not perform better. Deep learning is currently used in solving these data mining problems which very high accuracy. However, a recent report from the Massachusetts Institute of Technology states that computational limits of deep learning have reached. The report also stated that improvements can be achieved by using some optimization framework. Metaheuristic algorithms provide a framework for solving optimization problems and it is shown in this chapter some times metaheuristic algorithms outperform these machine learning algorithms and with less complexity. This chapter discusses different methods by which some common metaheuristic algorithms like Ant colony optimization, Genetic Algorithms, and Particle Swarm Optimization can be used to perform the different data mining tasks effectively.