Reviewerly/exHarmony huggingface dataset:
Information technology and online resources in the professional activities of preschool teachers

Purpose The research objectives are to interview rising preschool teachers studying at Russian universities, highlight the categories of information technologies used in preschool institutions based on the survey results and possible difficulties that may arise when working with them and develop tips for working with interactive technologies for preschool teachers. Design/methodology/approach The results of the survey showed a high level of involvement of information technology in the educational process. The majority of respondents (87%) use information technology in teaching and learning. The analysis of the answers shows relatively identical indicators of the use of different types of information technologies: 65% prefer more technological and 35% – applied. It was shown that the use of technological and applied ICT categories isn't similar among participants of the survey. To increase the level of students' and teachers’ knowledge with modern information technologies the program was created. Findings In preschool education, the kindergarten teacher or supervisor should act as the leader of the learning process, including the interactive one. The teacher should encourage children to independently use the ICT tools and guide the process. Originality/value Given the rapid pace of development of science and technology, the curriculum needs to be deepened and expanded by diversifying activities. It is worth paying attention to modern methods of teaching and learning, which require the use of pedagogical innovations, the acquaintance of students and teachers with possible ways to expand the activities and update the latest teaching methods in educational process.
Decoding of Space-Symmetric Rank Errors

This paper investigates the decoding of certain Gabidulin codes over a channel with space-symmetric errors. Space-symmetric errors are additive error matrices that have the property that their column and row spaces are equal. We show that for channels restricted to space-symmetric errors, with high probability errors of rank up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 (n-k)/3$</tex> can be decoded with a Gabidulin code of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> and dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> , using a weak-self orthogonal basis as code locators.
One-Shot Quantum State Redistribution and Quantum Markov Chains

We revisit the task of quantum state redistribution in the one-shot setting, and design a protocol for this task with communication cost in terms of a measure of distance from quantum Markov chains. More precisely, the distance is defined in terms of quantum max-relative entropy and quantum hypothesis testing entropy. Our result is the first to operationally connect one-shot quantum state redistribution and quantum Markov chains, and can be interpreted as an operational interpretation for a possible one-shot analogue of quantum conditional mutual information. The communication cost of our protocol is lower than all previously known ones and asymptotically achieves the well-known rate of quantum conditional mutual information. Thus, our work takes a step towards the important open question of near-optimal characterization of the one-shot quantum state redistribution. A full version of this paper is accessible at: https://arxiv.org/pdf/2104.08753.pdf
A New Accurate Clustering Approach for Detecting Different Densities in High Dimensional Data

Clustering is a data analysis method for extracting knowledge by discovering groups of data called clusters. Density-based clustering methods have proven to be effective for arbitrary-shaped clusters, but they have difficulties to find low-density clusters, near clusters with similar densities, and clusters in high-dimensional data. Our proposal consists in a new clustering algorithm based on spatial density and probabilistic approach. Sub-clusters are constituted using spatial density represented as probability density function (p.d.f) of pairwise distances between points. To agglomerate similar sub-clusters we combine spatial and probabilistic distances. We show that our approach outperforms main state-of-the-art density-based clustering methods on a wide variety of datasets.
The Anti-Hate Brigade: How a Group of Thousands Responds Collectively to Online Vitriol

Do you think about what you ought to say, or what you wish others would say when you read the comment fields in social media? Would you dare to share your opinion more frequently if you knew that others in the same thread supported you? Are you already taking action to disrupt those who are spreading hate on the Internet, and do you wish you had the support of others who are doing the same?These are the words that greet visitors to the Facebook group #jagärhär, a Sweden-based collective of thousands of people who have made a regular practice of responding en masse to what they regard as hateful comments online. Members of #jagärhär (which means “I am here”) seek out hatred in comment threads of newspaper articles posted on Facebook and then respond together, following a strict set of rules which includes keeping a respectful and non-condescending tone and never spreading prejudice or rumors.#jagärhar is by far the largest and best-organized collective effort to respond directly to hatred online, anywhere in the world, as far as we know. It is also one of only two civil society efforts against hatred online to have been replicated in numerous other countries. In this detailed account of its efforts– the first qualitative study of such a group – Cathy Buerger shares her findings on how and why #jagärhär members do what they do, how working collectively influences members’ ability and willingness to respond to hatred, and how the group’s strategy is carefully designed to take advantage of Facebook’s algorithms and influence ideas and discourse norms among the general public – not necessarily the ones writing the hateful comments.
EMG Controlled Electric Wheelchair

In this work, a controller for an electric wheelchair using electromyography (EMG) was developed. The commercial Myo armband that contains 8 surface EMG sensors, and an Inertial Measurement Unit (IMU) was used to collect raw EMG signals from the forearm muscles of a user. A preprocessing stage to denoise and condition the raw EMG signals was implemented. Then, the following features were extracted from each channel: Mean Absolute Value, Slope Sign Change, Waveform Length, Root Mean Squared, and Hjorth Parameters. The extracted feature vectors were used as inputs to an ANN that classified the hand gestures. Five hand gesture were selected to control the motion of the electric wheel chair as follows: Spread fingers (go forward), wave right (turn right), wave left (turn left), fist (go backwards), and rest position (stop). In order to find the optimal set of hyperparameters for the ANN, the Keras Tuner was used. Once the network was trained the TensorFlow Lite (TFLite) file was transferred to the Raspberry Pi for real time use. The ANN performed with a 96% accuracy rate with a delay of only 400 ms. The Raspberry Pi is where all of the signal processing and hand gesture recognition was performed. The hand gesture detected was sent over a general-purpose input/output connection to the Arduino that then communicates with the DC motors through a Sabertooth motor controller to operate electric wheelchair.
Attacks on Blockchain Based Digital Identity

An identity is a representation, in a given context, of a user, device, process or service which makes it unique or distinctive. It consists of attributes, traits, or preferences unique to an individual and may contain their date of birth, bank account number(s), social security number, passport information, family members’ information, (cryptographic) credentials, biometrics, or other personally identifying information (PII) necessary to uniquely identifying the individual. An attacker in possession of such information can damage the individual in various ways such as financially by accessing their bank accounts, or infiltrate their organizations, or putting blame on the individual for some wrongdoing.
Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking

Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs, where the DFT of an image has symmetry on the Fourier domain. To enhance the robustness and discriminative ability of the filters, many efforts have been devoted to optimizing the learning process. Regularization methods, such as spatial regularization or temporal regularization, used in existing DCF trackers aim to enhance the capacity of the filters. Most existing methods still fail to deal with severe appearance variations—in particular, the large scale and aspect ratio changes. In this paper, we propose a novel framework that employs adaptive spatial regularization and temporal regularization to learn reliable filters in both spatial and temporal domains for tracking. To alleviate the influence of the background and distractors to the non-rigid target objects, two sub-models are combined, and multiple features are utilized for learning of robust correlation filters. In addition, most DCF trackers that applied 1-dimensional scale space search method suffered from appearance changes, such as non-rigid deformation. We proposed a 2-dimensional scale space search method to find appropriate scales to adapt to large scale and aspect ratio changes. We perform comprehensive experiments on four benchmarks: OTB-100, VOT-2016, VOT-2018, and LaSOT. The experimental results illustrate the effectiveness of our tracker, which achieved a competitive tracking performance. On OTB-100, our tracker achieved a gain of 0.8% in success, compared to the best existing DCF trackers. On VOT2018, our tracker outperformed the top DCF trackers with a gain of 1.1% in Expected Average Overlap (EAO). On LaSOT, we obtained a gain of 5.2% in success, compared to the best DCF trackers.
Incorporating Question Information to Enhance the Performance of Automatic Short Answer Grading

Automatic short answer grading (ASAG) is focusing on tackling the problem of automatically assessing students' constructed responses to open-ended questions. ASAG is still far from being a reality in NLP. Previous work mainly concentrates on exploiting feature extraction from the textual information between the student answer and the model answer. A grade will be assigned to the student based on the similarity of his/her answers and the model answer. However, ASAG models trained by the same type of features lack the capacity to deal with a diversity of conceptual representations in students' responses. To capture multiple types of features, prior knowledge is utilized in our work to enrich the obtained features. The whole model is based on the Transformer. More specifically, a novel training approach is proposed. Forward propagation is added in the training step randomly to exploit the textual information between the provided questions and student answers in a training step. A feature fusion layer followed by an output layer is introduced accordingly for fine-tuning purposes. We evaluate the proposed model on two datasets (the University of North Texas dataset and student response analysis (SRA) dataset). A comparison is conducted on the ASAG task between the proposed model and the baselines. The performance results show that our model is superior to the recent state-of-the-art models.
Towards Artificial Intelligence Driven Immersive Environments in Virtual Reality for Industrial Applications

Natural interaction plays a significant role in the credibility of the virtual reality (VR) environments; unwanted or unexpected user interaction with the objects in the environment will negatively affect the immersion level for the user. Users have different level of skills when it comes to using VR. Therefore, handcrafting rule-based robust behaviour interaction that adapts to varying abilities of users is an ongoing challenge. In this study, the potential of using a data-driven method allowing researchers to gain insight into user behavioural data is investigated. A VR data replay and annotation method that allows for the analysis and classification of VR collected data through a graphical user interface is introduced. This method is applied to data collected from a VR lab study, including users with different skills in VR. Finally, the system is used to identify unwanted user interaction and Machine learning methods are investigated as an alternative for user interaction classification.
Remote Teaching Advanced Rendering Topics Using the Rayground Platform

Rayground is a novel online framework for fast prototyping and interactive demonstration of ray tracing algorithms. It aims to set the ground for the online development of ray-traced visualization algorithms in an accessible manner for everyone, stripping off the mechanics that get in the way of creativity and the understanding of the core concepts. Due to the COVID-19 pandemic, remote teaching and online coursework have taken center stage. In this work, we demonstrate how Rayground can incorporate advanced instructive rendering media during online lectures as well as offer attractive student assignments in an engaging, hands-on manner. We cover things to consider when building or porting methods to this new development platform, best practices in remote teaching and learning activities, and time-tested assessment and grading strategies suitable for fully online university courses.
Microseismic event location using artificial neural networks

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsMicroseismic event location using artificial neural networksAuthors: Denis AnikievUmair bin WaheedFrantišek StaněkDmitry AlexandrovLeo EisnerDenis AnikievGFZ German Research Centre for GeosciencesSearch for more papers by this author, Umair bin WaheedKing Fahd University of Petroleum and MineralsSearch for more papers by this author, František StaněkColorado School of MinesSearch for more papers by this author, Dmitry AlexandrovSeismik s.r.o.Search for more papers by this author, and Leo EisnerSeismik s.r.o.Search for more papers by this authorhttps://doi.org/10.1190/segam2021-3582729.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRecent advances in the field of machine learning coupled with available computational resources provide a great opportunity to address location of seismic events. Recently a new approach was proposed that uses feed-forward neural networks. First, the method utilizes only the P-wave arrival times instead of full waveforms. Second, instead of relying on historical training data, the neural network is trained on synthetically generated data. Once trained, the network can be deployed to locate real events by feeding their observed P-wave arrival times as input. The main challenge in the application of feed-forward neural networks to real datasets is in changing set or receivers where we pick time arrivals, leading to a non-regular input. We show how this problem can be overcome with a transfer learning approach and validate it by locating microseismic events that occurred during a hydraulic fracturing operation in Oklahoma.Keywords: machine learning, microseismic, earthquake, artificial intelligence, hydraulic fracturingPermalink: https://doi.org/10.1190/segam2021-3582729.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Denis Anikiev, Umair bin Waheed, František Staněk, Dmitry Alexandrov, and Leo Eisner, (2021), "Microseismic event location using artificial neural networks," SEG Technical Program Expanded Abstracts : 1661-1665. https://doi.org/10.1190/segam2021-3582729.1 Plain-Language Summary Keywordsmachine learningmicroseismicearthquakeartificial intelligencehydraulic fracturingPDF DownloadLoading ...
A Multi-Restart Dynamic Harris Hawk Optimization Algorithm for the Economic Load Dispatch Problem

In this study, we propose an algorithm based on a recently proposed metaheuristic called the Harris Hawk Optimization (HHO). We utilized a dynamic control strategy to enhance the exploration capability. To further avoid trapping in local optima, we incorporate opposition-based learning (OLB) and a multi-restart strategy. The proposed algorithm is used to solve economic load dispatch (ELD) problems with non-smooth cost functions. The ELD problems have a very large search space and therefore it is difficult to find global optimum using analytical methods. The results of the proposed approach are very competitive compared with notable results from previous research.
A Data Layout with Good Data Locality for Single-Machine based Graph Engines

Graph engines have been used in many applications to handle big graphs efficiently. The majority of the research to improve their performance has focused primarily on the design of efficient graph processing. This paper claims,however,the focus should be given also to graph storage design. This is because good storage design can improve both CPU performance and I/O performance of graph engines. In this paper,we propose an efficient data layout for single-machine based graph engines. We identify the common node access pattern of the graph algorithms running on single-machine based graph engines. Based on this finding,we propose the breadth-first (BF) data layout which places the nodes processed together in the same or adjacent storage space so that they can be accessed together as much as possible. The experimental results show that the BF data layout improves both CPU and I/O performances significantly in all single-machine based graph engines.
On the integration of Machine Learning algorithms and Operations Research techniques in the development of a hybrid Recommender System

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.
An Invasive Target Detection and Localization Strategy Using Pan-Tilt-Zoom Cameras for Security Applications

The widely applied security monitoring system mainly relies on human beings and lacks intelligence and flexibility. This paper designs a practical security monitoring system to quickly detect and localize the invasive animals, which includes the YOLOVS, DeepSORT, and proportional-integral-derivative (PID) methods. The proposed system has two parts, i.e., (1) the target identification and detection part, and (2) target localization part. The identification and detection part detects the suspicious invasive target. To resolve the inaccurate and time-lag problems in the target recognition, a strategy combined with YOLOV5 and DeepSOPT is developed. Then, multiple pan-tilt-zoom (PTZ) cameras in the target localization part will keep their lens to follow the target. The target position can be calculated through the rotation angle of the PTZ modules. Furthermore, an improved PID controller using particle swarm optimization (PSO) is proposed to control the PTZ for target localization. The simulation results demonstrate that the propose system can quickly detect the invasive target, and accurately localize the target.
An Efficient Steganalysis of Medical Images by Using Deep Learning Based Discrete Scalable Alex Net Convolutionary Neural Networks Classifier

Steganalysis is the technique that tries to beat steganography by detecting and removing secret information. Steganalysis involves the detection of a message embedded in a picture. Deep Learning (DL) advances have offered alternative approaches to many difficult issues, including the field of image steganalysis using deep-learning architecture based on convolutionary neural networks (CNN). In recent years, many CNN architectures have been established that have enhanced the exact identification of steganographic images. This work presents a novel architecture which involves a preprocessing stage using histogram equalization and adaptive recursive median filter banks to reduce image noise, a feature extraction stage using shearlet multilinear local embedding methods and then finally the classification can be done by using the discrete scalable Alex NET CNN classifier. Performance was evaluated on the RGB-BMP Steganalysis Dataset with different experimental setups. To prove the effectiveness of the suggested algorithm it can be compared with the other existing methodologies. This work improves classification accuracies on all other existing algorithms over test data.
Social Presence Outside the Augmented Reality Field of View

Augmented reality headsets in use today have a large area in which the real world can be seen, but virtual content cannot be displayed. Users perceptions of content in this area is not well understood. This work studies participants perception of a virtual character in this area by grounding this question in relevant theories of perception and performing a study using both behavioral and self-report measures. We find that virtual characters within the augmented periphery receive lower social presence scores, but we do notfind a difference in task performance. These findings inform application design and encourage future work in theories of AR perception and perception of virtual humans.
Improved K-means Clustering Algorithm by Combining with Multiple Factors

The traditional K-means clustering algorithm has the problems that the number of clusters needs to be determined artificially, the clustering results are easily affected by the initial clustering centers and isolated points, and the iterative process is computationally complicated. To address above problems, an improved K-means clustering algorithm combining multi-point optimization(MFK-means) is proposed. The proposed algorithm is improved from the following four aspects. Firstly, the number of clusters is jointly determined by combining the contour coefficient method and the elbow rule. Secondly, the optimized outlier detection algorithm is used to exclude the influence of isolated points on the clustering results. Thirdly, the initial clustering centers are gradually determined based on the outlier candidate set and the maximum-minimum distance idea. Finally, the heuristic method is used to reduce the computational effort of the iterative process. The experimental results on the UCI dataset show that the proposed improved algorithm has higher clustering accuracy and better stability than the traditional K-means algorithm and another improved algorithm.