Reviewerly/exHarmony huggingface dataset:
PERSONALIZED TRAJECTORIES OF UNIVERSITY TEACHERS PROFESSIONAL DEVELOPMENT IN DIGITAL SPHERE

The article is dedicated to the question of teachers’ postgraduate education system design for digital competence development. The need in the design of the above-mentioned system is justified and the adaptive system for teachers’ postgraduate education in digital sphere at Borys Grinchenko Kyiv University is described that provides the possibility of personalized trajectories of teachers’ professional development formation. According to the requirements of the educational institution the adaptiveness of the system is implemented in two directions: considering timing of testing and mini-courses completion; considering personal needs of a teacher according to their knowledge level, professional field and defined goals. The model of the system is developed that is designed based on self-assessment, self-education and micro-learning principles. The adaptive system is implemented in the e-learning system with the help of the following structural elements - the diagnostic test and the sets of mini-courses with topic-based organization that allow to implement personalized trajectories of teachers’ education. The utilized approaches to the diagnostic test formation are specified including ensuring its integration, variability and validity as well as the principle of its utilization to define teachers’ level of digital competence according to the developed Corporate standard of digital competence. Using the example of the compulsory level of digital competence Analyst-Researcher (A) the utilization of the sets of mini-courses is shown for all levels of digital competence - Integrator (B1), Expert (B2), Leader (C1) and Innovator (C2). The system allows a teacher to track in their personal cabinet their progress and define their own professional development trajectory and with the help of embedded business analytics tools utilization to get a visualized coherent picture of professional digital activities results. The role of the adaptive system in the organization of teachers’ postgraduate education and the future perspectives of the system implementation results utilization are outlined.
Proximity tracing applications for COVID-19: data privacy and security

Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Given the high rates of contagion, which makes difficult an effective manual tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations.
Speckle noise suppression method using multi-azimuth SAR images.

Abstract Synthetic aperture radar images play an important role in military and civilian fields, but the presence of speckle noise has an impact on subsequent tasks such as target detection and target interpretation. With the development of multi-azimuth observation mode, the obtained multi-azimuth image sequences have high similarities. Therefore, combined with multi-azimuth image sequences, a novel method of SAR image speckle noise suppression based on clustering is proposed in this paper. In this method, multi-azimuth joint filtering framework based on two-level filtering is proposed, in which pre-filtering for single image and joint filtering based on Non-local Means algorithm for multi-azimuth image are used to suppress the noise. And k-means clustering is used to optimize the search area in the multi-azimuth joint filtering, so as to effectively suppress speckle noise while retaining structural details.
Hate Speech in the Arab Electronic Press and Social Networks

Nowadays we are witnessing an open world, characterized by globalization which is accompanied by a technology through which information circulates without borders, especially with the widespread use of social networking sites being the most common communication tool, that gives access through various applications to a large space for the presentation of multiple ideas, including extremist ideas, and the spread of hate speech. This paper introduces a system of detection of hate speech in the texts of Arabic read media and social media, which is based on a combined use of NLP, and machine learning methods. The training of the detection model is done on a large Dataset of articles, tweets and comments, collected, balanced and tokenized afterwards using BERT in Arabic. The trained model detects hate speech in Arabic and various Arabic based dialects, by classifying the texts into two classes: Neutral and Abusive. The above-mentioned model is evaluated using precision metrics, recall and f1 score, it has reached an accuracy of 83%.
Neural response time analysis: Explainable artificial intelligence using only a stopwatch

Abstract How would you describe the features that a deep learning model composes if you were restricted to measuring observable behaviours? Explainable artificial intelligence (XAI) methods rely on privileged access to model architecture and parameters that is not always feasible for most users, practitioners and regulators. Inspired by cognitive psychology research on humans, we present a case for measuring response times (RTs) of a forward pass using only the system clock as a technique for XAI. Our method applies to the growing class of models that use input‐adaptive dynamic inference and we also extend our approach to standard models that are converted to dynamic inference post hoc. The experimental logic is simple: If the researcher can contrive a stimulus set where variability among input features is tightly controlled, differences in RT for those inputs can be attributed to the way the model composes those features. First, we show that RT is sensitive to difficult, complex features by comparing RTs from ObjectNet and ImageNet. Next, we make specific a priori predictions about RT for abstract features present in the SCEGRAM data set, where object recognition in humans depends on complex intrascene object‐object relationships. Finally, we show that RT profiles bear specificity for class identity and therefore the features that define classes. These results cast light on the model's feature space without opening the black box.
Webpage Matching Based on Visual Similarity

With the rapid development of the Internet, the types of webpages are more abundant than in previous decades. However, it becomes severe that people are facing more and more significant network security risks and enormous losses caused by phishing webpages, which imitate the interface of real webpages and deceive the victims. To better identify and distinguish phishing webpages, a visual feature extraction method and a visual similarity algorithm are proposed. First, the visual feature extraction method improves the Vision-based Page Segmentation (VIPS) algorithm to extract the visual block and calculate its signature by perceptual hash technology. Second, the visual similarity algorithm presents a one-to-one correspondence based on the visual blocks’ coordinates and thresholds. Then the weights are assigned according to the tree structure, and the similarity of the visual blocks is calculated on the basis of the measurement of the visual features’ Hamming distance. Further, the visual similarity of webpages is generated by integrating the similarity and weight of different visual blocks. Finally, multiple pairs of phishing webpages and legitimate webpages are evaluated to verify the feasibility of the algorithm. The experimental results achieve excellent performance and demonstrate that our method can achieve 94% accuracy.
Session centered Recommendation Utilizing Future Contexts in Social Media

Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender ( SRec ), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network ( CNN ) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.
Create laboratory business intelligence dashboards for free using R: A tutorial using the flexdashboard package

This tutorial described use of the R flexdashboard package and its possibilities for customization to develop a dashboard to monitor critical result reporting in a clinical laboratory. Several examples of interactive components were demonstrated. Sample code and exercises were provided to facilitate learning.
Communicating across educational boundaries: accommodation patterns in adolescents’ online interactions

Abstract This paper studies linguistic accommodation patterns in a large corpus of private online conversations produced by Flemish secondary school students. We use Poisson models to examine whether the teenagers adjust their writing style depending on their interlocutor’s educational profile, while also taking into account the extent to which these adaptation patterns are influenced by the authors’ own educational background or by other aspects of their socio-demographic profiles. The corpus does reveal accommodation patterns, but the adjustments do not always mirror variation patterns related to educational profiles. While salient features like expressive markers seem to lead to pattern-matching, less salient features appear less prone to ‘adequate’ adjustment. Lack of familiarity with the online behavior of students from other educational tracks is a factor too, since online communication clearly proceeds primarily within ‘same-education’ networks. The focus on cross-educational communication is quite unique in this respect and highly relevant from a sociological perspective.
Disparity refinement based on feature classification and local propagation for stereo matching

Stereo matching usually makes up of four steps: cost computation, cost aggregation, disparity optimization, and disparity refinement. The disparity refinement is used to further eliminate mismatches caused by occlusion, low texture, and other factors. The popular refinement methods are based on the consistency check of left and right two disparity maps. For efficiency, we propose a novel multistep disparity refinement framework using only one-sided image, which is organized into four main steps: leftmost occlusion detection, four-directional scanline outlier detection, black hole detection and eight-directional disparity propagation. Experimental results on Middlebury datasets show that our method is comparable with other postprocessing strategies, especially in occlusion handling, retaining object shapes and preserving discontinuities.
A two-level formal model for Big Data processing programs

This paper proposes a model for specifying data flow-based parallel data processing programs agnostic of target Big Data processing frameworks. The paper focuses on the formal abstract specification of non-iterative and iterative programs, generalizing the strategies adopted by data flow Big Data processing frameworks. The proposed model relies on Monoid Algebra and Petri Nets to abstract Big Data processing programs in two levels: a higher level representing the program data flow and a lower level representing data transformation operations (e.g., filtering, aggregation, join). We extend the model for data processing programs proposed in [1], for modeling iterative data processing programs. The general specification of these programs implemented by data flow-based parallel programming models is essential given the democratization of iterative and greedy Big Data analytics algorithms. Indeed, these algorithms call for revisiting parallel programming models to express iterations. The paper gives a comparative analysis of the iteration strategies proposed by Apache Spark, DryadLINQ, Apache Beam, and Apache Flink. It discusses how the model achieves to generalize these strategies.
Through the screens: interaction for learning in virtual environments

The digital technologies mediation, supose particular forms of knowledge construction which claim deep explorations, especially the interaction between actants and their ways of learning on virtual platforms. This article presents the results of an investigation situated at the crossroads between Education, Communication and Visual Design, carried out from a qualitative analysis of interpretive position. Starting from a case study based on virtual ethnography, the research is based on in-depth interviews, from which successively organized data were obtained from the emerging categories of the research itself (uses, skills, preferences and relationships). The conclusions suggest the need for a system of its own for on line education –that exceeds comparisons–, in which the assemblages disclosed here were considered and particular resources proposed that promote the participants learning.
Image recognition algorithms based on deep learning

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.
An Adaptive Hybrid Particle Swarm Optimizer for Constrained Optimization Problem

Particle Swarm Optimization (PSO) is a well-known nature-inspired algorithm that is inspired by the social behavior of bird flocking and fish schooling. It has proved efficient in solving real-time problems due to its simplicity and good exploitation ability. However, due to poor exploration of search space, PSO gets trapped in local optima and gives less accurate results. On the other hand, Butterfly Optimization Algorithm (BOA) is good in exploration but converges slowly. This paper proposes a novel hybrid based on PSO and BOA, namely PSOBOA with few improvements based on the advantage and uniqueness of these algorithms. Firstly, a parameter-free penalty function is used to handle constraint violations so that the search process does not slacken when handling the constraints. Secondly, a self-adaptive approach has been adopted in PSO as well as BOA to ensure a smooth transition from exploration to exploitation, and no user interference. Thirdly, to improve the convergence rate and avoid local optima stagnation, a conditional approach has been used in the local and global search of BOA. The proposed algorithm PSOBOA overcomes the shortcomings of PSO and BOA and maximizes the performance. It is applied to solve structural optimization problems, such as pressure vessel design and welded-beam design problem, where the objectives, decision variables, and constraints are different. The experimental results and the convergence curves demonstrate better optimization performance of PSOBOA compared with quite a few state-of-the-art algorithms.
A combination of forecasting internal and external crises in managing the development of educational institution

The article states that management requires forecasting of internal and external crises, timely preparation for which provides the educational institution, as a subject of market relations, competitive advantages and opportunities for development. To solve this problem, an approach to forecasting crisis phenomena in the management of the educational institution has been developed, which takes into account the peculiarities of external and internal crises, is based on formalizing the forecasting process of various components and provides grounds for using forecasts for development.
Radar Emitter Recognition Method Based on Bispectrum and Improved Multi-channel Convolutional Neural Network

Aiming at the problem that a kind of bispectrum one-dimensional data is extracted as the individual identification feature of radar emitter resulting in the loss of a lot of useful information in a complex electromagnetic environment, we make full use of the characteristic of symmetry of bispectrum structures, fuse the extracted bispectrum structure, and input an improved multi-channel one-dimensional convolutional neural network in parallel in this paper. Softmax classifier is used for radar emitter identification, so as to improve the recognition rate of individual radar emitter. The simulation experiment uses four kinds of homotype radar to test. Experimental results show that compared with the radar emitter identification method based on 1D-CNN for a single bisspectral one-dimensional feature, the proposed method has the ability of deep feature mining, reduces the loss of useful information, and improves the individual recognition rate of radar emitter effectively.
Source Attribution of Online News Images by Compression Analysis

The rapid increase in the amount of online disinformation warrants new and robust digital forensics methods for validating purported sources of multimodal news articles. We conducted a survey of news photojournalists for insights into their workflows. A high percentage (91%) of respondents reported standardized photo publishing procedures, which we hypothesize facilitates source verification. In this work, we demonstrate that the online news sites leave predictable and discernible patterns in the compression settings of the images they publish. We propose novel, simple, and very efficient algorithms to analyze the image compression profiles for news source verification and identification. We evaluate the algorithms' effectiveness through extensive experiments on a newly-released dataset of over 64K images from over 34K articles collected from 30 news sites. The image compression features are modeled by Naive Bayes variants or XGBoost classifiers for source attribution and verification. For these news sources we are able to achieve very strong performance with the proposed algorithms resulting in 0.92–0.94 average AUC for source verification under a closed set scenario, and compelling open set generalization with only 0.0–0.04 reduction in the average AUC.
Xatu: Richer Neural Network Based Prediction for Video Streaming

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
Customizable Memory Training in Virtual Reality with Personal Memoirs

Memory training methods are often not designed for all age groups, which causes older people to be failed by educational systems. However, the brain remains plastic for a lifetime. The application discussed in this paper demonstrates how approaches to learning psychology can be implemented in virtual reality to create an individualized learning environment that addresses all age groups. In addition, personal memoirs were used to address the episodic part of the brain and to increase motivation. In a small usability study, twelve participants tested the application and filled out a questionnaire. This showed that the design and usability of this application, using a virtual learning environment customized to the user as well as different perception channels, has been successful. Moreover, the wellbeing was rated positively as well.
An Innovative Security Screening Architecture for Detecting Illicit Goods and Threats

Every year, millions of letters/parcels containing illicit goods are detected by customs authorities, which use traditional security screening equipment. However this equipment cannot detect all kinds of illicit goods and the detection procedure heavily depends on the attention of the customs officer. In order to achieve sufficiently fast intelligent screening of the large volumes of letters/parcels and detect all common kinds of threats, this paper proposes a highly innovative architecture well-beyond the state-of–art. In particular the proposed architecture monitors every letter/parcel by incorporating: (a) terahertz/X-ray sensors, (b) chemical, biological, radiological and nuclear (CBNR) sensors, (c) artificial robot-noses for narcotics, explosives etc., (d) magnetometers for weapons, firearms, banknotes etc., (e) acoustic sensors for liquids/gases/solids, (f) weight/pressure sensors to measure weight distribution, size and shape. Sensory information can be: (a) used to create a “Spectral Signatures Dictionary of Illicit Goods and Threats”, (b) fused to segment/isolate illicit goods and (c) visualized in the form of annotated high-resolution tensor-structured (3D/4D) multisensory image data. The proposed solution also gathers available information for the sender/recipient from various resources, while it also analyzes data from the dark web. All information is forwarded to an AI-based knowledge infrastructure.