Reviewerly/exHarmony huggingface dataset:
Mastering the Electronic Educational Environment of a University by Educators with a Different Overall Digital Competency Index

The article describes the approach to the digital competency. Today the generalized knowledge of the humankind has been digitized and materialized in the technical environment. It can be easily accessed anytime and from anywhere and has become practically indestructible as it has been repeatedly duplicated. In the 21st century, we are dealing with a different subject of education, a network persona who has an extended range and faster pace of interaction. As a result, each individual can now exercise a greater degree of influence on the environment, whereas self-organizing capabilities of groups are growing. The question is, are the educators ready for this challenge? They handle these challenges differently and experience various kinds of problems and difficulties. The survey was conducted. The respondents were teachers of humanities in technical university. The digital competency index for each respondent was calculated, and then the mean value of digital competency among teachers of humanities in a technical university was found. It was revealed that the respondents manifested the highest competence in the field of content, and were least competent in communication, which shows how important it is to develop communication in the digital educational environment. The data obtained were also processed using mathematical statistics by applying Fisher angular transformation. A program for psychological and educational support for the educators in the process of mastering Learning Management System Moodle was developed.
Multi-label image recognition by using semantics consistency, object correlation, and multiple samples

An image can be annotated from the local perspective, based on objects visually present. An image can also be annotated from the global perspective, based on implicit emotion or meanings derived from it. We propose three points relatively little studied before. First, semantics remain the same even if the image is manipulated by some geometric processes. Second, object correlation is important in image labelling. We propose to use a standard recurrent neural network to take object sequences in random orders. Third, we observe that some entity can be represented by multiple image samples, and multiple samples can be jointly considered to improve recognition performance. These three points are implemented in a network that jointly considers global and local information. With comprehensive evaluation studies, we verify that a simple network with these points is effective and is able to achieve competitive performance compared to the state of the arts.
Nonlocal generalized quantum measurements of bipartite spin products without maximal entanglement

Abstract Measuring a nonlocal observable on a space-like separated quantum system is a resource-hungry and experimentally challenging task. Several theoretical measurement schemes have already been proposed to increase its feasibility, using a shared maximally-entangled ancilla. We present a new approach to this problem, using the language of generalized quantum measurements, to show that it is actually possible to measure a nonlocal spin product observable without necessarily requiring a maximally-entangled ancilla. This approach opens the door to more economical arbitrary-strength nonlocal measurements, with applications ranging from nonlocal weak values to possible new tests of Bell inequalities. The relation between measurement strength and the amount of ancillary entanglement needed is made explicit, bringing a new perspective on the links that tie quantum nonlocality, entanglement and information transmission together.
Selection of the Optimal Class of Features for Recognition of the Azerbaijani Handprinted Characters

The article explores the application of methods for solving binary optimization problems on the example of the problem of selecting features for recognizing handwritten characters of the Azerbaijani. The results of various algorithms for solving the binary optimization problem are analyzed, such as sequential selection forward and backward, as well as genetic algorithms for determining more informative features from a given class.
Patterns of conflict speech and young adult feminist identity construction on Tumblr

Abstract Interactions on social media platforms are becoming increasingly relevant from an identity construction perspective. Conflict speech, in particular, is a form of interaction that is especially common in online spaces and constructs identity through polarization, strengthening the in-group while excluding the out-group. The young adult feminist identity has established a strong presence in online spaces, specifically the microblogging platform Tumblr. This study seeks to analyze the role of conflict speech in young adult feminist identity construction through focusing on recontextualization of comments and name-calling strategies. Within this analysis, it is possible to determine the importance of conflict speech as it strengthens the collective feminist identity while allowing those in the in-group to exclude and dismiss conflictual comments stemming from those in the out-group.
Curiosity-Driven Reinforced Learning of Undesired Actions in Autonomous Intelligent Agents

Autonomous exploring agents are encouraged to explore unknown states in an environment when equipped with an intrinsic motivating factor such as curiosity. Although intrinsic motivation is a useful mechanism for an autonomous exploring agent in an environment that provides sparse rewards, it doubles as a mechanism for causing the agents to act in undesirable ways. In this paper, we show that highly-curious agents, attached with neural networks trained with the Machine Learning Agent Toolkit's (ML-Agents) implementation of the Proximal Policy Optimization (PPO) algorithm, and Intrinsic Curiosity Module (ICM), learn undesirable or reckless behaviors relatively early in the training process. We also show that strong correlations in the PPO training statistics of misbehaving agents may indicate when an actual human should intervene for safety during the RL training process.
Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.
Security analysis of KXB10 QKD protocol with higher-dimensional quantum states

Quantum key distribution (QKD) is one of the exciting applications of quantum mechanics. It allows the sharing of secret keys between two communicating parties with unconditional security. A variety of QKD protocols have been proposed since the inception of the BB84 protocol. Among different implementation techniques of QKD protocols, there is a category which exploits higher dimensions qubit states to encode classical bits. In this paper, we focus on such a QKD protocol called KXB10, which uses three bases with higher dimensions. Analysis of the generalized dimension quantum states is performed by evaluating it based on the index transmission error rate ITER. We find that there is a direct relationship between qubit dimensions and ITER for the KXB10 protocol.
Context-Aware Wearables

We present Connected Companion (CoCo), a health tracking wearable that provides users with timely, context-relevant notifications aimed at improving wellness. Traditionally, self-tracking wearables report basic health data such as resting heart rate; these data are visualised and positive behaviours (e.g. exercising often) are encouraged with rudimentary gamification (e.g. award badges) and notification systems. CoCo is the first wearable to combine caffeine, alcohol and cortisol sensors, a context network (which predicts user context), and a wellness model (which establishes per-user wellness measures). Working in tandem these provide users with notifications that encourage discrete behaviours intended to optimise user-wellness per very specific biological and social contexts. The paper describes the (sometimes unexpected) results of a user-study intended to evaluate CoCo's efficacy and we conclude with a discussion about the power and responsibility that comes with attempts to build context-aware computing systems.
DRAGON: diversity regulated adaptive generator online

Approaches based on Procedural Content Generation (PCG) are more and more diffused among video game developers. They offer many advantages, among which two of the most notables are the opportunity to lighten the burden of level designers and the possibility to produce personalized experiences for the players. In the present work we focus especially on the second aspect, while the first one is addressed as a side effect. In particular, we present DRAGON (Diversity Regulated Adaptive Generator Online), an algorithm for procedurally generating “monster” archetypes for multiplayer games basing also on the players’ preferences. The generation process exploits the genetic algorithm paradigm, opportunely adapted, and modified in order to guarantee enough flexibility to the game or level designers. Ideally, the archetypes produced by DRAGON can be employed for any game genre and setting. DRAGON has been implemented as a plugin for one of the state-of-the-art game engines and tested with game developers. Moreover, a simulation has been conducted for the end-users.
Cyberthreats are going mobile and it's time to take action

The world recently experienced an accelerated shift towards a remote working environment under Covid-19. Before the pandemic, workforces primarily relied on desktops and corporate network servers, with only 5% of UK workers operating from home. 1 That number is now 72%. As many of us continue to work away from the office and try to juggle personal responsibilities, we are finding new ways to stay productive – this includes using our mobile devices more.
A coarse to fine network for fast and accurate object detection in high‐resolution images

Because of the popularisation of high-resolution images, detecting objects in these images quickly and accurately has attracted increasing attention in recent studies. Current convolutional neural networks (CNN)-based detection methods have limitations in detecting small objects owing to the interference of scale variation. In this work, we propose an improved generic framework based on YOLOv3. Equipped with multiresolution supervision for training and multiresolution aggregation for inference, this method can deal with the challenge of scale variation in high-resolution images. At first, we move up the multiscale prediction position and add a dilated convolution module on YOLOv3 to improve the accuracy of detection, especially for small objects. Then, we present a coarse to fine method to reduce the detection time. Experiments on a COCO dataset show that our approach achieves 2.8% better accuracy compared with the previous YOLOv3. On a Dataset for Object deTection in Aerial images dataset (a high-resolution remote sensing dataset), our approach outperformed the YOLOv3 by nearly three percentage points in mean average precision. Moreover, it is up to three times faster as well and two times smaller than the previous YOLOv3.
Leveraging Phone Numbers for Spam detection in Online Social Networks

Online Social Networks (OSNs) are platforms that have gained immense traction from society today. Social media has reshaped our social world and has been playing a pivotal role in sculpting our personal and professional goals. While it provides invaluable information to millions of individuals daily, it has also become one of the most popular places for spam campaigns. In this paper, we design an algorithm for the recognition of spam campaigns, specifically focusing on a phone-numbers based approach. We build a system for spam campaign recognition with an emphasis on phone numbers in the light of the malicious activity that is vandalizing our online experience. This research focuses on data extracted from monitoring the following social networking channels: Tumblr, Twitter, and Flickr. The paper serves as an analytical lens for spam posts accumulated over four months. Regular expressions are used for data cleaning to identify posts containing phone numbers. We collected over 18 million spam posts and filtered the spam-containing posts using regular expressions. Next, we used a Bayesian Model called Latent Dirichlet Allocation (LDA) to perform a statistical model for detecting the category of the posts. We further use the bag-of-words and the tf-idf means to this data and apply cosine similarity for the similarity measure.
Distributed Random Number Generator on Hedera Hashgraph

Random number generators (RNG) are both building blocks and services on the top of distributed ledgers like blockchains and directed acyclic graphs. In the paper, the authors provide implementation and demonstration of the fault-tolerant RNG algorithm–No-Dealer–for a public directed acyclic graph-based platform called Hedera Hashgraph. The performance tests on the Hedera testnet show that the system meets the real-world requirements. The proposed solution can be used as an RNG service.
Patch-based co-occurrence filter with fast adaptive kernel

Which kind of edges should be preserved in texture smoothing, that is a major concern for experts. More recently, co-occurrence filter (CoF) gives a refreshing answer: in contrast to ordinary edges, the prominent structure edges (i.e., boundaries) ought to be given a prior consideration. In texture smoothing, CoF employs co-occurrence matrix to separate different edges in an image to preserve structures. However, the collection of co-occurrence information in the filter is pixel-wise, which is fragile to noises. To overcome the drawback, we extend CoF to a novel version, named patch-based co-occurrence filter with fast adaptive kernel (FAKPCoF). Different from acquiring pixel-wise co-occurrence matrix, FAKPCoF collects the co-occurrence information based on patches, which is more robust to noises. And rather than spatial Gaussian in CoF, an adaptive kernel is employed to keep the sharpness of edges. Meanwhile, to break through the computational bottleneck induced by the adaptive operation, a fast variant of the adaptive kernel is designed. Thorough experimental results demonstrate that the proposed FAKPCoF reveals more powerful performance both in texture smoothing and structure-preserving.
Application of Electromagnetic Simulation in the Course of “Microwave Technology and Antenna”

As a basic course for undergraduates, “microwave technology and antenna” is offered in the majors of electronic information in most universities. The basis of this course is complex and abstract knowledge concepts, which has always been considered as a difficult course for teachers and students. This article attempts to provide some suggestions for the teaching reform of this course from the perspective of introducing electromagnetic simulation. Through further construction, it can be built into an electromagnetic virtual simulation test online platform to better serve this course.
Function Approximation by Deep Neural Networks with Parameters $$\{0,\pm \frac{1}{2}, \pm 1, 2\}$$

Abstract In this paper, it is shown that $$C_\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>C</mml:mi> <mml:mi>β</mml:mi> </mml:msub> </mml:math> -smooth functions can be approximated by deep neural networks with ReLU activation function and with parameters $$\{0,\pm \frac{1}{2}, \pm 1, 2\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mo>±</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> <mml:mo>,</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> . The $$l_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>l</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> and $$l_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>l</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> parameter norms of considered networks are thus equivalent. The depth, the width and the number of active parameters of the constructed networks have, up to a logarithmic factor, the same dependence on the approximation error as the networks with parameters in $$[-1,1]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> . In particular, this implies that the nonparametric regression estimation with constructed networks achieves, up to logarithmic factors, the same minimax convergence rates as with sparse networks with parameters in $$[-1,1]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> .
Robustness Test of SIMON-32, SPECK-32, and SIMECK-32 Algorithms Using Fixed-Point Attacks

Abstract SIMON-32 and SPECK-32 are a family of lightweight block ciphers publicly released by the National Security Agency (NSA) in June 2013. Meanwhile, SIMECK-32 is a lightweight block cipher based on the combination of good design components from SIMON- 32 and SPECK-32 block ciphers. In this paper, we tested the robustness of the SIMON-32, SPECK-32, and SIMECK-32 algorithms against fixed-point attacks. Block cipher fixed-points may facilitate fixed-point attacks if an adversary can control the block cipher key input. The results of fixed-point attacks on the SIMON-32, SPECK-32, and SIMECK-32 algorithms show that all three variants of the algorithm have fixed-points.
Multi-modal, Multi-labeled Sports Highlight Extraction

Video highlight extraction has been as important as many other research on video analysis. To focus on sports highlight extraction, we aim to adopt machine learning techniques to provide an efficient approach to catch the most important moments in sports games of only a short period of time in such a fast-forward age. To elaborate the procedure, we examine frame by frame in a video to summarize what happened in a sports game which may contain the exciting moments, funny moments, game turning point, or even controversial events to name a few. One of the special treatment in this work is that we emphasize the multi-modal approach. Sports video contains a variety of modalities such as images, audios, scores and game time, etc. In this research, we explore different fusion strategies (e.g., the fusion on latent features or early-stage features) to form multi-modal data for modeling in order to utilize rich information to improve the result of highlight extraction. Furthermore, we consider the multi-label model based on the factors that affect the highlights to extract the joint features, and combine with the multi-modal model to further improve the final performance. The experiment results show the outstanding performance of the proposed method on the task of sports highlight extraction.
Simulation framework for practical cyber security training in the public service

The public service sector is a key target of cyberattacks. In order to prevent and effectively tackle such attacks, organisations should continuously develop their defence capabilities. As part of developing such capabilities, public service cybersecurity training is required to teach students about cyberattacks. The present study uses quantitative research techniques including (i) how to identify key requirements for the practical aspects of public service cybersecurity training and (ii) sampling to utilise international best practices from cybersecurity education and conceptual architectures from existing public service organisations. A schematic structure with a two-level practical training course is proposed. On the first level, the students learn about the defence mechanisms of their own info-communication devices and try to prevent attacks in a simulated environment. On the second level, the students apply protection strategies against cyberattacks in organisational infrastructure. Finally, a technical framework is defined to simulate cyberattacks against (a) personal devices and (b) a fictional organisational infrastructure. The specification of a public service cybersecurity training programme should not only focus on theoretical education but also provide practical knowledge to students. By simulating specific attacks, theoretical and practical knowledge can be combined. As a result, students will be able to recognise threats and potential risks from cyberspace.
Data Stream Clustering for Big Data Sets: A comparative Analysis

Abstract The world is growing rapidly with constantly increasing the data. There are innumerable people around the world who use different types of applications, whether it is for reservation, marketing, shopping or knowledge in the form of text, image, audio and video. Only data is being generated everywhere and this growing data which is large and high dimensional is in nature is generally known as “big data”. For the organizations, it is a big task to cluster streaming big data successfully. In this paper, we are presenting a survey of data stream clustering algorithms applied over big data and big datasets. The paper shows the comparative analysis of all the studied methods and also review the evolution and progression of data stream clustering algorithm for big datasets. The paper also analyses the proposed and implemented algorithms in recent years.