Reviewerly/exHarmony huggingface dataset:
CAAE: A Novel Wireless Spectrum Anomaly Detection Method with Multiple Scoring Criterion

To sense and understand how to use the wireless spectrum, people have proposed various anomaly spectrum detection methods. We judge it as anomaly behavior if the received signal is unauthorized or the radiation of an expected signal is changed. We propose CAAE, a novel wireless spectrum anomaly detection method, to detect the two kinds of anomaly behaviors. CAAE is a complex adversarial autoencoder that can realize feature extraction and image reconstruction of input data through convolution and deconvolution networks. We train CAAE in a semi-supervised learning fashion and various values in the calculation process would change if the anomaly spectrum is input after the model training is completed. Therefore, we propose the multiple scoring criterion to help improve the detection accuracy of our model. The time-frequency waterfall graphs are input and we do two sets of experiments to prove the validity of our model. The experimental results show that the comprehensive detection capability of CAAE model is superior to the comparison algorithms for our dataset.
Drivers for and Barriers to the Cross-border Implementation of the Once-Only Principle

Abstract The once-only principle (OOP) aims to reduce interactions between citizens and governments, but many factors challenge its cross-border implementation. Building on the results of the “The Once-Only Principle Project” (TOOP, 2017–2021), an analysis was undertaken of the factors that either support or hinder implementation of the cross-border OOP. Five domains of factors were examined - technological, organizational, institutional aspects, actors and miscellaneous. This research highlights the importance of awareness of the OOP, and its inherent benefits, as a key driver. Also, the activities of supranational entities are of key significance, as it is establishing a critical legal framework. Co-ordination between different levels of government and different countries remains an important barrier. One specific issue discovered and addressed during the project but uncovered here, relates to identity matching, and this requires EU level intervention to reach an effective and efficient solution.
Hanging Out Online: Social Life During the Pandemic

In March 2020, the government of India ordered a nationwide lockdown to prevent the spread of Covid-19. This led to the shutdown of educational institutes throughout the country, restricting all activities to online mediums. The shift has affected how students engage with each other, where rather than in-person interaction, they meet through a variety of online tools. In this paper, we discuss how the normal everyday routine of 'hanging out' with friends has been transformed during a prolonged lockdown of over ten months and counting. We investigate the opportunities and challenges students encounter when socializing online through various online modes including video calls, communal movie watching and social media. We discuss how social interaction; in particular, hanging out with friends has been transformed through these technologies and its implications for facilitating spontaneous interaction, negotiating intimacy, mutual understanding, and accessibility to different social groups. Finally, we conclude with a discussion of how these factors impact the transition from in-person to online modes of casual social interaction.
Influence of the Indoor Hockey “Push &amp; Flick” Methodology on the Ball Speed During the Penalty Corner Shooting

Indoor hockey is an official non-Olympic discipline, regulated by International Hockey Federation (FIH), practiced in a hall with a handball size pitch. The main objective of the game is a victory achieved by marking more goals than the opponent. The format of the game was created in the 1950s and 1960s. Later, in 1966, the first Indoor hockey rules were published. In 1968, the World Headquarters officially recognized the discipline as an integral part of hockey. The penalty corner is one of the most important game situations in hockey (both outdoor and indoor field hockey) with 40% of all goals resulting from this tactical situation. This number may reach 46% or even 68%. The aim of this paper is to study the influence of the indoor hockey “Push & Flick” methodology on the ball speed improvement during the penalty corner shooting in the potentially effective goal zones. Using variation analysis and InterCriteria Analysis the research team has sought to establish values and possible relations and dependencies between indicators reflecting the ball speed of zone shooting. Four elite indoor hockey players from the team of the National Sports Academy in Bulgaria, participants in the European Indoor Hockey Clubs Challenge, have been involved in the experiment. According to the requirements of the experimental “Push & Flick” methodology, the duration of the specialized training has been set to 16 weeks. Each player has performed 4,800 shootings, or approximately 300 shootings each week. Tests have been carried out at the beginning (the first week) and at the end (the sixteenth week) of the experiment in order to determine the speed of the ball during the shooting – push/flick from a penalty corner spot (9 m, central from the goal line). The speed of the ball has been measured with a sports radar Ra-Vid Pro Sport™ (Accuracy: ±0.1 km/h, Speed range: 1–480 km/h, Stopwatch within 1/100 s, 10 m sec acquisition time, 12-degree radar beam, 1200 to 38.4 K baud, Available in mph or km/h, Maximum Range, Sports: 400–500 ft., Autos: 1.75 miles) located just behind the net and the corresponding shooting areas. This report will demonstrate the effectiveness of the specialized methodologies related to the preparation of penalty corners “specialists”. In addition, InterCriteria Analysis applied for processing the data reveals important dependencies related to the refinement of the technique of pushing and flicking.
Examining and Predicting Teacher Professional Development by Machine Learning Methods

Core quality and ability development analysis is important for evaluating and deepening the professional development of teachers. It is also useful for improving the quality of education and promotes the growth of the next generation. This research tries to investigate how prominent teachers regulate their professional development. The research also attempts to provide suggestions about current professional status of teachers. The answer to these questions will help teachers to better comprehend how teachers develop. To perform factor analysis for teacher professional development, we develop a set of questionnaire scheme, and collected a certain number of samples through the survey. Professional development is transformed to a classification problem. This study applies machine learning (ML) methods to identify significant attributes that prominent teachers shown in class education, and predict the level of teacher professional development. Eight ML methods are taken to classify the samples. Hyperparameter optimization is performed to improve prediction accuracy. The simulation results show that the ensemble method, support vector machine and artificial neural network are the top three ML methods for the problem. Hyperparameter optimization does not show great impact on the performance of the ensemble and support vector machine methods. The accuracy can reach above 85% by tuning the artificial neural network method through hyperparameter optimization. This study provides an important basis for the future intelligent analysis of teacher professional development.
Unsupervised Person Re-Identification Via Global-Level And Patch-Level Discriminative Feature Learning

Due to the lack of labeled data, it is usually difficult for an unsupervised person re-identification (re-ID) model to learn discriminative features. To address this issue, we propose a global-level and patch-level unsupervised feature learning framework that utilizes both global and local information to obtain more discriminative features. For global-level learning, we design a global similarity-based loss (GSL) to leverage the similarities between whole images. Along with a memory-based non-parametric classifier, the GSL pulls credible samples closer to help train a discriminative model. For patch-level learning, we use a patch generation module to produce different patches. Applying the patch-based discriminative feature learning loss and image-level feature learning loss, the patch branch in the network can learn better representative patch features. Combining the global-level learning with patch-level learning, we obtain a more distinguishable re-ID model. Experimental results obtained on Market-1501 and DukeMTMC-reID datasets validate that our method has great superiority and effectiveness in unsupervised person re-ID.
Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning

Abstract With regard to video streaming services under wireless networks, how to improve the quality of experience (QoE) has always been a challenging task. Especially after the arrival of the 5G era, more attention has been paid to analyze the experience quality of video streaming in more complex network scenarios (such as 5G-powered drone video transmission). Insufficient buffer in the video stream transmission process will cause the playback to freeze [1]. In order to cope with this defect, this paper proposes a buffer starvation evaluation model based on deep learning and a video stream scheduling model based on reinforcement learning. This approach uses the method of machine learning to extract the correlation between the buffer starvation probability distribution and the traffic load, thereby obtaining the explicit evaluation results of buffer starvation events and a series of resource allocation strategies that optimize long-term QoE. In order to deal with the noise problem caused by the random environment, the model introduces an internal reward mechanism in the scheduling process, so that the agent can fully explore the environment. Experiments have proved that our framework can effectively evaluate and improve the video service quality of 5G-powered UAV.
Aerial Image Color Balancing Based on Rank-Deficient Free Network

In the process of digital image acquisition, color inconsistency is a common issue due to the influence of photographic conditions. The inconsistency of color affects the quality of digital orthophoto products and subsequent applications significantly, and this issue remains challenging both in the academia and industry. This paper proposes a color equalization method for aerial image based on block adjustment and an improved color balance model based on the analysis of causes for color deviation. Two nonlinear functions are used to simulate the process of color deviation and correct the color of images. Due to the existence of error, the color corrected images still have minor deviations in the color of the overlapped regions. Therefore, this research introduces an error equation to address this issue and uses the least square principle to calculate the adjustment. The simulation experiments and real experiments are carried out on several groups of aerial image data respectively. The results show that the method not only achieves better visual effect but also has a higher adjustment precision. Finally, the rationality and parameters of the model proposed in this paper are discussed.
Single Image Super-Resolution Via Global-Context Attention Networks

In the last few years, single image super-resolution (SISR) has benefited a lot from the rapid development of deep convolutional neural networks (CNNs), and the introduction of attention mechanisms further improves the performance of SISR. However, previous methods use one or more types of attention independently in multiple stages and ignore the correlations between different layers in the network. To address these issues, we propose a novel end-to-end architecture named global-context attention network (GCAN) for SISR, which consists of several residual global-context attention blocks (RGCABs) and an inter-group fusion module (IGFM). Specifically, the proposed RGCAB extracts representative features that capture non-local spatial interdependencies and multiple channel relations. Then the IGFM aggregates and fuses hierarchical features of multi-layers discriminatively by considering correlations among layers. Extensive experimental results demonstrate that our method achieves superior results against other state-of-the-art methods on publicly available datasets.
Domain Usability Evaluation

Contemporary software systems focus on usability and accessibility from the point of view of effectiveness and ergonomics. However, the correct usage of the domain dictionary and the description of domain relations and properties via their user interfaces are often neglected. We use the term domain usability (DU) to describe the aspects of the user interface related to the terminology and domain. Our experience showed that poor domain usability reduces the memorability and effectiveness of user interfaces. To address this problem, we describe a method called ADUE (Automatic Domain Usability Evaluation) for the automated evaluation of selected DU properties on existing user interfaces. As a prerequisite to the method, metrics for formal evaluation of domain usability, a form stereotype recognition algorithm, and general application terms filtering algorithm have been proposed. We executed ADUE on several real-world Java applications and report our findings. We also provide proposals to modify existing manual usability evaluation techniques for the purpose of domain usability evaluation.
On the Amplitude Amplification of Quantum States Corresponding to the Solutions of the Partition Problem

In this paper we investigate the effects of a quantum algorithm which increases the amplitude of the states corresponding to the solutions of the partition problem by a factor of almost two. The study is limited to one iteration.
Speed Scaling with Multiple Servers under a Sum-Power Constraint

The problem of scheduling jobs and choosing their respective speeds with multiple servers under a sum-power constraint to minimize the flow time + energy is considered.
Advancing the State of Maritime Cybersecurity Guidelines to Improve the Resilience of the Maritime Transportation System

Generally, cybersecurity guidelines are geared to take on an integral role in providing industries with a suitable and comprehensive set of standards to inform and direct the design and implementation of their (critical) information technology infrastructure as well as support and govern their operational decisions in order to enhance their cybersecurity posture and resilience. However, in this paper we show that current maritime cybersecurity guidelines have two main shortcomings in that they neither provide a set of holistic recommendations to the key stakeholders in the maritime transportation system nor are the current guidelines sufficiently grounded in research. Thus, as a second contribution in this paper, we propose a comprehensive outline aimed to allow the stakeholders to develop comprehensive and thus more effective cybersecurity guidelines for the maritime transportation system.
Computational Errors and Suspension in a PWK Epistemic Agent

Abstract In a computational program there can be two kinds of errors: (i) critical errors and (ii) non-critical errors. A critical error stops the program in a global way, which means the error cannot be fixed in the subsequent computation process. A non-critical error partially stops the computation program, and the error can be fixed in the subsequent computation process. We argue that two kinds of errors correspond to two kinds of suspension and can be modeled using Paraconsistent Weak Kleene ($ {\textsf{PWK}}$) belief revision theory, with the help of a new interpretation of the third value of $ {\textsf{PWK}}$, that is, off-topic. According to this new interpretation, if a proposition obtains the third value $\textbf{u}$, it means it is off-topic. Within our framework of $ {\textsf{PWK}}$ belief revision theory, we will show that a non-critical error corresponds to a non-critical suspension and that a critical error corresponds to a critical suspension.
Performance Analysis of Serverless Execution Environments

Serverless computing has introduced a novel Cloud delivery model that abstracts the infrastructure, platform, and execution layers, allowing users to focus on code development. With such abstraction, users lose insight into the factors that affect their serverless functions' execution performance. In this paper, we investigate the factors affecting serverless functions' execution performance. We examine the impact of container choices, language interpreter versions, and compilation options on a function's execution performance and execution consistency. The results of our investigation illustrate that seemingly trivial decisions can result in significant performance degradations for serverless functions' executions
Social and Professional Partnership in Preparation of Pedagogical Personnel: Regional Practices

Ensuring the competitiveness of pedagogical universities and their graduates is one of the strategic tasks in the field of education and the country’s economy. Its achievement requires orientation of the basic educational programs and technologies for their implementation to the needs of the modern school. This necessitates a rethinking of approaches to organizing the training of future teachers, strengthening its practical orientation by expanding the area of responsibility of direct employers. The central issue of the article is the role and directions of implementing partnerships between the employer and the pedagogical university in preparing future teachers in the current educational situation. The purpose of this article is to identify promising areas of its development on the basis of an analysis of the existing regional experience in the training of teachers based on the implementation of social and professional partnerships. The study of the role and directions of the implementation of partnerships between the employer and the pedagogical university in the preparation of modern graduates – future teachers was carried out on the basis of the application of theoretical and empirical research methods. An analysis of the role of the employer in the training process was carried out on the basis of questionnaires and expert interviews. The main material was the pedagogical experience of teachers of the Institute of Mathematics, Physics and Informatics of KSPU named after V.P. Astafiev, and employers – directors of educational institutions of secondary education of the Krasnoyarsk Territory and the Republic of Khakassia, as well as the results of a study of the problems of training future teachers. Based on empirical data, the article concludes that social and professional partnerships are a necessary part of the training of future teachers in accordance with modern requirements and standards. The role and directions of implementing partnerships between employers and a pedagogical university in the preparation of future teachers are detailed. The practical significance lies in the use of the results obtained in the system of higher pedagogical education in order to ensure high-quality training of graduates.
Sparse STATIS-Dual via Elastic Net

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.
Can a corpus-driven lexical analysis of human and machine translation unveil discourse features that set them apart?

Abstract There is still much to learn about the ways in which human and machine translation differ with regard to the contexts that regulate the production and interpretation of discourse. The present study explores whether a corpus-driven lexical analysis of human and machine translation can unveil discourse features that set the two apart. A balanced corpus of source texts aligned with authentic, professional translations and neural machine translations was compiled for the study. Lexical discrepancies in the two translation corpora were then extracted via a corpus-driven keyword analysis, and examined qualitatively through parallel concordances of source texts aligned with human and machine translation. The study shows that keyword analysis not only reiterates known problems of discourse in machine translation such as lexical inconsistency and pronoun resolution, but can also provide valuable insights regarding contextual aspects of translated discourse deserving further research.
On probabilistic argumentation and subargument-completeness

Abstract Probabilistic argumentation combines probability theory and formal models of argumentation. Given an argumentation graph where vertices are arguments and edges are attacks or supports between arguments, the approach of probabilistic labellings relies on a probability space where the sample space is any specific set of argument labellings of the graph, so that any labelling outcome can be associated with a probability value. Argument labellings can feature a label indicating that an argument is not expressed, and in previous work these labellings were constructed by exploiting the subargument-completeness postulate according to which if an argument is expressed then its subarguments are expressed and through the use of the concept of ‘subargument-complete subgraphs’. While the use of such subgraphs is interesting to compare probabilistic labellings with other works in the literature, it may also hinder the comprehension of a relatively simple framework. In this short communication, we revisit the construction of probabilistic labellings and demonstrate how labellings can be specified without reference to the concept of subargument-complete subgraphs. By doing so, the framework is simplified and yields a more natural model of argumentation.
A Highly Modular Architecture for Canned Pattern Selection Problem

Due to the continuously increasing rate of data production from multiple sources, especially from social media, data analysis techniques are focusing on identifying patterns in the formed graphs and extracting knowledge from them. Most techniques till now, begin with given patterns and calculate the coverage in the graph. Here, we propose a graph mining architecture that focus on finding small sub-graph patterns, referred to as canned pattern, from a database of graphs without any domain knowledge of the graph. These patterns can be used to expedite the query formulation time, increase the domain knowledge and support the data analysis. The canned pattern should maximize coverage and diversity over the graph database while minimizing the cognitive-load of the patterns. The approach presented here is based on an innovative modular architecture that combines state-of-art techniques to extract these patterns and validate the extracted result.