Reviewerly/exHarmony huggingface dataset:
RDVI: A Retrieval–Detection Framework for Verbal Irony Detection

Verbal irony is a common form of expression used in daily communication, where the intended meaning is often opposite to the literal meaning. Accurately recognizing verbal irony is essential for any NLP application for which the understanding of the true user intentions is key to performing the underlying tasks. While existing research has made progress in this area, verbal irony often involves connotative knowledge that cannot be directly inferred from the text or its context, which limits the detection model’s ability to recognize and comprehend verbal irony. To address this issue, we propose a Retrieval–Detection method for Verbal Irony (RDVI). This approach improves the detection model’s ability to recognize and comprehend verbal irony by retrieving the connotative knowledge from the open domain and incorporating it into the model using prompt learning. The experimental results demonstrate that our proposed method outperforms state-of-the-art models.
A Graph Neural Network Node Classification Application Model with Enhanced Node Association

This study combines the present stage of the node classification problem with the fact that there is frequent noise in the graph structure of the graph convolution calculation, which can lead to the omission of some of the actual edge relations between nodes and the appearance of numerous isolated nodes. In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the method of external referencing of similar word nodes, combined with Graph Convolutional Neural Network (GCN), Graph Attention Network (GAT), and the early stop algorithm. In order to demonstrate the applicability of the model, this paper employs two distinct types of node datasets for its investigations. The first is the Cora dataset, which is widely used in node classification at this time, and the second is a small-sample Stock dataset created by Eastern Fortune’s stock prospectus of the Science and Technology Board (STB). The experimental results demonstrate that the ENode-GAT model proposed in this paper obtains 85.1% classification accuracy on the Cora dataset and 85.3% classification accuracy on the Stock dataset, with certain classification advantages. It also verifies the future applicability of the model to the fields of stock classification, tender document classification, news classification, and government announcement classification, among others.
Comparative Analysis of Professional-Value Attitudes of the Youth in Russia and Kazakhstan in Education

Introduction. In the modern world, there is a global transition to a new value paradigm of education, upbringing, and professional self-determination. Therefore, the study of educational and professional attitudes of young people gains prominence. The aim of the research is to identify the factors influencing the formation of a system of value trajectories and attitudes of young people in Russia and Kazakhstan and to predict social scenarios for their development. Materials and Methods. To study the educational and professional value trajectories and attitudes of young people in Russia and Kazakhstan, the authors conducted an international empirical sociological study in 2021. The data collection method is a survey of young people aged 14–35 years living in Russia and Kazakhstan. Results. The analysis revealed a high level of socio-cultural, value potential of young people in Russia and Kazakhstan in the educational space. Young people from both countries point out the importance of higher education in personal and professional career growth and self-realization. The professional attitudes and desired professional orientations of the Russian and Kazakh youth are heterogeneous. The main factors in choosing a profession are the interest of the profession, it`s financially rewarding, and prestige of the educational institution. Risk factors include low demand for the profession in the labor market, lack of personal interest in the profession, and low income. As a possible scenario have been identified the demand for IT competencies in the Russian and Kazakhstan labor markets, the transformation of value-professional attitudes towards greater freedom and creativity, socio-professional mobility and flexibility of young people. Discussion and Conclusion. The obtained data and recommendations can be used by educational organizations, authorities implementing state policy in the field of education to improve the work on the formation and development of value trajectories and attitudes of young people in Russia and Kazakhstan in the educational space and in the field of professional self-determination.
Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications

Abstract In multimedia correspondence, steganography schemes are commonly applied. To reduce storage capacity, multimedia files, including images, are always compressed. Most steganographic video schemes are, therefore, not compression tolerant. In the frame sequences, the video includes extra hidden space. Artificial intelligence (AI) creates a digital world of real-time information for athletes, sponsors, and broadcasters. AI is reshaping business, and although it has already produced a significant impact on other sectors, the sports industry is the newest and most receptive one. Human-centered AI for web applications has substantially influenced audience participation, strategic plan execution, and other aspects of the sports industry that have traditionally relied heavily on statistics. Thus, this study presents the motion vector steganography of sports training video integrating with the artificial bee colony algorithm (MVS-ABC). The motion vector stenography detects the hidden information from the motion vectors in the sports training video bitstreams. Artificial bee colony (ABC) algorithm optimizes the block assignment to inject a hidden message into a host video, in which the block assignment is considered a combinatorial optimization problem. The experimental analysis evaluates the data embedding performance using steganographic technology compared with existing embedding technologies, using the ABC algorithm compared with other genetic algorithms. The findings show that the proposed model can give the highest performance in terms of embedding capacity and the least error rate of video steganography compared with the existing models.
Comparative Analysis of Feature Extraction Methods for Intelligence Estimation Based on Resting State EEG Data

In this paper, we investigate a modification of the method of data generation for multiple code paths within a single launch of the genetic algorithm. This method allows the consideration of the remoteness of paths initiated by different test cases by introducing an additional additive component into the fitness function. Previous studies have shown that the parameter defining the relationship between the different components of the fitness function has a rather strong effect on code coverage. To eliminate this effect, we propose the modification of the first component of the fitness function, which is responsible for path complexity. This modification is based on a dynamic change in code statement weights between generations to achieve greater population diversity. We propose several methods for implementing this modification, divided into two groups. In the first group, the statement weights change depending only on the fact of statement coverage in a generation, and the rate of change depends on the number of previous generations in which it was covered. In the second group, the rate of change depends on the proportion of statement coverage by the test sets in the previous generation. Each of the proposed methods is investigated to achieve complete coverage with different values of the parameter defining the ratio of the components of the fitness function. As a result, the best method is determined, which eliminates the need to determine this parameter for each testing code, thus achieving a greater universality for the algorithm.
The nomadic subject as a cinematographic hero by Alain Resnais

The author of the work raises the issue of the phenomenon of nomadism in the context of the work of the French director Alain Resnais. The object of the research is the early works of the director, such films as "Last year in Marienbad", "Hiroshima, my love", "I love you, I love". The purpose of the work is to answer the question of how nomadic strategies allow the author to create non-linear narrative structures, oscillating characters and flickering characters in his films, and how understanding these strategies can help viewers and researchers in the act of deciphering works. The author also analyzes a number of phenomena related to nomadism, such as the rhizome, the body without organs, deterritorialization, escape, flicker. The novelty of the research lies in the application of nomadic strategies to the cinema of Alain Resnais. This approach makes it possible to use the current philosophical and art history prism to study narrative structures, intraframe space and characters. The author argues that Rene's cinematography can be viewed through a position of resistance to forms of power - suppression and control - which manifests itself in the violation of traditional forms of narration and the creation of split characters. To study the mentioned aspects of Rene's work, the author resorts to the philosophy of Gilles Deleuze and Felix Guattari. The author also explains the position of the modern viewer and describes the strategies of looking, which can lead to a state of pleasure from interacting with nomadic works.
Object detection in traffic videos: an optimized approach using super-resolution and maximal clique algorithm

Abstract Detection of small objects is one of the main challenges to be improved in deep learning, mainly due to the small number of pixels and scene’s context, leading to a loss in performance. In this paper, we present an optimized approach based on deep object detection models that allow the detection of a higher number of elements and improve the score obtained for their class inference. The main advantage of the presented methodology is that it is not necessary to modify the internal structure of the selected convolutional neural network model or re-training for a specific scene. Our proposal is based on detecting initial regions to generate several sub-images using super-resolution (SR) techniques, increasing the number of pixels of the elements, and re-infer over these areas using the same pre-trained model. A reduced set of windows is calculated in the super-resolved image by analyzing a computed graph that describes the distances among the preliminary object detections. This analysis is done by finding maximal cliques on it. This way, the number of windows to be examined is diminished, significantly speeding up the detection process. This framework has been successfully tested on real traffic sequences obtained from the U.S. Department of Transportation. An increase of up to 44.6% is achieved, going from an average detection rate for the EfficientDet D4 model of 14.5% compared to 59.1% using the methodology presented for the first sequence. Qualitative experiments have also been performed over the Cityscapes and VisDrone datasets.
A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?

COVID-19 was an unprecedented pandemic that changed the lives of everyone. To handle the virus’s rapid spread, governments and big tech companies, such as Google and Apple, implemented Contact Tracing Applications (CTAs). However, the response by the public was different in each country. While some countries mandated downloading the application for their citizens, others made it optional, revealing contrasting patterns to the spread of COVID-19. In this study, in addition to investigating the privacy and security of the Canadian CTA, COVID Alert, we aim to disclose the public’s perception of these varying patterns. Additionally, if known of the results of other nations, would Canadians sacrifice their freedoms to prevent the spread of a future pandemic? Hence, a survey was conducted, gathering responses from 154 participants across Canada. Next, we questioned the participants regarding the COVID-19 pandemic and their knowledge and opinion of CTAs before presenting our findings regarding other countries. After showing our results, we then asked the participants their views of CTAs again. The arrangement of the preceding questions, the findings, and succeeding questions to identify whether Canadians’ opinions on CTAs would change, after presenting the proper evidence, were performed. Among all of our findings, there is a clear difference between before and after the findings regarding whether CTAs should be mandatory, with 34% of participants agreeing before and 56% agreeing afterward. This hints that all the public needed was information to decide whether or not to participate. In addition, this exposes the value of transparency and communication when persuading the public to collaborate. Finally, we offer three recommendations on how governments and health authorities can respond effectively in a future pandemic and increase the adoption rate for CTAs to save more lives.
OpenDSU: digital sovereignty in PharmaLedger

Introduction: Distributed ledger networks, chiefly those based on blockchain technologies, currently are heralding a next-generation of computer systems that aims to suit modern users’ demands. Over the recent years, several technologies for blockchains, off-chaining strategies, as well as decentralised and respectively self-sovereign identity systems have shot up so fast that standardisation of the protocols is lagging behind, severely hampering the interoperability of different approaches. Moreover, most of the currently available solutions for distributed ledgers focus on either home users or enterprise use case scenarios, failing to provide integrative solutions addressing the needs of both. Methods: Herein, we introduce the OpenDSU platform that allows to interoperate generic blockchain technologies, organised–and possibly cascaded in a hierarchical fashion–in domains. To achieve this flexibility, we seamlessly integrated a set of well conceived components that orchestrate off-chain data and provide granularly resolved and cryptographically secure access levels, intrinsically nested with sovereign identities across the different domains. The source code and extensive documentation of all OpenDSU components described herein are publicly available under the MIT open-source licence at https://opendsu.com . Results: Employing our platform to PharmaLedger, an inter-European network for the standardisation of data handling in the pharmaceutical industry and in healthcare, we demonstrate that OpenDSU can cope with generic demands of heterogeneous use cases in both, performance and handling substantially different business policies. Discussion: Importantly, whereas available solutions commonly require a pre-defined and fixed set of components, no such vendor lock-in restrictions on the blockchain technology or identity system exist in OpenDSU, making systems built on it flexibly adaptable to new standards evolving in the future.
Review of Egbert, Biber &amp; Gray (2022): Designing and Evaluating Language Corpora: A Practical Framework for Corpus Representativeness

Abstract This article reviews Designing and Evaluating Language Corpora: A Practical Framework for Corpus Representativeness
A data-driven multidimensional assessment model for English listening and speaking courses in higher education

Based on multiple assessment approach, this study used factor analysis and neural network modeling methods to build a data-driven multidimensional assessment model for English listening and speaking courses in higher education. We found that: (1) Peer assessment, student self-assessment, previous academic records, and teacher assessment were the four effective assessors of the multi-dimensional assessment of English listening and speaking courses; (2) The multidimensional assessment model based on the four effective assessors can predict the final academic performance of students in English listening and speaking courses, with previous academic records contributing the most, followed by peer assessment, teacher assessment, and student self-assessment. Therefore, a multidimensional assessment model for English listening and speaking courses in higher education was proposed: the academic performance of students (on a percentage basis) should be composed of 29% previous academic records, 28% peer assessment, 26% teacher assessment, and 17% student self-assessment. This model can guide teachers to intervene with students who need help in a timely manner, based on various assessors, thereby effectively improving their academic performance.
US-Kapitol-Erstürmung: Aufstand der unteren Mittelschicht

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Surfing Information: The Challenge of Intelligent Decision-Making

Reinforcement learning (RL) is indispensable for building intelligent decision-making agents. However, current RL algorithms suffer from statistical and computational inefficiencies that render them useless in most real-world applications. We argue that high-value information in the real world is essential for intelligent decision-making; however, it is not addressed by most RL formalisms. Through a closer investigation of high-value information, it becomes evident that, to exploit high-value information, there is a need to formalize intelligent decision-making as bounded-optimal lifelong RL. Thus, the challenge of achieving intelligent decision-making is summarized as effectively surfing information, specifically regarding handling the non-IID (independent and identically distributed) information stream while operating with limited resources. This study discusses the design of an intelligent decision-making agent and examines its primary challenges, which are (a) online learning for non-IID data streams, (b) efficient reasoning with limited resources, and (c) the exploration–exploitation dilemma. We review relevant problems and research in the field of RL literature and conclude that current RL methods are insufficient to address these challenges. We propose that an agent capable of overcoming these challenges could effectively surf the information overload in the real world and achieve sample- and compute-efficient intelligent decision-making.
Assistant Based Speech Recognition Support for Air Traffic Controllers in a Multiple Remote Tower Environment

Assistant Based Speech Recognition (ABSR) systems for air traffic control radiotelephony communication have shown their potential to reduce air traffic controllers’ (ATCos) workload. Related research activities mainly focused on utterances for approach and en-route traffic. This is one of the first investigations of how ABSR could support ATCos in a tower environment. Ten ATCos from Lithuania and Austria participated in a human-in-the-loop simulation to validate ABSR support within a prototypic multiple remote tower controller working position. The ABSR supports ATCos by (1) highlighting recognized callsigns, (2) inputting recognized commands from ATCo utterances in electronic flight strips, (3) offering correction of ABSR output, (4) automatically accepting ABSR output, and (5) feeding the digital air traffic control system. This paper assesses human factors such as workload, situation awareness, and usability when ATCos are supported by ABSR. Those assessments result from a system with a relevant command recognition rate of 82.9% and a callsign recognition rate of 94.2%. Workload reductions and usability improvement with p-values below 0.25 are obtained for the case when the ABSR system is compared to the baseline situation without ABSR support. This motivates the technology to be brought to a higher technology readiness level, which is also confirmed by subjective feedback from questionnaires and objective measurement of workload reduction based on a performed secondary task.
Language transformations in the reflection of digital media reality

The aim of the study is to identify the semantic and pragmatic transformations of language in the conditions of digital media reality reflection. The basic notions related to the transfer of the real world into the network information space are being defined based on the material of Russian, English and French, including such notions as ‘digital media’, ‘digital media reality’, ‘digital worldview’, ‘digital culture’ and ‘digital discursive practices’. The study is novel in that it is the first to outline the status of digital media reality as a substitute for the real worldview and digital media resources as a priority in receiving, processing and transmitting information involving a multi-format language material. As a result, the main characteristics of both digital media reality and the language transformations associated with the reflection of the digital worldview were revealed. Examples of discussion of smart home projects on various Internet platforms, including advertising texts, online dictionaries, blogs, social networks, forums and electronic corpora, were given as illustrations, as well as the possibilities of presenting the SMART HOME concept in the European digital media reality were shown.
DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.