Reviewerly/exHarmony huggingface dataset:
Analysis of binocular visual perception technology of underwater robot

Vision technology plays an important role when AUVs (Autonomous Underwater Vehicles) operate underwater. In this paper, the three-dimensional mode of binocular stereo vision is constructed to complete the positioning of the target. This article then explains the problem of distorted underwater images and introduces a method to correct distorted images. Based on underwater physical imaging, the underwater image processing methods are divided into underwater image enhancement and underwater image restoration. The research status of the two ways is analyzed and reviewed. The advantages and disadvantages of the above methods are summarized and discussed, and the future development trend is predicted.
Research on the Influence of Transformational Leadership on Teachers’ Craftsmanship in Higher Vocational Colleges

In vocational colleges, actively popularizing the connotation of the craftsman spirit and improving the student's understanding of the craftsman spirit are conducive to creating a good atmosphere, driving the teaching and education work of vocational colleges, and highlighting the social functions of vocational colleges in the new era.All this requires a good leader of higher vocational colleges.As a leader of higher vocational colleges, can he make the faculty "willing" to serve the vision of higher vocational colleges?Many scholars and managers are looking for answers, and have put forward the leadership trait theory, leadership behavior theory, and leadership contingency theory.Among them, transformational leadership, as one of the latest leadership theories, can motivate employees to the maximum extent and promote employees from "I want to do" to "I want to do".Based on this point, this paper takes the practical problems as the starting point, actively seeks the influence of the transformational leadership of higher vocational colleges on the craftsmanship spirit of the college staff, deeply studies the solution path, emphasizes seeking truth from facts, respecting the objective environment, scientifically adjusts the cultivation methods and channels of craftsmanship spirit, sets off a learning upsurge inside the campus, and trains more and more teachers and workers with craftsmanship spirit in the new era.
GOOD, BETTER &amp; THE BEST SECURITY

AbstractIf the goal of an organisation is to build a culture of cyber security awareness the cyber security leadership and the security team must be approachable and engaged with the business. This may sound like a simple mission but it's not. Cyber security and IT in general suffers from a very binary ”Is it on or off?” view of the organisation. in contrast to the business which is far more nuanced in its perspectives ”on-ish, off-ish or on & off at the very same time - business is rarely 100% certain of anything - especially in these turbulent and dynamic times. The desire to demonstrate or measure security through the application of Key Performance Indicators (KPI) becomes counter intuitive to actual progress in strengthening the organisational cyber posture. The number of ”insert security metric here” does little to advance the organisation culture and awareness as the metric is likely to be meaningless outside of the narrow field of vision of the security team. If you are genuinely interested in improving or building a culture of security within your organisation it's time to spend less time measuring and more time communicating. Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. https://www.iafci.org/MembersOnly/Federal_Agencies/2022_Annual_Internet_Crime_Report_IC3.aspx.2. https://www.techtarget.com/searchsecurity/news/365532537/Secureworks-IR-team-saw-BEC-attacks-double-in-2022.
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Using Transformers and Stacked Embeddings

Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles.Emotion detection from complex dialogues is challenging and often requires context/domain understanding.Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed.Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models.We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions.
The SIGMORPHON 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task.The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year's task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses.Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14% in the crosslingual subtask and 14% in the very-low resource subtask.The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.
YNU-HPCC at WASSA 2023: Using Text-Mixed Data Augmentation for Emotion Classification on Code-Mixed Text Message

Emotion classification on code-mixed texts has been widely used in real-world applications.In this paper, we build a system that participates in the WASSA 2023 Shared Task 2 for emotion classification on code-mixed text messages from Roman Urdu and English.The main goal of the proposed method is to adopt a text-mixed data augmentation for robust code-mixed text representation.We mix texts with both multilabel (track 1) and multi-class (track 2) annotations in a unified multilingual pre-trained model, i.e., XLM-RoBERTa, for both subtasks.Our results show that the proposed text-mixed method performs competitively, ranking first in both tracks, achieving an average Macro F 1 score of 0.9782 on the multi-label track and of 0.9329 on the multi-class track.
Superconductivity claim electrifies social media

Report of room-temperature material goes viral, but many physicists are skeptical
Cascade RCNN with Hybrid Attention and Dual Pooling for Soccer Player Detection

Sports video analysis is of great significance to football players’ tactical deployment and daily training, and player detection is the basis of football intelligence analysis. However, in sports videos, there are problems such as the camera is difficult to capture fast athletes, and dense crowds causing occlusions. It is difficult for existing target detection algorithms to accurately identify athletes. To this end, based on Cascade RCNN, this paper proposes an anti-blurring and anti-occlusion football player detection model. First, we propose a novel Hybrid Attention Module (HAM) to refine the feature pyramid and enhance features for object occlusion and blurring. In addition, we introduce dual pooling (DA) to capture context information and enhance feature expression when providing candidate regions for the subsequent RPN. We verified the performance of our method on the football player detection data set. The experimental results show that our method is superior to other models, and can effectively solve the problems of missed detection and false detection of athletes caused by occlusion and blurring. It can be effectively applied in Sports video analysis systems.
Application of AI, Big Data and Cloud Computing Technology in Smart Factories

AI, big data and cloud computing technology are used as a tool to extend intelligence and improve our ability to understand and transform the world, resulting in a new AI economic form. In recent years, the combination of artificial intelligence and industrial manufacturing is increasingly attracting attention. The integration of multiple information and the injection of high-tech and intelligent technologies will greatly promote the transformation from traditional manufacturing to new smart factories. This paper analyzes the basic concepts and characteristics of AI, big data and cloud computing, as well as the characteristics and technical framework construction of smart factories. Given the characteristics of manufacturing enterprises and the need for in-depth application of information systems, it describes how AI, big data and cloud computing technologies are applied in smart factories through practical application scenarios.
Examining Bias in Opinion Summarisation through the Perspective of Opinion Diversity

Opinion summarisation is a task that aims to condense the information presented in the source documents while retaining the core message and opinions.A summary that only represents the majority opinions will leave the minority opinions unrepresented in the summary.In this paper, we use the stance towards a certain target as an opinion.We study bias in opinion summarisation from the perspective of opinion diversity, which measures whether the model generated summary can cover a diverse set of opinions.In addition, we examine opinion similarity, a measure of how closely related two opinions are in terms of their stance on a given topic, and its relationship with opinion diversity.Through the lens of stances towards a topic, we examine opinion diversity and similarity using three debatable topics under COVID-19.Experimental results on these topics revealed that a higher degree of similarity of opinions did not indicate good diversity or fairly cover the various opinions originally presented in the source documents.We found that BART (Lewis et al., 2020) and ChatGPT can better capture diverse opinions presented in the source documents.
A new steganography technique based on genetic algorithm

As technology grows, it affect our daily lives becomes more penetrating. Our private information may be violated when communications media are used, to achieve information security this paper was based on a new technique of steganography using cryptography, color model and genetic algorithm. First, the secret text was encrypted with the advanced encryption standard (AES) algorithm. Second, the hiding was applied in two stages. First, the cover-image was converted in to the hue saturation intensity (HSI) color model and select one of the models, then divide it into a group of blocks and hiding the text in them by using least significant bit (LSB) method on specified bytes randomly, then using the genetic algorithm that calculates peak signal-to-noise-ratio (PSNR) for all blocks after the hiding process and thus obtain the best value for PSNR for the optimal block. Second, includes the final hiding of all blocks based on the results of the first stage of the best random distribution of bytes according to the results of the genetic algorithm. The PSNR and mean square error (MSE) measures were adopted to prove the accuracy and efficiency of the results.
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Image-to-Image (I2I) translation methods based on generative adversarial networks (GANs) require large amounts of training data, without which they will suffer from over-fitting and train divergence, and trained models are sub-optimal. In addition, it would be very difficult for the model to synthesize high-frequency signals, deteriorating the synthesis quality. To address these, this paper proposes W2GAN, which mainly introduces the ideas of the Importance Weight and Wavelet transformation to achieve the I2I translation trained on limited-data. Concretely, this paper first alleviates the over-fitting and train divergence by the adversarial loss with importance weight, which aims to improve the influence of the high-quality generated images during the training generator, thus enhancing the generator to deceive the discriminator. Then, the high-frequency features of the wavelet transformation are applied to the decoder, and wavelet-AdaIN normalization is proposed to prevent deficiency of high-frequency information, which adaptively integrates high-frequency statistical characteristics from generated features and real image high-frequency information. Qualitative and quantitative results on the AFHQ and CelebA-HQ datasets demonstrate the merits of the W2GAN. Noticeably, this paper achieves state-of-the-art FID and KID on AFHQ and CelebA-HQ datasets.
Two Types of a Musical-Creative Process – Two Forms of a Musical Composition

The study aims to show the interaction of the external and internal forms of a musical work as purely musical and conceptual logos structures using the example of V. G. Korolevsky’s First String Quartet. The paper analyses the scientific state of the problem of musical-creative thinking, sheds light on the issue of coexistence and interaction of musical and conceptual thinking in a composer’s creative process, which leave their formbuilding trace in the sound matter of a musical work, these spheres of formbuilding are differentiated into external and internal forms, i.e. the focus of the objective (universal) and subjective (personal) meaning, as well as their fundamental difference is determined. Scientific novelty lies in the approach to the analysis of V. G. Korolevsky’s First String Quartet from the position of the internal form, the starting point of which is the paradigmatic structure of the typical content, or the objective meaning, its ending point is the form of meaning. As a result, it has been found that the form uniting Korolevsky’s Quartet on the inside is a labyrinth branching into two intonational directions, represented by the themes of the chorale and fugue, with a fairly clearly marked fork point – the theme of the cross.
Synthesis of Arts as a Method of Artistic Perception Development: A Practical Aspect

The aim of the research is to substantiate that the synthesis of art forms enriches and deepens the cognitive, educational and developmental possibilities of art in the process of studying works of art. The paper identifies the specific characteristics of music lessons and the role of perception as a psychological aspect in the artistic and formative process; presents the fundamental aspects in the organisation of perception of works of art in music lessons; considers individual methods that satisfy the needs for emotional contact and fantasising, interest and the novelty effect, which leads to the mobilisation of creative efforts in music lessons. The scientific novelty of the research lies in proving that the artistic and formative process is based on the synthesis of the notions of “an emotion-idea” / “an idea-emotion”. As a result of the research, the poly-artistic methods of perception of works of various art forms have been developed and put into practice: voicing works of painting, sculpture, architecture on the basis of a common emotion-idea; combining works of different art forms on the basis of stylistic similarities; comprehending the depth of understanding an artistic image.
Russian Folk Songs in F. I. Chaliapin’s Repertoire: Interpretation of Folklore Singing Traditions in Academic Performance

The study aims to prove that F. I. Chaliapin became an unsurpassed interpreter of Russian songs included in his repertoire. The paper presents a detailed performance analysis of Chaliapin’s interpretation of folk songs “Eh, Vanka” and “Dubinushka (Russian Laborers’ Song)”; shows the interpretations of folklore singing traditions in F. I. Chaliapin’s academic performance. The novelty of the paper lies in substantiating the fact that intonation in F. I. Chaliapin’s creative work becomes the main conduit for the performing idea of a composition. As a result, it has been determined that such traditional techniques of folk culture as the complete freedom of intonation and rhythmic utterance; bending, stops inside a phrase with the help of agogic extensions; an abundance of fermata over a note and over a rest; strict restraint and concentration focused on the movement of thought and feeling are traced in Chaliapin’s style of performing folk songs.
“An Optimistic Tragedy” by A. Kholminov: From Chaos to Harmony

The aim of the study is to identify the categories characterising the diversity of the opera genre that have become integral components of A. Kholminov’s operas. The paper presents the content-related essence of V. Vishnevsky’s play “An Optimistic Tragedy”, which formed the basis of A. Kholminov’s opera. The scientific novelty of the study lies in determining the main feature of the Commissar’s portrait, i.e. melodiousness based on the couplet form, simple diatonic motifs supported by stable degrees, uncomplicated harmonic connections, declamations. As a result, it has been found that A. Kholminov’s opera “An Optimistic Tragedy” has all the defining features of the opera of the XX century: introducing epistolary heritage and documentary information into the libretto; polystylistics at the stage level; using the means of related art forms; orientation to mass character; “a director’s split-screen”.
Novam Viam or the “Dead End” of Opera in the Modern Socio-Cultural Space

The aim of the study is to comprehend in a theoretical manner the principles of opera as a synthetic genre, namely taking into account the symbiosis of the properties related to the plot and storyline and musical properties themselves. The paper examines the development of opera in modern realities, the phenomenon of hybridity and openness of the genre. The author focuses on the mixed nature of opera as a result of the search for new means of expression. The scientific novelty of the study lies in defining modern opera as a hybrid, mixed genre, which together with modern technologies, means of performance and interpretation creates new contexts of musical expression, temporality and imaginary space. As a result, it has been determined that the evolutionary movement of opera has a centuries-old history and has always consisted of the confrontation of the new and the conservative, the elitist and the democratic, while opera was able to absorb the “intonation vocabulary of a specific era” and remain viable.
Extinction-Based Exposure Therapies Using Virtual Reality

The focus of this chapter is an overview of integrating virtual reality (VR) technology within the context of exposure therapy for anxiety disorders, a gold standard treatment, with a focus on how VR can help facilitate extinction learning processes integral to these interventions. The chapter will include an overview of advantages of incorporating VR within exposure therapy, and benefits specifically within an inhibitory learning approach for extinction training. A review of the empirical literature on the effectiveness of VR exposure therapy for specific phobia and PTSD will be provided, as well as practical overview of how to effectively incorporate VR within exposure therapy.
Active broad learning with multi-objective evolution for data stream classification

Abstract In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted.
INTRODUCING ARTIFICIAL INTELLIGENCE TO THE MIDDLE SCHOOL THROUGH THE LOGIC PROGRAMMING LANGUAGE PROLOG

The article presents the results and practical experience of the introduction of Artificial Intelligence training in secondary school. The initial stage and the current state of the introduction of experimental learning in two Bulgarian schools are discussed. Our experience in teaching students in different forms of learning and different age groups is presented, as well as the joint teaching of teachers and students. Particular attention is paid to logic programming using the Prolog language. The chronology of our approach is examined, with the introduction of Artificial Intelligence in education presented in three stages – beginning, current state and future intentions. We consider the three-year period to be sufficient for approbation of the curriculum, the used methodology and teaching materials. An original approach to the training of teachers and students is proposed. A summary analysis of the learning outcomes is presented in an appropriate form. Based on the accumulated experience, a critical analysis of the used approaches and teaching aids was made. Conclusions were made for continuing the study with students and improving the methods, approaches and teaching materials.
ARTIFICIAL INTELLIGENCE AND LOGIC PROGRAMMING IN STEM EDUCATION

In this article, the authors present one possibility for introducing artificial intelligence training, specifically Prolog logic programming, into the secondary school. The choice of Prolog logic programming as an appropriate artificial intelligence topic for secondary school study is motivated. The training is planned to take place within the school’s STEM centers. The introduction is carried out through the training of science, mathematics and informatics teachers for the subsequent training of students in the school’s STEM centers. The authors describe the structure and content of the curriculum created. The first results of the implementation of this program in teacher training are presented. One of the goals is to create a network of STEM centers offering training in artificial intelligence. The preferred form is project-based learning and multidisciplinary lessons. For the better organization of this training, a project called „Digital Bulgaria in Prolog“ has been proposed. The content of the project is demonstrated with a selected concrete example. Within this project, during the training courses, training resources are created that can be used by the participants of the network. Our experience shows that such organized training stimulates the teamwork of teachers from different specialties.