Reviewerly/exHarmony huggingface dataset:
Learning advantages of online old-time jams

During the COVID-19 pandemic, participants in the old-time community of practice employed readily available streaming and teleconferencing platforms to jam online. Old-time musicians use acoustic instruments – primarily fiddle, banjo, mandolin, guitar and bass – to play traditional fiddle tunes from the United States, and jams are important sites for both learning and community building. Old-time jams were primarily facilitated using Zoom, Facebook Live and YouTube Live – platforms that allow a leader or small group to broadcast from a single location while other participants play on mute. While these platforms cannot successfully replicate in-person jams, for some players they provided a superior learning experience. This study draws on quantitative, qualitative and participant–observer data to categorize and describe the ways in which online jamming helped old-time players to sustain musical growth during the pandemic. While the inaudibility of individual participants might seem to be a major drawback of these platforms, it in fact benefited developing players in multiple ways. Participants also valued the opportunity to learn from recorded sessions, see and hear more clearly, maintain autonomy over their learning processes, access online resources and meet new challenges. For these reasons, online jams continue to serve as a valuable learning tool even as players return to in-person activities.
The International Conference on Music Education Technology 2023: A report

This report provides an overview of the organization and content of the International Conference on Music Education Technology 2023 (ICMdT2023) , a hybrid event hosted towards the end of the global lockdown in early 2023. Under the theme ‘Unfold the Future of Music Education through Technology’, the Conference attracted 640 academics, researchers, practitioners and professionals from different regions, all of whom participated online or in person to exchange and share their experiences and research projects on various aspects of music education through the use of technology. It included 36 workshops and paper presentations, with topics ranging from digital pedagogies for teaching and learning music online to the application of artificial intelligence (AI) and interdisciplinary approaches for music education. Challenges faced by the organizing committee are outlined, coupled with some recommendations for those wishing to host similar events that can improve knowledge exchange and encourage delegates’ networking experiences.
HPPython: Extending Python with HPspmd for Data Parallel Programming

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world.
Classification of Brain Functional Connections for Tone Processing in Deaf Children Based on 1D-CNN

Purpose: This study investigates variations in brain functional connectivity and activation regions during the processing of three different tones (first tone, second tone, third tone) in both deaf and hearing children, employing deep learning methods. Additionally, the research aims to discern differences in these brain responses between deaf and typically developing children. Methods: The study involves five deaf children and two typically developing children as participants, with resting-state functional magnetic resonance imaging (fMRI) scans conducted using a functional MRI scanner. The research workflow includes data preprocessing and fMRI data analysis. Specifically, a one-dimensional convolutional network is employed to extract features and classify the input brain functional connectivity data. Results: The study finds that the one-dimensional convolutional network is well-suited for capturing local patterns within sequential data and effectively extracting information pertaining to connectivity patterns from the one-dimensional sequence of connection weights. This makes it a suitable choice for processing brain functional connectivity data, especially in the context of deaf children.
Optimization of Chinese Character Font Recognition Model Based on Incremental Learning

Chinese character font recognition is a vital research area with applications in text recognition, artistic font design, and handwriting recognition. Traditional methods for this task are time-consuming, labor-intensive, and error-prone. The rise of deep learning has opened new possibilities in this field. However, neural network models often struggle with generalization and robustness, especially with new data, leading to catastrophic forgetting of old data. To address this, we propose an optimization algorithm for the Chinese character font recognition model SwordNet, combining parameter isolation and regularization. We designed an incremental module for SwordNet, enhancing it for better performance and scalability. The loss function incorporates a regularization term based on knowledge distillation principles, limiting significant parameter changes for old data and ensuring the model retains knowledge while learning new data. To tackle the diversity of Chinese character fonts and aid model recognition, we introduced a data augmentation method using noise, rotation, and occlusion. Experiments demonstrate that our regularized incremental learning optimization algorithm achieves a 98.81% accuracy rate with only new data, comparable to models trained on both new and old datasets. Notably, it also reduces training time by approximately 30%, marking a significant improvement in efficiency and accuracy for Chinese character font recognition.
The Design of an Intelligent Security System with Face Recognition and Human Action Analysis

Security systems are extremely important in the workplaces, educational establishments and other public places. Nowadays, in a wide range of scenarios, such as offices, schools, universities, factories, can be detected various anomaly behaviors, which may lead to potential danger. It is extremely important, as it can help to reduce risk from both personal and property safety. Thus, the purpose of the current study is to improve security systems with deep learning techniques, as computational power and hardware itself recently have improved tremendously. Human-Machine Interaction also benefits in this case, as machines can help people analyze the environment and prevent danger in time, thus creating a safer environment. In order to recognize, distinguish and analyze people's behavior, a new approach of combination of Face Recognition with masks and Human-Action Recognition algorithm with deep learning techniques is introduced in this security system. Provided approach consists of algorithms, which are considered as SOTA at each specific domain, and trained for a customly collected dataset. As for the result, performance of the current method appeared to be fairly robust, which means it can be used in public places, such as offices, schools, etc.
CLIP-M-Cap: CLIP Mean Teacher for Image Captioning

Abstract. Image description plays a pivotal role in the realm of computer vision language tasks, as its core mission revolves around predicting relevant textual information given an input image. In this research paper, we introduce an innovative model (Clip-M-cap) to tackle this challenge. Drawing inspiration from the Mean Teacher approach, we present a fresh network architecture that comprises two branches. One of these branches acts as a reference for the other, providing essential support during training. In the image feature extraction phase of our model, we harness CLIP to extract distinctive image features. In the subsequent caption generation stage, we enlist the assistance of GPT-2 to enhance the accuracy of caption generation. During the training phase, we meticulously train our model on two distinct datasets, MS-COCO and Flickr30k. Subsequently, we rigorously evaluate the performance of our trained model across three domains: MS-COCO, Flickr30k, and Cross-Domain, consistently achieving outstanding results in all three scenarios.
A Hybrid Improved Lion Swarm Optimization Algorithm

Abstract: When solving multimodal optimization problems, the lion swarm optimization (LSO) algorithm will face many problems, such as low individual diversity, slow search speed, premature convergence or even falling into local extremum. The traditional approach is to introduce chaotic search, gaussian mutation or other mutation strategies to enhance the local search ability of the LSO algorithm. These improvements have been verified and the performance of the algorithm can be improved to a certain extent. However, these improved strategies lack effective use of population information, which will still affect the performance of the algorithm in search speed and search accuracy. To solve this problem, on the basis of the above-mentioned improved algorithm, this paper introduces the distribution estimation algorithm and proposes a hybrid improved LSO algorithm. The hybrid improved algorithm analyzes and learns the structure of the problem by constructing a probability model for the dominant group, and guides the efficient optimization of individuals in the population according to this information. It is verified in five standard test functions and ten test functions of IEEE CEC 2021. Compared with the traditional improved LSO algorithm, the results show that the hybrid improved LSO algorithm is superior.
CLPLM-EE: Contrastive Learning Pre-training Model for Event Extraction In New Domain

The event extraction task recognizes events in natural language text and extracts the event triggers and arguments. The majority of existing methods that based on closed domains have poor generalization and are difficult to extend to new domains. As a result, event extraction models for new domains requires general event knowledge learned from large amounts of unsupervised data. Fortunately, existing studies have shown that a contrastive pre-training model for information extraction can better obtain prior knowledge from large unannotated data. However, such methods have insufficient perception of event structure, have not learnt enough about event representations, and thier contrastive learning methods used to construct negative samples lead to semantic conflicts. To adress the above problem, we propose CLPLM-EE, a contrastive pre-training MODEL for event extraction in new domians. CLPLM-EE contains has an encoder with event semantics and event structure awareness to learn the most common knowledge of the event. In addition, CLPLM-EE learns high-quality event representations by eliminating false negative samples and using a weighting mechanism to avoid the semantic conflicts generated in Contrastive learning pre-training.
VERY-RL: An Adaptive Image Steganography Algorithm Based on Reinforcement Learning

Adaptive image steganalysis algorithm is one of the important research direction in the field of information security. Nowadays, digital image steganography based on deep learning has been widely used to protect sensitive data during transmission or storage. However, it is a great challenge to achieve the accurate location of the best steganography position in the cover image and improve the model training accuracy is a great challenge. To solve the above problems, a novel digital image steganography algorithm based on reinforcement learning—VERY-RL, is proposed to obtain the optimal embedding strategy. Meanwhile, according to the designed distortion function, the parameters of VERY-RL are updated by gradient backpropagation to minimize the embedding loss. Experimental results show that, compared with other steganographic methods based on reinforcement learning, the detection error rate of VERY-RL is improved by 21.94% when resisting steganalysis detection. In addition, the FLOPs of the model are reduced by 24.8%. It can be seen that VERY-RL improves the localization accuracy of pixel modification bits, and the steganography network has a stronger representation ability, indicating that VERY-RL has better steganographic security.
Research on Named Entity Recognition in the Steel Industry Based on MacBERT

Abstract. In response to the lack of larger-scale and high-quality NER datasets and research on NER in the steel industry, this paper constructs a NER dataset for the steel industry that includes 4835 pieces of data, and annotates four entity categories: device, material, process, and product, A NER research method based on MacBERT_large-BiLSTM-CRF model was built. This method first utilizes the MacBERT model to generate semantically rich dynamic word vectors, then inputs the word vectors into the BiLSTM network model to obtain global features, and finally uses the CRF model to add effective constraints to the test labels to ensure the effectiveness of the generated labels. The model was compared with three other models, and the experimental results showed that the precision of the model was 90.01%, the recall rate was 91.02%, and the F1 value was 90.51%. The recognition performance of the model was superior to the other three models.
Contribution to determining the limits of the efficiency of target tracking

The track quality measuring by the track probability existence in order to determine the limits of the possibility of effective target tracking is present in this paper. The most known algorithms for radar target tracking do not have tools to measure track quality. There is no valid procedure that guarantees the verification of the optimal algorithm for a specific application in targets tracking. Standard Integrated Track Splitting (ITS) is the efficient, fully automatic multi target tracking algorithm with initialization, maintenance and deletion tracks and is well-known single and multi target tracking algorithm, also proposed for the purpose of testing the limits of successful tracking. The paper allows the user to choose optimal parameter by the proposed parameters - thresholds of confirmed and false tracks in order to achieve better false track discrimination (FTD). In particular, a diagram of RMSE position errors is also given in the comparison of the proposed algorithm.
Modeling Publication Collection and Processing

Here, we summarize the main objectives of modeling COVID-19 in the literature and categorize the research on COVID-19 modeling. These research objectives and categorizations provide structural answers to how the modeling research addresses the aforementioned COVID-19 disease, problems, data and modeling complexities by quantifying COVID-19 problems.
Review Objectives, Questions and Methods

In this chapter, we discuss our review objectives, questions, and methods for both global and modeling-specific research literature, which drive the results and findings in Part I and Part II, respectively.
Overall Publication Collection and Processing

Here, we introduce the collection of COVID-19 research publications, their supplementary information, and their publication impact metrics. Then we describe how the publications were processed. We further define the measures that were used to evaluate the COVID-19 publications.
State-Space Model with One Binary and Two Continuous Observations

Abstract In this chapter, we will consider a more complicated form of the state equation—one that contains both a forgetting factor and an external input. We will also extend the earlier state-space model to the case where we now have one binary observation and two continuous observations. As before, however, we will first consider a scenario motivating the need for such a state-space model.
Visualization and exploration of linked data using virtual reality

Abstract In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.
From Corpus Data to Constructional Networks: Analyzing Language with the Usage-Based Construction Grammar Framework

Abstract Construction Grammar (CxG) is an innovative approach to language that has become increasingly popular in the Anglosphere over the last 30 years. In CxG, the basic units of linguistic analysis are constructions: arbitrary and conventional form-meaning pairings, reminiscent of Saussure’s linguistic sign, but applied to levels of linguistic analysis beyond the lexicon. A large body of research has provided ample evidence in support of CxG. However, the theory remains unknown to many colleagues outside the Anglosphere. In this paper, I highlight a particularly interesting strand of CxG that is referred to as ‘usage-based’, an approach that assumes constructions are learned based on input frequency, that is, through repeated exposure to and use of a linguistic structure (hence usage- based). The main aim of this paper is thus to demonstrate how corpus data can be analyzed to find evidence for ‘entrenchment’ of linguistic structures and thus, the existence of constructions. I will illustrate this procedure by applying so-called covarying-collexeme analyses to data from the Slovak National Corpus (SNC) and the Slovak Web 2011 corpus from which I extracted 785 tokens of the so-called Comparative Correlative (CC) construction (e.g. Čím viac čítam, tým viac rozumiem).
Improving Text Descriptive Writing Skills In Middle-School Students: Investigating Image Media

This research aims to improve students' skills in writing descriptive text through the use of image media for class VII students at SMPN Satap Lelumpang. The low pre-test results of students are caused by teachers still using conventional methods of teaching writing. So the researchers decided to use a new method that had never been used before, namely using image media. The research carried out was classroom action research in two cycles. Each cycle consists of four stages, namely planning, action, observation, and reflection. The research was carried out at SMPN Satap Lelumpang, with the research subjects being class VII students. Data collection techniques include written tests and observation sheets. The results of research using image media show a significant increase. This can be seen from the students' learning results: in Cycle I, the average score was 4.87 in the poor category, increasing to 8.32 in the good category in Cycle II. The completeness of learning outcomes increased from 48.1% in Cycle I to 92% in Cycle II. This shows that students' skills in writing descriptive text increase through the use of image media.
Tutoring, essential accompaniment in the university school environment

University insertion is a unique experience that provides opportunities for growth and academic and cultural strengthening; therefore, the tutor presence and tutorial actions are essential in the accompaniment during higher education to finish satisfactorily. This study is of a qualitative and quantitative nature with a focused character that expects to describe the usefulness of tutoring before, during and after virtual accompaniment, as well as tutoring advantages and disadvantages with different tutors. The composition of the information presented is from the tutelage Hernandez Ruiz (student) of the Tourism Administration bachelor 2019 generation at the Universidad Autonoma del Carmen. According to the presented results, the tutor figure is prevailing for an adequate and successful school trajectory, for which it is required to continue with the pertinent actions developed by the tutor 4 (current) and other pertinent strategies to successfully complete the education program
The impact of Virtual Tutoring on the students of the Faculty of Pedagogy of the Universidad Veracruzana

This paper is designed under the qualitative paradigm, in order to collect the perception together with the level of satisfaction of the students of the Faculty of Pedagogy of the Universidad Veracruzana, regarding the virtual tutoring service, carried out before the pandemic of COVID-19, which imposed virtuality as a new environment for the Academic Tutor, agent who, in a strategy of innovation of the tutorial practice had to be trained in the handling of digital tools, this because the tutorial moments were executed through distance modalities and in an asynchronous way. Adapting the technological resources, training and platforms of the University Institution, being the presence of a figure such as the Tutor, increasingly necessary in this new modality, where a sense of social and educational uncertainty prevailed
The Students’ Interest in Learning Intercultural Communication Using The Integration of Mobile Learning Technology

The objective of this study is to describe students' interest in learning intercultural communication through local wisdom, specifically Makassar ethnicity, integrated with mobile learning technology. This study uses a quantitative descriptive method. The researchers chose one class, which consisted of 27 students in the English Education Department at Muhammadiyah University of Makassar. The researchers used two research instruments, namely questionnaires and documentation. With the following process: Creating questions After creating questions for the questionnaire, the researcher comes to class and distributes participant questionnaires. The researcher gives students 30 minutes to answer the questionnaire, adding up the plated questionnaire data. Data analysis was carried out through descriptive analysis by calculating the frequency, mean score, percentage, and average using SPSS. From the results of the descriptive analysis, it is known that the average score of interest in learning intercultural communication through the local wisdom of Makassar Ethnic using mobile learning integration is 74.55, which indicates that there is a lot of interest. This outcome is similarly related to the average percentage of survey responses, where 35.49% of students said that they were interested. This means that students in the English Education Department of Muhammadiyah University of Makassar are interested in learning English intercultural communication through the local wisdom of Makassar ethnicity.