Reviewerly/exHarmony huggingface dataset:
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR

Abstract Africa has a very poor doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day—a heavy patient burden compared with developed countries—but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African English speech, 67,577 clips from 2,463 unique speakers across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.
Concreteness ratings for 36,000 Estonian words

We present a collection of concreteness ratings for 35,979 words in Estonian. The data were collected via a web application from 2278 native Estonian speakers. Human ratings of concreteness have not been collected for Estonian beforehand. We compare our results to Aedmaa et al. (2018), who assigned concreteness ratings to 240,000 Estonian words by means of machine learning. We show that while these two datasets show reasonable correlation (R = 0.71), there are considerable differences in the distribution of the ratings, which we discuss in this paper. Furthermore, the results also raise questions about the importance of the type of scale used for collecting ratings. While most other datasets have been compiled based on questionnaires entailing five- or seven-point Likert scales, we used a continuous 0-10 scale. Comparing our rating distribution to those of other studies, we found that it is most similar to the distribution in Lahl et al. (Behavior Research Methods, 41(1), 13-19, 2009), who also used a 0-10 scale. Concreteness ratings for Estonian words are available at OSF .
High-precision point cloud registration method based on volume image correlation

Abstract With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight, 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on fast Fourier transform and an inverse compositional Gaussian Newton optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the iterative closest point (ICP) method, and its practicality has also been verified in impeller reconstruction experiments.
A BERT-based Framework for Production Line Fault Maintenance Knowledge Graph Construction

The manufacturing industry is currently experiencing a wave of digitalization and intelligent transformation. With continuous upgrades of production equipment and systems, the complexity and diversity of production line faults are continuously increasing. Moreover, there is a weak correlation among fault-related information and a low utilization rate of fault knowledge. In light of the problems, an automatic knowledge graph construction framework is proposed for production line fault maintenance. The framework employs both BERT-based models and template-based methods to extract entities and relationships from text data. First, by obtaining relevant engineering documents from the production line and considering their characteristics, a pattern layer of the production line fault maintenance knowledge graph (PLFMKG) is constructed. Then, the BERT-BILSTM-CRF deep learning algorithm is applied to extract corresponding entities from the engineering documents related to production line faults. Subsequently, a template-based method is employed to extracting relationships. Meanwhile, a Chinese named entity recognition dataset is also established. consisting of three hundred sentences from engineering documents related to production line faults. Experimental results show that the BERT-based knowledge extraction method outperforms classic BiLSTM-CRF and BERT-LSTM-CRF models in processing production line fault maintenance data, validating its effectiveness. Finally, we visualize the constructed knowledge graph using the Neo4j graph database. The production line fault maintenance knowledge graph provides data support for subsequent production line fault reasoning and decision-making processes.
An Entity Relation Extraction Algorithm Incorporating Multi-Attention Mechanisms and Remote Supervision

Entity-relation extraction is one of the important tasks in information extraction, which is widely used in natural language processing, construction of knowledge graph, and information reasoning work. Remote supervised algorithms utilize heuristic alignment of knowledge base with corpus, which can generate large-scale labeled corpus resources without human involvement. For the problem that noise is introduced into remote supervised algorithms leading to a decrease in the recognition rate, this paper introduces a multi-level attention mechanism into remote supervised learning algorithms, using word attention to strengthen the role of key words related to entity relations, and using sentence attention to further emphasize the role of key sentences; for the problem that entity pairs may store more than one relation, relational attention is invoked to find a relation in sentence packages that have the same entity pairs The exact expression of the relationship is found in the package of sentences with the same entity pairs. Experiments are conducted on the Freebase+NYT corpus. The results indicate that the algorithm designed in this paper is significantly improved compared to the classical algorithms.
Component-based Development Method for Intelligent Robot Systems

With the advent of the intelligent manufacturing era, the demand for industrial robots has increased, and the scenarios and functional requirements of industrial robots have become increasingly complex. Developers need to design robot control systems based on specific functions, environments, and tasks, which often suffer from poor flexibility, high development costs, and long development cycles. To address the issues of low system development efficiency, poor reusability, and limited scalability in traditional code-based robot control system development methods, this paper proposes a component-based development method for robot control systems. By adopting a component-based modeling and integration approach, the robot control system is built based on component models, providing a fast, efficient, and scalable solution for robot control system development.
Semantic Metadata Integration Support Method for Editable Re-flowable Document OOXML and Fixed-layout Document PDF

Document semantic annotation based on metadata lays the foundation for the automatic understanding and processing of document information. At present, most common documents can only support a small amount of preset metadata, and cannot support semantic metadata from different vocabularies. In addition, metadata cannot be shared among different types of documents. To solve this problem, this paper proposes a metadata support method for the mainstream editable document in OOXML format. This method satisfies that any semantic metadata can be edited along with editable documents, and the metadata in it can be converted into the fixed layout document in PDF format, thus realizing cross-document metadata support. In addition, this method does not need to change document format standards. Thus documents with metadata can be supported by existing tools. The feasibility of the proposed method is verified by the implementation of the system. The result has a good application value for automatic understanding and intelligent processing of documents.
Shortcut Learning of Large Language Models in Natural Language Understanding

Shortcuts often hinder the robustness of large language models.
Power Coordination Control of Wind-Solar Farms Cluster Based on Discrete Particle Swarm Algorithm

With the continuous integration of wind farms and photovoltaic power stations into the grid, the grid infrastructure in regions is gradually lagging behind the rapid growth of new energy generation capacity, posing challenges to the stability and reliability of the power system. In order to address this issue, researchers have proposed a new energy grid dispatch optimization method based on the Discrete Particle Swarm Algorithm (DPSO). This method aims to optimize power dispatching to maximize the integration capacity of new energy sources, reduce the demand for thermal power generation, and enhance the overall security and economic efficiency of the grid. This study first considers steady-state and dynamic active power balance constraints, which are crucial for the normal operation of the power system. Then, researchers employ the Discrete Particle Swarm Algorithm for power dispatch optimization. This algorithm simulates the behavior of particle swarms in nature when searching for the best positions to find optimal solutions. Through the Multi-Objective Particle Swarm Optimization (MPSO) algorithm, they are able to achieve the optimal power dispatch for new energy integration. To validate the effectiveness of this algorithm, simulations were conducted based on real data. The simulation results demonstrate that this algorithm can significantly increase the utilization of new energy, reduce the power demand of thermal power generation, and contribute not only to reducing reliance on traditional energy sources but also to enhancing the overall security of the grid, reducing carbon emissions, and running the power system more economically efficiently. This research provides a strong reference and reference for further research and applications in the field of new energy grid dispatching.
Formation of Russian Patriotism Among Migrant Teenagers Based on the Materials of Advanced Training Courses of the Federal State Educational Institution “Academy of the Ministry of Education of Russia”

The relevance of the study lies in the fact that in recent decades a large number of labor migrants have been arriving on the territory of the Russian Federation. Some of them move to Russia with their children. Will the new place of residence for migrant teenagers become a new homeland? Will they be able to become patriots of Russia? How can educational organizations carry out work on the formation of patriotic education among migrant adolescents? How can a teacher prepare for the formation of patriotic education of migrant teenagers? The modern system of additional professional pedagogical education offers teachers a wide variety of professional development programs, including the formation of patriotic education. FSAOU DPO “Academy of the Ministry of Education of Russia” maintains a federal register of additional professional training programs, which contains about 2,100 programs. A large proportion of programs are devoted to the topic of education and, in particular, are aimed at improving the professional competencies of teaching staff in the formation of patriotic education among students. The article considers the program of the Federal State Educational Educational Institution “Academy of the Ministry of Education of Russia” “School of modern teacher (subject): achievements of Russian science” (60 hours). Its purpose is to improve the professional competencies of teachers in the field of formation of personal results of students – the values of scientific knowledge, civic understanding of the role of Russian science, patriotic attitude to the achievements of Russian scientists. The categories of students in these programs are teachers of Russian language and literature, history and social studies, biology, geography, physics, chemistry, mathematics and computer science. Each subject has its own program. The program consists of two parts: basic and subject-methodical. The basic part of the program contains a module dedicated to the study of the state symbols of the Russian Federation. It reflects the issues of the main functions of the state symbols of the Russian Federation, the main milestones in the history of the coat of arms, anthem and flag of the Russian Federation. Knowledge, ideas about Russian symbols, and embedded in the materials of the module, represent the basis for the formation of a respectful attitude towards it. Further, the authors describe the subject-methodical part of the PC program Patriotic education is embedded in every school lesson and the materials of the PC programs show us scientific achievements in a particular subject, upon familiarization with which patriotism is formed. Thus, the materials of the advanced training programs “School of a modern teacher (subject): achievements of Russian science” can be used to form Russian patriotism among migrant adolescents.
Bayesian Semiparametric Local Clustering of Multiple Time Series Data

In multiple time series data, clustering the component profiles can identify meaningful latent groups while also detecting interesting change points in their trajectories. Conventional time series clustering methods, however, suffer the drawback of requiring the co-clustered units to have the same cluster membership throughout the entire time domain. In contrast to these “global” clustering methods, we develop a Bayesian “local” clustering method that allows the functions to flexibly change their cluster memberships over time. We design a Markov chain Monte Carlo algorithm to implement our method. We illustrate the method in several real-world datasets, where time-varying cluster memberships provide meaningful inferences about the underlying processes. These include a public health dataset to showcase the more detailed inference our method can provide over global clustering alternatives, and a temperature dataset to demonstrate our method’s utility as a flexible change point detection method. Supplemental materials for this article, including R codes implementing the method, are available online.
Infrared image super-resolution enhancement technology based on improved real-ESRGAN

Due to the defects such as low resolution, lack of hierarchy, and blurred visual effects in infrared images, the accuracy of detecting infrared images is much lower than that of detecting visible images. In this study, we propose a super-resolution enhancement method for single-frame infrared images based on improved Real-ESRGAN to resolve the problem of low resolution and lack of detailed texture of infrared images so that we can improve the accuracy of object detection. We add attention mechanism based on the original network to improve the network’s ability to extract the detailed texture. In addition, we also adjust the training epochs of the generator and discriminator to accelerate the generator update and prevent the discriminator from converging too fast. We also use pooling layers for downsampling to remain the important detailed texture features and make it easier for convolution layers to extract detailed features. The experimental results show that compared with original Real-ESRGAN, when improving the resolution twice as before, our improved network reach an increment of 6.23% of PSNR and 13.9% of SSIM, and when improving the resolution four times as before, the increment is 0.95% of PSNR and 4.42% of SSIM. In object detection, we use YOLOv5 to detect super-resolution infrared images generated by our improved network and the original infrared images and reach an increment of 2.95% of mAP. These promising results confirm that our network works effectively as a method of infrared image super-resolution enhancement and improves object detection accuracy.
Text Mining Business Policy Documents

In a time when the employment of natural language processing techniques in domains such as biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the authors implement a set of natural language processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for processing internal business policies. This framework relies on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision, recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was positively evaluated using a qualitative assessment.
Development of integrated photonic AI based on reservoir computing with wavelength nodes

Understanding and dealing with complex, large-scale, nonlinear complex systems such as those studied in the fields of bionic computing, climate change, molecular modelling, economic development, and language processing are important. In recent decades, artificial intelligence (AI) technology has played a prominent role in dealing with complex systems and problems, and the continuous improvement of the performance of various AI computing equipment is one of the decisive factors for its rapid development.
Simplifying Learning Experience on a Personalized Content Recommendation System for Complex Text Material in E-Learning

Complex material is difficult to absorb in e-learning environments, which distracts and lowers learning outcomes. This initiative proposes that consumers watch simple movies with the same topic to improve learning. Text analytics recommends tailored videos to consumers. The algorithm can make more tailored recommendations by evaluating text interaction and learning preferences. The system simplifies learning and makes material more complicated and intelligible. Visual videos aid learning by improving memory and comprehension. Analyze the data before using the advice. NLP can extract key text content and context. Review results are used to develop related topics and themes. Next, find relevant video content using keywords and keywords list. Previous video data can be used to recommend video material. Videos should simplify content to help consumers understand and remember it. Text interactions should be considered when personalising video suggestions. Create user profiles using engagement indicators like time on page, scroll depth, and click behaviour.
Nudging Motivation to Learn English Through a ChatGPT Smartphone-Based Hybrid Model

The artificial intelligence revolution as a medium and learning technology is increasingly popular in EFL. The trend of using ChatGPT is increasing. ChatGPT is a media and technology that helps in accelerating learning to help write, becomes teaching materials, learning resources with a conversation system. The purpose of this research is to find out the motivation to learn by developing the ChatGPT function as a teaching material in EFL classes among undergraduate students at universities. As an AI-based teaching material, ChatGPT was chosen with consideration of the ease of accessing fast information.
Effects of the spatial environment on tourists’ willingness to adopt robot services

Much of the literature on individuals' low adoption of robots revolves around service quality or consumer psychology; physical environmental factors are scarcely addressed. This paper investigates consumers' willingness to adopt robot services based on the spatial environment. A pair of experimental studies revealed that people are more willing to use robot services when the setting is spacious, whereas the opposite is true when the setting is compact. We also examined illusion of control as a mediator of the above impact. Furthermore, we identified consumers' knowledge of robots as a boundary condition.
Classification of Uncertainty Sources for Reliable Bayesian Estimation

Recursive Bayesian estimation has emerged as a key tool for estimating the unknown state of a system. The wide range of applications has resulted in a correspondingly wide variety of estimation algorithms. The Kalman filter and its derivatives, like extended and unscented Kalman filters, are the most prominent examples, while non-Gaussian full-blown filters are on the rise with the increasing availability of computational power. The filtering results are naturally accompanied by an assessment of the estimate’s uncertainty. However, this assessment may mislead the user into believing that the estimate is reliable, i.e., that the uncertainty reported by the filter matches the actual uncertainty. For a filter to assess its uncertainty correctly, often strict requirements must be met. The misalignment can be attributed to different origins, for which this work proposes a classification covering different stages of a filter design. Approximations and assumptions made in each class impair the filter’s reliability. This paper provides a conceptual perspective on how reliability can be defined and how it can be assessed. An example of a reliability index is examined in a simulated scenario to illustrate how it can contribute to a better understanding of the overall performance of a filter.
System Architecture Analysis: Artificial Intelligence of Things

This research aims to analyze and design the system architecture of artificial intelligence of things on visual programming. The research methodology involved theories and research papers relating to the system architecture, artificial intelligence, the internet of things, and artificial intelligence of things and the analysis and design of the artificial intelligence of things. The research method is used in content analysis form and data analysis by content analysis technique. The expected benefit is that it applies artificial intelligence of things which is one of the emerging technologies transforming in real-world industry 4.0 era for education.