Reviewerly/exHarmony huggingface dataset:
Demonstration of XRStudio Tools for Creation of Room-size Immersive Experiences within an Extended Reality Environment

Immersive experiences are mostly created with desktop-based tools using flat displays, mouse and keyboard; as a result immersive experiences have a 2D look and interaction: user sit or stand looking forward, and interact with elements within reach of their arms as in a desktop. The objective of this work is to facilitate creating room-size immersive experiences that take advantage of tethered head mounted displays and hand-held controllers to create while walking freely in space with full body movement freedom. To this end we propose XRStudio, a set of tools that permit to create within an extended reality environment so that authors can walk to move objects in space or manipulate elements with hand and full body movements as in the real world.
Research on feature vector construction and classification recognition of typical mine microseismic waveforms

In the realm of mining seismology, the identification of microseismic waveforms is a critical step for enhancing the precision of microseismic event localization. Addressing the complexities and diverse sources of noise in subterranean mining environments, this study has developed a novel methodology for the automated categorization and recognition of characteristic microseismic waveforms. Initially, a comprehensive database for mining-induced microseismic waveforms was established, catering to their non-stationary and nonlinear attributes. Based on the distinct features of different microseismic waveforms, this study introduces an approach for classification and identification that relies on waveform characteristics. A feature selection model, KNN-LOO (K-Nearest Neighbor - Leave-One-Out), was employed to optimize the feature vectors, in addition to the utilization of Principal Component Analysis (PCA) for dimensionality reduction of the feature vectors. Ultimately, an enhanced BT-SVM (Binary Tree-Support Vector Machine) model was developed to accomplish the automated classification and identification of mining-induced microseismic waveforms. Experimental results indicate that, compared to conventional SVM classification methods, the proposed methodology not only exhibits theoretical and mechanistic advancements but also significantly boosts performance in practical applications. Specifically, the average misclassification rate for each category of waveforms is reduced to less than 5%, and the overall accuracy has increased by 20%. This considerably elevates both the accuracy and speed of microseismic waveform classification and recognition, thereby providing valuable insights for monitoring and analyzing microseismic activities within mining operations as well as enhancing the safety protocols for mineral extraction.
Misaligned RGB-Infrared Object Detection via Adaptive Dual-Discrepancy Calibration

Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in mAP50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches.
DIGITALIZATION OF EDUCATION AT THE PRESENT STAGE OF DEVELOPMENT OF THE INFORMATION SOCIETY

The article theoretically substantiates, developed and practically implemented a structural and functional model for the formation of ICT competence of students of a pedagogical university, taking into account interdisciplinary integration; the pedagogical conditions that contribute to the formation of ICT competence of students of a pedagogical university are determined: interdisciplinary integration as the basis for building an interdisciplinary course, the content of which is aimed at the formation of ICT competence; informational, didactic, technological components as a basis for the formation of ICT competence; integration of formal, non-formal and informal education to implement the integrity of the acquired knowledge, skills, experience as the basis for the formation of ICT competence of future teachers; the creation and use of a set of tasks as special means of forming ICT competence, the educational process of a pedagogical university will be built in accordance with the conditions for the digitalization of education and aimed at the formation of students' ICT competence as the basis for the implementation of their future professional activities.
Converging Data Mesh and Microservice Principles into a Unified Logical Architecture

Companies have invested in Domain-Driven Design to support data-driven business, but this strategy has led to bottlenecks, demanding the decentralisation of analytical data by domain. This paper proposes a converged strategy for a domain-specific decentralisation of analytical data. Sequentially applied, the elicitation of cloud computing microservices, their identification uses using functionally decomposed UML use cases as a result of the domain-driven design, the application of a logical architecture derivation method followed by the application of microservices patterns, and, finally, the application of data mesh principles to organize packets or reorganize objects in new packet boundaries, result in the derivation of a converged architecture containing both microservices quantum and data mesh quantum as a distributed architecture mesh.
Generating Complex Dynamic Forms in Low-Code Development Platforms

This article highlights the potential of creating a library for generating dynamic forms using the JSON Schema within the framework of the Low-Code platform. Low-Code platforms are designed to enable users to build workflows that reflect the business processes of their organizations. Forms play a key role in these workflows, as they are used to process data and execute business processes. As these processes grow more complex, users require more robust solutions to manage them, leading to the greater complexity of forms and the need to dynamically change the structure, scope of data, and layout. The library has a structure that divides forms into logical sections and individual form fields, making it easier for users to manage complex forms. The library was thoroughly tested and analysed in terms of efficiency in generating forms of various sizes. The article presents a comprehensive solution for generating dynamic forms on the Low-Code platform. The use of JSON Schema in combination with a well-designed library facilitates the management of complex forms and more effective implementation of business processes for users. The results of tests and analyses show that the library is a viable solution for generating dynamic forms, providing users with the tools they need to achieve their goals.
An unsupervised colorization method of thermal infrared image based on edge consistency

Due to the lack of pixel level structural matching data, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images. Therefore, this paper proposes a unsupervised learning method based on CycleGAN. On the basis of CycleGAN, a pre trained edge monitor is introduced to calculate the edge feature map before and after image transformation, and the edge similarity loss function is calculated as the basis for optimizing the neural network parameters. The experimental results show that the proposed method effectively reduces the loss of effective edge information during the coloring process and suppresses the generation of abnormal edge information during the coloring process.
Text sentiment analysis model based on RoBERTa-BiLSTM-Attention

This paper aims to apply sentiment analysis techniques to cope with the rapid growth of public opinion data. Due to the complexity of Chinese semantics and the sparsity and high-dimensionality of text data, sentiment analysis faces great challenges. To this end, this paper proposes a deep learning model based on RoBERTa. The model uses the RoBERTa model to encode text into word vectors, and weights the word vectors with a weight matrix to enhance the emotional features in the sentence. Then, the contextual features of the word vectors are extracted by BiLSTM, and the hidden feature vectors are weighted by the attention mechanism, and finally classified by the fully connected layer. Experiments show that compared with the BERT model and other text classification models, the accuracy of the model proposed in this paper is significantly improved in the three public data sets.
A research on intelligent recommendation algorithms based on federated learning

Federated learning is a promising new paradigm for privacy protection. By dispersing training data storage locally rather than centrally constructing large-scale datasets, federated learning provides a mechanism for training models that protects privacy for various data-hungry applications, such as recommendation algorithms for mitigating information overload. This article implements a family of algorithms with common characteristics in a federated learning framework for model parameter transfer. Through comparative experiments, the performance differences of these algorithms under this federated learning framework are examined, and their model performance is compared and analyzed under their respective centralized learning forms. The conclusion is that the algorithm family combined with neural networks has higher performance and is more stable in this federated learning framework, with less performance loss.
Behavior steganography in social networks based on AES encryption algorithm

With the continuous development of steganalysis technology and the change of network environment, the performance of traditional steganography is unsatisfactory. Due to the popularization of social networks, the use of people's behavior on social networks to achieve steganographic transmission has emerged as a hot direction in the field of information hiding. To address the drawback of low transmission security in traditional behavior steganography, this paper proposes a behavior steganography scheme in social networks based on AES encryption algorithm. The scheme uses the AES encryption algorithm to encrypt the information before transmission, which effectively enhances the security of the transmission. The experiments are performed and the results show that the proposed scheme has good feasibility and high security, and its performance is excellent.
Technology research of φ-OTDR fiber perimeter intrusion system based on CNN-LSTM algorithm

The fiber optic sensing perimeter security system has broad application prospects due to its unique characteristics such as long transmission distance, wide monitoring range, high sensitivity, and strong real-time performance. Based on DWT and STFT, this paper extracts the features of the intrusion source signal and uses the CNN-LSTM deep learning algorithm to intelligently classify the signal, improving the separability of disturbance events and accurately judging the intrusion source type. The average accuracy rate of classification can reach more than 96%, with excellent intelligence and low false alarm performance. This technology is a perfect replacement for traditional perimeter alarm systems such as infrared beams and electronic fences, especially applicable for real-time measurement of vibration-related events such as long-distance, omni-directional, and multi-point illegal intrusion, illegal destruction, and structural damage. In addition, the application of distributed optical fiber sensing system in the industrial chain can improve product quality, ensure production safety, and realize production line automation.This research will improve the reliability and accuracy of the perimeter security system, improve the public security environment, save social resources, and improve social security.
Research on Chinese short text classification based on ERNIE-TEXTCNN model

The development of the Internet has led to a sharp increase in the amount of Chinese text data, and the classification of short Chinese texts has problems such as fewer words, more ambiguity, sparse features, and irregular features, which leads to poor results of traditional text classification techniques. This paper designs and implements the ERNIE-TEXTCNN model. The model uses ERNIE as the word vector model, TEXTCNN extracts features from the input text vector, and softmax obtains the final classification result. Through comparative experiments with other methods, it is shown that the ERNIE-TEXTCNN model can extract text feature information very well, and has a good classification effect of Chinese short texts.
Research on the knowledge elements extraction method in aviation domain based on head-tail pointers

Traditional relationship extraction requires manually labeling a small number of relationships and corresponding entities in a specific field as the training set. However, when switching to a new scene, it is necessary to redefine the rules and relabel the data. This will consume a lot of resources. With the continuous change of data types in the aviation field, it is necessary to design a general entity-relationship extraction model. For the research of open relation extraction in aviation field, we propose an open knowledge element extraction model based on Roberta + head-tail recognizer called RHTR. We constructed and made public a knowledge meta-corpus in the field of aviation. The model inputs the source text into Roberta to obtain the corresponding word embedding. The embedding is fed to the head-tail recognizer to do twice five classifications to get the head and tail sequence. Matching to the position information in the sentence based on the head and tail sequence, so as to get the corresponding label information. Then the label information is matched using the proximity principle to obtain the knowledge elements of the target. RHTR achieves 85.0% precision and 80.6% recall on the aviation field corpus. This fully verifies the superiority of the model. RHTR can obtain more kinds of knowledge elements while ensuring the accuracy of knowledge elements.
Research on mine object detection algorithm based on improved YOLOv4

In the detection of mining vehicles and pedestrians, the classical object detection model is not suitable for micro-small hardware equipment due to its large size and large number of parameters.However, the existing lightweight models are difficult to balance detection accuracy and real-time. In this study, a lightweight detection algorithm Depthwise MobileNetv3 -YOLOv4based on improved Mobilenet-YOLO is proposed to implement the compression of the network model. At the same time, the use of deep separable convolution on the detection head improves the ability of the network to pay attention to the target feature information, Finally, the Siou activation function is used to improve the problem of slow network convergence. The experimental results show that the Depthwise MobileNetv3-YOLOv4 network parameters are reduced by 90% compared with the original network under the condition of ensuring accuracy, and compared with other network models, Depthwise MobileNetv3-YOLOv4 is more suitable for vehicle volume.
SimAM-based optimization algorithm for small target detection

With the development of deep learning, small target detection application scenarios are gradually becoming more widespread, and higher demands are made on small target detection accuracy. The attention mechanism is a simple and convenient way to improve target detection accuracy. In order to improve the detection performance of the attention mechanism for small targets, a SimAM-based attention method for small target detection is proposed by us, which changes the activation method of SimAM and add hyperparameters to improve the detection effect of small targets. The experimental results show that the algorithm has good detection performance.
Multi-source combined transfer learning for text classification

The objective of transfer learning is to learn well-established knowledge from label-rich training data to handle learning tasks on the target domain, where the labeled samples can be hardly collected and the data distribution is different from that of the training data. Nowadays, the label information in the target domain may exist in multiple different source domains, but none of which can cover all the information. To make use of the imperfect source domains, many novel transfer learning algorithms explore potential properties among them to improve the completeness of transferred knowledge. However, these works only build on the original source domains, limiting the possibility to uncover additional supplementary information. In this paper, we propose novel multi-source combined transfer learning(MSCTL). MSCTL first replaces original source domains with a new training set space which can provide richer latent information by correlating multiple source domains. Then, through a triplex-complementary strategy, MSCTL extracts and integrates threefold concepts from the space to make the knowledge complementary and integral. Furthermore, we design MSCTL as an optimization problem and provide an iterative algorithm to solve it. Finally, massive experiments are performed to verify the effectiveness of our method.
Research on road detection algorithm based on improved YOLOv5s

This paper proposes a road condition detection approach based on modified YOLOv5s to address the issues of low detection accuracy and high leakage rate in the current road condition detection algorithms. Firstly, the ASPP module is introduced to not only expand the sensory field to obtain rich background information but also retain the key information of small targets in the original feature map, thus improving the detection precision in complex traffic environments and solving the problem of small target miss detection; secondly, through the localization loss function SIoU, a new angle loss is added to the penalty term to further improve the detection accuracy and convergence speed of the model. The experimental results show that the improved model achieves 84.8% detection precision, 89.1% mAP, and a detection speed of 29 frames/s. The improved model can be applied to various complex traffic environments and meet the requirements of road condition detection in the realm of intelligent driving.
Multi-modal sentiment analysis based on expression and text

Sentiment analysis has always been a hot research topic in the field of natural language processing. With the development of artificial intelligence and big data technology, many data such as text, images, and videos that have personal emotional tendencies are accidentally generated. Initially, researchers mainly focused on studying text data, but over time, more and more people began to realize the limitations of single-mode analysis. By introducing multi-modal data such as images and videos, more dimensional information can be provided for sentiment analysis, thereby improving performance. Therefore, more and more researchers are exploring richer sources of emotional information to improve the effectiveness of sentiment analysis. This article proposes a multi-modal sentiment analysis method based on deep learning and designs an experimental model targeted towards video modal data. In terms of text, this article proposes a GloVe-based BiGRU word vector model to process data. For images, the method changes from using simple frame extraction to speaker anchoring, uses object detection technology to segment the region of interest for the subsequent model to extract image expression features. A pre-trained ResNet101 model is used to obtain vectors and generate a sequenced image matrix, which is then input to LSTM for processing. From the experimental results, it can be seen that this method performs better than existing methods in terms of accuracy and F1 scores.
Transformer with modified self-attention for flat-lattice Chinese NER

As a fundamental task in the field of NLP, Chinese NER has attracted many researchers. Recently, the lattice structure supported by the auxiliary lexicon has been replaced by a new flat-lattice structure to accommodate the input of the Transformer encoder. The positional information in the flat-lattice is regarded as an information flow input into the calculation of self-attention. Due to the addition of this information flow, a variant of self-attention is used here. Although the acquisition of the distance feature relies on the positions of two tokens represented as characters or words in the flat-lattice, the current method only considers the interaction of the distance feature on the former token, rather than taking both tokens into account. In this paper, we propose a new variant of self-attention, which not only considers the influence of the distance feature on the former token but also considers the influence of the distance feature on the latter token. Experiments on two open datasets show that our proposed model outperforms other state-of-the-art models.
Entity linking for Chinese short texts based on multi-task learning

Entity linking task aims to establish unambiguous links between entity mentions in text and target entities in the knowledge base. Traditional entity linking models rely on complete context information in long texts. However, with the fast-growing amount of internet data, most of the data is in short text which presents a range of challenges including poor contextual semantics and disordered word order. Additionally, compared to English, Chinese poses greater challenges for entity linking task due to its unique language characteristics. To address this, we propose a multi-task learning based entity linking model for Chinese short text. By utilizing the fine-grained entity typing task as an auxiliary task for entity linking, and sharing the BERT-BiLSTM encoder, the model can leverage the implicit relationship between the two tasks to maximize potential information in the text and enhance performance in entity linking. Experimental results on Chinese short text datasets demonstrate that our proposed model outperforms a range of deep-learning based entity linking models, as well as the single-task entity linking model with the same structure, fully verifying the effectiveness of the proposed approach.