Reviewerly/exHarmony huggingface dataset:
Visual Analysis and Recognition of Virtual Reality Resolution Based on Pupil Response and Galvanic Skin Response

The ability to efficiently assess the resolution of VR videos is critical for the implementation and advertising of VR products. Pupil responses and Galvanic Skin Response (GSR) are direct and objective mirrors of the emotional activity of humans. They are unaffected by subjective will and has excellent real-time performance, which can be used for VR quality assessment. However, there is little work to combine the two signals to evaluate VR resolution so far. Whether subjects' visual patterns alter with the VR resolution changes is also an interesting point that has not been studied yet. In this paper, a dataset containing subjects' pupil responses and GSR under different VR resolutions was built. Based on it, Area of Interest (AOI) was utilized to analyze subjects' visual patterns and found there were differences and similarities under varied VR video resolutions. To extract signal features at different VR resolutions more efficiently, a hybrid attention network was proposed. Experiment results demonstrated that the model can distinguish pupil responses and GSR signals under different VR video resolutions more efficiently, providing a feasibility verification for us to detect VR video resolutions using physiological signals.
A Study of Chinese Semantic Analysis Based on Enhanced Lexicon

Sentiment analysis using natural language processing and machine learning techniques can determine the sentiment or emotional tone expressed in large amounts of text. However, due to the presence of Chinese polysemous words, current sentiment analysis lacks effective analysis for various specific domains. As a result, sentiment analysis in Chinese faces challenges. To address this issue, this paper presents a novel domain-specific sentiment analysis system. The system combines the advantages of two major sentiment analysis methods: lexicon-based sentiment analysis and machine learning analysis. First, the Naive Bayesian classification algorithm is used to extract words, accurately discriminate their emotional polarity, and form an enhanced lexicon suitable for a specific domain. Then, three commonly used machine learning classifiers are used to compare the effects of semantic analysis before and after using the enhanced lexicon. Experimental statistics show that the sentiment orientation accuracy of the Chinese financial commentary sentiment analysis system proposed in this paper can be improved by about 5%.
YONA: An Intelligence Question-Answering System Based on Artificial Intelligence

The purpose of this paper is designing an intelligent question-answering system. In the context of education informatization, university students tend to lack guidance in their studies. This paper proposes an intelligent Q&A system for university teaching that integrates intelligent dialogue and machine learning technologies. The proposed system includes intelligent Q&A module, the tutor recommend & match module, tutor Q&A system module, Q&A forum Module and user compasrison report module. The system utilizes BERT model, collaborative filtering recommendation algorithm, multi-threaded programming, TF-IDF algorithm and K-Means algorithm to implement the modules in the system. The proposed system deployed with Aliyun cloud server, CDN, load balancing, client testing, and online optimization. The pilot of this system demonstrated excellent performance: an average of 4 queries per person, with an average query duration of 7 minutes. The success rate of problem resolution reached 85%, and the average response time was 30 seconds. This system will promote the health of the education industry and enable intelligent learning diagnosis. The authors hope to contribute to improving the quality of life of college students and society by designing an intelligent question and answer system.
Deformable Convolution Based Feature Extraction for Multi-View Stereo

Deep learning based multi-view stereo algorithms have shown great reconstruction performance. We propose a method called DCMVSNet for depth inference from multi-view images. We choose to build a cost volume pyramid in a coarse-to- fine manner that leads to a lightweight network. It has an efficient feature extraction module based on deformable convolutional network. The proposed multi-stage network architecture can fully utilize multi-scale feature maps to achieve better reconstruction results. Experiments on popular datasets demonstrate that the proposed method achieves the exceptional reconstruction performance.
A Self-Verification Evaluation Method of Accuracy for RGBD 3D Reconstruction

3D reconstruction with RGBD images is a popular research issues in 3D vision as the easily accessibility of depth camera which can out depth images directly. The accuracy evaluation of a 3D reconstruction algorithm is indispensable no matter for academic research or for industrial application. Most evaluation methods used now, such as Absolutely Trajectory Error or 3D surface error, rely on the ground truth values provided beforehand as reference. These methods can only be used on datasets providing these reference values which are difficult to obtain. Actually for the practical reconstruction of a physical object or scene, there is no ground truth to refer and it is hard to assess the quality of the reconstructed results. A self-verification evaluation method of accuracy for RGBD 3D reconstruction without need any ground truth is proposed in this work to address this issue. During the reconstruction process, we generate depth images based on the estimated camera poses and 3D model. With the input depth images as reference, an accuracy metric is elaborated by computing the differences between the generated depth values and the inputted depth values. Experimental results illustrate the effectiveness of our proposed method which can give evaluation results consistent with the ATE metric.
Improved Algorithm for Small Target Detection of Traffic Signs on YOLOv5s

Aiming to address the issues of small target size, complex background, and low recognition accuracy of traffic signs in road scenes, an improved YOLOv5s algorithm is proposed. The algorithm introduces a Coordinate Attention in the feature fusion network, enhancing the model's ability to integrate spatial coordinate information and preserve a wider range of positional information. The Efficient Decoupled Heads are used to replace the Detection Heads in YOLOv5s, with an additional layer for small object detection, improving the model's generalization ability, robustness, and utilization of small object information. The Normalized Wasserstein Distance (NWD) is introduced to mitigate the sensitivity of IoU to small target position deviations. The algorithm is trained on the TT100K dataset with data augmentation and enhancement. Experimental results demonstrate that the improved algorithm achieves a 4.4% increase in mAP@0.5 compared to the original algorithm.
Clustering Driven Multi-Hop Graph Attention Network for Speaker Diarization

Recently, the segmented utterance-level modeling approach based on Graph Attention Network (GAT) has been proved to be effective in Clustering-based Speaker Diarization (CSD). However, these existing methods only rely on the message passing by a single neighbor per layer, ignoring the influence of sub-region and global information. In this paper, we propose clustering driven multi-hop Graph Attention Network (CD-MGAT) with the multi-hop neighbor module and the clustering-oriented prototype module, which effectively explores the sub-region and global information for each segmented utterance. Specifically, the developed modules can adaptively interact with each other by clustering-consistency loss, which ensures the consistency of learning between the prototype and speaker embedding. Extensive experiments demonstrate the effectiveness of our solution on the AMI datasets.
Design and Implementation of Image Monitoring System Based on Cortex A53

At present, with the rapid development of economy and technology, the quality of the people's life has been generally improved, and the importance of the security system has become increasingly prominent. Among them, the image monitoring system has attracted more and more attention because of its intuitive and rich information content, making it an important part of the security system. Based on this, an image monitoring system is designed. The system uses the GEC6818 platform and is equipped with the Samsung Cortex-A53 series high-performance eight-core processor S5P6818. The system uses ubuntu, selects the camera to collect the surrounding environment image in real time, studies the data structure and principle of the image acquisition software, introduces the process and key steps of image acquisition, and completes the design of the image acquisition program using API functions, The collected image is displayed on the touch screen after being filtered and enhanced. After debugging, the system has reached the expected goal. The system can play a good reference role in the security of homes, communities and other places.
Chinese News Headline Classification Method Based on ERNIE

To solve the problems of semantic ambiguity and feature sparsity in news headline classification tasks, a news headline classification method based on ERNIE is proposed. Firstly, the input text is embedded by the pre-trained language model Enhanced Representation through Knowledge Integration (ERNIE) to obtain dynamic word vectors, solving the problem of polysemy. Then, we use TextCNN and set multiple sizes of convolutional kernels to extract different levels of semantic information from the text. Finally, softmax is used to normalize the output, and the category corresponding to the maximum probability value is taken as the classification result output. Experimental results show that this method can effectively solve the problems of sparse features and semantic ambiguity, and improve the classification performance of news texts.
A Hybrid Model Based on GAT and TAGCN for Node Classification

In recent years, deep learning has been developing rapidly, especially the graph neural network plays an increasingly important role on more and more non-Euclidean data structures, such as social networks, recommender systems, molecular structures and other fields, and thus develops more rapidly. Convolutional operation has shown very good feature extraction effect on 2D data because of its translation without deformation, and its extension to graph convolutional networks on non-Euclidean data can also produce very good results. Now there are many very mature graph convolutional network algorithms, but although they can achieve good results on specific data structures, there are still some limitations. The hybrid model is able to make good use of its strengths and avoid its weaknesses. In this paper, we propose the AT-TAGCN model for node classification of graphs. Firstly, we construct the GAT model and TAGCN model respectively, and design their neural network layers, and then we combine the two, first use the neural network layer of GAT to process and then use the network layer of TAGCN to process, and then pass forward according to the corresponding order, and do the mean processing to get the final result, meanwhile, we use the dropout layer and the L2 regularization to alleviate the overfitting condition, so as to categorize the data of the graph Classification. In this paper, the CORA dataset is selected for the classification data. The results show that this method is more effective than the traditional methods.
Opening the Black Box: Interpretable Machine Learning Reveals the Relationship Between Lexical Diversity and Writing Quality

Machine learning serves as the core component of artificial intelligence (AI), however, there is scant interdisciplinary research between AI and applied linguistics. Very few studies touched upon the aspect of how to leverage interpretable machine learning to solve the problem in language learning, teaching, and assessment. This study aims to lift the veil and fill this gap by employing interpretable machine-learning approaches to explore the relationship between lexical diversity and writing quality. The findings contributed to providing a finer-grained understanding of the CET-4 writing construct and its rating scale. Furthermore, it may lend empirical evidence for the Dynamic Systematic Theory (DST) in language acquisition, verify the validity of automatic essay scoring, and advance teachers' language assessment literacy in the long run.
Contrastive Learning-Based Multi-Modal Entity Alignment and Feature Fusion Method for Electric Power Knowledge Graph

This paper proposes a Contrastive Learning-Based Multi-Modal Entity Alignment and Feature Fusion Method (CMEAF) for addressing the challenge of poor entity alignment in Chinese electric power knowledge graph retrieval tasks. Firstly, we combine the ideas of multi-modal contrastive learning from CLIP and momentum contrastive learning from MoCo to design an improved contrastive loss function (MCCL). By introducing a more flexible negative sample selection strategy and incorporating momentum updates, the performance of contrastive learning is enhanced. Secondly, in the multi-modal feature fusion stage, we propose a Cross-Modal Attention Fusion (CMAF) method that leverages cross-modal attention mechanisms to fully exploit the interactive information between image and text features, achieving superior multi-modal feature fusion. Lastly, in the model pre-processing stage, we adopt the Mixup data augmentation idea and propose a Joint Data Augmentation approach based on image-text pairs (TMIXUP) to enhance downstream retrieval performance. Experimental results demonstrate significant performance improvements of our proposed method in multi-modal entity alignment and feature fusion tasks. Compared to the baseline model WUKONG, CMEAF achieves a 2.5% increase in average recall rate on the MUGE dataset and a 4.1% improvement in MR performance on the Flickr30k-CN dataset. On the self-built POWER dataset, CMEAF also achieves excellent retrieval performance with an MR score of 93.7%. Furthermore, this paper conducts visual analysis to further showcase the advantages of multi-modal fusion. The proposed method effectively aligns Chinese cross-modal entities with high accuracy and practicality.
Points and Descriptors-Net Model for SIFT Features Learning

This paper presents a feature extraction network called PD-Net (Points and Descriptors-Net) that extracts feature points and generates feature descriptors from images. The model operates on full-size images using a fully convolutional model and computes the coordinates of feature points and feature point descriptors in a single forward pass. By building a self-coding and decoding deep neural network and learning a dataset containing SIFT features, the model is able to quickly and robustly acquire the features of the image, and the loss function of the descriptors is calculated using a sparse approach during training to achieve results similar to those of the dense method. Compared to LIFT, SuperPoint, SIFT and ORB, the comprehensive performance of our model achieves good results on the HPatches dataset
Wearable Design Innovations Enabled by Artificial Intelligence: A Technical Review

Wearable technology has become increasingly popular in recent years, offering a range of applications including health monitoring, personal assistance, and fitness tracking. Artificial intelligence (AI) has played a significant role in enhancing the capabilities of wearable devices, enabling intelligent perception, personalized recommendations, multimodal interaction, health monitoring, intelligent homes, and smart cities. This technical review paper presents an overview of the key technologies involved in wearable design and how AI has enabled innovative solutions in the field. Furthermore, this paper proposes novel wearable design innovations, which are supported by AI algorithms, such as deep learning and natural language processing.
Modulation pattern recognition based on ShuffleNet neural network

Abstract: Modulation pattern recognition is widely used in civil and military fields. In this paper, a modulation pattern recognition technology based on convolutional neural networks is proposed and the accuracy of modulation signal recognition based on three kinds of convolutional neural networks is compared to analyze the performance of the neural network model. The experimental results show that the convolutional neural network can achieve feature extraction and classification recognition at the same time. The simulation results show that ShuffleNet neural network has higher recognition accuracy and classification consistency for modulation pattern recognition, and its feature extraction performance is better than that of VGG and ResNet neural network. During the experiment, modulation pattern recognition based on VGG neural network model has high computational complexity and model size, which requires high hardware level. In this paper, modulation pattern recognition technology based on ShuffleNet neural network is proposed. Combined with the characteristics of neural network: point-to-point deep convolution and channel random rearrangement, the network model is constructed. The theoretical analysis and simulation results show that the modulation pattern recognition method based on ShuffleNet network model has lower computational complexity, fewer model parameters and smaller model size while ensuring the recognition accuracy.
Application of fast facial marker detector in head radiotherapy

In this paper, a fast face marker detector for head radiotherapy patient posing is investigated. The 3D coordinate data of feature points of head radiotherapy patients' faces are collected by an RGB-D camera, and the 3D coordinate data of feature points of head radiotherapy patients' faces are extracted for fast face marking using a face feature point matching algorithm with a lightweight backbone network model. By comparing the marked patient head feature point data with the real-time data of the head of radiotherapy patients, the comparison error is used as the basis for the pose operation. The results show that the detector can achieve the desired positional effect. Therefore, this detector can meet the clinical demand for a short time and high precision posing system for precise radiotherapy.
Automated Speech Recognition System for Dispatching Call Recordings in The Underground Coal Mines

In this paper, we proposed an automated speech recognition system focus on the dispatching call recordings in the underground coal mines which promoted the development of intelligent coal mining. The main challenges of the speech recognition system are the noise of recordings and the dialect speech. We employed a voice activity detection module to preprocess the recordings, this module is able to reduce the noise and segment the long recording speech; then the Conformer model with CTC algorithm is utilized to train the ASR module. To get better performance, the WenetSpeech pretrained model is embedded for fine-tuning. The result shows that compared with the other general speech recognition systems, our ASR system has great advance in recognizing the dispatching call recordings of Huaibei dialect.
Learnable Sparsity Structured Pruning for Acoustic Pre-trained Models

Large-scale pre-trained models bring significant gains to many speech-related tasks. However, it is still challenging to use these large models when computing power of terminal equipment is limited. Pruning is an effective method to reduce memory footprint and cost calculation. The imperfect evaluation criteria of existing pruning methods and the complex fine tuning process result in a relatively high loss of accuracy. To solve these problems, we propose a structured pruning method, which introduced the upper confidence bound of importance scores to assess the potential of each component of the model more accurately. In addition, we also introduce a set of learnable pruning threshold parameters that can be learned via stochastic gradient descent, thereby reducing the hyper-parameter tuning. We apply our method to HuBERT models on automatic speech recognition (ASR) task. Our result shows that for all pruning granularity and pruning ratios, our method yields higher accuracy and speedup ratios in the inference process.When sparsity was 60%, our method performed only 0.63% down.
The design of rotation-symmetric Gaussian low-pass filter (RSGLPF) and its applications

In this study, a rotation-symmetric Gaussian low-pass filter (RSGLPF) is designed, and two application methods of the filer are provided. The results of the two methods are different, and researchers and engineers can choose which method to use according to their own needs. In data related research, noise reduction is one of the key steps in most data processing. However, for many non-data processing researchers, it is crucial to choose a simple and effective noise reduction algorithm. Additionally, traditional Gaussian filters could cause some degree of detail damage in the image, we have provided a method to effectively solve this problem. In the present research, there are many low-pass filters. Some of them have simple applications, but the filtering effect may not be very obvious. Others have complex application processes or require high parameter settings. Our research results have both a significant filtering effect and an easy-to-use application process. Even non-experts in this field can apply by following the steps. Moreover, the traditional Gaussian filter will cause some detail damage to images in the process of denoising. We have provided a method that can effectively reduce the detail damage of the original image. Gaussian filter, as a linear smoothing filter, is suitable for eliminating Gaussian noise and has been widely used in image processing, signal processing and other fields.[1, 3] Therefore, our (RSGLPF) is improved on the original Gaussian filter, and can be better applied to the noise reduction process. The results can also show that the denoising effect is indeed more obvious. The results show that the image is smoother, the boundary is more blurred, and the high-frequency signal of the waveform is greatly reduced. The research results provided a practical algorithm model for denoising in signal processing.
Pedagogical support for achieving subject educational results in the context of digitalization

The article is devoted to the organization of schoolchildren&rsquo;s education in a digital educational environment and the achievement of subject educational results by them, the main mechanism of which is pedagogical support. The article provides an analysis of scientific and pedagogical literature in the field of defining and substantiating the content of pedagogical support. The main ideas and approaches of domestic scientists to the problem of pedagogical support are considered, its structure is considered, the content of pedagogical support is substantiated in solving learning problems in the context of digitalization of education. Under the pedagogical support for the achievement of subject educational results by schoolchildren in a digital educational environment, the authors understand the totality of resources and conditions aimed at enhancing the cognitive activity of students to ensure the effectiveness of the educational process. The authors include digital educational and methodological support, hardware, software, learning management systems; to the conditions &ndash; the readiness of the teacher to create a digital educational environment in the subject and work in it, a set of didactic conditions that contribute to the achievement of subject educational results by schoolchildren in a digital educational environment. As a result, the authors propose a theoretically substantiated content of pedagogical support for the process of achieving subject educational results by schoolchildren in a digital educational environment.