Reviewerly/exHarmony huggingface dataset:
An online-to-offline service recommendation method based on two-layer knowledge networks

This paper introduces a novel method aimed at enhancing online-to-offline (O2O) services recommendations by utilizing two-layer knowledge networks. The primary objective of this method is to assist consumers in efficiently navigating the myriad of options available when choosing O2O services. Using co-occurrence relationships, we construct a two-layer knowledge network system, comprising a service knowledge network based on service usage information as the first layer and a consumer knowledge network, built on “co-used” behaviors as the second layer. The former is established upon service use data, while the latter is founded on “co-used” behaviors among consumers. The features and information of these two knowledge networks can complement each other to produce precise and effective recommendations. Empirical findings gained from our experiments demonstrate that: (1) the proposed recommendation method outperforms widely-used and state-of-the-art recommendation methods; (2) both the service knowledge network and consumer knowledge network play an equally significant role in O2O service recommendations; (3) the location of O2O services is an essential factor in consumers’ choices for services. Notably, this research also identifies the optimal parameter settings for the proposed recommendation method.
Study on Facial Recognition Method Based on YOLOv5

Abstract This study focuses on the facial recognition algorithm. To address limitations of facial recognition, this paper proposes a deep learning method called YOLOv5-Attention for facial feature extraction, recognition. The algorithm in the article is designed based on YOLOv5 and incorporates an attention mechanism module to enhance the expression ability of the model’s eyebrow features and improve its overall performance. The experimental results demonstrate that the algorithm proposed in this paper achieves a harmonic mean of 94% for eyebrow recognition accuracy. This highlights the effectiveness and reliability of the algorithm, which can be applied to eyebrow recognition and dramatic character facial recognition, as well as the research of general facial recognition.
Improving Tibetan End-To-End Speech Recognition with Transfer Learning

Abstract End-to-end architecture has shown outstanding performance in the field of speech recognition, but achieving such performance typically requires a large amount of annotated data. In languages with sufficient corpus and resources, satisfactory recognition results have been achieved. However, for some low-resource data with insufficient training data, the lack of training data remains a bottleneck in building speech recognition systems. This paper presents an approach that utilizes self-supervised feature extraction and transfer learning to enhance the performance of acoustic models in low-resource languages. The proposed strategy involves retraining the fundamental acoustic model, originally trained on resource-rich languages, using a limited amount of low-resource speech data within an end-to-end architecture. By doing so, it aims to construct an improved acoustic model tailored for low-resource languages. The results of the model in the Tibetan language dataset show significant improvement, with a relative decrease of 26% in word error rate from 13.8% to 10.2% on the test set.
ATDAD: One-class adversarial learning for tabular data anomaly detection

The detection of tabular data anomalies is widely used in cybersecurity, medicine, and other fields. However, modelling tabular data is challenging. Unlike images and videos, tabular data have unknown structures and are of mixed type. Although most anomaly detection methods focus on images and videos, we propose a one-class adversarial learning method for tabular data anomaly detection (ATDAD). ATDAD changes the structure of generative adversarial networks (GANs) to better model high-dimensional complex tabular data. ATDAD adds dual encoders to stabilize the training and ensure cycle consistency of the samples and their reconstructions. ATDAD does not require any anomalous samples as prior knowledge in either the training or testing phases. To the best of our knowledge, ATDAD is the first study that focuses on the detection of tabular data anomalies using a GAN. Experiments show that ATDAD outperforms existing unsupervised anomaly detection methods for tabular data.
Survey of Noise-Against Techniques for Extracting Stable Skeleton

Abstract Stable skeletons are promising as a compact, concise, and efficient descriptor since they can reflect much critical information about the original object, such as topology, connectivity, etc. However, extracting stable skeletons from images is very challenging since most existing skeleton extraction methods, also named skeletonization methods in some literature, are sensitive to noise, which limits the application of the skeletons in the recognition field. Many denoising methods have been proposed to extract stable skeletons to overcome this problem. However, up to now, there are few review papers to conclude these denoising methods and present their pros and cons. Therefore, In this paper, we survey existing denoising techniques for extracting stable skeletons from images. We first categorize these denoising methods, analyze their core idea, and then present their pros and cons for comparison. In addition, we also offer the potential research direction and possibly challenge.
Pipe Routing Design for Aero-Engine Using Monte Carlo Tree Search Method

Abstract Pipe routing design (PRD) is a complex process that involves a large search space and requires experienced professionals. Despite advancements in aero-engine design, PRD remains a stage that is not completely automated. In this article, we present a new approach to PRD by formulating it as a Markov decision process and proposing a pipe routing design method based on Monte Carlo Tree Search (PRD-MCTS). Firstly, this paper uses an intelligent algorithm to look for enough paths for each pair of joints. Secondly, PRD-MCTS regards each path as a candidate choice, and then PRD-MCTS randomly chooses a path and calculates the probability of collision-free routing until the set time. The method selects the path with the highest probability and updates the environment for the next selection. A simplified environment from the aero engine verifies the correctness of the methods.
A Camera Calibration Method for Limited Motion of Target in Space

Abstract In modern industrial production, vision measurement systems are widely used for product quality inspection. Camera calibration is a crucial step in vision measurement systems that directly affects the accuracy of measurement results. However, in practical applications, the limited space for measurement and restricted movement of calibration targets present new challenges for camera calibration. In this paper, we propose a camera calibration method that is suitable for calibration targets with limited movement space. This method only requires one image of the calibration target to complete system calibration, greatly simplifying the on-site calibration work. Experimental results show that our method has a re-projection error of 0.0187 pixels, indicating high reliability and applicability. The application of this method in the inspection of seamless steel pipes demonstrates its potential for practical industrial production.
Multitasking evolutionary algorithm based on adaptive seed transfer for combinatorial problem

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs.
Anomaly Pixel Detection via Dual-Branch Uncertainty Metrics

Abstract Anomaly detection aims to detect abnormal samples from normal data. Existing methods mainly use synthesis-based approach and uncertainty-based approach. Moreover, the above-mentioned methods need to re-train part/all of the deep network, which limits its application scenarios especially real-time scenarios. We propose a novel retraining-free anomaly detection method, which includes a dual-branch anomaly detection mechanism to detect abnormal pixels from two views. In the pseudo-supervised branch, we use the Copy post method to transform the unsupervised anomaly detection task into a supervised binary detection task. In the prototype detection branch, we use prototypes to improve the accuracy of abnormal pixel detection. The anomaly results of the two branches are fused by means of soft voting to produce anomaly detection results with higher recall. Our experiments show that our method significantly outperforms other methods and achieves excellent results on different datasets, which also shows that our method has good generalization ability. Therefore, our proposed method can effectively improve the effectiveness and safety of anomaly detection in real-time scenarios, such as autonomous driving.
SU-Next: An Image Segmentation Model of Breast Tumors Based on U-Next and Attention Mechanisms

Abstract Breast cancer is one of the most common malignant tumors endangering women’s health today, and timely screening and targeted treatment of such tumors can effectively reduce the incidence of breast cancer. The commonly used method of breast tumors diagnosis is to detect the pathological image of breast tumor to find the spread of cancer cells and the difference in tissue morphology over time. There are a large number of complex problems such as interlaminar variation characteristics and structural and morphological diversity in the pathological sections of breast tumors, and the tumors must be accurately segmented before breast tumor detection, and the accuracy of breast tumor image segmentation directly affects the results of subsequent detection. Aiming at the problem that the U-Next network model is not clearly defined in the marginal region of breast tumors, we propose a SU-Next network model. The model adds an attention module to U-Next, which focuses on the feature values related to the current task and discards irrelevant features. By testing on the BUSI breast dataset, the effectiveness of this method in breast image segmentation was verified. Taking IoU and Dice as evaluation indicators, they reached 64.26% and 77.40%, respectively, and compared with U-Next, SU-Next improved by 1% and 1.19% respectively.
A Study of Chan-Vese Model with the Introduction of Edge Information

Abstract In image segmentation, the image edge information and the selection of edge-stopping function have a extremely important influence on the segmentation effect. As a classical geometric active contour model, Chan-Vese model completely utilizes image global information for segmentation, which has the problems of inability to handle grayscale uneven images and slow curve evolution. This paper introduces edge information into the Chan-Vese model, chooses a new edge stopping function and simplifies the driving force term to build a new image segmentation model. The new model can detect the target fast, displaying better detection effect on the targets of images with uneven grayscale and the detection effect of the target in the uneven gray image is better
A Fast Image Matching Method Based on Improved SURF

Abstract In order to solve the problems of low matching accuracy, slow speed and high system overhead in image matching methods, a rotation binary descriptor construction method based on Speed Up Robust Features (SURF) feature point detection is designed by using different Fast Library for Approximate Nearest Neighbors (FLANN) parameters and the filtering mechanism to screen out wrong matches according to the types of feature descriptors constructed in different feature extraction algorithms. This method ensures scale and rotation invariant while simplifying the representation of feature descriptors and speeding up the calculation speed in the initial stage of matching by combining the binary characteristics of descriptors. Finally, the Hamming distance is used as the filtering mechanism to improve the success rate of the final matching. The experimental results show that the accuracy of image matching is improved by 1.5% and the matching time is improved by 0.116s, while the robustness of the image to noise and rotation is ensured.
Heralded hyperparallel Fredkin gate with robust fidelity

We propose a heralded hyperparallel Fredkin gate for a three-photon system with the help of the optical property of a quantum dot (QD) embedded in a microcavity. This hyper-Fredkin gate performing both on spatial and polarized degrees of freedom (DoFs) of the three-photon system is equal to double independent three-photon Fredkin gate in one DoF, which has advantages over consuming a less quantum resource, accelerating calculation speed, and inhibiting photon-dissipation noise. Meanwhile, the fidelity of our hyper-Fredkin gate, in principle, approach unit even for the non-identical QD-cavity systems, as the imperfections involved in practical input–output processes of three photons can be converted into heralded errors by clicking the detectors rather than infidelity. Furthermore, the flexibility of the gate can be directly extended to construct some types of hybrid hyper-Fredkin gates with the same high fidelity and efficiency, accessibly performed under existing experimental techniques, as well as used large-scale quantum information processing.
A Comparative Study of Contrastive Learning-Based Few-Shot Unsupervised Algorithms for Efficient Deep Learning

Abstract The rapid development in computer vision algorithms, particularly in target detection algorithms based on deep neural networks, has led to significant advancements in various fields. However, recent research on target detection algorithms has shifted towards small sample scenarios with long-tailed distribution of categories, where achieving high accuracy target detection with limited data has become a crucial research topic. Deep neural networks often face several challenges when dealing with limited data, including convergence problems, overfitting, and poor generalization performance. In such scenarios, categories with little data are easily overwhelmed by the negative gradients generated by other categories during network training, which affects the final detection results. To address this issue, an unsupervised contrast learning algorithm has been proposed that can achieve accurate results on a small number of reference datasets without requiring a large number of datasets. One recently proposed contrast learning algorithm is PixelCL, which combines information from image depth in pretraining to improve results. The results of this algorithm show that it can obtain more accurate results through contrast learning with limited sample data. These findings highlight the potential of contrast learning algorithms in addressing negative gradient issues and improving the performance of deep neural networks in target detection tasks with limited data.
PF-BERxiT: Early exiting for BERT with parameter-efficient fine-tuning and flexible early exiting strategy

The industrial usage of huge pre-training language models such as BERT and ALBERT are limited by the computational probability problem in the fine-tuning process and overthinking problem in the inference process. PF-BERxiT has been proposed to optimize the pre-trained languages with a novel parameter-efficient fine-tuning method and a flexible early exiting strategy. Significantly, the new parameter-efficient fine-tuning model integrates a bottleneck adapter architecture parallel to the transformer architecture, and only the adapter's parameters are adjusted. In addition, we integrate an extra sub-learning module to learn the samples' characteristics, improving the accuracy and efficiency simultaneously. The flexible exiting strategy allows the model to exit early if the similarity score of adjacent layers is less than the threshold for pre-defined times. It is more flexible than previous early exiting methods, for it can simultaneously adjust the similarity score thresholds and patience parameters according to the request traffic. Extensive experiments are conducted on the GLUE benchmark, demonstrating that: (1) PF-BERxiT outperforms conventional training and parameter-efficient strategies with only a few parameters fine-tuned. (2) PF-BERxiT strikes a better balance between model performances and speedup ratios than previous state-of-the-art (SOTA) early exiting methods such as PABEE and BERxiT. (3) Ablation studies in the fine-tuning process demonstrate that the best bottleneck dimension r of the adapters is 32, and the adapters placed parallel to the feed-forward module are more efficient. (4) Ablation studies in the inference process demonstrate that for variants of PF-BERxiT with different similarity scores, PF-BERxiT-kl and PF-BERxiT-bikl attain better speedup-accuracy trade-offs than PF-BERxiT-rekl. Our PF-BERxiT helps attain a better trade-off between performance and efficiency, providing a reference for the efficient application of neural computing.
A Multi-hop Path Query Answering Model for Knowledge Graph based on Neighborhood Aggregation and Transformer

Abstract Multi-hop path query answering is a complex task in which a path needs to be inferred from a knowledge graph that contains a head entity and multi-hop relations. The objective is to identify the corresponding tail entity accurately. The representation of the path is a critical factor in this task. However, existing methods do not adequately consider the context information of the entities and relations in the path. To address this issue, this paper proposes a novel multi-hop path query answering model that utilizes an enhanced reasoning path feature representation to incorporate intermediate entity information and improve the accuracy of path query answering. The proposed model utilizes the neighborhood to aggregate the entity representation in the reasoning path. It employs a recurrent skipping network to splice the embedding of the relationship and the entity in the reasoning path based on their weight. Additionally, the model adds the position representation to obtain the reasoning path representation. Moreover, the model uses Bi-GRU and Transformer to obtain the local and global context features of each entity in the reasoning path. Finally, the reasoning path feature representation is input into the feedforward neural network and predicted through the softmax layer. The effectiveness of the proposed model in addressing the multi-hop path query answering task is demonstrated by experimental results on the WN18RR and FB15K-237 datasets. Specifically, the proposed model outperforms existing methods in terms of accuracy.
Image Enhancement Algorithm of Foggy Sky with Sky based on Sky Segmentation

Abstract In recent years, image defogging has become a research hotspot in the field of digital image processing. Through defogging enhancement processing, the visual quality of foggy images can be significantly improved, and it is also an important part of subsequent image processing. To overcome the limitations of traditional image dehazing, an image enhancement algorithm for foggy image with sky based on sky segmentation is proposed. Firstly, based on K-means clustering and sky feature analysis, sky region recognition is performed for fog image with sky. Secondly, according to the pixels of sky region, the rough transmittance is corrected, and the dehazing image is obtained by dark channel prior image dehazing based on guided filter. Finally, the dehazing image is equalized by bi-histogram equalization. This algorithm effectively avoids the problem of color distortion and halo caused by the traditional dark channel prior dehazing algorithm in the sky area, and makes the restored foggy image have better global and local contrast.
On UV Photon Distribution of Discharge in GIS Busbar Based on Ray Tracing

Abstract In order to clarify the distribution law of the ultraviolet photon field in gas insulated switchgear busbar, meanwhile to correct the detection range of the ultraviolet sensor, the optical model of the gas insulated switchgear busbar based on the uniform theory of diffraction is proposed based on ray tracing. the distributions of the radiation ultraviolet fields are calculated when partial discharge occurs at the key positions such as basin insulator and conductor. Based on the average irradiance, maximum irradiance, and attenuation characteristics, the distribution law in the busbar is further analyzed, which provides a new method to confirm the location of ultraviolet sensors.
Information theory-guided heuristic progressive multi-view coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided heuristic Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.