Reviewerly/exHarmony huggingface dataset:
Microseismic positioning of an isolated working face under complex geological conditions and its engineering application

In view of the inability to accurately locate vibrations in isolated workings under complex geological conditions, an adaptive rotational categorization method, a downhill comparison method based on time-frequency analysis (TFA-DC method), a variable-step acceleration search method, and a dual-phase seismic source location method (TD-DL method) are proposed. A set of integrated software with features of “visualization”, “interactive” and “one-click” was developed for microseismic data processing. Results show that compared with the improved STA/LTA method, the recognition accuracy of the island working face microseismic signal by the adaptive wheel classification method is increased by 4.8%. Compared with the improved STA/LTA method, the TFA-DC method has the advantage that it can simultaneously pick up the exact p wave and the peak S wave, and the failure ratio is 0. Compared with simulated annealing algorithm and genetic algorithm, stepwise accelerated search method has better results. The standard deviation of objective function value, location error and wave velocity error are all 0. Method improves the positioning of the TD - DL detector coordinates, dual phase and coherence of known information, such as the positioning result positioning error is only the p wave and S wave ChanZhen phase positioning method of 9.5% and 14.5%, to a certain extent offset the p wave and S wave ChanZhen phase calculation of the positioning error, so as to improve the effect of source localization precision of the inversion.
Vietnamese Students' Perceptions of Utilizing Social Media to Enhance English Vocabulary: A Case Study at Van Lang University

Social media (such as Facebook, TikTok, Instagram, YouTube, etc.) is widely used for people's entertainment and education across the globe. Furthermore, utilizing this sort of technology assists learners in mastering foreign languages such as English, Chinese, French, Japanese, and so on, which was proved by many foreign language experts and researchers worldwide. The study aims to find out students' perceptions of using social media to develop English vocabulary. A mix of quantitative and qualitative methods was exploited with surveys for 154 students from different majors at Van Lang University (VLU) in Ho Chi Minh City (HCMC), Vietnam, and semi-structured interviews for 15. After the investigation, the research results show students' deep insights in using social media to improve their English vocabulary and its positive effects on students' learning vocabulary. Thanks to the findings, lecturers can consider more teaching methods with social media in their English courses.
Adversarial Guided Gradient Estimation Hashing for Cross-modal Retrieval

Due to low storage cost and fast query speed, deep hashing methods are widely used in cross-modal retrieval. However, the “heterogeneous gap” between multi-modal data is still a challenge. Moreover, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output. Existing solutions usually use relaxation techniques, but this inevitably produces quantization error, leading to sub-optimal hash code. In this paper, we propose Adversarial Guided Gradient Estimation Hashing (AGEH). Firstly, in order to bridge the heterogeneous gap between different modal data, a cross-modal adversarial feature learning network is constructed to learn cross-modal semantic associations. Secondly, to solve the discrete optimization problem of hash code, we propose a hashing optimization strategy based on gradient estimation for sign function, which strictly uses sign function to maintain discrete constraints in forward propagation, while in back propagation, the gradient is directly transmitted to the previous layer and thus avoid quantization error. Extensive experiments conducted on two cross-modal benchmark datasets show that our proposed AGEH outperforms several state-of-the-art methods.
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features makes the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning.
Neural Network Reasoning Algorithm of Large-Scale Gragh Based on Parallel Computing

With the continuous development of intelligent computing power, neural networks are widely used in all walks of life. Traditional neural networks, such as convolutional neural networks and fully connected networks, as the carrier of method models, have played an important role in solving many structural data problems. However, for unstructured graph data, it has its own diversity and complexity. For large-scale graphs, it is still challenging to quickly calculate graph neural networks for processing graphs due to the limitations of software and hardware tools and resources. For this reason, this paper proposes a method of multi-GPU block parallel computing based on graph segmentation, so as to improve the computing efficiency of message passing graph neural network in dealing with large-scale graph problems.
SNSE: State Novelty Sampling Exploration

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are summed directly as intrinsic rewards and extrinsic rewards. However intrinsic rewards are non-stationary, which directly contaminates extrinsic environmental rewards and changes the optimization objective of the policy to maximize the sum of intrinsic and extrinsic rewards. This could lead the agent to a mixture policy that neither conducts exploration nor task score fulfillment resolutely. This adopts a simple and generic perspective, where we explicitly disentangle extrinsic reward and intrinsic reward. Through the multiple sampling mechanism, our method, State Novelty Sampling Exploration (SNSE), cleverly decouples the intrinsic and extrinsic rewards, so that the two can take their respective roles. Letting intrinsic rewards directly guide the agent to explore novel samples during the exploration phase, and that our policy optimization goal is still to maximize extrinsic rewards. In sparse rewards environments, our experiments show that SNSE can improve the efficiency of exploring unknown states and improve the final performance of the policy. Under dense rewards, SNSE do not make the policy produce optimization bias and cause performance loss.
An Attention-Lightweight YOLOv5 Aerial Dense Object Detection Method

The paper proposes an improved YOLOv5-CBG (SX-CBAM, Ghostbottleneck) real-time target detection network for the problem of false detection and high missed detection rate in real-time detection caused by a large number of dense small targets in complex aerial images. The network first adds a hybrid pooling layer to the attention mechanism CBAM (Convolutional Block Attention Module), and integrates the residual path into its structure, and then embeds the improved SX-CBAM into the YOLOv5 detection network to improve its detection performance. Secondly, combined with the lightweight Ghost bottleneck module to reduce the computational cost of the network and ensure the real-time performance of target detection. The results of comparison experiments on the dataset VisDrone2019 show that the mAP50 of YOLOv5-CBG is 5.2% higher and the GFLOPs are reduced by 1.4 compared to YOLOv5, demonstrating that the improved detection network reduces the false detection rate and missed detection rate for dense small targets while satisfying real-time target detection.
Multimodal Co-Attention Mechanism for One-stage Visual Grounding

Visual grounding aims to locate a specific region in a given image guided by a natural language query. It relies on the alignment of visual information and text semantics in a fine-grained fashion. We propose a one-stage visual grounding model based on cross-modal feature fusion, which regards the task as a coordinate regression problem and implement an end-to-end optimization. The coordinates of bounding box are directly predicted by the fusion features, but previous fusion methods such as element-wise product, summation, and concatenation are too simple to combine the deep information within feature vectors. In order to improve the quality of the fusion features, we incorporate co-attention mechanism to deeply transform the representations from two modalities. We evaluate our grounding model on publicly available datasets, including Flickr30k Entities, RefCOCO, RefCOCO+ and RefCOCOg. Quantitative evaluation results show that co-attention mechanism plays a positive role in multi-modal feature fusion for the task of visual grounding.
High-Level Design and Rapid Implementation of Blockchain-Based Real Time Supply Chain Platform

Blockchain technology as a foundation for distributed ledgers provides a cutting-edge foundation for a brand-new, transparent, decentralised transaction system across industries and businesses. The features of this technology that are intrinsic increase confidence by providing transparency and traceability in any data, products, or financial resource transaction. Real-time monitoring and tracking are critical for delivering a unified perspective of global supply chains management (SCM) that involves multiple stakeholders. This study suggests a blockchain-based supply chain platform that secures all the transactions with Identity Access Management (IAM) and works with multiple stakeholders while establishing transparency and traceability amongst them. This paper focuses on the platform that the authors have created to keep all the data secure pertaining to the parties involved with the supply chain like a producer, retailer, distributor, auditor, and customer at a single place whose access to that is being protected and private at the same time. We discuss Security Handling and Privacy, as well as how the smart contract plays a role in this SCM. We also look at the test outputs, implementation areas, and a brief discussion about the findings.
Domain Adaptation Method For Document Object Detection

Documents are essential information carriers, and obtaining digital information from document images cannot be overemphasized. Document images can be considered objects consisting of multiple semantic elements or regions. Correctly detecting different semantic objects in a document has been of considerable research significance and relevance. As a critical step and method for digitizing document image information, document object detection is also the basis of many downstream document tasks. In this paper, based on the classical target detection framework Faster RCNN model, we apply "strong alignment" at the image level and "weak alignment" at the instance level to mitigate the domain differences between source and target domains by training domain classifiers in the scenario where there are domain differences between different language documents and documents with varying styles of layout. A new loss function is designed to obtain a reliable relative comparison between the most possible positive case and the most difficult negative case, and to distinguish the more difficult categories. This scheme effectively migrates the detection knowledge learned by the model in the source domain data to the target domain data, which significantly improves the generalization performance and robustness of the document-oriented object detection model.
Graph-Augmented Multi-Level Representation Learning for Session-based Recommendation

Session-based recommendation has received extensive attention in recent years due to its excellent performance in recommending for anonymous users. However, most existing studies ignore the intention revealed by several consecutive items in a session and do not consider similar transitional relations across sessions. To this end, we propose a graph-augmented multi-level representation learning method for session-based recommendation. Specifically, we construct multi-level session graphs for each session to capture fine-grained user preferences. In addition, we utilize subgraphs of global transition graph to augment the representation of each session. Contrastive learning is adopted to maximize the consistency of the representation of the same session under different subgraphs. Experimental results on two real-world datasets prove that the proposed method achieves excellent performance compared to several competitive baselines.
Uncertainty Estimation Based Doubly Robust Learning for Debiasing Recommendation

In recommender systems, the interactions between users and items are sparse, which means most of the interactions between users and items are missing. More importantly, the loss of these interactions is non-random, which leads to selection bias and makes it difficult to predict the ratings accurately. Most recent methods combine the error-imputation-based (EIB) estimator and the inverse-propensity-score (IPS) estimator to overcome this problem. However, these methods may result in even higher variance because of the inaccurate error imputation, so we propose to make an uncertain estimate of error imputation and eliminate imputed error with excessive variance. We developed a novel framework for recommender system debiasing by estimating the inaccuracy of imputed errors. Specifically, we use dropout technology to calculate the variance of imputed error. Then, we filter imputed errors based on variance. Imputed errors with higher variance are less likely to be reserved which improves the credibility and quality of imputed errors. In this way, the performance of the estimator can be further improved. We conduct extensive experiments on two real-world datasets which demonstrate that our proposed framework has strong stability and outperforms the state-of-the-art.
A Chinese text classification model based on radicals and character distinctions

Chinese characters are generally correlated with their semantic meanings, and the structure of radicals, in particular, can be a clear indication of how characters are related to each other. In the Chinese characters simplification movement, some different traditional characters have been transferred into one simplified character (many-to-one mapping), resulting in the phenomenon of ’one simplified character corresponding to many traditional characters. Compared to the simplified characters, the traditional characters contain richer structural information, which is also more meaningful to semantic understanding. Traditional approaches of text modelling often overlook the structural content of Chinese characters and the role of human cognitive behaviour in the process of text comprehension. Hence, we propose a Chinese text classification model derived from the construction methods and evolution of Chinese characters. The model consists of two branches: the simplified and the traditional, with an attention module based on the radical classification in each branch. Specifically, we first develop a sequential modelling structure to obtain sequence information of Chinese texts. Afterwards, an associated word module using the part head as a medium is designed to filter out keywords with high semantic differentiation among the auxiliary units. An attention module is then implemented to balance the importance of each keyword in a particular context. Our proposed method is conducted on three datasets to demonstrate validity and plausibility.
TempCast: A Multi-modal Transformer for Short-Term Temperature Forecasting

Accurate weather forecasting benefits us in a variety of ways, from scheduling flights to agricultural harvests. However, existing temperature forecasting models have two problems, one is the accumulation of errors caused by autoregressive models, and the other is difficult to predict complex and varying temperatures such as those in highlands. In this paper, we proposed TempCast, a multi-modal Transformer model for short-term temperature prediction. The model has two features: (i) Modeling the features entirely by self-attention, which can effectively capture the exact long-term dependent coupling between output and input. And multiple predictions are obtained at once using a generative decoder, (ii) The modeling of multi-source data through a decoupled multi-modal fusion mechanism can effectively come to cope with the drastic changes of weather in highlands and mountains, etc. The experimental results show that the method can well achieve short-term temperature prediction and significantly outperforms all traditional methods in several indicators. The method also provides a new solution idea for multi-modal temperature prediction. Our code and data are available at https://github.com/Adam618/Temp Cast.
Deep Hashing via Dynamic Similarity Learning for Image Retrieval

Hashing has been commonly used in large-scale image retrieval. Due to the explosive expansion of data, traditional deep hashing methods are not able to extract features explicitly, which leads to inefficient learning of hash codes. Accordingly, in the proposed method, we use the latest backbone network called ConvNeXt for feature extraction, which not only has superior performance for feature extraction from larger scales datasets, but also has fewer parameters with higher training efficiency. Consequently, to capture the true similarity among images, different from existing methods that pre-define a similarity matrix, we learn the similarity matrix during training. We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE, and ImageNet. The proposed method shows superior performance compared with several state-of-the-art techniques.
Pedestrian Multi-Objective Tracking Based on Work-Yolo

Aiming at the problem of low pedestrian tracking accuracy caused by illumination and occlusion in intelligent surveillance videos, a pedestrian tracking algorithm based on Work-Yolo detection combined with DeepSORT is proposed. To improve the accuracy of the detector, the attention module CBAM is used to fuse with the Backbone and Neck parts of Yolov5s network for enhancing pedestrian features. The BiAdd structure is used to fuse features of different scales about BiFPN, Dilated ConV is proposed to reduce the number of model parameters and extract better shallow features. Work-Yolo head decoupled head separates prediction classification and regression tasks, solving the problem of missed detection when pedestrians are obscured. The Lite-shufflenet lightweight structure is proposed to extract appearance features, and retrain the pedestrian re-identification dataset to reduce the identity switching caused by pedestrian occlusion. Pedestrian detection experiments are conducted on 3247 intersection pedestrian datasets, and the final detection accuracy rate was 94.2% and the recall rate was 90.6%. The video taken at the indoor entrance and exit of the scene, four videos are randomly selected for multi-target tracking experiments, the MOTA is improved by 10%, and the detection speed reaches 15fps, which meets the requirements of industrial applications.
Exploring Learning with Deep Heterogeneous Descriptor-based Sampling

The training of deep learning models becomes more complex with the advancement of technology. Specifically, advanced computation hardware has a significant effect on the speed at which models can be trained. Improvements in training procedures are more likely to reduce time and capital costs. As a rule, samples have unequal importance in a dataset. Early model training will be accelerated with the help of informative samples. In order to maximize diversity of features, we develop DHDS (Deep Heterogeneous Descriptor-based Sampling), a novel method based on instance-level diversity sampling, which clusters examples according to deep similarity metrics. Furthermore, we perform extensive experiments, and the result demonstrates the DHDS approach accelerates network training and improves accuracy during the initial training stage.
An automatic photometric augmentation technique to recognize faces with single sample per person

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Tariq Siddique, Ibrahim Venkat, Asem Kasem, Sharul Tazrajiman; An automatic photometric augmentation technique to recognize faces with single sample per person. AIP Conference Proceedings 10 January 2023; 2643 (1): 040011. https://doi.org/10.1063/5.0110448 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Point-of-Interests Recommendation based on User-POI Coupling Relationships

This paper proposes a POI recommendation framework based on deep neural network. Firstly, we use k-means algorithm to cluster the POIs according to their geographical locations, so that POIs with high location proximity would be grouped into one category. Then, a convolutional neural network model is designed to study explicit correlations between users and POIs in aspects of respective features (such as correlations between users’ age and POIs’ location). Meanwhile, we use another neural network model to simulate matrix factorization method in machine learning. It can dig into implicit correlations between users and POIs according to users’ ratings of POIs. Lastly, the explicit and implicit correlations between users and POIs are integrated to comprehensively represent user-POI coupling relationships. After, we put the user-POI coupling relationships into a fully connected network for recommendation. The model proposed in this paper has been evaluated on the Yelp dataset and the experimental results show that the model proposed in this paper achieves high recommendation accuracy.
The Mudstone Weathering Detection Algorithm Based on Multi-branch Network

Nowadays, researchers have done a lot of work and discussion on the theory of deep learning. In addition, how to apply deep learning theory to the engineering field is also a hot issue that we should pay attention to. In this background, this paper discusses how to solve some problems in mudstone image data in the field of geology, such as mudstone image data is imbalanced, etc., to propose an effective detection model, which is expected to be able to detect and identify mudstone image weathering and lithology images quickly, efficiently and intelligently. For these problems, this paper designs a multi-branch detection network for weathering detection of mudstone images, and the experiments show that this network structure has better detection effect than the original neural network structure.