Reviewerly/exHarmony huggingface dataset:
Efficient Task Scheduling for Federated Learning-Driven Image Recognition in Cloud-Edge Collaborative Environments

Cloud-edge collaboration and artificial intelligence integration have advanced federated learning image recognition model training. However, in cloud-edge collaborative federated learning, imbalanced sample data often leads to low global model aggregation accuracy. To address this, a cloud-edge collaborative federated learning model aggregation method for sample imbalance is proposed, which considers local model accuracy, stability, and sample size as evaluation weights to address the issue of uneven sample distribution affecting global model aggregation. By increasing the contribution weight of high-quality local models, the impact of sample imbalance and low-quality models on the global model is reduced. This method is implemented through plugins and compared with FedAvg model aggregation method in multiple imbalanced sample scenarios. The experimental results demonstrate that this method mitigates the impact of sample diversity on global model aggregation and improves accuracy in imbalanced-sampling scenarios.
SWACL: Sentimental Words Aware Contrastive Learning for Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) aims to predict the emotional polarity of multiple modalities, such as text, video, and audio. Previous studies have focused extensively on fusing multimodal features while ignoring the value of implicit textual knowledge. This implicit knowledge within the text can be incorporated into a multimodal fusion network to improve the simultaneous representation of text, video, and auditory modalities, thereby enhancing the prediction performance of MSA. In this paper, we propose a sentimental words aware cross-modal contrastive learning strategy for multimodal sentiment analysis. It is intended to guide the network to obtain sentimental and common-sense knowledge from the text so that it can be fused with multiple modalities to improve the final representation of multimodal features. We conduct extensive experiments on the CMU-MOSI and CMU-MOSEI public datasets. The experimental results demonstrate the efficacy of our approach in comparison to baseline models corresponding to different fusion techniques.
Design of Shore-based Interactive Software based on QT

The ocean is a huge treasure house of resources. With the exploitation of the earth’s surface resources exhausted, people are increasingly exploring and exploiting the ocean. Autonomous Underwater Vehicle (AUV) is an indispensable tool in the process of human exploration of the sea, and plays an important role in marine development, seabed exploration and marine investigation. However, due to the unpredictable underwater environment, the control of AUV and real-time monitoring of its status are essential when the aircraft is conducting sea trials. To solve this problem, this paper designs a shore-based control software. This paper aims at the AUV developed based on MOOS system, uses the Qt development platform to complete the system design, and realizes the real-time control and status monitoring of the AUV. Through the sea experiment, the related functions of the shore-based operating system were tested, and the results proved the reliability and practicability of the system, which can provide guarantee for the safe operation of AUV.
Multi-perspective Text Matching Algorithm Based on Multi-granularity Feature Convolution

The core of Chinese text matching task lies in mining the deep semantic information inside the text, exploring the semantic similarities and differences between different texts, and then analyzing the semantic similarity between two texts to be matched. To address the problems of single text granularity feature, insufficient capture of potential semantic information at multiple granularities and weak interactive matching of coding features in Chinese text matching, we propose a multi-perspective text matching model based on multi-granularity features convolution (MpmMfc). The model first extracts characters, words and associated phrases by multi-pattern partitioning and performs initial encoding, then uses a two-way gate loop control unit to initially extract the contextual semantic information in the encoding. Then a multi-grain size high-dimensional encoding matrix is constructed and convolutional neural network is used to capture the granularity features to improve the characterization of multi-granularity semantic information. Finally, the multi-granularity convolutional matrices of two texts are cross-cosine matched by multi-perspective matching patterns to enhance the interaction strength of multi-granularity feature information. The results achieved in the experiments of LCQMC, a spoken expression class dataset, and BQ, a financial class dataset, are better than the currently available non-BERT type text matching models.
An Ontology-enhanced Knowledge Graph Embedding Method

Knowledge graph embedding maps entities and relationships of the graph into low dimensional dense vectors to expresse the semantic information, meantime provides effective support for downstream tasks such as link prediction. However, the existing knowledge graph embedding methods mainly focus on the explicit structured information in the graph and rarely use the entailed rich ontological knowledge. Therefore in the paper, a method for injecting ontology information into the embedding model is proposed, ontology information including class hierarchy information and relationship attribute constraints,especially symmetry attributes are considerd. By taking ontology information as extra constraints, the loss function is further refined.the generation of training samples is optimized and the number of false negative samples is limited. Experiments on the two datasets of DBpedia15K and NELL show that the embedding model can be further optimized by injecting ontology information. Specially, the hit rate of triple prediction is 63.70% for the no-type, and for type-triples the MR and the H@10 are 13.51 and 96.59% respectively. The proposed model has better performance than the basic model, which further confirms the effectiveness of the prior knowledge of ontology in knowledge graph embedding learning.
Multi-Scale TransUnet Combined with CBAM for Nuclear Image Segmentation

The nucleus carries a wealth of genetic information that controls and regulates the characteristics and functions of cells. Its morphology and distribution are highly crucial for the differentiation and grading of tumors. However, with the widespread adoption of medical imaging technology, the task of handling massive cell images has become challenging and time-consuming, demanding specialized knowledge and expertise from medical professionals. The traditional nuclear image segmentation methods require expensive time consuming and exhibit limited adaptability to different environments. To address these problems, we propose a novel multi-scale TransUnet combined with Convolutional Block Attention Module (CBAM) method for nuclear image segmentation. Firstly, in the downsampling phase of the encoder, we introduce dilated convolution units with varying dilation rates. This enables the model to integrate multi-scale feature information, thereby expanding the receptive field during downsampling to extract cell nucleus information more comprehensively. In addition, to enhance the ability of target feature extraction, CBAM is added to the up-sampling of the decoder to sequentially acquire inter-channel dependencies and spatial pixel-level relationships. The experimental results show that the Dice coefficient of our model on the MoNuSeg dataset reaches 86.3%, and the crossover ratio of IoU reaches 84.2%. Compared with the second-best models (TransUnet), the performance of our model is improved by 1.22% and 0.45% in terms of IoU and Dice similarity coefficient, respectively.
Research on Topic Identification of Safety Hazard Information in Oilfield Enterprises

To address the issues of semantic sparsity and insufficient co-occurrence information encountered when using the Latent Dirichlet Allocation (LDA) topic model for identifying topics in production safety hazard texts, a method is proposed for mining and analyzing safety hazard texts from both word and topic perspectives. At the word level, the Pointwise Mutual Information (PMI) algorithm is employed for co-occurrence analysis of hazard texts, revealing the associations between key hazard words through the co-occurrence of textual feature words. At the topic level, the PMI-LDA topic model is utilized to model and represent the production safety hazard texts, with the quality of the modeling results evaluated based on indicators such as topic similarity and log-likelihood. The results demonstrate that the combination of the PMI algorithm and the LDA topic model can effectively identify the major types and characteristics of safety hazards recorded in the oilfield enterprise's production safety hazard data, thereby providing valuable support for hazard investigation and control work.
Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements

To address the problems of inaccurate event element extraction, inability to directly handle long texts and errors in the fine-tuning stage of the task due to insufficient semantic information and incomplete semantic feature samples in the current Chinese domain event extraction model, this paper proposes the Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements (MiSE). In the embedding stage, the algorithm is designed a coding structure that fusing multiple information: Using the Roformer model based on rotational encoding for word embedding and text embedding, and augment the Roformer word embedding matrix by mapping the word embedding matrixs of event trigger, argument information and their location information to it , forming an vector matrix that incorporates multiple information; the matrix is then placed into the BiLSTM for forward and backward chain calculation to enhance the model's ability to extract semantic information from Chinese text. In the decoding stage, using the decoding structure to obtain the set of tags with the highest probability. Comparing this algorithm with several Chinese event extraction algorithms on the Baidu DuEE dataset and its extraction results outperformed other event extraction algorithms in all evaluation metrics.
A Semi-Supervised Graph Neural Network with Confidence Discrimination

Existing graph neural network methods usually depend on a large amount of labeled data, but labeled data is often scarce in the real world. In the case of less labeled data, utilizing correct pseudo-labels for model training can improve the model performance effectively. However, existing pseudo-labeling methods often use a fixed confidence threshold for all classes, leading to class imbalance and low data utilization. To solve this problem, a semi-supervised graph neural network method based on confidence discrimination is proposed, which can make full use of unlabeled data to facilitate semi-supervised node classification. Our method considers the learning state and difficulty of different classes of nodes and designs an adaptive confidence discrimination module. It assigns different confidence thresholds to each class of node, and uses unlabeled nodes with high confidence to expand the label set continuously. Our method can learn more discriminative node features to improve the model performance. On five publicly available datasets, the accuracy of the proposed method is improved by 2.1% on average compared with other methods, and in particular by 5.7% on the Flickr dataset. Extensive experiments verify the effectiveness of the proposed method.
Network Pruning via Weight Enhancement of Adaptive Exemplar Filters

The redundant architecture and inefficient parameters of Convolutional Neural Networks (CNNs) can be drastically decreased by the model compression approaches while the network model's high performance is maintained. The structured model compression technique filter pruning is a persistent subject of research since it is hardware-friendly. Whereas many filter pruning methods use training data and human empirical design to determine important filters, this might result in inaccurate results after fine-tuning and the creation of suboptimal compression networks. In this paper, we propose the Weight Enhancement of Adaptive Exemplar Filter pruning algorithm (WEAEF), which merely processes the weights of filters to decide which filters to keep at each layer automatically, while strengthening the critical filter weights thereby improving the accuracy of the pruned network without destroying the model structure. Affinity Propagation (AP), a clustering algorithm, uses similarity information sent between data points to automatically locate grouping centers. Inspiring by this, we implement the AP algorithm to make filters as data points for discovering filters that, via the similarities of filters, represent the performance of all filters within the corresponding layer. To be able to obtain enhanced accuracy after fine-tuning, we further creatively deliver the Weight Enhancement (WD) module, which improves accuracy by boosting the utility of the filter weights yet maintaining the model's structure. Our method has been shown to be superior to other state-of-the-art methods in the datasets CIFAR-10 and ILSVRC-2012.
Automated Machine Learning in Waste Classification: A Revolutionary Approach to Efficiency and Accuracy

In recent years, waste segregation, treatment, and recycling have become critical global issues, drawing significant attention worldwide. However, the efficiency of recycling processes can be influenced by numerous factors. Among these, waste classification plays a crucial role, and researchers have explored the application of machine learning to automate this step. Nonetheless, traditional machine learning approaches often require skilled professionals to invest substantial time in debugging, and achieving satisfactory accuracy can be challenging. To address this concern, we propose leveraging Automated Machine Learning (AutoML) to enhance the speed and accuracy of waste classification, thereby expediting the implementation of waste segregation policies. Our findings indicate that AutoML outperforms traditional machine learning models, requiring less time and energy, while achieving an impressive accuracy and precision rate of 95.1%.
CardiacSegFormer: Transformer for Semantic Segmentation of Cardiac Images

Cardiac image segmentation is a sub task of medical image segmentation, which has very important clinical significance in the prevention and diagnosis of cardiac diseases. Recently, segmentation models based on the Transformer architecture have achieved remarkable performance due to their advantages in handling long-range dependencies and capturing global contextual information. However, the Transformer based segmentation model only uses simple unprocessed skip connections to compensate for information loss during the down-sampling process, resulting in its potential in cardiac image segmentation remains largely unexplored. In this work, we propose CardiacSegFormer, which consists of various improvements, such as the application of self-attention modules in skip connections to facilitate better information fusion. At the same time, low-entropy constraint is introduced, cosine annealing warm restart algorithms are used to help training a more effective model. Experiment on Automated Cardiac Diagnosis Challenge (ACDC) datasets demonstrate that the proposed method has a Dice Similarity Coefficient (DSC) of 91.84%, which is superior to the current the-state-of-art model. To conclusion, this work provides a high-performing baseline model for cardiac image segmentation tasks, showcasing its value in the improvement and enhancement of related models.
Adaptive Fusion Attention Network for Facial Expression Recognition

In this paper, we propose a novel facial expression recognition network, called Adaptive Fusion Attention Network (AFAN). Facial expression recognition is different from traditional image classification tasks. There are potentially similar features between different categories, and the differences between them may be subtle and difficult to distinguish. To solve this problem, we propose AFAN, which not only can get multi-dimensional features, but also can generate an attention map with stronger representation ability. The comparative experiments on AffectNet-7 dataset verify the excellent performance of the proposed method on facial expression recognition tasks.
Rare Association Rule Mining Based on Reinforcement Learning

In some cases, it is more important to discover rare association rules than frequent itemsets. Unique rules represent rare cases, activities or events in real-world applications. Extracting critical activities from massive regular data is crucial. In this study, we introduce RARM-RL, a reinforcement learning-based approach for discovering rare association rules. In RARM-RL, the environment specifies iterative steps to extract rare association rules from the data set. In this method, the agent takes actions to either add or remove an item from the current set of items. After each action, the agent receives a reward from the environment. By repeating this process and trying different actions with varying rewards, the agent is trained to maximize the total reward. The goal is to learn an optimal strategy that allows the agent to form as many rare association rules as possible. Extensive experiments have been conducted to test the effectiveness of this method in mining rare association rules, and the results have shown promising potential for its applicability in different scenarios (such as agent transferability).
Deep Learning-Based Network Channel Quality Prediction Algorithm in High-Speed Mobile Scenarios

As a comfortable and fast new generation popular transportation, high-speed rail(HSR) has benefited from the development of mobile Internet technology, and passengers' demand for information during HSR journeys has been increasing. At the same time, the demand for streaming media services by users is also constantly increasing. However, in high-speed mobile scenarios, streaming media transmission faces frequent network channel quality fluctuations and unpredictable issues. To address these problems, this paper proposes a channel quality prediction algorithm based on Long Short Term Memory Network (LSTM) and Convolutional Neural Network (CNN), called the CNN-LSTM model. This model can predict the available channel quality in real-time and improve the efficiency of streaming media transmission, enhancing the user's viewing experience. Through extensive simulation experiments, the feasibility and effectiveness of the channel quality prediction algorithm are validated.
FTAN: Exploring Frame-Text Attention for lightweight Video Captioning

Traditional video captioning approaches employ LSTM as a lightweight decoder. However, these methods focus on fully extracting visual features, but pay less attention to textual information, resulting in relatively low-quality performance. Recent transformer-based methods achieve more accurate results, but at the cost of excessive computing resources. In this paper, we propose a lightweight model for video captioning named Frame-Text Attention Network (FTAN), aiming to make full use of both visual and textual features to obtain more accurate captions. We develop a novel text attention module in FTAN, which uses the hidden state of LSTM as query to generate attentive text features. Then the attentive text features are merged with visual features, which are used as input for LSTM to generate more accurate captions. To the best of our knowledge, we are the first to introduce attention mechanism to extract more textual information hidden in LSTM architecture in video captioning. Extensive experiments demonstrate the effectiveness of FTAN. FTAN outperforms the state-of-the-art LSTM-based method on MSVD dataset by 0.8 in CIDEr-D and is about one-fourth of the transformer-based methods in terms of parameters.
Adaptive Target Location Method for Cross-in-a-circle Apron Using Monocular High-Speed Camera

The recognition of airport apron targets and the determination of target positions are critically important in positioning the apron when there are abnormal target positions or unmanned aerial vehicles (UAVs) engaged in autonomous cargo retrieval. This is crucial for ensuring the safety and efficiency of the apron. However, challenges such as resource-intensive processes and suboptimal accuracy in position solving have been encountered due to limitations in hardware equipment and the complexity of multi-sensor fusion. In this paper, we propose an adaptive target location method for airport aprons based on monocular high-speed cameras. This method employs adaptive frame differencing and perspective transformation based on geometric shape extraction to detect apron objects and calculate their azimuth and deflection from the apron center. Importantly, our approach relies solely on a single visible-light camera, eliminating the need for complex multi-sensor fusion involving binocular matching, Global Navigation Satellite Systems (GNSS), or Inertial Measurement Units (IMU). Experimental results demonstrate that the proposed method achieves position solving accuracy within 1 meter and real-time recognition and solving capabilities for apron objects.
Contextualizing Answer Ranking: Embracing Multi-dimensional Enhanced Knowledge

Generative question answering (QA) involves the challenging task of producing contextually relevant answers to questions in text generation. Existing methodologies primarily solving the task of generative QA such as sequence-to-sequence models and autoregressive models utilize diverse strategies to improve answer quality in the generation process. However, these models might struggle with contextual coherence and generate answers that may not align with related knowledge. Therefore, we propose a two-stage framework CARMEK for generative QA which incorporates re-ranking stage and integrates diverse knowledge sources within the re-ranking stage, to achieve contextually relevant answers. Through rigorous experiments on diverse datasets, our method demonstrates improvements in ROUGE-1/2/L metrics, highlighting its effectiveness in generative QA.
Adaptive Non-Local Regression Prior based on Transformer for Image Deblurring

The non-local regression prior has shown promising results in image deblurring by effectively combining local continuity and non-local self-similarity of images. However, traditional non-local regression priors have limited representation capacity, and existing non-local regression-based methods often require iterative solutions to complex optimization problem. Additionally, these methods involve manually tuning hyper-parameters, which can be difficult and time-consuming. To address these limitations, we propose an adaptive non-local regression prior based on the Transformer model for image deblurring. Different from other learning-based approaches, the self-attention mechanism in the Transformer module better aligns with the modeling process of non-local regression prior, and the global computation capability of Transformer enables effective modeling of global context information. By unfolding the iterative process of the model into a neural network, we introduce a novel deep unfolding network. The proposed network is trained end-to-end, allowing all parameters to be jointly optimized to facilitate image restoration. The extensive experiments show that our proposed model outperforms state-of-the-art model-based and learning-based methods both in terms of PSNR metrics and visual quality.