Reviewerly/exHarmony huggingface dataset:
Improved Safety Helmet-Wearing Detection Algorithm Based on YOLOv5

This paper presents an improved algorithm, TNS-YOLOv5, based on YOLOv5s to address issues in existing safety helmet wearing detection algorithms for small and obstructed targets. TNS-YOLOv5 incorporates the NAM module, Transformer Encoder structure, and SIoU loss as the regression loss function. NAM focuses on important channels and spatial features, improving recognition accuracy. The Transformer Encoder’s multi-head attention layer enhances target feature extraction and performs well in detecting occluded objects. SIoU loss accelerates network convergence and improves regression accuracy by redefining distance loss through vector angles. Experimental results demonstrate that TNS-YOLOv5 significantly improves accuracy and detection speed, achieving excellent detection results in safety helmet wearing detection tasks.
Consistent Solutions for Optimizing Search Space of Beam Search

Research on math word problems has made significant advancements due to the emergence of language models. Large language models have excelled in a variety of reasoning tasks. Still, due to the demand for low costs, research on the upper bound of small language models in reasoning tasks and the limitation of the knowledge they can accommodate has drawn attention. In line with previous work on math word problems, we discover that models that only learned a single solution lacked reasoning ability during the decoding process, further exacerbating the error accumulation caused by exposure bias that will fail generalization. To tackle this problem, we suggest using the commutative property to generate a consistent solution set for each data in the training set. Then, we use it as additional training data to optimize the search space in beam search. On this foundation, we will go into great detail about how consistent solutions training affects the work process of beam search. In addition, we found significant differences between models trained using consistent solutions and those trained without consistent solutions, so the model ensemble technique is applied to improve model performance. In the NLPCC-2023 shared task 3, our model ultimately ranks fourth with an accuracy of 23.66%.
Conversational Aspect-Based Sentiment Quadruple Analysis with Consecutive Multi-view Interaction

With the development of information technology, the increasing amount of content on the web has made aspect-based sentiment analysis an essential tool for extracting information about emotional states. However, most of the existing work focuses on a single text, while little attention is paid to the task of sentiment analysis in complex texts such as dialogues, in which the quadruple of target-aspect-opinion-sentiment may appear in different speakers during one conversation thread. In this paper, we proposed a novel framework that was built by a Chinese pre-trained language model and a grid tagging classifier. In addition, we use multi-view interaction with three consecutive multi-head attention modules to improve the performance and robustness of our model. Besides, based on the excellent performance of the Chinese pre-training model, the English version is transferred from the final Chinese weights to achieve cross-lingual transfer. To improve the generalization ability of the model, cross-validation is used to select the best one. Our model ranks first on track 4 of the NLPCC-2023 shared task on conversational aspect-based sentiment quadruple analysis. Our code is publicly available at https://github.com/Joint-Laboratory-of-HUST-and-PAIC/nlpcc2023-shared-task-diaASQ.
Review Generation Combined with Feature and Instance-Based Domain Adaptation for Cross-Domain Aspect-Based Sentiment Analysis

The supervised learning methods have proven effective for Aspect-Based Sentiment Analysis (ABSA). However, sufficient data with fine-grained labels is essential for supervised learning, which hinders their effectiveness in many domains lacking fine-grained labeled data. Unsupervised domain adaptation methods are proposed to address this issue, but these methods exit some limitations and are difficult to satisfy the requirements of Cross-Domain ABSA. This paper proposes a joint approach named Review Generation Combined with Feature and Instance-Based Domain Adaptation (RGFI) for Cross-Domain ABSA. Based on Bert, RGFI not only uses the approach of review generation but also unifies the feature and instance-based domain adaptation methods for cross-domain ABSA tasks. Compared with other state-of-the-art domain adaptation methods, experiment results on four benchmarks demonstrate the significant effect of RGFI-based approaches in both cross-domain End-to-End ABSA and cross-domain Aspect Extraction tasks.
ZeroGen: Zero-Shot Multimodal Controllable Text Generation with Multiple Oracles

Automatically generating textual content with desired attributes is an ambitious task that individuals have pursued long. Existing work has made a series of progress in incorporating unimodal controls into language models (LMs), whereas how to efficiently generate controllable sentences with multimodal signals remains an open question. To tackle the puzzle, we propose a new paradigm of zero-shot controllable text generation with multimodal signals (ZeroGen). Specifically, ZeroGen leverages controls of text and image successively from token level to sentence level and maps them into a unified probability space at decoding, which customizes the LM outputs by weighted addition without extra training with images. To achieve better inter-modal trade-offs, we further introduce an effective dynamic weighting mechanism to regulate all control weights. Moreover, we conduct substantial experiments to probe the relationship of being in-depth or in-width between signals from distinct modalities. Encouraging empirical results on three downstream tasks show that ZeroGen outperforms its counterparts on the captioning task by a large margin and shows great potential in multimodal news generation with a higher degree of control (Our code will be released at https://github.com/ImKeTT/ZeroGen ).
Multi-perspective Feature Fusion for Event-Event Relation Extraction

Event-Event Relation Extraction (EERE) is a crucial task in the information extraction domain, which aims to obtain the relation between events and understand the relation structure of the event chain in a document. Existing methods mainly focused on single-event relations, such as causality, while only utilizing trigger words or semantic span as model inputs, ignoring the impact of event arguments and contextual features on events. Hence, we propose a novel Multi-Perspective Graph feature Fusion (MPGF) model. We model event triggers, arguments, sentences, and document context as heterogeneous graphs and transform event relation prediction into a graph edge classification problem. Applying three graph feature fusion strategies from different perspective to complete the node representation learning and learn local and global semantic features. At the same time, our model architecture can be flexibly adjusted according to different graph algorithm combinations. Extensive experiments on two benchmark datasets show that MPGF significantly outperforms previous SOTA methods.
CAMG: Context-Aware Moment Graph Network for Multimodal Temporal Activity Localization via Language

Temporal Activity Localization via Language (TALL) is a challenging task for language based video understanding, especially when a video contains multiple moments of interest and the language query has words describing complex context dependencies between the moments. Latest studies have proposed various ways to exploit the temporal context of adjacent moments, but two apparent limitations remained. First, only limited context information was encoded based on RNNs or 2-D convolutions, which highly depended on the pre-sorting of proposals and lacked flexibility. Second, semantically correlated content in different moments was ignored, i.e., semantic context. To address these limitations, we propose a novel GCN-based framework, i.e., Context-Aware Moment Graph (CAMG) network, to jointly model the temporal context and semantic context. Also, we design a multi-step fusion scheme to aggregate object, motion and textual features. A Query-Gated Integration Module is further designed to select queried objects and filter out noisy ones. Our model achieves superior performance to state-of-the-art methods on two widely-used benchmark datasets.
Multi-step Review Generation Based on Masked Language Model for Cross-Domain Aspect-Based Sentiment Analysis

It has been proved that supervised learning methods are effective for Aspect-Based Sentiment Analysis (ABSA). However, the supervised learning methods rely heavily on fine-grained labeled data, and many domains lack labeled data, hindering the ABSA task’s effectiveness. To solve this problem, many unsupervised domain adaptation methods are proposed to transfer knowledge from the source domain with plenty of labeled data to any target domain lacking labeled data. Inspired by the implementation principle of the masked language model and the experience of predecessors, we propose a new domain adaptation paradigm named Multi-Step Review Generation (MSRG) in this paper. MSRG aims to achieve domain adaptation by generating target-domain reviews with fine-grained labels through multi-step prediction based on the source-domain labeled reviews. Pre-trained masked language model plays a significant role in MSRG. Besides, we also propose two new training strategies to adapt to MSRG. Experiment results on four benchmarks demonstrate that MSRG can achieve significant improvement compared with the state-of-the-art domain adaptation methods in both Cross-Domain End2End ABSA and Cross-Domain Aspect Extraction tasks.
Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model

This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the feedback space. 30 common tags are identified based on clustering 363k comment phrases. By predicting the corresponding tags for an input, learners can gain insights on how to improve their work. However, the various tag types and non-exhaustive annotation pose challenges for model training. To address this, we propose a soft-label-driven approach to construct a more accurate relationship between samples and the tag space. We use a relevance matrix between different tags to adjust the positive label’s confidence across the entire tag space. Additionally, we propose a tag-aware regression-based ranking model that further improves performance. Our experiments demonstrate that the proposed soft-label approach performs better than a hard-label approach, and the proposed ranking model enhances performance. Our data and code are available at: https://github.com/Zhe0311/DetailedFeedback2ChineseWritingLearners .
NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering

Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous information, and the types of reasoning are mainly divided into numerical reasoning and span extraction. Current numerical reasoning methods simply use LSTMs to autoregressively decode program sequences, and each decoding step produces either an operator or an operand. However, step-by-step decoding suffers from exposure bias, and the accuracy of program generation drops sharply as the decoding steps unfold due to error propagation. In this paper, we propose a non-autoregressive program generation framework, which facilitates program generation in parallel. Our framework, which independently generates complete program tuples containing both operators and operands, can address the error propagation issue while significantly boosting the speed of program generation. Experiments on the ConvFinQA and MultiHiertt datasets show that our non-autoregressive program generation method can bring about substantial improvements over the strong FinQANet ( $$+5.06$$ Exe Acc and $$+4.80$$ Prog Acc points) and MT2Net ( $$+7.97$$ EM and $$+6.38$$ F1 points) baselines, establishing the new state-of-the-art performance, while being much faster ( $$\sim $$ 21x) in program generation. Finally, with increasing numbers of numerical reasoning steps the performance drop of our method is significantly smaller than that of the baselines.
A Unified Generation Approach for Robust Dialogue State Tracking

While dialogue state tracking by generation-based approaches allows for better scalability and generalization, they suffer from two major limitations. First, most generation-based models adopt a multi-task learning framework that may cause gradient conflicts and low training efficiency. Second, since the dialogue state of the previous turn is usually taken as an input for the current turn, there exists inconsistency between training and inference, which is identified as turn-level exposure bias. To address the first limitation, we propose the idea of state-transition sequence and transform multi-task learning into a single generation task. To alleviate turn-level exposure bias, we propose a slot-perturb strategy to reduce the over-reliance on the previous dialogue state. Experimental results show that our method achieves a new state of the art on the MultiWOZ 2.4 dataset and performs competitively on MultiWOZ 2.1. Besides, we demonstrate that the unified generation framework with slot-perturb improves the convergence speed and relieves error accumulation.
RSpell: Retrieval-Augmented Framework for Domain Adaptive Chinese Spelling Check

Chinese Spelling Check (CSC) refers to the detection and correction of spelling errors in Chinese texts. In practical application scenarios, it is important to make CSC models have the ability to correct errors across different domains. In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models. Specifically, we employ pinyin fuzzy matching to search for terms, which are combined with the input and fed into the CSC model. Then, we introduce an adaptive process control mechanism to dynamically adjust the impact of external knowledge on the model. Additionally, we develop an iterative strategy for the RSpell framework to enhance reasoning capabilities. We conducted experiments on CSC datasets in three domains: law, medicine, and official document writing. The results demonstrate that RSpell achieves state-of-the-art performance in both zero-shot and fine-tuning scenarios, demonstrating the effectiveness of the retrieval-augmented CSC framework. Our code is available at https://github.com/47777777/Rspell .
IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENINGKATKAN POLA PENJUALAN

Medicine is a necessity for someone who is sold to people with disease. Therefore, every pharmacy or hospital must have a data processing system so that each transaction data can be used to make reports. From this report a useful result will be created to determine which drugs are most frequently purchased and sold so as to be able to determine the amount of stock at the pharmacy. But at the present time drug sales transaction data continues to increase every day so that it has accumulated because the system used is a system for storing or archiving bookkeeping without utilizing the transaction data, besides that the problem that often arises is the lack of maximum customer service at pharmacies because the drugs or needs that consumers are looking for are still not available. The a priori algorithm functions as a candidate for possible item combinations, then tests whether the combination meets the minimum support and minimum confidence parameters which are the threshold values ​​given by the user, so that it finds patterns in the form of products that will often be purchased together or products that tend to appear together in a transaction.&#x0D; &#x0D;
Traffic Scene Captioning with Multi-Stage Feature Enhancement

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn more detailed content in the traffic scene image. Second, the scene knowledge graph is applied to the decoding process, and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again, so that the model can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable captions. This paper reports extensive experiments on the challenging MS-COCO dataset, evaluated by five standard automatic evaluation metrics, and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods, especially achieving a score of 129.0 on the CIDEr-D evaluation metric, which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.
FedTC: A Personalized Federated Learning Method with Two Classifiers

Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaboratively without sharing raw data. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy and ensuring robustness. In this paper, we propose FedTC, a personalized federated learning method with two classifiers that can retain personalized information in the local model and improve the model’s performance on local data. FedTC divides the model into two parts, namely, the extractor and the classifier, where the classifier is the last layer of the model, and the extractor consists of other layers. The classifier in the local model is always retained to ensure that the personalized information is not lost. After receiving the global model, the local extractor is overwritten by the global model’s extractor, and the classifier of the global model serves as an additional classifier of the local model to guide local training. The FedTC introduces a two-classifier training strategy to coordinate the two classifiers for local model updates. Experimental results on Cifar10 and Cifar100 datasets demonstrate that FedTC performs better on heterogeneous data than current studies, such as FedAvg, FedPer, and local training, achieving a maximum improvement of 27.95% in model classification test accuracy compared to FedAvg.
HybridHR-Net: Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model. Instead of fully connected layer features, we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach. Using a serial nature technique, the features that were selected are combined into a single vector, and then the results are categorized by machine learning classifiers. Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%, 98.7%, 100%, 99.7%, and 96.8%, respectively. Additionally, a comparison of the proposed framework with contemporary methods is done to demonstrate the increase in accuracy.
Research on Multi-Blockchain Electronic Archives Sharing Model

The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access control contract are designed; Secondly, the access object credit evaluation algorithm based on particle swarm optimization radial basis function (PSO-RBF) neural network is used to dynamically generate the access node credit threshold combined with the access policy, so as to realize the intelligent access right management method. Finally, combined with the above models and algorithms, the workflow of electronic archives sharing and utilization model of multi blockchain is constructed. The experimental results show that the time-consuming of the process increases linearly with the number of continuous access to electronic archives blocks, and the secure access control of sharing and utilization is feasible, secure and effective.
Hardware and Software Optimizations for Capsule Networks

Among advanced Deep Neural Network models, Capsule Networks (CapsNets) have shown high learning and generalization capabilities for advanced tasks. Their capability to learn hierarchical information of features makes them appealing in many applications. However, their compute-intensive nature poses several challenges for their deployment on resource-constrained devices. This chapter provides an optimization flow at the software and at the hardware level for improving the energy efficiency of the CapsNets’ execution.