Reviewerly/exHarmony huggingface dataset:
Improving Chinese Named Entity Recognition by Glyph and Part-of-Speech

Chinese characters are a unique type of morpheme-syllabic characters that are integral to the ideographic writing system used in the Chinese language. The most widely used word formation method in Chinese is the formation of compound words from roots according to syntactic relationships, which is basically the same as the syntax of combining words into phrases. However, the appropriate use of glyph information and lexical information in natural language processing tasks is still limited. To solve above problem, this paper proposes a novel approach, named GP-NER, to enhance Chinese character representation by glyph features and inject word information with part-of-speech information. Specifically, GP-NER uses a gate mechanism to enhance the representation of characters by combining glyph features, and then uses part-of-speech information to enhance the contextual representation of text sequences through attention mechanisms. Experimental results demonstrate the effectiveness of the proposed model.
Adaptive Contour Noise Generation Network for Semi-supervised Medical Data Segmentation

When training convolutional neural networks, noise distribution in data can affect segmentation accuracy. Therefore, many neural networks will strengthen the segmentation ability of the model by artificially adding noise. However, because the noise distribution of the input data is not known, the model training process can only generate random noise, e.g., gaussian noise, but cannot simulate real-world noise. In the medical imaging domain, more attention is paid to the image contours, which can also be used for noising simulation. Meanwhile, considering the existence of rich unlabeled data sets in medical images, this paper proposes a semi-supervised framework for uncertainty estimation by using Monte Carlo Dropout and generates adaptive noise using the uncertainty map. Specifically, the framework consists of three parts, including a noise generation model, a teacher model, and a student model. The noise generation part processes the data input to the other two parts and uses the contour feature identified by the posteriori uncertainty map to simulate priori noise to guide the process of training segmentation model. Meanwhile, the student model updates its parameters based on the teacher model output and the ground truth annotations by minimizing a joint loss. Segmentation experiments on left atrium 3D data show that our method has competitive results with other SOTA semi-supervised methods.
Imbalanced Multi-class Classification Based on Bi-Criterion Evolution Sampling and Ensemble Learning

Imbalanced dataset is a common phenomenon in real word applications. However, most of current classification approaches usually assume that the dataset is balanced, which results in undesirable classification performance, especially in multi-class classification. Here, we propose an approach based on bi-criterion evolution sampling and ensemble learning (BCEC) for imbalanced multi-class classification. Specifically, BCEC employs an evolution algorithm based on both Pareto criterion and non-Pareto criterion to undersample from each majority class for building balanced datasets. Then, BCEC separately characterizes each balanced dataset by a multi-layer perceptron and utilizes the ensemble learning based on Bagging strategy to integrate the classification results. Extensive experiments over UCI datasets demonstrate that our approach significantly outperforms several state-of-the-art imbalanced classification methods in terms of G-means.
Leveraging the Invariant Side of Dynamic Trichomonas Vaginalis via the Fusion of Optical Flow

Trichomoniasis is a common sexually transmitted disease caused by Trichomonas vaginalis and automatic trichomonas vaginalis (TV) detection is a problem of great concern in video object detection. However, existing algorithms are inadequate to identify and localize TV through the microscopic camera efficiently; the defocus, motion blur, resolution and computational efficiency, remain the major problems. To bridge the gap, we propose to learn the invariant side of the dynamic TV by capturing the optical flow. To make use of the motion information, we introduce OF-YOLO, a general-purpose framework for catching hold of the motion feature. We test it on a dataset with 1278 Trichomonas video clips including 51336 frames. Experiment results show how the OF-YOLO significantly boosts the detection performance on real-world scenes.
Blockchain-based Regulatory Framework for Logistics Management

Logistics transportation have developed rapidly in recent years. As technology advances, the goods that can be transported by logistics are becoming more and more expensive. Besides, due to the increasing demand for transportation, complex logistics management has become a problem. In order to avoid huge economic losses caused by management problems, we proposed a blockchain-based regulatory framework for logistics management. With this framework, the logistics transmission process becomes transparent and well regulated. The key information of the whole logistics transportation process will be recorded on the chain, including key transportation nodes, cargo status, operators, etc. Besides, we further use these on-chain information for intelligent supervision. The logistics information is encoded into a sequence vector, and deep learning is used for automated supervision to reduces the difficulty of administrator’s work. If there is an abnormal state of an item or a strange shipping route, it will cause an alarm. The framework includes three modules, an information registration module, a data encoding module, and a data monitoring module. We implement these modules based on Hyperledger Fabric. The experiments on real-world datasets show that our framework is efficient.
P4AI Application Based on Expert System to Evaluate Appropriate Profession

P4AI is a platform that supports quality improvement in the field of education which functions to provide special references for students based on industry 4.0. This platform is a form of transforming choices based on big data and local wisdom in the 4.0 era. This study aims to build a Profile of Measurement of Educational and Professional Interest in Era 4.0 in the Indonesian context through an Artificial Intelligence based mobile application abbreviated as P4AI. The Artificial Intelligence used in the development of this mobile application is an expert system where the system uses instruments obtained through gathering information from various experts so that through these instruments, the system can determine the type of user intelligence that is the most powerful based on the answers from user test work.
A Novel Spark-Based Algorithm for Mining Frequent Utility Patterns

We are now living in the era of big data thanks to technological innovation. It is very important to discover valuable information in massive data. High utility itemset mining and frequent itemset mining are commonly used techniques in data mining, but they only consider a single factor, ignoring some frequent and efficient itemsets. So as to mine more valuable information, a method for mining skyline frequent utility patterns is proposed. Facing multi-dimensional and large-scale datasets, how to quickly find skyline points is a new challenge. In this article, a parallel algorithm is proposed within the framework of Spark, using Transformations and Actions operators to mine skyline patterns. Through the analysis of experimental data, it is proved that the proposed method has high computing efficiency and high search efficiency, and can effectively mine skyline frequent utility itemsets.
Overcoming Performance Inequity in Quantum Dynamics Framework for Optimization of Symmetric and Asymmetric Double-Well Function

In recent years, a study focused on the quantum dynamics framework (QDF) process has revealed significant, random structural bias in QDF’s optimization of symmetric and asymmetric forms of double-well functions (DWF). Such bias means that the algorithm tends to favor specific solutions in the search space while ignoring other possible solutions. This tendency may cause the algorithm to become trapped in local optima, preventing it from finding global optima and ultimately compromising its performance. To address this performance gap, this paper proposes a dimension-decoupling sampling mechanism and random-neighborhood sampling mechanism. The proposed mechanism effectively reduces the coupling between dimensions during the optimization of the objective function and increases the search space diversity, thereby mitigating the structural bias of the algorithm. It successfully solves the issue of performance degradation observed in the optimization of symmetric double-well functions. We refer to the modified algorithm as the dimension-decoupling and random-neighborhood quantum dynamics framework (DR-QDF).
Contextual Entity Linking via Knowledge Representation Learning and Co-attention

The goal of entity linking (EL) is to automatically map mentions in natural language to corresponding entities in the knowledge graph (KG). Nonetheless, EL is replete with challenges due to the natural language’s diversity and ambiguity. Most advanced EL models do not fully utilize the valuable KG structure information for denoising context to disambiguate. This paper proposes an EL model, KRL-CoAtt, that encodes and integrates the KG structure information into the EL process for disambiguation based on knowledge representation learning (KRL), and introduces co-attention mechanism to filter the context information by obtaining weight coefficients for context words and triplets, improving local similarity calculation precision. KRL-CoAtt acquires EL results by adopting loopy belief propagation (LBP) to combine local similarity and global coherence. Extensive experimental results demonstrate the superiority and effectiveness of KRL-CoAtt compared with other state-of-the-art methods.
Spelling Check for Handwritten Homework and Exams

Word spelling is a fundamental skill at the center of K-12 English education. While there is a lot of academic research and commercial software on handwriting recognition, little effort has been made to recognize misspelled words, which is an important step toward AI grading. One significant challenge is the infinite possibility of wrong spelling but extremely limited training data compared to correct words. We observe that the current popular OCR algorithms such as CRNN and CNN-Ngram suffer prediction shift phenomenon. To this end, we propose a spelling checking framework consisting of self-supervised pre-training, supervised fine-tuning, and post-processing. To address the long-tailed balancing problem, we propose a novel data generation and a novel dataloader. Experimental results on the misspelling dataset demonstrate the effectiveness of the proposed framework, and the generated and augmented data both contribute to improving the performance.
Route Planning of Helicopter Mountain Fire Rescue Based on High Altitude Environment

Helicopters are often used for emergency rescue. This paper mainly solves the problem of emergency rescue of helicopters in high plateau geographical environment. Combined with the flight characteristics of helicopters that can hover in a small range and take off and land vertically, this paper uses $\mathrm{A}^{\star}$ search algorithm (A-Star) to substitute the path planning under the real data of Liangshan GIS. Firstly, the spatial grid of the mountain terrain map data is divided, and then the helicopter rescue flight path is constructed using MATLAB simulation software and the obstacle avoidance function is well realized. Liangshan Yi Autonomous Prefecture, which has a high plateau environment and a fire prone area, is selected as the research object for case analysis. The results show that the helicopter can achieve emergency rescue and real-time path planning in high plateau terrain, which has high application value after experimental demonstration.
An Improved Algorithm of Mining Frequent Item Sets Based on Pruning

It is an important problem in association rules to effectively mine the relationship between transaction items. For data sets with different characteristics, the same algorithm may have different performances. Aiming at sparse item set mining, this paper proposes an improved mining algorithm based on an H-struct structure. Based on the original structure, a pruning improvement is proposed to reduce the complexity of the search process. The experiment shows that our algorithm is feasible and practical.
GLexicon: Glyph and Lexicon-based Embedding Model for Chinese NER

Recently, many works have proved to be effective for Chinese named recognition (NER) by incorporating the word lexicons. However, previous works fuse lexicon information and ignore two important Chinese language characteristics: glyph and pinyin, which carry significant syntax and semantics information for sequence tagging tasks. This paper proposes GLexicon, which incorporates both lexicon features and Chinese language features (glyph and pinyin) into the character-based NER model. GLexicon entirely leverages the lexicon information and deeply fuses Chinese glyph and pinyin features compared with existing methods. Specifically, we first design the embedding scheme to preserve the lexicon-matching results as many as possible in character representations. Then, we deeply fuse the Chinese glyph and pinyin information into a lexicon-based model to enhance the expressiveness of the Chinese NER task. The experimental results on four Chinese public NER datasets show that GLexicon can achieve state-of-the-art performance.
An Automated On-Bed Rehabilitation System Design Based on Pressure Sensory Data Analysis

On-bed physical exercise is significant and necessary during rehabilitation process for patients with motor disorders. An automated, self-managed on-bed rehabilitation system is in demand. Such a system can be achieved and monitored with pressure mapping and posture classification. In this paper we present an automated physical rehabilitation system developed based on pressure mapping and advanced sensory data mining for bed ridden patients to conduct self-rehabilitation. The system is developed with three modules: 1) sensing module; 2) posture recognition module and 3) evaluation ampersand feedback module. Sensing module provides raw data and generates pressure map; Posture recognition module processes pressure data and recognizes postures; Evaluation ampersand feedback module is designed to provide condition evaluations and rehabilitation recommendations. Such a design can automatically provide instructions and guidance for next step exercise, without the involvement of physical therapist. The experimental results have demonstrated the effectiveness of this system. It could serve as a prototype of automated self- rehabilitation system.
Intelligent Analysis and Application of Judicial Big Data Sharing Based on Blockchain

In the era of continuous development of big data, the application of data sharing in the judicial field is still in its infancy, which improves the work efficiency to a certain extent, but also exposes the problems of mutual trust between departments, data security, personal privacy and so on. Based on the outstanding characteristics of regional chain technology such as decentralization and immutability, blockchain has been widely used in this field, and it has also been used as an important means to promote judicial modernization, which solves these problems well. This paper focuses on the use of regional chains to solve the problems arising in the process of judicial big data sharing, and proposes to use high-value judicial evidence chains to obtain storage methods, devices and readable storage media on the basis of blockchain to achieve intelligent data sharing.
Enhancing Scene Text Recognition by Strengthening Attention Alignment

Scene text recognition (STR) refers to recognizing text instance images from natural scenes as text. STR has been a hot area of computer vision because it has various applications. The state-of-the-art methods are under the attention-based encoder-decoder framework. However, most of the attention-based methods usually suffer from the problem of attention drift in many situations. We found that a significant cause of attention drift is that the extracted visual features are insufficient. To alleviate this problem, we propose an attention-tuned model, which strengthens attention alignment by learning better visual features. We first adopt deformable convolution to extract good visual features. Next, a stacked self-attention architecture with intermediate supervision is responsible for refining the representation of visual features. Moreover, spatial position encoding is used to guide attention alignment operation. Extensive experiments on standard benchmarks demonstrate that the proposed model achieves state-of-the-art performance for regular and irregular scene text recognition.
Semi-Supervised Semantic Segmentation with Scope Consistency Constraint in TRUS Image

In full supervised learning, the number of pixel-level labels for learning is small, and the complexity of neural network learning objectives is complex. A cross-label training framework based on Scope Consistency Constraint (SCC) is proposed for this challenge. Our framework mainly establishes task-level consistency through the outputs of two backbone networks and provides each other with pseudo labels for each other to learn. In addition, our framework introduces weak labels into the learning process of the segmentation network, significantly reducing the labeling cost. The experiment shows that the segmentation accuracy is better than the original segmentation network framework with the same amount of data. In addition, the semi-label data is introduced. The framework presented in this paper is not limited to a specific segmentation framework.
Model for Handwritten Mathematical Expression Recognition Based on ResNet and Attention

Handwritten mathematical expression recognition is still a challenging problem due to its two-dimensional structure and the complexity of handwriting. Handwritten Mathematical Expression Recognition (HMER) methods based on encoder-decoder has achieved good results, but there are still some problems in the existing model, such as feature loss caused by multiple pooling of convolution neural network in coding operation and low efficiency of parsing feature caused by inaccurate attention in decoding operation. In view of the above problems, this paper focuses on offline handwritten mathematical expression recognition, and adopts the encoder-decoder model, It is proposed to use the residual network (ResNet) as the encoder for position coding, combine the gated recurrent unit (GRU) of attention mechanism as the decoder for effective decoding analysis, accurately carry out structure analysis and character recognition, and finally realize the end-to-end operation of input pictures and output LaTex strings. The proposed model was tested on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) dataset, the experimental results show that this method can achieve relatively good recognition effect.
A Few-shot Classification Method with Coordinate Attention and Cross Attention

Deep learning tries to learn embedding functions from a large amount of data. A lot of work shows that deep learning has surpassed humans in areas such as image classification and image segmentation. Few-shot learning focuses on learning from unknown classes with limited labels, and the data scarcity of few-shot learning is destined to be a very challenging task. Our work studies the role of the attention mechanism in few-shot learning. In the pre-training process of the two-stage few-shot learning, this study introduces the coordinate attention mechanism to improve the ability of the model to focus on key areas. In the subsequent meta-training process, this study applies the cross-attention module to the support set embedding and the query set embedding, which generates cross-attention maps for each pair of class features and query sample features, alleviating the problem that the Transformer focuses more on the support set features during the meta-training process. Our work has significantly improved the accuracy rate based on the original works and is close to the existing state-of-the-art performance on the general few-shot classification dataset miniImageNet and tieredImageNet.
A Method of Sentiment Classification for Chinese Aspect Level Based on Information Fusion

In this paper, we present an enhanced approach to Aspect-Based Sentiment Classification (ABSC), a vital subtask of Aspect-Based Sentiment Analysis (ABSA) that focuses on identifying the sentiment polarity (positive, neutral, or negative) towards specific aspects within textual data, such as product reviews or customer feedback. This fine-grained analysis holds significant value for businesses and organizations, as it enables them to gain deeper insights into customer opinions and preferences. Although recent research has shown that fine-tuning pre-trained models (PTMs) is more effective for addressing ABSC challenges compared to previous syntax-based methods, there remains room for improvement. We propose a novel model that fine-tunes PTMs by concurrently considering context information, opinion words information, and prompt information, effectively capturing the intricate relationships between aspects and their corresponding sentiment expressions. This comprehensive approach allows for a more accurate classification of sentiment polarity for each aspect. We evaluate our model on several Chinese datasets, covering various domains such as hotels, cars, and phones, demonstrating its robustness and adaptability. On these Chinese datasets, our proposed model outperforms the previous state-of-the-art (SOTA) models in terms of accuracy. Additionally, we present an in-depth analysis of our model’s performance, discussing its strengths and limitations, and provide suggestions for future research directions in the field of ABSC.
Cross-chain Supervision Mechanism of Distributed Notaries for Consortium Blockchain

In recent years, consortium blockchain has played a significant role in fields such as logistics traceability and data transactions. However, due to the large differences in the underlying implementations of consortium blockchains, it is difficult to establish effective data sharing between different chains. Cross-chain technology is an important means to break the data isolation status of consortium blockchains, but the existing cross-chain technologies cannot meet the requirements of cross-chain supervision of consortium blockchain. There is no clear authority to divide cross-chain requests and inter-chain interoperability, and the contract calls between chains may lead to malicious tampering with ledger data during the cross-chain process, making them unsuitable for cross-chain supervision scenarios. In view of the above problems, this paper propose a cross-chain supervision mechanism of distributed notaries for consortium blockchain. The mechanism selects high-credit nodes from each consortium blockchain to serve as notaries and forms a notary chain as a medium for cross-chain sharing. By grading the identities of access chains, the security of cross-chain operations is ensured, the scalability of cross-chain is realized by modular design, and the communication message protocol designed for cross-chain supervision ensures the security of the supervision process. Finally, we compare and analyze our proposed solution with existing mechanisms and verify the practicability of the proposed scheme through simulation experiments.