Reviewerly/exHarmony huggingface dataset:
Semantic Role Labeling for Japanese Using Span-Based Models

We propose a span-based model for Japanese Semantic Role Labeling (SRL) with deep neural networks. Most previous studies of semantic role labeling have been conducted in English, however, it is not clear that previously proposed models are effective in Japanese. Besides, most of the previous studies relating to Japanese SRL focus on the dependency-based SRL task. Thus, in this paper, we apply span-based models to a Japanese corpus NPCMJ-PT which is annotated with semantic role labels and has about 52,500 entries. The semantic roles are defined with 32 types of semantic role labels such as Arg0, Arg1, and ArgM-LOC that are similar to PropBank. In the span-based models, there are two primary modeling approaches. One is a model that estimates a semantic role label for each span, and the other is a model that estimates a span for each label. Thus, we apply the two types of span-based models to a Japanese SRL task. The experimental results demonstrate that the model, which estimates a span for each semantic role label, achieved the highest F1 score of 77.54, in comparison to the other model.
Multiscale Models for Real-Time Human Pose Estimation

Although the lightweight OpenPose uses the depth-wise separable convolution to form the backbone to improve the computing efficiency of the model on the CPU, the lightweight OpenPose also has the following shortcomings. (1) The model has weak feature processing ability. (2) The model cannot identify multi-scale features well. In order to solve the above problems, a multi-scale model is proposed. We adjusted the network structure of the lightweight OpenPose model with reference to the network structure of RES2NET. The modified model not only recognizes multi-scale features but also has strong feature processing capabilities. The AP value of our model on the COCO2017 validation set is 0.412. The AP value of Multiscale Models is 0.037 higher than lightweight OpenPose on the COCO2017 validation set. Our future direction is to discover the influence of the model's ability to extract multi-scale features on the model AP value.
Text Recognition Based on Weakly Supervised Learning

Aiming at the problems of irregular text region and fuzzy text in picture, this paper proposes a text recognition method based on weakly supervised learning. The method is based on explicit rectify module, vision module, language module and fusion module. The vision module corrects the irregular text region through the correction module; the vision module extracts features and recognizes them through the convolution neural network and the location attention mechanism, and outputs the predicted strings; the language module learns sequence information through the attention mechanism and corrects the predicted strings by the vision module; finally, the output results of the vision module and the language module are combined according to the weight in the fusion module. Get the final prediction. The language module in this method prevents the direct interference of image blur and enhances the accuracy of text recognition, experiments on several common data sets demonstrate the effectiveness of the proposed method.
GF2DA: Gaussian Filter Fourier Domain Adaptation

Deep learning has significantly advanced in computer vision, but the performance of these well-trained models often degrades in cross-domain situations due to domain differences. To solve this challenge, adversarial learning is widely utilized in domain adaptation to reduce the differences between domains and explore domain-invariant information. However, as a result of the instability associated with adversarial learning and the neglect of domain-specific information, it often falls into the sub-optimal solution. In this paper, we propose a set of straightforward and powerful methods which do not use adversarial learning to reduce the performance degradation that occurs in cross-domain tasks. Specifically, we first propose GF2DA to reduce the distribution gap by shifting the source to the target. We also provide a method called KCW, which re-weights the training loss by calculating the class distribution of the inputted images, to solve the class imbalance of both domains. Compared with the adversarial-based DA methods, our methods are straightforward but powerful without additional feature-level operations. We provide a baseline for adversarial-free domain adaptive semantic segmentation. Extensive experiments prove that our methods can perform similarly or even better than adversarial-based methods on cross-domain semantic segmentation.
BSDIB: Balanced Self-Distillation Information Bottleneck for Long-Tailed Visual Recognition

Real-world data are usually long-tailed, which results in poor performance of visual recognition systems. Current prevailing methods decouple the training procedure into representation learning and classification. However, the two stages in such methods are independent, resulting in sub-optimal solutions. In this paper, we propose a one-stage method, namely balanced self-distillation information bottleneck (BSDIB), to take care of both representation learning and classification simultaneously. Specifically, a re-weighting strategy is used to obtain an unbiased classifier, and the class-dependent self-distillation information bottleneck is utilized to promote data representations. Extensive experiments on three commonly-used long-tailed datasets indicate that BSDIB achieves SOTA performance and the visualization results show that our approach focuses more on the target object.
On Cognitive Level Classification of Assessment-items Using Pre-trained BERT-based Model

Outcome based education (OBE) is gaining popularity nowadays due to its effectiveness in preparing learners for their future roles as active participants. Bloom's taxonomy is a well-known educational model as it helps implement the OBE. It offers teachers a tool to encourage progressive thinking and intellectual development in students. Bloom's Taxonomy helps categorize cognitive skills and learning objectives, while the cognitive level classification of assessment items determines the complexity needed to complete the assessment. By using both the cognitive level classification and Bloom's Taxonomy, educators can create assessments that accurately measure students' abilities and target specific learning outcomes. This paper proposes a model that can accurately categorize assessment items according to their cognitive complexity. The paper utilizes the cognitive domains of Bloom's Taxonomy and focuses specifically on the first four levels: remembering, understanding, applying, and analysing. Some previous attempts have been made in this field but, to our best knowledge, this is the first time the Bidirectional architecture of Deep Learning (DL) algorithms and BERT-based Transformer model have been used. By comparing several machine learning (ML) algorithms, DL algorithms with Bidirectional layers and pre-trained BERT-based Transformer model, it has been found that the BERT model scores the highest among all the other ML and DL algorithms. The performance is evaluated based on accuracy, precision, recall, and F1-score. The BERT-based Transformer model has an accuracy of 89%, where some of the other algorithms performed considerably well (BiLSTM: 84%, LSTM: 83%, BiGRU: 83%, RF: 83%). Also, compared to the state-of-the-art model, the transformer model scored higher. This suggests that the model can be used to deploy as the classification model. The developed model will help educators with the exact assessment items to implement OBE. Overall, this research contributes to the field of NLP and educational assessment by demonstrating the potential of ML and DL approaches in accurately classifying assessment items.
Optimization-Based Trajectory Planning for Autonomous Driving at Mining Area with Irregular Boundary

Abstract. Autonomous driving technology has been widely used in mining areas, and how to get a safe, smooth and easy-to-track path has gradually become the focus of research. Currently, most planners are oriented to structured roads and perform poorly in unstructured road scenarios. Due to the irregular and variable boundary geometry of the mining scene, the commonly used solution is to decouple the trajectory planning problem into path planning and the speed planning problem. In other words, the planning module first performs path planning and then finds the corresponding speed profile as the final result, which creates a path tracking task for the control module. This decoupling method may lose the optimality of the trajectory due to the problem of the matching degree between the path solution and the speed solution. In this paper, we present a trajectory planning method based on the idea of optimal control problem (OCP). Firstly, the hybrid A* method is used to obtain rough paths and convert them into rough trajectories based on the assumption of constant speed in the mining area. Then, an optimal control problem (OCP) is established by taking the state quantity and control quantity of the vehicle as the optimization objective, and it is discretized into a nonlinear problem (NLP). In order to solve the problem efficiently, the vehicle kinematic constraints are considered as the penalty term in the objective function. The non-convex collision avoidance constraints are transformed into convex terms by constructing a corridor. At the same time, a soft collision avoidance constraint is set as a penalty term to avoid the loss of path smoothness due to too conservative hard constraints. Finally, the proposed method is validated in two typical mining scenarios: loading and road driving areas. The results show that the proposed method enables finding a trajectory within the safety margin, and considerably improves the curvature and its rate.
Temporal Knowledge Graph Reasoning Method Based on Multi-Dimensional Entity Encoding

Temporal knowledge graph (TKG) is a knowledge graph sequence composed of multiple relational events under different time stamps. The purpose of TKG reasoning is to predict possible future events according to historical knowledge graph. The crucial to this task is to mine and explore the evolution law of TKG from historical sequence. To tackle the problems regarding the incomplete entity information extraction and lack of effective decoding of multi-dimensional information in the existing research, a reasoning model of TKG based on multi-dimensional entity encoding is proposed. multi-dimensional entity coding aims to introduce three entity feature coding, including historical transient graph coding, historical evolution pattern coding, and entity segment feature coding. In addition, to decode multi-dimensional encoding information efficiently, we introduce multi-dimensional perceptual convolutional neural network (CNN) to learn coding patterns of different dimensions. Experiments show that the model in this paper is superior to existing methods in entity prediction tasks.
Data-to-text Generation with Data Control and Multi-loss Fusion

The goal of data-to-text generation is to generate fluent and fidelity natural language based on input data. At present, most models focus on data planning. Generating text according to the planning can effectively improve the fidelity to the generated text, but they lack attention to the fluency of the generated text, resulting in poor readability of the generated text. In order to address this issue, we propose a data-to-text generation model based on a pre-training language model. A control module is cleverly designed inside the language model to improve the fidelity to generated text. In training stage, multiple loss functions are cleverly designed to optimize the fluency and fidelity of generated text. Finally, the experimental results of RotoWire datasets show that the strong prior knowledge based on the Generative Pre-Training 2.0 (GPT2) can effectively improve the fluency of the generated text, and the proposed model in this paper can generate and control the fluency and fidelity of the generated text simultaneously.
A Robust Text Recognition System for Noisy Document Images

The widespread application of optical character recognition (OCR) has solved the text detection and recognition problems in the field of document images. However, OCR encounters many interference factors in the context of big data, which causes the single OCR recognition rate to decrease. In this paper, we combine deep learning with image preprocessing to investigate noisy document images. Specifically, we implement an OCR system that can deal with photo angle tilt, uneven lighting, text tilt, and seals or fingerprints on paper. Experimental results show that our system can reach an average recognition rate of 98.30% and successfully reconstruct the document structure after recognition. Code: https://github.com/biyanqing/Chinese-OCR.
An Improved WPA Based on RHC Method and 2D-equal-step Path Generation Method is Proposed for Solving the Single UAV Online Path Planning Problem in Dynamic Mission Environment

Based on the premises of transparent posture and dynamic mission environment, the mathematical modeling of the single Unmanned Aerial Vehicle (UAV) online path planning problem is carried out. Then, Receding Horizon Control method (RHC) and 2D-equal-step path generation methods are briefly introduced, which are combined with the Improved Wolf Pack Algorithm (IWPA) to solve the single UAV online path planning problem modeled in the previous. The simulation results show that the new algorithm can be used to solve the single UAV online path planning problem aiming at moving targets in dynamic mission environment, and the performance of the improved wolf pack algorithm is more powerful than the original wolf pack algorithm in this process of application.
Exploring the Impact of Lexicon-based Knowledge Transfer for Hate Speech Detection in Indonesia Code-Mixed Languages

In this study, our objective is to examine the influence of external knowledge from a lexicon on knowledge transfer for mitigating the language shift issue in the detection of hate speech in code-mixed languages. To accomplish this, we constructed a lexicon based on findings from previous studies. Subsequently, we implemented several machine learning models with various scenarios to assess the impact of the lexicon. The experimental results demonstrate that incorporating lexicon features leads to improved performance in detecting hate speech within code-mixed languages. Particularly, utilizing a lexicon that encompasses both implicit and explicit lexicons yields the most favorable outcomes in this investigation. This research provides valuable insights into understanding the detection of hate speech in code-mixed Indonesian languages and contributes to the advancement of more robust systems aimed at fostering a safer and more inclusive online environment. By leveraging the lexicon and exploring the interplay between implicit and explicit elements in hate speech, this study enhances our understanding of addressing hate speech challenges in Indonesia code-mixed languages and paves the way for developing more effective detection mechanisms.
A New Scheme for Digital Watermarking of Color Image Based on DCT and (2, 2) Visual Cryptography

Digital watermarking is a very effective technology in digital copyright protection. To improve the security and robustness of digital watermarking technology, a new color image digital watermarking scheme based on visual cryptography and DCT is proposed. Firstly, the watermark is preprocessed by the (2, 2) visual cryptography, in which pixel expansion is 2, and two shared images are obtained, and one of them is used as the watermark information to be embedded in the carrier image. Secondly, the carrier image is divided into 8×8 blocks, then discrete cosine transform is performed, and then the watermark information is embedded in the medium frequency coefficients of the DCT domain by the additive embedding method according to a certain principle. When verifying copyright information, just stack and reduce the extracted image with another shared image, without requiring a lot of calculation and complex cryptography knowledge. Experiments show that this scheme is not only secretive, the watermark information cannot be detected, it can meet the needs of copyright protection, but also has high security and strong robustness. The watermark information cannot be restored by any attack method that does not meet the recovery conditions; It can against many common attacks such as white noise, Gaussian noise, salt and pepper noise, speckle noise, median filter, Gaussian low-pass filter, JPEG compression and so on. Therefore the proposed scheme is effective, and the overall performance is good.
Multi-Perspective Semantic Enhanced Model for Heterogeneous Graph Embedding with Contrastive Learning

Heterogeneous information network embedding aims to learn node representations from a low-dimensional space while preserving the network's complex structural and semantic information. Recently, inspired by the successful application of contrastive learning in computer vision, graph contrastive learning additive embedding of heterogeneous information networks has also entered the vision of researchers. The current means of graph expansion somewhat lead to changes in the underlying semantics. Hence, contrast schemes that can adequately capture rich and lossless semantics are still in the exploration stage. Given this, we propose a new Multi-perspective Semantic-enhanced Contrastive Learning framework for heterogeneous information network embedding (MSCL). Specifically, we design the contrast goals separately from global and local perspectives. First, we capture complementary semantic information for heterogeneous graphs using two sub-structures, network schema and meta-path. Then, considering that different meta-paths also represent different higher-order semantics from each other, we specifically design contrast targets within the meta-path perspective to learn the independence and semantic consistency between them. In addition, we propose a positive sampling strategy to improve the confidence level of the positive sample set in both semantic and structural dimensions. Extensive experiments on three real-world networks show that the proposed approach outperforms state-of-the-art methods.
A Comprehensive Summary on Category of Web Usage Mining

The World Wide Web has many distinctive characteristics that make it fascinating and challenging for web mining tasks. There is an immense amount of knowledge accessible online and a fertile field for study in data mining. In this paper, the researcher surveyed the research in web usage mining from different opinions as explained in their study. For the survey, the researcher focused on web usage mining categories as defined by different researchers. And concluded that web usage mining aims to discover the paradigm that underlies the web's relation structures. Web usage mining focuses on techniques, which, while the user interacts with the web, may predict behavior.
Siamese Network-based Framework for Open-set Domain Generalization

Deep learning has made great progress in many fields, such as computer vision and natural language processing. But the performance of traditional deep learning models will be seriously degraded when facing the domain shift, which means that the distribution of test data and training data is significantly different. A large number of Domain Generalization (DG) methods have been proposed to enhance the generalizability of models. However, traditional DG methods are based on the assumption that the category space of training data and test data is consistent, which is always untenable in practice. Therefore, this paper further studies the open-set domain generalization problem when the category spaces of training data and test data are inconsistent. This paper proposes an open-set domain generalization framework based on the Siamese network, which generates images in the unknown categories through patch-shuffling, and treats generated images as negative samples to negatively supervise models. Thus models are forced to learn the critical feature representations, the over-fitting of models reduces, and then the performance of models on open-set domain generalization tasks is enhanced. The experimental results show that the proposed framework achieves state-of-the-art on the two open-set domain generalization benchmarks.
Design of Virtual Reality Interactive Product Software Based on Artificial Intelligence

Abstract. Virtual reality (VR) technology is a new type of practical technology that was developed in the 20th century. Virtual reality involves the use and synthesis of computers, computer information and simulation technology. It is based on the latest high-tech developments in the use of computer technology, 3D graphics technology, multimedia technology, simulation technology, imaging technology, servo technology and other equipment to create a realistic 3D visual, tactile, olfactory and other sensory experience Virtual worlds create for people who are in them a sense of immersion. With the continuous development of social productivity and science and technology, the demand for virtual reality technology is growing in all walks of life, and virtual reality technology has made great strides and is gradually becoming a new field of science and technology. This paper first introduces artificial intelligence (AI), VR and interactive products, and then proves the progressiveness of AI based VR interactive product software through experiments (the interface interaction deviation is about 0.1 in the xyz axis, far lower than other VR interactive products).
The Impact of Artificial Intelligence on Higher Art Education

This paper evaluates the artistic value of the works created by the artificial intelligence image generation model based on people's cognition of the images generated by the artificial intelligence image models and the data of the evaluation experiments. Analyze the characteristics of the current artificial intelligence image generation model and its help to higher art education, and on this basis, discusses the measures and countermeasures that college art education should take in the era of artificial intelligence, points out the impact of artificial intelligence on human art and the importance and necessity of making changes in higher art education.
Exploring Hierarchical Multi-Label Text Classification Models using Attention-Based Approaches for Vietnamese language

The Hierarchical Attention-based Recurrent Neural Network (HARNN) is a system designed to categorize documents efficiently, taking into account both the content of the texts and their hierarchical category structure. This system is comprised of three primary components: the Document Representation Layer (DRL), which is used for semantic encoding, the Hierarchical Attention-based Recurrent Layer (HARL), that models dependencies between different hierarchical levels, and the Hybrid Predicting Layer (HPL), which is responsible for accurate category predictions. In this research, we put HARNN to the test, using a dataset of Vietnamese articles from VnExpress. We then contrast the performance of four different word embeddings (Word2Vec, FastText, PhoBERT, and BERT multilingual). Additionally, we introduce a domain-based approach for the HARNN model to compare the accuracy with the original manner. Experimental findings indicate that HARNN performs effectively in the context of Vietnamese language and that our domain-based approach can be advantageous in specific domains HMTC task.
Adaptive Learning of User's Interests

Today, users are confronted with a constant overflow of information. Alternative concepts are urgently needed to tackle this problem. This paper presents a new adaptive learning algorithm and managing the interests of users. This method utilizes a fresh approach of reinforcement learning. Furthermore, a framework is constructed, and the initial outcomes of the experiments are presented and discussed. Lastly, an intelligent webcrawler with a user-friendly interface is introduced which can propose further literature based on a user model.