In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classific...
In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.
作者：
Jiawei SunJia HaoXiaoning ZhangSchool of Mechanical Engineering
Beijing Institute of Technology China School of Mechanical Engineering
Beijing Institute of Technology China Yangtze Delta Region Academy Beijing Institute of Technology China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application?Ministry of Industry and Information Technology Beijing Institute of Technology China and Tianjin Key Laboratory of Space Environment Simulation Technology China
Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problem...
ISBN:
(纸本)9798400709449
Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. Thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. The cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components.
Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operat...
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As the study of graph pattern matching (GPM) attracts more and more scholars, the research of GPM in the medical field begins to emerge, from protein structure analysis to breast cancer classification diagnosis, and G...
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This study aimed to explore the relationship between physiological performance indicators and both intrinsic and extrinsic cognitive loads during a space robot grasping task, which involves teleoperation requiring hig...
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This study aimed to explore the relationship between physiological performance indicators and both intrinsic and extrinsic cognitive loads during a space robot grasping task, which involves teleoperation requiring high levels of spatial and motor coordination, attention, and decision-making. The hypothesis posited that operational mode would influence extrinsic cognitive load, while task difficulty would impact intrinsic cognitive load. Additionally, a correlation was expected between physiological performance indicators and cognitive load. To test this, a 2x2 factorial experiment was conducted with independent variables being task difficulty (high vs. low) and operational mode (joystick vs. keys), and dependent variables including cognitive load, performance, and learning outcomes. Subjective scales were used to assess intrinsic and extrinsic cognitive load, and physiological data were collected to examine their correlation with cognitive load. The results indicate that operational mode and task difficulty significantly affect cognitive load in space exploration contexts. Specifically, the 'Joystick' mode was associated with higher extrinsic cognitive load, likely due to the challenges it presents in precise control during complex tasks. Performance metrics showed that attention allocation varied by operational mode, with the 'Joystick' mode requiring greater focus on the side view and end-effector, in line with cognitive load theory. Physiological metrics, particularly pupil diameter and EEG data, were crucial in understanding these effects;a decrease in pupil diameter with increased task difficulty reflected heightened intrinsic cognitive load. EEG data further revealed significant variations in brain activity across frontal, prefrontal, and occipital regions, with distinct changes in alpha, theta, and beta frequency bands, highlighting the substantial impact of cognitive load on neural processes. These findings underscore the importance of integrating cognitive
Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the late...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.
Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus...
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With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the informa...
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Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment ...
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Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.
This paper presents a Scientific Literature Management Platform (SLMP, demo link 1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literatu...
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ISBN:
(数字)9798331508821
ISBN:
(纸本)9798331508838
This paper presents a Scientific Literature Management Platform (SLMP, demo link 1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM 2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge.
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