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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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Growth of High-Quality InP Nanowires with PN Junctions Using an Improved Chemical Vapor Deposition

Growth of High-Quality InP Nanowires with PN Junctions Using...

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2024 International Conference on Next Generation Electronics and Photonics, INGEP 2024

作者： Yao, Xinliang Liu, Xingpeng Yuan, Kui Su, Jiande Song, Shuxiang Liu, Linsheng Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips Key Laboratory of Integrated Circuits and Microsystems Education Department of Guangxi Zhuang Autonomous Region School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin541004 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

Nanowires grown by traditional chemical vapor deposition (CVD) systems often suffer from low crystal quality and difficulties in realizing complex device structures. To address these issues, we propose an improved CVD system with a source switching unit to enhance the controllability of source materials. By combining reverse gas flow and high-temperature degassing processes, high-quality InP nanowires with pn junctions were successfully grown. Scanning electron microscopy and photoluminescence spectroscopy revealed that the as-grown nanowires exhibit smooth surfaces, no significant defects, and high crystal quality. Furthermore, a single InP nanowire with a pn junction demonstrated rectifying characteristics and fast photoswitch response (less than 14.2 ms). These results suggest that the improved CVD method provides a new approach for low-cost fabrication of high-quality nanodevices. © Published under licence by IOP Publishing Ltd.

关键词： Nanowires

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

intelligent Transportation Systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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A Hf0.5Zr0.5O2 Non-Volatile Capacitor Based on SrTiO3 Substrate 3

A Hf0.5Zr0.5O2 Non-Volatile Capacitor Based on SrTiO3 Substr...

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3rd International Conference on New Materials, Machinery, and Vehicle Engineering, NMMVE 2024

作者： Liu, Xingpeng Wei, Chunshu Tang, Huiping Peng, Yiming Jiang, Chunsheng Aurora Technologies Co. Ltd. Guangzhou511453 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China

ISBN: (纸本)9781510686267

The rapid of development of the Internet of Things (Iot), 5G communications, and smart technologies placing higher demands on storage-integrated hardware devices. The discovery of HfO2-based ferroelectric materials has opened up a new avenue for achieving low-power logic and high-density non-volatile memory by utilizing this silicon-tolerant ferroelectric material. In this work, a 10 nm Hf0.5Zr0.5O2 (HZO) ferroelectric films ware prepared on SrTiO3 (STO) substrate using an atomic layer deposition system (ALD). After rapid annealing treatment, the surface roughness of HZO films showed nanometer level (Ra∼1.033 nm, Rq∼1.321 nm). HZO ferroelectric films with molybdenum (Mo) electrodes exhibit low coercive field (Ec∼1.48 MV/cm) and excellent ferroelectric property (2Pr∼56.143 μC/cm2, and 2Pmax∼ 81.070 μC/cm2). Under the fatigue signal of 100 kHz, the device exhibits stable anti-fatigue characteristics. Mo/HZO/Mo/STO devices exhibit fast response time (1ms) and stable polarization retention characteristics (over 103 s). This work introduces a novel method for the next-generation non-volatile memory solutions (NVM). © 2024 SPIE.

关键词： Ferroelectric films

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Generative Negative Sample Enhancement Based Few-Shot Named Entity Recognition 11th

Generative Negative Sample Enhancement Based Few-Shot Named ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Duan, Zhen Xiao, Shenghua Chen, Jie Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei 230601 China

ISBN: (纸本)9783031927409

Few-shot Named Entity Recognition (Few-shot NER) aims to identify and classify unseen named entity types with a limited labeled samples. In recent years, large language models have achieved remarkable results in various NLP tasks due to their excellent zero/few-shot generalization capabilities. Recent research has extensively focused on using large language models to study the named entity recognition task. Negative sample entities have been demonstrated to be effective when performing this task using in-context learning paradigm. However, the problem of low efficiency and poor quality in manually constructing negative sample entities has been exposed. To address this issue, in this paper, we propose a Few-shot NER method based on generative negative sample enhancement (GNSE). Specifically, GNSE first utilizes a large language model to pre-identify potential entity spans in the support set text paragraphs in a zero-shot manner and generate the semantics of the spans based on the paragraph context. Then, the pre-identified candidate spans are compared with the ground-truth labels to determine positive or negative sample entity spans. Finally, based on Chain-of-Thought, the span semantics, positive and negative sample entities, and ground-truth labels are inserted into the example template for the in-context learning paradigm, and the test text from the query set is concatenated to construct a complete prompt. GNSE was evaluated on three widely used datasets: CoNLL’03, WNUT’17 and GUM. Experiments show that GNSE has achieved better performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Few-shot Named Entity Recognition In-context Learning Large Language Model Prompt Engineering

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Ingredient-Guided RGB-D Fusion Network for Nutritional Assessment

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 156-166页

作者： Feng, Zhihui Xiong, Hao Min, Weiqing Hou, Sujuan Duan, Huichuan Liu, Zhonghua Jiang, Shuqiang Shandong Normal University School of Information Science and Enginering Jinan250358 China Macquarie University Centre for Health Informatics Macquarie ParkNSW2109 Australia Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang Ocean University School of Information Engineering Zhoushan316022 China

The nutritional value of agricultural products is an important indicator for evaluating their quality, which directly affects people's dietary choices and overall well-being. Nutritional assessment studies provide a scientific basis for the production, processing, and marketing of food by analyzing the nutrients they contain. Traditional methods often struggle with suboptimal accuracy and can be time consuming, as well as a shortage of professionals. The progress in artificial intelligence has revolutionized dietary health by offering more accessible methods for food nutritional assessment using vision-based approaches. However, existing vision-based methods using RGB images often face challenges due to varying lighting conditions, impacting the accuracy of nutritional assessment. An alternative is the RGB-D fusion method, which combines RGB images and depth maps. Yet, these methods typically rely on simple fusion techniques that do not ensure precise assessment. Additionally, current vision-based methods struggle to detect small components like oils and sugars on food surfaces, crucial for determining ingredient information and ensuring accurate nutritional assessment. In this pursuit, we propose a novel ingredient-guided RGB-D fusion network that integrates RGB images with depth maps and enables more reliable nutritional assessment guided by ingredient information. Specifically, the multifrequency bimodality fusion module is designed to leverage the correlation between the RGB image and the depth map within the frequency domain. Furthermore, the progressive-fusion module and ingredient-guided module leverage ingredient information to explore the potential correlation between ingredients and nutrients, thereby enhancing the guidance for nutritional assessment learning. We evaluate our approach on a variety of ablation settings on Nutrition5k, where it consistently outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Medicinal chemistry

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KF-TMIAF: An Efficient key-Frames-Based Temporal Modeling of Images and AU Features structure for Masked Facial Expression Recognition

KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gu, Huijie Wang, Hanpu He, Shiwei Li, Mengyan Zhou, Jianmeng Chen, Tong Southwest University College of Electronic and Information Engineering Chongqing400715 China Swu Chongqing Key Laboratory of Generic Technology and System of Service Robots China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China

ISBN: (纸本)9798350386226

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one's genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results. © 2024 IEEE.

关键词： Convolutional neural networks

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Beyond MLE: Convex Learning for Text Generation 37

Beyond MLE: Convex Learning for Text Generation

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37th Conference on Neural information Processing Systems, NeurIPS 2023

作者： Shao, Chenze Ma, Zhengrui Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

ISBN: (纸本)9781713899921

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at https://***/ictnlp/Convex-Learning. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Maximum likelihood estimation

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User-Level Large Scale Load Forecasting Based on GPU 9

User-Level Large Scale Load Forecasting Based on GPU

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9th Annual International Conference on Network and information Systems for Computers, ICNISC 2023

作者： Qi, Jinshan Wang, Jiangtao Liang, Xun Xu, Zhiming Huaiyin Normal University Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis School of Computer Science and Technology Huai'an China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350306682

Load forecasting is an essential part in power system management and planning. In recent years, more and more researchers started to focus on the behavior of terminal customers, trying to improve the accuracy of load forecasting. This paper proposes to use the neuron network model to predict each user's load first, and then aggregate all the forecasting results to get higher-level prediction. In order to speed up the process, we also design a model based on CUDA. We conducted several experiments on real-world data (160 thousand consumers' load profiles) and the results confirm the effectiveness and efficiency of our method. © 2023 IEEE.

关键词： Graphics processing unit

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