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Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective

Cross-domain NER with Generated Task-Oriented Knowledge: An ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Zhihao Lee, Sophia Yat Mei Wu, Junshuang Zhang, Dong Li, Shoushan Cambria, Erik Zhou, Guodong School of Computer Science & Technology NLP Lab Soochow University China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong Beijing Jinghang Research Institute of Computing and Communication China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP), enabling learning from source to target domains with limited data. Previous studies often rely on manually collected entity-relevant sentences from the web or attempt to bridge the gap between tokens and entity labels across domains. These approaches are time-consuming and inefficient, as these data are often weakly correlated with the target task and require extensive pre-training. To address these issues, we propose automatically generating task-oriented knowledge (GTOK) using large language models (LLMs), focusing on the reasoning process of entity extraction. Then, we employ task-oriented pre-training (TOPT) to facilitate domain adaptation. Additionally, current cross-domain NER methods often lack explicit explanations for their effectiveness. Therefore, we introduce the concept of information density to better evaluate the model's effectiveness before performing entity recognition. We conduct systematic experiments and analyses to demonstrate the effectiveness of our proposed approach and the validity of using information density for model evaluation. © 2024 Association for Computational Linguistics.

关键词： Domain Knowledge

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A Conceptual Evaluation of the data as Partner (DAP) Framework 9

A Conceptual Evaluation of the Data as Partner (DAP) Framewo...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Hassan, Sonia Sobnath, Drishty Snell, Esther Isiaq, Olufemi Solent University Department of Science and Engineering Southampton United Kingdom Creative Computing Institute Universiy of Arts Department of Computer and Data Science and Ai United Kingdom

ISBN: (纸本)9798350397758

This paper explores the innovative data As Partner (DAP) philosophy as a transformative approach to organisational data strategy, drawing upon established concepts of information value, technology acceptance, and inclusivity for people with disabilities (PwD) in smart cities. The study conducted a comprehensive systematic review, after meticulous screening, 20 eligible papers were selected, forming the foundation for deriving the key components of the DAP framework. Subsequently, a qualitative focus group study engaged 18 participants from diverse sectors to evaluate the practical applicability of the DAP conceptual framework. Thematic analysis of the focus group sessions revealed five key themes: practical value, pragmatic performance, ethical perspective, clarity of the framework, and enhancement tools. These findings offer valuable insights for refining the DAP framework and optimising its integration into real-world scenarios, in empowering individuals in smart cities, especially those with disabilities. Future research should explore larger and more diverse samples to generalise findings and assess the framework's long-term impact through longitudinal evaluations. By adopting user-centred methodologies, the DAP framework can be further enhanced as a strategic tool, empowering data to actively participate in organisational decision-making processes. © 2023 IEEE.

关键词： Smart city

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Historical Test-time Prompt Tuning for Vision Foundation Models 38

Historical Test-time Prompt Tuning for Vision Foundation Mod...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Jingyi Huang, Jiaxing Zhang, Xiaoqin Shao, Ling Lu, Shijian College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University of Technology China UCAS-Terminus AI Lab University of Chinese Academy of Sciences China

Test-time prompt tuning, which learns prompts online with unlabelled test samples during the inference stage, has demonstrated great potential by learning effective prompts on-the-fly without requiring any task-specific annotations. However, its performance often degrades clearly along the tuning process when the prompts are continuously updated with the test data flow, and the degradation becomes more severe when the domain of test samples changes continuously. We propose HisTPT, a Historical Test-time Prompt Tuning technique that memorizes the useful knowledge of the learnt test samples and enables robust test-time prompt tuning with the memorized knowledge. HisTPT introduces three types of knowledge banks, namely, local knowledge bank, hard-sample knowledge bank, and global knowledge bank, each of which works with different mechanisms for effective knowledge memorization and test-time prompt optimization. In addition, HisTPT features an adaptive knowledge retrieval mechanism that regularizes the prediction of each test sample by adaptively retrieving the memorized knowledge. Extensive experiments show that HisTPT achieves superior prompt tuning performance consistently while handling different visual recognition tasks (e.g., image classification, semantic segmentation, and object detection) and test samples from continuously changing domains. © 2024 Neural information processing systems foundation. All rights reserved.

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Multitask learning for Arabic Dialects Identification and Machine Translation

Multitask learning for Arabic Dialects Identification and Ma...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Dhleima, Mohamed Tourad, Mohamedou Cheikh Telmoud, Cheikh Abdelkader Ahmed Abdelmounaim, Abdali Nanne, Mohamedade Farouk Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania Research Team Control and Informatics for Intelligent Systems and Green Energy FST cadi Ayyad Marrakech Morocco

ISBN: (纸本)9783031714283

Multitask learning (MTL) is a popular approach in natural language processing (NLP) to improve the performance of related functions. This paper presents a comprehensive approach to address dialect identification and translation challenges in Arabic NLP. The proposed model utilizes state-of-the-art transformer architectures for both tasks, ensuring robust performance in capturing contextual dependencies and linguistic nuances inherent in Arabic dialects. The primary objectives are to accurately identify Arabic dialects and facilitate their translation to Modern Standard Arabic (MSA) using a shared representation learning approach. The model architecture consists of a shared embedding layer followed by a transformer encoder for each task, employing multi-head self-attention mechanisms for hierarchical contextual information extraction. The model’s performance is evaluated using the F1-score for dialect classification, and the BLEU-score for translation performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source Computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Automatic Stress Recognition System with Deep Learning using Multimodal Psychological data

Automatic Stress Recognition System with Deep Learning using...

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2022 International Conference on Electronic Systems and Intelligent computing, ICESIC 2022

作者： Praveenkumar., S. Karthick, T. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665483858

There are numerous applications for detecting stress states from physiological inputs. Human stress detection may be used to enhance the human experience as well as monitor and prevent stress-related disorders. Previous research has leveraged existing machine learning techniques for automated stress identification. However, there are also some limitations. Deep learning allows you to discover basic patterns in your body's reactions that are often difficult to notice. This study offers a convolutional network-based approach for detecting human stress utilizing multimodal data sets obtained by smart sensors for physiological and motion, which can aid humans in avoiding health issues caused by stress. Sensor information like triaxial accelerates, heartbeat, body temperature, respiration, electromyographic (EMG) data for physiological states stressed and non-stressed, are included in the WESAD data collection. The use of Long short - term memory method and the DNN method to detect stress in people in real-time is investigated in this study. Our research is based on their physical reactions. The study's findings provide an assessment of both models' ability to predict real-time users' mental expressions based on physiological parameter fluctuation. © 2022 IEEE.

关键词： Deep neural networks

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain Systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Assessing the feasibility of Blockchain Technology in Automotive Industry 9

Assessing the feasibility of Blockchain Technology in Automo...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2022

作者： Balachander, S. Murugan, A. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Blockchain is a decentralised, distributed public ledger that has recently garnered enormous traction. The automotive industry, one of the most profitable markets worldwide, is undoubtedly responsible for the transformations in modern civilisation over the past century. This paper primarily focuses on the issue of the complexity of existing technologies in the field and the possibility of applying Blockchain technology as a replacement in the automotive sector. In addition, the researcher has considered compatible precedents for identifying lacunae in the existing technologies. As a result, the adoption of Blockchain technology provides access to a wide range of potential automotive applications that can develop modern business models moreover even transform the established automotive partaking economy. Further, after examining the advantages, limitations, opportunities, and risks, some suggestions are made to guide enterprises and researchers in future advancements in the cyber-resilient automotive sector. © 2022 IEEE.

关键词： Blockchain

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STB-GraCapsNet: A Novel Capsule Network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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