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Named Entity recognition via Noise Aware Training Mechanism with Data Filter

Named Entity Recognition via Noise Aware Training Mechanism ...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Huang, Xiusheng Chen, Yubo Wu, Shun Zhao, Jun Xie, Yuantao Sun, Weijian Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781954085541

Named entity recognition (NER) is a fundamental task in natural language processing, these is a long held belief that datasets benefit the model. However, not all the data help with generalization, and some samples may contain ambiguous entities or noisy labels. the existing methods can not distinguish hard samples from noisy samples well, and becomes particularly challenging in the case of overfitting. this paper proposes a new method called Noise-Aware-with-Filter (NAF) to solve the issues from two sides. From the perspective of the data, we design a Logit-Maximum-Difference (LMD) mechanism, which maximizes the diversity between different samples to help the model identify noisy samples. From the perspective of the model, we design an Incomplete-Trust (In-trust) loss function, which boosts LCRF with a robust Distrust-Cross-Entropy(DCE) term. Our proposed Intrust can effectively alleviate the overfitting caused by previous loss function. Experiments on six real-world Chinese and English NER datasets show that NAF outperforms the previous methods, and which obtained the state-of-the-art(SOTA) results on the CoNLL2003 and CoNLL++ datasets.

关键词： Natural language processing systems

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Proceedings - 2021 IEEE/ACM conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021

Proceedings - 2021 IEEE/ACM Conference on Connected Health: ...

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6th IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021

ISBN: (纸本)9781665439657

the proceedings contain 37 papers. the topics discussed include: machine learning prediction of TBI from mobility, gait and balance patterns;improve image-based skin cancer diagnosis with generative self-supervised learning;RT-ACL: identification of high-risk youth patients and their most significant risk factors to reduce anterior cruciate ligament reinjury risk;detection and analysis of interrupted behaviors by public policy interventions during COVID-19;information extraction from patient care reports for intelligent emergency medical services;high-confidence data programming for evaluating suppression of physiological alarms;EDA-based data stream pattern analysis and peak detection algorithm for substance users;and sensor-based human activity recognition for elderly in-patients with a Luong self-attention network.

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Deep Active Learning Image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 23

Deep Active Learning Image Classification Algorithm Based on...

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Proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Yuliang Pang Ying Liu Yu Hao Yanchao Gong Daxiang Li Zhijie Xu Center for Image and Information Processing Xi'an University of Posts and Telecommunications China Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing Xi'an University of Posts and Telecommunications China International Joint-Research Center for Wireless Communication and Information Processing University of Huddersfield China

ISBN: (纸本)9798400707674

the success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. this paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. this makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. this algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. the proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance.

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the 75th anniversary of Professor Vladimir Fursov 6

The 75th anniversary of Professor Vladimir Fursov

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6th international conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Sokolov, Vladimir Russian Acad Sci Samara Sci Ctr Samara Russia

ISBN: (纸本)9781728170411

the paper briefly tells about the life and scientific activity of Professor Vladimir A. Fursov. Professor Vladimir Fursov is the Head of the Department of Supercomputers and General Informatics of the Samara National Research University named after academician S.P. Korolev (Samara University) and the leading researcher of the Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS) - the branch of the Federal Scientific Research Center "Crystallography and Photonics" of the Russian Academy of Sciences. Professor Vladimir Fursov is a well-known specialist in the field of image processing and pattern recognition. the paper analyzes his recent contribution to the development of the theory of identification, methods of images processing and recognition and supercomputing technologies.

关键词： scientific activity supercomputers and general informatics image processing pattern recognition theory of identification

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6th international Symposium on Emerging Technologies for Education, SETE 2021

6th International Symposium on Emerging Technologies for Edu...

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6th international Symposium on Emerging Technologies for Education, SETE 2021

ISBN: (纸本)9783030928353

the proceedings contain 43 papers. the special focus in this conference is on Emerging Technologies for Education. the topics include: the Syntax and Semantics of Verbs of Searching;writing Collaboratively in the Continuation Task via Shared Docs;Reflections on Applying Innovative Project-Based Learning: Shadow Play in OBTL Classroom;Online Collision Avoidance Algorithm for Lightweight Web3D Robot Based on M-BVH;the Teaching Design of Sequence Limit Based on Modern Education Technology;Reducing EFL Learners’ Error of Sound Deletion with ASR-Based Peer Feedback;the Digital Competence of Vocational Education Teachers and of Learners with and Without Cognitive Disabilities;An Action Research of Using SAMR to Guide Blended Learning Adoption During Covid-19;a Parsing Scheme of Mind-Map Images;student Experiences on Using Process-Centric thesis Management Tool;Research on OBE Online Teaching Mode Combined with Expression recognition—Taking Digital Image Processing Course as an Example;a Fitness Education and Scoring System Based on 3D Human Body Reconstruction;threat Analysis of IoT Security Knowledge Graph Based on Confidence;Adversarial Training Leaded Robust MRC Method;deep Knowledge Tracking Based on Exercise Semantic Information;automated Analysis of Student Verbalizations in Online Learning Environments;a Model of Teachers’ Excellent Teaching Behaviors Based on Natural Language Processing;examining the Efficacy of Video-Based Multimodal three-Dimension Input on the Acquisition of English Phrases;synchronous Computer Mediated Communication in English Language Classes During the Pandemic: A Case Study of Wuhan;systematic Evaluation of Research Progress on Technology-Enhanced Language Learning: Content Analysis and Knowledge Mapping;chatbots for Learning: A Facebook Messenger ‘Bot’.

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Improving Design Quality of Software Using Machine Learning Techniques 6

Improving Design Quality of Software Using Machine Learning ...

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6th international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Prabha, C. Lakshmi Shivakumar, N. Thiagarajar Coll Engn Comp Sci & Engn Madurai Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. the JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. the datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.

关键词： Design patterns Design pattern mining Machine learning techniques Object-oriented metrics

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle

Enhancing Interoperability of Digital Twin in the Maintenanc...

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international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dedy Ariansyah Bens Pardamean Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. this paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. the results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management.

关键词： Ontologies Software systems Data models Enterprise resource planning Digital twins Prognostics and health management Interoperability

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Identification and Sorting of Waste using Artificial Intelligence Based on Convolutional Neural Network

Identification and Sorting of Waste using Artificial Intelli...

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international conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

作者： Fahmi Fahmi Baharsyah Pratama Lubis Department of Electrical Engineering Universitas Sumatera Utara Medan INDONESIA

ISBN: (纸本)9798350320725

Indonesia is one of the biggest producers of waste in the world. Every day there is rubbish that is not thrown in the trash, those that have been thrown in its place but are still wrong in distinguishing the type of garbage. In general, one of the types of waste is based on its nature, some are easy to decompose (organic), and some are difficult to decompose (inorganic). this research introduces five organic and inorganic waste objects by seeing whether the system can read objects at every position in the background and the number of objects in 1 picture frame. this system uses Tensorflow with a sample of 100 images for each object. Each sample object is given an identifier according to the object’s name and type. the sample is given an identifier for the system to learn to recognize the patterns and shapes of objects from the data objects we take. the results showed that the average percentage of system accuracy in detecting objects reached 90%, with the highest data accuracy reaching 99% tested on the banana peel, leaves, grass, cardboard, styrofoam, broken glass, bottles, cans, and nails. Furthermore, the authors try to put two or more different objects in the same frame and produce an average percentage value of 90%, as well as testing two or more similar objects in one frame simultaneously and producing an average value of 90%. From the test results, grass and bottle objects are samples that do not have read errors in the system design. For testing two or more objects in one frame, the author puts two similar objects and objects of different types. the result is that the system can recognize objects in the frame.

关键词： Shape Neural networks Nails Glass products pattern recognition Object recognition Convolutional neural networks

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

9th IAPR TC3 International Workshop on Artificial Neural Net...

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

ISBN: (纸本)9783030583088

the proceedings contain 24 papers. the special focus in this conference is on Artificial Neural Networks in pattern recognition. the topics include: Named entity disambiguation at scale;geometric attention for prediction of differential properties in 3d point clouds;preface;deep learning methods for image guidance in radiation therapy;how (not) to measure bias in face recognition networks;feature extraction: A time window analysis based on the x-ite pain database;pain intensity recognition - an analysis of short-time sequences in a real-world scenario;a deep learning approach for efficient registration of dual view mammography;deep transfer learning for texture classification in colorectal cancer histology;applications of generative adversarial networks to dermatologic imaging;typing plasmids with distributed sequence representation;kp-yolo: A modification of yolo algorithm for the keypoint-based detection of qr codes;using mask r-cnn for image-based wear classification of solid carbide milling and drilling tools;a hybrid deep learning approach for forecasting air temperature;intentional image similarity search;using cnns to optimize numerical simulations in geotechnical engineering;going for 2d or 3d? investigating various machine learning approaches for peach variety identification;a transfer learning end-to-end arabic text-to-speech (tts) deep architecture;ml-based trading models: An investigation during covid-19 pandemic crisis;innvestigate-gui - explaining neural networks through an interactive visualization tool;structured (de)composable representations trained with neural networks;long distance relationships without time travel: Boosting the performance of a sparse predictive autoencoder in sequence modeling.

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Samara scientific school of digital image processing 6

Samara scientific school of digital image processing

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6th international conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Zheltov, Sergey State Res Inst Aviat Syst GosNIIAS Moscow Russia

the paper briefly describes the establishment and development of the Samara scientific school of digital image processing. the examples of the fundamental results and solutions of applied problems are provided. the au... 详细信息

ISBN: (纸本)9781728170411

关键词： scientific school computer-aided image processing pattern recognition

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