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Lexicon-matched Word Injection for Chinese NER 22

Lexicon-matched Word Injection for Chinese NER

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proceedings of the 2022 5th international conference on machine learning and machine Intelligence

作者： Qingyu Wang Guokai Sun Jianguo Sun Yuan Zhuang Lu Li Tianyi Gao School of Computer Science and Technology Harbin Engineering University China and Wuhan Sanjiang Aerospace Network Communication Co. LTD China School of Computer Science and Technology Harbin Engineering University China Cyberspace Security Lab Xidian University China Network Security Department Defence Industry Secrecy Examination and Certification Center China Network Security Technology Application Center Wuhan Sanjiang Aerospace Network Communication Co. LTD China

ISBN: (纸本)9781450397551

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have been proved to be effective for exploiting abundant word boundary information, it is difficult to model global semantic interactions due to the inherent one-way sequential nature of the DAG structure. Meanwhile, more interfering lexicon words have been introduced leading to word boundary *** address the above issues, this paper proposes a knowledge fusion method based on lexicon matching word injection, which captures sentence context features through a pre-trained learning model and then injects lexicon knowledge into each character. With the power of Transformer and well-designed encoding, it becomes easier to obtain accurate word information using the character encoding vector of transformer encoder model. Besides, the self-attention module integrating characters with different words is fully leveraged to improve recognition accuracy. Experiments on three Chinese public datasets show that the proposed method outperformed other lexicon-based methods in performance and efficiency.

关键词： Chinese named entity recognition Word injection Multi-headed self-attention

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Functionality Recognition on Binary Code with Neural Representation learning 4

Functionality Recognition on Binary Code with Neural Represe...

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4th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Tian, Zhenzhou Li, Jinrui Xue, Peng Tian, Jie Mao, Hengchao Huang, Yaqian Xi'an University of Posts and Telecommunications School of Computer Science and Technology China

ISBN: (纸本)9781450384087

the functionality recognition of binary code has important application value in malware analysis, software forensics, binary code similarity analysis and other applications. Most of the existing methods are based on source code or machine learning strategies to carry out program similarity analysis, and this similarity analysis is also applied to a pair of programs, there are limitations in detection accuracy and quantity. Inspired by the recent great success of neural networks and representation learning in various program analysis tasks, We propose NPFI to analyze the binary code of the program and identify its functionality from the perspective of assembly instruction sequence. To evaluate the performance of NPFI, we built a large dataset consisting of 39,000 programs from six different categories collected from Google Code Jam. A large number of experiments show that the accuracy of NPFI in binary code function recognition can reach 95.8%, which is much better than the existing methods. © 2021 ACM.

关键词： Binary codes

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application of ViT Models with Hyper Attention Mechanism on Skin Cancer Classification 5

Application of ViT Models with Hyper Attention Mechanism on ...

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5th international conference on Signal Processing and machine learning, CONF SPML 2025

作者： Li, Mohan Huang, Xiaolin Cai, Wanyun Zhao, Haoqi Sun, Xiaofei University of North Carolina at Chapel Hill School of Art and Science Chapel Hill27514 United States University of Birmingham School of Geography Earth and Environmental Sciences BirminghamB15 2TT United Kingdom Hubei University of Technology Detroit Green Technology Institute WuHan430068 China Tongji University School of Computer Science and Technology Shanghai200092 China University of British Columbia Vancouver School of Economics VancouverV6T 1L4 Canada

ISBN: (纸本)9781837243235

the ViT model has excellent performance in image classification tasks and has been applied in several fields. However, ViT is difficult to be retrained and applied under limited hardware conditions and data due to the large number of parameters and high computational complexity of self-attention. To solve this problem, this paper introduces an embedded hyper-attention layer to replace the original self-attention layer in skin cancer image classification, aiming to improve computational efficiency by reducing the parameters. In practice, the parameters of the Densenet121 ViT model are greatly reduced with comparable accuracy. Our study shows the potential for improving model classification efficiency in hardware-constrained environments, making it possible to efficiently train and apply ViT models in such situations. © the Institution of Engineering & Technology 2025.

关键词： Image classification

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Functionality Recognition on Binary Code with Neural Representation learning 21

Functionality Recognition on Binary Code with Neural Represe...

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proceedings of the 2021 4th international conference on Artificial Intelligence and Pattern Recognition

作者： Zhenzhou Tian Jinrui Li Peng Xue Jie Tian Hengchao Mao Yaqian Huang Xi'an University of Posts and Telecommunications School of Computer Science and Technology China

ISBN: (纸本)9781450384087

关键词： Binary code Neural network Program functionality identification

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A Sustainable and Secure Cloud resource provisioning system in Industrial Internet of things (IIoT) based on Image Encryption 22

A Sustainable and Secure Cloud resource provisioning system ...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： C.T Dora Pravina Mrunalini Upendra Buradkar Md Khalid Jamal Ashish Tiwari Udit Mamodiya Dinesh Goyal Mathematics Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology India Electronics & Telecommunication Engineering St. Vincent Pallotti College of Engineering & Technology India Computer Application Integral University Lucknow India Computer science and engineering Amity University Lucknow campus India PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology India CSE Poornima Institute of Engineering and Technology India

ISBN: (纸本)9781450399937

An organization can be created through the Industrial Internet of things (IIoT), which can transform an existing bound structure. the Industrial Internet of things (IIoT) and its remote organization requirements for current sensors are very productive. the IIoT's introduced sensors monitor the conditions of contemporary machines and devices. Silicon Cloud demonstrated the advantages of modern distributed computing advancements for intelligent and planning applications. As a result, constant quality and security have emerged as the primary concerns in the IIoT. this presents several distinct and consistently increasing wagers on the advanced system. the research deals with the aim of cutting-edge handheld GPS devices. In a similar vein, recent region-based organizations make use of regional data. there is enough fragile data in the region data provided to these area-based expert communities to warrant overemphasis. As a result, experts are working on finding a solution for safe regional data. Most of the investigation frameworks that were suggested were designed to make them believe that the research objectives are fulfilled rather than precise headings of the client's region.

关键词： Information Internet of things (IoT) Cloud applications Searching Methodology Optimization

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4th international conference on Intelligent Human Systems Integration, IHSI 2021

4th International Conference on Intelligent Human Systems In...

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4th international conference on Intelligent Human Systems Integration, IHSI 2021

ISBN: (纸本)9783030680169

the proceedings contain 134 papers. the special focus in this conference is on Intelligent Human Systems Integration. the topics include: the Openness of Open Innovation in the Product Development Process;Mouse Tracking IAT in Customer Research: An Investigation of Users’ Implicit Attitudes Towards Social Networks;uncanny Valley in 3D Emoji: Investigating User Perceptions of Realistic Representations;blended Design Strategies to Plan Again the New Territorial Networking and Its Interconnections;smart Retail and Phygital Customer Journey in the Kids and Toys Stores;Co-design of Gesture-Based Arabic Sign Language (ArSL) Recognition;Developing an application for Walking in Nature for Post COVID-19;A Study on the Attitude and Practice of Chinese Teachers and Students Towards VR Teaching for Foreign Language learning;Lean Manufacturing for Production Management and Strategic Planning to Increase Productivity in SMEs Engaged in Manufacturing Chemicals;service Oriented Software Architecture for Vehicle Diagnostics;implementation of Lean Manufacturing Tools to Improve the Flow of Productivity in a Craft Shoe Workshop;gestalt Prototyping Framework - Evaluation Tool;social Media Happiness Expression through the Virtual Reality: Cultural Differences on Instagram;flexible Manufacturing Systems: A Methods Engineering and Operations Management Approach;Lean Manufacturing Production Management Model Under a Change Management Approach to Enhance Production Efficiency of Textile and Clothing SMEs;supporting Collective Intelligence of Human-machine Teams in Decision-Making Scenarios;calculation of the Level of Service in the Gondola in Supermarkets;using Drones for Tourism: Exploring Exciting Places in Ecuador;visual Perception Based on Gestalt theory;design of a Ventilation System for the Uchucchacua-Buenaventura Oyón-Lima Mine.

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Explainable Convolutional Neural Network with Facial Emotion Enabled Music Recommender System 22

Explainable Convolutional Neural Network with Facial Emotion...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Udendhran R Mridul Garg Abhishek Kumar Yadav Computational Intelligence SRM Institute of Technology India

ISBN: (纸本)9781450399937

In the modern world, there is a lot of stress and exhaustion, which increases hopelessness and poor health. Medical research shows that listening to music that matches one's mood can be uplifting. A music listener finds it very difficult to choose what music to listen to from the vast and remarkable array of selections that are currently available. According on the user's mood, there have been a number of suggestions for the applications that are available for these issues, such as music, dining, and shopping. One industry where there is a significant opportunity to provide customers a wide range of options based on their preferences and other data is music. It is well understood that humans use face expressions to express what they want to say and the context in which they want to say. face is one of the most important organs in bodies. the emotions of an individual can be approximated to a certain degree of precision by using certain traits discernible on the face. With the development of technology, it is now possible to use a camera to recover recognizable facial traits as inputs. Songs from a personalized playlist are played while the mood is being determined using the data collected. More than 50% users feel that at some point, the number of tracks in their library is so huge that they were unable to play the song which they really desired to play. this removes the time-consuming process of manually choosing music or changing playlists and enables the building of a playlist that is suitable for the individual based on their emotional state or mood. In order to develop an automated playlist generation approach that uses emotion recognition to recommend track, a number of algorithms will be examined. the application is built in a way that will allow user to play tracks based on his facial expression.

关键词： CCS Concepts •Computing methodologies machine learning algorithms Feature selection

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Deep learning Dermoscopy: Unveiling CNN-SVM Synergy in Skin Lesion Detection

Deep Learning Dermoscopy: Unveiling CNN-SVM Synergy in Skin ...

引用

Intelligent Technologies (CONIT), international conference on

作者： Ankita Suryavanshi Shiva Mehta Ayushi Jain Siddhant thapliyal Shanmugasundaram Hariharan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the created algorithm was assessed rigorously, employing numerous performance metrics that were calculated per the categories of skin lesion detection. the precision for each class ranged from 75.00% to 87.85%, indicating that the majority of the model’s positive predictions were events that did indeed occur. To manage Class 5 to be the best in precision compared to other levels. the recall metrics, which means the announced level of the model’s accuracy in searching for all examples from each class, has a performance range of 72.84% to 84.81%, with Class 2 having the greatest recall. the F1-Score, which includes accuracy and recall, corresponds to a percentage that ranges from 73.90% to 83.00%. Further, the study applied various types of averages in order to establish how the model would apply to different classes. the model workout was done on the wise 79.30%, 79.40%, and 79.23% micro-average for accuracy, recall, and F1-score. Results thus attest that an equal performance in all the classes was achieved without the effect of any class imbalance. the weighted mean, giving similar consideration to the occurrence of real cases in each class, generated accuracy, recall, and F1 scores of 79.48%, 79.24%, and 79.24%, respectively, having a small better bent for classes having greater prevalence. Finally, the Micro-average is demonstrated as it is made up of contributions of all classes, and it shows consistent performance across all those Groups, having accuracy, recalling, and F1-Score all equal to 79.24%. As a result of this research, AI skin lesion identification systems are exposed to the application of CNN-SVM models that can perfectly manage medical diagnostics in future, and the shorter the adaptions and the further model simplification, the higher the relevance of performance evaluation options.

关键词： Support vector machines Performance evaluation Adaptation models Accuracy Neural networks Predictive models Prediction algorithms

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A Hybrid Deep learning Model for Breast Cancer Detection and Classification 23

A Hybrid Deep Learning Model for Breast Cancer Detection and...

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proceedings of the 2023 13th international conference on Communication and Network Security

作者： Shanshan Tu Wenlong Li Xin Ai Hongchen Li Qingqing Yue Sadaqat Ur Rehman Department of Informatics Beijing University of Technology China Department of Informatics University of Salford UK

ISBN: (纸本)9798400707964

One of the main areas of study in diagnostic radiology and medical imaging is computer-aided diagnosis (CAD). In reality, a significant number of CAD systems have been used to help doctors identify breast tumours early on mammograms. Medical image analysis algorithms are helpful in providing a better and more accurate comprehension of medical images as well as in boosting the dependability of medical images in the healthcare system because therapy and illness diagnosis are so crucial in medical imaging. Leveraging advanced machine learning techniques has become an invaluable tool for healthcare professionals, enhancing early detection and personalizing treatment ***, in this work, we began by mentioning several cutting-edge techniques for detecting breast cancer using a deep learning methodology. the primary drawback of current research is that existing models only concentrate on identifying or detecting benign or malignant tumours, rather than specific types of tumours such adenosis, phyllodes tumour, or lobular carcinoma. We used a number of lightweight deep learning models, like ShuffleNet, to create a flexible model. [11]Additionally, in order to achieve recognition, we create the Resnet50 classical CNN model based on transfer *** incorporating transfer learning, the model can effectively use pre-trained networks to enhance its learning capability, potentially yielding better performance than training from *** of lightweight deep learning models, like ShuffleNet, to create a flexible model. [4] A new multi-label breast cancer tissues classification model that combines the benefits of Resnet and the Attention mechanism is also taken into *** innovative approach is designed to focus on specific regions of interest within the images, which can potentially lead to a higher accuracy in detecting subtler signs of various tumour types.

关键词： Attention Mechanism Breast cancer ResNet detection

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Test Suites Generation using UML Modelling and Heuristic Techniques: A Systematic Study 22

Test Suites Generation using UML Modelling and Heuristic Tec...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Kirti Gupta Parul Goyal Computer Application and Information Technology Shri Guru Ram Rai University India

ISBN: (纸本)9781450399937

Software testing is not only crucial, but also a difficult stage in the system of software development life cycle. During the software testing phase, debuggers create test suites as input for the system being tested in order to evaluate the system's events. An eager role is played by creating test suites for identifying errors and enhancing the prominence of the product. this paper expands on a thorough analysis of generation of test suites processes used in many situations, including implementation of test suites utilizing a single UML diagram, a combinational UML diagram, and meta-heuristic methods. Together with this analysis, a selection of eminence literature from journals, symposia, workshops, and conferences is utilized. Issues with software testing have been identified by looking at the literature study that is based on methods used to develop test suites, coverage requirements, data foundation, testing level, and produced components. Deficits are acknowledged based on systematic study, and future efforts are advised. through the use of inclusion-exclusion criteria, newly proposed coverage criteria, and test suite generating approaches, writers were able to identify the limitations of the work that was provided after examining the deliberative literature. the findings examined 74 significant studies in opposition to 6 research issues. By responding to the carefully considered study questions, the purpose has been satisfied. Following a review of the literature, it can be said that all kinds of generation techniques are frequently used in a range of testing scenarios, but that the period from 2010 to 2022 witnessed the greatest use of all techniques.

关键词： Meta-Heuristic Algorithms

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