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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

Journal of Computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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Internet of Things IoMT data Aggregation Using Machine Learning 2

Internet of Things IoMT Data Aggregation Using Machine Learn...

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2nd IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

作者： Pulabaigari, Lakshmi Sagar Sivakumar, C. School of Computing Mohan Babu University Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Mohan Babu University Dept. of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350372847

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available. © 2024 IEEE.

关键词： Internet of things

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AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception 24

AesExpert: Towards Multi-modality Foundation Model for Image...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Yipo Sheng, Xiangfei Yang, Zhichao Yuan, Quan Duan, Zhichao Chen, Pengfei Li, Leida Lin, Weisi Shi, Guangming School of Artificial Intelligence Xidian University Xi'an China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

The highly abstract nature of image aesthetics perception (IAP) poses a significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in MLLMs falling short of aesthetics perception capabilities. To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models. Specifically, to align MLLMs with human aesthetics perception, we construct a corpus-rich aesthetic critique database with 21,904 diverse-sourced images and 88K human natural language feedbacks, which are collected via progressive questions, ranging from coarse-grained aesthetic grades to fine-grained aesthetic descriptions. To ensure that MLLMs can handle diverse queries, we further prompt GPT to refine the aesthetic critiques and assemble the large-scale aesthetic instruction tuning dataset, i.e. AesMMIT, which consists of 409K multi-typed instructions to activate stronger aesthetic capabilities. Based on the AesMMIT database, we fine-tune the open-sourced general foundation models, achieving multi-modality Aesthetic Expert models, dubbed AesExpert. Extensive experiments demonstrate that the proposed AesExpert models deliver significantly better aesthetic perception performances than the state-of-the-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision. Project Page: https://***/aes-expert/. © 2024 ACM.

关键词： aesthetic critique

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Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization 38

Sharpness-Aware Minimization Activates the Interactive Teach...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Mingwei Cao, Xiaofeng Tsang, Ivor W. School of Artificial Intelligence Jilin University China Singapore College of Computing and Data Science Nanyang Technological University Singapore

Teaching is a potentially effective approach for understanding interactions among multiple intelligences. Previous explorations have convincingly shown that teaching presents additional opportunities for observation and demonstration within the learning model, such as data distillation and selection. However, the underlying optimization principles and convergence of interactive teaching lack theoretical analysis, and in this regard co-teaching serves as a notable prototype. In this paper, we discuss its role as a reduction of the larger loss landscape derived from Sharpness-Aware Minimization (SAM). Then, we classify it as an iterative parameter estimation process using Expectation-Maximization. The convergence of this typical interactive teaching is achieved by continuously optimizing a variational lower bound on the log marginal likelihood. This lower bound represents the expected value of the log posterior distribution of the latent variables under a scaled, factorized variational distribution. To further enhance interactive teaching's performance, we incorporate SAM's strong generalization information into interactive teaching, referred as Sharpness Reduction Interactive Teaching (SRIT). This integration can be viewed as a novel sequential optimization process. Finally, we validate the performance of our approach through multiple experiments. © 2024 Neural information processing systems foundation. All rights reserved.

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Graph Disentangled Collaborative Filtering based on Multi-order Similarity Constraint 10

Graph Disentangled Collaborative Filtering based on Multi-or...

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10th IEEE International Conference on data science and Advanced Analytics, DSAA 2023

作者： Liu, Yaoze Du, Junwei Li, Haojie Liu, Guanfeng Qingdao University of Science and Technology School of Data Science Qingdao China Macquarie University School of Computing North Ryde Australia

ISBN: (纸本)9798350345032

Disentangled collaborative filtering can explicitly generate embeddings based on users' interests and help improve the interpretability and robustness of recommendations. However, the existing disentangled graph collaborative filtering methods rely solely on direct interaction constraints between nodes to learn node embeddings, which cannot represent higher-order constraints between nodes and node-type differences, resulting in suboptimal node representations and negatively affecting recommendation performance. To address this problem, we propose a Multi-order Similarity Constraint Disentangled Graph Collaborative Filtering (DGCF-MSC) method, which considers not only direct interaction constraints between nodes but also designs a neighborhood enhancement mechanism based on high-order relationships between homogeneous nodes. We realize the disentanglement of heterogeneous type nodes in different feature spaces in a graph convolutional neural network to make the generated embedding more interpretable and improve the performance of graph collaborative filtering. We conduct extensive experiments with three recommendation system datasets and the results demonstrate that DGCF-MSC outperforms the existing disentangled graph collaborative filtering methods in all performance metrics. Our code is released on https://***/lustrelake/DGCF_MSC. © 2023 IEEE.

关键词： Collaborative filtering

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Vanessa : Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis

Vanessa : Visual Connotation and Aesthetic Attributes Unders...

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

作者： Xiao, Luwei Mao, Rui Zhang, Xulang He, Liang Cambria, Erik School of Computer Science and Technology East China Normal University Shanghai China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Prevailing research concentrates on superficial features or descriptions of images, revealing a significant gap in the systematic exploration of their connotative and aesthetic attributes. Furthermore, the use of cross-modal relation detection modules to eliminate noise from comprehensive image representations leads to the omission of subtle contextual information. We present Vanessa, a visual connotation and aesthetic Attributes understanding network for multimodal aspect-based sentiment analysis. It incorporates a multi-aesthetic attributes aggregation (MA3) module that models intra- and inter-dependencies among bi-modal representations as well as emotion-laden aesthetic attributes. Moreover, we devise a self-supervised contrastive learning framework to explore the pairwise relevance between images and text via the Gaussian distribution of their CLIP scores. By dynamically clustering and merging multimodal tokens, Vanessa effectively captures both implicit and explicit sentimental cues. Extensive experiments on two widely adopted benchmarks verify Vanessa's effectiveness. © 2024 Association for Computational Linguistics.

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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Skin Cancer Classification using CNN and Transfer Learning (TL) 7

Skin Cancer Classification using CNN and Transfer Learning (...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350359299

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Fraud Detection in Financial Transactions: A Machine Learning Approach 9

Fraud Detection in Financial Transactions: A Machine Learnin...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350365092

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector. © 2024 IEEE.

关键词： Logistic regression

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Unveiling the Digital Shadows: A Comprehensive Exploration of Network Forensics Methodologies 2

Unveiling the Digital Shadows: A Comprehensive Exploration o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Nandini, Dhanalakota Sai Jyothika Lanitha, B. Azath, H. Ganeshan, R. Ahmed, Sajjad VIT Bhopal University Kothrikalan School of Computing Science and Engineering Sehore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India VIT Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331534950

The Internet has experienced significant growth in attacks that affect network security features such as confidentiality, integrity, and availability. Network security and network forensics work together to ensure system integrity, availability, and confidentiality. Network security measures prevent unau-theorized access, attacks, and breaches, while network forensics investigates and analyses the aftermath of security incidents. Network forensics refers to the process of collecting, analyzing, and preserving digital evidence from network activity in order to investigate and respond to security incidents, breaches, or other malicious activities. It involves reconstructing the events to determine attack methods, compromised data, and potential perpetrators. It lies at the intersection of digital forensics, incident response and network security. This paper is going to analyse the methodologies which are used in network forensics and application of new technologies in each network forensics methodology through ten figures. This paper critically examines network forensics methodologies, focusing on the evolution of these methods and the integration of emerging technologies. It assesses new strategies and discusses inherent issues and limitations of existing techniques, emphasizing the need for cutting-edge techniques to enhance the effectiveness of network forensic processes. © 2024 IEEE.

关键词： Network intrusion

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