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2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Li, Haili Wang, Xiaodong Zhou, Yunyan Liu, Weijie Pan, Shilong College of Computer Science and Technology National University of Defense Technology Hunan Changsha China Unit 63893 of PLA Henan Luoyang China Unit 63891 of PLA Henan Luoyang China

ISBN: (纸本)9798400711237

Event extraction is a fundamental and complex task in information extraction, aiming at automatically identifying and extracting structured event information from unstructured text. However, the same word may have different meanings in different contexts, necessitating semantic disambiguation during the classification process. The polysemy of triggers presents a significant challenge to accurate EE. Existing surveys primarily focus on different domains, fields, or technical paradigms, but none provide a systematic summary of the semantic disambiguation techniques employed. This study classifies these techniques into three categories based on the underlying technical frameworks and offers a comprehensive overview of the most advanced methods in each category. Finally, we summarize the application scenarios, strengths, and limitations of existing approaches, and discuss potential directions for future research. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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advanced Spam Detection Techniques in Social Media: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches

Advanced Spam Detection Techniques in Social Media: A Compre...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Akshatha, P.S. Abbas, Syed Muqaddam Vijay, D.V. Suhas, B. Prasad, Ramesh D'Souza, Sonia New Horizon College of Engineering Department of Ai&ml Bengaluru India

ISBN: (纸本)9798331529833

The rapid growth of social media platforms has led to increased spam content, ranging from unwanted advertisements to harmful misinformation, negatively affecting user experience and platform security. Traditional spam detection methods often need to address the evolving complexity of spam tactics. This study explores advanced spam detection techniques using machine learning (ML) and deep learning (DL). We propose a structured framework involving pre-processing, feature extraction, and model training to enhance detection accuracy. We evaluate algorithms such as decision trees, ensemble models, and neural networks, examining their strengths and limitations in classifying spam. Our findings show that advanced ML and DL techniques outperform traditional methods, providing more efficient and accurate spam detection. This study offers valuable insights for improving spam detection systems, ensuring safer social media environments and paving the way for future research combating spam online. © 2025 IEEE.

关键词： Adversarial machine learning

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th international conference on advanced research in Computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and Technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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Spatiotemporal Attention Fusion Network for Multiple Object Tracking of Unmanned Aerial Vehicle 4

Spatiotemporal Attention Fusion Network for Multiple Object ...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Qin, Sinlin Jiang, Lei Zhang, Jianlin Liu, Dongxu Li, Meihui National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu610209 China Key Laboratory of Optical Engineering Chinese Academy of Sciences Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350108 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The task of multiple object tracking from the perspective of Unmanned Aerial Vehicle (UAV) is becoming increasingly important and has a wide range of applications. However, conventional multiple object trackers do not fully exploit temporal and spatial information, facing challenges such as target blurring and variable trajectories due to the high-speed motion of UAV. In this paper, we propose STAF(Spatiotemporal Attention Fusion Network), which is based on spatiotemporal multi-head attention and fully integrates information from video sequence frames, enhancing the detection capability of targets. To better handle the camera shake, we develop an appearance feature update algorithm based confidence. The proposed method has demonstrated improvements on the VisDrone2019 dataset. © 2025 SPIE.

关键词： Object tracking

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Few-Shot Φ-OTDR Event Identification Based on Attention Prototypical Network 4

Few-Shot Φ-OTDR Event Identification Based on Attention Pro...

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4th international Computational Imaging conference, CITA 2024

作者： Hu, Liqin Li, Yujiao Qin, Ying Yu, Kuanglu Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510688834

Phase-sensitive optical time-domain reflectometer (Φ-OTDR) is a distributed, highly sensitive optical sensing system with high localization accuracy, and it is widely used in the fields of intrusion identification, pipeline monitoring, and et al. With the rapid development of machine learning and deep learning, it has become a major trend to utilize these advanced methods for Φ-OTDR disturbance event identification. Although these methods can achieve better results when trained on large-scale datasets, in practice, the low appearing frequency of certain category of events and the difficulty in data collection lead to a lack of training samples. In addition, labeling the collected samples is a time-consuming and labor-intensive task. In order to solve all the above problems and improve the classification performance of Φ-OTDR disturbance events with limited samples, we propose a method based on an attention prototypical network. Under the setting of 3-way 3-shot, i.e. only 3 samples per category, our method achieves a remarkable accuracy of 93.02%, demonstrating high validity and reliability in the few-shot disturbance event classification task. The experimental results show that our method has the potential to be applied in distributed fiber optic sensor systems, providing reliable technical support for practical engineering applications. © 2025 SPIE.

关键词： Reflectometers

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research on the Application of Intelligent Analysis System in the Promotion of Evaluation Indicators for the Development of New Rural Cultural Industries - Analysis of the Promotion Path and Key Elements Integrating TPB, TAM and DOI 24

Research on the Application of Intelligent Analysis System i...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Li, Jiayao Hu, Yubo Ji, Haokang Zhang, Yuchen College of Art and Communication China Jiliang University Zhejiang Hangzhou China Hubei University of Science and Technology Hubei Xianning China FutureFront Interdisciplinary Research Institute Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400710094

This article examines the application of intelligent analysis systems in the promotion of evaluation indicators for the development of the cultural industry in new rural areas. As China places emphasis on rural revitalization and the integration of cultural industries, the lack of scientific and quantitative evaluation standards has become a challenge to the development of the cultural industry in new rural areas. This study employs the Analytic Hierarchy Process (AHP) to establish a set of evaluation indicators and constructs a hybrid model combining the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Diffusion of Innovations (DOI) to analyze the acceptance, adaptability, and effectiveness of these indicators in the promotion process. Through a pilot study in Wenzhou City, Zhejiang Province, this research utilizes an intelligent analysis system to collect and analyze data, aiming to provide strategic recommendations to enhance the acceptance and usage rate of evaluation indicators for the development of the cultural industry in new rural areas. © 2024 Copyright held by the owner/author(s).

关键词： Evaluation Indicators

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 IEEE.

关键词： Dataset Condensation Deep Hashing Retrieval

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A Framework for Ontology-Based engineering Systems: Advances and Open Questions About Knowledge Capture and Use in Aerospace Manufacturing 5th

A Framework for Ontology-Based Engineering Systems: Advances...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Mas, Fernando Arista, Rebeca Skrzek, Murillo Oliva, Manuel Morales-Palma, Domingo Szejka, Anderson Luis Univ Seville Seville Spain Airbus SAS Toulouse France Pontif Catholic Univ Parana PUCPR Curitiba Parana Brazil Airbus Def & Space Seville Spain CT Engn Grp Madrid Spain

ISBN: (纸本)9783031807749;9783031807756

Modern Ontology-Based engineering (OBE) systems, built over the foundations of Knowledge-Based engineering (KBE), leverage ontology-based technologies and the semantic web to capture and formalise knowledge holistically. This comprehensive knowledge repository can subsequently feed design and decision support tools with robust generative features. However, the integration and reuse of knowledge poses challenges for which it is necessary to develop appropriate methods and tools capable of handling diverse types of knowledge from both human experience and databases. The objective of this paper is to discuss the current limitations of OBE systems to address knowledge integration and reuse, based on the research and experiences of the authors in the aerospace manufacturing industry. This aims to provide a set of research Questions (RQ) and the author's position to them, to serve as future research work and topics of discussion for the scientific community.

关键词： Ontology-Based engineering (OBE) Knowledge-Based engineering (KBE) Models for Manufacturing (MfM) Ontologies

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CLASS MATE: Cross-Lingual Semantic Search for Material science Driven by Knowledge Graphs

CLASS MATE: Cross-Lingual Semantic Search for Material Scien...

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21st international conference on The Semantic Web (ESWC)

作者： Perevalov, Aleksandr Chinchghare, Jiveshwari Krishna, Mouli Sharma, Shivam Lal, Amal Nimmy Deshwal, Aryman Both, Andreas Ngonga-Ngomo, Axel-Cyrille Paderborn Univ Paderborn Germany Leipzig Univ Appl Sci Leipzig Germany DATEV eG Nurnberg Germany

ISBN: (纸本)9783031789519;9783031789526

As diverse linguistic backgrounds contribute valuable insights to scientific research, effective Cross-Lingual Semantic Search (CLSS) mechanisms, which often remain overlooked, become crucial. This paper introduces CLASS MATE(https://***/)-a CLSS application working over material science knowledge graphs (KGs). Our work aims to bridge the digital language divide in the research community by employing advanced knowledge representation techniques. In particular, (1) we acquire our KG containing chemical substances with multilingual entity labels;(2) we implement a symbolic similarity-based named entity recognition algorithm;and (3) we develop a demo application employing the previous steps for retrieving information requested by a user from our KG and LOD sources in multiple languages. Our industry partner Springer Nature provided us with a KG as an information source to understand information needs. To the best of our knowledge, we made the first contribution to CLSS within material science.

关键词： Cross-Lingual Semantic Search Question Answering

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