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检索条件"机构=Cyber Security in Computer Science and Engineering"

共 2596 条记录，以下是411-420 订阅

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

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3rd IEEE International Conference on AI in cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

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SecureSem: Sensitive Text Classification Based on Semantic Feature Optimization 15th

SecureSem: Sensitive Text Classification Based on Semantic ...

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15th EAI International Conference on Digital Forensics and cyber Crime, ICDF2C 2024

作者： Qin, Kangyuan Liu, Ran Yu, Min Li, Gang Liu, Mingqi Li, Jingyuan Huang, Weiqing Jiang, Jianguo Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Information Technology Deakin University Geelong Australia Department of Computer Science Beijing Technology and Business University Beijing China

ISBN: (纸本)9783031893629

With the development of big data technology, the management of sensitive information online has become increasingly challenging, requiring innovative approaches for its effective control and classification. This paper addresses specific challenges in the classification of sensitive text in order to mitigate potential threats associated with the uncontrolled dissemination of sensitive information. Existing methods have shortcomings in extracting features from sensitive text, mainly due to insufficient feature extraction and interference caused by common features across domains. To address these challenges, we propose SecureSem, consisting of FeaEmbark and FeaPristine, which introduces innovative features to improve information exploitation within pre-trained language models such as BERT. The FeaEmbark module strengthens semantic features through feature standardisation operations, and the FeaPristine module purifies features through matrix mapping. Our experimental results, conducted on sensitive and general datasets, demonstrate the effectiveness of the SecureSem model. For example, our model achieves an improvement from 91.86% to 96.86% compared to BERT. This study improves sensitive text classification by introducing semantic features and optimisation operations, paving the way for further advances in the field. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Classification (of information)

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks 4

Intelligent Kidney Abnormality Detection in CT Images Using ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bajpai, Chhavi Anitha, S. Gaur, Manish Raghini, M. Kaliappan, S. Khan, Imran Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India Acs College of Engineering Department of CSE-Cyber Security Bangalore India Institute of Engineering and Technology Aktu Department of Computer Science & Engineering Lucknow India Velammal College of Engineering and Technology Department of Artificial Intell igence and Data Science Madurai India Lovely Professional University Department of Research and Development Jalandhar India Sharda School of Nursing Science and Research Sharda University Department of Medical Surgical Nursing Greater Noida India

ISBN: (纸本)9798350352931

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging. © 2024 IEEE.

关键词： computerized tomography

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Big Data Analysis of Cognitive Cloud Computing Based Intelligent Healthcare System 10

Big Data Analysis of Cognitive Cloud Computing Based Intelli...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Shah, Nirav Saxena, Aakansha Kumar, Yogesh Silveroak University Department of Cse Ahmedabad India School of It Artificial Intelligence & Cyber Security Rashtirya Raksha University India Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350382471

The medical sector domain is one of the most rapidly rising and demanding fields in the current era. With the increased usage of technology and the internet, it has become critical for healthcare professionals to adapt and exploit digital technologies in order to stay up with changing patient needs. Big Data can revolutionize the domain of healthcare by providing significant improvements in disease diagnosis, treatment planning, and drug development. Cognitive cloud computing-based intelligent healthcare systems can provide a comprehensive solution to the medical sector processing and analyzing large amounts of healthcare data. This study investigates the role, advantages, and disadvantages of using Big Data in the medical industry and outlines the structure of an intelligent healthcare system using cognitive cloud computing. In addition to this, the contribution of the researchers in this particular realm has been also discussed in detail. © 2023 IEEE.

关键词： Artificial Intelligence Big Data Cognitive Cloud Computing Deep Learning Healthcare Machine Learning

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Deep Learning Algorithms for Early Alzheimer's Diagnosis: From Data to Prognosis

Deep Learning Algorithms for Early Alzheimer's Diagnosis: Fr...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Buchepalli Praneeth P Siva Deekshith Reddy P Bhuvaneshwar Mohana Minal Moharir Ashok Kumar A R Computer Science and Engineering RV College of Engineering® Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

Alzheimer's disease (AD) is a neurological condition that worsens over time and affects millions of older people worldwide. An early diagnosis and correct classification of AD are essential for improving patient condition, enabling prompt intervention, and allocating healthcare resources. Healthcare professionals can use the system's predictions and classifications to make judgements about treatment regimens, patient counselling, and resource allocation. Additionally, by offering insightful information on the course of disease, potential therapeutic targets, and creation of fresh remedies, the system advances research into AD. To build reliable models for the precise diagnosis and classification of the disease, deep learning techniques were used to offer a cutting-edge AD prediction and classification system. To train and optimize deep neural networks, the system uses an AD dataset that consists of Magnetic resonance imaging(MRI) images. The dataset consists of 4 classes, Mild Demented, Moderate Demented, Non-Demented and Very Mild Demented. The proposed approach seeks to improve the precision, sensitivity, and specificity of AD prediction and classification by utilizing the power of deep learning. To identify complex patterns and correlations in the data, different deep learning architectures such as ResNet, MobileNet, and Xception are considered. ResNet exhibits the highest predictive accuracy among the three models, achieving an impressive 93%, followed closely by MobileNet at 91%, while Xception achieves an accuracy of 88%. The system's predictive models and classification algorithms can distinguish between various disease phases of the disease.

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Every Time Can Be Different: A Data Dynamic Protection Method Based on Moving Target Defense 28

Every Time Can Be Different: A Data Dynamic Protection Metho...

引用

28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tang, Zhimin Ma, Duohe Sun, Xiaoyan Chen, Kai Wang, Liming Jiang, Junye Institute of Information Engineering Chinese Academy of Sciences Beijing China California State University Department of Computer Science Sacramento United States School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350300482

Traditional defense methods are hard to change the inherent vulnerabilities of static data storage, single data access, and deterministic data content, leading to frequent data leakage incidents. Moving target defense (MTD) techniques can increase data diversity and unpredictability by dynamically shifting the data attack surface. However, in the existing methods, the data lacks sufficient dynamics due to insufficient shifting space and shifting frequency of attack surface, and legitimate users are inevitably greatly affected. This study proposes a data MTD method that the data changes dynamically based on real-Time multi-source user access information. Through the multidimensional user stratification mechanism, we establish a novel dynamic data model that uses the combination of random deception strategies to convert metadata properties and content of data based on the user risk levels, while data remains unchanged for legitimate users. Multiple sets of experiments demonstrate the effectiveness and low consumption of our data dynamic defense approach. © 2023 IEEE.

关键词： Digital storage

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Analysis of Credit Card Fraudulent Transactions Using Machine Learning and Artificial Intelligence

Analysis of Credit Card Fraudulent Transactions Using Machin...

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Intelligent cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Sriprada Ramesh Simna T M Mohana Department of Chemical Engineering RV College of Engineering® Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The reliance on credit cards for everyday purchases has become ubiquitous in modern society. This poses security threats as cybercriminals are constantly adopting new techniques to commit fraudulent activities. The need for financial organizations to protect their customer's data is at most important. Machine learning (ML) algorithms provide robust fraud detection capabilities and hence can be applied in in developing advanced systems that continuously monitor transactions in real-time, swiftly identifying suspicious patterns indicative of fraudulent activity. Proposed work focuses on comparing for ML models such as Random Forest, Logistic Regression, SVM, and XGBOOST for detecting credit card frauds. The objective is to maximize the detection of fraud transactions while reducing false positives. The f1 score of the models are compared to identify the most efficient algorithm. XGBOOST and Random Forest proved to perform the best in credit card fraud detection with a fl-score of 0.81.

关键词： Support vector machines Logistic regression Machine learning algorithms Forestry Organizations Credit cards Real-time systems Fraud security Random forests

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Towards Robust Temporal Activity Localization Learning with Noisy Labels 30

Towards Robust Temporal Activity Localization Learning with ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Daizong Qu, Xiaoye Fang, Xiang Dong, Jianfeng Zhou, Pan Nan, Guoshun Tang, Keke Fang, Wanlong Cheng, Yu Wangxuan Institute of Computer Technology Peking University China School of Cyber Science and Engineering Huazhong University of Science of Technology China Interdisciplinary Graduate Programme Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang Gongshang University China School of Cyberspace Security Beijing University of Posts and Telecommunications China Cyberspace Institute of Advanced Technology Guangzhou University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9782493814104

This paper addresses the task of temporal activity localization (TAL). Although recent works have made significant progress in TAL research, almost all of them implicitly assume that the dense frame-level correspondences in each video-query pair are correctly annotated. However, in reality, such an assumption is extremely expensive and even impossible to satisfy due to subjective labeling. To alleviate this issue, in this paper, we explore a new TAL setting termed Noisy Temporal activity localization (NTAL), where a TAL model should be robust to the mixed training data with noisy moment boundaries. Inspired by the memorization effect of neural networks, we propose a novel method called Co-Teaching Regularizer (CTR) for NTAL. Specifically, we first learn a Gaussian Mixture Model to divide the mixed training data into preliminary clean and noisy subsets. Subsequently, we refine the labels of the two subsets by an adaptive prediction function so that their true positive and false positive samples could be identified. To avoid single model being prone to its mistakes learned by the mixed data, we adopt a co-teaching paradigm, which utilizes two models sharing the same framework to teach each other for robust learning. A curriculum strategy is further introduced to gradually learn the moment confidence from easy to hard. Experiments on three datasets demonstrate that our CTR is significantly more robust to the noisy training data compared to the existing methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Gaussian distribution

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Image Forgery Detection using Convolutional Neural Networks and Blockchain Technology 24

Image Forgery Detection using Convolutional Neural Networks ...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Khalaf, Luay Ibrahim Jumaili, Mustafa Lateef Fadhil Almashhadany, Mohammed Thakir Mahmood Aljanabi, Mohammed S. Hasan, Taif S. Algburi, Sameer Department of Computer Science College of Computer Science and Mathematics Tikrit University Iraq Computer Engineering Techniques Dept Al-Maarif University College Al Anbar Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Computer Science Department Bayan University Erbil Kurdistan Iraq Department of Computer Engineering Techniques Al Hikma University College Baghdad Iraq Department of Cyber Security and Cloud Computing Al-Ma'moon University College Al-Washash Baghdad Iraq Al-Kitab University Kirkuk36015 Iraq

ISBN: (纸本)9798400716928

In the era of digital media, the growth of picture forgeries poses a huge threat to content validity and reliability. Previous techniques for picture forgery detection have found constraints in accuracy and scalability, forcing the creation of more robust systems. This article suggests a unique technique that merges convolutional neural networks (CNNs) and blockchain technology to address this critical issue. Leveraging a carefully curated dataset combining authentic and forged photos generated through various manipulation techniques, we trained our algorithm to detect and categorize forged content with high accuracy. Our test outcomes revealed impressive performance, with the highest achievable accuracy, precision, recall, and F1-score values standing at 0.95, 0.92, 0.94, and 0.93, respectively. This method represents a considerable leap in image forgery detection and shows promise for fighting digital tampering in varied real-world applications. © 2024 ACM.

关键词： Blockchain

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