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Construction of Anti-Terrorism intelligence Graph Based on DIKW Framework

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Construction of Anti-Terrorism Intelligence Graph Based on D...

IEEE International Conference on High Performance computing and Communications (HPCC)

作者： GAO Peng YUAN Haoran LIU Yanfei MAO Bowen DUAN Yucong WANG Zhenhua Gansu Police Vocational College Gansu China Artificial Intelligence and Big Data Academy Chongqing City Science and Technology College Chongqing China Department of Intelligence and Computing Department of Infor Security Tianjin University Chongqing Police College Chongqing China Department of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Hainan University Haikou China Department of Tactics Chongqing Police College Chongqing China

“Construction of anti-Terrorism intelligence Graph Based on the DIKW Framework” simplifies the process of handling decentralized and diverse sources of intelligence in counter-terrorism. It uses the DIKW model to consolidate and structure data for better efficiency. It creates a data graph to illustrate relationships, making data more accessible. This data is then used to form an information graph, connecting data with relevant knowledge. From this, intelligent models are extracted, aiding in counter-terrorism prevention and prediction. This approach emphasizes transforming data into intelligence and knowledge hierarchically to improve counter-terrorism efforts.

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Fast and Continual Knowledge Graph Embedding via Incremental LoRA

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arXiv 2024年

作者： Liu, Jiajun Ke, Wenjun Wang, Peng Wang, Jiahao Gao, Jinhua Shang, Ziyu Li, Guozheng Xu, Zijie Ji, Ke Li, Yining School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Institute of Computing Technology Chinese Academy of Sciences China College of Software Engineering Southeast University China

Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating catastrophic forgetting of old knowledge but neglect efficient learning for the emergence of new knowledge. However, in real-world scenarios, knowledge graphs (KGs) are continuously growing, which brings a significant challenge to fine-tuning KGE models efficiently. To address this issue, we propose a fast CKGE framework (FastKGE), incorporating an incremental low-rank adapter (IncLoRA) mechanism to efficiently acquire new knowledge while preserving old knowledge. Specifically, to mitigate catastrophic forgetting, FastKGE isolates and allocates new knowledge to specific layers based on the fine-grained influence between old and new KGs. Subsequently, to accelerate fine-tuning, FastKGE devises an efficient IncLoRA mechanism, which embeds the specific layers into incremental low-rank adapters with fewer training parameters. Moreover, IncLoRA introduces adaptive rank allocation, which makes the LoRA aware of the importance of entities and adjusts its rank scale adaptively. We conduct experiments on four public datasets and two new datasets with a larger initial scale. Experimental results demonstrate that FastKGE can reduce training time by 34%-49% while still achieving competitive link prediction performance against state-of-the-art models on four public datasets (average MRR score of 21.0% vs. 21.1%). Meanwhile, on two newly constructed datasets, FastKGE saves 51%-68% training time and improves link prediction performance by 1.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning

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arXiv 2024年

作者： Liu, Yuze Liu, Tingjie Zhang, Tiehua Xia, Youhua Wang, Jinze Shen, Zhishu Jin, Jiong Yu, Fei Richard Ant Group Shanghai China Shenzhen China School of Computer Science and Technology Tongji University Shanghai China Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world. Recent works discovered that the performance of LLMs is heavily dependent on the input prompt. However, prompt engineering is usually done manually in a trial-and-error fashion, which can be labor-intensive and challenging in order to find the optimal prompts. To address these problems and unleash the utmost potential of LLMs, we propose a novel LLMsagnostic framework for prompt optimization, namely GRL-Prompt, which aims to automatically construct optimal prompts via reinforcement learning (RL) in an end-to-end manner. To provide structured action/state representation for optimizing prompts, we construct a knowledge graph (KG) that better encodes the correlation between the user query and candidate in-context examples. Furthermore, a policy network is formulated to generate the optimal action by selecting a set of in-context examples in a rewardable order to construct the prompt. Additionally, the embedding-based reward shaping is utilized to stabilize the RL training process. The experimental results show that GRL-Prompt outperforms recent state-ofthe- art methods, achieving an average increase of 0.10 in ROUGE-1, 0.07 in ROUGE-2, 0.07 in ROUGE-L, and 0.05 in BLEU. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

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Computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for Pavement Crack Segmentation

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MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for...

International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： Zaid Al-Huda Bo Peng Mugahed A. Al-antari Riyadh Nazar Ali Algburi Radhwan A. A. Saleh Khaled Moghalles School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Artificial Intelligence College of Software & Convergence Technology Sejong University Seoul Korea Aeronautical Technical engineering College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey School of Information Science and Technology Southwest Jiaotong University Chengdu China

Crack segmentation is a critical and demanding task in pavement engineering. Recent studies using Convolutional Neural Networks (CNNs) or vision Transformer (ViT) for crack segmentation have shown promising results, but there are still challenges due to the inhomogeneous crack intensity, complex pavement environments, limited labeled training datasets, and noise in most crack images. These challenges make it difficult to distinguish cracks from noise. To address these challenges, we propose MDAU-Net, a multi-scale U-Net with dual attention module based on an encoder-decoder architecture for pavement crack segmentation. MDAU-Net exploits multi-scale input to preserve spatial awareness in all encoder layers, and the dual attention module (DAM) combines global average pooling (GAP) attention module and position attention (PC) module to extract more comprehensive and discriminative features for crack boundary recognition and improve crack segmentation effectiveness. For optimizing the segmentation network and capturing detailed crack information, a hybrid weighted segmentation loss function is developed. Comprehensive experimental evaluations on two benchmark datasets (DeepCrack and Crack500) demonstrate that MDAU-Net achieves state-of-the-art segmentation performance. These results show that MDAU-Net is a promising approach for pavement crack segmentation and can be used to improve the safety and durability of pavement infrastructure.

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Rice Leaves Disease Diagnose Empowered with Transfer Learning

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Computer Systems Science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection

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arXiv 2025年

作者： Li, Yuqi Lin, Xingyou Zhang, Kai Yang, Chuanguang Guo, Zhongliang Gou, Jianping Li, Yanli Institute of Computing Technology Chinese Academy of Sciences China College of Information Engineering Sichuan Agricultural University China Shenzhen University of Advanced Technology China School of Computer Science University of St Andrews United Kingdom College of Computer and Information Science College of Software Southwest University China School of Artificial Intelligence and Computer Science Nantong University China The School of Electrical and Computer Engineering The University of Sydney Australia

Federated Learning (FL) provides novel solutions for machine learning (ML)-based lithography hotspot detection (LHD) under distributed privacy-preserving settings. Currently, two research pipelines have been investigated to aggregate local models and achieve global consensus, including parameter/non-parameter based (also known as knowledge distillation, namely KD). While these two kinds of methods show effectiveness in specific scenarios, we note they have not fully utilized and transferred the information learned, leaving the potential of FL-based LDH remains unexplored. Thus, we propose FedKD-hybrid in this study to mitigate the research gap. Specifically, FedKD-hybrid clients agree on several identical layers across all participants and a public dataset for achieving global consensus. During training, the trained local model will be evaluated on the public dataset, and the generated logits will be uploaded along with the identical layer parameters. The aggregated information is consequently used to update local models via the public dataset as a medium. We compare our proposed FedKD-hybrid with several state-of-the-art (SOTA) FL methods under ICCAD-2012 and FAB (real-world collected) datasets with different settings;the experimental results demonstrate the superior performance of the FedKD-hybrid algorithm. Our code is available at https://***/itsnotacie/NN-FedKD-hybrid. © 2025, CC BY-NC-ND.

关键词： Federated learning

Learning without Forgetting for Vision-Language Models

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arXiv 2023年

作者： Zhou, Da-Wei Zhang, Yuanhan Wang, Yan Ning, Jingyi Ye, Han-Jia Zhan, De-Chuan Liu, Ziwei School of Artificial Intelligence Nanjing University National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China S-Lab College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former ones. While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning generalizable representations with the aid of textual information. However, when continually trained with new classes, VLMs often suffer from catastrophic forgetting of former knowledge. Applying VLMs to CIL poses two major challenges: 1) how to adapt the model without forgetting;and 2) how to make full use of the multi-modal information. To this end, we propose PROjectiOn Fusion (PROOF) that enables VLMs to learn without forgetting. To handle the first challenge, we propose training task-specific projections based on the frozen image/text encoders. When facing new tasks, new projections are expanded, and former projections are fixed, alleviating the forgetting of old concepts. For the second challenge, we propose the fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, the model can capture better task-specific semantic information that facilitates recognition. Extensive experiments on nine benchmark datasets with various continual learning scenarios and various VLMs validate that PROOF achieves state-of-the-art performance. Code is available at https://***/zhoudw-zdw/PROOF. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

Research on Decentralized Access Control in Big Data Environment 4

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Research on Decentralized Access Control in Big Data Environ...

2021 4th International Symposium on Big Data and Applied Statistics, ISBDAS 2021

作者： Liu, Nianzu Ma, Weichen Shen, Danjie Guo, Bing College of Computer Science Sichuan University Chengdu610065 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

With the rapid increase of network data, there are many challenges of security about data sharing in big data environment. The traditional attribute-based access control model generally adopts the centralized method. In the process of practical operation, once the decision node is attacked, the method will bring security risks to data resources. In order to improve the security of centralized access control and adapt to both dynamic and fine-grained characteristics of data access control in big data environment. The decentralized access control method for big data is proposed by combining the blockchain technology with attribute-based access control model. © Published under licence by IOP Publishing Ltd.

关键词： Big data