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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Correction: Prompt4LJP: prompt learning for legal judgment prediction

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The Journal of Supercomputing 2025年第5期81卷 1-2页

作者： Huang, Qiongyan Xia, Yuhan Long, Yunfei Fang, Hui Liang, Ruiwei Guan, Yin Xu, Ge College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computer and Big Data Minjiang University Fuzhou China Fujian Mental Health Human-Computer Interaction Technology Research Center Minjiang University Fuzhou China Fuzhou Institute of Oceanography Fuzhou China

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Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

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Computers, Materials & Continua 2020年第10期65卷 529-541页

作者： Yatian Shen Yubo Mai Xiajiong Shen Wenke Ding Mengjiao Guo School of Computer Science and Engineering Southeast UniversityNanjing210000China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475000China Swinburne Data Science Research Institute Swinburne University of TechnologyVictoria3122Australia

Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic *** the same time,the predicate-argument structure in a sentence is important information for semantic role labeling *** this work,we introduce the auxiliary deep neural network model,which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role *** on the framework of joint learning,part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role *** addition,we introduce the argument recognition layer in the training process of the main task-semantic role labeling,so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main *** the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicate-argument,our model achieved the F1 value of 89.0%on the WSJ test set of CoNLL2005,which is superior to existing state-of-the-art model about 0.8%.

关键词： Part-of-speech tagging semantic role labeling multi-task learning

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WANCO: WEAK ADVERSARIAL NETWORKS FOR CONSTRAINED OPTIMIZATION PROBLEMS

arXiv

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arXiv 2024年

作者： Bao, Gang Wang, Dong Zou, Boyi School of Mathematical Sciences Zhejiang University Zhejiang China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

This paper focuses on integrating the networks and adversarial training into constrained optimization problems to develop a framework algorithm for constrained optimization problems. For such problems, we first transform them into minimax problems using the augmented Lagrangian method and then use two (or several) deep neural networks(DNNs) to represent the primal and dual variables respectively. The parameters in the neural networks are then trained by an adversarial process. The proposed architecture is relatively insensitive to the scale of values of different constraints when compared to penalty based deep learning methods. Through this type of training, the constraints are imposed better based on the augmented Lagrangian multipliers. Extensive examples for optimization problems with scalar constraints, nonlinear constraints, partial differential equation constraints, and inequality constraints are considered to show the capability and robustness of the proposed method, with applications ranging from Ginzburg-Landau energy minimization problems, partition problems, fluid-solid topology optimization, to obstacle problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

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Pre-activation distributions expose backdoor neurons 22

Pre-activation distributions expose backdoor neurons

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Runkai Zheng Rongjun Tang Jianze Li Li Liu School of Data Science The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen

ISBN: (纸本)9781713871088

Convolutional neural networks (CNN) can be manipulated to perform specific behaviors when encountering a particular trigger pattern without affecting the performance on normal samples, which is referred to as backdoor attack. The backdoor attack is usually achieved by injecting a small proportion of poisoned samples into the training set, through which the victim trains a model embedded with the designated backdoor. In this work, we demonstrate that backdoor neurons are exposed by their pre-activation distributions, where populations from benign data and poisoned data show significantly different moments. This property is shown to be attack-invariant and allows us to efficiently locate backdoor neurons. On this basis, we make several proper assumptions on the neuron activation distributions, and propose two backdoor neuron detection strategies based on (1) the differential entropy of the neurons, and (2) the Kullback-Leibler divergence between the benign sample distribution and a poisoned statistics based hypothetical distribution. Experimental results show that our proposed defense strategies are both efficient and effective against various backdoor attacks.

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Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC

Joint Activity Detection and Channel Estimation in Massive M...

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IEEE International Conference on Communications (ICC)

作者： Ye Xue An Liu Yang Li Qingjiang Shi Vincent Lau Shenzhen Research Institute of Big Data Shenzhen China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Tongji University Shanghai China Dept. of ECE The Hong Kong University of Science and Technology Hong Kong

In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorization-minimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods.

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Using Meta-Heuristic Methods to Detect Botnet Attacks in Internet of Things

SSRN

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SSRN 2023年

作者： Mardani, Shayan Boroujeni, Farsad Zamani Akbari, Mehdi Department of Engineering Sepahan Institute of Higher Education Science and Technology Iran Department of Computer Engineering Islamic Azad University Science and Research Branch Tehran Iran Faculty of Computer Engineering Islamic Azad University Najafabad Branch Najafabad Iran Big Data Research Center Islamic Azad University Najafabad Branch Najafabad Iran

The need for timely detection of botnet attacks due to the increase in the number of Internet of Things devices is very necessary to minimize security risks and ensure network security. Recently, botnet attack detection methods are based on artificial intelligence techniques;And in order to deal with the growing dimensions of Internet of Things data, they have used various filtering and wrapping feature selection methods. In this paper, for the challenge of IoT data dimensions in detecting botnet attacks, a comparison has been made between 7 different old and new meta-heuristic methods including PSO, Giza, RSO, Firefly, PEC, BAT and GWO. In this model, decision tree algorithm is used to detect attacks. The experimental results of the methods on two data sets IOT-BotNet and N-BaIoT show that all meta-heuristic methods in binary classification have similar performance and have been able to achieve accuracy above 99%. In detecting the type of attacks (multi-class classification), the results show that the best performance belongs to RSO, Giza, which reached an accuracy of over 99% and are among the latest meta-heuristic methods. The results of the methods show that meta-heuristic feature selection algorithms can effectively reduce data dimensions;So that the accuracy of the proposed attack detection model has increased compared to previous methods. © 2023, The Authors. All rights reserved.

关键词： Botnet

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A Graph Learning Framework for Prediction of Missing Landmines Using Airborne Magnetometry in Iot Environment

SSRN

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SSRN 2023年

作者： Barnawi, Ahmed Kumar, Krishan Kumar, Neeraj Alzahrani, Bander Almansour, Amal The Department of Computing and IT King Abdulaziz University Jeddah Saudi Arabia The KAU Cloud Computing and Big Data Research Group Saudi Arabia The Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed to be University Punjab Patiala India

The continued use of landmines and the presence of already deployed landmines endanger countless lives worldwide. Detecting the landmines is a challenging task as it includes carefully surveying the target region using a suitable sensing device. One of the most trending sensory methods is the magnetometer sensing. The magnetometer sensor is used to visualize magnetic anomalies by measuring the magnetic field strength of magnetized objects especially in IoT environment. The process can be automated using a UAV-based survey by applying signal processing and computer vision techniques using the captured images. However, airborne magnetic detection fails to detect some of the landmines due to environmental conditions, thus the technology utilization is limited. On the other hand, the missing magnetic signature of some mines poses a severe risk to the mine removal team and civilians as they can be accidentally triggered. The work proposed is a further development of ongoing effort to come up with automated detection system. This paper focuses on post processing of magnetometry signature image of landmines deployed as rows in minefields, where mines are deployed in mu+++++ltiple rows patterns. The framework includes (1) a graph-based approach to identify the row clusters present in the pattern, (2) identify the row’s discontinuities and (3) localize the missing signatures via interpolation. A simulated dataset is used to test and evaluate the proposed strategies. The results signify the adequate performance of the proposed method in the identification of missing magnetic signatures of row pattern deployments which enhances reliability of the automatic airborne magnetic sensing of landmines. © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Transient Session Key Derivation Protocol for Key Escrow Prevention in Public Key Infrastructure 1st

Transient Session Key Derivation Protocol for Key Escrow Pre...

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1st EAI International Conference on Internet of Everything, IoECon 2022

作者： Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen Maolood, Ismail Yaqub Sibahee, Mustafa A. Al Ma, Junchao Aldarwish, Abdulla J. Y. Faculty of Biological and Physical Sciences Tom Mboya University Homabay40300 Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen518118 China – System Information Ministry of Higher Education and Scientific Research Erbil Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq

ISBN: (纸本)9783031252211

The Internet of Things (IoT) devices have been deployed to realize smart environments such as smart cities, smart homes, smart health and smart grids. In these domains, the IoT devices collect and forward high volumes of sensitive and private data. It is therefore important that security schemes be developed to protect the exchanged data. In this regard, a myriad of authentication protocols have been developed over the recent past. However, these schemes deploy cryptographic primitives that result in extremely high communication, storage and computation complexities. In addition, some of these protocols still have numerous security and privacy issues that render them unsuitable for deployment in an IoT environment. For instance, untraceability, anonymity, key escrow problems and attack vectors such as de-synchronization and forgery attacks are frequently ignored. In this paper, a transient session key derivation protocol is developed to address some of these security and efficiency challenges. The security analysis executed shows that this protocol offers untraceability, device anonymity and perfect forward key secrecy. In addition, it is robust against de-synchronization, known secret key leakage, eavesdropping and forgery attacks. In terms of operational efficiency, this protocol incurs the lowest computation and communication complexities. © 2023, ICST institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Authentication

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