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Debiasing Graph Representation Learning Based on Information Bottleneck

IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11092-11105页

作者： Ziyi Zhang Mingxuan Ouyang Wanyu Lin Hao Lan Lei Yang School of Software Engineering South China University of Technology Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computing Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computer Science and Technology Tsinghua University Beijing China

Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on the adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of graph representation learning based on fairness information bottleneck (GRAFair), a new framework based on a variational graph autoencoder (VGAE). The crux of GRAFair is the conditional fairness bottleneck (CFB), where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability.

关键词： Representation learning Adversarial machine learning Graph neural networks Optimization Electronic mail Training Predictive models Message passing Encoding Vectors

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC 48

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Dayong Sun, Yu Li, Weisheng Xie, Lele Lu, Xin Dufaux, Frederic Zhu, Ce Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science China University of Central Arkansas Computing Department of Computer Science United States Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering China Chongqing University of Posts and Telecommunications School of Information and Communication Engineering China United Kingdom Université Paris-Saclay Cnrs CentraleSupélec Laboratoire des Signaux et Systèmes France University of Electronic Science and Technology of China School of Information and Communication Engineering China

ISBN: (纸本)9781728163277

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss. © 2023 IEEE.

关键词： Gaussian distribution

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FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd China

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services None of modern ANNS systems can address these issues simultaneously. We present FusionANNS, a high-throughput, low-latency cost-efficient, and high-accuracy ANNS system for billion scale datasets using SSDs and only one entry-level GPU The key idea of FusionANNS lies in CPU/GPU collabo rative filtering and re-ranking mechanisms, which signifi cantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We imple ment FusionANNS and compare it with the state-of-the-art SSD-based ANNS system–SPANN and GPU-accelerated in memory ANNS system–RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic

Guardians of Truth: Exploring Techniques for Veracity Identi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P. Suthanthiradevi G Revathy P Sathish Kumar B Karthikeyan Lakshmi.V Muthu Data Science and Business Systems School of Computing SRM institute of Science and Technology India Department of Computer science and engineering Vels Institute of science Technology and Advanced studies India Computer and Communication Engineering Rajalakshmi institute of technology India School of Computing SASTRA Deemed University India Department of Computer Science and Engineering St. Joseph’s Institute of Technology India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score.

关键词： COVID-19 Social networking (online) Pandemics Blogs Predictive models Transformers Prediction algorithms

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： M. Kandan Swagata Sarkar Meenakshi Sharma V. Rajeswari P. Poonkuzhali Srimathi S Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Department of CSE Global Group of Institute Amritsar Information Technology Panimalar Engineering College R.M.D Engineering College Kavaraipettai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters.

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Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL

arXiv

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arXiv 2024年

作者： Liu, Geling Tan, Yunzhi Zhong, Ruichao Xie, Yuanzhen Zhao, Lingchen Wang, Qian Hu, Bo Li, Zang School of Cyber Science and Engineering Wuhan University China Big Data and AI Platform Department Tencent China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China

Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Optimization Study of BP Neural Network Based on Genetic Algorithm

Optimization Study of BP Neural Network Based on Genetic Alg...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Yongheng Cai Zhuye Zhou Zehui Li School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R 2 ), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios.

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Robust Zero Watermarking Algorithm for Medical Volume data Based on LBP 10th

Robust Zero Watermarking Algorithm for Medical Volume Data B...

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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Liu, Wenyi Li, Jingbing Liu, Jing Ma, Jixin School of Information and Communication Engineering Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou310058 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

ISBN: (纸本)9789811934391

With the gradual rise of telemedicine, a large number of images will be transmitted on the network during network consultation, which may cause privacy disclosure. How to more effectively ensure the information security of patients and the copyright of medical images has attracted great attention of scholars in relevant fields. This paper proposed a robust watermarking method for medical volume data based on local binary pattern (LBP) transformation. Firstly, taking the Medical Volume data as the research object, the low frequency feature vector of the Medical Volume data is extracted by combining local binary mode transform and discrete cosine transform. Then hash perception was performed. Finally, we use zero watermark technology to embed the hash sequence and chaotic technology to encrypt watermark. Experimental prove that the proposed algorithm has strong anti-attack abilities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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IoT Smart City Air Quality Monitoring System Using Arduino

IoT Smart City Air Quality Monitoring System Using Arduino

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Intelligent computing and Control for engineering and Business Systems (ICCEBS)

作者： N. Gopinath A. Chinnasamy Sachin K Sakthiraj S Ravindharan S Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems School of computing SRMIST kattankulathur campus Chennai

Many factors, including population growth, increased vehicle use, industrialization, and urbanisation, have contributed to an increase in pollution levels throughout time, which has a negative impact on human wellbeing by adversely affecting the health of those exposed to it. To keep an eye on In this project, we'll build an IOT-based air pollution monitoring system that will track the air quality via an internet-connected web server and sound an alarm whenit drops below a certain threshold, which occurs when harmful gases like CO2, smoke, alcohol, benzene, and NH3 are present in sufficient quantities. On the LCD and on the website, the air quality will be displayed in PPM for easy monitoring. You can use your mobiledevice to check the pollution level as part of this IOT project from anywhere.

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MG-Net: learn to customize QAOA with circuit depth awareness 24

MG-Net: learn to customize QAOA with circuit depth awareness

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yang Qian Xinbiao Wang Yuxuan Du Yong Luo Dacheng Tao School of Computer Science Faculty of Engineering University of Sydney New South Wales Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798331314385

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency.

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