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arXiv 2024年

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computer Science University of Nottingham China

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt Generator (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training

arXiv

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arXiv 2023年

作者： Chen, Chang Li, Min Wu, Zhihua Yu, Dianhai Yang, Chao Center for Data Science Peking University China School of Mathematical Sciences Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Baidu Inc China Institute for Computing and Digital Economy Peking University China

Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to fully exploit the underlying heterogeneous network environments. In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology. Based on communication modeling, we abstract the dispatch problem into an optimization objective and obtain the approximate dispatch pattern under different topologies. On top of that, we design a topology-aware auxiliary loss, which can adaptively route the data to fit in the underlying topology without sacrificing the model accuracy. Experiments show that TA-MoE can substantially outperform its counterparts on various hardware and model configurations, with roughly 1.01x-1.61x, 1.01x-4.77x, 1.25x-1.54x improvements over the popular DeepSpeed-MoE, FastMoE and FasterMoE systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Network topology

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The Comparison of Survival Right Censored Log-Logistic Models for Breast Cancer Bootstrap data

The Comparison of Survival Right Censored Log-Logistic Model...

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computing (ICOCO), IEEE International Conference on

作者： Nurul Izzah Binti Ali Siti Afiqah Muhamad Jamil Nurain Ibrahim Nur Solehah Mohammad Zuki School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Breast cancer is a significant global health concern, with increasing incidence and mortality rates particularly affecting women worldwide. In Malaysia, limited data exists on how survival rates vary across different age groups, despite evidence that breast cancer behavior differs by age. Due to time constraints, this study only included data from 32 patients and five variables. This study addresses the gap by examining survival rates in two age groups of breast cancer patients from Johor, Malaysia, using right-censored data from 32 women treated at Hospital Sultan Ismail. The cutoff point of $\mathbf{5 2}$ years is based on the mean age of the dataset, which helps simplify the analysis while ensuring meaningful comparison within the available data. Due to limited data, bootstrapping methods were employed to address small sample sizes, while survival analysis, specifically the Kaplan-Meier method, was used to assess survival patterns. Contrary to previous findings, older patients showed a higher probability of survival. The accuracy of bootstrapping method was validated through coverage probability, which exceeded $70 \%$ for all datasets. The log-logistic model with 100 replications was the best fit based on statistical measures like AIC, BIC, and p-values. Despite no significant interactions between variables, this study highlights the need for further research with additional variables and parametric models to improve breast cancer survival analysis in Malaysia

关键词： Analytical models Accuracy Hospitals Computational modeling Mortality Probability Breast cancer data models Parametric statistics Time factors

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

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arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification

引用

Computers, Materials & Continua 2023年第3期74卷 6001-6017页

作者： Idris Rabiu Naomie Salim Maged Nasser Aminu Da’u Taiseer Abdalla Elfadil Eisa Mhassen Elnour Elneel Dalam School of Computing Univerti Teknologi MalaysiaJohor81310Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial ResearchUniversiti Teknologi MalaysiaJohor81310Malaysia Ibrahim Badamasi Babangida University LapaiPMB 11Niger SteateNigeria UNITAR Graduate School UNITAR International UniversityTierra CrestJln SS6//3Petaling Jaya47301SelangorMalaysia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Mathematics-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online *** to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for *** majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed *** improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data *** paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment *** improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data *** a drift is detected,the proposed method builds and adds a new classifier to the *** add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the *** this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification *** experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy *** proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection *** all cases,the results show the efficiency of our proposed method.

关键词： data streams sentiment analysis concept drift ensemble classification adaptive window

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Two-Stage Feature Selection for PM2.5 Prediction: adjcor-TRBFNN Approach to Address Multicollinearity in Air Quality data in Urban Area

Two-Stage Feature Selection for PM2.5 Prediction: adjcor-TRB...

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computing (ICOCO), IEEE International Conference on

作者： Siti Khadijah Arafin Nurain Ibrahim Nur Azimah Abdul Rahim Mahayaudin M. Mansor Ahmad Zia Ul-Saufie Nor Azura Md Ghani School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

PM2.5 is a dangerous pollutant that has been shown to significantly affect human health due to its small size ($\leq 2.5$ micrometers). Urban areas often experience higher PM2.5 levels due to high traffic volume, industrial activities, and population density. Accurate prediction of PM2.5 levels not only enhances the body of knowledge on air quality modeling but also provides governments with critical insights to implement timely interventions, reduce public health risks, and guide policymaking for air quality management. Thus, this study aims to classify the next day’s air quality based on PM2.5 levels in Banting, an urban area in Selangor, Malaysia, using a machine learning approach. Radial Basis Function Neural Network (RBFNN) is a neural network method widely used for prediction and classification analysis due to it can handle complex nonlinear relationship in the dataset. However, RBFNN does not address multicollinearity in air quality data. To address this, we used the adjcorT feature selection method to identify the significant features affecting next-day PM2.5 levels, while considering the multicollinearity issues in air quality data. Thus, a two-stage feature selection method, adjcorT-RBFNN, was implemented in this study to enhance the classification of nextday PM2.5 levels. The research utilized hourly secondary data from the Department of Environment Malaysia (DOE), spanning 2018 to 2022. The findings indicate that PM10, PM2.5, CO, NO2, humidity, SO2, wind speed, and wind direction are significant features for classifying next-day PM2.5 levels in Banting. Moreover, the proposed adjcorT-RBFNN technique outperformed the RBFNN method in terms of accuracy, specificity, precision, F1 score, and AUROC, with scores of $0.761,0.752,0.752,0.761$, and 0.761, respectively. Hence, the proposed two-stage feature selection method, adjcorT-RBFNN, can assist future researchers in improving PM2.5 level predictions in other urban areas.

关键词： Accuracy Wind speed Urban areas Radial basis function networks Machine learning Humidity Feature extraction Air quality Wind forecasting Public healthcare

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

引用

arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

Multi-Scale Clinical-Guided Binocular Fusion Framework for P...

引用

IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang 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

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep Learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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