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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Liu Jialun Wu Yuhua Wei Bing Mao Chen Li Tieliang Gong School of Computer Science and Technology National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665429825

Healthcare Representation learning has been a key element to achieving state-of-the-art performance on healthcare prediction. Recent advances based Electronic Healthcare Records(EHRs) are mostly devoted to extracting temporal progression patterns with temporal model and their variants. Although these works have shown excellent performances in healthcare prediction, the unified temporal pattern may not be suitable for individuals in all healthcare conditions. Moreover, some studies ususally introduce complex Deep Neural Networks models and medical prior knowledge to get compact representation, causing great computational burden. In this paper, we propose a general health care representation model, named AEFNet. We only leverage three simple convolution operations and a set of up and down sampling to ensure performance and model complexity equally, which achieves adaptively extract distinct individual key feature in a light manner. AEFNet can shrink and refine highly suitable scale information adaptively and comletely. Breaking traditional fixed convolution scale or multi-scale, AEFNet achieves scale adaptively to extract the most significant information and context relationship. Finally, We validate our method on the public dataset MIMIC-III, and the evaluation results indicate that our method can significantly outperform other remarkable baseline models.

关键词： Adaptation models Convolution Computational modeling Neural networks MIMICs Medical services Feature extraction

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Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models

arXiv

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arXiv 2024年

作者： An, Yang Tong, Yuhao Wang, Weikai Su, Steven W. Jinan Key Lab of Intelligent Rehabilitation Robotics College of Artificial Intelligence and Big data for Medical Sciences Shandong First Medical University Shandong Academy of Medical Sciences China Faculty of Engineering and IT University of Technology SydneyNSW2007 Australia The Sixth Mobile Detachment of the First Mobile Corps The Chinese People’s Armed Police Force China

The present study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. This integration addresses key challenges associated with traditional EEG data acquisition, including participant burden, privacy concerns, and the financial costs of obtaining high-fidelity clinical data. Our methodology enhances the generation of EEG signals with detailed temporal and spectral features, enriching the authenticity and diversity of synthetic datasets. The uniqueness of our approach lies in its capacity to concurrently model time-domain characteristics, such as waveform morphology, and frequency-domain features, including rhythmic brainwave patterns, within a cohesive generative framework. This is executed through the reinforcement learning model’s autonomous selection of parameter update strategies, which steers the diffusion process to accurately reflect the complex dynamics inherent in EEG signals. We validate the efficacy of our approach using both the BCI Competition IV 2a dataset and a proprietary dataset, each collected under stringent experimental conditions. Our results indicate that the method preserves participant privacy by generating synthetic data that lacks biometric identifiers and concurrently improves the efficiency of model training by minimizing reliance on large annotated datasets. This research offers dual contributions: firstly, it advances EEG research by providing a novel tool for data augmentation and the advancement of machine learning algorithms;secondly, it enhances brain-computer interface technologies by offering a robust solution for training models on diverse and representative EEG datasets. Collectively, this study establishes a foundation for future investigations in neurological care and the development of tailored treatment protocols in neurorehabilitation. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation

arXiv

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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Vulnerability Detection With Graph Attention Network And Metric Learning

TechRxiv

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TechRxiv 2022年

作者： Zhang, Chunyong Liu, Bin Fan, Qi Xin, Yang Zhu, Hongliang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China College of Information and Network Engineering Anhui Science and Technology University Bengbu233030 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Static code vulnerability detection is a critical topic in software security. Existing software analysis methods have a high rate of false positives and false negatives. Researchers are interested in employing deep learning to discover vulnerabilities automatically, thanks to the recent success of deep learning algorithms in other application *** paper aims at the problem of insufficient and effective extraction of syntax and semantics, the issue of data imbalance, and the problem of overlapping feature distributions between vulnerable and non-vulnerable samples. We illustrate how to create models in a more principled way. We build GSM, a systematic vulnerability detection model based on Graph Attention Network, Sampling methods, and Metric Learning, one phase for one problem solution. When compared to the state-of-the-art approaches, our method achieves 11.5%, 12.3%, 12.57%, and 7.90% improvement in Precision, Recall, F1-Score, and AUC, respectively. Finally, based on the methods proposed in each stage of this paper, we put forward directions and suggestions for more efficient vulnerability detection tasks in the following research. © 2022, CC BY.

关键词： Learning algorithms

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Navigation Line Extraction Algorithm for Corn Spraying Robot Based on Improved Yolov8s Network

SSRN

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SSRN 2023年

作者： Diao, Zhihua Guo, Peiliang Zhang, Baohua Zhang, Dongyan Yan, Jiaonan He, Zhendong Zhao, Suna Zhao, Chunjiang Zhang, Jingcheng School of Electrical Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China Nanjing211800 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China School of Automation Hangzhou Dianzi University Hangzhou310000 China

Aiming at the shortcomings of the existing navigation line extraction algorithm for corn spraying robot in complex farmland environment, such as poor extraction effect and poor adaptability, this paper proposed a navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network. This algorithm proposes to use corn plant core as the identification target to locate corn plants. Firstly, a new spatial pyramid structure ASPPF is proposed in this paper, and an improved YOLOv8s model ASPPF-YOLOv8s is proposed for more accurate detection of corn plant cores. Secondly, the center coordinates of the network detection box were used to locate the corn crop row feature points. Finally, the least squares method was used to fit the corn crop row lines. Experimental results showed that ASPPF-YOLOv8s network had a good effect on corn plant cores extraction under different growth periods and environmental pressures. MAP and F1 of ASPPF-YOLOv8s network increased from 86.4% and 86% of YOLOv7 network, 88.8% and 87% of YOLOv8s network, 88.6% and 89% of ASPP-YOLOv8s network to 90.2% and 91%. The average fitting time and average angle error of the centerline of crop row were reduced from 82.6ms and 0.97°for method of Zhang et al., 74.8ms and 0.75°for method of Hu et al., 67ms and 2.03°for method of Yang et al. to 45ms and 0.63°by using the center coordinates of corn plant core detection box combined with least square method. The accuracy rate increased from 91.4% for method of Zhang et al., 93.6% for method of Hu et al. and 87.35% for method of Yang et al. to 94.35%. It shows that the navigation line extraction algorithm proposed in this paper can meet the requirements of real-time and accuracy of visual navigation of corn spraying robot, and can be used to extract navigation line of corn spraying robot in complex farmland environment. © 2023, The Authors. All rights reserved.

关键词： Extraction

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Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks 5

Fine-grained Scheduling in FPGA-Based Convolutional Neural N...

引用

5th IEEE International Conference on Cloud Computing and big data Analytics, ICCCBDA 2020

作者： Zhang, Wei Liao, Xiaofei Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781728160245

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13 times of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4 times+ performance improvement compared MKL with 20 threads running on 10 core Intel processors. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

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Automatically labeling Aging Scenarios with a Machine Learning Approach 1

引用

24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

arXiv

引用

arXiv 2023年

作者： Xiao, Zhiqing Wang, Haobo Jin, Ying Feng, Lei Chen, Gang Huang, Fei Zhao, Junbo College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University China CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group China

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces;(ii)-we further develop a fine-grained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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基于污点和概率的逃逸恶意软件多路径探索

引用

Security and Safety 2023年第3期2卷 83-106页

作者：徐钫洲张网羌卫中金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution ***,malware can employ evasive techniques to detect the analysis environment and alter its behavior *** known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown *** this paper,we present Antitoxin,a prototype for automatically exploring evasive *** utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive *** probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain ***,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path *** is achieved through forced execution,which forcefully sets the outcomes of branches on selected ***,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration ***,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual *** experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic *** probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and signific

关键词： Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection

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