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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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Biogas Fermentation Monitoring Research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

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Information and Communication technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

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VG-Net: Sensor Time Series Anomaly Detection with Joint Variational Autoencoder and Graph Neural Network

VG-Net: Sensor Time Series Anomaly Detection with Joint Vari...

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Smart World Congress (SWC), IEEE

作者： Shuai Wang Shenghao Liu Xiaoxuan Fan Haijun Wang Yinxin Zhou Hanjun Gao Hongwei Lu Xianjun Deng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hunan Technical College of Railway High-speed China Nanjing University of Posts and Telecommunications China China National Nuclear Power Co Ltd China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Intelligent Cyber-Physical systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data reliability is crucial for the normal operation of the system. While most existing methods are capable of identifying anomalies in high-dimensional time series data from sensors, they often lack adequate accuracy and fail to adequately consider the correlation between sensor nodes. To address these limitations, we propose an unsupervised framework which is a combination of variational autoencoder and graph neural network(VG-Net). By jointly optimizing the reconstruction module and the prediction module, VG-Net effectively models the correlation between sensor nodes while simultaneously detecting anomalies. The reconstruction module is responsible for modeling the latent representation space and reconstructing the time series data through variational autoencoder. The prediction module leverages bidirectional long short term memory networks and neural tensor network to learn the relationships between sensor nodes, and uses graph neural network to predict the future behavior of the data. Experiments on eight real and synthetic datasets demonstrate that, compared with existing methods, VG-Net can accurately model the relationship between sensor nodes and enhance the anomaly detection performance of high-dimensional time series data.

关键词： Time series analysis Autoencoders Sensor phenomena and characterization Predictive models data models Graph neural networks Reliability Anomaly detection Intelligent sensors Synthetic data

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2D/3D Shape Model Registration with X-ray Images for Patient-Specific Spine Geometry Reconstruction 16th

2D/3D Shape Model Registration with X-ray Images for Patient...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Jiang, Yanxin Zhai, Haoyu Li, Kang Li, Lei Tao, Hairong Shao, Moyu Cheng, Xiaomin Wang, Hongkai School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Dalian116024 China West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China Department of Vascular Surgery The Second Affiliated Hospital of Dalian Medical University Liaoning Dalian China Shanghai Key Laboratory of Orthopaedic Implants Department of Orthopaedic Surgery Shanghai Ninth People’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai200011 China Jiangsu Yunqianbai Digital Technology Co. Ltd. Xuzhou221000 China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian116024 China

ISBN: (纸本)9789819964796

The use of preoperative CT and intraoperative fluoroscopic-guided surgical robotic assistance for spinal disease treatment has gained significant attention among surgeons. However, the intraoperative robotic systems lack guidance based on three-dimensional anatomical structures, rendering them unusable when there is a mismatch between preoperative CT and intraoperative fluoroscopy. Additionally, the continuous X-ray imaging during fluoroscopy exposes the patient to significant radiation risks. This paper proposes a 2D/3D registration method to reconstruct 3D patient spine geometry from frontal-view X-ray images. Our aim is to combine this method with intraoperative robotic assistance for spine procedures, enabling three-dimensional spatial navigation for the robotic system and reducing patient radiation exposure. By utilizing 45 frontal-view X-ray images of patients with scoliosis, we trained a 3D trunk anatomy SSM specific to scoliosis. For the registration, a 2D registration-back projection strategy was employed, which first generates the simulated X-ray projection image of the 3D SSM, then non-rigidly registers the projection image with the patient X-ray image, and finally back-project the 2D registration into 3D to guide the shape deformation of the 3D SSM. This approach was evaluated using X-ray images of 10 scoliosis patients. The results demonstrate the ability to reconstruct the anatomical structure and curvature of the spine in X-ray images. The registration accuracy of the SSM was assessed by visualizing the alignment between the registered model and X-ray images, as well as calculating the average distance between the vertebral disc center points of the model and expert-annotated anatomical landmarks in the patient’s X-rays. The average distance between the registered model’s vertebral disc centers and the patient’s X-ray anatomical positions was 11.38 mm. The registered model exhibited a high degree of alignment with the curvature of the spine in X-ray i

关键词： Three dimensional computer graphics

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DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

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Computers in Biology and Medicine 2024年 175卷 108536-108536页

作者： Luo, Ding Tong, Zhuohao Wen, Lu Bai, Mingze Jin, Xiaojie Liu, Zerong Li, Yinghong Xue, Weiwei School of Pharmaceutical Sciences Chongqing University Chongqing401331 China Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China College of Pharmacy Gansu University of Chinese Medicine Lanzhou730000 China Central Nervous System Drug Key Laboratory of Sichuan Province Sichuan Credit Pharmaceutical Co. Ltd. Sichuan 646100 China Key Laboratory of Biorheological Science and Technology Ministry of Education College of Bioengineering Chongqing University Chongqing400030 China

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://***, complemented by a mirror site available at http://***. © 2024

关键词： database systems

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Dual-Channel Recalibration and Feature Fusion Method for Liver Image Classification 17th

Dual-Channel Recalibration and Feature Fusion Method for Liv...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Niu, Tingting Zhang, Xiaolong Deng, Chunhua Chen, Ruoqin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Tianyou Hospital Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845285

Aiming at the problem of poor medical image recognition accuracy caused by the large individual differences in liver pathological images and the large single feature size, a liver image classification method based on dual-channel recalibration and feature fusion is proposed. Firstly, we construct a dual-channel recalibration model based on attention mechanism, suppress useless features and calculate different feature channel weights, and embed them into the Inception_ResNet_V2 network structure. Secondly, we design fully connected layers on the basis of two-dimensional and three-dimensional convolutional neural networks, then add feature fusion layers to obtain deep semantic information in different dimensions. Finally, we use pre-training models to initialize the network structure and input the fused features into the XGBoost classifier for classification prediction. Experiments have been carried out with liver cancer race imaging datasets and a hospital patient dataset. Experimental results show that this method is superior to previous ones. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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基于污点和概率的逃逸恶意软件多路径探索

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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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Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data 3

Quantitative monitoring of sugarcane typhoon disaster based ...

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3rd International Conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Qian, Lisha Chen, Shuisen Jiang, Hao Dai, Xuemei Jia, Kai Guangzhou Institute of Geography Guangdong Academy of Sciences Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Res. Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou China Shaoguan ShenBay Low Carbon Digital Technology Co. Ltd. Guangdong Shaoguan China Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

With a foreseen increase in the number of agrometeorological disasters due to climate change, especially in the field of crop lodging. This paper presented an innovative monitoring technique to explore the application potential of muti-source remote sensing data. Based on the sugarcane planting area extracted from sentinel-1 time-series data and combination of Landsat-8 and sentinel-2 MSI images before and after Super Typhoon Hato, a vegetation index distance leveling method was come out and then was applied to assess the sugarcane lodging in Dagang Town, Nansha District, Guangzhou City. The region was caused by strong wind and rainstorm on August 23, 2017. The validation results showed that the multi-temporal Sentinel-1 image data can effectively extract the sugarcane planted area before and after lodging with an accuracy of 87.83%. Compared with other vegetation indices (RVI/LSWI/NBR/EVI/DVI), NDVI was the most sensitive in response to sugarcane lodging. The validation accuracy of extracting farmland damage extent reached 71.64%, among them, the affected area of sugarcane reached 711.33 ha. The study further illustrates the capability of the image vegetation index difference method on monitoring of sugarcane lodging degree at the reginal scale. © 2022 IEEE.

关键词： Remote sensing

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A Uniform Representation Model for Oct-Based Fingerprint Presentation Attack Detection and Reconstruction

SSRN

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SSRN 2023年

作者： Zhang, Wentian Liu, Haozhe Liu, Feng Ramachandra, Raghavendra The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Gjøvik2818 Norway

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS building. Thus, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mean Intersection of Union (mIOU) and 0.937 Pixel Accuracy (PA). By comparing with the recognition performance on surface 2D fingerprints (e.g. commercial and high resolution), the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved. © 2023, The

关键词： Optical tomography

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