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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Binghong Zhang, Zhengqian Wang, Haiyu Yu, Tianshuo Wang, Dong Wang, Jialiang Ren, Junjun Liu, Yongzhuang Jiang, Wei Harbin Institute of Technology School of Mathematics School of Computer Science and Technology Harbin China Harbin Institute of Technology Department of Fundamental Education Harbin China University of Glasgow James Watt School of Engineering Glasgow United Kingdom Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology School of Mathematics Harbin China

ISBN: (纸本)9798350386226

In the field of Medical image processing, Transformers and CNNs bring filter-based local sensing and attention-based global modelling, which strengthened their dominant position. However, the scales required in different applications vary, and improper usage or selection on models can lead to performance decrease, resulting in more serious consequences in downstream tasks. The recent advanced Structured State Space Model has shown its potential as a baseline model, and is further proved to maintaining implicit duality with the attention mechanism, The corresponding model Mamba2 shows its long-range modelling ability with optimized long-term memory and linear computational *** this paper, we propose SSD-MIR, a novel architecture leveraging the advantages of state space duality mechanism. SSD-MIR includes three parts: input projection and feature representation, deep feature transformation, output projection and feature inversion. With the three components sequentially connected to form our model, it is able to model long-range dependencies and learn the mapping between low-quality input and high-quality output simultaneously. In the main deep feature transformation part, depthwise convolution is used for additional feature mapping as an alternative of 1D causal convolution, and an optimized quad-direction scanning mechanism is implemented to form the overall 2D state space dual module. The corresponding wrapper module is built in MetaFormer style with the usage of residual connection and LayerNorm. We perform extensive experiments on multiple medical image restoration tasks, and the results are superior to the state-of-the-arts, demonstrating the strength and efficacy of our model. © 2024 IEEE.

关键词： Mapping

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Explainable Database Management System Configuration Tuning through Counterfactuals 40

Explainable Database Management System Configuration Tuning ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Shao, Xinyue Wang, Hongzhi Zhu, Xiao Mu, Tianyu Zhang, Yan Harbin Institute of Technology School of Computer Science Harbin China

ISBN: (纸本)9798350317152

Data management system configuration optimization has been the focus of database-related research. It can improve the adaptability of DBMS to various business scenarios by selecting the right parameter combination. However, this has been a daunting task because of the vast number of configuration 'knobs' involved, and the information about the effects of each knob usually comes from valuable experience, which is very labor-intensive. While machine learning techniques show promise in certain database tasks, they encounter challenges when applied to configuration optimization. Traditional machine learning models excel in prediction tasks through data fitting, but they lack direct applicability in configuration tuning. Moreover, the lack of explainability in machine learning poses a significant obstacle in quantifying the impact of individual knobs on the database performance. Affected by the above factors, this paper proposes CFTune, a method that can accurately evaluate the performance of a DBMS under each configuration using experience and achieve automatic DBMS configuration tuning with counterfactual techniques. Based on various optimization requirements, the approach can offer configuration tuning advice with minimal modification knobs for DBMS that perform badly under specified workloads. Furthermore, to address model shifts resulting from training data, we offer additional strategies to enhance the robustness of the tuning advice. The experimental results demonstrate that the CFTune effectively optimizes the configuration and enhances the performance. © 2024 IEEE.

关键词： Database systems

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3D Electromechanical Coupling Simulation Under Heart Failure: Exploring Reentry Phenomena and Arrhythmogenesis

3D Electromechanical Coupling Simulation Under Heart Failure...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Wei Bu, Xianda Li, Qince Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Heart failure alters the electrophysiological properties of cardiomyocytes, leading to changes in the overall mechanical function of the heart, with significant implications for human health. Current research on heart failure predominantly focuses on two-dimensional electrophysiological models, which limits their ability to fully capture the complexity of heart failure. In contrast, our work integrates both electrophysio-logical and mechanical aspects by developing a cardiomyocyte model incorporating heart failure remodeling within a three-dimensional electromechanical coupling model, providing a more comprehensive understanding of heart failure dynamics. We investigated the variations in electromechanical coupling properties of the left ventricle under heart failure conditions, focusing on key indicators such as ventricular action potentials, myocardial contractility, ventricular volume, and pressure. The simulation results successfully reproduced the impact of heart failure on both electrophysiological and mechanical characteristics. Additionally, the simulation observed reentrant waves under heart failure conditions, further revealing that heart failure predisposes the heart to reentrant arrhythmias. In conclusion, the 3D electromechanical coupling model presented in this study offers crucial insights into the mechanisms of reentry phenomena and arrhythmogenesis under heart failure conditions. © 2024 IEEE.

关键词： Heart

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Qualitative and Quantitative Model Checking Against Recurrent Neural Networks

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Journal of computer science & technology 2024年第6期39卷 1292-1311页

作者： Zhen Liang Wan-Wei Liu Fu Song Bai Xue Wen-Jing Yang Ji Wang Zheng-Bin Pang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Institute of Software Chinese Academy of SciencesBeijing 100190China

Recurrent neural networks (RNNs) have been heavily used in applications relying on sequence data such as time series and natural languages. As a matter of fact, their behaviors lack rigorous quality assurance due to the black-box nature of deep learning. It is an urgent and challenging task to formally reason about the behaviors of RNNs. To this end, we first present an extension of linear-time temporal logic to reason about properties with respect to RNNs, such as local robustness, reachability, and some temporal properties. Based on the proposed logic, we formalize the verification obligation as a Hoare-like triple, from both qualitative and quantitative perspectives. The former concerns whether all the outputs resulting from the inputs fulfilling the pre-condition satisfy the post-condition, whereas the latter is to compute the probability that the post-condition is satisfied on the premise that the inputs fulfill the pre-condition. To tackle these problems, we develop a systematic verification framework, mainly based on polyhedron propagation, dimension-preserving abstraction, and the Monte Carlo sampling. We also implement our algorithm with a prototype tool and conduct experiments to demonstrate its feasibility and efficiency.

关键词： recurrent neural network model checking temporal logic qualitative/quantitative verification

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Optimized Click Prediction on Mobile Devices via Device-Cloud Synergy 30

Optimized Click Prediction on Mobile Devices via Device-Clou...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Pan, Shuyuan Lu, Anqi Hu, Youbing Li, Lingzhi Li, Zhijun Soochow University School of Computer Science and Technology Suzhou China Harbin Institute of Technology School of Computer Science and Technology Harbin China

ISBN: (纸本)9798331515966

The rapid growth of deep learning-based services and applications underscores the need for efficient neural network model deployment. Traditional cloud-centric solutions, despite their computational power, face significant challenges such as high energy consumption, network transmission delays, and user privacy concerns. Conversely, performing high-performance inference on resource-constrained mobile devices, especially for tasks like advertising click prediction, presents its own set of difficulties. To address these challenges, we propose a device-cloud collaboration system utilizing a difficult-case discriminator. This system classifies input samples based on semantic information into difficult and simple cases. Difficult cases are processed in the cloud using a large model, while simple cases are handled on the device by a smaller model. This approach maximizes system resources and protects user privacy. Evaluations on public datasets show that our system significantly outperforms other advertisement methods in click prediction accuracy and uploading efficiency. Compared to the device-only approach, our system improves the area under the curve (AUC) by 8.9%, and compared to the cloud-centric approach, it reduces the upload ratio by 34%. Moreover, deploying our system on a specific smartphone demonstrates substantial improvements in private real datasets. © 2024 IEEE.

关键词： click prediction device-cloud collaboration discriminator mobile device

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Facile fabrication of graphene/g-C_(3)N_(4) for electromagnetic wave absorption

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Nano Research 2024年第3期17卷 1687-1698页

作者： Qiang Su Yunfei He Dongdong Liu Daguang Li Long Xia Xiaoxiao Huang Bo Zhong School of Materials Science and Engineering Harbin Institute of Technology WeihaiWeihai 264209China Weihai Yunshan Technology Co. Ltd.Weihai 264200China State Key Laboratory on Integrated Optoelectronics College of Electronic Science&EngineeringJilin UniversityChangchun 130012China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China

With the development of the miniaturization of electronic equipment and lightweight weapon equipment,there are new requirements for electromagnetic wave absorption material(EMWAM).EMWAM has outstanding electromagnetic wave absorption properties and lightweight characteristics become an important direction of *** this study,graphene/g-C_(3)N_(4)(GGCN)EMWAM was first synthesized in situ by simple heat treatment,in which the g-C_(3)N_(4) had a porous structure and dispersed on the surface of *** impedance matching of the GGCN was well adjusted by decreasing the dielectric constant and attenuation constant due to the g-C_(3)N_(4) semiconductor property and the graphite-like *** EMW loss mechanism of GGCN was also analyzed by simulating GGCN’s electric field mode distribution and resistance loss power *** analysis result shows that the distribution of g-C_(3)N_(4) among GGCN sheets can produce more polarization effects and relaxation effects by increasing the lamellar ***,the polarization loss of GGCN could be increased successfully by porous g-C_(3)N_(4).Ultimately,the EMW absorption property of GGCN is optimized significantly,and GGCN exhibits excellent EMW absorption *** the thickness is 2 mm,the effective absorption bandwidth(EAB)can reach 4.6 GHz,and when the thickness is 4.5 mm,the minimum reflection loss(RLmin)at 4.56 GHz can reach-34.69 ***,the practical application of EMWAM was studied by radar cross-section(RCS)simulation,showing that GGCN has a good application prospect.

关键词： graphene/g-C_(3)N_(4) impedance matching simulation analysis dielectric loss excellent electromagnetic wave absorption

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Voice Spoofing Detection using Feature Adaptive One-Class Classification Based on Graph Neural Network 24

Voice Spoofing Detection using Feature Adaptive One-Class Cl...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Lan, Haiyan Li, Yike Wang, Yuhua Dong, Linfan Han, Zhiying Yang, Kai College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710834

To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unknown attacks, this paper combines the representation ability of graph neural networks with the learning objective of one-class classification, and proposes a feature adaptive one-class voice detection framework based on graph neural networks. The core idea of the feature adaptive one-class classification loss (FAOC) in the framework is to cluster the real voices affected by similar factors more closely, learn several classification boundaries of the real voices, and optimize the distance of the real voices with different distributions, so that the model can obtain more general features of the real voices. At the same time, the relationship between different frequency bands or time segments of the voice is modeled by using graph neural networks, and the graph embedding features with strong recognition ability are obtained, which can better exploit the performance advantages of FAOC. A series of experiments are conducted on the ASVspoof2019 LA and ASVspoof2021 LA datasets, and the results on the ASVspoof2021 LA show that the EER and min-tDCF are improved by 45.8% and 48.2% compared to the baseline. The results show that the proposed method has good performance for voice synthesis attack detection. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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High temperature mechanical and fatigue properties of sol-derived C/Mullite composites

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Ceramics International 2025年第16期51卷 21776-21787页

作者： Zhang, Wei Hong, Sen Ma, Qingsong Zeng, Kuanhong Mao, Weiguo College of Materials Science and Engineering Changsha University of Science & Technology Changsha410114 China Science and Technology on Advanced Ceramic Fibers &Composites Laboratory College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin150001 China

To reveal the in-situ mechanical properties evolution and failure behavior of C/Mullite composites at high temperature, the in-situ mechanical and fatigue properties of the composites at high temperature were systematically studied in present study. The results suggested that C/Mullite composites presented admirable high temperature mechanical properties in air. The flexural strength of the composites at 1200 °C∼1400 °C was higher than that at room temperature. The composites showed retention of ∼120 % in flexural strength at 1400 °C. The enhanced flexural strength was ascribed to the released thermal stress. The composites showed the fracture toughness of ∼13.5 MPa m1/2 at 1200 °C. Although the fracture toughness of the composites decreased to about 5.6 MPa m1/2 at 1500 °C due to oxidation, the composites exhibited strength retention of ∼94 % at this moment. Furthermore, C/Mullite composites exhibited excellent fatigue performance at 1200 °C in vacuum. The composites was survived after 105 cycles of fatigue loading when the stress level did not exceed 75 %, and there was no decline in in-situ mechanical properties of the composites after 105 cycles of fatigue loading under the stress level of 70 %. The present study further verified the application potential of C/Mullite composites as thermal protection materials. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Thermal fatigue

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Intelligent fitting global real-time task scheduling strategy for high-performance multi-core systems

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CAAI Transactions on Intelligence technology 2022年第2期7卷 244-255页

作者： Junpeng Wu Enyuan Zhao Sizhao Li Yanqiang Wang College of Computer Science and Technology Harbin Engineering UniversityHarbinChina

With the development of high-performance computing,it is possible to solve large-scale computing ***,the irregularity and access characteristics of computing problems bring challenges to the realisation and performance *** the performance of a single core makes it challenging to maintain Moore's law,and multi-core processors emerge.A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core *** high-performance computing,the granularity of computing tasks leads to the complexity of scheduling *** high system performance,load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field,especially in specific multi-core hardware *** this study,global real-time task scheduling is implemented in a high-performance multi-core *** system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling *** the cluster scheduling policy,tasks are allowed to preempt the core with low priority,and the priority of tasks that access memory is dynamically improved,higher than that of all the tasks without memory *** intelligent fitting method is also *** the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold,the priority of the task is promoted to the highest priority,pre-empting the core without the access memory *** results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.

关键词： genetic algorithms intelligent systems

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Semantic segmentation of airborne LiDAR point cloud based on global geometric attention network

Semantic segmentation of airborne LiDAR point cloud based on...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Li, Han Men, Chaoguang Liu, Yongmei Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798331527624

Deep learning-based semantic segmentation of airborne laser scanning (ALS) point clouds receives extensive attention from researchers in the field of remote sensing and computing. However, due to the special properties of ALS point cloud data, it makes difficulties in the semantic segmentation task. Therefore, a new global geometric attention network (GGAN) for ALS point cloud data semantic segmentation is proposed in this paper. Firstly, a local geometric attention convolution module is proposed to embed local geometric feature information. Then, a global relationship aware attention module was inserted into the network to improve the global perception of the geometric information. The experimental results of semantic segmentation show that the GGPN method can achieve satisfactory classification results. Semantic segmentation experiments are conducted on the ISPRS benchmark point cloud dataset to evaluate the performance of the GGAN network, and the method in this paper achieves an overall accuracy of 81.6 and the average F1 of 71.4 which is better than most of the existing popular methods. © 2024 IEEE.

关键词： Semantic Segmentation

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