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A proof system in PADS

A proof system in PADS

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10th International colloquium on Theoretical Aspects of computing, ICTAC 2013

作者： Yao, Xinghua Zhang, Min Chen, Yixiang MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9783642397172

The PADS (Process Algebra for Demand and Supply) framework is an approach to model resource demand and supply for the formal analysis of hierarchical scheduling. Inspired by the demand relation in PADS, we propose a weak demand relation covering several cases which can not be described by a demand relation. And we explore some properties of weak demand relation which are similar to properties of demand relation. Especially, if two tasks are in a weak demand relation then their schedulabilities are closely related. Furthermore, we present a proof system for the weak demand relation in a decomposing-composing way, which helps to compare two tasks' schedulabilities. Finally, we prove that the proof system is sound and complete with respect to the semantic definition of weak demand relation. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Semantics

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PSTeC: A location-time driven modelling formalism for probabilistic real-time systems 18th

PSTeC: A location-time driven modelling formalism for probab...

引用

18th International GI/ITG conference on Measurement, Modelling and Evaluation of computing Systems and Dependability and Fault Tolerance, MMB and DFT 2016

作者： He, Kangli Chen, Yixiang Zhang, Min Zhang, Yuanrui MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9783319315584

Internet of Things (IoT) and Cyber-Physical Systems (CPS) have become important topics in both theory and industry. In some application domains, such as when specifying the behaviour of precision mechanics, we need to include features of spatial-temporal consistency. How to model probabilistic real-time systems in such domains is a challenge. This paper presents a modelling formalism, called PSTeC, for describing the behaviour of probabilistic real-time systems focusing on spatial-temporal consistency with nondeterministic, probabilistic and real-time aspects. The consistency restricts a process to start and finish at the required location and time. communications between agents is specified by interactive actions. The language we propose is an extension of STeC, which is a specification language for location-aware real-time systems, adding probabilistic operations so as to support the incorporation of probabilistic aspects. We first give a formal definition of the syntax for PSTeC, then focus on the details of its operational semantics, which maps a PSTeC term onto a Probabilistic Spatial-Temporal Transition System (PSTTS) following the structured operational semantics style. A simple example demonstrates the expressiveness of PSTeC. © Springer International Publishing Switzerland 2016.

关键词： Semantics

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SAMPLE EFFICIENT LUNG SEGMENTATION USING GROUP STRUCTURED coNDITIONAL VARIATIONAL DATA IMPUTATION

SAMPLE EFFICIENT LUNG SEGMENTATION USING GROUP STRUCTURED CO...

引用

2021 IEEE International conference on Multimedia and Expo, ICME 2021

作者： Li, Yan Cao, Guitao Cao, Wenming MOE Research Center for Software/Hardware Co-Design Engineering East China Normal University China College of Information Engineering Shenzhen University China

ISBN: (纸本)9781665438643

Patients infected with coVID-19 can lead to their Chest X-rays (CXRs) with opacifications rendered regions, which may produce incomplete lung segmentation in automated image analysis models. To tackle this issue, we propose a Group structured conditional Variational data Imputation model to capture the missing data accurately with conditional distribution, where the high-dimensional probability distribution is narrow down to a small latent space to account for unobserved features. This work particularly arises in the fight against coVID-19 that effectively modeling a segmentation of plausible can be presented to a subsequent automated risk scoring and treatment. We train this model with limited CXRs data to demonstrate the abilities on the task of data imputation and proved to be effective though with relatively small datasets. © 2021 IEEE

关键词： coVID-19

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A software trustworthiness measure based on the decompositions of trustworthy attributes and its validation

A software trustworthiness measure based on the decompositio...

引用

International conference on Industrial engineering, Management Science and applications, ICIMSA 2015

作者： Tao, Hongwei Chen, Yixiang Pang, Jianmin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou45002 China MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China

ISBN: (纸本)9783662471999

There exist many software trustworthiness measures based on the decompositions of attributes. However, few of them are concerned with using more rigorous approaches to measure software trustworthiness and carrying out the theoretical validation of measures. Axiomatic approaches formalize the empirical understanding of a software attribute through the definition of a set of desirable software measure properties. It can provide precise and formal terms for the quantification of software attributes. We once used axiomatic approaches to measure software trustworthiness based on multi-dimensional attributes, in this paper, based on previous works, we apply axiomatic approaches to measure software trustworthiness based on the decompositions of attributes, propose the desirable properties of software trustworthiness measures based on the decompositions of attributes, present a software trustworthiness measure and validate this measure from the theory by proving that it complies with the set of properties. compared with the popular software trustworthiness measures, this measure can evaluate software trustworthiness better in view of our properties. © Springer-Verlag Berlin Heidelberg 2015.

关键词： computers

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DCFG: Discovering Directional counterFactual Generation for Chest X-rays

DCFG: Discovering Directional CounterFactual Generation for ...

引用

2021 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Yan Liu, Shasha Wu, Chunwei Xi, Xidong Cao, Guitao Cao, Wenming East China Normal University MOE Research Center for Software/Hardware Co-Design Engineering China Shenzhen University College of Information Engineering China

ISBN: (纸本)9781665401265

While Deep Neural Networks (DNNs) are achieving state-of-the-art performance on medical domains across a variety of tasks, the need for explainability of model predictions in these high-stakes tasks is still lacking. Current for the explainability in model predictions potentially relies on the supervised counterfactual generation that is time-consuming and direction uncontrollable. Yet, the counterfactual generation needs to be easy to implement and have a controllable direction. In light of this trend, we propose an approach for the unsupervised latent direction search of black-box models that are steerable to the user by enabling the user to effectively explore counterfactual generation in a directional way, without relying on domain- or data-specific assumptions. To identify these explainable directions, we use Principal component Analysis (PCA), a general manifold learning framework to extract low-dimensional subspaces based on a local noise injection of the pre-trained generative model, so that a small perturbation in the subspaces would provide enough change in the resulting data. With experiments on three real-world CXR datasets involving 6 tasks, we find that our approach is capable of learning explainable predictions that discard unrelated confounding factors. Moreover, our method enables practitioners to edit directions to better understand which features are used for predictions. © 2021 IEEE.

关键词： Forecasting

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Buffer with Feature Tokens: combating confusion in Medical Image continual Learning

Buffer with Feature Tokens: Combating Confusion in Medical I...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Zheming Chen, Weiting Fan, Jiahao Yu, Ruizhi East China Normal University Shanghai Key Laboratory of Trustworthy Computing Moe Research Center of Software/Hardware Co-Design Engineering China

ISBN: (纸本)9798350386226

Due to its complexity and privacy concerns, medical data is often difficult to collect fully at once. New data emerges with the discovery of new diseases and advances in medical technology, but privacy concerns limit its storage. When deep learning models require training on datasets of new diseases, they often suffer from catastrophic forgetting, where the models' performance on previously learned data degrades significantly. Recently, pre-trained continual learning methods have been proposed. Unlike earlier continual learning methods, these new approaches do not require learning excessive amounts of new knowledge, furthermore, they can still achieve good performance even when the pre-trained feature extractor is completely frozen. For these approaches, the performance degradation is not primarily due to catastrophic forgetting, but rather confusion. To combat confusion, we propose a method using a buffer with feature tokens (BWFT) which is cheaper and safer than directly storing the original data. Additionally, we propose a feature merge method to further reduce storage and computation costs. In the class-incremental scenario, our method outperforms state-of-the-art methods on multiple medical datasets as well as natural image datasets. code is available at https://***/1345475677/BWFT. © 2024 IEEE.

关键词： contrastive Learning

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CA-Res2UNet++: a Deep Residual UNet Based Method for Brain Tumor Segmentation in Multimodal MRI 2

CA-Res2UNet++: a Deep Residual UNet Based Method for Brain T...

引用

2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Hu, Yujiao Chen, Weiting Wu, Wenjing Long, Jianghao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Verifying the correct composition of distributed components: Formalisation and Tool 13

Verifying the correct composition of distributed components:...

引用

13th International Workshop on Foundations of coordination Languages and Self-Adaptive Systems, FOCLASA 2014

作者： Henrio, Ludovic Kulankhina, Oleksandra Liu, Dongqian Madelaine, Eric Univ. of Nice Sophia Antipolis CNRS France INRIA Sophia Antipolis Méditérannée France MoE Engineering Research Center for Software/Hardware Co-design Technology and Application ECNU Shanghai China

This article provides formal definitions characterizing well-formed composition of components in order to guarantee their safe deployment and execution. Our work focuses on the structural aspects of component composition;it puts together most of the concepts common to many component models, but never formalized as a whole. Our formalization characterizes correct component architectures made of functional and non-functional aspects, both structured as component assemblies. Interceptor chains can be used for a safe and controlled interaction between the two aspects. Our well-formed components guarantee a set of properties ensuring that the deployed component system has a correct architecture and can run safely. Finally, those definitions constitute the formal basis for our Eclipse-based environment for the development and specification of component-based applications. © 2015 L. Henrio, O. Kulankhina, D. Liu, E. Madelaine.

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RFID Based Motion Direction Estimation in Gate Systems 22

RFID Based Motion Direction Estimation in Gate Systems

引用

22nd IEEE International conference on computer Supported cooperative Work in design, CSCWD 2018

作者： Jie, Wu Minghua, Zhu Bo, Xiao Wei, He MOE Research Center for Software Hardware Co-Design Engineering and Application East China Normal University Shanghai China Anti-forgery Department Third Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9781538614822

The RFID based school gate system is used to estimate students entering or leaving the school when they go through the school gate with RFID tags. In general, the accuracy of the estimation of RFID is sensitive to complex electromagnetic environment changing. For example, the estimation success rate is high in some circumstance but low when there is a car parking in front of directional readers. In this paper, a method is presented by which the direction of the readers would be automatically adjusted according to the received information of the tags. With this method, the accuracy of the estimation of RFID could maintain high even if the electromagnetic environment changes. Experiments also were carried out to evaluate the feasibility of the proposed method. The results showed that the method is highly suitable to keep a stable success rate of motion direction estimation in school gate systems. © 2018 IEEE.

关键词： Radio frequency identification (RFID)

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Multi-scale Transformation Based Image Representation and Analysis of Amplitude-integrated EEG 2

Multi-scale Transformation Based Image Representation and An...

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2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Wu, Wenjing Chen, Weiting Hu, Yujiao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

Amplitude-integrated electroencephalography (aEEG) is widely adopted for recognizing neonatal neurological disorders in clinics. Previous work has mainly analyzed aEEGs from a time series perspective, while clinicians are more concerned about images. This paper studies the efficacy of image representations in aEEG interpretation. To this end, we employ Amplitude-frequency contour map (AFCM) to express information on local amplitude distributions and overall temporal trend. During its generation, Multi-scale Box-cox transformation is introduced to flexibly control the degree of amplitude compression and capture details in different amplitude regions. Moreover, one- and two-dimensional features are extracted and combined to enhance model predictions. The experimental results show the potential of image representation for aEEG recognition. This method can provide new ideas to analyze time series signals like aEEGs from the perspective of combining time series and image. © 2023 SPIE.

关键词： Electroencephalography

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