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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Yan Xie, Guoqi Jin, Linlin Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (纸本)9781728120584

A new generation of automotive electronic systems is a typical cyber-physical systems (CPS) based on an integrated architecture, in which multiple functions can be executed on one electronic control unit (ECU) and one function can be assigned to different ECU. Heterogeneity and dynamics are essential characteristics of automotive CPS (ACPS). Considering heterogeneity and dynamics of ACPS, optimizing functional safety and energy consumption in system design is still a challenge. In this paper, we focus on the collaborative optimization of functional safety and energy consumption with global method in the context of prioritizing functional safety. Considering the requirements of functional safety, we propose a deadline driven processor merging for multiple dynamic functions (DPMMDF) algorithm to maximizes the number of functions that meet deadlines when a function arrives dynamically from the perspective of global optimization. Further, we use a global energy saving for multiple dynamic functions (GESMDF) algorithm, combined with the integrated architecture features, to optimize the energy consumption of ACPS by selecting multiple candidate executable ECUs for a task. Experimental results show that combination of the proposed two algorithms can effectively reduce deadline missed ratio (DMR), and minimize energy consumption compared with existing methods. © 2019 IEEE.

关键词： Global optimization

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A Sentiment Classification Model Based on Multiple Multi-classifier Systems 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yang, Kehua Liao, Chenglin Zhang, Wei College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University ChangshaHunan410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

With the fast development of the Internet, especially the development of the Web technique, more and more people express their views on the Internet. The content of these views involve various aspects of our life, such as books, movies, musics, policies, commodities, technical services, etc. Since these views are usually subjective and possess great potential commercial and social value, the study of sentiment analysis is followed. At the early stage of sentiment analysis, researchers used lexicon based method to solve sentiment analysis problem. With the improvement in machine learning and Natural Language Processing technology, machine learning based method are becoming more and more popular in the field of sentiment analysis. In this study, we propose an ensemble sentiment classification model based on three multi-classifier systems. The proposed model uses majority voting as an ensemble method of multiple multi-classifier systems. The effectiveness of the classification model is verified on five public datasets. Experimental results show that the proposed model is superior to traditional sentiment classification methods and simple multi-classifier systems in the performance of classification accuracy. By using the sentiment classification model proposed in this paper, the accuracy improvement in different domains also show that our proposed model has certain generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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TMI: Two-dimensional maintainability index for automotive software maintainability measurement

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Journal of Systems Architecture 2025年 166卷

作者： Jiao Xie Jialing Yang Sirong Zhao Jianmei Lei Kaiwei Fan Guoqi Xie Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha 410082 Hunan China Center of Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing 401120 China China Automotive Engineering Research Institute Co Ltd Electronic Communication and Software Evaluation Research Center Chongqing 401122 China

The mass adoption of autonomous driving and Advanced Driver-Assistance System (ADAS) technologies increases the complexity and maintenance cost of automotive-embedded software during model-based development. Through model-based development tools (e.g., MATLAB/Simulink), the code is automatically generated by architectural transformation; however, unreasonable software architecture design could make the final generated code be difficult to maintain. Currently, both Microsoft Maintainability Index (MMI) and Bosch Maintainability Index (BMI) are used to measure the maintainability of code, but neither software architecture. We establish a four-layer maintainability index model by analyzing the characteristics of automotive software architecture, software quality model, and industry-related standards. We then propose a Two-dimensional Maintainability Index (TMI), meaning the code and architecture dimensions are simultaneously considered. This is the first work of maintainability measurement to combine code and architecture dimensions while ensuring low redundancy. TMI utilizes nine index metrics to guide architecture refactoring and code optimization, significantly improving software maintainability.

关键词： MATLAB

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Instance-Aware Style-Swap for Disentangled Attribute-Level Image Editing 17th

Instance-Aware Style-Swap for Disentangled Attribute-Level ...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhou, Xinjiao Jiang, Bin Yang, Chao Hu, Haotian Sun, Minyu College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

Recent studies have shown that attribute-level image editing could be achieved by modifying the latent code of style-based Generative Adversarial network (StyleGAN). However, many existing methods suffer from semantic entanglement, which leads to undesirable changes when editing a specific attribute. To solve this problem, we focus on the S space of StyleGAN2 and propose a simple and effective Optimization-based Instance-Aware Style-Swap method, which could perform a natural transformation effect on arbitrary source images and reference images by searching instance-aware swap coefficients with the guidance of prior information. By further analyzing the learned swapping coefficients, we can identify the style channels connected with a specific target attribute. We verify the effectiveness of our proposed methods in the field of face attribute editing. Extensive experiments have demonstrated that our method can achieve controllable and fine-grained image editing along various attributes. Qualitative and quantitative results show the advantages of our method compared with other semantic image editing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Collaborative Multi-head Contextualized Sparse Representations for Real-Time Open-Domain Question Answering 17th

Collaborative Multi-head Contextualized Sparse Representatio...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Sun, Minyu Jiang, Bin Zhou, Xinjiao Zhang, Bolin Yang, Chao College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

An efficient method of representing and retrieving information is an essential component of open domain QA. There are question and answer models that allow for real-time responses with speed benefit and scalability. Nonetheless, due to the limitations of existing phrase models, their accuracy is low. In this paper, we improve the contextualized sparse representation to strengthen the connection between contextual information. We achieve better answer retrieval by enhancing the embedding quality of the model for phrase representation. Specifically, based on original contextualized sparse representations, we transform the single self-attention into collaborative multi-head attention so that attention heads can connect and pay attention to crucial information in different context locations. Compared with learning sparse vectors in n-gram vocabulary space by rectified self-attention, collaborative multi-head attention performs better on the SQuAD dataset. Due to the increased efficiency of critical information representation, the model improves to varying degrees on both of the two evaluation metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vector spaces

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Optimizing Word Embedding for Fine-Grained Sentiment Analysis 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Wei Zhang, Yue Yang, Kehua Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Word embeddings have been extensively used for various Natural Language Processing tasks. However, word vectors trained based on corpus context information fail to distinguish words with the same context but different semantics, which may lead to a similar word vector with opposite semantic terms. This will affect some Natural Language Processing tasks, such as fine-grained sentiment analysis tasks. In this paper, a new word vectors optimization model is proposed. This model can be applied to any pre-trained word vectors. Within a certain range, it can make the opposite semantic words away from each other, and the same semantic words are close to each other. The experimental results show that our model can improve the traditional word embedding in the fine-grained emotional analysis task of Chinese Weibo, and the optimized word vector using our model outperforms the unoptimized word vector. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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Real-time depth estimation with an optimized encoder-decoder architecture on embedded devices 21

Real-time depth estimation with an optimized encoder-decoder...

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作者： Tu, Xiaohan Xu, Cheng Liu, Siping Xie, Guoqi Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781728120584

As a strategy to obtain dense depth maps from a single image, sparse depths, or the fusion of both (RGBd), depth estimation receives much attention. Usually relying on high-performance workstations, existing depth prediction methods, are not particularly suitable for embedded devices. Moreover, the accuracy of depth estimation needs to be improved. Towards real-time and reliable depth prediction on portable embedded platforms, we propose an encoder-decoder architecture (EDA). Specifically, these are the three contributions of this work. (1) We present a light-weight encoder, which takes three modalities of data as inputs, namely, RGB images, sparse depths, or RGBd. The encoder is efficient to extract features on portable devices. (2) A novel decoder is designed to obtain accurate and high-resolution depth maps. (3) We use the existing TVM to deploy, compile, and optimize EDA on embedded platforms, for further improvements in the computational speed and power of depth estimation. In real scenarios, the optimized EDA achieves remarkable results with low latency. Through extensive experimental evaluation on two public datasets, we show that the optimized EDA outperforms the state of the arts in accuracy and computational resources. To the best of our knowledge, this is the first paper that provides an efficient encoder, learnable decoder, and end-to-end optimized encoder-decoder architecture for precise depth prediction. © 2019 IEEE.

关键词： Forecasting

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A unified architecture for supporting operations of AES and ECC

A unified architecture for supporting operations of AES and ...

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2011 4th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2011

作者： Wang, Yi Li, Renfa Embedded Systems and Networking Laboratory Hunan Province Key Laboratory of Network and Information Security Hunan University Changsha 410082 China

ISBN: (纸本)9780769545752

In the recent years, embedded systems began to be used in sensitive applications such as personal digital assistants and smart cards. Due to very strict cost and power constrains, the support for cryptography provided by these devices is often limited to either public or private key primitives. This limitation is much more evident in devices where the cryptographic algorithms are implemented using hardware resources. In this paper, we propose an extension of a public-key cryptosystem to support also private-key, and we evaluate our architecture on FPGA platforms. In particular, we propose a new arithmetic unit in which the polynomial modular multiplication of ECC is extended to compute also the polynomial arithmetic operations over binary extended field of AES. We compare our design with an architecture obtained by instantiating state of the art implementation of AES and ECC and we evaluate the trade-offs. The experimental results show that our proposed architecture takes up less hardware resources. Nevertheless, the achieved performances are better compared to the ECC reference core, while the ones compared to AES only implementation are comparable with the state of the art.1 © 2011 IEEE.

关键词： Smart cards

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Brief Industry Paper: Response Time Evaluation of Cross-Domain Communication in CAN-FD and TSN 44

Brief Industry Paper: Response Time Evaluation of Cross-Doma...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Ma, Wenhong Huang, Xiaoyi Wei, Dongsheng Li, Renfa Zhang, Yunfei Xie, Guoqi Chang, Wanli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province China Tencent Future Network Lab Tencent Corporation China

ISBN: (纸本)9798350328578

With the advancement of intelligence and networked automotive, the domain-centralized architecture, which employs time sensitive networking (TSN) as the inter-domain backbone network and control area network with flexible data rate (CAN-FD) as the intra-domain network, has garnered significant attention. However, cross-domain end-to-end communication involves multiple components, and significant disparities between TSN and CAN-FD render response time analysis within domain-centralized architecture for mixed-critical traffic exceptionally complex. In this paper, we develop a cross-domain with TSN and CAN-FD end-to-end response time evaluation tool, which analyzes the response time of mixed-critical traffic under different design options segment by segment. We specifically analyze the waiting times of different messages in the domain control unit when faced with the design options of one-to-one and multi-to-one conversion of CAN-FD and TSN frames. The proposed evaluation tool can be easily extended to different design options to support more application scenarios. Theoretical computational analysis and real hardware measurements show the effectiveness of our tool. © 2023 IEEE.

关键词： network architecture

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ACWB: Access-characteristic write buffer management scheme for flash memory 21

ACWB: Access-characteristic write buffer management scheme f...

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作者： Zhu, Lei Liu, Yan Zhang, Zaimei Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan410082 China School of Economics and Management Changsha University of Science and Technology Changsha Hunan410076 China

ISBN: (纸本)9781728120584

Compared with traditional hard disk drives (HDD), solid state drives (SDD) have distinct characteristics, such as efficient continuous access and random reading capability. Using of SSD is greatly limited due to the out-of-place update and endurance problem. Many flash-aware buffer schemes have been proposed to reduce write and erase operations. However, a good buffer management scheme should reduce the effects of random writes and suit access patterns. In this paper, we propose an access characteristic write buffer management scheme (ACWB) on flash memory that integrates write buffer and cost regulation based on access characteristic. Experimental results show that ACWB increases the response time by 35%, reduces erase times by 25%, and improves the hit ratio by 27% on the average compared with current flash-aware write buffer schemes. © 2019 IEEE.

关键词： Flash memory

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