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33rd International conference on software engineering and Knowledge engineering, SEKE 2021

作者： Chen, Cheng Dai, Yifan Shen, Yuqi Cai, Jinfang Chen, Liangyu Engineering Research Center of 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 Institute of Vocational and Adult Education East China Normal University Shanghai China

ISBN: (纸本)1891706527

Formula retrieval is an important research topic in Mathematical Information Retrieval (MIR). Most studies have focused on comparing formulae to determine the similarity between mathematical documents. However, two similar formulae may appear in completely different knowledge domains and have different meanings. Based on N-ary Tree-based Formula Embedding Model (NTFEM), we introduce a new hybrid retrieval model combining formula with its surrounding text for more accurate retrieval. Using keywords extraction technology, we extract keywords from text around the formula which can supplement the semantic information of formula. Then we get the representation vectors of keywords by FastText N-gram embedding model, and the representation vectors of formulae by NTFEM. Finally, documents are first sorted according to the similarity of keywords, and then the ranking results are optimized by formula similarity. Experimental results show that the accuracy of top-10 results is at least 20% higher than that of NTFEM and can be 50% in some specific topics. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Embeddings

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A Novel Fault Diagnosis Method Based on Ensemble Feature Selection in The Industrial IoT Scenario

A Novel Fault Diagnosis Method Based on Ensemble Feature Sel...

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IEEE International conference on Systems, Man and Cybernetics

作者： Huadong Xu Minghua Zhu Bo Xiao Yunzhou Qiu MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9781665442084

Fault diagnosis as a research hotspot in the field of prognostics and health management (PHM) has attracted the attention of academia and industry. Deep learning is widely used in academia due to its strong self-learning ability. However, as a "black-box" model, the features extracted by deep learning have poor interpretability which can’t be understood by IoT devices. In this paper, we propose a novel fault diagnosis method based on ensemble feature selection. We separately evaluate our method on the four-fault hydraulic components, which are derived from the real-world hydraulic time series data sets. The results show that compared with the deep learning method the proposed method can greatly reduce the complexity of the model without much reduction accuracy. Meanwhile, the balanced ensemble feature selection method we proposed is better than traditional ensemble methods such as union, intersection, and weighted linear aggregation. After testing, the method we proposed can be applied to the industrial IoT fault diagnosis.

关键词： Fault diagnosis Deep learning Industries Time series analysis Hydraulic systems Filtering algorithms Feature extraction

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Strategies for Intelligent Registration of IoT Devices

Strategies for Intelligent Registration of IoT Devices

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IEEE conference on Wireless communications and Networking

作者： Wenjing Yue Xianzhong Liu School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-design Engineering Research Center MOE Shanghai China

The heterogeneity of the underlying devices introduced significant technical challenges for interoperability in the Internet of Things (IoT), as a rapidly increasing landscape of IoT devices. A unified form of device registration is an important method to shield the heterogeneity. However, most solutions were to focus on the abstraction of device data. Notably, device description information is also necessary. For example, we can deploy the operation temperature of the device in its range to ensure normal operation of the device. However, the device description information registration methods mainly rely on developers entering manually, which causes inefficiency and high information error rate under the cases of many devices. Therefore, it is necessary to develop an excellent strategy for the intelligent registration of IoT *** this paper, we proposed the device intelligent registration strategy by a semantic ontology-based IoT device registration middleware (IDRM): (1) It achieves automatic registration by using automatic recognition and ontology matching of banner information from device application protocol. (2) It realizes registration with converting manual submitted by device vendor users into ontology-based device information. Our experimental results show that IDRM implements Plug-and-Play (PnP) for devices by providing an intelligent registration strategy to shield the heterogeneity of underlying devices.

关键词： Temperature distribution Protocols Semantics Reinforcement learning Manuals Ontologies Information retrieval

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DCFG: Discovering Directional counterFactual Generation for Chest X-rays

DCFG: Discovering Directional CounterFactual Generation for ...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (纸本)9781665429825

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.

关键词： Biological system modeling Perturbation methods Neural networks Predictive models Sampling methods Market research Task analysis

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MGC: Multiple-Gray-code for 3D NAND Flash based High-Density SSDs

MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density...

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IEEE Symposium on High-Performance computer Architecture

作者： Yina Lv Liang Shi Qiao Li congming Gao Yunpeng Song Longfei Luo Youtao Zhang School of Computer Science and Technology East China Normal University Shanghai China Ministry of Education Software/Hardware Co-Design Engineering Research Center School of Informatics Xiamen University Xiamen China Computer Science Department University of Pittsburgh Pittsburgh USA

QLC (4-bit-per-cell) and more-bit-per-cell 3D NAND flash memories are increasingly adopted in large storage systems. While achieving significant cost reduction, these memories face degraded performance and reliability issues. The industry has adopted two-step programming (TSP), rather than one-step programming, to perform fine-granularity program control and choose gray-code encoding, as well as LDPC (Low-Density Parity-Check code) for error correction. Different flash manufacturers often integrate different gray-codes in their products, which exhibit different performance and reliability characteristics. Unfortunately, a fixed gray-code encoding design lacks the ability to meet the dynamic read and program performance requirements at both application and device *** this paper, we propose MGC, a multiple-gray-code encoding strategy, that adaptively chooses the best gray-code to meet the optimization goals at runtime. In particular, MGC first extracts the performance and reliability requirements based on application-level access patterns and detects the reliability degree of SSD. It then determines the appropriate gray-code to encode the data, either from host/user application or due to garbage collection, before writing the pages to the flash memory. MGC is integrated in FTL (flash translation layer) and enhances the flash controller to enable runtime gray-code arbitration. We evaluate the proposed MGC scheme. The results show that MGC achieves better performance and lifetime guarantee compared with state-of-the-arts and introduces little overhead.

关键词： Performance evaluation Three-dimensional displays Runtime Programming Writing Encoding Parity check codes

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Parallel Implementation of H.265 Intra-Frame coding Based on FPGA Heterogeneous Platform 22

Parallel Implementation of H.265 Intra-Frame Coding Based on...

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22nd IEEE International conference on High Performance computing and communications, 18th IEEE International conference on Smart City and 6th IEEE International conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Chen, Wenjie He, Qunfang Li, Shen Xiao, Bo Chen, Mingsong Chai, Zhilei East China Normal University National Trusted Embedded Software Engineering Technology Research Center Shanghai China East China Normal University MoE Engineering Research Center for Software/Hardware Co-design Technology Shanghai China Jiangnan University School of IoT Engineering Wuxi China

ISBN: (纸本)9781728176499

The new video coding algorithm H.265 achieved twice compression rate than the prevailing H.264, but the encoding speed is reduced several times, making it difficult for the real-Time applications such as video surveillance and remote meeting. In this paper, we design a parallel intra-frame mode decision architecture based on FPGA to accelerate the coding. We prove that the mode decision problem of multi-level large coding unit of H.265 can be transformed into the accumulation of multiple single-level small coding unit mode decision. Furthermore, we design how mode decision is accelerated by fine-grained parallelism within coding unit, and pipelining among coding units, then implement them on FPGA. The experimental results show that the proposed architecture achieves maximum 93.6\times acceleration compare with the ARM A53 platform, with an insignificant loss of video quality. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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CHECKING coNTINUOUS STOCHASTIC LOGIC AGAINST QUANTUM coNTINUOUS-TIME MARKOV CHAINS

arXiv

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arXiv 2022年

作者： Mei, Jingyi Xu, Ming Guan, Ji Deng, Yuxin Yu, Nengkun Shanghai Key Lab of Trustworthy Computing East China Normal University China Shanghai Key Lab of Trustworthy Computing & MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States

Verifying quantum systems has attracted a lot of interest in the last decades. In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is famous for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method. © 2022, CC BY.

关键词： Stochastic systems

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An MQTT-based Scalable Remote control Framework with NodeMCU and Cloud platfrom

An MQTT-based Scalable Remote Control Framework with NodeMCU...

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International conference on Mechanical, control and computer engineering (ICMCCE)

作者： Xiaotong Chi Minghua Zhu Jing Yang Wenjie Wang MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China

Wi-Fi(IEEE802.11x) has an unshakable position in the field of wireless communication technology due to its low-cost, high-speed characteristics with wide coverage and large data transmission volume. In the Internet of Things(IoT) fields such as smart home, smart medical care and smart transportation, applications for remote control via easy-peasy Wi-Fi modules are in abundance. The development of cloud platform enables devices scattered in different areas to access the Internet for centralized control and breaks geographical restrictions. In the communication process, choosing an appropriate, reliable and lightweight application layer protocol with less load can better guarantee the quality of data transmission and so improve efficiency. In this paper, we proposed an scalable remote control framework based on MQTT application protocol. We utilize the simple-to-deploy, easy-to-develop NodeMCU board as edge wireless Wi-Fi nodes called SA, LK and SS NMN. combined with a single-chip microcomputer deployed with MQTT application layer protocol through TCP transparent transmission, the control system will be connected into cloud platform with proper burned SDK. In this way, people can control devices remotely, and cooperate with RocketMQ messages for message flow. We deployed a relatively complete experimental system, and verified the feasibility and reliability of the framework by testing the message delivery time and efficiency of pub/sub scenarios. The experimental results show that the speed of uplink and downline of this framework is within and above reason, and the efficiency is still high in the QoS2 state.

关键词： Wireless communication Cloud computing Protocols Microcomputers Reliability Data communication Uplink

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A comprehensive Survey of Few-shot Learning: Evolution, applications, Challenges, and Opportunities

arXiv

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arXiv 2022年

作者： Song, Yisheng Wang, Ting Mondal, Subrota K. Sahoo, Jyoti Prakash The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The Faculty of Information Technology Macau University of Science and Technology Macao China The Department of Computer Science & Information Technology Institute of Technical Education and Research Siksha 'o' Anusandhan University India

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. To enrich this survey, in each subsection we provide in-depth analysis and insightful discussion about recent advances on these topics. Moreover, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research. © 2022, CC BY.

关键词： Surveys

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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