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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Hao, Li Shi, Jianqi Su, Ting Huang, Yanhong National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/software Co-Design Technology and Application Engineering Research Center Shanghai China Nanyang Technological University Singapore Singapore Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9781728133423

A programmable logic controller (PLC) is essentially a computer dedicated to industrial control which is widely used in the field of global automation control. However, PLC software bugs can result in economic losses and even personal safety issues. PLC software must be thoroughly tested regarding function, structure, safety, and other aspects to avoid accidents. Existing PLC tools are mainly based on the manual setting of input data, which is not only unable to be well automated but also cannot provide information about code coverage. This paper presents an automated test case generation approach for a Structured Text (ST) language to reduce the cost of testing, using dynamic symbolic execution. We apply this method to implement the coverage-based automated test case generation tool STAutoTester. We have evaluated STAutoTester on 21 programs. The experimental results show that STAutoTester can effectively handle these programs. For 11 ST programs, STAutoTester reduces, on average, 87.5% of generated test cases compared to SYMPLC. © 2019 IEEE.

关键词： software testing

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Research on a location-based task assignment for mobile sensing

Research on a location-based task assignment for mobile sens...

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International econference on computer and Knowledge engineering (ICCKE)

作者： Xiulan Wang Shiyan Wang MoE Engineering research Center for Software / Hardware Co ─design Technology & Application China East China Normal University Sanghai China

ISBN: (纸本)9781509035878

in recent years, mobile phone sensing applications has been regarded as new paradigm to obtain ubiquitous environment data. Research on motivating smart phone users to participate in mobile sensing contributing their resources and maximizing the profit of platform is in full swing. Unfortunately, the location-based optimal task assignment problem in mobile sensing is an NP-hard problem. We transform the problem to the maximum weight independent set problem (MWIS) and proposed a polynomial-time approximation scheme (PTAS) to approximate this problem in the intersection graph models. We conducted the simulation experiment and it turns out that the practical performance of the proposed near optimal task assignment algorithm corroborates the theoretical analysis.

关键词： Transforms computational modeling Delays Mobile communication Sensors Monitoring

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HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation

arXiv

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arXiv 2022年

作者： Xia, Jun Zhang, Yi Yue, Zhihao Hu, Ming Wei, Xian Chen, Mingsong The MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China

Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation. FL suffers from model inaccuracy and slow convergence due to the model heterogeneity of the AIoT devices involved. Although various existing methods try to solve the bottleneck of the model heterogeneity problem, most of them improve the accuracy of heterogeneous models in a coarse-grained manner, which makes it still a great challenge to deploy large-scale AIoT devices. To alleviate the negative impact of this problem and take full advantage of the diversity of each heterogeneous model, we propose an efficient framework named HierarchyFL, which uses a small amount of public data for efficient and scalable knowledge across a variety of differently structured models. By using self-distillation and our proposed ensemble library, each hierarchical model can intelligently learn from each other on cloud servers. Experimental results on various well-known datasets show that HierarchyFL can not only maximize the knowledge sharing among various heterogeneous models in large-scale AIoT systems, but also greatly improve the model performance of each involved heterogeneous AIoT device. copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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An Automatic Test Case Generation Method based on SysML Activity Diagram

An Automatic Test Case Generation Method based on SysML Acti...

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第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Yiqun Xu Linbo Wu MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Institute of Computer Application Research Chinese Academy of Engineering Physics

Model-Based Systems engineering(MBSE) is a new method to engineer the modern large, complex systems. MBSE has a good guarantee for system reliability and it can improve the efficiency of system development. So, MBSE has received extensive attention from academia and industry. SysML is an auxiliary language for MBSE. SysML activity diagram can model the behavior of the system. In this paper, we use SysML activity diagram to generate test case. There are two main things in our work. One is that we have designed an automated SysML activity diagram test case generation method. Second, we develop a test case generation tool to support related applications in the industry.

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A Range/Angle-based Localization Framework in WSN: A Clustering Approach

A Range/Angle-based Localization Framework in WSN: A Cluster...

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Sensing, Measurement, communication and Internet of Things Technologies (SMC-IoT), International conference on

作者： Sijie Kong Bo Xiao MOE Research Center for Software/Hardware Co-Design Engineering and Application Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

In this paper, we propose a localization algorithm framework for wireless sensor networks (WSNs) based on cluster implementation. The algorithm addresses the issue of information locality in traditional distributed localization algorithms by utilizing clustering. In contrast to centralized algorithms, this algorithm achieves a more balanced node energy consumption without the need for additional special nodes. Through clustering, nodes pass appropriate information to their targets, and through optimizations within and between clusters, relatively accurate location information is obtained. After multiple rounds of clustering-localization iterations, the accuracy of node localization significantly improves. Importantly, simulation results demonstrate that the algorithm can adapt to various deployment scenarios by adjusting clustering parameters to balance energy consumption and localization accuracy.

关键词： Location awareness Wireless sensor networks Energy consumption Simulation Clustering algorithms Sensors Internet of Things

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Satellite Federated Fine-Tuning for Foundation Models: Architecture design and System Optimization

Satellite Federated Fine-Tuning for Foundation Models: Archi...

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2024 IEEE Global communications conference, GLOBEcoM 2024

作者： Zhu, Yan Yang, Peng Zhu, Jingyang Wen, Dingzhu Wang, Ting Zhou, Yong Shi, Yuanming Jiang, Chunxiao East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350351255

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite. © 2024 IEEE.

关键词： Satellite ground stations

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An Efficient Scheduling Algorithm for Distributed Heterogeneous Systems with Task Duplication Allowed

An Efficient Scheduling Algorithm for Distributed Heterogene...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Hao Shi Yixiang Chen Jinyi Xu Software Engineering Institute East China Normal University Shanghai China MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

Due to the restriction of data transmission speed and bandwidth between processing units, communication delays are significantly impacting the complete time of applications in distributed heterogeneous systems. Most of the existing methods considering communication delays are approximate. The performance of solutions given by these heuristic methods are depends on the input instance. Another common choice is optimization methods, such as genetic algorithms and branch-and-bound search. However, these algorithms solve general situations with low efficiency. This paper proposes an efficient scheduling algorithm with task duplication allowed, called DHSA. Task duplication allows tasks executing multiple times to trade extra computation for communication delays, which is widely used in parallel system scheduling approximately. To address the complexity explosion caused by allowing all-task duplication, we decompose the scheduling into allocation and ordering stages and optimize them separately with solving optimization model and slack-interval aware ordering approach. Extensive experiments show that DHSA gives more optimal solutions within feasible time comparing to two state-of-the-art methods, particularly for applications with medium to high communication load.

关键词： Schedules Scheduling algorithms computational modeling Optimization methods Scheduling Delays complexity theory

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FPGA-Accelerated for constrained High Dispersal Network

FPGA-Accelerated for Constrained High Dispersal Network

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IEEE International Symposium on Parallel and Distributed Processing with applications and IEEE International conference on Ubiquitous computing and communications (ISPA/IUCC)

作者： Yanliang Chen Minghua Zhu Bo Xiao Dan Meng MOE Research Center for Software/Hardware Co-Design Engineering and Application East China Normal University Shanghai China

ISBN: (纸本)9781538637913

In recent years, the Deep Neural Network (DNN) has been successfully used in image classification. Most of existing DNN often need to learn a very large set of parameters, which require a huge amount of computational resources and time to train these model parameters using the gradient descent and back-propagation procedure. To solve this issue, the PCANet has been developed for high efficient design and training of the DNN. compared with traditional DNN, PCANet has simpler structure and better performance, which makes it attractive for hardware design. To overcome the limitations of PCANet and significantly improve its performance, we have proposed a novel model named constrained High Dispersal Network (CHDNet) which is a variant of PCANet. In this paper, we implement the CHDNet on the Xilinx ZYNQ FPGA to ensure the instantaneity of the system with lower power than personal computer needed by taking advantage of the algorithmic parallelism and ZYNQ architecture. Our experimental results over two major datasets, the MNIST dataset for handwritten digits recognition, and the Extended Yale B dataset for face recognition, demonstrate that our model of implementation on FPGA is more than 15x faster than software implementation on PC (Intel i7-4720HQ, 2.6GHz).

关键词： Field programmable gate arrays hardware Principal component analysis Acceleration Machine learning Graphics processing units computer architecture

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PSTEP - A Novel Probabilistic Event Processing Language for Uncertain Spatio-temporal Event Streams of Internet of Vehicles

PSTEP - A Novel Probabilistic Event Processing Language for ...

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IEEE International conference on software Quality, Reliability and Security companion (QRS-C)

作者： Huiyong Li Yuanrui Zhang Yixiang Chen MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai P.R.China

Internet of Vehicles (IoV, shortly) is a typical system of Internet of Things. Spatio-Temporal event stream is one of basic features of IoV. These event streams often are uncertain due to the limit of the monitoring device and the high speed of vehicles. Developing an event processing language to process these spatio-temporal event streams with uncertainty is a challenge issue. The goal of this paper is to develop a Probabilistic Event Processing Language, called as Probabilistic Spatio-Temporal Event Processing language (PSTEP, shortly), dealing with this challenge issue. In PSTEP, we use the Possible World Model to express uncertain spatio-temporal events of IoV and assign a spatio-temporal event with a probability which is the threshold value for processing the existence of an event. We establish its syntax and operational semantics. Finally, a case study is given to show the effectiveness of the PSTEP language.

关键词： Data models Spatial databases Probabilistic logic Monitoring Intelligent vehicles Internet of things Syntactics

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MSTC^∗:Multi-robot coverage Path Planning under Physical constrain

MSTC<sup>∗</sup>:Multi-robot Coverage Path Planning under P...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Jingtao Tang Chun Sun Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing Engineering Research Center of Software/Hardware Co-Design Technology and Application (MoE) and School of Software Engineering East China Normal University Shanghai

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC∗ for multi-robot coverage path planning (mCPP) based on spiral spanni... 详细信息

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC^∗ for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm incorporates strict physical constraints like terrain traversability and material load capacity. We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and real field terrains in simulation environments. The experimental results show that our method significantly outperforms existing spiral-STC based mCPP methods. Our algorithm can find a set of well-balanced workload distributions for all robots and therefore, achieve the overall minimum time to complete the coverage.

关键词： Spirals Automation conferences Path planning Task analysis Robots Load modeling

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