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Towards Tolerating Soft Errors for Embedded systems

SN Computer Science 2021年第2期2卷 1-10页

作者： Sadi, Muhammad Sheikh Ahmed, Waseem Jürjens, Jan Khulna University of Engineering & Technology Khulna Bangladesh Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia IST/ISST University of Koblenz-Landau and Fraunhofer Institute for Software and Systems Engineering Koblenz/Dortmund Germany

Exponential growth in the number of transistors for each chip along with increasing clock frequencies and operational voltages and decreasing load capacitance are aggravating the possibility of occurrence of soft errors in embedded systems. Transistors on current chips have components separated by only a few hundred atoms;hence, a small voltage glitch can alter the state of the transistor, thus causing soft errors in the systems. The impact will be a matter of great concern when the line widths will shrink further. These complicated linkages among the components in chips directly affect the reliability of embedded systems and cause them to be sensitive to soft errors. The common approach to address such errors is focused on post-design phases that are complex and costly to implement. However, reliability, which is a vital non-functional attribute of a system, should be validated at the design phase, particularly for critical systems. This paper proposes an efficient approach to measure and minimize the potential threats of soft errors for embedded systems in the early design phase of system-level design. The methodology is validated against a system model that must have high reliability. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Criticality analysis Embedded systems Heuristic metrics Reliability risks Soft errors

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A Cython Bound Tracklet- Tracklet Correlation for Resident Space Objects

A Cython Bound Tracklet- Tracklet Correlation for Resident S...

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IEEE Conference on Aerospace

作者： Kathrin Rack Simon Schmitz Martin Stoffers Benjamin Hofmann Michael Meinel Hauke Fiedler Thomas Schildknecht Department High-Performance Computing Institute for Software Technology German Aerospace Center Linder Hohe Koln Germany Department Space Flight Technology Institute for Space Operations and Astronaut Training German Aerospace Center Weßling Germany Department Intelligent and Distributed Systems Institute for Software Technology German Aerospace Center Berlin Germany Astronomical Institute University of Bern Bern Switzerland

Almost all humankind depends on satellite technology orbiting the Earth. However, space operations are increasingly endangered by the rapidly growing number of orbiting objects and debris. To avoid collisions with debris particles and to keep the space infrastructure alive, it is mandatory to monitor cooperative and uncooperative resident space objects. For this purpose, we are developing a software system called Backbone Catalogue of Relational Debris Information (BACARDI). It provides a database with orbit information of resident space objects and related products like ephemerides, which enables us to derive close approaches and provide collision warnings. The challenge is to detect, identify and track new resident space objects that were recently launched, created by fragmentations and collisions or recently observed due to improved sensor tech-nologies. Typically, passive optical telescopes observe objects for a relatively short time. A short time series of observation is called a tracklet. To derive the full orbit of an object, at least a pair of tracklets is needed. In case the tracklets cannot be associated to an already known object, it should therefore be identified which tracklets belong to the same object. The algorithm performing this task is called the Tracklet- Tracklet Correlation (TTC). Our aim is to integrate the TTC into BACARDI. Therefore, we have to consider that the individual algorithm meets different requirements than the overall software. On the one hand, BAC- ARDI is a software system that needs to be easily maintainable, extendable and usable and is thus written in Python. On the other hand, the TTC has to be computationally fast, which is why it is written in C++. To fulfil these opposing requirements, we present a successful linkage of a fast C++ based TTC to a Python-based BACARDI. To perform this task, we used the pro-gramming language Cython. The Cython syntax is a superset of the Python syntax and provides additional rules to address

关键词： Space vehicles Codes Correlation software algorithms C++ languages Object recognition Task analysis

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Learning domain-aware detection head with prompt tuning 23

Learning domain-aware detection head with prompt tuning

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Haochen Li Rui Zhang Hantao Yao Xinkai Song Yifan Hao Yongwei Zhao Ling Li Yunji Chen Intelligent Software Research Center Institute of Software CAS Beijing China and University of Chinese Academy of Sciences Beijing China State Key Lab of Processors Institute of Computing Technology CAS Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS Beijing China State Key Lab of Processors Institute of Computing Technology CAS Beijing China and University of Chinese Academy of Sciences Beijing China

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reducing the domain bias of the detection backbone by inferring a discriminative visual encoder, while ignoring the domain bias in the detection head. Inspired by the high generalization of vision-language models (VLMs), applying a VLM as the robust detection backbone following a domain-aware detection head is a reasonable way to learn the discriminative detector for each domain, rather than reducing the domain bias in traditional methods. To achieve the above issue, we thus propose a novel DAOD framework named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies the learnable domain-adaptive prompt to generate the dynamic detection head for each domain. Formally, the domain-adaptive prompt consists of the domain-invariant tokens, domain-specific tokens, and the domain-related textual description along with the class label. Furthermore, two constraints between the source and target domains are applied to ensure that the domain-adaptive prompt can capture the domains-shared and domain-specific knowledge. A prompt ensemble strategy is also proposed to reduce the effect of prompt disturbance. Comprehensive experiments over multiple cross-domain adaptation tasks demonstrate that using the domain-adaptive prompt can produce an effectively domain-related detection head for boosting domain-adaptive object detection. Our code is available at https://***/Therock90421/DA-Pro.

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B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

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11th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665468190

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. © 2022 IEEE.

关键词： Semantic Segmentation

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Hot Spot Locating and Optimization of Calculation Errors in Arithmetic Expressions 7

Hot Spot Locating and Optimization of Calculation Errors in ...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Jinchen, Xu Yuanyuan, Xia Jiangwei, Hao Hao, Cao State Key Laboratory of Mathematical Engineering and Advanced Computing No. 62 Science Avenue High-Tech Zone Henan Zhengzhou450001 China National Key Laboratory of Science and Technology on Integrated Information Systems Technology Institute of Software Chinese Academy of Sciences No. 4 Zhongguancun South Fourth Street Haidian District Beijing10190 China

ISBN: (纸本)9781510656437

Error analysis is an important support for improving the precision of floating-point calculations. However, the floating-point error distribution does not have dominant characteristics, making it difficult to optimize floating-point calculation precision. This paper proposes the LFEE (Locating hot spots of floating-point expression errors) algorithm. The algorithm analyzes the error detection result of the expression to obtain the error hot spot, i.e., the step with the maximum error in the expression calculation sequence, followed by preforming precision optimization scheme for the error hot spot. The experimental results show that the LFEE algorithm can accurately locate the error hot spots and improve the accuracy of the function by optimizing the error hot spots. © 2022 SPIE. All rights reserved.

关键词： Error detection

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Spatial Temporal Transpose Convolutional Networks for Action Recognition

Spatial Temporal Transpose Convolutional Networks for Action...

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International Conference on Computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton Partitioning algorithms Computational complexity Monitoring

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FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

arXiv

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arXiv 2023年

作者： Niu, Changan Li, Chuanyi Ng, Vincent Lo, David Luo, Bin State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Human Language Technology Research Institute University of Texas at Dallas RichardsonTX United States School of Computing and Information Systems Singapore Management University Singapore

While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs;(2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems;and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks. © 2023, CC BY.

关键词： Semantics

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Improved Action Recognition Algorithm for Video Surveillance Based on PoseC3D

Improved Action Recognition Algorithm for Video Surveillance...

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International Conference on Computer and Communications (ICCC)

作者： Ruifeng Guo Chao Yang Changyi Deng Ying Bai Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The precise recognition of human action is of great significance for intelligent monitoring and video analysis. Previous researchers have utilized many methods to detect human action, but the accuracy and robustness of these methods remain to be improved. Skeleton-based action recognition methods have reported high accuracy and robustness, but these methods have not fully utilized multiple modalities for feature fusion. In order to solve the above problems, a multi-modality feature fusion based video surveillance personnel action recognition method is proposed by combining the two methods. The SlowOnly network is used to extract RGB modality features. The Faster R-CNN and Lite-HRNet networks are used to obtain skeleton modality data. The PoseC3D network is used to extract skeleton modality features. The early and late fusion of RGB modality features and skeleton modality features are performed. Then the action recognition results of video surveillance personnel are obtained. The experimental results on the NTU RGB+D public dataset under the X-Sub standard show the following points. In the action recognition model based on a single skeleton modality, PoseC3D has a higher recognition accuracy than GCN methods, reaching 92.8%. In the action recognition model based on RGB modality, recognition accuracy has reached 95.4%. The action recognition model based on multi-modality feature fusion has a higher recognition accuracy than the recognition model based on a single modality, reaching 96.1%.

关键词： Accuracy Computational modeling Feature extraction Video surveillance Skeleton Robustness Personnel Standards Monitoring

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