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Asia-Pacific Conference on Software Engineering

作者： Chaopeng Luo Yuanliang Zhang Haochen He Zhouyang Jia Teng Wang Shulin Zhou Si Zheng Shanshan Li National University of Defense Technology Changsha China Key Laboratory of Satellite Information Intelligent Processing and Application Research Beijing China National Innovation Institute of Defense Technology Beijing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Runtime misconfiguration can lead to software performance degradation and even cause failure. It is usually caused by invalid parameter values set by users. Developers typically perform sanity checks during the configuration parsing stage to prevent invalid parameter values. However, we discovered that even valid values that pass these checks can also lead to unexpected severe consequences. Our study reveals the underlying reason: the value of runtime configuration parameters may interact with other constants and variables when propagated and used, altering its original effect on software behavior. Consequently, parameter values may no longer be valid when encountering complex runtime environments and workloads. Therefore, it is extremely challenging for users to properly configure the software before it starts running. This paper presents the first comprehensive and in-depth study (to the best of our knowledge) on how configuration affects software at runtime through the interaction with constants, and variables (PCV Interaction). Parameter values represent user intentions, constants embody developer knowledge, and variables are typically defined by the runtime environment and workload. This interaction essentially illustrates how different roles jointly determine software behavior. In this regard, we studied 705 configuration parameters from 10 large-scale Software systems. We reveal that a large portion of configuration parameters interact with constants/variables after parsing. We analyzed the interaction patterns and their effects on software runtime behavior. Furthermore, we highlighted the risks of PCV interaction and identified potential issues behind specific interaction patterns. Our findings expose the “double edge” of PCV interaction, providing new insights and motivating the development of new automated techniques to help users configure software appropriately and assist developers in designing better configurations.

关键词： Degradation Runtime environment Focusing Software performance Software systems Software Software reliability

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result proves that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 IEEE.

关键词： Reinforcement learning

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation

arXiv

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arXiv 2022年

作者： Shao, Chenze Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive translation (NAT) models are typically trained with the cross-entropy loss, which forces the model outputs to be aligned verbatim with the target sentence and will highly penalize small shifts in word positions. Latent alignment models relax the explicit alignment by marginalizing out all monotonic latent alignments with the CTC loss. However, they cannot handle non-monotonic alignments, which is non-negligible as there is typically global word reordering in machine translation. In this work, we explore non-monotonic latent alignments for NAT. We extend the alignment space to non-monotonic alignments to allow for the global word reordering and further consider all alignments that overlap with the target sentence. We non-monotonically match the alignments to the target sentence and train the latent alignment model to maximize the F1 score of non-monotonic matching. Extensive experiments on major WMT benchmarks show that our method substantially improves the translation performance of CTC-based models. Our best model achieves 30.06 BLEU on WMT14 En-De with only one-iteration decoding, closing the gap between non-autoregressive and autoregressive models.2 Copyright © 2022, The Authors. All rights reserved.

关键词： Alignment

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CB-YOLOv5 Algorithm for Small Target Detection in Aerial Images 4

CB-YOLOv5 Algorithm for Small Target Detection in Aerial Ima...

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4th International Conference for Emerging technology, INCET 2023

作者： Li, Yingjie Wang, Yitian Zhao, Huaici Shenyang University of Chemical Technology College of Information Engineering Shenyang110142 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China

ISBN: (纸本)9798350335750

Aerial images are often plagued by background interference, and small targets with indistinct features, leading to low accuracy, high false detection rates, and high miss detection rates. To address these challenges, a small target detection algorithm based on YOLOv5, Coordinate-attention and Bidirectional-feature-pyramid-network YOLOv5 (CB-YOLOv5), is proposed in this paper. Considering the small number of pixels occupied by small targets and their indistinct features, a fourth target detection layer is added by concatenating the feature map from quadruple down-sampling during feature extraction with the feature map output from 8-fold up-sampling during feature fusion. Additionally, a coordinate attention mechanism is introduced during the feature extraction stage to improve small target localization and enhance detection accuracy. Finally, the original Path Aggregation Networks (PANet) structure is replaced with a weighted Bidirectional Feature Pyramid Network (BiFPN) structure during the feature fusion stage to improve the network's ability to fuse feature maps of different scales. The simulation results demonstrate that the CB-YOLOv5 improves mAP50 by 9.4%, mAP75 by 9.7%, and mAP50:95 by 7.8% compared to the original YOLOv5s model. Thus, the effectiveness of the CB-YOLOv5 algorithm for detecting small targets in aerial images is validated. © 2023 IEEE.

关键词： Feature extraction

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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A Semantic Overlay on Blockchain for Supporting Traceability in Cross-border Logistics

A Semantic Overlay on Blockchain for Supporting Traceability...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xiaoping Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Traceability is important for maintaining quality of logistics transactions and improving logistics efficiency. Implementing traceability for cross-border logistics is facing challenges when it lacks global centralized logistics information management services. Blockchain techniques enable a decentralized cross-border logistics management by publishing logistics transaction on blockchain transactions to support privacy protection and traceability. Logistics transactions involves complex interactions among participants. How to publish those interactions on a blockchain platform is a challenge. We propose a semantic overlay model that publishes a logistics transaction as a semantic link on a blockchain platform for supporting traceability. A semantic link schema model is designed and published on the blockchain platform to validate semantics links for ensuring the correctness of logistics transaction execution. The proposed semantic overlay on blockchain supports further extension of semantic computing in decentralized applications.

关键词： Privacy Overlay networks Publishing Law Computational modeling Semantics Smart contracts

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Diagnosis method of kiwifruit foliar diseases based on improved YOLOv4-tiny

Diagnosis method of kiwifruit foliar diseases based on impro...

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2021 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2021

作者： Ye, Tianyu Wu, Zhaoming Wang, Shengqian Deng, Chengzhi Tang, Cong School of Information Engineering Nanchang Institute of Technology Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang China

ISBN: (纸本)9781665439602

To solve the problem of slow diagnosis speed of kiwifruit foliar surface diseases and insufficient diagnosis ability of small target diseases, a lightweight network model based on YOLOv4-Tiny is proposed. Firstly, by introducing a depthwise separable convolution at the end of the backbone network, the number of parameters is reduced while the accuracy of diagnosis is guaranteed, and the training and diagnosis speed is improved. Secondly, SPP-Net is introduced in the Neck to realize the fusion of multiple receptive fields and the aggregation of multi-scale information, thereby improving the diagnostic accuracy of the model. Lastly, the multi-feature fusion FPN model is modified to improve the diagnosis ability of small target diseases, and then improve the diagnosis accuracy. The experimental results show that our method is superior to YOLOv4-Tiny on mAP@O.5, diagnosis speed, model size and small target disease diagnosis ability. © 2021 IEEE.

关键词： Convolution

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Multispectral and Hyperspectral Image Fusion Via Joint Low-Rank and Smooth Tensor Prior

Multispectral and Hyperspectral Image Fusion Via Joint Low-R...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fan Li Yong Deng Shaoquan Zhang Yuyun Liang Hong Wang Chengzhi Deng Jun Li School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China School of Information Engineering Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Changjiang Institute of Survey Planning Design and Research Wuhan China TidBit Global Propell AI Norway

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Multispectral and hyperspectral image fusion has emerged as a highly effective technique for obtaining images with both high spatial and spectral resolution. This is an ill-posed problem that poses significant challenges to the optimization solution, which is often mitigated by incorporating low-rank and smooth priors to restrict the solution space. Traditionally, these priors are combined additively using nuclear norm and total variation (TV) regularization. However, the intricate interactions between these priors make it difficult to accurately characterize the prior structure using an additive approach. Moreover, their influence is heavily dependent on the trade-off parameter between the regularization terms. To address this issue, we propose a novel fusion method leveraging a joint low-rank and smooth tensor prior (LRST). The LRST method introduces a tensor nuclear norm on the gradient maps of various dimensions of the target image capitalizing on the analogous manifold structures shared between the original image and its gradient map, which seamlessly integrates the two priors into a unified regularization framework. This facilitates the precise and convenient exploitation of spatial and spectral correlations inherent in the desired hyperspectral image. Experimental findings demonstrate that compared to state-of-the-art fusion methods, the LRST approach yields finely fused images.

关键词： Manifolds Tensors TV Correlation Additives Geoscience and remote sensing Spatial resolution

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