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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Alexander Stringer Geoffrey Dolinger Timothy Sharp Debra Hogue Joseph Karch Lesya Borowska Justin G. Metcalf US Air Force's Oklahoma City Air Logistics Complex and the 76 Software Engineering Group School of Electrical and Computer Engineering and the Advanced Radar Research Center at the University of Oklahoma Department of Computer Science University of Oklahoma

Modern navigation applications are confronting the challenge of path identification and prediction. Because of the inherent noisiness of measurement techniques, positional data often contains errors. Over time, when attempting to predict future positions, these errors can compound, causing drift in the target's path. Filtering or regression techniques can use periodic high-fidelity measurements, like those from GPS sensors, to correct positional data and reduce historic errors. These techniques have been much less effective in specialized environments where GPS is intermittent or denied. At the same time, the development of semi- and fully-autonomous systems has increased the need for accurate predictive navigation. To address this problem, previous methods were proposed for repairing tracking data using causality-aware machine learning (ML). It combined a long short-term memory (LSTM) network that predicted target paths with the non-dominated sorting genetic algorithm II (NSGA-II) which identified and evaluated counterfactual paths. Here te authors expand on that work by improving the simulated data used to train and test the system, expanding the GA by adding additional first principles-based objective functions, improving upon the LSTM implementation, and utilizing Extended Kalman Filter (EKF) pre-processing. System testing is conducted on Matlab-generated navigation scenarios, and results are compared to both EKF correction and spline interpolation. The proposed counterfactual track repair (CTR) tool produces paths with less repeatability than traditional approaches. However, it is shown to consistently generate more realistic path corrections, lower error in predictions based on its corrected paths, and is highly configurable, demonstrating the value and utility of this approach.

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Thematic Domain Analysis for Ocean Modeling

arXiv

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arXiv 2022年

作者： Jung, Reiner Gundlach, Sven Hasselbring, Wilhelm Software Engineering Group Department of Computer Science Kiel University Christian-Albrechts-Platz 4 Kiel24118 Germany

Ocean science is a discipline that employs ocean models as an essential research asset. Such scientific modeling provides mathematical abstractions of real-world systems, e.g., the oceans. These models are then coded as implementations of the mathematical abstractions. The developed software systems are called models of the real-world system. To advance the state in engineering such ocean models, we intend to better understand how ocean models are developed and maintained in ocean science. In this paper, we present the results of semi-structured interviews and the Thematic Analysis (TA) of the interview results to analyze the domain of ocean modeling. Thereby, we identified developer requirements and impediments to model development and evolution, and related themes. This analysis can help to understand where methods from software engineering should be introduced and which challenges need to be addressed. We suggest that other researchers extend and repeat our TA with model developers and research software engineers working in related domains to further advance our knowledge and skills in scientific modeling. © 2022, CC BY-NC-ND.

关键词： Oceanography

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S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation

arXiv

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arXiv 2024年

作者： Wang, Gui Li, Yuexiang Chen, Wenting Ding, Meidan Cheah, Wooi Ping Qu, Rong Ren, Jianfeng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Group University Engineering Research Center of Digital Medicine and Healthcare Guangxi Medical University Guangxi Nanning China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong School of Computer Science University of Nottingham Nottingham United Kingdom

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a Small-Size-Sensitive Mamba (S3-Mamba), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S3-Mamba, especially in segmenting small lesions. Our code is available at https://***/ErinWang2023/S3-Mamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

arXiv

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arXiv 2024年

作者： Tian, Hongduan Liu, Feng Liu, Tongliang Du, Bo Cheung, Yiu-Ming Han, Bo TMLR Group Hong Kong Baptist University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong TMLR Group University of Melbourne Australia Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan Univeristy China

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training

arXiv

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arXiv 2023年

作者： Liu, Chong Zhang, Yuqi Wang, Hongsong Chen, Weihua Wang, Fan Huang, Yan Shen, Yi-Dong Wang, Liang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Alibaba Group Beijing100102 China Department of Computer Science and Engineering Southeast University Nanjing210096 China Beijing100190 China

Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. However, the close relations between coarse- and fine-grained representations for each modality are important for image-text retrieval but almost neglected. As a result, such previous works inevitably suffer from low retrieval accuracy or heavy computational cost. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework. This framework is consistent with human cognition, as humans simultaneously pay attention to the entire sample and regional elements to understand the semantic content. To this end, a Token-Guided Dual Transformer (TGDT) architecture which consists of two homogeneous branches for image and text modalities, respectively, is proposed for image-text retrieval. The TGDT incorporates both coarse- and fine-grained retrievals into a unified framework and beneficially leverages the advantages of both retrieval approaches. A novel training objective called Consistent Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra- and inter-modal semantic consistencies between images and texts in the common embedding space. Equipped with a two-stage inference method based on the mixed global and local cross-modal similarity, the proposed method achieves state-of-the-art retrieval performances with extremely low inference time when compared with representative recent approaches. Code is publicly available: ***/LCFractal/TGDT. © 2023, CC BY.

关键词： Semantics

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Design and Implementation of a Following Vehicle Based on ROS and UWB

Design and Implementation of a Following Vehicle Based on RO...

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Automation, Control and Robots (ICACR), International Conference on

作者： Yun-Shuai Yu Chin-Wei Chen Department of Computer Science and Information Engineering National Formosa University Yunlin Taiwan Software Research & Development Center Hon Hai Technology Group (Foxconn) New Taipei City Taiwan

Autonomous vehicle technology has gained significant attention in various industries recently. This paper presents the design and implementation of a following vehicle that utilizes the Robot Operating System (ROS) and Ultra-Wideband (UWB) RF transceiver modules to achieve precise target localization, map the surrounding environment, and navigate accordingly. A mobile application has also been developed to track the following vehicle’s position.

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The effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks

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Journal of Optical Communications 2024年第s1期45卷 s253-s259页

作者： Eid, Mahmoud M. A. Zaki Rashed, Ahmed Nabih Ahammad, Mohd. Sultan Paul, Bikash Kumar Ahmed, Kawsar Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Sukrabad Dhaka1207 Bangladesh

This study presents the effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks. The received signal power and max Q factor are measured in the presence of vertical-cavity surface-emitting lasers (VCSELs) bias current and modulation current at maximum propagation distance for the wireless network. The signal is enhanced with increasing of bias and modulation peak currents of the VCSEL device. The optimum received power and max. Q factor is also examined at Tx./Rx. the pointing error of 0.1 mrad and propagation reach of 5 km at available bit rates transmission of 10 Gb/s. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Surface emitting lasers

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Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing 22

Effectively Generating Vulnerable Transaction Sequences in S...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Jianzhong Su Hong-Ning Dai Lingjun Zhao Zibin Zheng Xiapu Luo School of Software Engineering Sun Yat-sen University China and Ant Group China Department of Computer Science Hong Kong Baptist University China School of Software Engineering Sun Yat-sen University China The Hong Kong Polytechnic University China

ISBN: (纸本)9781450394758

As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).

关键词： Reinforcement learning Fuzzing Smart contract

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

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International Conference on computer Vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

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Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

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computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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