IF 2 4区计算机科学

ETRI Journal Pub Date : 2026-10-20 Epub Date: 2026-09-24 DOI: 10.4218/etrij.2026-0204

Yangkoo Lee, Eun Hye Jang, Mi Chang, Kezia Amanda Kurniadi, Jiwoo Han, Woojin Kim, Daesub Yoon

{"title":"STPA-based analysis of driver misuse risk scenarios in level 3 automated driving","authors":"Yangkoo Lee, Eun Hye Jang, Mi Chang, Kezia Amanda Kurniadi, Jiwoo Han, Woojin Kim, Daesub Yoon","doi":"10.4218/etrij.2026-0204","DOIUrl":"https://doi.org/10.4218/etrij.2026-0204","url":null,"abstract":"This paper proposes an analytical framework that integrates the SOTIF perspective with System-Theoretic Process Analysis (STPA) to systematically analyze driver misuse in Level 3 automated driving environments. In Lv.3 systems, drivers must intervene when Takeover Requests (TOR) occur, making misuse a critical safety risk even when the system operates as intended. Driver misuse is modeled as inadequate control actions within the control structure, and the four psychological states that induce misuse—overtrust, distrust, distraction, and interference—are treated as the causal factors of those unsafe control actions. Following the STPA procedure, 41 causal scenarios were derived and evaluated using severity, likelihood, and controllability. Clustering analysis was then applied to identify recurring risk patterns and representative Risk Archetypes. The results show that many high-risk scenarios are strongly related to drivers' failures to perceive or properly respond to TOR. The proposed framework provides a structured basis for understanding driver misuse risks and supports the design of driver monitoring, improved HMI strategies, and multilayer safety assistance mechanisms.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 5","pages":"868-883"},"PeriodicalIF":2.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2026-0204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149222842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prey carrying and passive wing load alleviation reconfigure longitudinal stabilization in a gliding barn owl. 猎物携带和被动机翼负荷减轻重新配置纵向稳定滑翔仓鸮。

IF 3.5 3区计算机科学

Bioinspiration & Biomimetics Pub Date : 2026-10-20 DOI: 10.1088/1748-3190/aeb0eb

Samuel C Stanton, Elias S Laroussi

{"title":"Prey carrying and passive wing load alleviation reconfigure longitudinal stabilization in a gliding barn owl.","authors":"Samuel C Stanton, Elias S Laroussi","doi":"10.1088/1748-3190/aeb0eb","DOIUrl":"10.1088/1748-3190/aeb0eb","url":null,"abstract":"An experimentally informed model of gliding barn-owl flight with prey carriage and passive wing load alleviation is developed to examine trim, static stability, and dynamic response. Baseline trim predictions agree with prior owl data and are shown to be more consistent with a minimum-sink condition than with minimum drag coefficient or maximum lift-to-drag ratio. A generalized static-stability analysis demonstrates that vertical center-of-gravity offset and drag-induced moments, including those associated with prey carriage, contribute directly to the pitching-moment balance, so that the pitching-moment slope is not determined by fore-aft center-of-gravity position alone. Prey carriage is found to drive the generalized static margin further into the unstable region and to shorten the time to double amplitude of the dominant mode. In the dynamic model, passive wing load alleviation attenuates the dominant unstable mode and shifts its character, as measured by unit-invariant modal participation factors, from a fast short-period-like response to a slower, more phugoid-like response as compliance increases. With passive alleviation present, modest proportional wing-pitch feedback is sufficient to place the system poles in the stable left-half plane. These results indicate that load carriage and passive compliance can reshape the stability properties of gliding flight, reducing the control required to maintain steady trajectories without requiring a classically stable configuration.","PeriodicalId":55377,"journal":{"name":"Bioinspiration & Biomimetics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM-guided semantic-aware trajectory optimization for UAV path planning using environmental context 基于环境上下文的llm引导语义感知轨迹优化无人机路径规划

IF 2 4区计算机科学

ETRI Journal Pub Date : 2026-10-20 Epub Date: 2026-10-06 DOI: 10.4218/etrij.2026-0245

Hanseob Lee, Hoon Jung

{"title":"LLM-guided semantic-aware trajectory optimization for UAV path planning using environmental context","authors":"Hanseob Lee, Hoon Jung","doi":"10.4218/etrij.2026-0245","DOIUrl":"https://doi.org/10.4218/etrij.2026-0245","url":null,"abstract":"Autonomous unmanned aerial vehicle path planning in urban environments requires both geometric safety and semantic awareness. Conventional planners based on fixed costs or predefined risk models struggle to reflect mission-dependent semantic priorities. We present a large language model (LLM)-guided semantic-aware planning framework in which the LLM serves as a bounded high-level decision module. OpenStreetMap data are transformed into class-wise semantic potential fields, and a route-relative context and natural-language mission description are used to infer validated semantic modulation coefficients. A deterministic B-spline optimizer generates smooth paths by jointly considering multiple factors. Comparisons with existing models demonstrate a competitive tradeoff between path efficiency and semantic safety. Mission-conditioned experiments reveal that different mission descriptions produce distinct semantic policies and path geometries under identical environmental conditions. Reliability analysis indicates that the deterministic downstream planner maintains stable safety-related performance under context perturbations and stochastic LLM outputs.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"48 5","pages":"896-910"},"PeriodicalIF":2.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2026-0245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149222794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-Guided Decomposition from a Long-Exposure Nighttime Snapshot into Video Sequence 从长曝光夜间快照到视频序列的事件引导分解

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2026-10-20 DOI: 10.1007/s11263-026-03057-x

Jianbo Cao, Yuqi Han, Siming Zheng, Guilin Xu, Qiang Zhang, Jinli Suo

引用次数: 0

Towards understanding and monitorability of reasoning LLMs 对推理法学硕士的理解和监控

IF 29.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2026-10-19 DOI: 10.1038/s42256-026-01333-6

引用次数: 0

Swin-WaveNet: frequency-guided boundary restoration for robust skin lesion segmentation. swwin - wavenet：用于稳健皮肤病变分割的频率引导边界恢复。

IF 6.7 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2026-10-19 DOI: 10.1186/s42492-026-00235-z

Zhiping Dan, Run Zhang, Tinglong Tang, Yirong Wu, Xiaosheng Yu, Shuifa Sun, Ben Wang

{"title":"Swin-WaveNet: frequency-guided boundary restoration for robust skin lesion segmentation.","authors":"Zhiping Dan, Run Zhang, Tinglong Tang, Yirong Wu, Xiaosheng Yu, Shuifa Sun, Ben Wang","doi":"10.1186/s42492-026-00235-z","DOIUrl":"https://doi.org/10.1186/s42492-026-00235-z","url":null,"abstract":"Accurate skin lesion segmentation remains difficult when dermoscopic boundaries are fragmented by hair occlusion, uneven illumination, or weak local contrast. To address this problem, the authors propose Swin-WaveNet, a frequency-guided encoder-decoder that combines a Swin Transformer backbone with wavelet decomposition and selective state-space modeling. Rather than passing skip features directly to the decoder, the proposed method separates low-frequency semantic information from high-frequency details, selectively refines boundary-related responses, and suppresses artifact-induced activation prior to feature reconstruction. The main results are reported as the mean and standard deviation of the sample over three runs. Swin-WaveNet achieved a Dice score of 89.69% ± 0.51% and a 95th-percentile Hausdorff distance (HD95) of [Formula: see text] pixels on the International Skin Imaging Collaboration (ISIC) 2018 dataset. Without fine-tuning on ISIC 2017, it obtained a Dice score of 91.69% ± 1.36% and an HD95 of [Formula: see text] pixels. On Kvasir-SEG, the model achieved a Dice score of 91.63% ± 0.57% and an HD95 of [Formula: see text] pixels. In a single-run component ablation, the complete model improved the Dice score by 0.96 percentage points and reduced HD95 by 1.16 pixels relative to the internal baseline. These results indicate that frequency-selective feature processing can help preserve weak or interrupted lesion boundaries.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DA-S²RL: Disease-aware shared-specific representation learning for heterogeneous image-EHR Alzheimer's disease diagnosis. DA-S2RL：异构图像- ehr诊断阿尔茨海默病的疾病感知共享特异性表征学习。

IF 7.2 1区计算机科学

Neural Networks Pub Date : 2026-10-19 DOI: 10.1016/j.neunet.2026.109760

Wei Wang, Yu Shi, Juan Cheng, Yu Liu

{"title":"DA-S2RL: Disease-aware shared-specific representation learning for heterogeneous image-EHR Alzheimer's disease diagnosis.","authors":"Wei Wang, Yu Shi, Juan Cheng, Yu Liu","doi":"10.1016/j.neunet.2026.109760","DOIUrl":"https://doi.org/10.1016/j.neunet.2026.109760","url":null,"abstract":"Combining electronic health records (EHR) with medical images is important for improving the diagnosis of Alzheimer's disease (AD). However, neuroimages and EHR differ greatly in semantic content and data distribution. Neuroimages mainly reflect brain structure and metabolic abnormalities, while EHR records clinical symptoms and behavioral changes. Directly fusing these heterogeneous modalities may mix shared disease information with modality-specific diagnostic cues, resulting in redundant and ambiguous representations. To address this problem, we propose DA-S2RL, a disease-aware shared-specific multimodal representation learning framework for AD diagnosis. DA-S2RL organizes heterogeneous image and EHR features into disease-related shared semantics and modality-specific diagnostic residuals. The core of DA-S2RL is the shared-specific cross-modal coding (SCC) module. It strengthens cross-modal semantic consistency, suppresses redundant correlations, and enables mutual refinement between shared semantics and diagnostic residuals. Before SCC, image features are integrated into a compact image representation, and EHR features are encoded into the same feature space. Experiments on ADNI and NACC demonstrate that explicitly organizing heterogeneous image-EHR representations into shared disease semantics and modality-specific residuals improves multimodal diagnostic learning under different image-EHR settings.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"206 Pt A","pages":"109760"},"PeriodicalIF":7.2,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance 用人类评估可解释的人工智能：信任、依赖、理解和表现

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-19 DOI: 10.1145/3856819

Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan

{"title":"Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance","authors":"Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan","doi":"10.1145/3856819","DOIUrl":"https://doi.org/10.1145/3856819","url":null,"abstract":"Explainable AI (XAI) is often evaluated through claims about trust, reliance, understanding, and performance, yet studies measure these constructs differently and frequently overlook confounding factors. This PRISMA-guided systematic review with narrative synthesis examines human-centered XAI evaluation across these four constructs. The review covers publications from 2015 to 2025. A merged search combined an OpenAlex-informed DOI/title catalog, prior surveys, citation chasing, and a Scopus coverage-completion stage identifying 9,026 records. A targeted revision update added two eligible publications, yielding 119 validated full-text publications. The synthesis highlights three patterns: trust and reliance can diverge; perceived understanding can exceed objective understanding; and interface salience or perceived accuracy can confound explanation effects. Aggregate advice acceptance, for example, cannot distinguish beneficial reliance from automation bias. We therefore recommend measuring acceptance separately for correct and incorrect AI outputs, testing understanding beyond self-report, documenting study context and interface conditions, and evaluating explanations under uncertainty or model failure. The review contributes construct definitions, a synthesis across designs and domains, an analysis of evaluation measures, and practical reporting guidelines. It also examines generated explanations and citation-based interfaces, supporting more reproducible and interpretable human-centered XAI evaluations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection and Mitigation of Data Integrity Attacks in Vehicle Platoons Using Unknown-Input Kalman Filters and Penalized Weighted Least Squares. 基于未知输入卡尔曼滤波和惩罚加权最小二乘的车辆队列数据完整性攻击检测与缓解。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-10-19 DOI: 10.1109/TCYB.2026.3740509

Chirag Tiwari Rao, Amir Dadashi, Amir Ameli, Hassan Naser

{"title":"Detection and Mitigation of Data Integrity Attacks in Vehicle Platoons Using Unknown-Input Kalman Filters and Penalized Weighted Least Squares.","authors":"Chirag Tiwari Rao, Amir Dadashi, Amir Ameli, Hassan Naser","doi":"10.1109/TCYB.2026.3740509","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3740509","url":null,"abstract":"This study enhances the security of vehicle platoon systems against false data injection attacks (FDIAs), which can compromise critical states, such as position, velocity, and acceleration. The proposed approach ensures safe and stable autonomous vehicle coordination by detecting and mitigating such attacks under flexible platoon conditions. Unlike the existing methods, it supports arbitrary communication topologies, varying platoon sizes, dynamic lead-vehicle behavior, and accounts for process and measurement noise-making it applicable to a wide range of real-world scenarios. A state-space model is formulated to capture the system dynamics, and an unknown input Kalman filter (UIKF) is employed to estimate both vehicle states and the lead vehicles acceleration as an unknown input. To counteract malicious data, a penalized weighted least squares (PWLSs) method is used to downweight suspicious measurements based on discrepancies between the observed and estimated values. The simulation results demonstrate the effectiveness of the proposed framework in detecting and mitigating FDIAs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Adaptive Background-Attentive Network With Anomaly Suppression for Hyperspectral Anomaly Detection. 基于异常抑制的自适应背景关注网络高光谱异常检测。

IF 9.7 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2026-10-19 DOI: 10.1109/TNNLS.2026.3741742

Xu Liu, Hao Yuan, Bohui Tang, Qingquan Li, Chao Yang, Tiezhu Shi, Daming Zhu

{"title":"Self-Adaptive Background-Attentive Network With Anomaly Suppression for Hyperspectral Anomaly Detection.","authors":"Xu Liu, Hao Yuan, Bohui Tang, Qingquan Li, Chao Yang, Tiezhu Shi, Daming Zhu","doi":"10.1109/TNNLS.2026.3741742","DOIUrl":"https://doi.org/10.1109/TNNLS.2026.3741742","url":null,"abstract":"In recent years, deep learning has emerged as one of the prominent technologies in hyperspectral anomaly detection (HAD) due to its powerful feature extraction capability. Deep learning-based methods aim to reconstruct the background image and use the reconstruction error as a criterion for anomaly detection. However, existing methods rarely exploit the spatial and spectral differences between background and anomalies to suppress anomaly reconstruction. To overcome this limitation, we propose a self-adaptive background-attentive network with anomaly suppression (BASNet). Specifically, a pyramid similarity downsampling module (PSDM) is employed to reduce the proportion of anomalies in the input image and improve multiscale background representation. To enhance the network's attention to background information, a multiscale attention module (MAM) is designed, and an inverse attention strategy is adopted to guide the network to focus on background regions. In addition, a depthwise separable blind-block convolution module (DBCM) is constructed for pixel reconstruction. This module applies weighted multiscale blind-block convolutions according to the anomaly score of each pixel, allowing the network to effectively handle anomalies at different scales. Due to the stochastic nature of deep learning, the optimal stopping epoch for network training is not fixed. Therefore, reconstructed image difference (RID) is developed to dynamically determine the stopping epoch. Extensive experiments on eight datasets demonstrate that the proposed BASNet achieves satisfactory detection performance.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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