Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage.
Recently, Feature Models have been extended with context to capture the correlation of configuration options with contextual influences and user customizations. Unfortunately, this extension makes the task of detecting anomalies harder. In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. By extending the reconfigurator engine HyVarRec, we present findings evidencing that QBF solvers can outperform the common techniques for anomaly analysis.
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（中国科学技术大学生命科学与医学部生物医学工程学院）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州高等研究院MIRACLE中心）；Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Stanford University（斯坦福大学）
Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection
School of Computing and Information Systems（计算与信息学院）；The University of Melbourne（墨尔本大学）；School of Coumputing and Information（计算与信息学院）；Department of Electrical and Electronic Engineering（电气与电子工程系）
Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection
School of Computing, Engineering \& Mathematical Sciences, La Trobe University, Melbourne, Australia School of Engineering；Applied Sciences, GIFT University, Gujranwala 52250, Pakistan
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness
Department of AI Semiconductor Engineering, Hanyang University, Seoul, Korea（汉阳大学人工智能半导体工程系）；Department of Electronic Engineering, Hanyang University, Seoul, Korea（汉阳大学电子工程系）
Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval
Hanoi University of Science and Technology（河内科技大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）；Vietnam National University, Ho Chi Minh City（胡志明市国家大学）；VinUniversity；Vietnamese-German University（越南德国大学）
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学海洋地质国家重点实验室、自动化与智能传感学院）
Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments
UC San Diego（加州大学圣地亚哥分校）；Massachusetts Institute of Technology（麻省理工学院）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Tongji University（同济大学）；Tsinghua University（清华大学）；University of Pennsylvania（宾夕法尼亚大学）
Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection
School of Engineering and Built Environment, Griffith University（格里菲斯大学工程与环境学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
Telenor Research and Innovation（Telenor研究与创新）；Telenor Denmark OSS & Tech Analytics（Telenor丹麦OSS与技术分析）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；SINTEF Digital（SINTEF数字）；Hance
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；The Hong Kong Polytechnic University（香港理工大学）；Pazhou Lab（琶洲实验室）
Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask
Peng Cheng Laboratory（鹏城实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Xi’an University of Electronic Science and Technology（西安电子科技大学）；Tencent（腾讯）
Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling
KIOS Research and Innovation Center of Excellence（基奥斯研究与创新中心）；University of Cyprus（塞浦路斯大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Aalborg University（奥尔堡大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）；Technical University of Denmark（丹麦技术大学）；Milestone Systems（Milestone系统）；University of Bucharest（布加勒斯特大学）；University of Surrey（萨里大学）
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Information Technology, Adelaide University（阿德莱德大学计算机科学与信息科技学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems
Yeshiva University（耶鲁大学）；Yale University（耶鲁大学）；University of California, San Diego（加州大学圣地亚哥分校）；North Carolina State University（北卡罗来纳州立大学）；University of Pennsylvania（宾夕法尼亚大学）
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Department of CS \& IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran；Department of Computer Engineering University of Zanjan (ZNU) Zanjan, Iran
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Provincial Engineering Research Center for Real-Time SmartTech in Urban Security Governance（浙江省实时智能科技在城市安全治理中的工程研究中心）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
