OpenRaiser/Intern-Atlas huggingface dataset:
Attention-aggregated Attack for Boosting the Transferability of Facial Adversarial Examples
Venue: arXiv cs.CV 2025
Year: 2025

Adversarial examples have revealed the vulnerability of deep learning models and raised serious concerns about information security. The transfer-based attack is a hot topic in black-box attacks that are practical to real-world scenarios where the training datasets, parameters, and structure of the target model are unknown to the attacker. However, few methods consider the particularity of class-specific deep models for fine-grained vision tasks, such as face recognition (FR), giving rise to unsatisfactory attacking performance. In this work, we first investigate what in a face exactly contributes to the embedding learning of FR models and find that both decisive and auxiliary facial features are specific to each FR model, which is quite different from the biological mechanism of human visual system. Accordingly we then propose a novel attack method named Attention-aggregated Attack (AAA) to enhance the transferability of adversarial examples against FR, which is inspired by the attention divergence and aims to destroy the facial features that are critical for the decision-making of other FR models by imitating their attentions on the clean face images. Extensive experiments conducted on various FR models validate the superiority and robust effectiveness of the proposed method over existing methods.
CB-cPIR: Code-Based Computational Private Information Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

A private information retrieval (PIR) scheme is a protocol that allows a user to retrieve a file from a database without revealing the identity of the desired file to a curious database. Given a distributed data storage system, efficient PIR can be achieved by making assumptions about the colluding capabilities of the storage servers holding the database. If these assumptions turn out to be incorrect, privacy is lost. In this work, we focus on the worst-case assumption: full collusion or, equivalently, viewing the storage system virtually as a single honest-but-curious server. We present CB-cPIR, a single-server code-based computational private information retrieval (cPIR) scheme that derives security from code-based cryptography. Specifically, the queries are protected by the hardness of decoding a random linear code. The scheme is heavily inspired by the pioneering code-based cPIR scheme proposed by Holzbaur, Hollanti, and Wachter-Zeh in [Holzbaur et al., "Computational Code-Based Single-Server Private Information Retrieval", 2020 IEEE ISIT] and fixes the vulnerabilities of the original scheme arising from highly probable rank differences in submatrices of the user's query. Recently, a new vulnerability was observed in [Lage, Bartz, "On the Security of a Code-Based PIR Scheme"], a simple modification to the scheme now fixes this vulnerability. For further validation of our scheme, we draw comparisons to the state-of-the-art lattice-based cPIR schemes.
AI-Based Feedback in Counselling Competence Training of Prospective Teachers
Venue: arXiv cs.HC 2025
Year: 2025

This study explores the use of AI-based feedback to enhance the counselling competence of prospective teachers. An iterative block seminar was designed, incorporating theoretical foundations, practical applications, and AI tools for analysing verbal, paraverbal, and nonverbal communication. The seminar included recorded simulated teacher-parent conversations, followed by AI-based feedback and qualitative interviews with students. The study investigated correlations between communication characteristics and conversation quality, student perceptions of AI-based feedback, and the training of AI models to identify conversation phases and techniques. Results indicated significant correlations between nonverbal and paraverbal features and conversation quality, and students positively perceived the AI feedback. The findings suggest that AI-based feedback can provide objective, actionable insights to improve teacher training programs. Future work will focus on refining verbal skill annotations, expanding the dataset, and exploring additional features to enhance the feedback system.
Directed Greybox Fuzzing via Large Language Model
Venue: arXiv cs.CR 2025
Year: 2025

Directed greybox fuzzing (DGF) focuses on efficiently reaching specific program locations or triggering particular behaviors, making it essential for tasks like vulnerability detection and crash reproduction. However, existing methods often suffer from path explosion and randomness in input mutation, leading to inefficiencies in exploring and exploiting target paths. In this paper, we propose HGFuzzer, an automatic framework that leverages the large language model (LLM) to address these challenges. HGFuzzer transforms path constraint problems into targeted code generation tasks, systematically generating test harnesses and reachable inputs to reduce unnecessary exploration paths significantly. Additionally, we implement custom mutators designed specifically for target functions, minimizing randomness and improving the precision of directed fuzzing. We evaluated HGFuzzer on 20 real-world vulnerabilities, successfully triggering 17, including 11 within the first minute, achieving a speedup of at least 24.8x compared to state-of-the-art directed fuzzers. Furthermore, HGFuzzer discovered 9 previously unknown vulnerabilities, all of which were assigned CVE IDs, demonstrating the effectiveness of our approach in identifying real-world vulnerabilities.
A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

The fusion of low-spatial-resolution hyperspectral images (HSIs) with high-spatial-resolution conventional images (e.g., panchromatic or RGB) has played a significant role in recent advancements in HSI super-resolution. However, this fusion process relies on the availability of precise alignment between image pairs, which is often challenging in real-world scenarios. To mitigate this limitation, we propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN). Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information at an early stage. Next, an Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies, followed by a dedicated multi-scale fusion block. To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear interpolation with depthwise separable convolutions. Experimental evaluations on two publicly available hyperspectral datasets demonstrate the competitive performance of our method.
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
Venue: arXiv cs.LG 2025
Year: 2025

Score-based Generative Models (SGMs) approximate a data distribution by perturbing it with Gaussian noise and subsequently denoising it via a learned reverse diffusion process. These models excel at modeling complex data distributions and generating diverse samples, achieving state-of-the-art performance across domains such as computer vision, audio generation, reinforcement learning, and computational biology. Despite their empirical success, existing Wasserstein-2 convergence analysis typically assume strong regularity conditions-such as smoothness or strict log-concavity of the data distribution-that are rarely satisfied in practice. In this work, we establish the first non-asymptotic Wasserstein-2 convergence guarantees for SGMs targeting semiconvex distributions with potentially discontinuous gradients. Our upper bounds are explicit and sharp in key parameters, achieving optimal dependence of $O(\sqrt{d})$ on the data dimension $d$ and convergence rate of order one. The framework accommodates a wide class of practically relevant distributions, including symmetric modified half-normal distributions, Gaussian mixtures, double-well potentials, and elastic net potentials. By leveraging semiconvexity without requiring smoothness assumptions on the potential such as differentiability, our results substantially broaden the theoretical foundations of SGMs, bridging the gap between empirical success and rigorous guarantees in non-smooth, complex data regimes.
Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement of generative image technology has introduced significant security concerns, particularly in the domain of face generation detection. This paper investigates the vulnerabilities of current AI-generated face detection systems. Our study reveals that while existing detection methods often achieve high accuracy under standard conditions, they exhibit limited robustness against adversarial attacks. To address these challenges, we propose an approach that integrates adversarial training to mitigate the impact of adversarial examples. Furthermore, we utilize diffusion inversion and reconstruction to further enhance detection robustness. Experimental results demonstrate that minor adversarial perturbations can easily bypass existing detection systems, but our method significantly improves the robustness of these systems. Additionally, we provide an in-depth analysis of adversarial and benign examples, offering insights into the intrinsic characteristics of AI-generated content. All associated code will be made publicly available in a dedicated repository to facilitate further research and verification.
Polar Coordinate-Based 2D Pose Prior with Neural Distance Field
Venue: arXiv cs.CV 2025
Year: 2025

Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB videos have achieved impressive performance on public datasets, their effectiveness in real-world sports scenarios is often hindered by motion blur, occlusions, and domain shifts across different pose representations. Fine-tuning these models can partially alleviate such challenges but typically requires large-scale annotated data and still struggles to generalize across diverse sports environments. To address these limitations, we propose a 2D pose prior-guided refinement approach based on Neural Distance Fields (NDF). Unlike existing approaches that rely solely on angular representations of human poses, we introduce a polar coordinate-based representation that explicitly incorporates joint connection lengths, enabling a more accurate correction of erroneous pose estimations. Additionally, we define a novel non-geodesic distance metric that separates angular and radial discrepancies, which we demonstrate is better suited for polar representations than traditional geodesic distances. To mitigate data scarcity, we develop a gradient-based batch-projection augmentation strategy, which synthesizes realistic pose samples through iterative refinement. Our method is evaluated on a long jump dataset, demonstrating its ability to improve 2D pose estimation across multiple pose representations, making it robust across different domains. Experimental results show that our approach enhances pose plausibility while requiring only limited training data. Code is available at: https://github.com/QGAN2019/polar-NDF.
LogisticsVLN: Vision-Language Navigation For Low-Altitude Terminal Delivery Based on Agentic UAVs
Venue: arXiv cs.RO 2025
Year: 2025

The growing demand for intelligent logistics, particularly fine-grained terminal delivery, underscores the need for autonomous UAV (Unmanned Aerial Vehicle)-based delivery systems. However, most existing last-mile delivery studies rely on ground robots, while current UAV-based Vision-Language Navigation (VLN) tasks primarily focus on coarse-grained, long-range goals, making them unsuitable for precise terminal delivery. To bridge this gap, we propose LogisticsVLN, a scalable aerial delivery system built on multimodal large language models (MLLMs) for autonomous terminal delivery. LogisticsVLN integrates lightweight Large Language Models (LLMs) and Visual-Language Models (VLMs) in a modular pipeline for request understanding, floor localization, object detection, and action-decision making. To support research and evaluation in this new setting, we construct the Vision-Language Delivery (VLD) dataset within the CARLA simulator. Experimental results on the VLD dataset showcase the feasibility of the LogisticsVLN system. In addition, we conduct subtask-level evaluations of each module of our system, offering valuable insights for improving the robustness and real-world deployment of foundation model-based vision-language delivery systems.
Augmenting Human Cognition through Everyday AR
Venue: arXiv cs.HC 2025
Year: 2025

As spatial computing and multimodal LLMs mature, AR is tending to become an intuitive "thinking tool," embedding semantic and context-aware intelligence directly into everyday environments. This paper explores how always-on AR can seamlessly bridge digital cognition and physical affordances, enabling proactive, context-sensitive interactions that enhance human task performance and understanding.
Ruled by the Representation Space: On the University's Embrace of Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

This paper explores the implications of universities' rapid adoption of large language models (LLMs) for studying, teaching, and research by analyzing the logics underpinning their representation space. It argues that by uncritically adopting LLMs, the University surrenders its autonomy to a field of heteronomy, that of generative AI, whose norms are not democratically shaped. Unlike earlier forms of rule-based AI, which sought to exclude human judgment and interpretation, generative AI's new normative rationality is explicitly based on the automation of moral judgment, valuation, and interpretation. By integrating LLMs into pedagogical and research contexts before establishing a critical framework for their use, the University subjects itself to being governed by contingent, ever-evolving, and domain-non-specific norms that structure the model's virtual representation space and thus everything it generates.
Faster MoE LLM Inference for Extremely Large Models
Venue: arXiv cs.CL 2025
Year: 2025

Sparse Mixture of Experts (MoE) large language models (LLMs) are gradually becoming the mainstream approach for ultra-large-scale models. Existing optimization efforts for MoE models have focused primarily on coarse-grained MoE architectures. With the emergence of DeepSeek Models, fine-grained MoE models are gaining popularity, yet research on them remains limited. Therefore, we want to discuss the efficiency dynamic under different service loads. Additionally, fine-grained models allow deployers to reduce the number of routed experts, both activated counts and total counts, raising the question of how this reduction affects the trade-off between MoE efficiency and performance. Our findings indicate that while deploying MoE models presents greater challenges, it also offers significant optimization opportunities. Reducing the number of activated experts can lead to substantial efficiency improvements in certain scenarios, with only minor performance degradation. Reducing the total number of experts provides limited efficiency gains but results in severe performance degradation. Our method can increase throughput by at least 10\% without any performance degradation. Overall, we conclude that MoE inference optimization remains an area with substantial potential for exploration and improvement.
Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images
Venue: arXiv cs.CV 2025
Year: 2025

Text-based pedestrian search (TBPS) in full images aims to locate a target pedestrian in untrimmed images using natural language descriptions. However, in complex scenes with multiple pedestrians, existing methods are limited by uncertainties in detection and matching, leading to degraded performance. To address this, we propose UPD-TBPS, a novel framework comprising three modules: Multi-granularity Uncertainty Estimation (MUE), Prototype-based Uncertainty Decoupling (PUD), and Cross-modal Re-identification (ReID). MUE conducts multi-granularity queries to identify potential targets and assigns confidence scores to reduce early-stage uncertainty. PUD leverages visual context decoupling and prototype mining to extract features of the target pedestrian described in the query. It separates and learns pedestrian prototype representations at both the coarse-grained cluster level and the fine-grained individual level, thereby reducing matching uncertainty. ReID evaluates candidates with varying confidence levels, improving detection and retrieval accuracy. Experiments on CUHK-SYSU-TBPS and PRW-TBPS datasets validate the effectiveness of our framework.
Troika algorithm: approximate optimization for accurate clique partitioning and clustering of weighted networks
Venue: arXiv cs.SI 2025
Year: 2025

Clique partitioning is a fundamental network clustering task, with applications in a wide range of computational sciences. It involves identifying an optimal partition of the nodes for a real-valued weighted graph according to the edge weights. An optimal partition is one that maximizes the sum of within-cluster edge weights over all possible node partitions. This paper introduces a novel approximation algorithm named Troika to solve this NP-hard problem in small to mid-sized networks for instances of theoretical and practical relevance. Troika uses a branch-and-cut scheme for branching on node triples to find a partition that is within a user-specified optimality gap tolerance. Troika offers advantages over alternative methods like integer programming solvers and heuristics for clique partitioning. Unlike existing heuristics, Troika returns solutions within a guaranteed proximity to global optimality. And our results indicate that Troika is faster than using the state-of-the-art integer programming solver Gurobi for most benchmark instances. Besides its advantages for solving the clique partitioning problem, we demonstrate the applications of Troika in community detection and portfolio analysis. Troika returns partitions with higher proximity to optimal compared to eight modularity-based community detection algorithms. When used on networks of correlations among stocks, Troika reveals the dynamic changes in the structure of portfolio networks including downturns from the 2008 financial crisis and the reaction to the COVID-19 pandemic. Our comprehensive results based on benchmarks from the literature and new real and random networks point to Troika as a reliable and accurate method for solving clique partitioning instances with up to 5000 edges on standard hardware.
Meta-reasoning Using Attention Maps and Its Applications in Cloud Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Metareasoning, a branch of AI, focuses on reasoning about reasons. It has the potential to enhance robots' decision-making processes in unexpected situations. However, the concept has largely been confined to theoretical discussions and case-by-case investigations, lacking general and practical solutions when the Value of Computation (VoC) is undefined, which is common in unexpected situations. In this work, we propose a revised meta-reasoning framework that significantly improves the scalability of the original approach in unexpected situations. This is achieved by incorporating semantic attention maps and unsupervised 'attention' updates into the metareasoning processes. To accommodate environmental dynamics, 'lines of thought' are used to bridge context-specific objects with abstracted attentions, while meta-information is monitored and controlled at the meta-level for effective reasoning. The practicality of the proposed approach is demonstrated through cloud robots deployed in real-world scenarios, showing improved performance and robustness.
Fixed-Length Dense Fingerprint Representation
Venue: arXiv cs.CV 2025
Year: 2025

Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and well-suited for large-scale matching. However, designing a robust representation that effectively handles diverse fingerprint modalities, pose variations, and noise interference remains a significant challenge. In this work, we propose a fixed-length dense descriptor of fingerprints, and introduce FLARE-a fingerprint matching framework that integrates the Fixed-Length dense descriptor with pose-based Alignment and Robust Enhancement. This fixed-length representation employs a three-dimensional dense descriptor to effectively capture spatial relationships among fingerprint ridge structures, enabling robust and locally discriminative representations. To ensure consistency within this dense feature space, FLARE incorporates pose-based alignment using complementary estimation methods, along with dual enhancement strategies that refine ridge clarity while preserving the original fingerprint modality. The proposed dense descriptor supports fixed-length representation while maintaining spatial correspondence, enabling fast and accurate similarity computation. Extensive experiments demonstrate that FLARE achieves superior performance across rolled, plain, latent, and contactless fingerprints, significantly outperforming existing methods in cross-modality and low-quality scenarios. Further analysis validates the effectiveness of the dense descriptor design, as well as the impact of alignment and enhancement modules on the accuracy of dense descriptor matching. Experimental results highlight the effectiveness and generalizability of FLARE as a unified and scalable solution for robust fingerprint representation and matching. The implementation and code will be publicly available at https://github.com/Yu-Yy/FLARE.
TailBench++: Flexible Multi-Client, Multi-Server Benchmarking for Latency-Critical Workloads
Venue: arXiv cs.DC 2025
Year: 2025

Cloud systems have rapidly expanded worldwide in the last decade, shifting computational tasks to cloud servers where clients submit their requests. Among cloud workloads, latency-critical applications -- characterized by high-percentile response times -- have gained special interest. These applications are present in modern services, representing an important fraction of cloud workloads. This work analyzes common cloud benchmarking suites and identifies TailBench as the most suitable to assess cloud performance with latency-critical workloads. Unfortunately, this suite presents key limitations, especially in multi-server scenarios or environments with variable client arrival patterns and fluctuating loads. To address these limitations, we propose TailBench++, an enhanced benchmark suite that extends TailBench to enable cloud evaluation studies to be performed in dynamic multi-client, multi-server environments. It allows reproducing experiments with varying client arrival times, dynamic query per second (QPS) fluctuations, and multiple servers handling requests. Case studies show that TailBench++ enables more realistic evaluations by capturing a wider range of real-world scenarios.