OpenRaiser/Intern-Atlas huggingface dataset:
Physics-Guided Image Dehazing Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Due to the domain gap between real-world and synthetic hazy images, current data-driven dehazing algorithms trained on synthetic datasets perform well on synthetic data but struggle to generalize to real-world scenarios. To address this challenge, we propose \textbf{I}mage \textbf{D}ehazing \textbf{D}iffusion \textbf{M}odels (IDDM), a novel diffusion process that incorporates the atmospheric scattering model into noise diffusion. IDDM aims to use the gradual haze formation process to help the denoising Unet robustly learn the distribution of clear images from the conditional input hazy images. We design a specialized training strategy centered around IDDM. Diffusion models are leveraged to bridge the domain gap from synthetic to real-world, while the atmospheric scattering model provides physical guidance for haze formation. During the forward process, IDDM simultaneously introduces haze and noise into clear images, and then robustly separates them during the sampling process. By training with physics-guided information, IDDM shows the ability of domain generalization, and effectively restores the real-world hazy images despite being trained on synthetic datasets. Extensive experiments demonstrate the effectiveness of our method through both quantitative and qualitative comparisons with state-of-the-art approaches.
Concurrency Testing in the Linux Kernel via eBPF
Venue: arXiv cs.OS 2025
Year: 2025

Concurrency is vital for our critical software to meet modern performance requirements, yet concurrency bugs are notoriously difficult to detect and reproduce. Controlled Concurrency Testing (CCT) can make bugs easier to expose by enabling control over thread interleavings and systematically exploring the interleaving space through scheduling algorithms. However, existing CCT solutions for kernel code are heavyweight, leading to significant performance, maintainability and extensibility issues. In this work, we introduce LACE, a lightweight CCT framework for kernel code empowered by eBPF. Without hypervisor modification, LACE features a custom scheduler tailored for CCT algorithms to serialize non-determistic thread execution into a controlled ordering. LACE also provides a mechanism to safely inject scheduling points into the kernel for fine-grained control. Furthermore, LACE employs a two-phase mutation strategy to integrate the scheduler with a concurrency fuzzer, allowing for automated exploration of both the input and schedule space. In our evaluation, LACE achieves 38\% more branches, 57\% overhead reduction and 11.4$\times$ speed-up in bug exposure compared to the state-of-the-art kernel concurrency fuzzers. Our qualitative analysis also demonstrates the extensibility and maintainability of LACE. Furthermore, LACE discovers eight previously unknown bugs in the Linux kernel, with six confirmed by developers.
Coping with Uncertainty in UX Design Practice: Practitioner Strategies and Judgment
Venue: arXiv cs.HC 2025
Year: 2025

The complexity of UX design practice extends beyond ill-structured design problems to include uncertainties shaped by shifting stakeholder priorities, team dynamics, limited resources, and implementation constraints. While prior research in related fields has addressed uncertainty in design more broadly, the specific character of uncertainty in UX practice remains underexplored. This study examines how UX practitioners experience and respond to uncertainty in real-world projects, drawing on a multi-week diary study and follow-up interviews with ten designers. We identify a range of practitioner strategies-including adaptive framing, negotiation, and judgment-that allow designers to move forward amid ambiguity. Our findings highlight the central role of design judgment in navigating uncertainty, including emergent forms such as temporal and sacrificial judgment, and extend prior understandings by showing how UX practitioners engage uncertainty as a persistent, situated feature of practice.
Tolerating Disasters with Hierarchical Consensus
Venue: arXiv cs.DC 2025
Year: 2025

Geo-replication provides disaster recovery after catastrophic accidental failures or attacks, such as fires, blackouts or denial-of-service attacks to a data center or region. Naturally distributed data structures, such as Blockchains, when well designed, are immune against such disruptions, but they also benefit from leveraging locality. In this work, we consolidate the performance of geo-replicated consensus by leveraging novel insights about hierarchical consensus and a construction methodology that allows creating novel protocols from existing building blocks. In particular we show that cluster confirmation, paired with subgroup rotation, allows protocols to safely operate through situations where all members of the global consensus group are Byzantine. We demonstrate our compositional construction by combining the recent HotStuff and Damysus protocols into a hierarchical geo-replicated blockchain with global durability guarantees. We present a compositionality proof and demonstrate the correctness of our protocol, including its ability to tolerate cluster crashes. Our protocol -ORION 1 -achieves a 20% higher throughput than GeoBFT, the latest hierarchical Byzantine Fault-Tolerant (BFT) protocol.
Optimizing Mouse Dynamics for User Authentication by Machine Learning: Addressing Data Sufficiency, Accuracy-Practicality Trade-off, and Model Performance Challenges
Venue: arXiv cs.CR 2025
Year: 2025

User authentication is essential to ensure secure access to computer systems, yet traditional methods face limitations in usability, cost, and security. Mouse dynamics authentication, based on the analysis of users' natural interaction behaviors with mouse devices, offers a cost-effective, non-intrusive, and adaptable solution. However, challenges remain in determining the optimal data volume, balancing accuracy and practicality, and effectively capturing temporal behavioral patterns. In this study, we propose a statistical method using Gaussian kernel density estimate (KDE) and Kullback-Leibler (KL) divergence to estimate the sufficient data volume for training authentication models. We introduce the Mouse Authentication Unit (MAU), leveraging Approximate Entropy (ApEn) to optimize segment length for efficient and accurate behavioral representation. Furthermore, we design the Local-Time Mouse Authentication (LT-AMouse) framework, integrating 1D-ResNet for local feature extraction and GRU for modeling long-term temporal dependencies. Taking the Balabit and DFL datasets as examples, we significantly reduced the data scale, particularly by a factor of 10 for the DFL dataset, greatly alleviating the training burden. Additionally, we determined the optimal input recognition unit length for the user authentication system on different datasets based on the slope of Approximate Entropy. Training with imbalanced samples, our model achieved a successful defense AUC 98.52% for blind attack on the DFL dataset and 94.65% on the Balabit dataset, surpassing the current sota performance.
A Unified QoS-Aware Multiplexing Framework for Next Generation Immersive Communication with Legacy Wireless Applications
Venue: arXiv cs.NI 2025
Year: 2025

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.
A Comprehensive Survey of Electrical Stimulation Haptic Feedback in Human-Computer Interaction
Venue: arXiv cs.HC 2025
Year: 2025

Haptic perception and feedback play a pivotal role in interactive experiences, forming an essential component of human-computer interaction (HCI). In recent years, the field of haptic interaction has witnessed significant advancements, particularly in the area of electrical haptic feedback, driving innovation across various domains. To gain a comprehensive understanding of the current state of research and the latest developments in electrical haptic interaction, this study systematically reviews the literature in this area. Our investigation covers key aspects including haptic devices, haptic perception mechanisms, the comparison and integration of electrical haptic feedback with other feedback modalities, and their diverse applications. Specifically, we conduct a systematic analysis of 110 research papers to explore the forefront of electrical haptic feedback, providing insights into its latest trends, challenges, and future directions.
A Comprehensive Study of Exploitable Patterns in Smart Contracts: From Vulnerability to Defense
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid advancement of blockchain technology, smart contracts have enabled the implementation of increasingly complex functionalities. However, ensuring the security of smart contracts remains a persistent challenge across the stages of development, compilation, and execution. Vulnerabilities within smart contracts not only undermine the security of individual applications but also pose significant risks to the broader blockchain ecosystem, as demonstrated by the growing frequency of attacks since 2016, resulting in substantial financial losses. This paper provides a comprehensive analysis of key security risks in Ethereum smart contracts, specifically those written in Solidity and executed on the Ethereum Virtual Machine (EVM). We focus on two prevalent and critical vulnerability types (reentrancy and integer overflow) by examining their underlying mechanisms, replicating attack scenarios, and assessing effective countermeasures.
Consistency-aware Fake Videos Detection on Short Video Platforms
Venue: arXiv cs.CV 2025
Year: 2025

This paper focuses to detect the fake news on the short video platforms. While significant research efforts have been devoted to this task with notable progress in recent years, current detection accuracy remains suboptimal due to the rapid evolution of content manipulation and generation technologies. Existing approaches typically employ a cross-modal fusion strategy that directly combines raw video data with metadata inputs before applying a classification layer. However, our empirical observations reveal a critical oversight: manipulated content frequently exhibits inter-modal inconsistencies that could serve as valuable discriminative features, yet remain underutilized in contemporary detection frameworks. Motivated by this insight, we propose a novel detection paradigm that explicitly identifies and leverages cross-modal contradictions as discriminative cues. Our approach consists of two core modules: Cross-modal Consistency Learning (CMCL) and Multi-modal Collaborative Diagnosis (MMCD). CMCL includes Pseudo-label Generation (PLG) and Cross-modal Consistency Diagnosis (CMCD). In PLG, a Multimodal Large Language Model is used to generate pseudo-labels for evaluating cross-modal semantic consistency. Then, CMCD extracts [CLS] tokens and computes cosine loss to quantify cross-modal inconsistencies. MMCD further integrates multimodal features through Multimodal Feature Fusion (MFF) and Probability Scores Fusion (PSF). MFF employs a co-attention mechanism to enhance semantic interactions across different modalities, while a Transformer is utilized for comprehensive feature fusion. Meanwhile, PSF further integrates the fake news probability scores obtained in the previous step. Extensive experiments on established benchmarks (FakeSV and FakeTT) demonstrate our model exhibits outstanding performance in Fake videos detection.
Meta knowledge assisted Evolutionary Neural Architecture Search
Venue: arXiv cs.NE 2025
Year: 2025

Evolutionary computation (EC)-based neural architecture search (NAS) has achieved remarkable performance in the automatic design of neural architectures. However, the high computational cost associated with evaluating searched architectures poses a challenge for these methods, and a fixed form of learning rate (LR) schedule means greater information loss on diverse searched architectures. This paper introduces an efficient EC-based NAS method to solve these problems via an innovative meta-learning framework. Specifically, a meta-learning-rate (Meta-LR) scheme is used through pretraining to obtain a suitable LR schedule, which guides the training process with lower information loss when evaluating each individual. An adaptive surrogate model is designed through an adaptive threshold to select the potential architectures in a few epochs and then evaluate the potential architectures with complete epochs. Additionally, a periodic mutation operator is proposed to increase the diversity of the population, which enhances the generalizability and robustness. Experiments on CIFAR-10, CIFAR-100, and ImageNet1K datasets demonstrate that the proposed method achieves high performance comparable to that of many state-of-the-art peer methods, with lower computational cost and greater robustness.
eNCApsulate: NCA for Precision Diagnosis on Capsule Endoscopes
Venue: arXiv cs.CV 2025
Year: 2025

Wireless Capsule Endoscopy is a non-invasive imaging method for the entire gastrointestinal tract, and is a pain-free alternative to traditional endoscopy. It generates extensive video data that requires significant review time, and localizing the capsule after ingestion is a challenge. Techniques like bleeding detection and depth estimation can help with localization of pathologies, but deep learning models are typically too large to run directly on the capsule. Neural Cellular Automata (NCA) for bleeding segmentation and depth estimation are trained on capsule endoscopic images. For monocular depth estimation, we distill a large foundation model into the lean NCA architecture, by treating the outputs of the foundation model as pseudo ground truth. We then port the trained NCA to the ESP32 microcontroller, enabling efficient image processing on hardware as small as a camera capsule. NCA are more accurate (Dice) than other portable segmentation models, while requiring more than 100x fewer parameters stored in memory than other small-scale models. The visual results of NCA depth estimation look convincing, and in some cases beat the realism and detail of the pseudo ground truth. Runtime optimizations on the ESP32-S3 accelerate the average inference speed significantly, by more than factor 3. With several algorithmic adjustments and distillation, it is possible to eNCApsulate NCA models into microcontrollers that fit into wireless capsule endoscopes. This is the first work that enables reliable bleeding segmentation and depth estimation on a miniaturized device, paving the way for precise diagnosis combined with visual odometry as a means of precise localization of the capsule -- on the capsule.
FreeBeacon: Efficient Communication and Data Aggregation in Battery-Free IoT
Venue: arXiv cs.NI 2025
Year: 2025

To improve sustainability, Internet-of-Things (IoT) is increasingly adopting battery-free devices powered by ambient energy scavenged from the environment. The unpredictable availability of ambient energy leads to device intermittency, bringing critical challenges to device communication and related fundamental operations like data aggregation. We propose FreeBeacon, a novel scheme for efficient communication and data aggregation in battery-free IoT. We argue that the communication challenge between battery-free devices originates from the complete uncertainty of the environment. FreeBeacon is built on the insight that by introducing just a small degree of certainty into the system, the communication problem can be largely simplified. To this end, FreeBeacon first introduces a small number of battery-powered devices as beacons for battery-free devices. Then, FreeBeacon features protocols for battery-free devices to achieve interaction with the beacon and to perform communication efficiently following customized schedules that implement different data aggregation schemes while achieving resilience. We evaluate FreeBeacon with extensive prototype-based experiments and simulation studies. Results show that FreeBeacon can consistently achieve an order of magnitude data aggregation efficiency when compared with the state-of-the-art approaches.
Latent Feature-Guided Conditional Diffusion for Generative Image Semantic Communication
Venue: arXiv cs.MM 2025
Year: 2025

Semantic communication is proposed and expected to improve the efficiency of massive data transmission over sixth generation (6G) networks. However, existing image semantic communication schemes are primarily focused on optimizing pixel-level metrics, while neglecting the crucial aspect of region of interest (ROI) preservation. To address this issue, we propose an ROI-aware latent representation-oriented image semantic communication (LRISC) system. In particular, we first map the source image to latent features in a high-dimensional semantic space, these latent features are then fused with ROI mask through a feature-weighting mechanism. Subsequently, these features are encoded using a joint source and channel coding (JSCC) scheme with adaptive rate for efficient transmission over a wireless channel. At the receiver, a conditional diffusion model is developed by using the received latent features as conditional guidance to steer the reverse diffusion process, progressively reconstructing high-fidelity images while preserving semantic consistency. Moreover, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism, allowing one model to work across various channel states. Experiments show that the proposed method significantly outperforms existing methods, in terms of learned perceptual image patch similarity (LPIPS) and robustness against channel noise, with an average LPIPS reduction of 43.3% compared to DeepJSCC, while guaranteeing the semantic consistency.
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
Venue: arXiv cs.RO 2025
Year: 2025

In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available: https://github.com/kav-institute/SemanticLiDAR.
HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user experience. Nonetheless, existing diffusion models predominantly concentrate on modeling static 3D scenes or object-level dynamics, constraining their capacity to provide truly immersive experiences. To address this issue, we propose HoloTime, a framework that integrates video diffusion models to generate panoramic videos from a single prompt or reference image, along with a 360-degree 4D scene reconstruction method that seamlessly transforms the generated panoramic video into 4D assets, enabling a fully immersive 4D experience for users. Specifically, to tame video diffusion models for generating high-fidelity panoramic videos, we introduce the 360World dataset, the first comprehensive collection of panoramic videos suitable for downstream 4D scene reconstruction tasks. With this curated dataset, we propose Panoramic Animator, a two-stage image-to-video diffusion model that can convert panoramic images into high-quality panoramic videos. Following this, we present Panoramic Space-Time Reconstruction, which leverages a space-time depth estimation method to transform the generated panoramic videos into 4D point clouds, enabling the optimization of a holistic 4D Gaussian Splatting representation to reconstruct spatially and temporally consistent 4D scenes. To validate the efficacy of our method, we conducted a comparative analysis with existing approaches, revealing its superiority in both panoramic video generation and 4D scene reconstruction. This demonstrates our method's capability to create more engaging and realistic immersive environments, thereby enhancing user experiences in VR and AR applications.
Extension-ranking Semantics for Abstract Argumentation Preprint
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allowing us to state that one set is "closer" to being acceptable than another. To evaluate the extension-ranking semantics, we introduce a number of principles that a well-behaved extension-ranking semantics should satisfy. We consider several simple base relations, each of which models a single central aspect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a number of approaches from the literature for ranking extensions to be usable in the context of extension-ranking semantics, and evaluate their behaviour.