OpenRaiser/Intern-Atlas huggingface dataset:
Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
Venue: arXiv cs.CR 2025
Year: 2025

Privacy-preserving machine learning (PPML) based on cryptographic protocols has emerged as a promising paradigm to protect user data privacy in cloud-based machine learning services. While it achieves formal privacy protection, PPML often incurs significant efficiency and scalability costs due to orders of magnitude overhead compared to the plaintext counterpart. Therefore, there has been a considerable focus on mitigating the efficiency gap for PPML. In this survey, we provide a comprehensive and systematic review of recent PPML studies with a focus on cross-level optimizations. Specifically, we categorize existing papers into protocol level, model level, and system level, and review progress at each level. We also provide qualitative and quantitative comparisons of existing works with technical insights, based on which we discuss future research directions and highlight the necessity of integrating optimizations across protocol, model, and system levels. We hope this survey can provide an overarching understanding of existing approaches and potentially inspire future breakthroughs in the PPML field. As the field is evolving fast, we also provide a public GitHub repository to continuously track the developments, which is available at https://github.com/PKU-SEC-Lab/Awesome-PPML-Papers.
ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present:(1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated image aesthetic dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public to advance the field.
Large Language Models Assisting Ontology Evaluation
Venue: arXiv cs.AI 2025
Year: 2025

Ontology evaluation through functional requirements, such as testing via competency question (CQ) verification, is a well-established yet costly, labour-intensive, and error-prone endeavour, even for ontology engineering experts. In this work, we introduce OE-Assist, a novel framework designed to assist ontology evaluation through automated and semi-automated CQ verification. By presenting and leveraging a dataset of 1,393 CQs paired with corresponding ontologies and ontology stories, our contributions present, to our knowledge, the first systematic investigation into large language model (LLM)-assisted ontology evaluation, and include: (i) evaluating the effectiveness of a LLM-based approach for automatically performing CQ verification against a manually created gold standard, and (ii) developing and assessing an LLM-powered framework to assist CQ verification with Protégé, by providing suggestions. We found that automated LLM-based evaluation with o1-preview and o3-mini perform at a similar level to the average user's performance.
Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library
Venue: arXiv cs.SE 2025
Year: 2025

The combination of computer vision and artificial intelligence is fundamentally transforming a broad spectrum of industries by enabling machines to interpret and act upon visual data with high levels of accuracy. As the biggest and by far the most popular open-source computer vision library, OpenCV library provides an extensive suite of programming functions supporting real-time computer vision. Bugs in the OpenCV library can affect the downstream computer vision applications, and it is critical to ensure the reliability of the OpenCV library. This paper introduces VISTAFUZZ, a novel technique for harnessing large language models (LLMs) for document-guided fuzzing of the OpenCV library. VISTAFUZZ utilizes LLMs to parse API documentation and obtain standardized API information. Based on this standardized information, VISTAFUZZ extracts constraints on individual input parameters and dependencies between these. Using these constraints and dependencies, VISTAFUZZ then generates new input values to systematically test each target API. We evaluate the effectiveness of VISTAFUZZ in testing 330 APIs in the OpenCV library, and the results show that VISTAFUZZ detected 17 new bugs, where 10 bugs have been confirmed, and 5 of these have been fixed.
BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
Venue: arXiv cs.RO 2025
Year: 2025

In the field of Learning from Demonstration (LfD), enabling robots to generalize learned manipulation skills to novel scenarios for long-horizon tasks remains challenging. Specifically, it is still difficult for robots to adapt the learned skills to new environments with different task and motion requirements, especially in long-horizon, multi-stage scenarios with intricate constraints. This paper proposes a novel hierarchical framework, called BT-TL-DMPs, that integrates Behavior Tree (BT), Temporal Logic (TL), and Dynamical Movement Primitives (DMPs) to address this problem. Within this framework, Signal Temporal Logic (STL) is employed to formally specify complex, long-horizon task requirements and constraints. These STL specifications are systematically transformed to generate reactive and modular BTs for high-level decision-making task structure. An STL-constrained DMP optimization method is proposed to optimize the DMP forcing term, allowing the learned motion primitives to adapt flexibly while satisfying intricate spatiotemporal requirements and, crucially, preserving the essential dynamics learned from demonstrations. The framework is validated through simulations demonstrating generalization capabilities under various STL constraints and real-world experiments on several long-horizon robotic manipulation tasks. The results demonstrate that the proposed framework effectively bridges the symbolic-motion gap, enabling more reliable and generalizable autonomous manipulation for complex robotic tasks.
FORTA: Byzantine-Resilient FL Aggregation via DFT-Guided Krum
Venue: arXiv cs.CR 2025
Year: 2025

Secure federated learning enables collaborative model training across decentralized users while preserving data privacy. A key component is secure aggregation, which keeps individual updates hidden from both the server and users, while also defending against Byzantine users who corrupt the aggregation. To this end, Jinhyun So et al. recently developed a Byzantine-resilient secure aggregation scheme using a secret-sharing strategy over finite-field arithmetic. However, such an approach can suffer from numerical errors and overflows when applied to real-valued model updates, motivating the need for secure aggregation methods that operate directly over the real domain. We propose FORTA, a Byzantine-resilient secure aggregation framework that operates entirely in the real domain. FORTA leverages Discrete Fourier Transform (DFT) codes for privacy and employs Krum-based outlier detection for robustness. While DFT decoder is error-free under infinite precision, finite precision introduces numerical perturbations that can distort distance estimates and allow malicious updates to evade detection. To address this, FORTA refines Krum using feedback from DFT decoder, improving the selection of trustworthy updates. Theoretical analysis and experiments show that our modification of Krum offers improved robustness and more accurate aggregation than standard Krum.
Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
Venue: arXiv cs.DC 2025
Year: 2025

Processing data at high speeds is becoming increasingly critical as digital economies generate enormous data. The current paradigms for timely data processing are edge computing and data stream processing (DSP). Edge computing places resources closer to where data is generated, while stream processing analyzes the unbounded high-speed data in motion. However, edge stream processing faces rapid workload fluctuations, complicating resource provisioning. Inadequate resource allocation leads to bottlenecks, whereas excess allocation results in wastage. Existing reactive methods, such as threshold-based policies and queuing theory scale only after performance degrades, potentially violating SLAs. Although reinforcement learning (RL) offers a proactive approach through agents that learn optimal runtime adaptation policies, it requires extensive simulation. Furthermore, predictive machine learning models face online distribution and concept drift that minimize their accuracy. We propose a three-step solution to the proactive edge stream processing autoscaling problem. Firstly, a GRU neural network forecasts the upstream load using real-world and synthetic DSP datasets. Secondly, a transfer learning framework integrates the predictive model into an online stream processing system using the DTW algorithm and joint distribution adaptation to handle the disparities between offline and online domains. Finally, a horizontal autoscaling module dynamically adjusts the degree of operator parallelism, based on predicted load while considering edge resource constraints. The lightweight GRU model for load predictions recorded up to 1.3\% SMAPE value on a real-world data set. It outperformed CNN, ARIMA, and Prophet on the SMAPE and RMSE evaluation metrics, with lower training time than the computationally intensive RL models.
Koopman Operator Based Linear Model Predictive Control for 2D Quadruped Trotting, Bounding, and Gait Transition
Venue: arXiv cs.RO 2025
Year: 2025

Online optimal control of quadrupedal robots would enable them to plan their movement in novel scenarios. Linear Model Predictive Control (LMPC) has emerged as a practical approach for real-time control. In LMPC, an optimization problem with a quadratic cost and linear constraints is formulated over a finite horizon and solved on the fly. However, LMPC relies on linearizing the equations of motion (EOM), which may lead to poor solution quality. In this paper, we use Koopman operator theory and the Extended Dynamic Mode Decomposition (EDMD) to create a linear model of the system in high dimensional space, thus retaining the nonlinearity of the EOM. We model the aerial phase and ground contact phases using different linear models. Then, using LMPC, we demonstrate bounding, trotting, and bound-to-trot and trot-to-bound gait transitions in level and rough terrains. The main novelty is the use of Koopman operator theory to create hybrid models of a quadrupedal system and demonstrate the online generation of multiple gaits and gaits transitions.
Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition
Venue: arXiv cs.LG 2025
Year: 2025

EEG-based emotion recognition plays an important role in developing adaptive brain-computer communication systems, yet faces two fundamental challenges in practical implementations: (1) effective integration of non-stationary spatial-temporal neural patterns, (2) robust adaptation to dynamic emotional intensity variations in real-world scenarios. This paper proposes SST-CL, a novel framework integrating spatial-temporal transformers with curriculum learning. Our method introduces two core components: a spatial encoder that models inter-channel relationships and a temporal encoder that captures multi-scale dependencies through windowed attention mechanisms, enabling simultaneous extraction of spatial correlations and temporal dynamics from EEG signals. Complementing this architecture, an intensity-aware curriculum learning strategy progressively guides training from high-intensity to low-intensity emotional states through dynamic sample scheduling based on a dual difficulty assessment. Comprehensive experiments on three benchmark datasets demonstrate state-of-the-art performance across various emotional intensity levels, with ablation studies confirming the necessity of both architectural components and the curriculum learning mechanism.
A Notification Based Nudge for Handling Excessive Smartphone Use
Venue: arXiv cs.HC 2025
Year: 2025

Excessive use of smartphones is a worldwide known issue. In this study, we proposed a notification-based intervention approach to reduce smartphone overuse without making the user feel any annoyance or irritation. Most of the work in this field tried to reduce smartphone overuse by making smartphone use more difficult for the user. In our user study (n = 109), we found that 19.3% of the participants are unwilling to use any usage-limiting application because a) they do not want their smartphone activities to get restricted or b) those applications are annoying. Following that, we devised a hypothesis to minimize smartphone usage among undergraduates. Finally, we designed a prototype for Android, "App Usage Monitor," and conducted a 3-week experiment through which we found proof of concept for our hypothesis. In our prototype, we combined techniques such as nudge and visualization to increase self-awareness among the user by leveraging notifications.
Information Theoretic Analysis of a Dual-Band MIMO Cellphone Antenna with ANSYS HFSS SBR+
Venue: arXiv cs.IT 2025
Year: 2025

Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on the deterministic Maxwell theory alone. In this paper, we investigate an information-theoretic analysis approach which we apply to evaluate the design of a dual-band, dual-polarized multiple-input multiple-output (MIMO) array on a cellphone. To this end, we use ANSYS HFSS, a commercial electromagnetic (EM) simulation software suitable for the numerical optimization of antenna systems. HFSS is used to obtain an accurate model of the cellphone MIMO antenna array and HFSS SBR+ is utilized to obtain channel matrices for a large number of users. Taking advantage of linear and optimal processing at the cellphone, we estimate the outage probability curves. The curves are then used to determine the diversity gain in a moderate signal-to-noise ratio (SNR) regime and the multiplexing gain at a high SNR regime. This approach is then compared with the method of estimating the diversity gain from the envelope correlation coefficients or the beam-coupling matrix showing substantial differences in the two methodologies.
HistoryFinder: Advancing Method-Level Source Code History Generation with Accurate Oracles and Enhanced Algorithm
Venue: arXiv cs.SE 2025
Year: 2025

Reconstructing a method's change history efficiently and accurately is critical for many software engineering tasks, including maintenance, refactoring, and comprehension. Despite the availability of method history generation tools such as CodeShovel and CodeTracker, existing evaluations of their effectiveness are limited by inaccuracies in the ground truth oracles used. In this study, we systematically construct two new oracles -- the corrected CodeShovel oracle and a newly developed HistoryFinder oracle -- by combining automated analysis with expert-guided manual validation. We also introduce HistoryFinder, a new method history generation tool designed to improve not only the accuracy and completeness of method change histories but also to offer competitive runtime performance. Through extensive evaluation across 400 methods from 40 open-source repositories, we show that HistoryFinder consistently outperforms CodeShovel, CodeTracker, IntelliJ, and Git-based baselines in terms of precision, recall, and F1 score. Moreover, HistoryFinder achieves competitive runtime performance, offering the lowest mean and median execution times among all the research-based tools. While Git-based tools exhibit the fastest runtimes, this efficiency comes at the cost of significantly lower precision and recall -- leaving HistoryFinder as the best overall choice when both accuracy and efficiency are important. To facilitate adoption, we provide a web interface, CLI, and Java library for flexible usage.
Automated Safety Evaluations Across 20 Large Language Models: The Aymara LLM Risk and Responsibility Matrix
Venue: arXiv cs.AI 2025
Year: 2025

As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. This paper introduces Aymara AI, a programmatic platform for generating and administering customized, policy-grounded safety evaluations. Aymara AI transforms natural-language safety policies into adversarial prompts and scores model responses using an AI-based rater validated against human judgments. We demonstrate its capabilities through the Aymara LLM Risk and Responsibility Matrix, which evaluates 20 commercially available LLMs across 10 real-world safety domains. Results reveal wide performance disparities, with mean safety scores ranging from 86.2% to 52.4%. While models performed well in well-established safety domains such as Misinformation (mean = 95.7%), they consistently failed in more complex or underspecified domains, notably Privacy & Impersonation (mean = 24.3%). Analyses of Variance confirmed that safety scores differed significantly across both models and domains (p < .05). These findings underscore the inconsistent and context-dependent nature of LLM safety and highlight the need for scalable, customizable tools like Aymara AI to support responsible AI development and oversight.
X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Venue: arXiv cs.RO 2025
Year: 2025

Existing navigation methods are primarily designed for specific robot embodiments, limiting their generalizability across diverse robot platforms. In this paper, we introduce X-Nav, a novel framework for end-to-end cross-embodiment navigation where a single unified policy can be deployed across various embodiments for both wheeled and quadrupedal robots. X-Nav consists of two learning stages: 1) multiple expert policies are trained using deep reinforcement learning with privileged observations on a wide range of randomly generated robot embodiments; and 2) a single general policy is distilled from the expert policies via navigation action chunking with transformer (Nav-ACT). The general policy directly maps visual and proprioceptive observations to low-level control commands, enabling generalization to novel robot embodiments. Simulated experiments demonstrated that X-Nav achieved zero-shot transfer to both unseen embodiments and photorealistic environments. A scalability study showed that the performance of X-Nav improves when trained with an increasing number of randomly generated embodiments. An ablation study confirmed the design choices of X-Nav. Furthermore, real-world experiments were conducted to validate the generalizability of X-Nav in real-world environments.
Study of Delay-Calibrated Joint User Activity Detection, Channel Estimation and Data Detection for Asynchronous mMTC Systems
Venue: arXiv cs.IT 2025
Year: 2025

This work considers uplink asynchronous massive machine-type communications, where a large number of low-power and low-cost devices asynchronously transmit short packets to an access point equipped with multiple receive antennas. If orthogonal preambles are employed, massive collisions will occur due to the limited number of orthogonal preambles given the preamble sequence length. To address this problem, we propose a delay-calibrated joint user activity detection, channel estimation, and data detection algorithm, and investigate the benefits of oversampling in estimating continuous-valued time delays at the receiver. The proposed algorithm is based on the expectation-maximization method, which alternately estimates the delays and detects active users and their channels and data by noting that the collided users have different delays. Under the Bayesian inference framework, we develop a computationally efficient iterative algorithm using the approximate message passing principle to resolve the joint user activity detection, channel estimation, and data detection problem. Numerical results demonstrate the effectiveness of the proposed algorithm in terms of the normalized mean-squared errors of channel and data symbols, and the probability of misdetection.
CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus
Venue: arXiv cs.CR 2025
Year: 2025

The Controller Area Network (CAN) protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and message authentication. This paper presents a novel Intrusion Detection System (IDS) designed for the CAN environment, utilizing Hardware Performance Counters (HPCs) to detect anomalies indicative of cyber attacks. A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks, which trigger distinct HPC responses. Key HPC features are optimized through data extraction and correlation analysis to enhance classification efficiency. Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Self-supervised feature learning and pretraining methods in reinforcement learning (RL) often rely on information-theoretic principles, termed mutual information skill learning (MISL). These methods aim to learn a representation of the environment while also incentivizing exploration thereof. However, the role of the representation and mutual information parametrization in MISL is not yet well understood theoretically. Our work investigates MISL through the lens of identifiable representation learning by focusing on the Contrastive Successor Features (CSF) method. We prove that CSF can provably recover the environment's ground-truth features up to a linear transformation due to the inner product parametrization of the features and skill diversity in a discriminative sense. This first identifiability guarantee for representation learning in RL also helps explain the implications of different mutual information objectives and the downsides of entropy regularizers. We empirically validate our claims in MuJoCo and DeepMind Control and show how CSF provably recovers the ground-truth features both from states and pixels.