OpenRaiser/Intern-Atlas huggingface dataset:
Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study
Venue: arXiv cs.AI 2025
Year: 2025

In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. This study analyses the state of the art in the use of Semantic Web technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 48 systems are selected as representative of the current state of the art. We critically analyse the extent to which the selected systems address seven key challenges: interoperability, situation detection, situation prediction, decision support, context awareness, explainability, and uncertainty handling. We discuss the role and limitations of Semantic Web technologies in managing each challenge. We then conduct a quality assessment of the selected systems based on the data and devices used, system and components development, rigour of evaluation, and accessibility of research outputs. Finally, we propose a reference architecture to provide guidance for the design and development of new systems. This study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research.
World Models for Math Story Problems
Venue: arXiv cs.CL 2025
Year: 2025

Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an answer. Recent years have seen impressive performance on automatically solving these problems with large pre-trained language models and innovative techniques to prompt them. However, it remains unclear if these models possess accurate representations of mathematical concepts. This leads to lack of interpretability and trustworthiness which impedes their usefulness in various applications. In this paper, we consolidate previous work on categorizing and representing math story problems and develop MathWorld, which is a graph-based semantic formalism specific for the domain of math story problems. With MathWorld, we can assign world models to math story problems which represent the situations and actions introduced in the text and their mathematical relationships. We combine math story problems from several existing datasets and annotate a corpus of 1,019 problems and 3,204 logical forms with MathWorld. Using this data, we demonstrate the following use cases of MathWorld: (1) prompting language models with synthetically generated question-answer pairs to probe their reasoning and world modeling abilities, and (2) generating new problems by using the world models as a design space.
Computation of a Unified Graph-Based Rate Optimization Problem
Venue: arXiv cs.IT 2025
Year: 2025

We define a graph-based rate optimization problem and consider its computation, which provides a unified approach to the computation of various theoretical limits, including the (conditional) graph entropy, rate-distortion functions and capacity-cost functions with side information. Compared with their classical counterparts, theoretical limits with side information are much more difficult to compute since their characterizations as optimization problems have larger and more complex feasible regions. Following the unified approach, we develop effective methods to resolve the difficulty. On the theoretical side, we derive graph characterizations for rate-distortion and capacity-cost functions with side information and simplify the characterizations in special cases by reducing the number of decision variables. On the computational side, we design an efficient alternating minimization algorithm for the graph-based problem, which deals with the inequality constraint by a flexible multiplier update strategy. Moreover, simplified graph characterizations are exploited and deflation techniques are introduced, so that the computing time is greatly reduced. Theoretical analysis shows that the algorithm converges to an optimal solution. By numerical experiments, the accuracy and efficiency of the algorithm are illustrated and its significant advantage over existing methods is demonstrated.
Argumentative Characterizations of (Extended) Disjunctive Logic Programs
Venue: arXiv cs.AI 2025
Year: 2025

This paper continues an established line of research about the relations between argumentation theory, particularly assumption-based argumentation, and different kinds of logic programs. In particular, we extend known result of Caminada, Schultz and Toni by showing that assumption-based argumentation can represent not only normal logic programs, but also disjunctive logic programs and their extensions. For this, we consider some inference rules for disjunction that the core logic of the argumentation frameworks should respect, and show the correspondence to the handling of disjunctions in the heads of the logic programs' rules. Under consideration in Theory and Practice of Logic Programming (TPLP).
Slicing Unbalanced Optimal Transport
Venue: arXiv cs.LG 2025
Year: 2025

Optimal transport (OT) is a powerful framework to compare probability measures, a fundamental task in many statistical and machine learning problems. Substantial advances have been made in designing OT variants which are either computationally and statistically more efficient or robust. Among them, sliced OT distances have been extensively used to mitigate optimal transport's cubic algorithmic complexity and curse of dimensionality. In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers. In this paper, we bridge the gap between those two concepts and develop a general framework for efficiently comparing positive measures. We notably formulate two different versions of sliced unbalanced OT, and study the associated topology and statistical properties. We then develop a GPU-friendly Frank-Wolfe like algorithm to compute the corresponding loss functions, and show that the resulting methodology is modular as it encompasses and extends prior related work. We finally conduct an empirical analysis of our loss functions and methodology on both synthetic and real datasets, to illustrate their computational efficiency, relevance and applicability to real-world scenarios including geophysical data.
Friend or Foe Inside? Exploring In-Process Isolation to Maintain Memory Safety for Unsafe Rust
Venue: arXiv cs.CR 2025
Year: 2025

Rust is a popular memory-safe systems programming language. In order to interact with hardware or call into non-Rust libraries, Rust provides \emph{unsafe} language features that shift responsibility for ensuring memory safety to the developer. Failing to do so, may lead to memory safety violations in unsafe code which can violate safety of the entire application. In this work we explore in-process isolation with Memory Protection Keys as a mechanism to shield safe program sections from safety violations that may happen in unsafe sections. Our approach is easy to use and comprehensive as it prevents heap and stack-based violations. We further compare process-based and in-process isolation mechanisms and the necessary requirements for data serialization, communication, and context switching. Our results show that in-process isolation can be effective and efficient, permits for a high degree of automation, and also enables a notion of application rewinding where the safe program section may detect and safely handle violations in unsafe code.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Venue: arXiv cs.CL 2025
Year: 2025

Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instruction fine-tuning, by adapting the Evol-Instruct method to the domain of code. Through comprehensive experiments on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we unveil the exceptional capabilities of our model. It surpasses all other open-source Code LLMs by a substantial margin. Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+. Our code, model weights, and data are public at https://github.com/nlpxucan/WizardLM
Towards Automated Identification of Violation Symptoms of Architecture Erosion
Venue: arXiv cs.SE 2025
Year: 2025

Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation deviates from the intended architecture. Detecting symptoms of erosion, particularly architectural violations, at an early stage is crucial. This paper explores the automated identification of violation symptoms from developer discussions in code reviews. We developed 15 machine learning-based and 4 deep learning-based classifiers using three pre-trained word embeddings, and evaluated them on code review comments from four large open-source projects (OpenStack Nova/Neutron and Qt Base/Creator). To validate practical value, we conducted surveys and semi-structured interviews with developers involved in these discussions. We further compared traditional ML/DL classifiers with Large Language Models (LLMs) such as GPT-4o, Qwen-2.5, and DeepSeek-R1. Results show that SVM with word2vec achieved the best ML/DL performance with an F1-score of 0.779, while fastText embeddings also yielded strong results. Ensemble voting strategies enhanced traditional classifiers, and 200-dimensional embeddings generally outperformed 100/300-dimensional ones. LLM-based classifiers consistently surpassed ML/DL models, with GPT-4o achieving the best F1-score of 0.851, though ensembles added no further benefits. Overall, our study provides an automated approach to identify architecture violation symptoms, offers systematic comparisons of ML/DL and LLM methods, and delivers practitioner insights, contributing to sustainable architectural conformance in software systems.
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

Collaborative-learning-based recommender systems, such as those employing Federated Learning (FL) and Gossip Learning (GL), allow users to train models while keeping their history of liked items on their devices. While these methods were seen as promising for enhancing privacy, recent research has shown that collaborative learning can be vulnerable to various privacy attacks. In this paper, we propose a novel attack called Community Inference Attack (CIA), which enables an adversary to identify community members based on a set of target items. What sets CIA apart is its efficiency: it operates at low computational cost by eliminating the need for training surrogate models. Instead, it uses a comparison-based approach, inferring sensitive information by comparing users' models rather than targeting any specific individual model. To evaluate the effectiveness of CIA, we conduct experiments on three real-world recommendation datasets using two recommendation models under both Federated and Gossip-like settings. The results demonstrate that CIA can be up to 10 times more accurate than random guessing. Additionally, we evaluate two mitigation strategies: Differentially Private Stochastic Gradient Descent (DP-SGD) and a Share less policy, which involves sharing fewer, less sensitive model parameters. Our findings suggest that the Share less strategy offers a better privacy-utility trade-off, especially in GL.
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
Venue: arXiv cs.RO 2025
Year: 2025

Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance on these state augmentations, delay-resolved reinforcement learning algorithms train policies to learn optimal interactions with environments featuring observation or action delays. Although such methods can be directly trained on the real robots, due to sample inefficiency, limited resources, or safety constraints, a common approach is to transfer models trained in simulation to the physical robot. However, robotic simulations rely on approximated models of the physical systems, which hinders the sim2real transfer. In this work, we consider various uncertainties in modeling the robot or environment dynamics as unknown intrinsic disturbances applied to the system input. We introduce the disturbance-augmented Markov decision process (DAMDP) in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms. The proposed method is validated across several metrics on learning robotic reaching and pushing tasks and compared with disturbance-unaware baselines. The results show that the disturbance-augmented models can achieve higher stabilization and robustness in the control response, which in turn improves the prospects of successful sim2real transfer.
Environmental Sound Classification on An Embedded Hardware Platform
Venue: arXiv cs.SD 2025
Year: 2025

Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource constrained devices such as embedded systems. In this paper, we analyze how the performance of large-scale pre-trained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as a Raspberry Pi. We empirically study the role of CPU temperature, microphone quality and audio signal volume on performance. Our experiments reveal that the continuous CPU usage results in an increased temperature that can trigger an automated slowdown mechanism in the Raspberry Pi, impacting inference latency. The quality of a microphone, specifically with affordable devices such as the Google AIY Voice Kit, and audio signal volume, all affect the system performance. In the course of our investigation, we encounter substantial complications linked to library compatibility and the unique processor architecture requirements of the Raspberry Pi, making the process less straightforward compared to conventional computers (PCs). Our observations, while presenting challenges, pave the way for future researchers to develop more compact machine learning models, design heat-dissipative hardware, and select appropriate microphones when AI models are deployed for real-time applications on edge devices.
$\rm P$ has polynomial-time finite-state verifiers
Venue: arXiv cs.CC 2025
Year: 2025

Interactive proof systems whose verifiers are constant-space machines have interesting features that do not have counterparts in the better studied case where the verifiers operate under reasonably large space bounds. The language verification power of finite-state verifiers is known to be sensitive to the difference between private and public randomization. These machines also lack the capability of imposing worst-case superlinear bounds on their own runtime, and long interactions with untrustable provers can involve the risk of being fooled to loop forever. We analyze such verifiers under different bounds on the numbers of private and public random bits that they are allowed to use. This separate accounting for the private and public coin budgets as resource functions of the input length provides interesting characterizations of the collections of the associated languages. When the randomness bound is constant, the verifiable class is $\rm NL$ for private-coin machines, but equals just the regular languages when one uses public coins. Increasing the public coin budget while keeping the number of private coins constant augments the power: We show that the set of languages that are verifiable by such machines in expected polynomial time (with an arbitrarily small positive probability of looping) equals the complexity class $\rm P$. This hints that allowing a minuscule probability of looping may add significant power to polynomial-time finite-state automata, since it is still not known whether those machines can verify all of $\rm P$ when required to halt with probability 1, even with no bound on their private coin usage. We also show that logarithmic-space machines which hide a constant number of their coins are limited to verifying the languages in $\rm P$.
Clickbait Detection via Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success in a series of NLP downstream tasks. However, it is not yet known whether LLMs can be served as a high-quality clickbait detection system. In this paper, we analyze the performance of LLMs in the few-shot and zero-shot scenarios on several English and Chinese benchmark datasets. Experimental results show that LLMs cannot achieve the best results compared to the state-of-the-art deep and fine-tuning PLMs methods. Different from human intuition, the experiments demonstrated that LLMs cannot make satisfied clickbait detection just by the headlines.
GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy
Venue: arXiv cs.LG 2025
Year: 2025

Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulation policy for each rope, even for simple tasks such as rope goal reaching, which hinder their applications in our ever-changing world. To address this issue, we introduce GenORM, a framework that allows the manipulation policy to handle different deformable ropes with a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters. At the time of inference, given a new rope, GenORM estimates the deformable rope parameters by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations. With the help of a differentiable physics simulator, we require only a single real-world demonstration. Empirical validations on both simulated and real-world rope manipulation setups clearly show that our method can manipulate different ropes with a single demonstration and significantly outperforms the baseline in both environments (62% improvement in in-domain ropes, and 15% improvement in out-of-distribution ropes in simulation, 26% improvement in real-world), demonstrating the effectiveness of our approach in one-shot rope manipulation.
FP-IRL: Fokker-Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
Venue: arXiv cs.LG 2025
Year: 2025

Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed trajectories within a Markov decision process (MDP). However, most existing IRL methods require access to the transition function, either prescribed or estimated \textit{a priori}, which poses significant challenges when the underlying dynamics are unknown, unobservable, or not easily sampled. We propose Fokker--Planck inverse reinforcement learning (FP-IRL), a novel physics-constrained IRL framework tailored for systems governed by Fokker--Planck (FP) dynamics. FP-IRL simultaneously infers both the reward and transition functions directly from trajectory data, without requiring access to sampled transitions. Our method leverages a conjectured equivalence between MDPs and the FP equation, linking reward maximization in MDPs with free energy minimization in FP dynamics. This connection enables inference of the potential function using our inference approach of variational system identification, from which the full set of MDP components -- reward, transition, and policy -- can be recovered using analytic expressions. We demonstrate the effectiveness of FP-IRL through experiments on synthetic benchmarks and a modified version of the Mountain Car problem. Our results show that FP-IRL achieves accurate recovery of agent incentives while preserving computational efficiency and physical interpretability.
MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
Venue: arXiv cs.MM 2025
Year: 2025

Multimodal learning on video and text has seen significant progress, particularly in tasks like text-to-video retrieval, video-to-text retrieval, and video captioning. However, most existing methods and datasets focus exclusively on English. Despite Indonesian being one of the most widely spoken languages, multimodal research in Indonesian remains under-explored, largely due to the lack of benchmark datasets. To address this gap, we introduce the first public Indonesian video-text dataset by translating the English captions in the MSVD dataset into Indonesian. Using this dataset, we evaluate neural network models which were developed for the English video-text dataset on three tasks, i.e., text-to-video retrieval, video-to-text retrieval, and video captioning. Most existing models rely on feature extractors pretrained on English vision-language datasets, raising concerns about their applicability to Indonesian, given the scarcity of large-scale pretraining resources in the language. We apply a cross-lingual transfer learning approach by leveraging English-pretrained extractors and fine-tuning models on our Indonesian dataset. Experimental results demonstrate that this strategy improves performance across all tasks and metrics. We release our dataset publicly to support future research and hope it will inspire further progress in Indonesian multimodal learning.
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
Venue: arXiv cs.RO 2025
Year: 2025

Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network, which directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a united target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. The soft point-to-pixel correspondence is implicitly constructed on the intrinsic-independent normalized plane in the 2D-3D cost volume module. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. Extensive localization experiments are conducted on the KITTI, nuScenes, M2DGR, Argoverse, Waymo, and Lyft5 datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin and has a higher efficiency than the previous works. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Source codes are released at https://github.com/IRMVLab/I2PNet.