OpenRaiser/Intern-Atlas huggingface dataset:
Research on evolution and early warning model of network public opinion based on online Latent Dirichlet distribution model and BP neural network
Venue: arXiv cs.SI 2025
Year: 2025

Online public opinion is increasingly becoming a significant factor affecting the stability of the internet and society, particularly as the frequency of online public opinion crises has risen in recent years. Enhancing the capability for early warning of online public opinion crises is urgent. The most effective approach is to identify potential crises in their early stages and implement corresponding management measures. This study establishes a preliminary indicator system for online public opinion early warning, based on the principles of indicator system construction and the characteristics and evolution patterns of online public opinion. Subsequently, data-driven methodologies were employed to collect and preprocess public opinion indicator data. Utilizing grey relational analysis and the K-Means clustering algorithm, we classified online public opinion events into three levels: slight, warning, and severe. Furthermore, we constructed an online topic evolution model using the online Hierarchical Dirichlet Process model to analyze the thematic changes of online public opinion events across different warning levels. Finally, we developed an online public opinion early warning model using a Backpropagation (BP) neural network. The test results of early warning samples show that the model achieves high accuracy. Thus, in practical early warning applications, the BP neural network can be effectively utilized for predicting online public opinion events.
Efficient Finetuning for Dimensional Speech Emotion Recognition in the Age of Transformers
Venue: arXiv cs.SD 2025
Year: 2025

Accurate speech emotion recognition is essential for developing human-facing systems. Recent advancements have included finetuning large, pretrained transformer models like Wav2Vec 2.0. However, the finetuning process requires substantial computational resources, including high-memory GPUs and significant processing time. As the demand for accurate emotion recognition continues to grow, efficient finetuning approaches are needed to reduce the computational burden. Our study focuses on dimensional emotion recognition, predicting attributes such as activation (calm to excited) and valence (negative to positive). We present various finetuning techniques, including full finetuning, partial finetuning of transformer layers, finetuning with mixed precision, partial finetuning with caching, and low-rank adaptation (LoRA) on the Wav2Vec 2.0 base model. We find that partial finetuning with mixed precision achieves performance comparable to full finetuning while increasing training speed by 67%. Caching intermediate representations further boosts efficiency, yielding an 88% speedup and a 71% reduction in learnable parameters. We recommend finetuning the final three transformer layers in mixed precision to balance performance and training efficiency, and adding intermediate representation caching for optimal speed with minimal performance trade-offs. These findings lower the barriers to finetuning speech emotion recognition systems, making accurate emotion recognition more accessible to a broader range of researchers and practitioners.
Information entropy of complex probability
Venue: arXiv cs.IT 2025
Year: 2025

Probability theory is fundamental for modeling uncertainty, with traditional probabilities being real and non-negative. Complex probability extends this concept by allowing complex-valued probabilities, opening new avenues for analysis in various fields. This paper explores the information-theoretic aspects of complex probability, focusing on its definition, properties, and applications. We extend Shannon entropy to complex probability and examine key properties, including maximum entropy, joint entropy, conditional entropy, equilibration, and cross entropy. These results offer a framework for understanding entropy in complex probability spaces and have potential applications in fields such as statistical mechanics and information theory.
Disproving some theorems in Sharma and Chauhan et al. (2018, 2021)
Venue: arXiv cs.IT 2025
Year: 2025

The main objective of this work is to show, through counterexamples, that some of the theorems presented in the papers of Sharma \textit{et al.} (2018) and Chauhan \textit{et al.} ( 2021) are incorrect. Although they used these theorems to establish a sufficient condition for a multi-twisted (MT) code to be linear complementary dual (LCD), we show that this condition itself remains valid. We further improve this condition by removing the restrictions on the shift constants and relaxing the required coprimality condition. We show that compared to the previous condition, the modified condition is able to identify more LCD MT codes. Furthermore, without the need for a normalized set of generators, we develop a formula to determine the dimension of any $ρ$-generator MT code.
BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference
Venue: arXiv cs.SI 2025
Year: 2025

Social bots have become widely known by users of social platforms. To prevent social bots from spreading harmful speech, many novel bot detections are proposed. However, with the evolution of social bots, detection methods struggle to give high-confidence answers for samples. This motivates us to quantify the uncertainty of the outputs, informing the confidence of the results. Therefore, we propose an uncertainty-aware bot detection method to inform the confidence and use the uncertainty score to pick a high-confidence decision from multiple views of a social network under different environments. Specifically, our proposed BotUmc uses LLM to extract information from tweets. Then, we construct a graph based on the extracted information, the original user information, and the user relationship and generate multiple views of the graph by causal interference. Lastly, an uncertainty loss is used to force the model to quantify the uncertainty of results and select the result with low uncertainty in one view as the final decision. Extensive experiments show the superiority of our method.
Rebalanced Multimodal Learning with Data-aware Unimodal Sampling
Venue: arXiv cs.LG 2025
Year: 2025

To address the modality learning degeneration caused by modality imbalance, existing multimodal learning~(MML) approaches primarily attempt to balance the optimization process of each modality from the perspective of model learning. However, almost all existing methods ignore the modality imbalance caused by unimodal data sampling, i.e., equal unimodal data sampling often results in discrepancies in informational content, leading to modality imbalance. Therefore, in this paper, we propose a novel MML approach called \underline{D}ata-aware \underline{U}nimodal \underline{S}ampling~(\method), which aims to dynamically alleviate the modality imbalance caused by sampling. Specifically, we first propose a novel cumulative modality discrepancy to monitor the multimodal learning process. Based on the learning status, we propose a heuristic and a reinforcement learning~(RL)-based data-aware unimodal sampling approaches to adaptively determine the quantity of sampled data at each iteration, thus alleviating the modality imbalance from the perspective of sampling. Meanwhile, our method can be seamlessly incorporated into almost all existing multimodal learning approaches as a plugin. Experiments demonstrate that \method~can achieve the best performance by comparing with diverse state-of-the-art~(SOTA) baselines.
Nexar Dashcam Collision Prediction Dataset and Challenge
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents the Nexar Dashcam Collision Prediction Dataset and Challenge, designed to support research in traffic event analysis, collision prediction, and autonomous vehicle safety. The dataset consists of 1,500 annotated video clips, each approximately 40 seconds long, capturing a diverse range of real-world traffic scenarios. Videos are labeled with event type (collision/near-collision vs. normal driving), environmental conditions (lighting conditions and weather), and scene type (urban, rural, highway, etc.). For collision and near-collision cases, additional temporal labels are provided, including the precise moment of the event and the alert time, marking when the collision first becomes predictable. To advance research on accident prediction, we introduce the Nexar Dashcam Collision Prediction Challenge, a public competition on top of this dataset. Participants are tasked with developing machine learning models that predict the likelihood of an imminent collision, given an input video. Model performance is evaluated using the average precision (AP) computed across multiple intervals before the accident (i.e. 500 ms, 1000 ms, and 1500 ms prior to the event), emphasizing the importance of early and reliable predictions. The dataset is released under an open license with restrictions on unethical use, ensuring responsible research and innovation.
A Bridge to Nowhere: A Healthcare Case Study for Non-Reformist Design
Venue: arXiv cs.CY 2025
Year: 2025

In the face of intensified datafication and automation in public sector industries, frameworks like design justice and the feminist practice of refusal provide help to identify and mitigate structural harm and challenge inequities reproduced in digitized infrastructures. This paper applies those frameworks to emerging efforts across the U.S. healthcare industry to automate prior authorization -- a process whereby insurance companies determine whether a treatment or service is 'medically necessary' before agreeing to cover it. Federal regulatory interventions turn to datafication and automation to reduce the harms of this widely unpopular process shown to delay vital treatments and create immense administrative burden for healthcare providers and patients. This paper explores emerging prior authorization reforms as a case study, applying the frameworks of design justice and refusal to highlight the inherent conservatism of interventions oriented towards improving the user experience of extractive systems. I further explore how the abolitionist framework of non-reformist reform helps to clarify alternative interventions that would mitigate the harms of prior authorization in ways that do not reproduce or extend the power of insurance companies. I propose a set of four tenets for nonreformist design to mitigate structural harms and advance design justice in a broad set of domains.
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach
Venue: arXiv cs.LG 2025
Year: 2025

As specialized large language models (LLMs) become increasingly prevalent, model merging methods are being used to combine them to create a single multi-task model without requiring any additional data or training. However, these approaches fall short when the objective of merging is to increase the downstream model's performance on a particular task-specific benchmark. In this work, we propose LEWIS (Layer Wise Sparsity), a guided model-merging framework that uses activation-based layer importance to dynamically adjust layer-wise task-vector sparsity required for the merge process. LEWIS uses a calibration dataset to prioritize critical layers during the task-vector pruning process required for model merging. This approach guides existing merging methods by preserving essential layer-wise task-specific knowledge while ensuring the merged model performs the best at benchmarks resembling the calibration dataset. Our experiments demonstrate the effectiveness of LEWIS with performance improvements of code instruction-following and math-solving models created through model merging up to 4 percent and 11.3 percent, respectively, outperforming unguided data-less model merging approaches that use uniform-sparsity.
CRAFT: Characterizing and Root-Causing Fault Injection Threats at Pre-Silicon
Venue: arXiv cs.CR 2025
Year: 2025

Fault injection attacks (FIA) pose significant security threats to embedded systems as they exploit weaknesses across multiple layers, including system software, instruction set architecture (ISA), microarchitecture, and physical hardware. Early detection and understanding of how physical faults propagate to system-level behavior are essential to safeguarding cyberinfrastructure. This work introduces CRAFT, a framework that combines pre-silicon analysis with post-silicon validation to systematically uncover and analyze fault injection vulnerabilities. Our study, conducted on a RISC-V soft-core processor (cv32e40x) reveals two novel vulnerabilities. First, we demonstrate a method to induce instruction skips by glitching the clock (single-glitch attack), which prevents critical values from being loaded from memory, thus disrupting program execution. Second, we show a technique that converts a fetched legal instruction into an illegal one mid-execution, diverting control flow in a manner exploitable by attackers. Notably, we identified a specific timing window in which the processor fails to detect these illegal control-flow diversions, allowing silent, undetected corruption of the program state. By simulating 9248 FIA scenarios at pre-silicon and conducting root-cause analysis of the RISC-V pipeline, we trace the faults to a previously unreported vulnerability in a pipeline register shared between the instruction fetch and decode stages. Our approach reduced the search space for post-silicon experiments by 97.31%, showing pre-silicon advantages for post-silicon testing. Finally, we validate our identified exploit cases on real hardware (FPGA).
Seldonian Reinforcement Learning for Ad Hoc Teamwork
Venue: arXiv cs.LG 2025
Year: 2025

Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical applications, such as in some multiagent domains where agents, and possibly humans, need to interact to reach their goals without harming each other. In this work, we propose a novel offline RL approach, inspired by Seldonian optimization, which returns policies with good performance and statistically guaranteed properties with respect to predefined desirable behaviors. In particular, our focus is on Ad Hoc Teamwork settings, where agents must collaborate with new teammates without prior coordination. Our method requires only a pre-collected dataset, a set of candidate policies for our agent, and a specification about the possible policies followed by the other players -- it does not require further interactions, training, or assumptions on the type and architecture of the policies. We test our algorithm in Ad Hoc Teamwork problems and show that it consistently finds reliable policies while improving sample efficiency with respect to standard ML baselines.
Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis
Venue: arXiv cs.RO 2025
Year: 2025

The deployment of Large Language Models (LLMs) in robotic systems presents unique safety challenges, particularly in unpredictable environments. Although LLMs, leveraging zero-shot learning, enhance human-robot interaction and decision-making capabilities, their inherent probabilistic nature and lack of formal guarantees raise significant concerns for safety-critical applications. Traditional model-based verification approaches often rely on precise system models, which are difficult to obtain for real-world robotic systems and may not be fully trusted due to modeling inaccuracies, unmodeled dynamics, or environmental uncertainties. To address these challenges, this paper introduces a safety assurance framework for LLM-controlled robots based on data-driven reachability analysis, a formal verification technique that ensures all possible system trajectories remain within safe operational limits. Our framework specifically investigates the problem of instructing an LLM to navigate the robot to a specified goal and assesses its ability to generate low-level control actions that successfully guide the robot safely toward that goal. By leveraging historical data to construct reachable sets of states for the robot-LLM system, our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models. We validate the framework through experimental case studies in autonomous navigation and task planning, demonstrating its effectiveness in mitigating risks associated with LLM-generated commands. This work advances the integration of formal methods into LLM-based robotics, offering a principled and practical approach to ensuring safety in next-generation autonomous systems.
Rényi Divergences in Central Limit Theorems: Old and New
Venue: arXiv cs.IT 2025
Year: 2025

We give an overview of various results and methods related to information-theoretic distances of Rényi type in the light of their applications to the central limit theorem (CLT). The first part (Sections 1-9) is devoted to the total variation and the Kullback-Leibler distance (relative entropy). In the second part (Sections 10-15) we discuss general properties of Rényi and Tsallis divergences of order $α>1$, and then in the third part (Sections 16-21) we turn to the CLT and non-uniform local limit theorems with respect to these strong distances. In the fourth part (Sections 22-31), we discuss recent results on strictly subgaussian distributions and describe necessary and sufficient conditions which ensure the validity of the CLT with respect to the Rényi divergence of infinite order.
GeoDEN: A Visual Exploration Tool for Analysing the Geographic Spread of Dengue Serotypes
Venue: arXiv cs.HC 2025
Year: 2025

Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualisations. Over half of the global population live in dengue endemic regions. Understanding the spatiotemporal dynamics of the four closely related dengue serotypes, and their immunological interactions, remains a challenge at a global scale. To facilitate this understanding, we worked with dengue epidemiologists in a user-centered design framework to create GeoDEN, an exploratory visualisation tool that empowers experts to investigate spatiotemporal patterns in dengue serotype reports. The tool has several linked visualisations and filtering mechanisms, enabling analysis at a range of spatial and temporal scales. To identify successes and failures, we present both insight-based and value-driven evaluations. Our domain experts found GeoDEN valuable, verifying existing hypotheses and uncovering novel insights that warrant further investigation by the epidemiology community. The developed visual exploration approach can be adapted for exploring other epidemiology and disease incident datasets.
GeoFIK: A Fast and Reliable Geometric Solver for the IK of the Franka Arm based on Screw Theory Enabling Multiple Redundancy Parameters
Venue: arXiv cs.RO 2025
Year: 2025

Modern robotics applications require an inverse kinematics (IK) solver that is fast, robust and consistent, and that provides all possible solutions. Currently, the Franka robot arm is the most widely used manipulator in robotics research. With 7 DOFs, the IK of this robot is not only complex due to its 1-DOF redundancy, but also due to the link offsets at the wrist and elbow. Due to this complexity, none of the Franka IK solvers available in the literature provide satisfactory results when used in real-world applications. Therefore, in this paper we introduce GeoFIK (Geometric Franka IK), an analytical IK solver that allows the use of different joint variables to resolve the redundancy. The approach uses screw theory to describe the entire geometry of the robot, allowing the computation of the Jacobian matrix prior to computation of joint angles. All singularities are identified and handled. As an example of how the geometric elements obtained by the IK can be exploited, a solver with the swivel angle as the free variable is provided. Several experiments are carried out to validate the speed, robustness and reliability of the GeoFIK against two state-of-the-art solvers.
Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations
Venue: arXiv cs.RO 2025
Year: 2025

The growing adoption of batteries in the electric vehicle industry and various consumer products has created an urgent need for effective recycling solutions. These products often contain a mix of compliant and rigid components, making robotic disassembly a critical step toward achieving scalable recycling processes. Diffusion policy has emerged as a promising approach for learning low-level skills in robotics. To effectively apply diffusion policy to contact-rich tasks, incorporating force as feedback is essential. In this paper, we apply diffusion policy with vision and force in a compliant object prying task. However, when combining low-dimensional contact force with high-dimensional image, the force information may be diluted. To address this issue, we propose a method that effectively integrates force with image data for diffusion policy observations. We validate our approach on a battery prying task that demands high precision and multi-step execution. Our model achieves a 96\% success rate in diverse scenarios, marking a 57\% improvement over the vision-only baseline. Our method also demonstrates zero-shot transfer capability to handle unseen objects and battery types. Supplementary videos and implementation codes are available on our project website. https://rros-lab.github.io/diffusion-with-force.github.io/
Binomial Tabulation: A Short Story
Venue: arXiv cs.PL 2025
Year: 2025

We reconstruct some of the development in Richard Bird's [2008] paper Zippy Tabulations of Recursive Functions, using dependent types and string diagrams rather than mere simple types. This paper serves as an intuitive introduction to and demonstration of these concepts for the curious functional programmer, who ideally already has some exposure to dependent types and category theory, is not put off by basic concepts like indexed types and functors, and wants to see a more practical example. The paper is presented in the form of a short story, narrated from the perspective of a functional programmer trying to follow the development in Bird's paper. The first section recaps the original simply typed presentation. The second section explores a series of refinements that can be made using dependent types. The third section uses string diagrams to simplify arguments involving functors and naturality. The short story ends there, but the paper concludes with a discussion and reflection in the afterword.
Revisiting Abstractions for Software Architecture and Tools to Support Them
Venue: arXiv cs.SE 2025
Year: 2025

The mid-1990s saw the design of programming languages for software architectures, which define the high-level aspects of software systems including how code components were composed to form full systems. Our paper "Abstractions for Software Architecture and Tools to Support Them" presented a conceptual view of software architecture based on abstractions used in practice to organize software systems, a language that supported these abstractions, and a prototype implementation of this language. By invitation, we reflect on the paper's principal ideas about system-level abstractions, place the work in a historical context of steadily increasing abstraction power in software development languages and infrastructure, and reflect on how progress since the paper's 1995 publication has been influenced, directly or indirectly, by this work. We describe current manifestations of architectural ideas and current challenges. We suggest how the strategy we used to identify and reify architectural abstractions may apply to current opportunities.