OpenRaiser/Intern-Atlas huggingface dataset:
Training Spatial Ability in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Background: Spatial reasoning has been identified as a critical skill for success in STEM. Unfortunately, under-represented groups often have lower incoming spatial ability. Courses that improve spatial skills exist but are not widely used. Virtual reality (VR) has been suggested as a possible tool for teaching spatial reasoning since students are more accurate and complete spatial tasks more quickly in three dimensions. However, no prior work has developed or evaluated a fully-structured VR spatial skills course. Objectives: We seek to assess the effectiveness of teaching spatial reasoning in VR, both in isolation as a structured training curriculum and also in comparison to traditional methods. Methods: We adapted three modules of an existing pencil-and-paper course to VR, leveraging educational scaffolding and real-time feedback in the design. We evaluated our three-week course in a study with $n=24$ undergraduate introductory STEM students, capturing both quantitative spatial ability gains (using pre- and post test scores on validated assessments) and qualitative insights (from a post-study questionnaire). We also compared our VR course to an offering of a baseline non-VR course (using data collected in a previous study). Results and Conclusions: Students who took our VR course had significant spatial ability gains. Critically, we find no significant difference in outcomes between our VR course (3 meetings of 120 minutes each) and a baseline pencil and paper course (10 meetings of 90 minutes each), suggesting that spatial reasoning can be very efficiently taught in VR. We observed cybersickness at lower rates than are generally reported and most students reported enjoying learning in VR.
Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Venue: arXiv cs.CR 2025
Year: 2025

Underground mining operations rely on distributed sensor networks to collect critical data daily, including mine temperature, toxic gas concentrations, and miner movements for hazard detection and operational decision-making. However, transmitting raw sensor data to a central server for training deep learning models introduces significant privacy risks, potentially exposing sensitive mine-specific information. Federated Learning (FL) offers a transformative solution by enabling collaborative model training while ensuring that raw data remains localized at each mine. Despite its advantages, FL in underground mining faces key challenges: (i) An attacker may compromise a mine's local model by employing techniques such as sign-flipping attacks or additive noise, leading to erroneous predictions; (ii) Low-quality (yet potentially valuable) data, caused by poor lighting conditions or sensor inaccuracies in mines may degrade the FL training process. In response, this paper proposes MineDetect, a defense FL framework that detects and isolates the attacked models while mitigating the impact of mines with low-quality data. MineDetect introduces two key innovations: (i) Detecting attacked models (maliciously manipulated) by developing a history-aware mechanism that leverages local and global averages of gradient updates; (ii) Identifying and eliminating adversarial influences from unreliable models (generated by clients with poor data quality) on the FL training process. Comprehensive simulations across diverse datasets demonstrate that MineDetect outperforms existing methods in both robustness and accuracy, even in challenging non-IID data scenarios. Its ability to counter adversarial influences while maintaining lower computational efficiency makes it a vital advancement for improving safety and operational effectiveness in underground mining.
Understanding Textual Emotion Through Emoji Prediction
Venue: arXiv cs.CL 2025
Year: 2025

This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training advantage, while CNN demonstrates superior efficacy on rare emoji classes. This research shows the importance of architecture selection and hyperparameter tuning for sentiment-aware emoji prediction, contributing to improved human-computer interaction.
Personalized Real-time Jargon Support for Online Meetings
Venue: arXiv cs.HC 2025
Year: 2025

Effective interdisciplinary communication is frequently hindered by domain-specific jargon. To explore the jargon barriers in-depth, we conducted a formative diary study with 16 professionals, revealing critical limitations in current jargon-management strategies during workplace meetings. Based on these insights, we designed ParseJargon, an interactive LLM-powered system providing real-time personalized jargon identification and explanations tailored to users' individual backgrounds. A controlled experiment comparing ParseJargon against baseline (no support) and general-purpose (non-personalized) conditions demonstrated that personalized jargon support significantly enhanced participants' comprehension, engagement, and appreciation of colleagues' work, whereas general-purpose support negatively affected engagement. A follow-up field study validated ParseJargon's usability and practical value in real-time meetings, highlighting both opportunities and limitations for real-world deployment. Our findings contribute insights into designing personalized jargon support tools, with implications for broader interdisciplinary and educational applications.
Rethinking Reliability Using Network Coding: a Practical 5G Evaluation
Venue: arXiv cs.NI 2025
Year: 2025

This work presents the design and implementation of a real-time network coding system integrated into the IP layer of a 5G testbed, offering an alternative to conventional retransmission-based reliability mechanisms such as ARQ and HARQ. Using a netfilter-based packet interception framework, we inject forward erasure correction using Random Linear Network Coding (RLNC) into live traffic between a gNB and UE over a 3GPP RF link. We evaluate a block coding scheme, analyzing its impact on throughput, jitter, and resource usage. Results show that with appropriate code rate selection, RLNC can fully recover from packet losses using fewer transmissions than ARQ/HARQ and maintain a high throughput, particularly under moderate-to-high packet loss rates. These findings demonstrate that network coding can effectively replace retransmission-based reliability in future wireless systems, with the potential for more efficient resource utilization.
Meta-Metrics and Best Practices for System-Level Inference Performance Benchmarking
Venue: arXiv cs.PF 2025
Year: 2025

Benchmarking inference performance (speed) of Foundation Models such as Large Language Models (LLM) involves navigating a vast experimental landscape to understand the complex interactions between hardware and software components. However, evaluating every possible test configuration is impractical, unfeasible and unnecessary. To address this challenge, we introduce FMwork, a comprehensive and methodical approach to creating a controlled testing environment that accurately reflects and characterizes performance. FMwork comprises a set of benchmkaring best practices with three key components: 1) meta-metrics, 2) parameter selection, and 3) strategic cost-performance evaluation. Meta-metrics account for time and resources spent on benchmarking and the relative accuracy of the results compared to a larger body of measurements, representing the complete experimental space. FMwork operationalizes the meta-metrics and provides efficient strategies for parameter selection and cost-performance analysis. Using the framework, we show up to 24x improvement (speedup and/or resource savings) running sweeps of experiments compared to the ground truth. Even already considering a subset of experiments as reference point (using the power of two for batch sizes), reducing experimental output size from 1024 to 128 tokens yields another 2.7x gain while keeping 96.6% accuracy for an evaluation using Llama 3.1 8B model.
Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
Venue: arXiv cs.LG 2025
Year: 2025

This paper addresses source component shift adaptation, aiming to update predictions adapting to source component shifts for incoming data streams based on past training data. Existing online learning methods often fail to utilize recurring shifts effectively, while model-pool-based methods struggle to capture individual source components, leading to poor adaptation. In this paper, we propose a source component shift adaptation method via an offline decomposition and online mixing approach. We theoretically identify that the problem can be divided into two subproblems: offline source component decomposition and online mixing weight adaptation. Based on this, our method first determines prediction models, each of which learns a source component solely based on past training data offline through the EM algorithm. Then, it updates the mixing weight of the prediction models for precise prediction through online convex optimization. Thanks to our theoretical derivation, our method fully leverages the characteristics of the shifts, achieving superior adaptation performance over existing methods. Experiments conducted on various real-world regression datasets demonstrate that our method outperforms baselines, reducing the cumulative test loss by up to 67.4%.
Pose-Robust Calibration Strategy for Point-of-Gaze Estimation on Mobile Phones
Venue: arXiv cs.CV 2025
Year: 2025

Although appearance-based point-of-gaze (PoG) estimation has improved, the estimators still struggle to generalize across individuals due to personal differences. Therefore, person-specific calibration is required for accurate PoG estimation. However, calibrated PoG estimators are often sensitive to head pose variations. To address this, we investigate the key factors influencing calibrated estimators and explore pose-robust calibration strategies. Specifically, we first construct a benchmark, MobilePoG, which includes facial images from 32 individuals focusing on designated points under either fixed or continuously changing head poses. Using this benchmark, we systematically analyze how the diversity of calibration points and head poses influences estimation accuracy. Our experiments show that introducing a wider range of head poses during calibration improves the estimator's ability to handle pose variation. Building on this insight, we propose a dynamic calibration strategy in which users fixate on calibration points while moving their phones. This strategy naturally introduces head pose variation during a user-friendly and efficient calibration process, ultimately producing a better calibrated PoG estimator that is less sensitive to head pose variations than those using conventional calibration strategies. Codes and datasets are available at our project page.
The Conditional Regret-Capacity Theorem for Batch Universal Prediction
Venue: arXiv cs.IT 2025
Year: 2025

We derive a conditional version of the classical regret-capacity theorem. This result can be used in universal prediction to find lower bounds on the minimal batch regret, which is a recently introduced generalization of the average regret, when batches of training data are available to the predictor. As an example, we apply this result to the class of binary memoryless sources. Finally, we generalize the theorem to Rényi information measures, revealing a deep connection between the conditional Rényi divergence and the conditional Sibson's mutual information.
Design of a Timer Queue Supporting Dynamic Update Operations
Venue: arXiv cs.NI 2025
Year: 2025

Large-scale timers are ubiquitous in network processing, including flow table entry expiration control in software defined network (SDN) switches, MAC address aging in Ethernet bridges, and retransmission timeout management in TCP/IP protocols. Conventional implementations suffer from critical limitations: low timing accuracy due to large-scale timer traversal and high computational overhead for new timer insertion. This paper presents a hybrid-architecture hardware priority queue based on systolic arrays and shift registers for efficient timer queue management. The design uniquely supports five operations: enqueue, dequeue, delete, update, and peek.To the best of our knowledge, it is the first hardware priority queue enabling in-queue priority updates. By leveraging centralized Boolean logic encoding within systolic blocks, the design efficiently generates set/shift control signals while the novel push-first operation ensures FIFO ordering for same-priority timers without additional metadata. Experimental results demonstrate that the design operates at over 400 MHz on FPGAs, achieving a 2.2-2.8x reduction in resource consumption compared to state-of-the-art implementations.
Uncertainty-Aware Prediction of Parkinson's Disease Medication Needs: A Two-Stage Conformal Prediction Approach
Venue: arXiv cs.LG 2025
Year: 2025

Parkinson's Disease (PD) medication management presents unique challenges due to heterogeneous disease progression and treatment response. Neurologists must balance symptom control with optimal dopaminergic dosing based on functional disability while minimizing side effects. This balance is crucial as inadequate or abrupt changes can cause levodopa-induced dyskinesia, wearing off, and neuropsychiatric effects, significantly reducing quality of life. Current approaches rely on trial-and-error decisions without systematic predictive methods. Despite machine learning advances, clinical adoption remains limited due to reliance on point predictions that do not account for prediction uncertainty, undermining clinical trust and utility. Clinicians require not only predictions of future medication needs but also reliable confidence measures. Without quantified uncertainty, adjustments risk premature escalation to maximum doses or prolonged inadequate symptom control. We developed a conformal prediction framework anticipating medication needs up to two years in advance with reliable prediction intervals and statistical guarantees. Our approach addresses zero-inflation in PD inpatient data, where patients maintain stable medication regimens between visits. Using electronic health records from 631 inpatient admissions at University of Florida Health (2011-2021), our two-stage approach identifies patients likely to need medication changes, then predicts required levodopa equivalent daily dose adjustments. Our framework achieved marginal coverage while reducing prediction interval lengths compared to traditional approaches, providing precise predictions for short-term planning and wider ranges for long-term forecasting. By quantifying uncertainty, our approach enables evidence-based decisions about levodopa dosing, optimizing symptom control while minimizing side effects and improving life quality.
A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Defending backdoor attacks in Federated Learning (FL) under heterogeneous client data distributions encounters limitations balancing effectiveness and privacy-preserving, while most existing methods highly rely on the assumption of homogeneous client data distributions or the availability of a clean serve dataset. In this paper, we propose an FL backdoor defense framework, named CLIP-Fed, that utilizes the zero-shot learning capabilities of vision-language pre-training models. Our scheme overcomes the limitations of Non-IID imposed on defense effectiveness by integrating pre-aggregation and post-aggregation defense strategies. CLIP-Fed aligns the knowledge of the global model and CLIP on the augmented dataset using prototype contrastive loss and Kullback-Leibler divergence, so that class prototype deviations caused by backdoor samples are ensured and the correlation between trigger patterns and target labels is eliminated. In order to balance privacy-preserving and coverage enhancement of the dataset against diverse triggers, we further construct and augment the server dataset via using the multimodal large language model and frequency analysis without any client samples. Extensive experiments on representative datasets evidence the effectiveness of CLIP-Fed. Comparing to other existing methods, CLIP-Fed achieves an average reduction in Attack Success Rate, {\em i.e.}, 2.03\% on CIFAR-10 and 1.35\% on CIFAR-10-LT, while improving average Main Task Accuracy by 7.92\% and 0.48\%, respectively. Our codes are available at https://anonymous.4open.science/r/CLIP-Fed.
A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks
Venue: arXiv cs.LG 2025
Year: 2025

The Internet of Medical Things (IoMT) is driving a healthcare revolution but remains vulnerable to cyberattacks such as denial of service, ransomware, data hijacking, and spoofing. These networks comprise resource constrained, heterogeneous devices (e.g., wearable sensors, smart pills, implantables), making traditional centralized Intrusion Detection Systems (IDSs) unsuitable due to response delays, privacy risks, and added vulnerabilities. Centralized IDSs require all sensors to transmit data to a central server, causing delays or network disruptions in dense environments. Running IDSs locally on IoMT devices is often infeasible due to limited computation, and even lightweight IDS components remain at risk if updated models are delayed leaving them exposed to zero-day attacks that threaten patient health and data security. We propose a multi level IoMT IDS framework capable of detecting zero day attacks and distinguishing between known and unknown threats. The first layer (near Edge) filters traffic at a coarse level (attack or not) using meta-learning or One Class Classification (OCC) with the usfAD algorithm. Subsequent layers (far Edge, Cloud) identify attack type and novelty. Experiments on the CICIoMT2024 dataset show 99.77 percentage accuracy and 97.8 percentage F1-score. The first layer detects zero-day attacks with high accuracy without needing new datasets, ensuring strong applicability in IoMT environments. Additionally, the meta-learning approach achieves high.
Making Qwen3 Think in Korean with Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

We present a two-stage fine-tuning approach to make the large language model Qwen3 14B "think" natively in Korean. In the first stage, supervised fine-tuning (SFT) on a high-quality Korean reasoning dataset establishes a strong foundation in Korean logical reasoning, yielding notable improvements in Korean-language tasks and even some gains in general reasoning ability. In the second stage, we employ reinforcement learning with a customized Group Relative Policy Optimization (GRPO) algorithm to further enhance both Korean reasoning alignment and overall problem-solving performance. We address critical stability challenges in GRPO training - such as reward hacking and policy collapse - by introducing an oracle judge model that calibrates the reward signal. Our approach achieves stable learning (avoiding the collapse observed in naive GRPO) and leads to steady, incremental performance gains. The final RL-tuned model demonstrates substantially improved results on advanced reasoning benchmarks (particularly math and coding tasks) while maintaining knowledge and language proficiency, successfully conducting its internal chain-of-thought entirely in Korean.
Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Reinforcement learning in large reasoning models enables learning from feedback on their outputs, making it particularly valuable in scenarios where fine-tuning data is limited. However, its application in multi-modal human activity recognition (HAR) domains remains largely underexplored. Our work extends reinforcement learning to the human activity recognition domain with multimodal large language models. By incorporating visual reinforcement learning in the training process, the model's generalization ability on few-shot recognition can be greatly improved. Additionally, visual reinforcement learning can enhance the model's reasoning ability and enable explainable analysis in the inference stage. We name our few-shot human activity recognition method with visual reinforcement learning FAVOR. Specifically, our approach first utilizes a multimodal large language model (MLLM) to generate multiple candidate responses for the human activity image, each containing reasoning traces and final answers. These responses are then evaluated using reward functions, and the MLLM model is subsequently optimized using the Group Relative Policy Optimization (GRPO) algorithm. In this way, the MLLM model can be adapted to human activity recognition with only a few samples. Extensive experiments on four human activity recognition datasets and five different settings demonstrate the superiority of the proposed method.
Lower Bounds on Tree Covers
Venue: arXiv cs.DS 2025
Year: 2025

Given an $n$-point metric space $(X,d_X)$, a tree cover $\mathcal{T}$ is a set of $|\mathcal{T}|=k$ trees on $X$ such that every pair of vertices in $X$ has a low-distortion path in one of the trees in $\mathcal{T}$. Tree covers have been playing a crucial role in graph algorithms for decades, and the research focus is the construction of tree covers with small size $k$ and distortion. When $k=1$, the best distortion is known to be $Θ(n)$. For a constant $k\ge 2$, the best distortion upper bound is $\tilde O(n^{\frac 1 k})$ and the strongest lower bound is $Ω(\log_k n)$, leaving a gap to be closed. In this paper, we improve the lower bound to $Ω(n^{\frac{1}{2^{k-1}}})$. Our proof is a novel analysis on a structurally simple grid-like graph, which utilizes some combinatorial fixed-point theorems. We believe that they will prove useful for analyzing other tree-like data structures as well.
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challenge for understanding model vulnerabilities and improving robustness. In this context, we propose a new black-box attack method that leverages the interpretability of large models. We introduce the Sparse Feature Perturbation Framework (SFPF), a novel approach for adversarial text generation that utilizes sparse autoencoders to identify and manipulate critical features in text. After using the SAE model to reconstruct hidden layer representations, we perform feature clustering on the successfully attacked texts to identify features with higher activations. These highly activated features are then perturbed to generate new adversarial texts. This selective perturbation preserves the malicious intent while amplifying safety signals, thereby increasing their potential to evade existing defenses. Our method enables a new red-teaming strategy that balances adversarial effectiveness with safety alignment. Experimental results demonstrate that adversarial texts generated by SFPF can bypass state-of-the-art defense mechanisms, revealing persistent vulnerabilities in current NLP systems.However, the method's effectiveness varies across prompts and layers, and its generalizability to other architectures and larger models remains to be validated.