OpenRaiser/Intern-Atlas huggingface dataset:
CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
Venue: arXiv cs.LG 2025
Year: 2025

Cybersecurity systems are continuously producing a huge number of time-stamped events in the form of high-order tensors, such as {count; time, port, flow duration, packet size, . . . }, and so how can we detect anomalies/intrusions in real time? How can we identify multiple types of intrusions and capture their characteristic behaviors? The tensor data consists of categorical and continuous attributes and the data distributions of continuous attributes typically exhibit skew. These data properties require handling skewed infinite and finite dimensional spaces simultaneously. In this paper, we propose a novel streaming method, namely CyberCScope. The method effectively decomposes incoming tensors into major trends while explicitly distinguishing between categorical and skewed continuous attributes. To our knowledge, it is the first to compute hybrid skewed infinite and finite dimensional decomposition. Based on this decomposition, it streamingly finds distinct time-evolving patterns, enabling the detection of multiple types of anomalies. Extensive experiments on large-scale real datasets demonstrate that CyberCScope detects various intrusions with higher accuracy than state-of-the-art baselines while providing meaningful summaries for the intrusions that occur in practice.
We Need to Effectively Integrate Computing Skills Across Discipline Curricula
Venue: arXiv cs.CY 2025
Year: 2025

Computing is increasingly central to innovation across a wide range of disciplinary and interdisciplinary problem domains. Students across noncomputing disciplines need to apply sophisticated computational skills and methods to fields as diverse as biology, linguistics, and art. Furthermore, computing plays a critical role in "momentous geopolitical events", such as elections in several countries including the US, and is changing how people "work, collaborate, communicate, shop, eat, travel, get news and entertainment, and quite simply live". Traditional computing courses, however, fail to equip non-computing discipline students with the necessary computing skills - if they can even get into classes packed with CS majors. A pressing question facing academics today is: How do we effectively integrate computing skills that are useful for the discipline into discipline curricula? We advocate an approach where courses in discipline X include the computing relevant to the learning outcomes of that course, as used by practitioners in X. We refer to the computing skills relevant to a course in discipline X as an "ounce of computing skills", to highlight our belief regarding the amount of computing to be integrated in that course. In this article, we outline our insights regarding the development of an ounce of computing skills for a discipline course, and the evaluation of the developed ounce. The key takeaways are that the goal has to be to advance students in their disciplines, and only the disciplinary experts can tell us how computing is used in that discipline. Computer scientists know how to teach computing, but the classes can't be about CS values. The disciplinary values are paramount.
FunBench: Benchmarking Fundus Reading Skills of MLLMs
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have shown significant potential in medical image analysis. However, their capabilities in interpreting fundus images, a critical skill for ophthalmology, remain under-evaluated. Existing benchmarks lack fine-grained task divisions and fail to provide modular analysis of its two key modules, i.e., large language model (LLM) and vision encoder (VE). This paper introduces FunBench, a novel visual question answering (VQA) benchmark designed to comprehensively evaluate MLLMs' fundus reading skills. FunBench features a hierarchical task organization across four levels (modality perception, anatomy perception, lesion analysis, and disease diagnosis). It also offers three targeted evaluation modes: linear-probe based VE evaluation, knowledge-prompted LLM evaluation, and holistic evaluation. Experiments on nine open-source MLLMs plus GPT-4o reveal significant deficiencies in fundus reading skills, particularly in basic tasks such as laterality recognition. The results highlight the limitations of current MLLMs and emphasize the need for domain-specific training and improved LLMs and VEs.
Behavior Preference Regression for Offline Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Offline reinforcement learning (RL) methods aim to learn optimal policies with access only to trajectories in a fixed dataset. Policy constraint methods formulate policy learning as an optimization problem that balances maximizing reward with minimizing deviation from the behavior policy. Closed form solutions to this problem can be derived as weighted behavioral cloning objectives that, in theory, must compute an intractable partition function. Reinforcement learning has gained popularity in language modeling to align models with human preferences; some recent works consider paired completions that are ranked by a preference model following which the likelihood of the preferred completion is directly increased. We adapt this approach of paired comparison. By reformulating the paired-sample optimization problem, we fit the maximum-mode of the Q function while maximizing behavioral consistency of policy actions. This yields our algorithm, Behavior Preference Regression for offline RL (BPR). We empirically evaluate BPR on the widely used D4RL Locomotion and Antmaze datasets, as well as the more challenging V-D4RL suite, which operates in image-based state spaces. BPR demonstrates state-of-the-art performance over all domains. Our on-policy experiments suggest that BPR takes advantage of the stability of on-policy value functions with minimal perceptible performance degradation on Locomotion datasets.
B-OCL: An Object Constraint Language Interpreter in Python
Venue: arXiv cs.SE 2025
Year: 2025

The Object Constraint Language (OCL) has been widely used in the modeling community to complement software models for precisely defining constraints and business rules for the modeled systems. There is a limited number of tools supporting the definition and interpretation of OCL constraints, even less for a Python-based modelling approaches. In this paper, we introduce an OCL interpreter for Python. The interpreter has two components: parser and evaluator. We implement the OCL metamodel as a set of Python classes and design the grammar for the parser using the state-of-the-art ANTLR parser generator. The parser generates the syntax tree, that conforms with the OCL metamodel, after parsing each part of the OCL constraint. The evaluator then interprets the constraints using this syntax tree and the object diagram. In the end, the interpreter reports the result for all the constraints.
How Do Teachers Create Pedagogical Chatbots?: Current Practices and Challenges
Venue: arXiv cs.HC 2025
Year: 2025

AI chatbots have emerged as promising educational tools for personalized learning experiences, with advances in large language models (LLMs) enabling teachers to create and customize these chatbots for their specific classroom needs. However, there is a limited understanding of how teachers create pedagogical chatbots and integrate them into their lessons. Through semi-structured interviews with seven K-12 teachers, we examined their practices and challenges when designing, implementing, and deploying chatbots. Our findings revealed that teachers prioritize developing task-specific chatbots aligned with their lessons. Teachers engaged in various creation practices and had different challenges; novices in chatbot creation struggled mainly with initial design and technical implementation, while experienced teachers faced challenges with technical aspects and analyzing conversational data. Based on these insights, we explore approaches to supporting teachers' chatbot development and opportunities for designing future chatbot creation systems. This work provides foundational insights from teachers that can empower teacher-created chatbots, facilitating AI-augmented teaching.
Evaluating Polish linguistic and cultural competency in large language models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are becoming increasingly proficient in processing and generating multilingual texts, which allows them to address real-world problems more effectively. However, language understanding is a far more complex issue that goes beyond simple text analysis. It requires familiarity with cultural context, including references to everyday life, historical events, traditions, folklore, literature, and pop culture. A lack of such knowledge can lead to misinterpretations and subtle, hard-to-detect errors. To examine language models' knowledge of the Polish cultural context, we introduce the Polish linguistic and cultural competency benchmark, consisting of 600 manually crafted questions. The benchmark is divided into six categories: history, geography, culture & tradition, art & entertainment, grammar, and vocabulary. As part of our study, we conduct an extensive evaluation involving over 30 open-weight and commercial LLMs. Our experiments provide a new perspective on Polish competencies in language models, moving past traditional natural language processing tasks and general knowledge assessment.
Federated Conversational Recommender System
Venue: arXiv cs.IR 2025
Year: 2025

Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users in a conversational manner to learn their current and fine-grained preferences, a CRS can quickly derive recommendations that are relevant and justifiable. However, existing conversational recommendation systems (CRSs) typically rely on a centralized training and deployment process, which involves collecting and storing explicitly-communicated user preferences in a centralized repository. These fine-grained user preferences are completely human-interpretable and can easily be used to infer sensitive information (e.g., financial status, political stands, and health information) about the user, if leaked or breached. To address the user privacy concerns in CRS, we first define a set of privacy protection guidelines for preserving user privacy under the conversational recommendation setting. Based on these guidelines, we propose a novel federated conversational recommendation framework that effectively reduces the risk of exposing user privacy by (i) de-centralizing both the historical interests estimation stage and the interactive preference elicitation stage and (ii) strictly bounding privacy leakage by enforcing user-level differential privacy with meticulously selected privacy budgets. Through extensive experiments, we show that the proposed framework not only satisfies these user privacy protection guidelines, but also enables the system to achieve competitive recommendation performance even when compared to the state-of-the-art non-private conversational recommendation approach.
Privacy vs. Profit: The Impact of Google's Manifest Version 3 (MV3) Update on Ad Blocker Effectiveness
Venue: arXiv cs.CY 2025
Year: 2025

Google's recent update to the manifest file for Chrome browser extensions, transitioning from manifest version 2 (MV2) to manifest version 3 (MV3), has raised concerns among users and ad blocker providers, who worry that the new restrictions, notably the shift from the powerful WebRequest API to the more restrictive DeclarativeNetRequest API, might reduce ad blocker effectiveness. Because ad blockers play a vital role for millions of users seeking a more private and ad-free browsing experience, this study empirically investigates how the MV3 update affects their ability to block ads and trackers. Through a browser-based experiment conducted across multiple samples of ad-supported websites, we compare the MV3 to MV2 instances of four widely used ad blockers. Our results reveal no statistically significant reduction in ad-blocking or anti-tracking effectiveness for MV3 ad blockers compared to their MV2 counterparts, and in some cases, MV3 instances even exhibit slight improvements in blocking trackers. These findings are reassuring for users, indicating that the MV3 instances of popular ad blockers continue to provide effective protection against intrusive ads and privacy-infringing trackers. While some uncertainties remain, ad blocker providers appear to have successfully navigated the MV3 update, finding solutions that maintain the core functionality of their extensions.
Improving inference time in multi-TPU systems with profiled model segmentation
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we systematically evaluate the inference performance of the Edge TPU by Google for neural networks with different characteristics. Specifically, we determine that, given the limited amount of on-chip memory on the Edge TPU, accesses to external (host) memory rapidly become an important performance bottleneck. We demonstrate how multiple devices can be jointly used to alleviate the bottleneck introduced by accessing the host memory. We propose a solution combining model segmentation and pipelining on up to four TPUs, with remarkable performance improvements that range from $6\times$ for neural networks with convolutional layers to $46\times$ for fully connected layers, compared with single-TPU setups.
A Comparison of Object Detection and Phrase Grounding Models in Chest X-ray Abnormality Localization using Eye-tracking Data
Venue: arXiv cs.CV 2025
Year: 2025

Chest diseases rank among the most prevalent and dangerous global health issues. Object detection and phrase grounding deep learning models interpret complex radiology data to assist healthcare professionals in diagnosis. Object detection locates abnormalities for classes, while phrase grounding locates abnormalities for textual descriptions. This paper investigates how text enhances abnormality localization in chest X-rays by comparing the performance and explainability of these two tasks. To establish an explainability baseline, we proposed an automatic pipeline to generate image regions for report sentences using radiologists' eye-tracking data. The better performance - mIoU = 0.36 vs. 0.20 - and explainability - Containment ratio 0.48 vs. 0.26 - of the phrase grounding model infers the effectiveness of text in enhancing chest X-ray abnormality localization.
Training Tactile Sensors to Learn Force Sensing from Each Other
Venue: arXiv cs.RO 2025
Year: 2025

Humans achieve stable and dexterous object manipulation by coordinating grasp forces across multiple fingers and palms, facilitated by a unified tactile memory system in the somatosensory cortex. This system encodes and stores tactile experiences across skin regions, enabling the flexible reuse and transfer of touch information. Inspired by this biological capability, we present GenForce, the first framework that enables transferable force sensing across tactile sensors in robotic hands. GenForce unifies tactile signals into shared marker representations, analogous to cortical sensory encoding, allowing force prediction models trained on one sensor to be transferred to others without the need for exhaustive force data collection. We demonstrate that GenForce generalizes across both homogeneous sensors with varying configurations and heterogeneous sensors with distinct sensing modalities and material properties. This transferable force sensing is also demonstrated with high performance in robot force control including daily object grasping, slip detection and avoidance. Our results highlight a scalable paradigm for cross-sensor robotic tactile learning, offering new pathways toward adaptable and tactile memory-driven manipulation in unstructured environments.
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization
Venue: arXiv cs.AI 2025
Year: 2025

Workforce optimization plays a crucial role in efficient organizational operations where decision-making may span several different administrative and time scales. For instance, dispatching personnel to immediate service requests while managing talent acquisition with various expertise sets up a highly dynamic optimization problem. Existing work focuses on specific sub-problems such as resource allocation and facility location, which are solved with heuristics like local-search and, more recently, deep reinforcement learning. However, these may not accurately represent real-world scenarios where such sub-problems are not fully independent. Our aim is to fill this gap by creating a simulator that models a unified workforce optimization problem. Specifically, we designed a modular simulator to support the development of reinforcement learning methods for integrated workforce optimization problems. We focus on three interdependent aspects: personnel dispatch, workforce management, and personnel positioning. The simulator provides configurable parameterizations to help explore dynamic scenarios with varying levels of stochasticity and non-stationarity. To facilitate benchmarking and ablation studies, we also include heuristic and RL baselines for the above mentioned aspects.
KineSoft: Learning Proprioceptive Manipulation Policies with Soft Robot Hands
Venue: arXiv cs.RO 2025
Year: 2025

Underactuated soft robot hands offer inherent safety and adaptability advantages over rigid systems, but developing dexterous manipulation skills remains challenging. While imitation learning shows promise for complex manipulation tasks, traditional approaches struggle with soft systems due to demonstration collection challenges and ineffective state representations. We present KineSoft, a framework enabling direct kinesthetic teaching of soft robotic hands by leveraging their natural compliance as a skill teaching advantage rather than only as a control challenge. KineSoft makes two key contributions: (1) an internal strain sensing array providing occlusion-free proprioceptive shape estimation, and (2) a shape-based imitation learning framework that uses proprioceptive feedback with a low-level shape-conditioned controller to ground diffusion-based policies. This enables human demonstrators to physically guide the robot while the system learns to associate proprioceptive patterns with successful manipulation strategies. We validate KineSoft through physical experiments, demonstrating superior shape estimation accuracy compared to baseline methods, precise shape-trajectory tracking, and higher task success rates compared to baseline imitation learning approaches.
Identity documents recognition and detection using semantic segmentation with convolutional neural network
Venue: arXiv cs.CV 2025
Year: 2025

Object recognition and detection are well-studied problems with a developed set of almost standard solutions. Identity documents recognition, classification, detection, and localization are the tasks required in a number of applications, particularly, in physical access control security systems at critical infrastructure premises. In this paper, we propose the new original architecture of a model based on an artificial convolutional neural network and semantic segmentation approach for the recognition and detection of identity documents in images. The challenge with the processing of such images is the limited computational performance and the limited amount of memory when such an application is running on industrial oneboard microcomputer hardware. The aim of this research is to prove the feasibility of the proposed technique and to obtain quality metrics. The methodology of the research is to evaluate the deep learning detection model trained on the mobile identity document video dataset. The dataset contains five hundred video clips for fifty different identity document types. The numerical results from simulations are used to evaluate the quality metrics. We present the results as accuracy versus threshold of the intersection over union value. The paper reports an accuracy above 0.75 for the intersection over union (IoU) threshold value of 0.8. Besides, we assessed the size of the model and proved the feasibility of running the model on an industrial one-board microcomputer or smartphone hardware.
FAIR: Facilitating Artificial Intelligence Resilience in Manufacturing Industrial Internet
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence (AI) systems have been increasingly adopted in the Manufacturing Industrial Internet (MII). Investigating and enabling the AI resilience is very important to alleviate profound impact of AI system failures in manufacturing and Industrial Internet of Things (IIoT) operations, leading to critical decision making. However, there is a wide knowledge gap in defining the resilience of AI systems and analyzing potential root causes and corresponding mitigation strategies. In this work, we propose a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer. The proposed method can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model. The merits of the proposed method are elaborated using an MII testbed of connected Aerosol Jet Printing (AJP) machines, fog nodes, and Cloud with inference tasks via AI pipelines.
ViKANformer: Embedding Kolmogorov Arnold Networks in Vision Transformers for Pattern-Based Learning
Venue: arXiv cs.CV 2025
Year: 2025

Vision Transformers (ViTs) have significantly advanced image classification by applying self-attention on patch embeddings. However, the standard MLP blocks in each Transformer layer may not capture complex nonlinear dependencies optimally. In this paper, we propose ViKANformer, a Vision Transformer where we replace the MLP sub-layers with Kolmogorov-Arnold Network (KAN) expansions, including Vanilla KAN, Efficient-KAN, Fast-KAN, SineKAN, and FourierKAN, while also examining a Flash Attention variant. By leveraging the Kolmogorov-Arnold theorem, which guarantees that multivariate continuous functions can be expressed via sums of univariate continuous functions, we aim to boost representational power. Experimental results on MNIST demonstrate that SineKAN, Fast-KAN, and a well-tuned Vanilla KAN can achieve over 97% accuracy, albeit with increased training overhead. This trade-off highlights that KAN expansions may be beneficial if computational cost is acceptable. We detail the expansions, present training/test accuracy and F1/ROC metrics, and provide pseudocode and hyperparameters for reproducibility. Finally, we compare ViKANformer to a simple MLP and a small CNN baseline on MNIST, illustrating the efficiency of Transformer-based methods even on a small-scale dataset.