OpenRaiser/Intern-Atlas huggingface dataset:
A transformer-based deep q learning approach for dynamic load balancing in software-defined networks
Venue: arXiv cs.NI 2025
Year: 2025

This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mechanisms, such as Round Robin (RR) and Weighted Round Robin (WRR), are static and often struggle to adapt to fluctuating traffic conditions, leading to inefficiencies in network performance. In contrast, SDNs offer centralized control and flexibility, providing an ideal platform for implementing machine learning-driven optimization strategies. The core of this research combines a Temporal Fusion Transformer (TFT) for accurate traffic prediction with a DQN model to perform real-time dynamic load balancing. The TFT model predicts future traffic loads, which the DQN uses as input, allowing it to make intelligent routing decisions that optimize throughput, minimize latency, and reduce packet loss. The proposed model was tested against RR and WRR in simulated environments with varying data rates, and the results demonstrate significant improvements in network performance. For the 500MB data rate, the DQN model achieved an average throughput of 0.275 compared to 0.202 and 0.205 for RR and WRR, respectively. Additionally, the DQN recorded lower average latency and packet loss. In the 1000MB simulation, the DQN model outperformed the traditional methods in throughput, latency, and packet loss, reinforcing its effectiveness in managing network loads dynamically. This research presents an important step towards enhancing network performance through the integration of machine learning models within SDNs, potentially paving the way for more adaptive, intelligent network management systems.
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
Venue: arXiv cs.CV 2025
Year: 2025

In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing MR modalities pose significant challenges for tasks like brain tumor segmentation, especially in deep learning-based segmentation, as each modality provides complementary information crucial for improving accuracy. A promising solution is missing data imputation, where absent modalities are generated from available ones. While generative models have been widely used for this purpose, most state-of-the-art approaches are limited to single or dual target translations, lacking the adaptability to generate missing modalities based on varying input configurations. To address this, we propose an Adaptive Multi-Modality Diffusion Network (AMM-Diff), a novel diffusion-based generative model capable of handling any number of input modalities and generating the missing ones. We designed an Image-Frequency Fusion Network (IFFN) that learns a unified feature representation through a self-supervised pretext task across the full input modalities and their selected high-frequency Fourier components. The proposed diffusion model leverages this representation, encapsulating prior knowledge of the complete modalities, and combines it with an adaptive reconstruction strategy to achieve missing modality completion. Experimental results on the BraTS 2021 dataset demonstrate the effectiveness of our approach.
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Crack Segmentation in industrial concrete surfaces is a challenging task because cracks usually exhibit intricate morphology with slender appearances. Traditional segmentation methods often struggle to accurately locate such cracks, leading to inefficiencies in maintenance and repair processes. In this paper, we propose a novel diffusion-based model with a cross-conditional encoder-decoder, named CrossDiff, which is the first to introduce the diffusion probabilistic model for the crack segmentation task. Specifically, CrossDiff integrates a cross-encoder and a cross-decoder into the diffusion model to constitute a cross-shaped diffusion model structure. The cross-encoder enhances the ability to retain crack details and the cross-decoder helps extract the semantic features of cracks. As a result, CrossDiff can better handle slender cracks. Extensive experiments were conducted on five challenging crack datasets including CFD, CrackTree200, DeepCrack, GAPs384, and Rissbilder. The results demonstrate that the proposed CrossDiff model achieves impressive performance, outperforming other state-of-the-art methods by 8.0% in terms of both Dice score and IoU. The code will be open-source soon.
Multi-Platform Aggregated Dataset of Online Communities (MADOC)
Venue: arXiv cs.SI 2025
Year: 2025

The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing FAIR-compliant standardized access to cross-platform analysis of online social dynamics. MADOC aggregates and standardizes data from Bluesky, Koo, Reddit, and Voat (2012-2024), containing 18.9 million posts, 236 million comments, and 23.1 million unique users. The dataset enables comparative studies of toxic behavior evolution across platforms through standardized interaction records and sentiment analysis. By providing UUID-anonymized user histories and temporal alignment of banned communities' activity patterns, MADOC supports research on content moderation impacts and platform migration trends. Distributed via Zenodo with persistent identifiers and Python/R toolkits, the dataset adheres to FAIR principles while addressing post-API-era research challenges through ethical aggregation of public social media archives.
Analyzing and Exploiting Branch Mispredictions in Microcode
Venue: arXiv cs.CR 2025
Year: 2025

We present uSpectre, a new class of transient execution attacks that exploit microcode branch mispredictions to transiently leak sensitive data. We find that many long-known and recently-discovered transient execution attacks, which were previously categorized as Spectre or Meltdown variants, are actually instances of uSpectre on some Intel microarchitectures. Based on our observations, we discover multiple new uSpectre attacks and present a defense against uSpectre vulnerabilities, called uSLH.
Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi
Venue: arXiv cs.LG 2025
Year: 2025

Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (ViT) architectures evaluate long-range correlations, using repeated transformer encoders composed of fully connected layers. Both are designed to solve complex classification tasks but from different perspectives. This study demonstrates that CNNs and ViT architectures stem from a unified underlying learning mechanism, which quantitatively measures the single-nodal performance (SNP) of each node in feedforward (FF) and multi-head attention (MHA) sub-blocks. Each node identifies small clusters of possible output labels, with additional noise represented as labels outside these clusters. These features are progressively sharpened along the transformer encoders, enhancing the signal-to-noise ratio. This unified underlying learning mechanism leads to two main findings. First, it enables an efficient applied nodal diagonal connection (ANDC) pruning technique without affecting the accuracy. Second, based on the SNP, spontaneous symmetry breaking occurs among the MHA heads, such that each head focuses its attention on a subset of labels through cooperation among its SNPs. Consequently, each head becomes an expert in recognizing its designated labels, representing a quantitative MHA modus vivendi mechanism. This statistical mechanics inspired viewpoint enables to reveal macroscopic behavior of the entire network from the microscopic performance of each node. These results are based on a compact convolutional transformer architecture trained on the CIFAR-100 and Flowers-102 datasets and call for their extension to other architectures and applications, such as natural language processing.
FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces
Venue: arXiv cs.CL 2025
Year: 2025

Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recent advances in automated decision-making with language agent-based societies, this paper introduces FilmAgent, a novel LLM-based multi-agent collaborative framework for end-to-end film automation in our constructed 3D virtual spaces. FilmAgent simulates various crew roles, including directors, screenwriters, actors, and cinematographers, and covers key stages of a film production workflow: (1) idea development transforms brainstormed ideas into structured story outlines; (2) scriptwriting elaborates on dialogue and character actions for each scene; (3) cinematography determines the camera setups for each shot. A team of agents collaborates through iterative feedback and revisions, thereby verifying intermediate scripts and reducing hallucinations. We evaluate the generated videos on 15 ideas and 4 key aspects. Human evaluation shows that FilmAgent outperforms all baselines across all aspects and scores 3.98 out of 5 on average, showing the feasibility of multi-agent collaboration in filmmaking. Further analysis reveals that FilmAgent, despite using the less advanced GPT-4o model, surpasses the single-agent o1, showing the advantage of a well-coordinated multi-agent system. Lastly, we discuss the complementary strengths and weaknesses of OpenAI's text-to-video model Sora and our FilmAgent in filmmaking.
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections like fully homomorphic encryption (FHE) impose prohibitive computational overhead, while lightweight alternatives risk vulnerable data leakage through model updates. To address this issue, we present FAS (Fast and Secure Federated Learning), a novel approach that strategically combines selective homomorphic encryption, differential privacy, and bitwise scrambling to achieve robust security without compromising practical usability. Our approach eliminates the need for model pretraining phases while dynamically protecting high-risk model parameters through layered encryption and obfuscation. We implemented FAS using the Flower framework and evaluated it on a cluster of eleven physical machines. Our approach was up to 90\% faster than applying FHE on the model weights. In addition, we eliminated the computational overhead that is required by competitors such as FedML-HE and MaskCrypt. Our approach was up to 1.5$\times$ faster than the competitors while achieving comparable security results. Experimental evaluations on medical imaging datasets confirm that FAS maintains similar security results to conventional FHE against gradient inversion attacks while preserving diagnostic model accuracy. These results position FAS as a practical solution for latency-sensitive healthcare applications where both privacy preservation and computational efficiency are requirements.
Not Just a Number: A Multidimensional Approach to Ageing in HCI
Venue: arXiv cs.HC 2025
Year: 2025

The focus on managing problems that can arise for older adults has meant that extant HCI and Ageing research has not given the concepts of 'age' and 'ageing' the explicit theoretical attention they deserve. Attending to this gap, we critically examine a ten-year corpus of CHI publications through the lens of an existing typology which we have further developed to analyse how age is understood, interpreted and constructed in the field of HCI. Our resulting multidimensional typology of age in HCI elucidates the distinctive characteristics of older adults considered when designing with and for this user group, but also highlights the need for a more critical, reflexive, social constructivist approach to age in HCI. Applying this approach, we explore age as a multidimensional system of stratification to better understand the phenomenon of the age-based digital divide.
QuaRs: A Transform for Better Lossless Compression of Integers
Venue: arXiv cs.DS 2025
Year: 2025

The rise of integer-valued data, partly driven by the Internet of Things (IoT), has increased demand for efficient compression methods to reduce storage and transmission costs. Existing, speed-oriented methods rely on the ``smaller-numbers-less-bits'' principle, assuming unimodal distributions centered around zero. This assumption is often violated in practice, leading to suboptimal compression. We propose QuaRs, a transformation that reshapes arbitrary distributions into unimodal ones centered around zero, improving compatibility with fast integer compression methods. QuaRs remaps data based on quantiles, assigning smaller magnitudes to frequent values. The method is fast, invertible, and has sub-quadratic complexity. QuaRs enhances compression efficiency, even for challenging distributions, while integrating seamlessly with existing techniques.
Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network
Venue: arXiv cs.SI 2025
Year: 2025

Community detection is crucial in data mining. Traditional methods primarily focus on graph structure, often neglecting the significance of attribute features. In contrast, deep learning-based approaches incorporate attribute features and local structural information through contrastive learning, improving detection performance. However, existing algorithms' complex design and joint optimization make them difficult to train and reduce detection efficiency. Additionally, these methods require the number of communities to be predefined, making the results susceptible to artificial interference. To address these challenges, we propose a simple yet effective community detection algorithm that can adaptively detect communities without relying on data augmentation and contrastive optimization. The proposed algorithm first performs community pre-detection to extract global structural information adaptively. It then utilizes GCN to integrate local structures and attribute features. Subsequently, it combines global, local structures and attribute features in the feature space to discover community affiliations. Finally, a modularity maximization method is employed to optimize the communities based on these three types of information, thereby uncovering the community affiliation of each node. We conduct experimental comparisons across various graph datasets, evaluating the proposed algorithm against traditional methods and state-of-the-art community detection algorithms. The experimental results demonstrate that our algorithm achieves greater efficiency and accuracy in terms of both detection speed and effectiveness. The code is available at https://github.com/wuanghoong/Less-is-More.git.
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
Venue: arXiv cs.MA 2025
Year: 2025

Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a 15\% improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.
Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects
Venue: arXiv cs.CV 2025
Year: 2025

As the density of spacecraft in Earth's orbit increases, their recognition, pose and trajectory identification becomes crucial for averting potential collisions and executing debris removal operations. However, training models able to identify a spacecraft and its pose presents a significant challenge due to a lack of available image data for model training. This paper puts forth an innovative framework for generating realistic synthetic datasets of Resident Space Object (RSO) imagery. Using the International Space Station (ISS) as a test case, it goes on to combine image regression with image restoration methodologies to estimate pose from blurred images. An analysis of the proposed image recovery and regression techniques was undertaken, providing insights into the performance, potential enhancements and limitations when applied to real imagery of RSOs. The image recovery approach investigated involves first applying image deconvolution using an effective point spread function, followed by detail object extraction with a U-Net. Interestingly, using only U-Net for image reconstruction the best pose performance was attained, reducing the average Mean Squared Error in image recovery by 97.28% and the average angular error by 71.9%. The successful application of U-Net image restoration combined with the Resnet50 regression network for pose estimation of the International Space Station demonstrates the value of a diverse set of evaluation tools for effective solutions to real-world problems such as the analysis of distant objects in Earth's orbit.
The regret lower bound for communicating Markov Decision Processes
Venue: arXiv cs.LG 2025
Year: 2025

This paper is devoted to the extension of the regret lower bound beyond ergodic Markov decision processes (MDPs) in the problem dependent setting. While the regret lower bound for ergodic MDPs is well-known and reached by tractable algorithms, we prove that the regret lower bound becomes significatively more complex in communicating MDPs. Our lower bound revisits the necessary explorative behavior of consistent learning agents and further explains that all optimal regions of the environment must be overvisited compared to sub-optimal ones, a phenomenon that we refer to as co-exploration. In tandem, we show that these two explorative and co-explorative behaviors are intertwined with navigation constraints obtained by scrutinizing the navigation structure at logarithmic scale. The resulting lower bound is expressed as the solution of an optimization problem that, in many standard classes of MDPs, can be specialized to recover existing results. From a computational perspective, it is provably $Σ_2^\textrm{P}$-hard in general and as a matter of fact, even testing the membership to the feasible region is coNP-hard. We further provide an algorithm to approximate the lower bound in a constructive way.
Extension of the Poltyrev Bound to Binary Memoryless Symmetric Channels
Venue: arXiv cs.IT 2025
Year: 2025

The Poltyrev bound provides a very tight upper bound on the decoding error probability when using binary linear codes for transmission over the binary symmetric channel and the additive white Gaussian noise channel, making use of the code's weight spectrum. In the present work, the bound is extended to memoryless symmetric channels with a discrete output alphabet. The derived bound is demonstrated on a hybrid BSC-BEC channel. Additionally, a reduced-complexity bound is introduced at the cost of some loss in tightness.
Development of the Critical Reflection and Agency in Computing Index
Venue: arXiv cs.CY 2025
Year: 2025

As computing's societal impact grows, so does the need for computing students to recognize and address the ethical and sociotechnical implications of their work. While there are efforts to integrate ethics into computing curricula, we lack a standardized tool to measure those efforts, specifically, students' attitudes towards ethical reflection and their ability to effect change. This paper introduces the novel framework of Critically Conscious Computing and reports on the development and content validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students' attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students' perspectives, inform the design of targeted ethics interventions, and measure the effectiveness of computing ethics education initiatives.
Systematic comparison of gender inequality in scientific rankings across disciplines
Venue: arXiv cs.SI 2025
Year: 2025

The participation of women in academia has increased in the last few decades across many fields (e.g., Computer Science, History, Medicine). However, this increase in the participation of women has not been the same at all career stages. Here, we study how gender participation within different fields is related to gender representation in top-ranking positions in productivity (number of papers), research impact (number of citations), and co-authorship networks (degree of connectivity). We analyzed over 80 million papers published from 1975 to 2020 in 19 academic fields. Our findings reveal that women remain a minority in all 19 fields, with physics, geology, and mathematics having the lowest percentage of papers authored by women at 14% and psychology having the largest percentage at 39%. Women are significantly underrepresented in top-ranking positions (top 10% or higher) across all fields and metrics (productivity, citations, and degree), indicating that it remains challenging for early researchers (especially women) to reach top-ranking positions, as our results reveal the rankings to be rigid over time. Finally, we show that in most fields, women and men with comparable productivity levels and career age tend to attain different levels of citations, where women tend to benefit more from co-authorships, while men tend to benefit more from productivity, especially in pSTEMs. Our findings highlight that while the participation of women has risen in some fields, they remain under-represented in top-ranking positions. Greater gender participation at entry levels often helps representation, but stronger interventions are still needed to achieve long-lasting careers for women and their participation in top-ranking positions.