OpenRaiser/Intern-Atlas huggingface dataset:
Payment Channels with Proofs
Venue: arXiv cs.LO 2025
Year: 2025

The fundamental building blocks of the Bitcoin lightning network are bidirectional payment channels. We describe an extension of payment channels in the Proofgold network which allow the two parties to bet on whether a proposition will be proven by a certain time. These provide the foundation for a Proofgold lightning network that would allow parties to request proofs (by betting there will be no proof by a certain time) and other parties to provide proofs (and be rewarded by betting there will be a proof). The bets may also provide a way to approximate the probability that a certain proposition is provable (in the given amount of time). We describe the implementation of payment channels supporting proofs in Proofgold and discuss a potential lightning network that could be built as a result. One application of such lightning network would be a large decentralized infrastructure for fast collaborative formalization projects.
Enhancing 6G Network Security and Incident Response through Integrated VNF and SDN Technologies
Venue: arXiv cs.NI 2025
Year: 2025

Low-speed internet can negatively affect incident response in a number of ways, including decreased teamwork, delayed detection, inefficient action, and elevated risk. Delayed data acquisition and processing may result from inadequate internet connectivity, hindering security teams' ability to obtain the necessary information for timely and effective responses. Each of these factors may augment the organization's susceptibility to security incidents and their subsequent ramifications. This article establishes a virtual network function service delivery network (VNFSDN) through the integration of virtual network function (VNF) and software-defined networking (SDN) technologies. The VNFSDN approach enhances network security effectiveness and efficiency while reducing the danger of breaches. This method assists security services in rapidly assessing vast quantities of data generated by 6G networks. VNFSDN adapts dynamically to changing safety requirements and connection conditions through the use of SDN and VNF. This flexibility enables enterprises to mitigate or halt the impact of cyberattacks by swiftly identifying and addressing security threats. The VNFSDN enhances network resilience, allowing operators to proactively mitigate possible security attacks and minimize downtime. The incorporation of machine learning and artificial intelligence into VNFSDN can significantly improve network security and threat detection capabilities. The VNFSDN integrates VNF and SDN technologies to deliver security services that analyze vast quantities of 6G data in real time. As security requirements and network conditions evolve, it adapts dynamically to enhance network resilience and facilitate proactive threat detection.
Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content
Venue: arXiv cs.CL 2025
Year: 2025

Understanding semantic relations between two texts is crucial for many information and document management tasks, in which one must determine whether the content fully overlaps, is completely superseded by another document, or overlaps only partially, with unique information in each. Beyond establishing this relation, it is equally important to provide explainable outputs that specify which pieces of information are present, missing, or newly added between the text pair. In this study, we formally define semantic relations between two texts through the set-theoretic relation between their respective Answerable Question Sets (AQS), the sets of questions each text can answer. Under this formulation, Semantic Text Relation (STR), such as equivalence, inclusion, and mutual overlap, becomes a well-defined set relation between the corresponding texts' AQSs. The set differences between the AQSs also serve as an explanation or diagnostic tool for identifying how the information in the texts diverges. Using this definition, we construct a synthetic benchmark that captures fine-grained informational relations through controlled paraphrasing and deliberate information removal supported by AQS manipulations. We then use this dataset to evaluate several discriminative and generative models for classifying text pairs into STR categories, assessing how well different model architectures capture semantic relations beyond surface-level similarity. We publicly release both the dataset and the data generation code to support further research.
Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
Venue: arXiv cs.CV 2025
Year: 2025

Accurate and early diagnosis of malignant melanoma is critical for improving patient outcomes. While convolutional neural networks (CNNs) have shown promise in dermoscopic image analysis, they often neglect clinical metadata and require extensive preprocessing. Vision-language models (VLMs) offer a multimodal alternative but struggle to capture clinical specificity when trained on general-domain data. To address this, we propose a retrieval-augmented VLM framework that incorporates semantically similar patient cases into the diagnostic prompt. Our method enables informed predictions without fine-tuning and significantly improves classification accuracy and error correction over conventional baselines. These results demonstrate that retrieval-augmented prompting provides a robust strategy for clinical decision support.
An HPC Benchmark Survey and Taxonomy for Characterization
Venue: arXiv cs.DC 2025
Year: 2025

The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of hardware, software, and algorithms. Many benchmarks exist in the community, from individual niche benchmarks testing specific features, to large-scale benchmark suites for whole procurements. We survey the available HPC benchmarks, summarizing them in table form with key details and concise categorization, also through an interactive website. For categorization, we present a benchmark taxonomy for well-defined characterization of benchmarks.
An Adaptive Scoring Framework for Attention Assessment in NDD Children via Serious Games
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces an innovative adaptive scoring framework for children with Neurodevelopmental Disorders (NDD) that is attributed to the integration of multiple metrics, such as spatial attention patterns, temporal engagement, and game performance data, to create a comprehensive assessment of learning that goes beyond traditional game scoring. The framework employs a progressive difficulty adaptation method, which focuses on specific stimuli for each level and adjusts weights dynamically to accommodate increasing cognitive load and learning complexity. Additionally, it includes capabilities for temporal analysis, such as detecting engagement periods, providing rewards for sustained attention, and implementing an adaptive multiplier framework based on performance levels. To avoid over-rewarding high performers while maximizing improvement potential for students who are struggling, the designed framework features an adaptive temporal impact framework that adjusts performance scales accordingly. We also established a multi-metric validation framework using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation, and Spearman correlation, along with defined quality thresholds for assessing deployment readiness in educational settings. This research bridges the gap between technical eye-tracking metrics and educational insights by explicitly mapping attention patterns to learning behaviors, enabling actionable pedagogical interventions.
Automatic Detection of Inauthentic Templated Responses in English Language Assessments
Venue: arXiv cs.CL 2025
Year: 2025

In high-stakes English Language Assessments, low-skill test takers may employ memorized materials called ``templates'' on essay questions to ``game'' or fool the automated scoring system. In this study, we introduce the automated detection of inauthentic, templated responses (AuDITR) task, describe a machine learning-based approach to this task and illustrate the importance of regularly updating these models in production.
<think> So let's replace this phrase with insult... </think> Lessons learned from generation of toxic texts with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Modern Large Language Models (LLMs) are excellent at generating synthetic data. However, their performance in sensitive domains such as text detoxification has not received proper attention from the scientific community. This paper explores the possibility of using LLM-generated synthetic toxic data as an alternative to human-generated data for training models for detoxification. Using Llama 3 and Qwen activation-patched models, we generated synthetic toxic counterparts for neutral texts from ParaDetox and SST-2 datasets. Our experiments show that models fine-tuned on synthetic data consistently perform worse than those trained on human data, with a drop in performance of up to 30% in joint metrics. The root cause is identified as a critical lexical diversity gap: LLMs generate toxic content using a small, repetitive vocabulary of insults that fails to capture the nuances and variety of human toxicity. These findings highlight the limitations of current LLMs in this domain and emphasize the continued importance of diverse, human-annotated data for building robust detoxification systems.
The Impact of Team Diversity in Agile Development Education
Venue: arXiv cs.SE 2025
Year: 2025

Software Engineering is mostly a male-dominated sector, where gender diversity is a key feature for improving equality of opportunities, productivity, and innovation. Other diversity aspects, including but not limited to nationality and ethnicity, are often understudied.In this work we aim to assess the impact of team diversity, focusing mainly on gender and nationality, in the context of an agile software development project-based course. We analyzed 51 teams over three academic years, measuring three different Diversity indexes - regarding Gender, Nationality and their co-presence - to examine how different aspects of diversity impact the quality of team project outcomes.Statistical analysis revealed a moderate, statistically significant correlation between gender diversity and project success, aligning with existing literature. Diversity in nationality showed a negative but negligible effect on project results, indicating that promoting these aspects does not harm students' performance. Analyzing their co-presence within a team, gender and nationality combined had a negative impact, likely due to increased communication barriers and differing cultural norms.This study underscores the importance of considering multiple diversity dimensions and their interactions in educational settings. Our findings, overall, show that promoting diversity in teams does not negatively impact their performance and achievement of educational goals.
Leveraging Blockchain and Proxy Re-Encryption to secure Medical IoT Records
Venue: arXiv cs.CR 2025
Year: 2025

The integration of the Internet of Things (IoT) in healthcare has revolutionized patient monitoring and data collection, allowing real-time tracking of vital signs, remote diagnostics, and automated medical responses. However, the transmission and storage of sensitive medical data introduce significant security and privacy challenges. To address these concerns, blockchain technology provides a decentralized and immutable ledger that ensures data integrity, , and transparency. Unlike public blockchains, private blockchains are permissioned; the access is granted only to authorized participants; they are more suitable for handling confidential healthcare data. Although blockchain ensures security and trust, it lacks built-in mechanisms to support flexible and controlled data sharing; This is where Proxy Re-Encryption (PRE) comes into play. PRE is a cryptographic technique that allows encrypted data to be re-encrypted for a new recipient without exposing it to intermediaries. We propose an architecture integrating private blockchain and PRE to enable secure, traceable, and privacy-preserving data sharing in IoT-based healthcare systems. Blockchain guarantees tamper proof record-keeping, while PRE enables fine-grained access control, allowing medical professionals to securely share patient data without compromising confidentiality. This combination creates a robust security framework that enhances trust and efficiency in digital healthcare ecosystems.
Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques
Venue: arXiv cs.CR 2025
Year: 2025

In the realm of cybersecurity, phishing stands as a prevalent cyber attack, where attackers employ various tactics to deceive users into gathering their sensitive information, potentially leading to identity theft or financial gain. Researchers have been actively working on advancing phishing webpage detection approaches to detect new phishing URLs, bolstering user protection. Nonetheless, the ever-evolving strategies employed by attackers, aimed at circumventing existing detection approaches and tools, present an ongoing challenge to the research community. This survey presents a systematic categorization of diverse phishing webpage detection approaches, encompassing URL-based, webpage content-based, and visual techniques. Through a comprehensive review of these approaches and an in-depth analysis of existing literature, our study underscores current research gaps in phishing webpage detection. Furthermore, we suggest potential solutions to address some of these gaps, contributing valuable insights to the ongoing efforts to combat phishing attacks.
Spherical Brownian Bridge Diffusion Models for Conditional Cortical Thickness Forecasting
Venue: arXiv cs.CV 2025
Year: 2025

Accurate forecasting of individualized, high-resolution cortical thickness (CTh) trajectories is essential for detecting subtle cortical changes, providing invaluable insights into neurodegenerative processes and facilitating earlier and more precise intervention strategies. However, CTh forecasting is a challenging task due to the intricate non-Euclidean geometry of the cerebral cortex and the need to integrate multi-modal data for subject-specific predictions. To address these challenges, we introduce the Spherical Brownian Bridge Diffusion Model (SBDM). Specifically, we propose a bidirectional conditional Brownian bridge diffusion process to forecast CTh trajectories at the vertex level of registered cortical surfaces. Our technical contribution includes a new denoising model, the conditional spherical U-Net (CoS-UNet), which combines spherical convolutions and dense cross-attention to integrate cortical surfaces and tabular conditions seamlessly. Compared to previous approaches, SBDM achieves significantly reduced prediction errors, as demonstrated by our experiments based on longitudinal datasets from the ADNI and OASIS. Additionally, we demonstrate SBDM's ability to generate individual factual and counterfactual CTh trajectories, offering a novel framework for exploring hypothetical scenarios of cortical development.
DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) enables decentralized model training without sharing raw data, offering strong privacy guarantees. However, existing FL protocols struggle to defend against Byzantine participants, maintain model utility under non-independent and identically distributed (non-IID) data, and remain lightweight for edge devices. Prior work either assumes trusted hardware, uses expensive cryptographic tools, or fails to address privacy and robustness simultaneously. We propose DSFL, a Dual-Server Byzantine-Resilient Federated Learning framework that addresses these limitations using a group-based secure aggregation approach. Unlike LSFL, which assumes non-colluding semi-honest servers, DSFL removes this dependency by revealing a key vulnerability: privacy leakage through client-server collusion. DSFL introduces three key innovations: (1) a dual-server secure aggregation protocol that protects updates without encryption or key exchange, (2) a group-wise credit-based filtering mechanism to isolate Byzantine clients based on deviation scores, and (3) a dynamic reward-penalty system for enforcing fair participation. DSFL is evaluated on MNIST, CIFAR-10, and CIFAR-100 under up to 30 percent Byzantine participants in both IID and non-IID settings. It consistently outperforms existing baselines, including LSFL, homomorphic encryption methods, and differential privacy approaches. For example, DSFL achieves 97.15 percent accuracy on CIFAR-10 and 68.60 percent on CIFAR-100, while FedAvg drops to 9.39 percent under similar threats. DSFL remains lightweight, requiring only 55.9 ms runtime and 1088 KB communication per round.
Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in Large Language Models (LLMs) have demonstrated their remarkable capacity to process and reason over structured and unstructured data modalities beyond natural language. In this work, we explore the applications of Vision Language Models (VLMs), specifically a fine-tuned variant of LLaMa 3.2, to the task of identifying neutrino interactions in pixelated detector data from high-energy physics (HEP) experiments. We benchmark this model against a state-of-the-art convolutional neural network (CNN) architecture, similar to those used in the NOvA and DUNE experiments, which have achieved high efficiency and purity in classifying electron and muon neutrino events. Our evaluation considers both the classification performance and interpretability of the model predictions. We find that VLMs can outperform CNNs, while also providing greater flexibility in integrating auxiliary textual or semantic information and offering more interpretable, reasoning-based predictions. This work highlights the potential of VLMs as a general-purpose backbone for physics event classification, due to their high performance, interpretability, and generalizability, which opens new avenues for integrating multimodal reasoning in experimental neutrino physics.
Flow-Based Detection and Identification of Zero-Day IoT Cameras
Venue: arXiv cs.CR 2025
Year: 2025

The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for identifying known IoT cameras with supervised classifiers (zCamClassifier) and detecting zero-day cameras with one-class classifiers (zCamDetector). We analyzed ~40GB of traffic across three datasets: Set I (six commercial IoT cameras), Set II (five open-source IoT cameras, ~1.5GB), and Set III (four conferencing and two video-sharing applications as non-IoT traffic). From each, 62 flow-based features were extracted using CICFlowmeter. zCamInspector employs seven supervised models (ET, DT, RF, KNN, XGB, LKSVM, GNB) and four one-class models (OCSVM, SGDOCSVM, IF, DeepSVDD). Results show that XGB identifies IoT cameras with >99% accuracy and false negatives as low as 0.3%, outperforming state-of-the-art methods. For zero-day detection, accuracies reached 93.20% (OCSVM), 96.55% (SGDOCSVM), 78.65% (IF), and 92.16% (DeepSVDD). When all devices were treated as zero-day, DeepSVDD performed best with mean training/testing accuracies of 96.03%/74.51%. zCamInspector also achieved >95% accuracy for specific devices, such as Spy Clock cameras, demonstrating its robustness for identifying and detecting zero-day IoT cameras in diverse network environments.
Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We set up a consistent training paradigm and evaluate the optimizers based on metrics such as convergence speed and stability. We also include some other Machine Learning Evaluation metrics such as AUC, Precision, and Recall, which are central metrics to classification problems. Our results show that there are trade-offs between convergence speed and stability, as optimizers like Adagrad and Adadelta, which are more stable, took longer time to converge. Across all our metrics, we chose RMSProp to be the most effective optimizer for this heart disease prediction task because it offered a balanced performance across key metrics. It achieved a precision of 0.765, a recall of 0.827, and an AUC of 0.841, along with faster training time. However, it was not the most stable. We recommend that, in less compute-constrained environments, this method of choosing optimizers through a thorough evaluation should be adopted to increase the scientific nature and performance in training deep learning models.
Variational Rank Reduction Autoencoders for Generative Thermal Design
Venue: arXiv cs.LG 2025
Year: 2025

Generative thermal design for complex geometries is fundamental in many areas of engineering, yet it faces two main challenges: the high computational cost of high-fidelity simulations and the limitations of conventional generative models. Approaches such as autoencoders (AEs) and variational autoencoders (VAEs) often produce unstructured latent spaces with discontinuities, which restricts their capacity to explore designs and generate physically consistent solutions. To address these limitations, we propose a hybrid framework that combines Variational Rank-Reduction Autoencoders (VRRAEs) with Deep Operator Networks (DeepONets). The VRRAE introduces a truncated SVD within the latent space, leading to continuous, interpretable, and well-structured representations that mitigate posterior collapse and improve geometric reconstruction. The DeepONet then exploits this compact latent encoding in its branch network, together with spatial coordinates in the trunk network, to predict temperature gradients efficiently and accurately. This hybrid approach not only enhances the quality of generated geometries and the accuracy of gradient prediction, but also provides a substantial advantage in inference efficiency compared to traditional numerical solvers. Overall, the study underscores the importance of structured latent representations for operator learning and highlights the potential of combining generative models and operator networks in thermal design and broader engineering applications.