OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing Bugs in Login Processes of Android Applications: An Empirical Study
Venue: arXiv cs.SE 2025
Year: 2025

The login functionality, being the gateway to app usage, plays a critical role in both user experience and application security. As Android apps increasingly incorporate login functionalities, they support a variety of authentication methods with complicated login processes, catering to personalized user experiences. However, the complexities in managing different operations in login processes make it difficult for developers to handle them correctly. In this paper, we present the first empirical study of login issues in Android apps. We analyze 361 issues from 44 popular open-source Android repositories, examining the root causes, symptoms, and trigger conditions of these issues. Our findings indicate that the vast majority of the login issues are induced by the improper handling of complex state transitions during the login process, which can prevent users from logging in or misdirect them to incorrect subsequent actions. Additionally, we observed that issues related to this cause typically require the convergence of multiple trigger conditions to manifest. These findings can help developers to model the login processes which can help them to identify the causes of issues and design targeted test cases and precise test oracles. Our dataset has been made openly available to facilitate future research in this area.
Ensuring Reliability via Hyperparameter Selection: Review and Advances
Venue: arXiv cs.LG 2025
Year: 2025

Hyperparameter selection is a critical step in the deployment of artificial intelligence (AI) models, particularly in the current era of foundational, pre-trained, models. By framing hyperparameter selection as a multiple hypothesis testing problem, recent research has shown that it is possible to provide statistical guarantees on population risk measures attained by the selected hyperparameter. This paper reviews the Learn-Then-Test (LTT) framework, which formalizes this approach, and explores several extensions tailored to engineering-relevant scenarios. These extensions encompass different risk measures and statistical guarantees, multi-objective optimization, the incorporation of prior knowledge and dependency structures into the hyperparameter selection process, as well as adaptivity. The paper also includes illustrative applications for communication systems.
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data
Venue: arXiv cs.CR 2025
Year: 2025

Dataset distillation (DD) enhances training efficiency and reduces bandwidth by condensing large datasets into smaller synthetic ones. It enables models to achieve performance comparable to those trained on the raw full dataset and has become a widely adopted method for data sharing. However, security concerns in DD remain underexplored. Existing studies typically assume that malicious behavior originates from dataset owners during the initial distillation process, where backdoors are injected into raw datasets. In contrast, this work is the first to address a more realistic and concerning threat: attackers may intercept the dataset distribution process, inject backdoors into the distilled datasets, and redistribute them to users. While distilled datasets were previously considered resistant to backdoor attacks, we demonstrate that they remain vulnerable to such attacks. Furthermore, we show that attackers do not even require access to any raw data to inject the backdoors successfully. Specifically, our approach reconstructs conceptual archetypes for each class from the model trained on the distilled dataset. Backdoors are then injected into these archetypes to update the distilled dataset. Moreover, we ensure the updated dataset not only retains the backdoor but also preserves the original optimization trajectory, thus maintaining the knowledge of the raw dataset. To achieve this, a hybrid loss is designed to integrate backdoor information along the benign optimization trajectory, ensuring that previously learned information is not forgotten. Extensive experiments demonstrate that distilled datasets are highly vulnerable to backdoor attacks, with risks pervasive across various raw datasets, distillation methods, and downstream training strategies. Moreover, our attack method is efficient, capable of synthesizing a malicious distilled dataset in under one minute in certain cases.
Saflo: eBPF-Based MPTCP Scheduler for Mitigating Traffic Analysis Attacks in Cellular Networks
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents the $\underline{\textbf{saf}}$e sub$\underline{\textbf{flo}}$w (Saflo) eBPF-based multipath TCP (MPTCP) scheduler, designed to mitigate traffic analysis attacks in cellular networks. Traffic analysis attacks, which exploit vulnerabilities in Downlink Control Information (DCI) messages, remain a significant security threat in LTE/5G networks. To counter such threats, the Saflo scheduler employs multipath communication combined with additional security-related tasks. Specifically, it utilizes eBPF tools to operate in both kernel and user spaces. In the kernel space, the eBPF scheduler performs multipath scheduling while excluding paths disabled by the user-space programs. The user-space programs conduct security-related computations and machine learning-based attack detection, determining whether each path should be enabled or disabled. This approach offloads computationally intensive tasks to user-space programs, enabling timely multipath scheduling in kernel space. The Saflo scheduler was evaluated in a private LTE/5G testbed. The results demonstrated that it significantly reduces the accuracy of video identification and user identification attacks in cellular networks while maintaining reasonable network performance for users.
Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps
Venue: arXiv cs.SE 2025
Year: 2025

Bug reports are essential for developers to confirm software problems, investigate their causes, and validate fixes. Unfortunately, reports often miss important information or are written unclearly, which can cause delays, increased issue resolution effort, or even the inability to solve issues. One of the most common components of reports that are problematic is the steps to reproduce the bug(s) (S2Rs), which are essential to replicate the described program failures and reason about fixes. Given the proclivity for deficiencies in reported S2Rs, prior work has proposed techniques that assist reporters in writing or assessing the quality of S2Rs. However, automated understanding of S2Rs is challenging, and requires linking nuanced natural language phrases with specific, semantically related program information. Prior techniques often struggle to form such language to program connections - due to issues in language variability and limitations of information gleaned from program analyses. To more effectively tackle the problem of S2R quality annotation, we propose a new technique called AstroBR, which leverages the language understanding capabilities of LLMs to identify and extract the S2Rs from bug reports and map them to GUI interactions in a program state model derived via dynamic analysis. We compared AstroBR to a related state-of-the-art approach and we found that AstroBR annotates S2Rs 25.2% better (in terms of F1 score) than the baseline. Additionally, AstroBR suggests more accurate missing S2Rs than the baseline (by 71.4% in terms of F1 score).
Leveraging Geolocation in Clinical Records to Improve Alzheimer's Disease Diagnosis Using DMV Framework
Venue: arXiv cs.LG 2025
Year: 2025

Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV framework using Llama3-70B and GPT-4o as embedding models to analyze clinical notes and predict a continuous risk score associated with early AD onset. Framing the task as a regression problem, we model the relationship between linguistic features in clinical notes (inputs) and a target variable (data value) that answers specific questions related to AD risk within certain topic categories. By leveraging a multi-faceted feature set that includes geolocation data, we capture additional environmental context potentially linked to AD. Our results demonstrate that the integration of the geolocation information significantly decreases the error of predicting early AD risk scores over prior models by 28.57% (Llama3-70B) and 33.47% (GPT4-o). Our findings suggest that this combined approach can enhance the predictive accuracy of AD risk assessment, supporting early diagnosis and intervention in clinical settings. Additionally, the framework's ability to incorporate geolocation data provides a more comprehensive risk assessment model that could help healthcare providers better understand and address environmental factors contributing to AD development.
The 23andMe Data Breach: Analyzing Credential Stuffing Attacks, Security Vulnerabilities, and Mitigation Strategies
Venue: arXiv cs.CR 2025
Year: 2025

In October 2023, 23andMe, a prominent provider of personal genetic testing, ancestry, and health information services, suffered a significant data breach orchestrated by a cybercriminal known as ``Golem.'' Initially, approximately 14,000 user accounts were compromised by a credential smear attack, exploiting reused usernames and passwords from previous data leaks. However, due to the interconnected nature of 23andMe's DNA Relatives and Family Tree features, the breach expanded exponentially, exposing sensitive personal and genetic data of approximately 5.5 million users and 1.4 million additional profiles. The attack highlights the increasing threat of credential stuffing, exacerbated by poor password hygiene and the absence of robust security measures such as multi-factor authentication (MFA) and rate limiting. In response, 23andMe mandated password resets, implemented email-based two-step verification, and advised users to update passwords across other services. This paper critically analyzes the attack methodology, its impact on users and the company, and explores potential mitigation strategies, including enhanced authentication protocols, proactive breach detection, and improved cybersecurity practices. The findings underscore the necessity of stronger user authentication measures and corporate responsibility in safeguarding sensitive genetic and personal data.
Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Computational Fluid Dynamics (CFD) is crucial for automotive design, requiring the analysis of large 3D point clouds to study how vehicle geometry affects pressure fields and drag forces. However, existing deep learning approaches for CFD struggle with the computational complexity of processing high-resolution 3D data. We propose Factorized Implicit Global Convolution (FIGConv), a novel architecture that efficiently solves CFD problems for very large 3D meshes with arbitrary input and output geometries. FIGConv achieves quadratic complexity $O(N^2)$, a significant improvement over existing 3D neural CFD models that require cubic complexity $O(N^3)$. Our approach combines Factorized Implicit Grids to approximate high-resolution domains, efficient global convolutions through 2D reparameterization, and a U-shaped architecture for effective information gathering and integration. We validate our approach on the industry-standard Ahmed body dataset and the large-scale DrivAerNet dataset. In DrivAerNet, our model achieves an $R^2$ value of 0.95 for drag prediction, outperforming the previous state-of-the-art by a significant margin. This represents a 40% improvement in relative mean squared error and a 70% improvement in absolute mean squared error over previous methods.
Variation of sentence length across time and genre
Venue: arXiv cs.CL 2025
Year: 2025

The goal of this paper is threefold: i) to present some practical aspects of using full-text version of Corpus of Historical American English (COHA), the largest diachronic multi-genre corpus of the English language, in the investigation of a linguistic trend of change; ii) to test a widely held assumption that sentence length in written English has been steadily decreasing over the past few centuries; iii) to point to a possible link between the changes in sentence length and changes in the English syntactic usage. The empirical proof of concept for iii) is provided by the decline in the frequency of the non-finite purpose subordinator in order to. Sentence length, genre and the likelihood of occurrence of in order to are shown to be interrelated.
The Uniformly Rotated Mondrian Kernel
Venue: arXiv cs.LG 2025
Year: 2025

Random feature maps are used to decrease the computational cost of kernel machines in large-scale problems. The Mondrian kernel is one such example of a fast random feature approximation of the Laplace kernel, generated by a computationally efficient hierarchical random partition of the input space known as the Mondrian process. In this work, we study a variation of this random feature map by applying a uniform random rotation to the input space before running the Mondrian process to approximate a kernel that is invariant under rotations. We obtain a closed-form expression for the isotropic kernel that is approximated, as well as a uniform convergence rate of the uniformly rotated Mondrian kernel to this limit. To this end, we utilize techniques from the theory of stationary random tessellations in stochastic geometry and prove a new result on the geometry of the typical cell of the superposition of uniformly rotated Mondrian tessellations. Finally, we test the empirical performance of this random feature map on both synthetic and real-world datasets, demonstrating its improved performance over the Mondrian kernel on a dataset that is debiased from the standard coordinate axes.
Can Grammarly and ChatGPT accelerate language change? AI-powered technologies and their impact on the English language: wordiness vs. conciseness
Venue: arXiv cs.CL 2025
Year: 2025

The proliferation of NLP-powered language technologies, AI-based natural language generation models, and English as a mainstream means of communication among both native and non-native speakers make the output of AI-powered tools especially intriguing to linguists. This paper investigates how Grammarly and ChatGPT affect the English language regarding wordiness vs. conciseness. A case study focusing on the purpose subordinator in order to is presented to illustrate the way in which Grammarly and ChatGPT recommend shorter grammatical structures instead of longer and more elaborate ones. Although the analysed sentences were produced by native speakers, are perfectly correct, and were extracted from a language corpus of contemporary English, both Grammarly and ChatGPT suggest more conciseness and less verbosity, even for relatively short sentences. The present article argues that technologies such as Grammarly not only mirror language change but also have the potential to facilitate or accelerate it.
Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Cyber threat detection has become an important area of focus in today's digital age due to the growing spread of fake information and harmful content on social media platforms such as Twitter (now 'X'). These cyber threats, often disguised within tweets, pose significant risks to individuals, communities, and even nations, emphasizing the need for effective detection systems. While previous research has explored tweet-based threats, much of the work is limited to specific languages, domains, or locations, or relies on single-model approaches, reducing their applicability to diverse real-world scenarios. To address these gaps, our study focuses on multi-lingual tweet cyber threat detection using a variety of advanced models. The research was conducted in three stages: (1) We collected and labeled tweet datasets in four languages English, Chinese, Russian, and Arabic employing both manual and polarity-based labeling methods to ensure high-quality annotations. (2) Each dataset was analyzed individually using machine learning (ML) and deep learning (DL) models to assess their performance on distinct languages. (3) Finally, we combined all four datasets into a single multi-lingual dataset and applied DL and large language model (LLM) architectures to evaluate their efficacy in identifying cyber threats across various languages. Our results show that among machine learning models, Random Forest (RF) attained the highest performance; however, the Bi-LSTM architecture consistently surpassed other DL and LLM architectures across all datasets. These findings underline the effectiveness of Bi-LSTM in multilingual cyber threat detection. The code for this paper can be found at this link: https://github.com/Mmurrad/Tweet-Data-Classification.git.
PerPO: Perceptual Preference Optimization via Discriminative Rewarding
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs). To align MLLMs with human visual perception process, PerPO employs discriminative rewarding to gather diverse negative samples, followed by listwise preference optimization to rank them.By utilizing the reward as a quantitative margin for ranking, our method effectively bridges generative preference optimization and discriminative empirical risk minimization. PerPO significantly enhances MLLMs' visual discrimination capabilities while maintaining their generative strengths, mitigates image-unconditional reward hacking, and ensures consistent performance across visual tasks. This work marks a crucial step towards more perceptually aligned and versatile MLLMs. We also hope that PerPO will encourage the community to rethink MLLM alignment strategies.
MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction
Venue: arXiv cs.CV 2025
Year: 2025

Map construction task plays a vital role in providing precise and comprehensive static environmental information essential for autonomous driving systems. Primary sensors include cameras and LiDAR, with configurations varying between camera-only, LiDAR-only, or camera-LiDAR fusion, based on cost-performance considerations. While fusion-based methods typically perform best, existing approaches often neglect modality interaction and rely on simple fusion strategies, which suffer from the problems of misalignment and information loss. To address these issues, we propose MapFusion, a novel multi-modal Bird's-Eye View (BEV) feature fusion method for map construction. Specifically, to solve the semantic misalignment problem between camera and LiDAR BEV features, we introduce the Cross-modal Interaction Transform (CIT) module, enabling interaction between two BEV feature spaces and enhancing feature representation through a self-attention mechanism. Additionally, we propose an effective Dual Dynamic Fusion (DDF) module to adaptively select valuable information from different modalities, which can take full advantage of the inherent information between different modalities. Moreover, MapFusion is designed to be simple and plug-and-play, easily integrated into existing pipelines. We evaluate MapFusion on two map construction tasks, including High-definition (HD) map and BEV map segmentation, to show its versatility and effectiveness. Compared with the state-of-the-art methods, MapFusion achieves 3.6% and 6.2% absolute improvements on the HD map construction and BEV map segmentation tasks on the nuScenes dataset, respectively, demonstrating the superiority of our approach.
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents SOLOMON, a novel Neuro-inspired Large Language Model (LLM) Reasoning Network architecture that enhances the adaptability of foundation models for domain-specific applications. Through a case study in semiconductor layout design, we demonstrate how SOLOMON enables swift adaptation of general-purpose LLMs to specialized tasks by leveraging Prompt Engineering and In-Context Learning techniques. Our experiments reveal the challenges LLMs face in spatial reasoning and applying domain knowledge to practical problems. Results show that SOLOMON instances significantly outperform their baseline LLM counterparts and achieve performance comparable to state-of-the-art reasoning model, o1-preview. We discuss future research directions for developing more adaptive AI systems that can continually learn, adapt, and evolve in response to new information and changing requirements.
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Venue: arXiv cs.CV 2025
Year: 2025

Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled datasets. Recently, self-supervised foundation models have been extended to three-dimensional (3D) computed tomography (CT) data, generating compact, information-rich embeddings with 1408 features that achieve state-of-the-art performance on downstream tasks such as intracranial hemorrhage detection and lung cancer risk forecasting. However, these embeddings have been shown to encode demographic information, such as age, sex, and race, which poses a significant risk to the fairness of clinical applications. In this work, we propose a Variation Autoencoder (VAE) based adversarial debiasing framework to transform these embeddings into a new latent space where demographic information is no longer encoded, while maintaining the performance of critical downstream tasks. We validated our approach on the NLST lung cancer screening dataset, demonstrating that the debiased embeddings effectively eliminate multiple encoded demographic information and improve fairness without compromising predictive accuracy for lung cancer risk at 1-year and 2-year intervals. Additionally, our approach ensures the embeddings are robust against adversarial bias attacks. These results highlight the potential of adversarial debiasing techniques to ensure fairness and equity in clinical applications of self-supervised 3D CT embeddings, paving the way for their broader adoption in unbiased medical decision-making.
FedP$^2$EFT: Federated Learning to Personalize PEFT for Multilingual LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages. To improve client-specific performance, personalization via the use of parameter-efficient fine-tuning (PEFT) modules such as LoRA is common. This involves a personalization strategy (PS), such as the design of the PEFT adapter structures (e.g., in which layers to add LoRAs and what ranks) and choice of hyperparameters (e.g., learning rates) for fine-tuning. Instead of manual PS configuration, we propose FedP$^2$EFT, a federated learning-to-personalize method for multilingual LLMs in cross-device FL settings. Unlike most existing PEFT structure selection methods, which are prone to overfitting low-data regimes, FedP$^2$EFT collaboratively learns the optimal personalized PEFT structure for each client via Bayesian sparse rank selection. Evaluations on both simulated and real-world multilingual FL benchmarks demonstrate that FedP$^2$EFT largely outperforms existing personalized fine-tuning methods, while complementing other existing FL methods.