OpenRaiser/Intern-Atlas huggingface dataset:
Melanoma Classification Through Deep Ensemble Learning and Explainable AI
Venue: arXiv cs.LG 2025
Year: 2025

Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. However, the entire community must overcome the explainability limit to get the maximum benefit from DL for diagnostics in the healthcare domain. Because of the black box operation's shortcomings in DL models' decisions, there is a lack of reliability and trust in the outcomes. However, Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks, along with an approach to ensure the reliability of the predictions by utilizing XAI techniques to explain the basis of the predictions.
LLM-Driven Cost-Effective Requirements Change Impact Analysis
Venue: arXiv cs.SE 2025
Year: 2025

Requirements are inherently subject to changes throughout the software development lifecycle. Within the limited budget available to requirements engineers, manually identifying the impact of such changes on other requirements is both error-prone and effort-intensive. That might lead to overlooked impacted requirements, which, if not properly managed, can cause serious issues in the downstream tasks. Inspired by the growing potential of large language models (LLMs) across diverse domains, we propose ProReFiCIA, an LLM-driven approach for automatically identifying the impacted requirements when changes occur. We conduct an extensive evaluation of ProReFiCIA using several LLMs and prompts variants tailored to this task. Using the best combination of an LLM and a prompt variant, ProReFiCIA achieves a recall of 93.3% on a benchmark dataset and 95.8% on a newly created industry dataset, demonstrating its strong effectiveness in identifying impacted requirements. Further, the cost of applying ProReFiCIA remains small, as the engineer only needs to review the generated results, which represent between 2.1% and 8.5% of the entire set of requirements.
Language Modeling With Factorization Memory
Venue: arXiv cs.CL 2025
Year: 2025

We propose Factorization Memory, an efficient recurrent neural network (RNN) architecture that achieves performance comparable to Transformer models on short-context language modeling tasks while also demonstrating superior generalization in long-context scenarios. Our model builds upon Mamba-2, enabling Factorization Memory to exploit parallel computations during training while preserving constant computational and memory complexity during inference. To further optimize model efficiency and representational capacity, we develop a sparse formulation of Factorization Memory that updates only a subset of recurrent states at each step while preserving the strong performance of its dense counterpart. To our knowledge, this represents the first RNN architecture that successfully combines sparse memory activation with competitive performance across both short and long-context settings. This work provides a systematic empirical analysis of Factorization Memory in comparison to Transformer and Mamba-2 architectures.
A Technical Exploration of Causal Inference with Hybrid LLM Synthetic Data
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) offer a flexible means to generate synthetic tabular data, yet existing approaches often fail to preserve key causal parameters such as the average treatment effect (ATE). In this technical exploration, we first demonstrate that state-of-the-art synthetic data generators, both GAN- and LLM-based, can achieve high predictive fidelity while substantially misestimating causal effects. To address this gap, we propose a hybrid generation framework that combines model-based covariate synthesis (monitored via distance-to-closest-record filtering) with separately learned propensity and outcome models, thereby ensuring that (W, A, Y) triplets retain their underlying causal structure. We further introduce a synthetic pairing strategy to mitigate positivity violations and a realistic evaluation protocol that leverages unlimited synthetic samples to benchmark traditional estimators (IPTW, AIPW, substitution) under complex covariate distributions. This work lays the groundwork for LLM-powered data pipelines that support robust causal analysis. Our code is available at https://github.com/Xyc-arch/llm-synthetic-for-causal-inference.git.
GEDICorrect: A Scalable Python Tool for Orbit-, Beam-, and Footprint-Level GEDI Geolocation Correction
Venue: arXiv cs.CE 2025
Year: 2025

Accurate geolocation is essential for the reliable use of GEDI LiDAR data in footprint-scale applications such as aboveground biomass modeling, data fusion, and ecosystem monitoring. However, residual geolocation errors arising from both systematic biases and random ISS-induced jitter can significantly affect the accuracy of derived vegetation and terrain metrics. The main goal of this study is to develop and evaluate a flexible, computationally efficient framework (GEDICorrect) that enables geolocation correction of GEDI data at the orbit, beam, and footprint levels. The framework integrates existing GEDI Simulator modules (gediRat and gediMetrics) and extends their functionality with flexible correction logic, multiple similarity metrics, adaptive footprint clustering, and optimized I/O handling. Using the Kullback--Leibler divergence as the waveform similarity metric, GEDICorrect improved canopy height (RH95) accuracy from $R^2 = 0.61$ (uncorrected) to 0.74 with the orbit-level correction, and up to $R^2 = 0.78$ with the footprint-level correction, reducing RMSE from 2.62~m ($rRMSE = 43.13\%$) to 2.12~m ($rRMSE = 34.97\%$) at the orbit level, and 2.01~m ($rRMSE = 33.05\%$) at the footprint level. Terrain elevation accuracy also improved, decreasing RMSE by 0.34~m relative to uncorrected data and by 0.37~m compared to the GEDI Simulator baseline. In terms of computational efficiency, GEDICorrect achieved a $\sim2.4\times$ speedup over the GEDI Simulator in single-process mode (reducing runtime from $\sim84$~h to $\sim35$~h) and scaled efficiently to 24 cores, completing the same task in $\sim4.3$~h -- an overall $\sim19.5\times$ improvement. GEDICorrect offers a robust and scalable solution for improving GEDI geolocation accuracy while maintaining full compatibility with standard GEDI data products.
Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limits
Venue: arXiv cs.AR 2025
Year: 2025

The expansion of context windows in large language models (LLMs) to multi-million tokens introduces severe memory and compute bottlenecks, particularly in managing the growing Key-Value (KV) cache. While Compute Express Link (CXL) enables non-eviction frameworks that offload the full KV-cache to scalable external memory, these frameworks still suffer from costly data transfers when recalling non-resident KV tokens to limited GPU memory as context lengths increase. This work proposes scalable Processing-Near-Memory (PNM) for 1M-Token LLM Inference, a CXL-enabled KV-cache management system that coordinates memory and computation beyond GPU limits. Our design offloads token page selection to a PNM accelerator within CXL memory, eliminating costly recalls and enabling larger GPU batch sizes. We further introduce a hybrid parallelization strategy and a steady-token selection mechanism to enhance compute efficiency and scalability. Implemented atop a state-of-the-art CXL-PNM system, our solution delivers consistent performance gains for LLMs with up to 405B parameters and 1M-token contexts. Our PNM-only offloading scheme (PNM-KV) and GPU-PNM hybrid with steady-token execution (PnG-KV) achieve up to 21.9x throughput improvement, up to 60x lower energy per token, and up to 7.3x better total cost efficiency than the baseline, demonstrating that CXL-enabled multi-PNM architectures can serve as a scalable backbone for future long-context LLM inference.
Oitijjo-3D: Generative AI Framework for Rapid 3D Heritage Reconstruction from Street View Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Cultural heritage restoration in Bangladesh faces a dual challenge of limited resources and scarce technical expertise. Traditional 3D digitization methods, such as photogrammetry or LiDAR scanning, require expensive hardware, expert operators, and extensive on-site access, which are often infeasible in developing contexts. As a result, many of Bangladesh's architectural treasures, from the Paharpur Buddhist Monastery to Ahsan Manzil, remain vulnerable to decay and inaccessible in digital form. This paper introduces Oitijjo-3D, a cost-free generative AI framework that democratizes 3D cultural preservation. By using publicly available Google Street View imagery, Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline - multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery. The system produces photorealistic, metrically coherent reconstructions in seconds, achieving significant speedups compared to conventional Structure-from-Motion pipelines, without requiring any specialized hardware or expert supervision. Experiments on landmarks such as Ahsan Manzil, Choto Sona Mosque, and Paharpur demonstrate that Oitijjo-3D preserves both visual and structural fidelity while drastically lowering economic and technical barriers. By turning open imagery into digital heritage, this work reframes preservation as a community-driven, AI-assisted act of cultural continuity for resource-limited nations.
Fast Networks for High-Performance Distributed Trust
Venue: arXiv cs.CR 2025
Year: 2025

Organizations increasingly need to collaborate by performing a computation on their combined dataset, while keeping their data hidden from each other. Certain kinds of collaboration, such as collaborative data analytics and AI, require a level of performance beyond what current cryptographic techniques for distributed trust can provide. This is because the organizations run software in different trust domains, which can require them to communicate over WANs or the public Internet. In this paper, we explore how to instead run such applications using fast datacenter-type LANs. We show that, by carefully redesigning distributed trust frameworks for LANs, we can achieve up to order-of-magnitude better performance than naïvely using a LAN. Then, we develop deployment models for Distributed But Proximate Trust (DBPT) that allow parties to use a LAN while remaining physically and logically distinct. These developments make secure collaborative data analytics and AI significantly more practical and set new research directions for developing systems and cryptographic theory for high-performance distributed trust.
Design of a Turbo-based Deep Semantic Autoencoder for Marine Internet of Things
Venue: arXiv cs.IT 2025
Year: 2025

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.
Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model
Venue: arXiv cs.LG 2025
Year: 2025

The emergence of foundation models in neuroimaging is driven by the increasing availability of large-scale and heterogeneous brain imaging datasets. Recent advances in self-supervised learning, particularly reconstruction-based objectives, have demonstrated strong potential for pretraining models that generalize effectively across diverse downstream functional MRI (fMRI) tasks. In this study, we explore region-aware reconstruction strategies for a foundation model in resting-state fMRI, moving beyond approaches that rely on random region masking. Specifically, we introduce an ROI-guided masking strategy using the Automated Anatomical Labelling Atlas (AAL3), applied directly to full 4D fMRI volumes to selectively mask semantically coherent brain regions during self-supervised pretraining. Using the ADHD-200 dataset comprising 973 subjects with resting-state fMRI scans, we show that our method achieves a 4.23% improvement in classification accuracy for distinguishing healthy controls from individuals diagnosed with ADHD, compared to conventional random masking. Region-level attribution analysis reveals that brain volumes within the limbic region and cerebellum contribute most significantly to reconstruction fidelity and model representation. Our results demonstrate that masking anatomical regions during model pretraining not only enhances interpretability but also yields more robust and discriminative representations. In future work, we plan to extend this approach by evaluating it on additional neuroimaging datasets, and developing new loss functions explicitly derived from region-aware reconstruction objectives. These directions aim to further improve the robustness and interpretability of foundation models for functional neuroimaging.
A Big Step Forward? A User-Centric Examination of iOS App Privacy Report and Enhancements
Venue: arXiv cs.SE 2025
Year: 2025

The prevalent engagement with mobile apps underscores the importance of understanding their data practices. Transparency plays a crucial role in this context, ensuring users to be informed and give consent before any data access occurs. Apple introduced a new feature since iOS 15.2, App Privacy Report, to inform users about detailed insights into apps' data access and sharing. This feature continues Apple's trend of privacy-focused innovations (following Privacy Nutrition Labels), and has been marketed as a big step forward in user privacy. However, its real-world impacts on user privacy and control remain unexamined. We thus proposed an end-to-end study involving systematic assessment of the App Privacy Report's real-world benefits and limitations, LLM-enabled and multi-technique synthesized enhancements, and comprehensive evaluation from both system and user perspectives. Through a structured focus group study with twelve everyday iOS users, we explored their experiences, understanding, and perceptions of the feature, suggesting its limited practical impact resulting from missing important details. We identified two primary user concerns: the clarity of data access purpose and domain description. In response, we proposed enhancements including a purpose inference framework and domain clarification pipeline. We demonstrated the effectiveness and benefits of such enhancements for mobile app users. This work provides practical insights that could help enhance user privacy transparency and discusses areas for future research.
An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process
Venue: arXiv cs.CR 2025
Year: 2025

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as these data may contain anomalies due to many reasons such as sensor faults, environmental disturbances, or malicious intrusions. In this paper a lightweight and interpretable anomaly detection framework based on a first order Markov chain model has been proposed. The method discretizes continuous sensor readings into finite states and models the temporal dynamics of sensor transitions through a transition probability matrix. Anomalies are detected when observed transitions occur with probabilities below a computed threshold, allowing for real time detection without labeled data or intensive computation. The proposed framework was validated using the Intel Berkeley Research Lab dataset, as a case study on indoor environmental monitoring demonstrates its capability to identify thermal spikes, voltage related faults, and irregular temperature fluctuations with high precision. Comparative analysis with Z score, Hidden Markov Model, and Auto encoder based methods shows that the proposed Markov based framework achieves a balanced trade-off between accuracy, F1 score is 0.86, interoperability, and computational efficiency. The systems scalability and low resource footprint highlight its suitability for large-scale and real time anomaly detection in WSN deployments.
OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
Venue: arXiv cs.CV 2025
Year: 2025

This paper investigates Multi-Object Tracking (MOT) in panoramic imagery, which introduces unique challenges including a 360° Field of View (FoV), resolution dilution, and severe view-dependent distortions. Conventional MOT methods designed for narrow-FoV pinhole cameras generalize unsatisfactorily under these conditions. To address panoramic distortion, large search space, and identity ambiguity under a 360° FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes according to scene dynamics, offering a balanced and scalable solution for panoramic MOT. To support rigorous evaluation, we establish the EmboTrack benchmark, a comprehensive dataset for panoramic MOT that includes QuadTrack, captured with a quadruped robot, and BipTrack, collected with a bipedal wheel-legged robot. Together, these datasets span wide-angle environments and diverse motion patterns, providing a challenging testbed for real-world panoramic perception. Extensive experiments on JRDB and EmboTrack demonstrate that OmniTrack++ achieves state-of-the-art performance, yielding substantial HOTA improvements of +25.5% on JRDB and +43.07% on QuadTrack over the original OmniTrack. Datasets and code will be made publicly available at https://github.com/xifen523/OmniTrack.
Descriptive Model-based Learning and Control for Bipedal Locomotion
Venue: arXiv cs.RO 2025
Year: 2025

Bipedal balance is challenging due to its multi-phase, hybrid nature and high-dimensional state space. Traditional balance control approaches for bipedal robots rely on low-dimensional models for locomotion planning and reactive control, constraining the full robot to behave like these simplified models. This involves tracking preset reference paths for the Center of Mass and upper body obtained through low-dimensional models, often resulting in inefficient walking patterns with bent knees. However, we observe that bipedal balance is inherently low-dimensional and can be effectively described with simple state and action descriptors in a low-dimensional state space. This allows the robot's motion to evolve freely in its high-dimensional state space, only constraining its projection in the low-dimensional state space. In this work, we propose a novel control approach that avoids prescribing a low-dimensional model to the full model. Instead, our control framework uses a descriptive model with the minimum degrees of freedom necessary to maintain balance, allowing the remaining degrees of freedom to evolve freely in the high-dimensional space. This results in an efficient human-like walking gait and improved robustness.
Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
Venue: arXiv cs.SE 2025
Year: 2025

Code review (CR) is a crucial practice for ensuring software quality. Various automated review comment generation techniques have been proposed to streamline the labor-intensive process. However, existing approaches heavily rely on a single model to identify various issues within the code, limiting the model's ability to handle the diverse, issue-specific nature of code changes and leading to non-informative comments, especially in complex scenarios such as bug fixes. To address these limitations, we propose RevAgent, a novel agent-based issue-oriented framework, decomposes the task into three stages: (1) Generation Stage, where five category-specific commentator agents analyze code changes from distinct issue perspectives and generate candidate comments; (2) Discrimination Stage, where a critic agent selects the most appropriate issue-comment pair; and (3) Training Stage, where all agents are fine-tuned on curated, category-specific data to enhance task specialization. Evaluation results show that RevAgent significantly outperforms state-of-the-art PLM- and LLM-based baselines, with improvements of 12.90\%, 10.87\%, 6.32\%, and 8.57\% on BLEU, ROUGE-L, METEOR, and SBERT, respectively. It also achieves relatively higher accuracy in issue-category identification, particularly for challenging scenarios. Human evaluations further validate the practicality of RevAgent in generating accurate, readable, and context-aware review comments. Moreover, RevAgent delivers a favorable trade-off between performance and efficiency.