OpenRaiser/Intern-Atlas huggingface dataset:
The Technical Debt Gamble: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
Venue: arXiv cs.SE 2025
Year: 2025

Microservice architectures provide an intuitive promise of high maintainability and evolvability due to loose coupling. However, these quality attributes are notably vulnerable to technical debt (TD). Few studies address TD in microservice systems, particularly on a large scale. This research explores how TD manifests in a large-scale microservice-based industrial system. The research is based on a mixed-method case study of a project including over 100 microservices and serving over 15k locations. Results are collected via a quantitative method based static code analyzers combined with qualitative insights derived from a focus group discussion with the development team and a follow-up interview with the lead architect of the case study system. Results show that (1) simple static source code analysis can be an efficient and effective entry point for holistic TD discovery, (2) inadequate communication significantly contributes to TD, (3) misalignment between architectural and organizational structures can exacerbate TD accumulation, (4) microservices can rapidly cycle through TD accumulation and resolution, a phenomenon referred to as "microservice architecture technical debt gamble". Finally, we identify a set of fitting strategies for TD management in microservice architectures.
Fine-grained Image Retrieval via Dual-Vision Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

Fine-Grained Image Retrieval~(FGIR) faces challenges in learning discriminative visual representations to retrieve images with similar fine-grained features. Current leading FGIR solutions typically follow two regimes: enforce pairwise similarity constraints in the semantic embedding space, or incorporate a localization sub-network to fine-tune the entire model. However, such two regimes tend to overfit the training data while forgetting the knowledge gained from large-scale pre-training, thus reducing their generalization ability. In this paper, we propose a Dual-Vision Adaptation (DVA) approach for FGIR, which guides the frozen pre-trained model to perform FGIR through collaborative sample and feature adaptation. Specifically, we design Object-Perceptual Adaptation, which modifies input samples to help the pre-trained model perceive critical objects and elements within objects that are helpful for category prediction. Meanwhile, we propose In-Context Adaptation, which introduces a small set of parameters for feature adaptation without modifying the pre-trained parameters. This makes the FGIR task using these adjusted features closer to the task solved during the pre-training. Additionally, to balance retrieval efficiency and performance, we propose Discrimination Perception Transfer to transfer the discriminative knowledge in the object-perceptual adaptation to the image encoder using the knowledge distillation mechanism. Extensive experiments show that DVA has fewer learnable parameters and performs well on three in-distribution and three out-of-distribution fine-grained datasets.
Advancing Automated Speaking Assessment Leveraging Multifaceted Relevance and Grammar Information
Venue: arXiv cs.CL 2025
Year: 2025

Current automated speaking assessment (ASA) systems for use in multi-aspect evaluations often fail to make full use of content relevance, overlooking image or exemplar cues, and employ superficial grammar analysis that lacks detailed error types. This paper ameliorates these deficiencies by introducing two novel enhancements to construct a hybrid scoring model. First, a multifaceted relevance module integrates question and the associated image content, exemplar, and spoken response of an L2 speaker for a comprehensive assessment of content relevance. Second, fine-grained grammar error features are derived using advanced grammar error correction (GEC) and detailed annotation to identify specific error categories. Experiments and ablation studies demonstrate that these components significantly improve the evaluation of content relevance, language use, and overall ASA performance, highlighting the benefits of using richer, more nuanced feature sets for holistic speaking assessment.
Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective
Venue: arXiv cs.LG 2025
Year: 2025

The rapid adaptation ability of auto-regressive foundation models is often attributed to the diversity of their pre-training data. This is because, from a Bayesian standpoint, minimizing prediction error in such settings requires integrating over all plausible latent hypotheses consistent with observations. While this behavior is desirable in principle, it often proves too ambitious in practice: under high ambiguity, the number of plausible latent alternatives makes Bayes-optimal prediction computationally intractable. Cognitive science has long recognized this limitation, suggesting that under such conditions, heuristics or information-seeking strategies are preferable to exhaustive inference. Translating this insight to next-token prediction, we hypothesize that low- and high-ambiguity predictions pose different computational demands, making ambiguity-agnostic next-token prediction a detrimental inductive bias. To test this, we introduce MetaHMM, a synthetic sequence meta-learning benchmark with rich compositional structure and a tractable Bayesian oracle. We show that Transformers indeed struggle with high-ambiguity predictions across model sizes. Motivated by cognitive theories, we propose a method to convert pre-trained models into Monte Carlo predictors that decouple task inference from token prediction. Preliminary results show substantial gains in ambiguous contexts through improved capacity allocation and test-time scalable inference, though challenges remain.
Wavelet-based Global Orientation and Surface Reconstruction for Point Clouds
Venue: arXiv cs.CG 2025
Year: 2025

Unoriented surface reconstruction is an important task in computer graphics and has extensive applications. Based on the compact support of wavelet and orthogonality properties, classic wavelet surface reconstruction achieves good and fast reconstruction. However, this method can only handle oriented points. Despite some improved attempts for unoriented points, such as iWSR, these methods perform poorly on sparse point clouds. To address these shortcomings, we propose a wavelet-based method to represent the mollified indicator function and complete both the orientation and surface reconstruction tasks. We use the modifying kernel function to smoothen out discontinuities on the surface, aligning with the continuity of the wavelet basis function. During the calculation of coefficient, we fully utilize the properties of the convolutional kernel function to shift the modifying computation onto wavelet basis to accelerate. In addition, we propose a novel method for constructing the divergence-free function field and using them to construct the additional homogeneous constraints to improve the effectiveness and stability. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both orientation and reconstruction for sparse models. We align the matrix construction with the compact support property of wavelet basis functions to further accelerate our method, resulting in efficient performance on CPU. Our source codes will be released on GitHub.
Optimizing Multilingual Text-To-Speech with Accents & Emotions
Venue: arXiv cs.LG 2025
Year: 2025

State-of-the-art text-to-speech (TTS) systems realize high naturalness in monolingual environments, synthesizing speech with correct multilingual accents (especially for Indic languages) and context-relevant emotions still poses difficulty owing to cultural nuance discrepancies in current frameworks. This paper introduces a new TTS architecture integrating accent along with preserving transliteration with multi-scale emotion modelling, in particularly tuned for Hindi and Indian English accent. Our approach extends the Parler-TTS model by integrating A language-specific phoneme alignment hybrid encoder-decoder architecture, and culture-sensitive emotion embedding layers trained on native speaker corpora, as well as incorporating a dynamic accent code switching with residual vector quantization. Quantitative tests demonstrate 23.7% improvement in accent accuracy (Word Error Rate reduction from 15.4% to 11.8%) and 85.3% emotion recognition accuracy from native listeners, surpassing METTS and VECL-TTS baselines. The novelty of the system is that it can mix code in real time - generating statements such as "Namaste, let's talk about <Hindi phrase>" with uninterrupted accent shifts while preserving emotional consistency. Subjective evaluation with 200 users reported a mean opinion score (MOS) of 4.2/5 for cultural correctness, much better than existing multilingual systems (p<0.01). This research makes cross-lingual synthesis more feasible by showcasing scalable accent-emotion disentanglement, with direct application in South Asian EdTech and accessibility software.
When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard
Venue: arXiv cs.HC 2025
Year: 2025

This study investigated the impact of a theory-driven, explainable Learning Analytics Dashboard (LAD) on university students' human-AI collaborative academic abstract writing task. Grounded in Self-Regulated Learning (SRL) theory and incorporating Explainable AI (XAI) principles, our LAD featured a three-layered design (Visual, Explainable, Interactive). In an experimental study, participants were randomly assigned to either an experimental group (using the full explainable LAD) or a control group (using a visual-only LAD) to collaboratively write an academic abstract with a Generative AI. While quantitative analysis revealed no significant difference in the quality of co-authored abstracts between the two groups, a significant and noteworthy difference emerged in conceptual understanding: students in the explainable LAD group demonstrated a superior grasp of abstract writing principles, as evidenced by their higher scores on a knowledge test (p= .026). These findings highlight that while basic AI-generated feedback may suffice for immediate task completion, the provision of explainable feedback is crucial for fostering deeper learning, enhancing conceptual understanding, and developing transferable skills fundamental to self-regulated learning in academic writing contexts.
Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections
Venue: arXiv cs.RO 2025
Year: 2025

Reinforcement learning (RL) exhibits remarkable potential in addressing autonomous driving tasks. However, it is difficult to train a sample-efficient and safe policy in complex scenarios. In this article, we propose a novel hierarchical reinforcement learning (HRL) framework with a goal-conditioned collision prediction (GCCP) module. In the hierarchical structure, the GCCP module predicts collision risks according to different potential subgoals of the ego vehicle. A high-level decision-maker choose the best safe subgoal. A low-level motion-planner interacts with the environment according to the subgoal. Compared to traditional RL methods, our algorithm is more sample-efficient, since its hierarchical structure allows reusing the policies of subgoals across similar tasks for various navigation scenarios. In additional, the GCCP module's ability to predict both the ego vehicle's and surrounding vehicles' future actions according to different subgoals, ensures the safety of the ego vehicle throughout the decision-making process. Experimental results demonstrate that the proposed method converges to an optimal policy faster and achieves higher safety than traditional RL methods.
Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images
Venue: arXiv cs.CV 2025
Year: 2025

The high computational demands of Vision Transformers (ViTs) in processing a large number of tokens often constrain their practical application in analyzing medical images. This research proposes a Prompt-driven Adaptive Token ({\it PrATo}) pruning method to selectively reduce the processing of irrelevant tokens in the segmentation pipeline. The prompt-based spatial prior helps to rank the tokens according to their relevance. Tokens with low-relevance scores are down-weighted, ensuring that only the relevant ones are propagated for processing across subsequent stages. This data-driven pruning strategy improves segmentation accuracy and inference speed by allocating computational resources to essential regions. The proposed framework is integrated with several state-of-the-art models to facilitate the elimination of irrelevant tokens, thereby enhancing computational efficiency while preserving segmentation accuracy. The experimental results show a reduction of $\sim$ 35-55% tokens; thus reducing the computational costs relative to baselines. Cost-effective medical image processing, using our framework, facilitates real-time diagnosis by expanding its applicability in resource-constrained environments.
HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents our approach to multi-label emotion detection in Hausa, a low-resource African language, for SemEval Track A. We fine-tuned AfriBERTa, a transformer-based model pre-trained on African languages, to classify Hausa text into six emotions: anger, disgust, fear, joy, sadness, and surprise. Our methodology involved data preprocessing, tokenization, and model fine-tuning using the Hugging Face Trainer API. The system achieved a validation accuracy of 74.00%, with an F1-score of 73.50%, demonstrating the effectiveness of transformer-based models for emotion detection in low-resource languages.
LoRaIN: A Constructive Interference-Assisted Reliable and Energy-Efficient LoRa Indoor Network
Venue: arXiv cs.NI 2025
Year: 2025

LoRa is a promising communication technology for enabling the next-generation indoor Internet of Things applications. Very few studies, however, have analyzed its performance indoors. Besides, these indoor studies investigate mostly the RSSI and SNR of the received packets at the gateway, which, as we show, may not unfold the poor performance of LoRa and its MAC protocol, LoRaWAN, indoors in terms of reliability and energy-efficiency. In this paper, we extensively evaluate the performance of LoRaWAN indoors and then use the key insights to boost its reliability and energy-efficiency by proposing LoRaIN, LoRa Indoor Network, a new link-layer protocol that can be effectively used for indoor deployments. The approach to boosting the reliability and energy efficiency in LoRaIN is underpinned by enabling constructive interference with specific timing requirements analyzed both empirically and mathematically for different pairs of channel bandwidth and spreading factor and relaying precious acknowledgments to the end-devices with the assistance of several booster nodes. The booster nodes do not need any special capability and can be a subset of the LoRa end-devices. To our knowledge, LoRaIN is the first protocol for boosting reliability and energy-efficiency in indoor LoRa networks. We evaluate its performance in an indoor testbed consisting of one LoRaWAN gateway and 20 end-devices. Our extensive evaluation shows that when 15% of the end-devices operate as booster nodes, the reliability at the gateway increases from 62% to 95%, and the end-devices are approximately 2.5x energy-efficient.
REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) face an inherent challenge: their knowledge is confined to the data that they have been trained on. To overcome this issue, Retrieval-Augmented Generation (RAG) complements the static training-derived knowledge of LLMs with an external knowledge repository. RAG consists of three stages: indexing, retrieval, and generation. The retrieval stage of RAG becomes a significant bottleneck in inference pipelines. In this stage, a user query is mapped to an embedding vector and an Approximate Nearest Neighbor Search (ANNS) algorithm searches for similar vectors in the database to identify relevant items. Due to the large database sizes, ANNS incurs significant data movement overheads between the host and the storage system. To alleviate these overheads, prior works propose In-Storage Processing (ISP) techniques that accelerate ANNS by performing computations inside storage. However, existing works that leverage ISP for ANNS (i) employ algorithms that are not tailored to ISP systems, (ii) do not accelerate data retrieval operations for data selected by ANNS, and (iii) introduce significant hardware modifications, limiting performance and hindering their adoption. We propose REIS, the first ISP system tailored for RAG that addresses these limitations with three key mechanisms. First, REIS employs a database layout that links database embedding vectors to their associated documents, enabling efficient retrieval. Second, it enables efficient ANNS by introducing an ISP-tailored data placement technique that distributes embeddings across the planes of the storage system and employs a lightweight Flash Translation Layer. Third, REIS leverages an ANNS engine that uses the existing computational resources inside the storage system. Compared to a server-grade system, REIS improves the performance (energy efficiency) of retrieval by an average of 13x (55x).
Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election
Venue: arXiv cs.SI 2025
Year: 2025

During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks. Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate. Moving beyond traditional correlation analyses, we apply a causal inference technique: Convergent Cross Mapping, to uncover directional influences among political communities, revealing how some parties are more likely to initiate changes in activity while others tend to respond. This approach allows us to distinguish true influence from mere correlation, highlighting asymmetric relationships and hidden dynamics within the social media political network. Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases. These insights provide a novel framework for understanding political discourse dynamics and have practical implications for campaign strategists and media analysts seeking to monitor and respond to shifts in political influence in real time.
SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL framework for identifying and reducing the impact of such attackers. Phase 1 involves collecting model updates from participating clients and applying a dimensionality reduction approach to identify outlier patterns frequently associated with malicious behavior. Temporary models constructed from the client updates are evaluated on synthetic datasets to compute validation losses and support anomaly scoring. The idea of learning zones is presented in Phase 2, where weights are dynamically routed according to their contribution scores and gradient magnitudes. High-value gradient zones are given greater weight in aggregation and contribute more significantly to the global model, while lower-value gradient zones, which may indicate possible adversarial activity, are gradually removed from training. Until the model converges and a strong defense against poisoning attacks is possible, this training cycle continues Based on the experimental findings, SecureFed considerably improves model resilience without compromising model performance.
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
Venue: arXiv cs.LG 2025
Year: 2025

Multitask learning (MTL) has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, to solve them simultaneously while minimizing data sharing across users and organizations. MTL typically accomplishes this goal by learning a shared representation that captures common structure among the tasks by embedding data from all tasks into a common feature space. Despite being designed to be the smallest unit of shared information necessary to effectively learn patterns across multiple tasks, these shared representations can inadvertently leak sensitive information about the particular tasks they were trained on. In this work, we investigate what information is revealed by the shared representations through the lens of inference attacks. Towards this, we propose a novel, black-box task-inference threat model where the adversary, given the embedding vectors produced by querying the shared representation on samples from a particular task, aims to determine whether that task was present when training the shared representation. We develop efficient, purely black-box attacks on machine learning models that exploit the dependencies between embeddings from the same task without requiring shadow models or labeled reference data. We evaluate our attacks across vision and language domains for multiple use cases of MTL and demonstrate that even with access only to fresh task samples rather than training data, a black-box adversary can successfully infer a task's inclusion in training. To complement our experiments, we provide theoretical analysis of a simplified learning setting and show a strict separation between adversaries with training samples and fresh samples from the target task's distribution.
Closed-Loop Control of Electrical Stimulation through Spared Motor Unit Ensembles Restores Foot Movements after Spinal Cord Injury
Venue: arXiv cs.HC 2025
Year: 2025

Restoring movement of a paralyzed foot is a key challenge in helping individuals with neurological conditions such as spinal cord injury (SCI) to improve their quality of life. Neuroprostheses based on functional electrical stimulation (FES) can restore the physiological range of motion by stimulating the affected muscles using surface electrodes. We have previously shown that, despite chronic motor-complete SCI, it is possible to capture paralyzed hand movements in individuals with tetraplegia using spared and modulated motor unit (MU) activity decoded with non-invasive electromyography (EMG) sensors. This study investigated whether a wearable high-density surface EMG system could capture and control paralyzed foot kinematics in closed-loop control with an FES system. We found that all our participants with SCI (2 with chronic SCI and 3 with acute SCI) retained distinct spared EMG activity for at least three ankle movements, which allowed them to reliably control a digital cursor using their spared tibialis anterior and triceps surae MU activity. Movement separability was further reconfirmed by extracting task-modulated MU activity during foot flexion/extension (3-7 modulated MUs/participant). Three participants were further able to modulate and maintain their foot flexion/extension EMG levels with an accuracy of >70%. Lastly, we show that real-time control of a FES system using EMG from the affected limb can restore foot movements in a highly intuitive way, significantly improving the lost or pathological foot range of motion. Our system provides an intuitive approach for closed-loop control of FES that has the potential to assist individuals with SCI in regaining lost motor functions.