OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Earable Technology: Trends, Tools, and the Road Ahead
Venue: arXiv cs.HC 2025
Year: 2025

Earable devices, wearables positioned in or around the ear, are undergoing a rapid transformation from audio-centric accessories into multifunctional systems for interaction, contextual awareness, and health monitoring. This evolution is driven by commercial trends emphasizing sensor integration and by a surge of academic interest exploring novel sensing capabilities. Building on the foundation established by earlier surveys, this work presents a timely and comprehensive review of earable research published since 2022. We analyze over one hundred recent studies to characterize this shifting research landscape, identify emerging applications and sensing modalities, and assess progress relative to prior efforts. In doing so, we address three core questions: how has earable research evolved in recent years, what enabling resources are now available, and what opportunities remain for future exploration. Through this survey, we aim to provide both a retrospective and forward-looking view of earable technology as a rapidly expanding frontier in ubiquitous computing. In particular, this review reveals that over the past three years, researchers have discovered a variety of novel sensing principles, developed many new earable sensing applications, enhanced the accuracy of existing sensing tasks, and created substantial new resources to advance research in the field. Based on this, we further discuss open challenges and propose future directions for the next phase of earable research.
There's Waldo: PCB Tamper Forensic Analysis using Explainable AI on Impedance Signatures
Venue: arXiv cs.CR 2025
Year: 2025

The security of printed circuit boards (PCBs) has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. While detecting tampering on a PCB is the first step for verification, forensics is also needed to identify the modified component. One non-invasive and reliable PCB tamper detection technique with global coverage is the impedance characterization of a PCB's power delivery network (PDN). However, it is an open question whether one can use the two-dimensional impedance signatures for forensics purposes. In this work, we introduce a novel PCB forensics approach using explainable AI (XAI) on impedance signatures. Through extensive experiments, we replicate various PCB tamper events, generating a dataset used to develop an XAI algorithm capable of not only detecting tampering but also explaining why the algorithm makes a decision about whether a tamper event has happened. At the core of our XAI algorithm is a random forest classifier with an accuracy of 96.7%, sufficient to explain the algorithm's decisions. To understand the behavior of the classifier in the decision-making process, we utilized SHAP values as an XAI tool to determine which frequency component influences the classifier's decision for a particular class the most. This approach enhances detection capabilities as well as advancing the verifier's ability to reverse-engineer and analyze two-dimensional impedance signatures for forensics.
FIST: A Structured Threat Modeling Framework for Fraud Incidents
Venue: arXiv cs.CR 2025
Year: 2025

Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured Threat Framework), an innovative structured threat modeling methodology specifically designed for fraud scenarios. Inspired by MITRE ATT\&CK and DISARM, FIST systematically incorporates social engineering tactics, stage-based behavioral decomposition, and detailed attack technique mapping into a reusable knowledge base. FIST aims to enhance the efficiency of fraud detection and the standardization of threat intelligence sharing, promoting collaboration and a unified language across organizations and sectors. The framework integrates interdisciplinary insights from cybersecurity, criminology, and behavioral science, addressing both technical vectors and psychological manipulation mechanisms in fraud. This approach enables fine-grained analysis of fraud incidents, supporting automated detection, quantitative risk assessment, and standardized incident reporting. The effectiveness of the framework is further validated through real-world case studies, demonstrating its value in bridging academic research and practical applications, and laying the foundation for an intelligence-driven anti-fraud ecosystem. To the best of our knowledge, FIST is the first systematic, open-source fraud threat modeling framework that unifies both technical and psychological aspects, and is made freely available to foster collaboration between academia and industry.
When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk of training data privacy leakage. This paper systematically investigates the privacy threats posed by membership inference attacks (MIAs) targeting encoder models, focusing on contrastive learning frameworks. Through experimental analysis, we reveal the significant impact of model architecture complexity on membership privacy leakage: As more advanced encoder frameworks improve feature-extraction performance, they simultaneously exacerbate privacy-leakage risks. Furthermore, this paper proposes a novel membership inference attack method based on the p-norm of feature vectors, termed the Embedding Lp-Norm Likelihood Attack (LpLA). This method infers membership status, by leveraging the statistical distribution characteristics of the p-norm of feature vectors. Experimental results across multiple datasets and model architectures demonstrate that LpLA outperforms existing methods in attack performance and robustness, particularly under limited attack knowledge and query volumes. This study not only uncovers the potential risks of privacy leakage in contrastive learning frameworks, but also provides a practical basis for privacy protection research in encoder models. We hope that this work will draw greater attention to the privacy risks associated with self-supervised learning models and shed light on the importance of a balance between model utility and training data privacy. Our code is publicly available at: https://github.com/SeroneySun/LpLA_code.
LLM-Symbolic Integration for Robust Temporal Tabular Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data, which is a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive few-shot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs.
Investigating the Relationship between the Weighted Figure of Merit and Rosin's Measure
Venue: arXiv cs.CV 2025
Year: 2025

Many studies have been conducted to solve the problem of approximating a digital boundary by piece straight-line segments for the further processing required in computer vision applications. The authors of these studies compared their schemes to determine the best one. The initial measure used to assess the goodness of fit of a polygonal approximation was the figure of merit. Later,it was noted that this measure was not an appropriate metric for a valid reason which is why Rosin-through mathematical analysis-introduced a measure called merit. However,this measure involves an optimal scheme of polygonal approximation,so it is time-consuming to compute it to assess the goodness of fit of an approximation. This led many researchers to use a weighted figure of merit as a substitute for Rosin's measure to compare sub optimal schemes. An attempt is made in this communication to investigate whether the two measures-weighted figure of merit and Rosin's measure-are related so that one can be used instead of the other, and toward this end, theoretical analysis, experimental investigation and statistical analysis are carried out. The mathematical formulas for the weighted figure of merit and Rosin's measure are analyzed, and through proof of theorems,it is found that the two measures are theoretically independent of each other. The graphical analysis of experiments carried out using a public dataset supports the results of the theoretical analysis. The statistical analysis via Pearson's correlation coefficient and non-linear correlation measure also revealed that the two measures are uncorrelated. This analysis leads one to conclude that if a suboptimal scheme is found to be better (worse) than some other suboptimal scheme,as indicated by Rosin's measure,then the same conclusion cannot be drawn using a weighted figure of merit,so one cannot use a weighted figure of merit instead of Rosin's measure.
Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane
Venue: arXiv cs.NI 2025
Year: 2025

The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use of the match-action table (MAT) abstraction on the dataplane is misaligned with DL inference, leading to several key limitations, including accuracy degradation, limited scale, and lack of generality. This paper proposes Pegasus to address these limitations. Pegasus translates DL operations into three dataplane-oriented primitives to achieve generality: Partition, Map, and SumReduce. Specifically, Partition "divides" high-dimensional features into multiple low-dimensional vectors, making them more suitable for the dataplane; Map "conquers" computations on the low-dimensional vectors in parallel with the technique of fuzzy matching, while SumReduce "combines" the computation results. Additionally, Pegasus employs Primitive Fusion to merge computations, improving scalability. Finally, Pegasus adopts full precision weights with fixed-point activations to improve accuracy. Our implementation on a P4 switch demonstrates that Pegasus can effectively support various types of DL models, including Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and AutoEncoder models on the dataplane. Meanwhile, Pegasus outperforms state-of-the-art approaches with an average accuracy improvement of up to 22.8%, along with up to 248x larger model size and 212x larger input scale.
Option Pricing Using Ensemble Learning
Venue: arXiv cs.LG 2025
Year: 2025

Ensemble learning is characterized by flexibility, high precision, and refined structure. As a critical component within computational finance, option pricing with machine learning requires both high predictive accuracy and reduced structural complexity-features that align well with the inherent advantages of ensemble learning. This paper investigates the application of ensemble learning to option pricing, and conducts a comparative analysis with classical machine learning models to assess their performance in terms of accuracy, local feature extraction, and robustness to noise. A novel experimental strategy is introduced, leveraging parameter transfer across experiments to improve robustness and realism in financial simulations.Building upon this strategy, an evaluation mechanism is developed that incorporates a scoring strategy and a weighted evaluation strategy explicitly emphasizing the foundational role of financial theory. This mechanism embodies an orderly integration of theoretical finance and computational methods. In addition, the study examines the interaction between sliding window technique and noise, revealing nuanced patterns that suggest a potential connection relevant to ongoing research in machine learning and data science.
Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

Centralized electrical/electronic architectures and High-Performance Computers (HPCs) are redefining automotive software development, challenging traditional microcontroller-based approaches. Ensuring real-time, safety, and scalability in software-defined vehicles necessitates reevaluating how mixed-criticality software is integrated into centralized architectures. While existing research on automotive SoftWare Architectures (SWAs) is relevant to the industry, it often lacks validation through systematic, empirical methods. To address this gap, we conduct a systematic literature review focusing on automotive mixed-criticality SWAs. Our goal is to provide practitioner-oriented guidelines that assist automotive software architects and developers design centralized, mixed-criticality SWAs based on a rigorous and transparent methodology. First, we set up a systematic review protocol grounded in established guidelines. Second, we apply this protocol to identify relevant studies. Third, we extract key functional domains, constraints, and enabling technologies that drive changes in automotive SWAs, thereby assessing the protocol's effectiveness. Additionally, we extract techniques, architectural patterns, and design practices for integrating mixed-criticality requirements into HPC-based SWAs, further demonstrating the protocol's applicability. Based on these insights, we propose an exemplary SWA for a microprocessor-based system-on-chip. In conclusion, this study provides a structured approach to explore and realize mixed-criticality software integration for next-generation automotive SWAs, offering valuable insights for industry and research applications.
High Throughput Event Filtering: The Interpolation-based DIF Algorithm Hardware Architecture
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights (DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 x 720 and 428.45 MEPS for a resolution of 640 x 480. The average values of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.
A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

A fundamental challenge in car-following modeling lies in accurately representing the multi-scale complexity of driving behaviors, particularly the intra-driver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize inter-driver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven car-following framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines Gated Recurrent Units for discrete driving regime classification with Long Short-Term Memory networks for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent inter- and intra-driver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and Dynamic Time Warping, ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for acceleration (maximum MSE improvement reached 58.47\%), speed, and spacing metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval-augmented generation (RAG) based on large language models often falters on narrative documents with inherent temporal structures. Standard unstructured RAG methods rely solely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information, while knowledge graph RAG (KG-RAG) frameworks collapse every mention of an entity into a single node, erasing the evolving context that drives many queries. To formalize this challenge and draw the community's attention, we construct ChronoQA, a robust and discriminative QA benchmark that measures temporal, causal, and character consistency understanding in narrative documents (e.g., novels) under the RAG setting. We then introduce Entity-Event RAG (E^2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping, thereby preserving the temporal and causal facets needed for fine-grained reasoning. Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries. E^2RAG therefore offers a practical path to more context-aware retrieval for tasks that require precise answers grounded in chronological information.
Additive decomposition of one-dimensional signals using Transformers
Venue: arXiv cs.LG 2025
Year: 2025

One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields. It serves as a highly valuable pre-processing step for data analysis. While traditional decomposition techniques often rely on mathematical models, recent research suggests that applying the latest deep learning models to this problem presents an exciting, unexplored area with promising potential. This work presents a novel method for the additive decomposition of one-dimensional signals. We leverage the Transformer architecture to decompose signals into their constituent components: piece-wise constant, smooth (low-frequency oscillatory), textured (high-frequency oscillatory), and a noise component. Our model, trained on synthetic data, achieves excellent accuracy in modeling and decomposing input signals from the same distribution, as demonstrated by the experimental results.
IntentionESC: An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems
Venue: arXiv cs.CL 2025
Year: 2025

In emotional support conversations, unclear intentions can lead supporters to employ inappropriate strategies, inadvertently imposing their expectations or solutions on the seeker. Clearly defined intentions are essential for guiding both the supporter's motivations and the overall emotional support process. In this paper, we propose the Intention-centered Emotional Support Conversation (IntentionESC) framework, which defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. While Large Language Models (LLMs) excel in text generating, they fundamentally operate as probabilistic models trained on extensive datasets, lacking a true understanding of human thought processes and intentions. To address this limitation, we introduce the Intention Centric Chain-of-Thought (ICECoT) mechanism. ICECoT enables LLMs to mimic human reasoning by analyzing emotional states, inferring intentions, and selecting suitable support strategies, thereby generating more effective emotional support responses. To train the model with ICECoT and integrate expert knowledge, we design an automated annotation pipeline that produces high-quality training data. Furthermore, we develop a comprehensive evaluation scheme to assess emotional support efficacy and conduct extensive experiments to validate our framework. Our data and code are available at https://github.com/43zxj/IntentionESC_ICECoT.
Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review
Venue: arXiv cs.CV 2025
Year: 2025

With the wide application of computer vision in agriculture, image analysis has become the key to tasks such as crop health monitoring and pest detection. However, the significant domain shifts caused by environmental changes, different crop types, and diverse data acquisition methods seriously hinder the generalization ability of the model in cross-region, cross-season, and complex agricultural scenarios. This paper explores how domain adaptation (DA) techniques can address these challenges to improve cross-domain transferability in agricultural image analysis. DA is considered a promising solution in the case of limited labeled data, insufficient model adaptability, and dynamic changes in the field environment. This paper systematically reviews the latest advances in DA in agricultural images in recent years, focusing on application scenarios such as crop health monitoring, pest and disease detection, and fruit identification, in which DA methods have significantly improved cross-domain performance. We categorize DA methods into shallow learning and deep learning methods, including supervised, semi-supervised and unsupervised strategies, and pay special attention to the adversarial learning-based techniques that perform well in complex scenarios. In addition, this paper also reviews the main public datasets of agricultural images, and evaluates their advantages and limitations in DA research. Overall, this study provides a complete framework and some key insights that can be used as a reference for the research and development of domain adaptation methods in future agricultural vision tasks.