arXiv subjects

Yong Guan

Publications and source records attributed to Yong Guan.

At least 19 recordsLinked to original sources

arXiv2026

CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting

News-driven time series forecasting uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitoring and resource scheduling. In multi-agent settings, two challenges still remain. The first is degeneration of thought, where agents converge to similar evidence-seeking behaviors. The second is insufficient theoretical grounding, where strategy updates are often heuristic and lack a principled formulation. To address the above challenges, we propose CompEvo, a competition-induced evolution framework for multi-agent news-driven time series forecasting. For theoretical grounding, we introduce an evolutionary game formulation to guarantee equilibrium existence and optimization convergence. Building on this formulation, we construct a trainable multi-agent evolution framework that integrates strategy execution, fitness-based differentiable selection, and competition-induced strategy evolution. CompEvo enables heterogeneous agents to explore diverse news evidence, converts forecasting feedback into differentiable influence weights, and evolves agent strategies under competitive pressure to preserve effective logic while maintaining diversity. Experiments on four real-world datasets show that CompEvo reduces RMSE by 27.3% and MAPE by 26.2% on average over strong baselines. Further analysis indicates that CompEvo successfully maintains diverse and specialized agent behaviors.

cs.NE↗

arXiv2026

Static Attribution of Android Residential Proxy Malware Using Graph Kernels

Android residential proxy applications represent a growing class of potentially-unwanted programs (PUPs) that covertly route third-party traffic through end-user devices, enabling ad fraud, credential abuse, and evasion of geolocation controls by sophisticated threat actors. Attributing an unknown APK to a specific proxy network remains challenging due to code reuse, SDK embedding, and obfuscation across proxy families. We present a static-analysis pipeline for automated proxyware family attribution, extracting graph-structured representations (control-flow and function-call graphs) and behavioral signatures from a labeled corpus of 3,365 Android proxy apps spanning four commercial proxy networks. We evaluate Weisfeiler-Lehman graph kernel features alone and fused with binary capability vectors across multiple classifiers. Using 5-fold DEX-grouped cross-validation to prevent data leakage, SGD achieves a macro F1 of 0.985 on the expanded dataset. To support explainability, we map classifier decisions to automatically generated Yara rules, achieving per-family accuracies up to 88.45\% after filtering non-discriminative signatures. Finally, we discuss these results in the context of the broader ecosystem. We find that from the expanded dataset, the majority of applications (51.4\%) still available through APKPure still contain embedded proxy SDK code. Further analysis of developer accounts reveals that 23 developers are responsible for other applications also containing such functionality, suggesting continuous and ongoing commercial relationships between proxy providers and developers.

cs.CR↗

arXiv2025

ReVeal-MT: A Physics-Informed Neural Network for Multi-Transmitter Radio Environment Mapping

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling Secondary Users~(SUs) to access underutilized spectrum bands while protecting Primary Users~(PUs). While existing models have made progress, they often degrade in performance when multiple transmitters coexist, due to the compounded effects of shadowing, interference from adjacent transmitters. To address this challenge, we extend our prior work on Physics-Informed Neural Networks~(PINNs) for single-transmitter mapping to derive a new multi-transmitter Partial Differential Equation~(PDE) formulation of the Received Signal Strength Indicator~(RSSI). We then propose \emph{ReVeal-MT} (Re-constructor and Visualizer of Spectrum Landscape for Multiple Transmitters), a novel PINN which integrates the multi-source PDE residual into a neural network loss function, enabling accurate spectrum landscape reconstruction from sparse RF sensor measurements. ReVeal-MT is validated using real-world measurements from the ARA wireless living lab across rural and suburban environments, and benchmarked against 3GPP and ITU-R channel models and a baseline PINN model for a single transmitter use-case. Results show that ReVeal-MT achieves substantial accuracy gains in multi-transmitter scenarios, e.g., achieving an RMSE of only 2.66\,dB with as few as 45 samples over a 370-square-kilometer region, while maintaining low computational complexity. These findings demonstrate that ReVeal-MT significantly advances radio environment mapping under realistic multi-transmitter conditions, with strong potential for enabling fine-grained spectrum management and precise coexistence between PUs and SUs.

eess.SP↗

arXiv2025

APEX: Automatic Event Sequence Generation for Android Applications

Due to the event driven nature and the versatility of GUI designs in Android programs, it is challenging to generate event sequences with adequate code coverage within a reasonable time. A common approach to handle this issue is to rely on GUI models to generate event sequences. These sequences can be effective in covering GUI states, but inconsistent in exposing program behaviors that require specific inputs. A major obstacle to generate such specific inputs is the lack of a systematic GUI exploration process to accommodate the analysis requirements. In this paper, we introduce Android Path Explorer (APEX), a systematic input generation framework using concolic execution. APEX addresses the limitations of model-based sequence generation by using concolic execution to discover the data dependencies of GUI state transitions. Moreover, concolic execution is also used to prioritize events during the exploration of GUI, which leads to a more robust model and accurate input generation. The key novelty of APEX is that concolic execution is not only used to construct event sequences, but also used to traverse the GUI more systematically. As such, our experimental results show that APEX can be used to generate a set of event sequences that achieve high code coverage, as well as event sequences that reach specific targets.

cs.CR↗

arXiv2025

Design and Implementation of ARA Wireless Living Lab for Rural Broadband and Applications

Addressing the broadband gap between rural and urban regions requires rural-focused wireless research and innovation. In the meantime, rural regions provide rich, diverse use cases of advanced wireless, and they offer unique real-world settings for piloting applications that advance the frontiers of wireless systems (e.g., teleoperation of ground and aerial vehicles). To fill the broadband gap and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity terrestrial wireless x-haul and access platforms as well as low-earth-orbit (LEO) satellite communications platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems, and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment (UE), base stations (BS), edge, and cloud, including support for Bring Your Own Device (BYOD), ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as Open RAN, NextG, and agriculture applications.

cs.NI↗

arXiv2025

ReVeal: A Physics-Informed Neural Network for High-Fidelity Radio Environment Mapping

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling secondary users (SUs) to access underutilized spectrum bands while protecting primary users (PUs). However, current models are either not generalizable due to shadowing, interference, and fading or are computationally too expensive, limiting real-world applicability. To address the shortcomings of existing models, we derive a second-order partial differential equation (PDE) for the Received Signal Strength Indicator (RSSI) based on a statistical model used in the literature. We then propose ReVeal (Re-constructor and Visualizer of Spectrum Landscape), a novel Physics-Informed Neural Network (PINN) that integrates the PDE residual into a neural network loss function to accurately model the radio environment based on sparse RF sensor measurements. ReVeal is validated using real-world measurement data from the rural and suburban areas of the ARA testbed and benchmarked against existing methods.ReVeal outperforms the existing methods in predicting the radio environment; for instance, with a root mean square error (RMSE) of only 1.95 dB, ReVeal achieves an accuracy that is an order of magnitude higher than existing methods such as the 3GPP and ITU-R channel models, ray-tracing, and neural networks. ReVeal achieves both high accuracy and low computational complexity while only requiring sparse RF sampling, for instance, only requiring 30 training sample points across an area of 514 square kilometers.

eess.SP↗

arXiv2024

OpenEP: Open-Ended Future Event Prediction

Future event prediction (FEP) is a long-standing and crucial task in the world, as understanding the evolution of events enables early risk identification, informed decision-making, and strategic planning. Existing work typically treats event prediction as classification tasks and confines the outcomes of future events to a fixed scope, such as yes/no questions, candidate set, and taxonomy, which is difficult to include all possible outcomes of future events. In this paper, we introduce OpenEP (an Open-Ended Future Event Prediction task), which generates flexible and diverse predictions aligned with real-world scenarios. This is mainly reflected in two aspects: firstly, the predictive questions are diverse, covering different stages of event development and perspectives; secondly, the outcomes are flexible, without constraints on scope or format. To facilitate the study of this task, we construct OpenEPBench, an open-ended future event prediction dataset. For question construction, we pose questions from seven perspectives, including location, time, event development, event outcome, event impact, event response, and other, to facilitate an in-depth analysis and understanding of the comprehensive evolution of events. For outcome construction, we collect free-form text containing the outcomes as ground truth to provide semantically complete and detail-enriched outcomes. Furthermore, we propose StkFEP, a stakeholder-enhanced future event prediction framework, that incorporates event characteristics for open-ended settings. Our method extracts stakeholders involved in events to extend questions to gather diverse information. We also collect historically events that are relevant and similar to the question to reveal potential evolutionary patterns. Experiment results indicate that accurately predicting future events in open-ended settings is challenging for existing LLMs.

cs.CL↗

arXiv2024

Wireless Spectrum in Rural Farmlands: Status, Challenges and Opportunities

Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared white spaces such as those in the TVWS and CBRS spectrum bands offer opportunities to expand connectivity, innovate, and provide affordable access to high-speed Internet in under-served areas without additional cost to expensive licensed spectrum. However, the current methods to utilize these white spaces are inefficient due to very conservative models and spectrum policies, causing under-utilization of valuable spectrum resources. This hampers the full potential of innovative wireless technologies that could benefit farmers, small Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) operating in rural regions. This study explores the challenges faced by farmers and service providers when using shared spectrum bands to deploy their networks while ensuring maximum system performance and minimizing interference with other users. Additionally, we discuss how spatiotemporal spectrum models, in conjunction with database-driven spectrum-sharing solutions, can enhance the allocation and management of spectrum resources, ultimately improving the efficiency and reliability of wireless networks operating in shared spectrum bands.

cs.NI↗

arXiv2024

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For \textbf{ability modeling}, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. (2) For \textbf{data}, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For \textbf{evaluation criteria}, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models and a unique self-contrast metric for automatically evaluating knowledge-creating ability. We evaluate $28$ open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset and open-participation leaderboard are publicly released at https://kola.xlore.cn and will be continuously updated to provide references for developing LLMs and knowledge-related systems.

cs.CL↗

arXiv2024

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and codes can be obtained from https://github.com/THU-KEG/MAVEN-Argument.

cs.CL↗

arXiv2024

Event GDR: Event-Centric Generative Document Retrieval

Generative document retrieval, an emerging paradigm in information retrieval, learns to build connections between documents and identifiers within a single model, garnering significant attention. However, there are still two challenges: (1) neglecting inner-content correlation during document representation; (2) lacking explicit semantic structure during identifier construction. Nonetheless, events have enriched relations and well-defined taxonomy, which could facilitate addressing the above two challenges. Inspired by this, we propose Event GDR, an event-centric generative document retrieval model, integrating event knowledge into this task. Specifically, we utilize an exchange-then-reflection method based on multi-agents for event knowledge extraction. For document representation, we employ events and relations to model the document to guarantee the comprehensiveness and inner-content correlation. For identifier construction, we map the events to well-defined event taxonomy to construct the identifiers with explicit semantic structure. Our method achieves significant improvement over the baselines on two datasets, and also hopes to provide insights for future research.

cs.IR↗

arXiv2024

TacoERE: Cluster-aware Compression for Event Relation Extraction

Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information redundancy. To address these issues, we propose a cluster-aware compression method for improving event relation extraction (TacoERE), which explores a compression-then-extraction paradigm. Specifically, we first introduce document clustering for modeling event dependencies. It splits the document into intra- and inter-clusters, where intra-clusters aim to enhance the relations within the same cluster, while inter-clusters attempt to model the related events at arbitrary distances. Secondly, we utilize cluster summarization to simplify and highlight important text content of clusters for mitigating information redundancy and event distance. We have conducted extensive experiments on both pre-trained language models, such as RoBERTa, and large language models, such as ChatGPT and GPT-4, on three ERE datasets, i.e., MAVEN-ERE, EventStoryLine and HiEve. Experimental results demonstrate that TacoERE is an effective method for ERE.

cs.CL↗

arXiv2024

Improving behavior based authentication against adversarial attack using XAI

In recent years, machine learning models, especially deep neural networks, have been widely used for classification tasks in the security domain. However, these models have been shown to be vulnerable to adversarial manipulation: small changes learned by an adversarial attack model, when applied to the input, can cause significant changes in the output. Most research on adversarial attacks and corresponding defense methods focuses only on scenarios where adversarial samples are directly generated by the attack model. In this study, we explore a more practical scenario in behavior-based authentication, where adversarial samples are collected from the attacker. The generated adversarial samples from the model are replicated by attackers with a certain level of discrepancy. We propose an eXplainable AI (XAI) based defense strategy against adversarial attacks in such scenarios. A feature selector, trained with our method, can be used as a filter in front of the original authenticator. It filters out features that are more vulnerable to adversarial attacks or irrelevant to authentication, while retaining features that are more robust. Through comprehensive experiments, we demonstrate that our XAI based defense strategy is effective against adversarial attacks and outperforms other defense strategies, such as adversarial training and defensive distillation.

cs.CR↗

arXiv2024

Knowledge-Aware Neuron Interpretation for Scene Classification

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and more attentions. However, current methods rarely incorporate external knowledge and still suffer from three limitations: (1) Neglecting concept completeness. Merely selecting concepts may not sufficient for prediction. (2) Lacking concept fusion. Failure to merge semantically-equivalent concepts. (3) Difficult in manipulating model behavior. Lack of verification for explanation on original model. To address these issues, we propose a novel knowledge-aware neuron interpretation framework to explain model predictions for image scene classification. Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts. Our method, incorporating complete concepts, effectively provides better prediction explanations compared to baselines. Furthermore, for concept fusion, we introduce a knowledge graph-based method known as Concept Filtering, which produces over 23% point gain on neuron behaviors for neuron interpretation. At last, we propose Model Manipulation, which aims to study whether the core concepts based on ConceptNet could be employed to manipulate model behavior. The results show that core concepts can effectively improve the performance of original model by over 26%.

cs.CV↗

arXiv2023

Formalization of Robot Collision Detection Method based on Conformal Geometric Algebra

Cooperative robots can significantly assist people in their productive activities, improving the quality of their works. Collision detection is vital to ensure the safe and stable operation of cooperative robots in productive activities. As an advanced geometric language, conformal geometric algebra can simplify the construction of the robot collision model and the calculation of collision distance. Compared with the formal method based on conformal geometric algebra, the traditional method may have some defects which are difficult to find in the modelling and calculation. We use the formal method based on conformal geometric algebra to study the collision detection problem of cooperative robots. This paper builds formal models of geometric primitives and the robot body based on the conformal geometric algebra library in HOL Light. We analyse the shortest distance between geometric primitives and prove their collision determination conditions. Based on the above contents, we construct a formal verification framework for the robot collision detection method. By the end of this paper, we apply the proposed framework to collision detection between two single-arm industrial cooperative robots. The flexibility and reliability of the proposed framework are verified by constructing a general collision model and a special collision model for two single-arm industrial cooperative robots.

cs.RO↗

arXiv2023

A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)

The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.

cs.LG↗

arXiv2021

Reasoning about Iteration and Recursion Uniformly based on Big-step Semantics

A reliable technique for deductive program verification should be proven sound with respect to the semantics of the programming language. For each different language, the construction of a separate soundness proof is often a laborious undertaking. In language-independent program verification, common aspects of computer programs are addressed to enable sound reasoning for all languages. In this work, we propose a solution for the sound reasoning about iteration and recursion based on the big-step operational semantics of any programming language. We give inductive proofs on the soundness and relative completeness of our reasoning technique. We illustrate the technique at simplified programming languages of the imperative and functional paradigms, with diverse features. We also mechanism all formal results in the Coq proof assistant.

cs.PL↗

arXiv2020

Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in $100^\circ$ limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from $2.55 \times 10^{-3}$ μm$^{-1}$ in the FBP reconstruction to $1.21 \times 10^{-3}$ μm$^{-1}$ in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to $1.16 \times 10^{-3}$ μm$^{-1}$ and 0.932, respectively. For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials science.

eess.IV↗