pwc-archive/papers-with-abstracts huggingface dataset:
CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial Forecasting by Integrating Time Series Patterns and Salient Macroeconomic Announcements
Authors: Yang Zhang, Wenbo Yang, Jun Wang, Qiang Ma, Jie Xiong
Date: 2025-02-07 00:00:00
Tasks: counterfactual, Time Series

Accurately forecasting the impact of macroeconomic events is critical for investors and policymakers. Salient events like monetary policy decisions and employment reports often trigger market movements by shaping expectations of economic growth and risk, thereby establishing causal relationships between events and market behavior. Existing forecasting methods typically focus either on textual analysis or time-series modeling, but fail to capture the multi-modal nature of financial markets and the causal relationship between events and price movements. To address these gaps, we propose CAMEF (Causal-Augmented Multi-Modality Event-Driven Financial Forecasting), a multi-modality framework that effectively integrates textual and time-series data with a causal learning mechanism and an LLM-based counterfactual event augmentation technique for causal-enhanced financial forecasting. Our contributions include: (1) a multi-modal framework that captures causal relationships between policy texts and historical price data; (2) a new financial dataset with six types of macroeconomic releases from 2008 to April 2024, and high-frequency real trading data for five key U.S. financial assets; and (3) an LLM-based counterfactual event augmentation strategy. We compare CAMEF to state-of-the-art transformer-based time-series and multi-modal baselines, and perform ablation studies to validate the effectiveness of the causal learning mechanism and event types.
Physically Interpretable Representation and Controlled Generation for Turbulence Data
Authors: Tiffany Fan, Murray Cutforth, Marta D'Elia, Alexandre Cortiella, Alireza Doostan, Eric Darve
Date: 2025-01-31 00:00:00
Tasks: Dimensionality Reduction

Computational Fluid Dynamics (CFD) plays a pivotal role in fluid mechanics, enabling precise simulations of fluid behavior through partial differential equations (PDEs). However, traditional CFD methods are resource-intensive, particularly for high-fidelity simulations of complex flows, which are further complicated by high dimensionality, inherent stochasticity, and limited data availability. This paper addresses these challenges by proposing a data-driven approach that leverages a Gaussian Mixture Variational Autoencoder (GMVAE) to encode high-dimensional scientific data into low-dimensional, physically meaningful representations. The GMVAE learns a structured latent space where data can be categorized based on physical properties such as the Reynolds number while maintaining global physical consistency. To assess the interpretability of the learned representations, we introduce a novel metric based on graph spectral theory, quantifying the smoothness of physical quantities along the latent manifold. We validate our approach using 2D Navier-Stokes simulations of flow past a cylinder over a range of Reynolds numbers. Our results demonstrate that the GMVAE provides improved clustering, meaningful latent structure, and robust generative capabilities compared to baseline dimensionality reduction methods. This framework offers a promising direction for data-driven turbulence modeling and broader applications in computational fluid dynamics and engineering systems.
From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings
Authors: Tianyang Wang, Silin Chen, Yunze Wang, Yichao Zhang, Xinyuan Song, Ziqian Bi, Ming Liu, Qian Niu, Junyu Liu, Pohsun Feng, Xintian Sun, Benji Peng, Charles Zhang, Keyu Chen, Ming Li, Cheng Fei, Lawrence KQ Yan
Date: 2025-01-24 00:00:00

The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental approaches to ensure their biological relevance. This review explores the various methods and technologies used for experimental validation, including gene expression analysis, protein-protein interaction verification, and pathway validation. We also discuss the challenges involved in translating computational predictions to experimental settings and highlight the importance of collaboration between bioinformatics and experimental research. Finally, emerging technologies, such as CRISPR gene editing, next-generation sequencing, and artificial intelligence, are shaping the future of bioinformatics validation and driving more accurate and efficient biological discoveries.
Reconstructing 3D Flow from 2D Data with Diffusion Transformer
Authors: Fan Lei
Date: 2024-12-20 00:00:00

Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are slow and resource-intensive. In experimental fluid dynamics, PIV cost increases with dimensionality. Reconstructing 3D flow fields from 2D PIV data could reduce costs and expand application scenarios. Here, We propose a Diffusion Transformer-based method for reconstructing 3D flow fields from 2D flow data. By embedding the positional information of 2D planes into the model, we enable the reconstruction of 3D flow fields from any combination of 2D slices, enhancing flexibility. We replace global attention with window and plane attention to reduce computational costs associated with higher dimensions without compromising performance. Our experiments demonstrate that our model can efficiently and accurately reconstruct 3D flow fields from 2D data, producing realistic results.
MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents
Authors: Kaijie Zhu, Xianjun Yang, Jindong Wang, Wenbo Guo, William Yang Wang
Date: 2025-02-07 00:00:00

Recent research has explored that LLM agents are vulnerable to indirect prompt injection (IPI) attacks, where malicious tasks embedded in tool-retrieved information can redirect the agent to take unauthorized actions. Existing defenses against IPI have significant limitations: either require essential model training resources, lack effectiveness against sophisticated attacks, or harm the normal utilities. We present MELON (Masked re-Execution and TooL comparisON), a novel IPI defense. Our approach builds on the observation that under a successful attack, the agent's next action becomes less dependent on user tasks and more on malicious tasks. Following this, we design MELON to detect attacks by re-executing the agent's trajectory with a masked user prompt modified through a masking function. We identify an attack if the actions generated in the original and masked executions are similar. We also include three key designs to reduce the potential false positives and false negatives. Extensive evaluation on the IPI benchmark AgentDojo demonstrates that MELON outperforms SOTA defenses in both attack prevention and utility preservation. Moreover, we show that combining MELON with a SOTA prompt augmentation defense (denoted as MELON-Aug) further improves its performance. We also conduct a detailed ablation study to validate our key designs. Code is available at https://github.com/kaijiezhu11/MELON.
Learning Temporal Invariance in Android Malware Detectors
Authors: Xinran Zheng, Shuo Yang, Edith C. H. Ngai, Suman Jana, Lorenzo Cavallaro
Date: 2025-02-07 00:00:00
Tasks: Contrastive Learning, Learning Theory, Malware Detection

Learning-based Android malware detectors degrade over time due to natural distribution drift caused by malware variants and new families. This paper systematically investigates the challenges classifiers trained with empirical risk minimization (ERM) face against such distribution shifts and attributes their shortcomings to their inability to learn stable discriminative features. Invariant learning theory offers a promising solution by encouraging models to generate stable representations crossing environments that expose the instability of the training set. However, the lack of prior environment labels, the diversity of drift factors, and low-quality representations caused by diverse families make this task challenging. To address these issues, we propose TIF, the first temporal invariant training framework for malware detection, which aims to enhance the ability of detectors to learn stable representations across time. TIF organizes environments based on application observation dates to reveal temporal drift, integrating specialized multi-proxy contrastive learning and invariant gradient alignment to generate and align environments with high-quality, stable representations. TIF can be seamlessly integrated into any learning-based detector. Experiments on a decade-long dataset show that TIF excels, particularly in early deployment stages, addressing real-world needs and outperforming state-of-the-art methods.
Learning the Language of NVMe Streams for Ransomware Detection
Authors: Barak Bringoltz, Elisha Halperin, Ran Feraru, Evgeny Blaichman, Amit Berman
Date: 2025-02-07 00:00:00
Tasks: Language Modeling, Language Modelling, token-classification, Token Classification

We apply language modeling techniques to detect ransomware activity in NVMe command sequences. We design and train two types of transformer-based models: the Command-Level Transformer (CLT) performs in-context token classification to determine whether individual commands are initiated by ransomware, and the Patch-Level Transformer (PLT) predicts the volume of data accessed by ransomware within a patch of commands. We present both model designs and the corresponding tokenization and embedding schemes and show that they improve over state-of-the-art tabular methods by up to 24% in missed-detection rate, 66% in data loss prevention, and 84% in identifying data accessed by ransomware.
Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search
Authors: Zeren Luo, Zifan Peng, Yule Liu, Zhen Sun, Mingchen Li, Jingyi Zheng, Xinlei He
Date: 2025-02-07 00:00:00

Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AIPSEs by systematically defining the threat model, risk type, and evaluating responses to various query types. With data collected from PhishTank, ThreatBook, and LevelBlue, our findings reveal that AIPSEs frequently generate harmful content that contains malicious URLs even with benign queries (e.g., with benign keywords). We also observe that directly querying a URL will increase the number of main risk-inclusive responses, while querying with natural language will slightly mitigate such risk. Compared to traditional search engines, AIPSEs outperform in both utility and safety. We further perform two case studies on online document spoofing and phishing to show the ease of deceiving AIPSEs in the real-world setting. To mitigate these risks, we develop an agent-based defense with a GPT-4.1-based content refinement tool and a URL detector. Our evaluation shows that our defense can effectively reduce the risk, with only a minor cost of reducing available information by approximately 10.7%. Our research highlights the urgent need for robust safety measures in AIPSEs.
On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
Authors: Jaiden Fairoze, Guillermo Ortiz-Jimenez, Mel Vecerik, Somesh Jha, Sven Gowal
Date: 2025-02-07 00:00:00
Tasks: Retrieval

This work investigates the theoretical boundaries of creating publicly-detectable schemes to enable the provenance of watermarked imagery. Metadata-based approaches like C2PA provide unforgeability and public-detectability. ML techniques offer robust retrieval and watermarking. However, no existing scheme combines robustness, unforgeability, and public-detectability. In this work, we formally define such a scheme and establish its existence. Although theoretically possible, we find that at present, it is intractable to build certain components of our scheme without a leap in deep learning capabilities. We analyze these limitations and propose research directions that need to be addressed before we can practically realize robust and publicly-verifiable provenance.
Enhancing SQL Injection Detection and Prevention Using Generative Models
Authors: Naga Sai Dasari, Atta Badii, Armin Moin, Ahmed Ashlam
Date: 2025-02-07 00:00:00

SQL Injection (SQLi) continues to pose a significant threat to the security of web applications, enabling attackers to manipulate databases and access sensitive information without authorisation. Although advancements have been made in detection techniques, traditional signature-based methods still struggle to identify sophisticated SQL injection attacks that evade predefined patterns. As SQLi attacks evolve, the need for more adaptive detection systems becomes crucial. This paper introduces an innovative approach that leverages generative models to enhance SQLi detection and prevention mechanisms. By incorporating Variational Autoencoders (VAE), Conditional Wasserstein GAN with Gradient Penalty (CWGAN-GP), and U-Net, synthetic SQL queries were generated to augment training datasets for machine learning models. The proposed method demonstrated improved accuracy in SQLi detection systems by reducing both false positives and false negatives. Extensive empirical testing further illustrated the ability of the system to adapt to evolving SQLi attack patterns, resulting in enhanced precision and robustness.
Enhancing Phishing Email Identification with Large Language Models
Authors: Catherine Lee
Date: 2025-02-07 00:00:00

Phishing has long been a common tactic used by cybercriminals and continues to pose a significant threat in today's digital world. When phishing attacks become more advanced and sophisticated, there is an increasing need for effective methods to detect and prevent them. To address the challenging problem of detecting phishing emails, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. In this work, we take steps to study the efficacy of large language models (LLMs) in detecting phishing emails. The experiments show that the LLM achieves a high accuracy rate at high precision; importantly, it also provides interpretable evidence for the decisions.
Differential Privacy of Quantum and Quantum-Inspired-Classical Recommendation Algorithms
Authors: Chenjian Li, Mingsheng Ying
Date: 2025-02-07 00:00:00
Tasks: Privacy Preserving

We analyze the DP (differential privacy) properties of the quantum recommendation algorithm and the quantum-inspired-classical recommendation algorithm. We discover that the quantum recommendation algorithm is a privacy curating mechanism on its own, requiring no external noise, which is different from traditional differential privacy mechanisms. In our analysis, a novel perturbation method tailored for SVD (singular value decomposition) and low-rank matrix approximation problems is introduced. Using the perturbation method and random matrix theory, we are able to derive that both the quantum and quantum-inspired-classical algorithms are $\big(\tilde{\mathcal{O}}\big(\frac 1n\big),\,\, \tilde{\mathcal{O}}\big(\frac{1}{\min\{m,n\}}\big)\big)$-DP under some reasonable restrictions, where $m$ and $n$ are numbers of users and products in the input preference database respectively. Nevertheless, a comparison shows that the quantum algorithm has better privacy preserving potential than the classical one.
Confidence Elicitation: A New Attack Vector for Large Language Models
Authors: Brian Formento, Chuan Sheng Foo, See-Kiong Ng
Date: 2025-02-07 00:00:00
Tasks: Adversarial Robustness

A fundamental issue in deep learning has been adversarial robustness. As these systems have scaled, such issues have persisted. Currently, large language models (LLMs) with billions of parameters suffer from adversarial attacks just like their earlier, smaller counterparts. However, the threat models have changed. Previously, having gray-box access, where input embeddings or output logits/probabilities were visible to the user, might have been reasonable. However, with the introduction of closed-source models, no information about the model is available apart from the generated output. This means that current black-box attacks can only utilize the final prediction to detect if an attack is successful. In this work, we investigate and demonstrate the potential of attack guidance, akin to using output probabilities, while having only black-box access in a classification setting. This is achieved through the ability to elicit confidence from the model. We empirically show that the elicited confidence is calibrated and not hallucinated for current LLMs. By minimizing the elicited confidence, we can therefore increase the likelihood of misclassification. Our new proposed paradigm demonstrates promising state-of-the-art results on three datasets across two models (LLaMA-3-8B-Instruct and Mistral-7B-Instruct-V0.3) when comparing our technique to existing hard-label black-box attack methods that introduce word-level substitutions.
An Empirical Study of Code Obfuscation Practices in the Google Play Store
Authors: Akila Niroshan, Suranga Seneviratne, Aruna Seneviratne
Date: 2025-02-07 00:00:00

The Android ecosystem is vulnerable to issues such as app repackaging, counterfeiting, and piracy, threatening both developers and users. To mitigate these risks, developers often employ code obfuscation techniques. However, while effective in protecting legitimate applications, obfuscation also hinders security investigations as it is often exploited for malicious purposes. As such, it is important to understand code obfuscation practices in Android apps. In this paper, we analyze over 500,000 Android APKs from Google Play, spanning an eight-year period, to investigate the evolution and prevalence of code obfuscation techniques. First, we propose a set of classifiers to detect obfuscated code, tools, and techniques and then conduct a longitudinal analysis to identify trends. Our results show a 13% increase in obfuscation from 2016 to 2023, with ProGuard and Allatori as the most commonly used tools. We also show that obfuscation is more prevalent in top-ranked apps and gaming genres such as Casino apps. To our knowledge, this is the first large-scale study of obfuscation adoption in the Google Play Store, providing insights for developers and security analysts.
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
Authors: Abhinav Kumar, George Torres, Noah Guzinski, Gaurav Panwar, Reza Tourani, Satyajayant Misra, Marcin Spoczynski, Mona Vij, Nageen Himayat
Date: 2025-02-07 00:00:00
Tasks: Attribute, Federated Learning, Privacy Preserving, Reconstruction Attack

The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless, these solutions have predominantly been crafted for and assessed in the context of synchronous FL systems, with minimal focus on asynchronous FL. This gap arises in part due to the unique challenges posed by the asynchronous setting, such as the lack of coordinated updates, increased variability in client participation, and the potential for more severe privacy risks. These concerns have stymied the adoption of asynchronous FL. In this work, we first demonstrate the privacy vulnerabilities of asynchronous FL through a novel data reconstruction attack that exploits gradient updates to recover sensitive client data. To address these vulnerabilities, we propose a privacy-preserving framework that combines a gradient obfuscation mechanism with Trusted Execution Environments (TEEs) for secure asynchronous FL aggregation at the network edge. To overcome the limitations of conventional enclave attestation, we introduce a novel data-centric attestation mechanism based on Multi-Authority Attribute-Based Encryption. This mechanism enables clients to implicitly verify TEE-based aggregation services, effectively handle on-demand client participation, and scale seamlessly with an increasing number of asynchronous connections. Our gradient obfuscation mechanism reduces the structural similarity index of data reconstruction by 85% and increases reconstruction error by 400%, while our framework improves attestation efficiency by lowering average latency by up to 1500% compared to RA-TLS, without additional overhead.
Private Federated Learning In Real World Application -- A Case Study
Authors: An Ji, Bortik Bandyopadhyay, Congzheng Song, Natarajan Krishnaswami, Prabal Vashisht, Rigel Smiroldo, Isabel Litton, Sayantan Mahinder, Mona Chitnis, Andrew W Hill
Date: 2025-02-06 00:00:00
Tasks: Federated Learning

This paper presents an implementation of machine learning model training using private federated learning (PFL) on edge devices. We introduce a novel framework that uses PFL to address the challenge of training a model using users' private data. The framework ensures that user data remain on individual devices, with only essential model updates transmitted to a central server for aggregation with privacy guarantees. We detail the architecture of our app selection model, which incorporates a neural network with attention mechanisms and ambiguity handling through uncertainty management. Experiments conducted through off-line simulations and on device training demonstrate the feasibility of our approach in real-world scenarios. Our results show the potential of PFL to improve the accuracy of an app selection model by adapting to changes in user behavior over time, while adhering to privacy standards. The insights gained from this study are important for industries looking to implement PFL, offering a robust strategy for training a predictive model directly on edge devices while ensuring user data privacy.
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
Authors: Yixin Liu, Lie Lu, Jihui Jin, Lichao Sun, Andrea Fanelli
Date: 2025-02-06 00:00:00
Tasks: Audio Synthesis, Face Swapping, Misinformation

The rapid proliferation of generative audio synthesis and editing technologies has raised significant concerns about copyright infringement, data provenance, and the spread of misinformation through deepfake audio. Watermarking offers a proactive solution by embedding imperceptible, identifiable, and traceable marks into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to achieve both robust detection and accurate attribution simultaneously. This paper introduces Cross-Attention Robust Audio Watermark (XAttnMark), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility. Our approach achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing with strong editing strength. The project webpage is available at https://liuyixin-louis.github.io/xattnmark/.