pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics
Authors: Jie Yang, Yiqiu Tang, YongJie Li, Lihua Zhang, Haoran Zhang
Date: 2025-04-05 00:00:00
Tasks: Articles, Decision Making, Management, Sentiment Analysis

Dynamic hedging strategies are essential for effective risk management in derivatives markets, where volatility and market sentiment can greatly impact performance. This paper introduces a novel framework that leverages large language models (LLMs) for sentiment analysis and news analytics to inform hedging decisions. By analyzing textual data from diverse sources like news articles, social media, and financial reports, our approach captures critical sentiment indicators that reflect current market conditions. The framework allows for real-time adjustments to hedging strategies, adapting positions based on continuous sentiment signals. Backtesting results on historical derivatives data reveal that our dynamic hedging strategies achieve superior risk-adjusted returns compared to conventional static approaches. The incorporation of LLM-driven sentiment analysis into hedging practices presents a significant advancement in decision-making processes within derivatives trading. This research showcases how sentiment-informed dynamic hedging can enhance portfolio management and effectively mitigate associated risks.
Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets
Authors: Jie Yang, Yiqiu Tang, YongJie Li, Lihua Zhang, Haoran Zhang
Date: 2025-04-05 00:00:00
Tasks: Articles, Data Integration, Decision Making, Management

Large language models (LLMs) have emerged as powerful tools in the field of finance, particularly for risk management across different asset classes. In this work, we introduce a Cross-Asset Risk Management framework that utilizes LLMs to facilitate real-time monitoring of equity, fixed income, and currency markets. This innovative approach enables dynamic risk assessment by aggregating diverse data sources, ultimately enhancing decision-making processes. Our model effectively synthesizes and analyzes market signals to identify potential risks and opportunities while providing a holistic view of asset classes. By employing advanced analytics, we leverage LLMs to interpret financial texts, news articles, and market reports, ensuring that risks are contextualized within broader market narratives. Extensive backtesting and real-time simulations validate the framework, showing increased accuracy in predicting market shifts compared to conventional methods. The focus on real-time data integration enhances responsiveness, allowing financial institutions to manage risks adeptly under varying market conditions and promoting financial stability through the advanced application of LLMs in risk analysis.
Exploring Various Sequential Learning Methods for Deformation History Modeling
Authors: Muhammed Adil Yatkin, Mihkel Korgesaar, Jani Romanoff, Umit Islak, Hasan Kurban
Date: 2025-04-04 00:00:00

Current neural network (NN) models can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is unknown which NN architectures will perform the best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting deformation localization based on the earlier states in the form of deformation history. Following this investigation, the crucial incompatibility issues between the mathematical computation of the prediction process in the best-performing NN architectures and the actual values derived from the natural physical properties of the deformation paths are examined in detail.
Detecting Plant VOC Traces Using Indoor Air Quality Sensors
Authors: Seyed Hamidreza Nabaei, Ryan Lenfant, Viswajith Govinda Rajan, Dong Chen, Michael P. Timko, Bradford Campbell, Arsalan Heydarian
Date: 2025-04-03 00:00:00

In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount. Smart tools, especially VOC sensors, are crucial for monitoring indoor air quality, yet interpreting signals from various VOC sources remains challenging. A promising approach involves understanding how indoor plants respond to environmental conditions. Plants produce terpenes, a type of VOC, when exposed to abiotic and biotic stressors - including pathogens, predators, light, and temperature - offering a novel pathway for monitoring indoor air quality. While prior work often relies on specialized laboratory sensors, our research leverages readily available commercial sensors to detect and classify plant emitted VOCs that signify changes in indoor conditions. We quantified the sensitivity of these sensors by measuring 16 terpenes in controlled experiments, then identified and tested the most promising terpenes in realistic environments. We also examined physics based models to map VOC responses but found them lacking for real world complexity. Consequently, we trained machine learning models to classify terpenes using commercial sensors and identified optimal sensor placement. To validate this approach, we analyzed emissions from a living basil plant, successfully detecting terpene output. Our findings establish a foundation for overcoming challenges in plant VOC detection, paving the way for advanced plant based sensors to enhance indoor environmental quality in future smart buildings.
Proposed 2MW Wind Turbine for Use in the Governorate of Dhofar at the Sultanate of Oman
Authors: Osama Ahmed Marzouk, Omar Rashid Hamdan Al Badi, Maadh Hamed Salman Al Rashdi, Hamed Mohammed Eid Al Balushi
Date: 2025-03-31 00:00:00

In this work, we propose a preliminary design of a horizontal-axis wind turbine (HAWT) as a candidate for the Dhofar Wind Farm project, in the southern Omani Governorate "Dhofar", at the southwest part of the Sultanate of Oman. This wind farm (under construction) is considered to be the first commercial, utility-scale (50MW) wind farm in the GCC (Gulf Cooperation Council) area. The proposed wind turbine has an expected electricity generation of 2MW. We studied the wind atlas of Oman and from which we determined the maximum possible mean wind speed in the entire Sultanate and built our design based on that reference value, which is 6m/s (21.6km/h). After this, we applied a set of modeling equations that estimate the power output from the wind turbine rotor and matched the target electric power to the design variables using a MATLAB computer code. We reached a suitable design and we present here the distribution of the blade angle (twist angle), and the power per unit span along the rotor blade. The rotor design has 3 blades with a diameter of 70m and a rotational speed of 24rpm. This rotor gives 2.37MW of output power, which exceeds the target 2MW output, allowing for about 15% of power losses in the gearbox and generator. We utilized some commercial designs of wind turbines from different international manufacturers as references for typical limits or recommended values of some design parameters.
RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack
Authors: Weichen Dai, Zijie Dai, Zhijie Huang, Yixuan Pan, Xinhe Li, Xi Li, Yi Zhou, Ji Qi, Wu Jiang
Date: 2025-03-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveraging structured scientific data (e.g., chemical molecular properties in databases) that encapsulate centuries of accumulated scientific expertise. These structured datasets hold strategic significance for advancing AI for Science yet current approaches merely treat them as auxiliary supplements to unstructured text. This study pioneers a systematic investigation into enhancing LLMs with structured scientific data, using chemical molecular science as a testbed. We investigate the impact of incorporating molecular property data on LLM across distinct training phases, including continual pre-training, supervised fine-tuning, and reinforcement learning. Notably, to address the inherent limitation of numerical insensitivity in large models, we propose an innovative methodology termed "Reinforcement Learning with Database Feedback" (RLDBF). Experimental evaluations demonstrate the efficacy of the proposed approach, with the model exhibiting remarkable generalization capabilities on previously unseen data and other chemical tasks. The results substantiate the potential of our method in advancing the field of structured scientific data processing within LLMs.
Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing
Authors: Jan Lewen, Max Pargmann, Jenia Jitsev, Mehdi Cherti, Robert Pitz-Paal, Daniel Maldonado Quinto
Date: 2025-03-28 00:00:00

Concentrating Solar Power (CSP) plants are a key technology in the transition toward sustainable energy. A critical factor for their safe and efficient operation is the distribution of concentrated solar flux on the receiver. However, flux distributions from individual heliostats are sensitive to surface imperfections. Measuring these surfaces across many heliostats remains impractical in real-world deployments. As a result, control systems often assume idealized heliostat surfaces, leading to suboptimal performance and potential safety risks. To address this, inverse Deep Learning Raytracing (iDLR) has been introduced as a novel method for inferring heliostat surface profiles from target images recorded during standard calibration procedures. In this work, we present the first successful Sim-to-Real transfer of iDLR, enabling accurate surface predictions directly from real-world target images. We evaluate our method on 63 heliostats under real operational conditions. iDLR surface predictions achieve a median mean absolute error (MAE) of 0.17 mm and show good agreement with deflectometry ground truth in 84% of cases. When used in raytracing simulations, it enables flux density predictions with a mean accuracy of 90% compared to deflectometry over our dataset, and outperforms the commonly used ideal heliostat surface assumption by 26%. We tested this approach in a challenging double-extrapolation scenario-involving unseen sun positions and receiver projection-and found that iDLR maintains high predictive accuracy, highlighting its generalization capabilities. Our results demonstrate that iDLR is a scalable, automated, and cost-effective solution for integrating realistic heliostat surface models into digital twins. This opens the door to improved flux control, more precise performance modeling, and ultimately, enhanced efficiency and safety in future CSP plants.
Bregman-Hausdorff divergence: strengthening the connections between computational geometry and machine learning
Authors: Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner
Date: 2025-04-09 00:00:00

The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on the family of Bregman divergences, which includes the popular Kullback--Leibler divergence (also known as relative entropy). As a proof of concept, we use the resulting Bregman--Hausdorff divergence to compare two collections of probabilistic predictions produced by different machine learning models trained using the relative entropy loss. The algorithms we propose are surprisingly efficient even for large inputs with hundreds of dimensions. In addition to the introduction of this technical concept, we provide a survey. It outlines the basics of Bregman geometry, as well as computational geometry algorithms. We focus on algorithms that are compatible with this geometry and are relevant for machine learning.
Deep Learning-based Intrusion Detection Systems: A Survey
Authors: Zhiwei Xu, Yujuan Wu, Shiheng Wang, Jiabao Gao, Tian Qiu, Ziqi Wang, Hai Wan, Xibin Zhao
Date: 2025-04-10 00:00:00
Tasks: Deep Learning, Intrusion Detection, Log Parsing, Survey

Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made great progress due to their increasing generalizability. The rationale behind this is that by learning the underlying patterns of known system behaviors, IDS detection can be generalized to intrusions that exploit zero-day vulnerabilities. In this survey, we refer to this type of IDS as DL-based IDS (DL-IDS). From the perspective of DL, this survey systematically reviews all the stages of DL-IDS, including data collection, log storage, log parsing, graph summarization, attack detection, and attack investigation. To accommodate current researchers, a section describing the publicly available benchmark datasets is included. This survey further discusses current challenges and potential future research directions, aiming to help researchers understand the basic ideas and visions of DL-IDS research, as well as to motivate their research interests.
Exploring a Patch-Wise Approach for Privacy-Preserving Fake ID Detection
Authors: Javier Muñoz-Haro, Ruben Tolosana, Ruben Vera-Rodriguez, Aythami Morales, Julian Fierrez
Date: 2025-04-10 00:00:00
Tasks: Privacy Preserving

In an increasingly digitalized world, verifying the authenticity of ID documents has become a critical challenge for real-life applications such as digital banking, crypto-exchanges, renting, etc. This study focuses on the topic of fake ID detection, covering several limitations in the field. In particular, no publicly available data from real ID documents exists, and most studies rely on proprietary in-house databases that are not available due to privacy reasons. In order to shed some light on this critical challenge that makes difficult to advance in the field, we explore a trade-off between privacy (i.e., amount of sensitive data available) and performance, proposing a novel patch-wise approach for privacy-preserving fake ID detection. Our proposed approach explores how privacy can be enhanced through: i) two levels of anonymization for an ID document (i.e., fully- and pseudo-anonymized), and ii) different patch size configurations, varying the amount of sensitive data visible in the patch image. Also, state-of-the-art methods such as Vision Transformers and Foundation Models are considered in the analysis. The experimental framework shows that, on an unseen database (DLC-2021), our proposal achieves 13.91% and 0% EERs at patch and ID document level, showing a good generalization to other databases. In addition to this exploration, another key contribution of our study is the release of the first publicly available database that contains 48,400 patches from both real and fake ID documents, along with the experimental framework and models, which will be available in our GitHub.
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization
Authors: Yang Jiao, Xiaodong Wang, Kai Yang
Date: 2025-04-10 00:00:00
Tasks: Anomaly Detection, Bilevel Optimization, Code Generation, Medical Question Answering, Question Answering, RAG, Retrieval-augmented Generation

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications, e.g., medical question-answering, mathematical sciences, and code generation. However, they also exhibit inherent limitations, such as outdated knowledge and susceptibility to hallucinations. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to address these issues, but it also introduces new vulnerabilities. Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges: (1) their effectiveness declines sharply when only a limited number of poisoned texts can be injected into the knowledge database, (2) they lack sufficient stealth, as the attacks are often detectable by anomaly detection systems, which compromises their effectiveness, and (3) they rely on heuristic approaches to generate poisoned texts, lacking formal optimization frameworks and theoretic guarantees, which limits their effectiveness and applicability. To address these issues, we propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database while embedding a backdoor trigger within the prompt. When activated, the trigger causes the LLM to generate pre-designed responses to targeted queries, while maintaining normal behavior in other contexts. This ensures both high effectiveness and stealth. We formulate the attack generation process as a bilevel optimization problem leveraging a principled optimization framework to develop optimal poisoned texts and triggers. Extensive experiments across diverse LLMs and datasets demonstrate the effectiveness of PR-Attack, achieving a high attack success rate even with a limited number of poisoned texts and significantly improved stealth compared to existing methods.
Privacy-Preserving Vertical K-Means Clustering
Authors: Federico Mazzone, Trevor Brown, Florian Kerschbaum, Kevin H. Wilson, Maarten Everts, Florian Hahn, Andreas Peter
Date: 2025-04-10 00:00:00
Tasks: Clustering, Privacy Preserving

Clustering is a fundamental data processing task used for grouping records based on one or more features. In the vertically partitioned setting, data is distributed among entities, with each holding only a subset of those features. A key challenge in this scenario is that computing distances between records requires access to all distributed features, which may be privacy-sensitive and cannot be directly shared with other parties. The goal is to compute the joint clusters while preserving the privacy of each entity's dataset. Existing solutions using secret sharing or garbled circuits implement privacy-preserving variants of Lloyd's algorithm but incur high communication costs, scaling as O(nkt), where n is the number of data points, k the number of clusters, and t the number of rounds. These methods become impractical for large datasets or several parties, limiting their use to LAN settings only. On the other hand, a different line of solutions rely on differential privacy (DP) to outsource the local features of the parties to a central server. However, they often significantly degrade the utility of the clustering outcome due to excessive noise. In this work, we propose a novel solution based on homomorphic encryption and DP, reducing communication complexity to O(n+kt). In our method, parties securely outsource their features once, allowing a computing party to perform clustering operations under encryption. DP is applied only to the clusters' centroids, ensuring privacy with minimal impact on utility. Our solution clusters 100,000 two-dimensional points into five clusters using only 73MB of communication, compared to 101GB for existing works, and completes in just under 3 minutes on a 100Mbps network, whereas existing works take over 1 day. This makes our solution practical even for WAN deployments, all while maintaining accuracy comparable to plaintext k-means algorithms.
Malware analysis assisted by AI with R2AI
Authors: Axelle Apvrille, Daniel Nakov
Date: 2025-04-10 00:00:00
Tasks: Malware Analysis

This research studies the quality, speed and cost of malware analysis assisted by artificial intelligence. It focuses on Linux and IoT malware of 2024-2025, and uses r2ai, the AI extension of Radare2's disassembler. Not all malware and not all LLMs are equivalent but the study shows excellent results with Claude 3.5 and 3.7 Sonnet. Despite a few errors, the quality of analysis is overall equal or better than without AI assistance. For good results, the AI cannot operate alone and must constantly be guided by an experienced analyst. The gain of speed is largely visible with AI assistance, even when taking account the time to understand AI's hallucinations, exaggerations and omissions. The cost is usually noticeably lower than the salary of a malware analyst, but attention and guidance is needed to keep it under control in cases where the AI would naturally loop without showing progress.
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security
Authors: Caroline Panggabean, Chandrasekar Venkatachalam, Priyanka Shah, Sincy John, Renuka Devi P, Shanmugavalli Venkatachalam
Date: 2025-04-10 00:00:00
Tasks: Intrusion Detection

Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on the UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The proposed approach achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. These findings offer promising advancements in real-time threat detection and contribute to improved network security across various domains.
Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
Authors: Li An, Yujian Liu, Yepeng Liu, Yang Zhang, Yuheng Bu, Shiyu Chang
Date: 2025-04-09 00:00:00
Tasks: Representation Learning

Watermarking has emerged as a promising technique for detecting texts generated by LLMs. Current research has primarily focused on three design criteria: high quality of the watermarked text, high detectability, and robustness against removal attack. However, the security against spoofing attacks remains relatively understudied. For example, a piggyback attack can maliciously alter the meaning of watermarked text-transforming it into hate speech-while preserving the original watermark, thereby damaging the reputation of the LLM provider. We identify two core challenges that make defending against spoofing difficult: (1) the need for watermarks to be both sensitive to semantic-distorting changes and insensitive to semantic-preserving edits, and (2) the contradiction between the need to detect global semantic shifts and the local, auto-regressive nature of most watermarking schemes. To address these challenges, we propose a semantic-aware watermarking algorithm that post-hoc embeds watermarks into a given target text while preserving its original meaning. Our method introduces a semantic mapping model, which guides the generation of a green-red token list, contrastively trained to be sensitive to semantic-distorting changes and insensitive to semantic-preserving changes. Experiments on two standard benchmarks demonstrate strong robustness against removal attacks and security against spoofing attacks, including sentiment reversal and toxic content insertion, while maintaining high watermark detectability. Our approach offers a significant step toward more secure and semantically aware watermarking for LLMs. Our code is available at https://github.com/UCSB-NLP-Chang/contrastive-watermark.