pwc-archive/papers-with-abstracts huggingface dataset:
The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy
Authors: Xinyi Hou, Yanjie Zhao, Haoyu Wang
Date: 2025-03-06 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large Language Model (LLM) applications, including LLM app stores and autonomous agents, are shaping the future of AI ecosystems. However, platform silos, fragmented hardware integration, and the absence of standardized interfaces limit scalability, interoperability, and resource efficiency. While LLM app stores democratize AI, their closed ecosystems restrict modular AI reuse and cross-platform portability. Meanwhile, agent-based frameworks offer flexibility but often lack seamless integration across diverse environments. This paper envisions the future of LLM applications and proposes a three-layer decoupled architecture grounded in software engineering principles such as layered system design, service-oriented architectures, and hardware-software co-design. This architecture separates application logic, communication protocols, and hardware execution, enhancing modularity, efficiency, and cross-platform compatibility. Beyond architecture, we highlight key security and privacy challenges for safe, scalable AI deployment and outline research directions in software and security engineering. This vision aims to foster open, secure, and interoperable LLM ecosystems, guiding future advancements in AI applications.
Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study
Authors: Nenad Petrovic, Yurui Zhang, Moaad Maaroufi, Kuo-Yi Chao, Lukasz Mazur, Fengjunjie Pan, Vahid Zolfaghari, Alois Knoll
Date: 2025-03-06 00:00:00

Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. However, the application and advantages of multimodal summarization have not received much attention in model-based engineering (MBE), where it has become a cornerstone in the design and development of complex systems, leveraging formal models to improve understanding, validation and automation throughout the engineering lifecycle. UML and EMF diagrams in model-based engineering contain a large amount of multimodal information and intricate relational data. Hence, our study explores the application of multimodal large language models within the domain of model-based engineering to evaluate their capacity for understanding and identifying relationships, features, and functionalities embedded in UML and EMF diagrams. We aim to demonstrate the transformative potential benefits and limitations of multimodal summarization in improving productivity and accuracy in MBE practices. The proposed approach is evaluated within the context of automotive software development, while many promising state-of-art models were taken into account.
ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions
Authors: Julian Aron Prenner, Romain Robbes
Date: 2025-03-06 00:00:00
Tasks: Benchmarking, HumanEval

Modern Large Language Models (LLMs) have shown astounding capabilities of code understanding and synthesis. In order to assess such capabilities, several benchmarks have been devised (e.g., HumanEval). However, most benchmarks focus on code synthesis from natural language instructions. Hence, such benchmarks do not test for other forms of code understanding. Moreover, there have been concerns about contamination and leakage. That is, benchmark problems (or closely related problems) may appear in training set, strongly biasing benchmark results. In this work we investigate whether large language models can correctly predict runtime program behavior. To this end, we introduce ThrowBench, a benchmark consisting of over 2,400 short user-written programs written in four different programming languages. The majority of these programs throw an exception during runtime (due to a bug). LLMs are asked to predict whether a presented program throws an exception and, if so, which one. Evaluating our benchmark on six state-of-the-art code LLMs we see modest performance ranging from 19 to 38% (F1 score). Benchmarking a wider set of code capabilities could improve the assessment of code LLMs and help identify weak points in current models. Moreover, as ground-truth answers have been determined through program execution, leakage is not a concern. We release ThrowBench as well as all of our results together with this work.
Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
Authors: Simin Chen, Pranav Pusarla, Baishakhi Ray
Date: 2025-03-06 00:00:00
Tasks: Benchmarking

The rapid evolution of code largelanguage models underscores the need for effective and transparent benchmarking of their reasoning capabilities. However, the current benchmarking approach heavily depends on publicly available, human-created datasets. The widespread use of these fixed benchmark datasets makes the benchmarking process to be static and thus particularly susceptible to data contamination, an unavoidable consequence of the extensive data collection processes used to train Code LLMs. Existing approaches that address data contamination often suffer from human effort limitations and imbalanced problem complexity. To tackle these challenges, we propose \tool, a novel benchmarking suite for evaluating Code LLMs under potential data contamination. Given a seed programming problem, \tool employs multiple agents to extract and modify the context without altering the core logic, generating semantically equivalent variations. We introduce a dynamic data generation methods and conduct empirical studies on two seed datasets across 21 Code LLMs. Results show that \tool effectively benchmarks reasoning capabilities under contamination risks while generating diverse problem sets to ensure consistent and reliable evaluations.
WIP: Assessing the Effectiveness of ChatGPT in Preparatory Testing Activities
Authors: Susmita Haldar, Mary Pierce, Luiz Fernando Capretz
Date: 2025-03-05 00:00:00
Tasks: software testing

This innovative practice WIP paper describes a research study that explores the integration of ChatGPT into the software testing curriculum and evaluates its effectiveness compared to human-generated testing artifacts. In a Capstone Project course, students were tasked with generating preparatory testing artifacts using ChatGPT prompts, which they had previously created manually. Their understanding and the effectiveness of the Artificial Intelligence generated artifacts were assessed through targeted questions. The results, drawn from this in-class assignment at a North American community college indicate that while ChatGPT can automate many testing preparation tasks, it cannot fully replace human expertise. However, students, already familiar with Information Technology at the postgraduate level, found the integration of ChatGPT into their workflow to be straightforward. The study suggests that AI can be gradually introduced into software testing education to keep pace with technological advancements.
Analyzing the temporal dynamics of linguistic features contained in misinformation
Authors: Erik J Schlicht
Date: 2025-02-27 00:00:00
Tasks: Misinformation, named-entity-recognition, Named Entity Recognition

Consumption of misinformation can lead to negative consequences that impact the individual and society. To help mitigate the influence of misinformation on human beliefs, algorithmic labels providing context about content accuracy and source reliability have been developed. Since the linguistic features used by algorithms to estimate information accuracy can change across time, it is important to understand their temporal dynamics. As a result, this study uses natural language processing to analyze PolitiFact statements spanning between 2010 and 2024 to quantify how the sources and linguistic features of misinformation change between five-year time periods. The results show that statement sentiment has decreased significantly over time, reflecting a generally more negative tone in PolitiFact statements. Moreover, statements associated with misinformation realize significantly lower sentiment than accurate information. Additional analysis shows that recent time periods are dominated by sources from online social networks and other digital forums, such as blogs and viral images, that contain high levels of misinformation containing negative sentiment. In contrast, most statements during early time periods are attributed to individual sources (i.e., politicians) that are relatively balanced in accuracy ratings and contain statements with neutral or positive sentiment. Named-entity recognition was used to identify that presidential incumbents and candidates are relatively more prevalent in statements containing misinformation, while US states tend to be present in accurate information. Finally, entity labels associated with people and organizations are more common in misinformation, while accurate statements are more likely to contain numeric entity labels, such as percentages and dates.
Improving Access to Trade and Investment Information in Thailand through Intelligent Document Retrieval
Authors: Sirinda Palahan
Date: 2025-03-09 00:00:00
Tasks: Chatbot, Information Retrieval, Natural Language Queries, Navigate, Retrieval

Overseas investment and trade can be daunting for beginners due to the vast amount of complex information. This paper presents a chatbot system that integrates natural language processing and information retrieval techniques to simplify the document retrieval process. The proposed system identifies the most relevant content, enabling users to navigate the intricate landscape of foreign trade and investment more efficiently. Our methodology combines the BM25 model and a deep learning model to rank and retrieve documents, aiming to reduce noise in the document content and enhance the accuracy of the results. Experiments with Thai natural language queries have demonstrated the effectiveness of our system in retrieving pertinent documents. A user satisfaction survey further validated the system's effectiveness. Most respondents found the system helpful and agreed with the suggested documents, indicating its potential as a valuable tool for Thai entrepreneurs navigating foreign trade and investment.
Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation
Authors: Murong Yang, Shihui Ying, Xin-Jian Xu, Yue Gao
Date: 2025-03-08 00:00:00
Tasks: Clustering, Representation Learning, Riemannian optimization

Graph-based multi-view spectral clustering methods have achieved notable progress recently, yet they often fall short in either oversimplifying pairwise relationships or struggling with inefficient spectral decompositions in high-dimensional Euclidean spaces. In this paper, we introduce a novel approach that begins to generate hypergraphs by leveraging sparse representation learning from data points. Based on the generated hypergraph, we propose an optimization function with orthogonality constraints for multi-view hypergraph spectral clustering, which incorporates spectral clustering for each view and ensures consistency across different views. In Euclidean space, solving the orthogonality-constrained optimization problem may yield local maxima and approximation errors. Innovately, we transform this problem into an unconstrained form on the Grassmannian manifold. Finally, we devise an alternating iterative Riemannian optimization algorithm to solve the problem. To validate the effectiveness of the proposed algorithm, we test it on four real-world multi-view datasets and compare its performance with seven state-of-the-art multi-view clustering algorithms. The experimental results demonstrate that our method outperforms the baselines in terms of clustering performance due to its superior low-dimensional and resilient feature representation.
Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration
Authors: Jan Fillies, Adrian Paschke
Date: 2025-03-07 00:00:00
Tasks: Hate Speech Detection

Algorithmic hate speech detection faces significant challenges due to the diverse definitions and datasets used in research and practice. Social media platforms, legal frameworks, and institutions each apply distinct yet overlapping definitions, complicating classification efforts. This study addresses these challenges by demonstrating that existing datasets and taxonomies can be integrated into a unified model, enhancing prediction performance and reducing reliance on multiple specialized classifiers. The work introduces a universal taxonomy and a hate speech classifier capable of detecting a wide range of definitions within a single framework. Our approach is validated by combining two widely used but differently annotated datasets, showing improved classification performance on an independent test set. This work highlights the potential of dataset and taxonomy integration in advancing hate speech detection, increasing efficiency, and ensuring broader applicability across contexts.
Value of Information in Social Learning
Authors: Hiroto Sato, Konan Shimizu
Date: 2025-03-06 00:00:00
Tasks: Relation

This study extends Blackwell's (1953) comparison of information to a sequential social learning model, where agents make decisions sequentially based on both private signals and the observed actions of others. In this context, we introduce a new binary relation over information structures: an information structure is more socially valuable than another if it yields higher expected payoffs for all agents, regardless of their preferences. First, we establish that this binary relation is strictly stronger than the Blackwell order. Then, we provide a necessary and sufficient condition for our binary relation and propose a simpler sufficient condition that is easier to verify.
Unseen Fake News Detection Through Casual Debiasing
Authors: Shuzhi Gong, Richard Sinnott, Jianzhong Qi, Cecile Paris
Date: 2025-03-06 00:00:00
Tasks: Fake News Detection

The widespread dissemination of fake news on social media poses significant risks, necessitating timely and accurate detection. However, existing methods struggle with unseen news due to their reliance on training data from past events and domains, leaving the challenge of detecting novel fake news largely unresolved. To address this, we identify biases in training data tied to specific domains and propose a debiasing solution FNDCD. Originating from causal analysis, FNDCD employs a reweighting strategy based on classification confidence and propagation structure regularization to reduce the influence of domain-specific biases, enhancing the detection of unseen fake news. Experiments on real-world datasets with non-overlapping news domains demonstrate FNDCD's effectiveness in improving generalization across domains.
BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference
Authors: Tao Yang, Yang Hu, Feihong Lu, Ziwei Zhang, Qingyun Sun, JianXin Li
Date: 2025-03-04 00:00:00
Tasks: Causal Inference, Twitter Bot Detection

Social bots have become widely known by users of social platforms. To prevent social bots from spreading harmful speech, many novel bot detections are proposed. However, with the evolution of social bots, detection methods struggle to give high-confidence answers for samples. This motivates us to quantify the uncertainty of the outputs, informing the confidence of the results. Therefore, we propose an uncertainty-aware bot detection method to inform the confidence and use the uncertainty score to pick a high-confidence decision from multiple views of a social network under different environments. Specifically, our proposed BotUmc uses LLM to extract information from tweets. Then, we construct a graph based on the extracted information, the original user information, and the user relationship and generate multiple views of the graph by causal interference. Lastly, an uncertainty loss is used to force the model to quantify the uncertainty of results and select the result with low uncertainty in one view as the final decision. Extensive experiments show the superiority of our method.
Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning
Authors: Kaida Wu, Peihao Xiang, Chaohao Lin, Lixuan Chen, Ou Bai
Date: 2025-03-10 00:00:00
Tasks: Transfer Learning

This paper presents a novel method for real-time lifting-load estimation to enhance the control strategies of upper-limb assistive exoskeletons. By leveraging cost-effective insole pressure sensors, the proposed system extracts differential pressure data that minimizes disturbances from variations in body weight and sensor placement. Two modeling approaches are explored: a channel-based method that employs traditional regression techniques-Elastic Net, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP)-and a map-based method that utilizes transfer learning with a pre-trained MobileNetV2 model. The experiment is in the preliminary test stage, covering load ranges from 2 kg to 10 kg in increments of 0.5 kg, and collecting data from three subjects to test the approach. In the Channel-based method, the average Weighted Mean Absolute Percentage Error(WMAPE) for three subjects showed that the SVR achieved 13.46%, with the MLP performing similarly. In the Map-based method, using data from one subject, the Fully Fine-Tuned MobileNetV2 model reached a WMAPE of 9.74%. The results indicate that the integration of insole sensor technology with advanced machine learning models provides an effective solution for dynamic load estimation, potentially reducing the risks of over- and under-compensation in exoskeleton control.
Sum-of-Squares Data-driven Robustly Stabilizing and Contracting Controller Synthesis for Polynomial Nonlinear Systems
Authors: Hamza El-Kebir, Melkior Ornik
Date: 2025-03-10 00:00:00
Tasks: Autonomous Vehicles

This work presents a computationally efficient approach to data-driven robust contracting controller synthesis for polynomial control-affine systems based on a sum-of-squares program. In particular, we consider the case in which a system alternates between periods of high-quality sensor data and low-quality sensor data. In the high-quality sensor data regime, we focus on robust system identification based on the data informativity framework. In low-quality sensor data regimes we employ a robustly contracting controller that is synthesized online by solving a sum-of-squares program based on data acquired in the high-quality regime, so as to limit state deviation until high-quality data is available. This approach is motivated by real-life control applications in which systems experience periodic data blackouts or occlusion, such as autonomous vehicles undergoing loss of GPS signal or solar glare in machine vision systems. We apply our approach to a planar unmanned aerial vehicle model subject to an unknown wind field, demonstrating its uses for verifiably tight control on trajectory deviation.
Stochastic Tube-based Model Predictive Control for Cyber-Physical Systems under False Data Injection Attacks with Bounded Probability
Authors: Yuzhou Xiao, Senchun Chai, Li Dai, Yuanqing Xia, Runqi Chai
Date: 2025-03-10 00:00:00
Tasks: Model Predictive Control

This paper addresses the challenge of amplitude-unbounded false data injection (FDI) attacks targeting the sensor-to-controller (S-C) channel in cyber-physical systems (CPSs). We introduce a resilient tube-based model predictive control (MPC) scheme. This scheme incorporates a threshold-based attack detector and a control sequence buffer to enhance system security. We mathematically model the common FDI attacks and derive the maximum duration of such attacks based on the hypothesis testing principle. Following this, the minimum feasible sequence length of the control sequence buffer is obtained. The system is proven to remain input-to-state stable (ISS) under bounded external disturbances and amplitude-unbounded FDI attacks. Moreover, the feasible region under this scenario is provided in this paper. Finally, the proposed algorithm is validated by numerical simulations and shows superior control performance compared to the existing methods.
Diagnostic-free onboard battery health assessment
Authors: Yunhong Che, Vivek N. Lam, Jinwook Rhyu, Joachim Schaeffer, Minsu Kim, Martin Z. Bazant, William C. Chueh, Richard D. Braatz
Date: 2025-03-10 00:00:00
Tasks: Battery diagnosis, Decoder, Diagnostic, Interpretable Machine Learning, Prognosis

Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery's current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery's degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. In this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning. We demonstrate the versatility of this model framework by applying it to three battery-cycling datasets consisting of 422 cells under different operating conditions, highlighting the utility of an interpretable diagnostic-free, onboard battery diagnosis and prognosis model.
$\mathcal{H}_\infty$ Loop-shaping for Power Tracking Control of Wind Turbines
Authors: Aaron Grapentin, Christian A. Hans, Jörg Raisch
Date: 2025-03-10 00:00:00

In this paper, we present an advanced wind turbine control scheme for power maximization as well as for active power control, which is designed using $\mathcal{H}_\infty$ loop-shaping. Our approach involves the synthesis of two separate controllers for two different operating modes. To ensure smooth transitions between these modes, we implement a bumpless transfer strategy that reduces transient effects. A comprehensive case study demonstrates the efficacy of our control scheme, showing significant improvements in power tracking accuracy and a reduction in mechanical wear. Moreover, our control strategy comes with robust stability guarantees.
Optimal Control Selection over the Edge-Cloud Continuum
Authors: Xiyu Gu, Matthias Pezzutto, Luca Schenato, Subhrakanti Dey
Date: 2025-03-10 00:00:00

The emerging computing continuum paves the way for exploiting multiple computing devices, ranging from the edge to the cloud, to implement the control algorithm. Different computing units over the continuum are characterized by different computational capabilities and communication latencies, thus resulting in different control performances and advocating for an effective trade-off. To this end, in this work, we first introduce a multi-tiered controller and we propose a simple network delay compensator. Then we propose a control selection policy to optimize the control cost taking into account the delay and the disturbances. We theoretically investigate the stability of the switching system resulting from the proposed control selection policy. Accurate simulations show the improvements of the considered setup.