pwc-archive/papers-with-abstracts huggingface dataset:
From Questions to Insights: Exploring XAI Challenges Reported on Stack Overflow Questions
Authors: Saumendu Roy, Saikat Mondal, Banani Roy, Chanchal Roy
Date: 2025-04-03 00:00:00

The lack of interpretability is a major barrier that limits the practical usage of AI models. Several eXplainable AI (XAI) techniques (e.g., SHAP, LIME) have been employed to interpret these models' performance. However, users often face challenges when leveraging these techniques in real-world scenarios and thus submit questions in technical Q&A forums like Stack Overflow (SO) to resolve these challenges. We conducted an exploratory study to expose these challenges, their severity, and features that can make XAI techniques more accessible and easier to use. Our contributions to this study are fourfold. First, we manually analyzed 663 SO questions that discussed challenges related to XAI techniques. Our careful investigation produced a catalog of seven challenges (e.g., disagreement issues). We then analyzed their prevalence and found that model integration and disagreement issues emerged as the most prevalent challenges. Second, we attempt to estimate the severity of each XAI challenge by determining the correlation between challenge types and answer metadata (e.g., the presence of accepted answers). Our analysis suggests that model integration issues is the most severe challenge. Third, we attempt to perceive the severity of these challenges based on practitioners' ability to use XAI techniques effectively in their work. Practitioners' responses suggest that disagreement issues most severely affect the use of XAI techniques. Fourth, we seek agreement from practitioners on improvements or features that could make XAI techniques more accessible and user-friendly. The majority of them suggest consistency in explanations and simplified integration. Our study findings might (a) help to enhance the accessibility and usability of XAI and (b) act as the initial benchmark that can inspire future research.
Level Up Peer Review in Education: Investigating genAI-driven Gamification system and its influence on Peer Feedback Effectiveness
Authors: Rafal Wlodarski, Leonardo da Silva Sousa, Allison Connell Pensky
Date: 2025-04-03 00:00:00
Tasks: Specificity

In software engineering (SE), the ability to review code and critique designs is essential for professional practice. However, these skills are rarely emphasized in formal education, and peer feedback quality and engagement can vary significantly among students. This paper introduces Socratique, a gamified peer-assessment platform integrated with Generative AI (GenAI) assistance, designed to develop students' peer-review skills in a functional programming course. By incorporating game elements, Socratique aims to motivate students to provide more feedback, while the GenAI assistant offers real-time support in crafting high quality, constructive comments. To evaluate the impact of this approach, we conducted a randomized controlled experiment with master's students comparing a treatment group with a gamified, GenAI-driven setup against a control group with minimal gamification. Results show that students in the treatment group provided significantly more voluntary feedback, with higher scores on clarity, relevance, and specificity - all key aspects of effective code and design reviews. This study provides evidence for the effectiveness of combining gamification and AI to improve peer review processes, with implications for fostering review-related competencies in software engineering curricula.
Graph Network Modeling Techniques for Visualizing Human Mobility Patterns
Authors: Sinjini Mitra, Anuj Srivastava, Avipsa Roy, Pavan Turaga
Date: 2025-04-04 00:00:00
Tasks: Graph Matching

Human mobility analysis at urban-scale requires models to represent the complex nature of human movements, which in turn are affected by accessibility to nearby points of interest, underlying socioeconomic factors of a place, and local transport choices for people living in a geographic region. In this work, we represent human mobility and the associated flow of movements as a grapyh. Graph-based approaches for mobility analysis are still in their early stages of adoption and are actively being researched. The challenges of graph-based mobility analysis are multifaceted - the lack of sufficiently high-quality data to represent flows at high spatial and teporal resolution whereas, limited computational resources to translate large voluments of mobility data into a network structure, and scaling issues inherent in graph models etc. The current study develops a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics. Through experiments, we demonstrate how mobility data collected from taxicab trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
Embedding Method for Knowledge Graph with Densely Defined Ontology
Authors: Takanori Ugai
Date: 2025-04-02 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Retrieval

Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologies, specifically the relationships between properties. This study proposes a KGE model, TransU, designed for knowledge graphs with well-defined ontologies that incorporate relationships between properties. The model treats properties as a subset of entities, enabling a unified representation. We present experimental results using a standard dataset and a practical dataset.
GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media
Authors: Girma Yohannis Bade, Zahra Ahani, Olga Kolesnikova, José Luis Oropeza, Grigori Sidorov
Date: 2025-04-01 00:00:00
Tasks: Abusive Language, regression

The increasing misuse of social media has become a concern; however, technological solutions are being developed to moderate its content effectively. This paper focuses on detecting abusive texts targeting women on social media platforms. Abusive speech refers to communication intended to harm or incite hatred against vulnerable individuals or groups. Specifically, this study aims to identify abusive language directed toward women. To achieve this, we utilized logistic regression and BERT as base models to train datasets sourced from DravidianLangTech@2025 for Tamil and Malayalam languages. The models were evaluated on test datasets, resulting in a 0.729 macro F1 score for BERT and 0.6279 for logistic regression in Tamil and Malayalam, respectively.
Modeling Charging Demand and Quantifying Flexibility Bounds for Large-Scale BEV Fleets
Authors: Maria Parajeles Herrera, Gabriela Hug
Date: 2025-04-04 00:00:00

This paper presents a bottom-up method to model baseline charging power demand and quantify available flexibility for large-scale BEV fleets. The method utilizes geographic and sociodemographic information to represent the fleet's mobility and driving energy needs. It models the charging decisions of drivers based on their driving energy needs and range comfort level using real-world data. The flexibility quantification provides an hourly maximum and minimum bound for the charging power and limits the amount of daily flexible charging energy. We apply the methodology to the future fully electrified fleet of Switzerland as a case study and compare the spatio-temporal characteristics of the charging demand and flexibility of different geographic areas and urbanization levels.
Lambda/6 Suspended Patch Antenna
Authors: Luca Giangrande
Date: 2025-04-04 00:00:00

This work introduces a novel, compact antenna design based on a lambda-6th suspended patch configuration that is particularly suited for small-size wireless sensor nodes. The proposed design meets key requirements such as compactness, omnidirectionality, robust source matching over a designated bandwidth, interference immunity, and low costs by evolving the conventional square patch antenna. With a footprint of only 20-by-20 mm, the antenna incorporates a grounded metal shield to both reduce its effective dimensions below one-half wavelength and mitigate interference from nearby circuitry. Simulation results, conducted on a cost-effective FR4 substrate, demonstrate a resonance at 2.45 GHz with a return loss of -32.5 dB and a bandwidth of 50 MHz (at the -10 dB level), making this design an attractive candidate for integration into densely populated wireless sensor networks.
The Limits of "Fairness" of the Variational Generalized Nash Equilibrium
Authors: Sophie Hall, Florian Dörfler, Heinrich H. Nax, Saverio Bolognani
Date: 2025-04-04 00:00:00
Tasks: Fairness

Generalized Nash equilibrum (GNE) problems are commonly used to model strategic interactions between self-interested agents who are coupled in cost and constraints. Specifically, the variational GNE, a refinement of the GNE, is often selected as the solution concept due to it's non-discriminatory treatment of agents by charging a uniform ``shadow price" for shared resources. We study the fairness concept of v-GNEs from a comparability perspective and show that it makes an implicit assumption of unit comparability of agent's cost functions, one of the strongest comparability notions. Further, we introduce a new solution concept, f-GNE in which a fairness metric is chosen a priori which is compatible with the comparability at hand. We introduce an electric vehicle charging game to demonstrate the fragility of v-GNE fairness and compare it to the f-GNE under various fairness metrics.
State estimation for gas purity monitoring and control in water electrolysis systems
Authors: Lucas Cammann, Johannes Jäschke
Date: 2025-04-04 00:00:00
Tasks: State Estimation

Green hydrogen, produced via water electrolysis using renewable energy, is seen as a cornerstone of the energy transition. Coupling of renewable power supplies to water electrolysis processes is, however, challenging, as explosive gas mixtures (hydrogen in oxygen) might form at low loads. This has prompted research into gas purity control of such systems. While these attempts have shown to be successful in theoretical and practical studies, they are currently limited in that they only consider the gas purity at locations where composition measurements are available. As these locations are generally positioned downstream of the disturbance origin, this incurs considerable delays and can lead to undetected critical conditions. In this work, we propose the use of an Extended Kalman Filter (EKF) in combination with a simple process model to estimate and control the gas composition at locations where measurements are not available. The model uses noise-driven states for the gas impurity and is hence agnostic towards any mechanistic disturbance model. We show in simulations that this simple approach performs well under various disturbance types and can reduce the time spent in potentially hazardous conditions by up to one order of magnitude.
Bifurcation analysis of an opinion dynamics model coupled with an environmental dynamics
Authors: Anthony Couthures, Anastasia Bizyaeva, Vineeth S. Varma, Alessio Franci, Irinel-Constantin Morarescu
Date: 2025-04-04 00:00:00

We consider an opinion dynamics model coupled with an environmental dynamics. Based on a forward invariance argument, we can simplify the analysis of the asymptotic behavior to the case when all the opinions in the social network are synchronized. Our goal is to emphasize the role of the trust given to the environmental signal in the asymptotic behavior of the opinion dynamics and implicitly of the coupled system. To do that, we conduct a bifurcation analysis of the system around the origin when the trust parameter is varying. Specific conditions are presented for both pitchfork and Hopf bifurcation. Numerical illustration completes the theoretical findings.
Leveraging Network Topology in a Two-way Competition for Influence in the Friedkin-Johnsen Model
Authors: Aashi Shrinate, Twinkle Tripathy
Date: 2025-04-04 00:00:00

In this paper, we consider two stubborn agents who compete for `influence' over a strongly connected group of agents. This framework represents real-world contests, such as competition among firms, two-party elections, and sports rivalries, among others. Considering stubbornness of agents to be an immutable property, we utilise the network topology alone to increase the influence of a preferred stubborn agent. We demonstrate this on a special class of strongly connected networks by identifying the supporters of each of the stubborn agents in such networks. Thereafter, we present sufficient conditions under which a network perturbation always increases the influence of the preferred stubborn agent. A key advantage of the proposed topology-based conditions is that they hold independent of the edge weights in the network. Most importantly, we assert that there exists a sequence of perturbations that can make the lesser influential stubborn agent more influential. Finally, we demonstrate our results over the Sampson's Monastery dataset.
Policy Optimization Algorithms in a Unified Framework
Authors: Shuang Wu
Date: 2025-04-04 00:00:00

Policy optimization algorithms are crucial in many fields but challenging to grasp and implement, often due to complex calculations related to Markov decision processes and varying use of discount and average reward setups. This paper presents a unified framework that applies generalized ergodicity theory and perturbation analysis to clarify and enhance the application of these algorithms. Generalized ergodicity theory sheds light on the steady-state behavior of stochastic processes, aiding understanding of both discounted and average rewards. Perturbation analysis provides in-depth insights into the fundamental principles of policy optimization algorithms. We use this framework to identify common implementation errors and demonstrate the correct approaches. Through a case study on Linear Quadratic Regulator problems, we illustrate how slight variations in algorithm design affect implementation outcomes. We aim to make policy optimization algorithms more accessible and reduce their misuse in practice.
Probabilistic State Estimation of Timed Probabilistic Discrete Event Systems via Artificial Neural Networks [Draft Version]
Authors: Omar Amri, Carla Seatzu, Alessandro Giua, Dimitri Lefebvre
Date: 2025-04-04 00:00:00
Tasks: State Estimation

This paper is about the state estimation of timed probabilistic discrete event systems. The main contribution is to propose general procedures for developing state estimation approaches based on artificial neural networks. It is assumed that no formal model of the system exists but a data set is available, which contains the history of the timed behaviour of the systems. This dataset will be exploited to develop a neural network model that uses both logical and temporal information gathered during the functioning of the system as inputs and provides the state probability vector as output. Two main approaches are successively proposed (i) state estimation of timed probabilistic discrete event systems over observations: in this case the state estimate is reconstructed at the occurrence of each new observation; (ii) state estimation of timed probabilistic discrete event systems over time: in this case the state estimate is reconstructed at each clock time increment. For each approach, the paper outlines the process of data preprocessing, model building and implementation. This paper not only proposes groundbreaking approaches but also opens the door to further exploitation of artificial neural networks for the benefit of discrete event systems.
Controllability Analysis of Multi-Modal Acoustic Particle Manipulation in One-Dimensional Standing Waves
Authors: Dongjun Wu, Guilherme Perticarari, Thierry Baasch
Date: 2025-04-04 00:00:00

Acoustic manipulation in microfluidic devices enables contactless handling of biological cells for Lab-on-Chip applications. This paper analyzes the controllability of multi-particle systems in a one-dimensional acoustic standing wave system using multi-modal actuation. By modeling the system as a nonlinear control system, we analyze its global and local controllability, quantifying these properties in terms of mode numbers. Our results show that sufficient modes enable dense reachability sets, while mode mixing with 10 modes grants a strict notion of controllability to 80\% of the state space in a two-particle system. These findings offer theoretical insights for designing acoustic manipulation algorithms, supporting efficient control in biomedical applications.
Learning-Based Conformal Tube MPC for Safe Control in Interactive Multi-Agent Systems
Authors: Shuqi Wang, Yue Gao, Xiang Yin
Date: 2025-04-04 00:00:00
Tasks: Conformal Prediction, Model Predictive Control

Safety assurance in multi-agent systems with coupled dynamics is a fundamental yet challenging problem, especially when agents exhibit uncertain and state-dependent behaviors. Classical robust control often assumes worst-case disturbances, leading to overly conservative actions. In this work, we propose a learning-based framework that combines conformal prediction with model predictive control (MPC) to ensure probabilistic safety under action-level uncertainty. Unlike prior approaches that predict future states, we directly model the control action of the uncontrollable agent as a stochastic function of the joint state, trained via neural networks and calibrated using conformal prediction. This enables us to construct dynamic, probabilistically guaranteed reachable tubes for the uncontrollable agent. These tubes are then embedded into an MPC formulation to synthesize control actions for the controllable agent that ensure safe interactions over a finite planning horizon. We provide formal stepwise and cumulative safety guarantees, and demonstrate the effectiveness of our approach through a pedestrian-vehicle interaction scenario. Compared to baseline methods, our framework achieves higher safety rates while maintaining high performance in terms of speed and responsiveness.
Non-parametric B-spline decoupling of multivariate functions
Authors: Joppe De Jonghe, Mariya Ishteva
Date: 2025-04-04 00:00:00

Many scientific fields and applications require compact representations of multivariate functions. For this problem, decoupling methods are powerful techniques for representing the multivariate functions as a combination of linear transformations and nonlinear univariate functions. This work introduces an efficient decoupling algorithm that leverages the use of B-splines to allow a non-parametric estimation of the decoupling's internal functions. The use of B-splines alleviates the problem of choosing an appropriate basis, as in parametric methods, but still allows an intuitive way to tweak the flexibility of the estimated functions. Besides the non-parametric property, the use of B-spline representations allows for easy integration of nonnegativity or monotonicity constraints on the function shapes, which is not possible for the currently available (non-)parametric decoupling methods. The proposed algorithm is illustrated on synthetic examples that highlight the flexibility of the B-spline representation and the ease with which a monotonicity constraint can be added. The examples also show that if monotonic functions are required, enforcing the constraint is necessary.
On Differential Controllability and Observability Functions
Authors: Yu Kawano, Bart Besselink, Jacquelien M. A. Scherpen
Date: 2025-04-04 00:00:00

Differential balancing theory for nonlinear model reduction relies on differential controllability and observability functions. In this paper, we further investigate them from two different perspectives. First, we establish novel connections between these differential energy functions and their incremental counterparts by assuming the existence of the corresponding optimal state feedback for each controllability function. Specifically, an upper bound on the incremental controllability/observability function is provided by the corresponding differential energy function. Conversely, an upper bound on the differential controllability function can be estimated from the incremental controllability function. Furthermore, the differential observability function can be constructed from the incremental observability function. Second, we explore the positive definiteness of the differential controllability/observability function in the context of controllability/observability and stability.
Data-Driven Hamiltonian for Direct Construction of Safe Set from Trajectory Data
Authors: Jason J. Choi, Christopher A. Strong, Koushil Sreenath, Namhoon Cho, Claire J. Tomlin
Date: 2025-04-04 00:00:00

In continuous-time optimal control, evaluating the Hamiltonian requires solving a constrained optimization problem using the system's dynamics model. Hamilton-Jacobi reachability analysis for safety verification has demonstrated practical utility only when efficient evaluation of the Hamiltonian over a large state-time grid is possible. In this study, we introduce the concept of a data-driven Hamiltonian (DDH), which circumvents the need for an explicit dynamics model by relying only on mild prior knowledge (e.g., Lipschitz constants), thus enabling the construction of reachable sets directly from trajectory data. Recognizing that the Hamiltonian is the optimal inner product between a given costate and realizable state velocities, the DDH estimates the Hamiltonian using the worst-case realization of the velocity field based on the observed state trajectory data. This formulation ensures a conservative approximation of the true Hamiltonian for uncertain dynamics. The reachable set computed based on the DDH is also ensured to be a conservative approximation of the true reachable set. Next, we propose a data-efficient safe experiment framework for gradual expansion of safe sets using the DDH. This is achieved by iteratively conducting experiments within the computed data-driven safe set and updating the set using newly collected trajectory data. To demonstrate the capabilities of our approach, we showcase its effectiveness in safe flight envelope expansion for a tiltrotor vehicle transitioning from near-hover to forward flight.
A Robust Method for Fault Detection and Severity Estimation in Mechanical Vibration Data
Authors: Youngjae Jeon, Eunho Heo, Jinmo Lee, Taewon Uhm, Dongjin Lee
Date: 2025-04-04 00:00:00
Tasks: Fault Detection, Management

This paper proposes a robust method for fault detection and severity estimation in multivariate time-series data to enhance predictive maintenance of mechanical systems. We use the Temporal Graph Convolutional Network (T-GCN) model to capture both spatial and temporal dependencies among variables. This enables accurate future state predictions under varying operational conditions. To address the challenge of fluctuating anomaly scores that reduce fault severity estimation accuracy, we introduce a novel fault severity index based on the mean and standard deviation of anomaly scores. This generates a continuous and reliable severity measurement. We validate the proposed method using two experimental datasets: an open IMS bearing dataset and data collected from a fanjet electric propulsion system. Results demonstrate that our method significantly reduces abrupt fluctuations and inconsistencies in anomaly scores. This provides a more dependable foundation for maintenance planning and risk management in safety-critical applications.