pwc-archive/papers-with-abstracts huggingface dataset:
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis
Authors: Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi
Date: 2025-05-12 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Large Language Models (LLMs) are valued for their strong performance across various tasks, but they also produce inaccurate or misleading outputs. Uncertainty Estimation (UE) quantifies the model's confidence and helps users assess response reliability. However, existing UE methods have not been thoroughly examined in scenarios like Retrieval-Augmented Generation (RAG), where the input prompt includes non-parametric knowledge. This paper shows that current UE methods cannot reliably assess correctness in the RAG setting. We further propose an axiomatic framework to identify deficiencies in existing methods and guide the development of improved approaches. Our framework introduces five constraints that an effective UE method should meet after incorporating retrieved documents into the LLM's prompt. Experimental results reveal that no existing UE method fully satisfies all the axioms, explaining their suboptimal performance in RAG. We further introduce a simple yet effective calibration function based on our framework, which not only satisfies more axioms than baseline methods but also improves the correlation between uncertainty estimates and correctness.
Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation
Authors: Jianan Li, Yangtao Zhou, Zhifu Zhao, Qinglan Huang, Jian Qi, Xiao He, Hua Chu, Fu Li
Date: 2025-05-12 00:00:00
Tasks: Diagnostic, Recommendation Systems

Recommendation systems in AI-based medical diagnostics and treatment constitute a critical component of AI in healthcare. Although some studies have explored this area and made notable progress, healthcare recommendation systems remain in their nascent stage. And these researches mainly target the treatment process such as drug or disease recommendations. In addition to the treatment process, the diagnostic process, particularly determining which medical examinations are necessary to evaluate the condition, also urgently requires intelligent decision support. To bridge this gap, we first formalize the task of medical examination recommendations. Compared to traditional recommendations, the medical examination recommendation involves more complex interactions. This complexity arises from two folds: 1) The historical medical records for examination recommendations are heterogeneous and redundant, which makes the recommendation results susceptible to noise. 2) The correlation between the medical history of patients is often irregular, making it challenging to model spatiotemporal dependencies. Motivated by the above observation, we propose a novel Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation (DST-GKAN) with a two-stage learning paradigm to solve the above challenges. In the first stage, we exploit a task-adaptive diffusion model to distill recommendation-oriented information by reducing the noises in heterogeneous medical data. In the second stage, a spatiotemporal graph KANsformer is proposed to simultaneously model the complex spatial and temporal relationships. Moreover, to facilitate the medical examination recommendation research, we introduce a comprehensive dataset. The experimental results demonstrate the state-of-the-art performance of the proposed method compared to various competitive baselines.
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
Authors: Yi Zhang, Ruihong Qiu, Xuwei Xu, Jiajun Liu, Sen Wang
Date: 2025-05-12 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning (RL)

Model-based offline reinforcement learning (RL) has emerged as a promising approach for recommender systems, enabling effective policy learning by interacting with frozen world models. However, the reward functions in these world models, trained on sparse offline logs, often suffer from inaccuracies. Specifically, existing methods face two major limitations in addressing this challenge: (1) deterministic use of reward functions as static look-up tables, which propagates inaccuracies during policy learning, and (2) static uncertainty designs that fail to effectively capture decision risks and mitigate the impact of these inaccuracies. In this work, a dual-agent framework, DARLR, is proposed to dynamically update world models to enhance recommendation policies. To achieve this, a \textbf{\textit{selector}} is introduced to identify reference users by balancing similarity and diversity so that the \textbf{\textit{recommender}} can aggregate information from these users and iteratively refine reward estimations for dynamic reward shaping. Further, the statistical features of the selected users guide the dynamic adaptation of an uncertainty penalty to better align with evolving recommendation requirements. Extensive experiments on four benchmark datasets demonstrate the superior performance of DARLR, validating its effectiveness. The code is available at https://github.com/ArronDZhang/DARLR.
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
Authors: Yue Meng, Cheng Guo, Yi Cao, Tong Liu, Bo Zheng
Date: 2025-05-12 00:00:00
Tasks: Diversity, Learning-To-Rank, Recommendation Systems, regression, Re-Ranking

E-commerce recommendation systems aim to generate ordered lists of items for customers, optimizing multiple business objectives, such as clicks, conversions and Gross Merchandise Volume (GMV). Traditional multi-objective optimization methods like formulas or Learning-to-rank (LTR) models take effect at item-level, neglecting dynamic user intent and contextual item interactions. List-level multi-objective optimization in the re-ranking stage can overcome this limitation, but most current re-ranking models focus more on accuracy improvement with context. In addition, re-ranking is faced with the challenges of time complexity and diversity. In light of this, we propose a novel end-to-end generative re-ranking model named Sequential Ordered Regression Transformer-Generator (SORT-Gen) for the less-studied list-level multi-objective optimization problem. Specifically, SORT-Gen is divided into two parts: 1)Sequential Ordered Regression Transformer innovatively uses Transformer and ordered regression to accurately estimate multi-objective values for variable-length sub-lists. 2)Mask-Driven Fast Generation Algorithm combines multi-objective candidate queues, efficient item selection and diversity mechanism into model inference, providing a fast online list generation method. Comprehensive online experiments demonstrate that SORT-Gen brings +4.13% CLCK and +8.10% GMV for Baiyibutie, a notable Mini-app of Taobao. Currently, SORT-Gen has been successfully deployed in multiple scenarios of Taobao App, serving for a vast number of users.
A Reinforcement Learning Framework for Application-Specific TCP Congestion-Control
Authors: Jinming Xing, Muhammad Shahzad
Date: 2025-05-11 00:00:00
Tasks: Deep Reinforcement Learning

The Congestion Control (CC) module plays a critical role in the Transmission Control Protocol (TCP), ensuring the stability and efficiency of network data transmission. The CC approaches that are commonly used these days employ heuristics-based rules to adjust the sending rate. Due to their heuristics-based nature, these approaches are not only unable to adapt to changing network conditions but are also agnostic to the diverse requirements that different applications often have. Recently, several learning-based CC approaches have been proposed to adapt to changing network conditions. Unfortunately, they are not designed to take application requirements into account. Prior heuristics-based as well as learning-based CC approaches focus on achieving a singular objective, which is often to maximize throughput, even though a lot of applications care more about latency, packet losses, jitter, and different combinations of various network metrics. Motivated by this, we propose a Deep Reinforcement Learning (DRL) based CC framework, namely ASC, which allows any application to specify any arbitrary objectives that the network traffic of that application should achieve and is able to swiftly adapt to the changes in the objectives of the applications as well as to the changes in the network conditions. Our ASC framework further employs a client-server architecture that serves two purposes: 1) it makes ASC highly scalable in terms of the arrival and departure of TCP connections, and 2) it makes ASC very lightweight for the nodes maintaining the TCP connections. We implemented and extensively evaluated ASC in a variety of settings. Our results show that it can not only achieve various objectives but also outperforms prior approaches even in the specific objectives that those approaches were designed to achieve.
NetSight: Graph Attention Based Traffic Forecasting in Computer Networks
Authors: Jinming Xing, Guoheng Sun, Hui Sun, Linchao Pan, Shakir Mahmood, Xuanhao Luo, Muhammad Shahzad
Date: 2025-05-11 00:00:00
Tasks: Graph Attention

The traffic in today's networks is increasingly influenced by the interactions among network nodes as well as by the temporal fluctuations in the demands of the nodes. Traditional statistical prediction methods are becoming obsolete due to their inability to address the non-linear and dynamic spatio-temporal dependencies present in today's network traffic. The most promising direction of research today is graph neural networks (GNNs) based prediction approaches that are naturally suited to handle graph-structured data. Unfortunately, the state-of-the-art GNN approaches separate the modeling of spatial and temporal information, resulting in the loss of important information about joint dependencies. These GNN based approaches further do not model information at both local and global scales simultaneously, leaving significant room for improvement. To address these challenges, we propose NetSight. NetSight learns joint spatio-temporal dependencies simultaneously at both global and local scales from the time-series of measurements of any given network metric collected at various nodes in a network. Using the learned information, NetSight can then accurately predict the future values of the given network metric at those nodes in the network. We propose several new concepts and techniques in the design of NetSight, such as spatio-temporal adjacency matrix and node normalization. Through extensive evaluations and comparison with prior approaches using data from two large real-world networks, we show that NetSight significantly outperforms all prior state-of-the-art approaches. We will release the source code and data used in the evaluation of NetSight on the acceptance of this paper.
Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization
Authors: Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath, Alex Mathai, Atul Kumar, David Wenk
Date: 2025-05-11 00:00:00
Tasks: Knowledge Graphs

Industries such as banking, telecom, and airlines - o6en have large so6ware systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a so6ware system analysis tool that allows a subject ma=er expert (SME) or system architect to analyze a large so6ware system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent Entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the Entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.
OpenSky Report 2025: Improving Crowdsourced Flight Trajectories with ADS-C Data
Authors: Junzi Sun, Xavier Olive, Martin Strohmeier, Vincent Lenders
Date: 2025-05-01 00:00:00

The OpenSky Network has been collecting and providing crowdsourced air traffic surveillance data since 2013. The network has primarily focused on Automatic Dependent Surveillance--Broadcast (ADS-B) data, which provides high-frequency position updates over terrestrial areas. However, the ADS-B signals are limited over oceans and remote regions, where ground-based receivers are scarce. To address these coverage gaps, the OpenSky Network has begun incorporating data from the Automatic Dependent Surveillance--Contract (ADS-C) system, which uses satellite communication to track aircraft positions over oceanic regions and remote areas. In this paper, we analyze a dataset of over 720,000 ADS-C messages collected in 2024 from around 2,600 unique aircraft via the Alphasat satellite, covering Europe, Africa, and parts of the Atlantic Ocean. We present our approach to combining ADS-B and ADS-C data to construct detailed long-haul flight paths, particularly for transatlantic and African routes. Our findings demonstrate that this integration significantly improves trajectory reconstruction accuracy, allowing for better fuel consumption and emissions estimates. We illustrate how combined data captures flight patterns across previously underrepresented regions across Africa. Despite coverage limitations, this work marks an important advancement in providing open access to global flight trajectory data, enabling new research opportunities in air traffic management, environmental impact assessment, and aviation safety.
Interval reduced-order switched positive observers for uncertain switched positive linear systems
Authors: Naohisa Otsuka, Daiki Kakehi, Przemysław Ignaciuk
Date: 2025-05-09 00:00:00

In this paper, existence conditions and a design procedure of reduced-order switched positive observers for continuous- and discrete-time switched positive linear systems with uncertainty are established. In the analyzed class, arbitrary switching is permitted, whereas the uncertainty expressed via matrix inequalities concerns both the initial state and system parameters. Positive lower and positive upper interval switched observers are obtained. The proposed observers are of (n - p) order, where n is the dimension of the state vector and p is the rank of the output matrix, i.e., p-dimensional measurement information. Moreover, as a special case, existence conditions and a design procedure of reduced-order positive observers for uncertain positive linear systems without switching are provided. The theoretical findings are illustrated by two numerical examples for continuous- and discrete-time systems.
Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers
Authors: Bhuvan Saravanan, Pasanth Kumar M D, Aarnesh Vengateson
Date: 2025-05-07 00:00:00
Tasks: Benchmarking, Fault Detection

Accurate diagnosis of power transformer faults is essential for ensuring the stability and safety of electrical power systems. This study presents a comparative analysis of conventional machine learning (ML) algorithms and deep learning (DL) algorithms for fault classification of power transformers. Using a condition-monitored dataset spanning 10 months, various gas concentration features were normalized and used to train five ML classifiers: Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), XGBoost, and Artificial Neural Network (ANN). In addition, four DL models were evaluated: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Network (1D-CNN), and TabNet. Experimental results show that both ML and DL approaches performed comparably. The RF model achieved the highest ML accuracy at 86.82%, while the 1D-CNN model attained a close 86.30%.
History-Aware Transformation of ReID Features for Multiple Object Tracking
Authors: Ruopeng Gao, Yuyao Wang, Chunxu Liu, LiMin Wang
Date: 2025-03-16 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking

The aim of multiple object tracking (MOT) is to detect all objects in a video and bind them into multiple trajectories. Generally, this process is carried out in two steps: detecting objects and associating them across frames based on various cues and metrics. Many studies and applications adopt object appearance, also known as re-identification (ReID) features, for target matching through straightforward similarity calculation. However, we argue that this practice is overly naive and thus overlooks the unique characteristics of MOT tasks. Unlike regular re-identification tasks that strive to distinguish all potential targets in a general representation, multi-object tracking typically immerses itself in differentiating similar targets within the same video sequence. Therefore, we believe that seeking a more suitable feature representation space based on the different sample distributions of each sequence will enhance tracking performance. In this paper, we propose using history-aware transformations on ReID features to achieve more discriminative appearance representations. Specifically, we treat historical trajectory features as conditions and employ a tailored Fisher Linear Discriminant (FLD) to find a spatial projection matrix that maximizes the differentiation between different trajectories. Our extensive experiments reveal that this training-free projection can significantly boost feature-only trackers to achieve competitive, even superior tracking performance compared to state-of-the-art methods while also demonstrating impressive zero-shot transfer capabilities. This demonstrates the effectiveness of our proposal and further encourages future investigation into the importance and customization of ReID models in multiple object tracking. The code will be released at https://github.com/HELLORPG/HATReID-MOT.
Towards Autonomous 1/8th Offroad RC Racing -- The TruggySense Educational Platform
Authors: Robbe Elsermans, Jan Steckel
Date: 2025-05-12 00:00:00

This paper presents a state-of-the-art Data Acquisition System designed for off-road conditions, deployed on a Team Corally Kagama 1/8 Remote Controlled Vehicle. The system is intended to support Advanced Driver Assistance Systems in an educational context by providing valuable, consistent, and representative data. Key measurement systems are discussed to enable insights into the Remote Controlled Vehicles stability during and after off-road races. Furthermore, four experiments where conducted to evaluate the Data Acquisition Systems accuracy, stability, and consistency in replicating real-world vehicle behavior. The proposed Data Acquisition System platform serves as a solid foundation for use in engineering education, enabling integration with various Advanced Driver Assistance Systems algorithms to enhance vehicle control and overall performance, offering a new dimension to off-road racing. Additionally, realtime telemetry enables verification and validation of Advanced Driver Assistance Systems algorithms based on the live operating state of the Radio Controlled Vehicle during races
Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation
Authors: Gabriele Rosi, Fabio Cermelli
Date: 2025-05-06 00:00:00
Tasks: Open Vocabulary Semantic Segmentation, Prompt Engineering, Segmentation, Semantic Segmentation

Prompt engineering has shown remarkable success with large language models, yet its systematic exploration in computer vision remains limited. In semantic segmentation, both textual and visual prompts offer distinct advantages: textual prompts through open-vocabulary methods allow segmentation of arbitrary categories, while visual reference prompts provide intuitive reference examples. However, existing benchmarks evaluate these modalities in isolation, without direct comparison under identical conditions. We present Show or Tell (SoT), a novel benchmark specifically designed to evaluate both visual and textual prompts for semantic segmentation across 14 datasets spanning 7 diverse domains (common scenes, urban, food, waste, parts, tools, and land-cover). We evaluate 5 open-vocabulary methods and 4 visual reference prompt approaches, adapting the latter to handle multi-class segmentation through a confidence-based mask merging strategy. Our extensive experiments reveal that open-vocabulary methods excel with common concepts easily described by text but struggle with complex domains like tools, while visual reference prompt methods achieve good average results but exhibit high variability depending on the input prompt. Through comprehensive quantitative and qualitative analysis, we identify the strengths and weaknesses of both prompting modalities, providing valuable insights to guide future research in vision foundation models for segmentation tasks.
DGMR: Diffusion Guided Masked Reconstruction Framework for Multimodal Cloud Removal
Authors: Coupled and decoupled learning, diffusion guidance, masked reconstruction, noncloudy difference similarity (NDS)
Date: 2025-05-01 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters 2025 5
Tasks: Cloud Removal

Cloudy conditions affect the quality of captured data by optical satellites. Multimodal techniques rely on synthetic aperture radar (SAR) images to recover cloudy pixels in optical images. These techniques face challenges of noise, modality, and temporal differences. In this work, we propose a diffusion guided masked reconstruction (DGMR) framework for multimodal cloud removal, which consists of a masked reconstruction network (MRNet), conditional diffusion guidance model (CDGM), and noncloudy difference similarity (NDS) soft constraint. DGMR effectively extracts local-global relationships and combines complementary information using MRNet with coupled feature fusion and decoupled masked reconstruction. CDGM guides the intermediate features of MRNet to reconstruct more refined, cloud-free images. NDS ensures that the reconstructed output is consistent with temporal changes. DGMR achieves state-of-the-art results on four widely used benchmarks of the SEN12MS-CR, M3R-CR, and SMILE-CR datasets. The code and trained models are available at https://github.com/chouhan-avinash/DGMR/