pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management
Authors: Irshad A. Meer, Karl-Ludwig Besser, Mustafa Ozger, Dominic Schupke, H. Vincent Poor, Cicek Cavdar
Date: 2024-12-05 00:00:00
Tasks: Clustering, Deep Reinforcement Learning, Management

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach.
Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice
Authors: Hannes Westermann, Jaromir Savelka
Date: 2024-12-16 00:00:00

Interacting with the legal system and the government requires the assembly and analysis of various pieces of information that can be spread across different (paper) documents, such as forms, certificates and contracts (e.g. leases). This information is required in order to understand one's legal rights, as well as to fill out forms to file claims in court or obtain government benefits. However, finding the right information, locating the correct forms and filling them out can be challenging for laypeople. Large language models (LLMs) have emerged as a powerful technology that has the potential to address this gap, but still rely on the user to provide the correct information, which may be challenging and error-prone if the information is only available in complex paper documents. We present an investigation into utilizing multi-modal LLMs to analyze images of handwritten paper forms, in order to automatically extract relevant information in a structured format. Our initial results are promising, but reveal some limitations (e.g., when the image quality is low). Our work demonstrates the potential of integrating multi-modal LLMs to support laypeople and self-represented litigants in finding and assembling relevant information.
Coexistence Options and Performance Analysis of 100 Gbit/s Coherent PON in Brownfield DWDM Networks
Authors: Gabriele Di Rosa, Martin Kuipers, Jim Zou, Ognjen Jovanovic, Jörg-Peter Elbers
Date: 2024-12-20 00:00:00

We study system architectures for the coexistence of future coherent PON and DWDM networks. Considering deployed optical filters, we observe filtering penalties < 1dB at a laser frequency accuracy < 12GHz when using a cost-effective architecture.
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0
Authors: Ozlem Turgut, Ibrahim Kok, Suat Ozdemir
Date: 2024-12-16 00:00:00
Tasks: counterfactual, Diversity, Edge-computing, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to the diminishing natural resources, the limited arable land, and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML), and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture.
Risk-Sensitive Orbital Debris Collision Avoidance using Distributionally Robust Chance Constraints
Authors: Kanghyun Ryu, Jean-Baptiste Bouvier, Shazaib Lalani, Siegfried Eggl, Negar Mehr
Date: 2024-12-23 00:00:00
Tasks: Collision Avoidance

The exponential increase in orbital debris and active satellites will lead to congested orbits, necessitating more frequent collision avoidance maneuvers by satellites. To minimize fuel consumption while ensuring the safety of satellites, enforcing a chance constraint, which poses an upper bound in collision probability with debris, can serve as an intuitive safety measure. However, accurately evaluating collision probability, which is critical for the effective implementation of chance constraints, remains a non-trivial task. This difficulty arises because uncertainty propagation in nonlinear orbit dynamics typically provides only limited information, such as finite samples or moment estimates about the underlying arbitrary non-Gaussian distributions. Furthermore, even if the full distribution were known, it remains unclear how to effectively compute chance constraints with such non-Gaussian distributions. To address these challenges, we propose a distributionally robust chance-constrained collision avoidance algorithm that provides a sufficient condition for collision probabilities under limited information about the underlying non-Gaussian distribution. Our distributionally robust approach satisfies the chance constraint for all debris position distributions sharing a given mean and covariance, thereby enabling the enforcement of chance constraints with limited distributional information. To achieve computational tractability, the chance constraint is approximated using a Conditional Value-at-Risk (CVaR) constraint, which gives a conservative and tractable approximation of the distributionally robust chance constraint. We validate our algorithm on a real-world inspired satellite-debris conjunction scenario with different uncertainty propagation methods and show that our controller can effectively avoid collisions.
Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
Authors: Benny Bao-Sheng Li, Elena Wu, Hins Shao-Xuan Yang, Nicky Yao-Jin Liang
Date: 2024-12-20 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios.
Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles
Authors: Manele Ait Habouche, Mickaël Kerboeuf, Goulven Guillou, Jean-Philippe Babau
Date: 2024-12-17 00:00:00
Tasks: Autonomous Navigation

Unmanned Surface Vehicles (USVs) have become critical tools for marine exploration, environmental monitoring, and autonomous navigation. Accurate estimation of wave direction is essential for improving USV navigation and ensuring operational safety, but traditional methods often suffer from high costs and limited spatial resolution. This paper proposes a machine learning-based approach leveraging LSTM (Long Short-Term Memory) networks to predict wave direction using sensor data collected from USVs. Experimental results show the capability of the LSTM model to learn temporal dependencies and provide accurate predictions, outperforming simpler baselines.
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning
Authors: Erfan Doroudian, Hamid Taghavifar
Date: 2024-12-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Reinforcement Learning (RL)

This paper presents CLIP-RLDrive, a new reinforcement learning (RL)-based framework for improving the decision-making of autonomous vehicles (AVs) in complex urban driving scenarios, particularly in unsignalized intersections. To achieve this goal, the decisions for AVs are aligned with human-like preferences through Contrastive Language-Image Pretraining (CLIP)-based reward shaping. One of the primary difficulties in RL scheme is designing a suitable reward model, which can often be challenging to achieve manually due to the complexity of the interactions and the driving scenarios. To deal with this issue, this paper leverages Vision-Language Models (VLMs), particularly CLIP, to build an additional reward model based on visual and textual cues.
More complex environments may be required to discover benefits of lifetime learning in evolving robots
Authors: Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
Date: 2024-12-11 00:00:00

It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we investigate this further by comparing it in two different environments: an easy flat environment and a more challenging hills environment. We show that learning is significantly more beneficial in a hilly environment than in a flat environment and that it might be needed to evaluate robots in a more challenging environment to see the benefits of learning.
Coupled differential-algebraic equations framework for modeling six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft
Authors: Osama A. Marzouk
Date: 2024-12-23 00:00:00
Tasks: Computational Efficiency

This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be specified. It supports both direct and inverse flight dynamics analyses. In direct dynamics, the historical profiles of control inputs (deflection angles and engine thrust) are specified, and the resulting flight trajectory is predicted. In inverse dynamics, the desired flight trajectory and an additional constraint are specified to determine the required control inputs. The framework employs wind axes for linear-momentum equations and body axes for angular-momentum equations, incorporates two flight path angles, and provides formulas for aerodynamic force and moment coefficients. Key advantages include improved computational efficiency, elimination of Euler angle singularities, and independence from symmetry assumptions with regard to the aircraft's moments of inertia. The model also accounts for nonlinear air density variations with altitude, up to 20 km above mean sea level, making it suitable for accurate and efficient flight dynamics simulations.
Investigating Prosodic Signatures via Speech Pre-Trained Models for Audio Deepfake Source Attribution
Authors: Orchid Chetia Phukan, Drishti Singh, Swarup Ranjan Behera, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-12-23 00:00:00
Tasks: Audio Deepfake Detection, DeepFake Detection, Face Swapping, Speaker Recognition, speech-recognition, Speech Recognition

In this work, we investigate various state-of-the-art (SOTA) speech pre-trained models (PTMs) for their capability to capture prosodic signatures of the generative sources for audio deepfake source attribution (ADSD). These prosodic characteristics can be considered one of major signatures for ADSD, which is unique to each source. So better is the PTM at capturing prosodic signs better the ADSD performance. We consider various SOTA PTMs that have shown top performance in different prosodic tasks for our experiments on benchmark datasets, ASVSpoof 2019 and CFAD. x-vector (speaker recognition PTM) attains the highest performance in comparison to all the PTMs considered despite consisting lowest model parameters. This higher performance can be due to its speaker recognition pre-training that enables it for capturing unique prosodic characteristics of the sources in a better way. Further, motivated from tasks such as audio deepfake detection and speech recognition, where fusion of PTMs representations lead to improved performance, we explore the same and propose FINDER for effective fusion of such representations. With fusion of Whisper and x-vector representations through FINDER, we achieved the topmost performance in comparison to all the individual PTMs as well as baseline fusion techniques and attaining SOTA performance.
Uncovering the Visual Contribution in Audio-Visual Speech Recognition
Authors: Zhaofeng Lin, Naomi Harte
Date: 2024-12-22 00:00:00
Tasks: Audio-Visual Speech Recognition, Informativeness, speech-recognition, Speech Recognition, Visual Speech Recognition

Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visual contribution, and whether AVSR systems fully exploit the available cues in the visual domain, remains unclear. This paper assesses AVSR systems from a different perspective, by considering human speech perception. We use three systems: Auto-AVSR, AVEC and AV-RelScore. We first quantify the visual contribution using effective SNR gains at 0 dB and then investigate the use of visual information in terms of its temporal distribution and word-level informativeness. We show that low WER does not guarantee high SNR gains. Our results suggest that current methods do not fully exploit visual information, and we recommend future research to report effective SNR gains alongside WERs.
A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voice
Authors: Mary Paterson, James Moor, Luisa Cutillo
Date: 2024-12-20 00:00:00
Tasks: Benchmarking, Diagnostic, Specificity

Cases of laryngeal cancer are predicted to rise significantly in the coming years. Current diagnostic pathways are inefficient, putting undue stress on both patients and the medical system. Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient voice, which could help prioritise referrals more effectively. A major barrier in this field is the lack of reproducible methods. Our work addresses this challenge by introducing a benchmark suite comprising 36 models trained and evaluated on open-source datasets. These models classify patients with benign and malignant voice pathologies. All models are accessible in a public repository, providing a foundation for future research. We evaluate three algorithms and three audio feature sets, including both audio-only inputs and multimodal inputs incorporating demographic and symptom data. Our best model achieves a balanced accuracy of 83.7%, sensitivity of 84.0%, specificity of 83.3%, and AUROC of 91.8%.
Decoding Poultry Vocalizations -- Natural Language Processing and Transformer Models for Semantic and Emotional Analysis
Authors: Venkatraman Manikandan, Suresh Neethirajan
Date: 2024-12-11 00:00:00

Deciphering the acoustic language of chickens offers new opportunities in animal welfare and ecological informatics. Their subtle vocal signals encode health conditions, emotional states, and dynamic interactions within ecosystems. Understanding the semantics of these calls provides a valuable tool for interpreting their functional vocabulary and clarifying how each sound serves a specific purpose in social and environmental contexts. We apply advanced Natural Language Processing and transformer based models to translate bioacoustic data into meaningful insights. Our method integrates Wave2Vec 2.0 for raw audio feature extraction with a fine tuned Bidirectional Encoder Representations from Transformers model, pretrained on a broad corpus of animal sounds and adapted to poultry tasks. This pipeline decodes poultry vocalizations into interpretable categories including distress calls, feeding signals, and mating vocalizations, revealing emotional nuances often overlooked by conventional analyses. Achieving 92 percent accuracy in classifying key vocalization types, our approach demonstrates the feasibility of real time automated monitoring of flock health and stress. By tracking this functional vocabulary, farmers can respond proactively to environmental or behavioral changes, improving poultry welfare, reducing stress related productivity losses, and supporting more sustainable farm management. Beyond agriculture, this research enhances our understanding of computational ecology. Accessing the semantic foundation of animal calls may indicate biodiversity, environmental stressors, and species interactions, informing integrative ecosystem level decision making.
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services
Authors: Danush Venkateshperumal, Rahman Abdul Rafi, Shakil Ahmed, Ashfaq Khokhar
Date: 2024-12-09 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Emergency communication systems face disruptions due to packet loss, bandwidth constraints, poor signal quality, delays, and jitter in VoIP systems, leading to degraded real-time service quality. Victims in distress often struggle to convey critical information due to panic, speech disorders, and background noise, further complicating dispatchers' ability to assess situations accurately. Staffing shortages in emergency centers exacerbate delays in coordination and assistance. This paper proposes leveraging Large Language Models (LLMs) to address these challenges by reconstructing incomplete speech, filling contextual gaps, and prioritizing calls based on severity. The system integrates real-time transcription with Retrieval-Augmented Generation (RAG) to generate contextual responses, using Twilio and AssemblyAI APIs for seamless implementation. Evaluation shows high precision, favorable BLEU and ROUGE scores, and alignment with real-world needs, demonstrating the model's potential to optimize emergency response workflows and prioritize critical cases effectively.
Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge
Authors: Fuxiang Tao, Bahman Mirheidari, Madhurananda Pahar, Sophie Young, Yao Xiao, Hend ElGhazaly, Fritz Peters, Caitlin Illingworth, Dorota Braun, Ronan O'Malley, Simon Bell, Daniel Blackburn, Fasih Haider, Saturnino Luz, Heidi Christensen
Date: 2024-12-05 00:00:00

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.
ChainStream: An LLM-based Framework for Unified Synthetic Sensing
Authors: Jiacheng Liu, Yuanchun Li, Liangyan Li, Yi Sun, Hao Wen, Xiangyu Li, Yao Guo, Yunxin Liu
Date: 2024-12-13 00:00:00
Tasks: Code Generation

Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-concerning for end-users. In this paper, we propose to use natural language as the unified interface to process personal data and sense user context, which can effectively ease app development and make the data pipeline more transparent. Our work is inspired by large language models (LLMs) and other generative models, while directly applying them does not solve the problem - letting the model directly process the data cannot handle complex sensing requests and letting the model write the data processing program suffers error-prone code generation. We address the problem with 1) a unified data processing framework that makes context-sensing programs simpler and 2) a feedback-guided query optimizer that makes data query more informative. To evaluate the performance of natural language-based context sensing, we create a benchmark that contains 133 context sensing tasks. Extensive evaluation has shown that our approach is able to automatically solve the context-sensing tasks efficiently and precisely. The code is opensourced at https://github.com/MobileLLM/ChainStream.
LABIIUM: AI-Enhanced Zero-configuration Measurement Automation System
Authors: Emmanuel A. Olowe, Danial Chitnis
Date: 2024-12-07 00:00:00

The complexity of laboratory environments requires solutions that simplify instrument interaction and enhance measurement automation. Traditional tools often require configuration, software, and programming skills, creating barriers to productivity. Previous approaches, including dedicated software suites and custom scripts, frequently fall short in providing user-friendly solutions that align with programming practices. We present LABIIUM, an AI-enhanced, zero-configuration measurement automation system designed to streamline experimental workflows and improve user productivity. LABIIUM integrates an AI assistant powered by Large Language Models (LLMs) to generate code. LABIIUM's Lab-Automation-Measurement Bridges (LAMBs) enable seamless instrument connectivity using standard tools such as VSCode and Python, eliminating setup overhead. To demonstrate its capabilities, we conducted experiments involving the measurement of the parametric transfer curve of a simple two-transistor inverting amplifier with a current source load. The AI assistant was evaluated using different prompt scenarios and compared with multiple models, including Claude Sonnet 3.5, Gemini Pro 1.5, and GPT-4o. An expert solution implementing the Gradient-Weighted Adaptive Stochastic Sampling (GWASS) method was used as a baseline. The solutions generated by the AI assistant were compared with the expert solution and a uniform linear sweep baseline with 10,000 points. The graph results show that the LLMs were able to successfully complete the most basic uniform sweep, but LLMs were unable to develop adaptive sweeping algorithms to compete with GWASS. The evaluation underscores LABIIUM's ability to enhance laboratory productivity and support digital transformation in research and industry, and emphasizes the future work required to improve LLM performance in Electronic Measurement Science Tasks.