pwc-archive/papers-with-abstracts huggingface dataset:
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface
Authors: Sizhen Bian, Pixi Kang, Julian Moosmann, Mengxi Liu, Pietro Bonazzi, Roman Rosipal, Michele Magno
Date: 2024-08-25 00:00:00
Tasks: Brain Computer Interface, EEG, Eeg Decoding, Electroencephalogram (EEG), Motor Imagery

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decoding, maintaining performance across diverse user populations remains challenging due to feature distribution drift. This paper presents an effective approach to address this challenge by implementing a lightweight and efficient on-device learning engine for wearable motor imagery recognition. The proposed approach, applied to the well-established EEGNet architecture, enables real-time and accurate adaptation to EEG signals from unregistered users. Leveraging the newly released low-power parallel RISC-V-based processor, GAP9 from Greeenwaves, and the Physionet EEG Motor Imagery dataset, we demonstrate a remarkable accuracy gain of up to 7.31\% with respect to the baseline with a memory footprint of 15.6 KByte. Furthermore, by optimizing the input stream, we achieve enhanced real-time performance without compromising inference accuracy. Our tailored approach exhibits inference time of 14.9 ms and 0.76 mJ per single inference and 20 us and 0.83 uJ per single update during online training. These findings highlight the feasibility of our method for edge EEG devices as well as other battery-powered wearable AI systems suffering from subject-dependant feature distribution drift.
How to Measure Human-AI Prediction Accuracy in Explainable AI Systems
Authors: Sujay Koujalgi, Andrew Anderson, Iyadunni Adenuga, Shikha Soneji, Rupika Dikkala, Teresita Guzman Nader, Leo Soccio, Sourav Panda, Rupak Kumar Das, Margaret Burnett, Jonathan Dodge
Date: 2024-08-23 00:00:00
Tasks: Decision Making, Sequential Decision Making

Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space.
Phrasing for UX: Enhancing Information Engagement through Computational Linguistics and Creative Analytics
Authors: Nimrod Dvir
Date: 2024-08-23 00:00:00

This study explores the relationship between textual features and Information Engagement (IE) on digital platforms. It highlights the impact of computational linguistics and analytics on user interaction. The READ model is introduced to quantify key predictors like representativeness, ease of use, affect, and distribution, which forecast engagement levels. The model's effectiveness is validated through AB testing and randomized trials, showing strong predictive performance in participation (accuracy: 0.94), perception (accuracy: 0.85), perseverance (accuracy: 0.81), and overall IE (accuracy: 0.97). While participation metrics are strong, perception and perseverance show slightly lower recall and F1-scores, indicating some challenges. The study demonstrates that modifying text based on the READ model's insights leads to significant improvements. For example, increasing representativeness and positive affect boosts selection rates by 11 percent, raises evaluation averages from 3.98 to 4.46, and improves retention rates by 11 percent. These findings highlight the importance of linguistic factors in IE, providing a framework for enhancing digital text engagement. The research offers practical strategies applicable to fields like education, health, and media.
AttDiCNN: Attentive Dilated Convolutional Neural Network for Automatic Sleep Staging using Visibility Graph and Force-directed Layout
Authors: Md Jobayer, Md. Mehedi Hasan Shawon, Tasfin Mahmud, Md. Borhan Uddin Antor, Arshad M. Chowdhury
Date: 2024-08-21 00:00:00
Tasks: EEG, Sleep Staging

Sleep stages play an essential role in the identification of sleep patterns and the diagnosis of sleep disorders. In this study, we present an automated sleep stage classifier termed the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable automatic sleep staging. We employed a force-directed layout based on the visibility graph to capture the most significant information from the EEG signals, representing the spatial-temporal features. The proposed network consists of three compositors: the Localized Spatial Feature Extraction Network (LSFE), the Spatio-Temporal-Temporal Long Retention Network (S2TLR), and the Global Averaging Attention Network (G2A). The LSFE is tasked with capturing spatial information from sleep data, the S2TLR is designed to extract the most pertinent information in long-term contexts, and the G2A reduces computational overhead by aggregating information from the LSFE and S2TLR. We evaluated the performance of our model on three comprehensive and publicly accessible datasets, achieving state-of-the-art accuracy of 98.56%, 99.66%, and 99.08% for the EDFX, HMC, and NCH datasets, respectively, yet maintaining a low computational complexity with 1.4 M parameters. The results substantiate that our proposed architecture surpasses existing methodologies in several performance metrics, thus proving its potential as an automated tool in clinical settings.
No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data
Authors: Christopher Klugmann, Rafid Mahmood, Guruprasad Hegde, Amit Kale, Daniel Kondermann
Date: 2024-08-19 00:00:00
Tasks: Autonomous Driving

Labeling visual data is expensive and time-consuming. Crowdsourcing systems promise to enable highly parallelizable annotations through the participation of monetarily or otherwise motivated workers, but even this approach has its limits. The solution: replace manual work with machine work. But how reliable are machine annotators? Sacrificing data quality for high throughput cannot be acceptable, especially in safety-critical applications such as autonomous driving. In this paper, we present a framework that enables quality checking of visual data at large scales without sacrificing the reliability of the results. We ask annotators simple questions with discrete answers, which can be highly automated using a convolutional neural network trained to predict crowd responses. Unlike the methods of previous work, which aim to directly predict soft labels to address human uncertainty, we use per-task posterior distributions over soft labels as our training objective, leveraging a Dirichlet prior for analytical accessibility. We demonstrate our approach on two challenging real-world automotive datasets, showing that our model can fully automate a significant portion of tasks, saving costs in the high double-digit percentage range. Our model reliably predicts human uncertainty, allowing for more accurate inspection and filtering of difficult examples. Additionally, we show that the posterior distributions over soft labels predicted by our model can be used as priors in further inference processes, reducing the need for numerous human labelers to approximate true soft labels accurately. This results in further cost reductions and more efficient use of human resources in the annotation process.
Quality Assessment in the Era of Large Models: A Survey
Authors: ZiCheng Zhang, Yingjie Zhou, Chunyi Li, Baixuan Zhao, Xiaohong Liu, Guangtao Zhai
Date: 2024-08-17 00:00:00

Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment typically relied on small expert models tailored for specific tasks. While these smaller models are effective at handling their designated tasks and predicting quality levels, they often lack explainability and robustness. With the advancement of large models, which align more closely with human cognitive and perceptual processes, many researchers are now leveraging the prior knowledge embedded in these large models for quality assessment tasks. This emergence of quality assessment within the context of large models motivates us to provide a comprehensive review focusing on two key aspects: 1) the assessment of large models, and 2) the role of large models in assessment tasks. We begin by reflecting on the historical development of quality assessment. Subsequently, we move to detailed discussions of related works concerning quality assessment in the era of large models. Finally, we offer insights into the future progression and potential pathways for quality assessment in this new era. We hope this survey will enable a rapid understanding of the development of quality assessment in the era of large models and inspire further advancements in the field.
AVIN-Chat: An Audio-Visual Interactive Chatbot System with Emotional State Tuning
Authors: Chanhyuk Park, Jungbin Cho, Junwan Kim, Seongmin Lee, Jungsu Kim, SangHoon Lee
Date: 2024-08-15 00:00:00
Tasks: Chatbot

This work presents an audio-visual interactive chatbot (AVIN-Chat) system that allows users to have face-to-face conversations with 3D avatars in real-time. Compared to the previous chatbot services, which provide text-only or speech-only communications, the proposed AVIN-Chat can offer audio-visual communications providing users with a superior experience quality. In addition, the proposed AVIN-Chat emotionally speaks and expresses according to the user's emotional state. Thus, it enables users to establish a strong bond with the chatbot system, increasing the user's immersion. Through user subjective tests, it is demonstrated that the proposed system provides users with a higher sense of immersion than previous chatbot systems. The demonstration video is available at https://www.youtube.com/watch?v=Z74uIV9k7_k.
Validation of Practicality for CSI Sensing Utilizing Machine Learning
Authors: Tomoya Tanaka, Ayumu Yabuki, Mizuki Funakoshi, Ryo Yonemoto
Date: 2024-09-09 00:00:00

In this study, we leveraged Channel State Information (CSI), commonly utilized in WLAN communication, as training data to develop and evaluate five distinct machine learning models for recognizing human postures: standing, sitting, and lying down. The models we employed were: (i) Linear Discriminant Analysis, (ii) Naive Bayes-Support Vector Machine, (iii) Kernel-Support Vector Machine, (iv) Random Forest, and (v) Deep Learning. We systematically analyzed how the accuracy of these models varied with different amounts of training data. Additionally, to assess their spatial generalization capabilities, we evaluated the models' performance in a setting distinct from the one used for data collection. The experimental findings indicated that while two models -- (ii) Naive Bayes-Support Vector Machine and (v) Deep Learning -- achieved 85% or more accuracy in the original setting, their accuracy dropped to approximately 30% when applied in a different environment. These results underscore that although CSI-based machine learning models can attain high accuracy within a consistent spatial structure, their performance diminishes considerably with changes in spatial conditions, highlighting a significant challenge in their generalization capabilities.
Exact computation of Transfer Entropy with Path Weight Sampling
Authors: Avishek Das, Pieter Rein ten Wolde
Date: 2024-09-03 00:00:00

The ability to quantify the directional flow of information is vital to understanding natural systems and designing engineered information-processing systems. A widely used measure to quantify this information flow is the transfer entropy. However, until now, this quantity could only be obtained in dynamical models using approximations that are typically uncontrolled. Here we introduce a computational algorithm called Transfer Entropy-Path Weight Sampling (TE-PWS), which makes it possible, for the first time, to quantify the transfer entropy and its variants exactly for any stochastic model, including those with multiple hidden variables, nonlinearity, transient conditions, and feedback. By leveraging techniques from polymer and path sampling, TE-PWS efficiently computes the transfer entropy as a Monte-Carlo average over signal trajectory space. We apply TE-PWS to linear and nonlinear systems to reveal how transfer entropy can overcome naive applications of the data processing inequality in the presence of feedback.
Convolutional Beamspace Beamforming for Low-Complexity Far-Field and Near-Field MU-MIMO Communications
Authors: Chao Feng, Huizhi Wang, Yong Zeng
Date: 2024-09-02 00:00:00

Inter-user interference (IUI) mitigation has been an essential issue for multi-user multiple-input multiple-output (MU-MIMO) communications. The commonly used linear processing schemes include the maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean squared error (MMSE) beamforming, which may result in the unfavorable performance or complexity as the antenna number grows. In this paper, we introduce a low-complexity linear beamforming solution for the IUI mitigation by using the convolutional beamspace (CBS) technique. Specifically, the dimension of channel matrix can be significantly reduced via the CBS preprocessing, thanks to its beamspace and spatial filtering effects. However, existing methods of the spatial filter design mainly benefit from the Vandermonde structure of channel matrix, which only holds for the far-field scenario with the uniform plane wave (UPW) model. As the antenna size increases, this characteristic may vanish in the near-field region of the array, where the uniform spherical wave (USW) propagation becomes dominant. To gain useful insights, we first investigate the beamforming design and performance analysis of the CBS-based beamforming based on the UPW model. Our results unveil that the proposed CBS-based MMSE beamforming is able to achieve a near-optimal performance but demands remarkably lower complexity than classical ZF and MMSE schemes. Furthermore, our analysis is also extended to the near-field case. To this end, a novel optimization-based CBS approach is proposed for preserving spatial filtering effects, thus rendering the compatibility of the CBS-based beamforming. Finally, numerical results are provided to demonstrate the effectiveness of our proposed CBS-based beamforming method.
Performance Analysis of Outdoor THz Links under Mixture Gamma Fading with Misalignment
Authors: Hakim Jemaa, Simon Tarboush, Hadi Sarieddeen, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2024-09-01 00:00:00

The precision of link-level theoretical performance analysis for emerging wireless communication paradigms is critical. Recent studies have demonstrated the excellent fitting capabilities of the mixture gamma (MG) distribution in representing small-scale fading in outdoor terahertz (THz)-band scenarios. Our study establishes an in-depth performance analysis for outdoor point-to-point THz links under realistic configurations, incorporating MG small-scale fading combined with the misalignment effect. We derive closed-form expressions for the bit-error probability, outage probability, and ergodic capacity. Furthermore, we conduct an asymptotic analysis of these metrics at high signal-to-noise ratios and derive the necessary convergence conditions. Simulation results, leveraging precise measurement-based channel parameters in various configurations, closely align with the derived analytical equations.
Leveraging parallelizability and channel structure in THz-band, Tbps channel-code decoding
Authors: Hakim Jemaa, Hadi Sarieddeen, Simon Tarboush, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2024-09-01 00:00:00

As advancements close the gap between current device capabilities and the requirements for terahertz (THz)-band communications, the demand for terabit-per-second (Tbps) circuits is on the rise. This paper addresses the challenge of achieving Tbps data rates in THz-band communications by focusing on the baseband computation bottleneck. We propose leveraging parallel processing and pseudo-soft information (PSI) across multicarrier THz channels for efficient channel code decoding. We map bits to transmission resources using shorter code-words to enhance parallelizability and reduce complexity. Additionally, we integrate channel state information into PSI to alleviate the processing overhead of soft decoding. Results demonstrate that PSI-aided decoding of 64-bit code-words halves the complexity of 128-bit hard decoding under comparable effective rates, while introducing a 4 dB gain at a $10^{-3}$ block error rate. The proposed scheme approximates soft decoding with significant complexity reduction at a graceful performance cost.
Online Optimization for Learning to Communicate over Time-Correlated Channels
Authors: Zheshun Wu, Junfan Li, Zenglin Xu, Sumei Sun, Jie Liu
Date: 2024-09-01 00:00:00
Tasks: Generalization Bounds

Machine learning techniques have garnered great interest in designing communication systems owing to their capacity in tackling with channel uncertainty. To provide theoretical guarantees for learning-based communication systems, some recent works analyze generalization bounds for devised methods based on the assumption of Independently and Identically Distributed (I.I.D.) channels, a condition rarely met in practical scenarios. In this paper, we drop the I.I.D. channel assumption and study an online optimization problem of learning to communicate over time-correlated channels. To address this issue, we further focus on two specific tasks: optimizing channel decoders for time-correlated fading channels and selecting optimal codebooks for time-correlated additive noise channels. For utilizing temporal dependence of considered channels to better learn communication systems, we develop two online optimization algorithms based on the optimistic online mirror descent framework. Furthermore, we provide theoretical guarantees for proposed algorithms via deriving sub-linear regret bound on the expected error probability of learned systems. Extensive simulation experiments have been conducted to validate that our presented approaches can leverage the channel correlation to achieve a lower average symbol error rate compared to baseline methods, consistent with our theoretical findings.
Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO
Authors: Sangwoo Park, Ahmet Hasim Gokceoglu, Li Wang, Osvaldo Simeone
Date: 2024-08-26 00:00:00
Tasks: Quantization

The conventional approach to the fronthaul design for cell-free massive MIMO system follows the compress-and-precode (CP) paradigm. Accordingly, encoded bits and precoding coefficients are shared by the distributed unit (DU) on the fronthaul links, and precoding takes place at the radio units (RUs). Previous theoretical work has shown that CP can be potentially improved by a significant margin by precode-and-compress (PC) methods, in which all baseband processing is carried out at the DU, which compresses the precoded signals for transmission on the fronthaul links. The theoretical performance gain of PC methods are particularly pronounced when the DU implements multivariate quantization (MQ), applying joint quantization across the signals for all the RUs. However, existing solutions for MQ are characterized by a computational complexity that grows exponentially with the sum-fronthaul capacity from the DU to all RUs. This work sets out to design scalable MQ strategies for PC-based cell-free massive MIMO systems. For the low-fronthaul capacity regime, we present alpha-parallel MQ (alpha-PMQ), whose complexity is exponential only in the fronthaul capacity towards an individual RU, while performing close to full MQ. alpha-PMQ tailors MQ to the topology of the network by allowing for parallel local quantization steps for RUs that do not interfere too much with each other. For the high-fronthaul capacity regime, we then introduce neural MQ, which replaces the exhaustive search in MQ with gradient-based updates for a neural-network-based decoder, attaining a complexity that grows linearly with the sum-fronthaul capacity. Numerical results demonstrate that the proposed scalable MQ strategies outperform CP for both the low and high-fronthaul capacity regimes at the cost of increased computational complexity at the DU (but not at the RUs).
Extending Machine Learning Based RF Coverage Predictions to 3D
Authors: Muyao Chen, Mathieu Châteauvert, Jonathan Ethier
Date: 2024-08-19 00:00:00

This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed.
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
Authors: Yuhao Pan, Xiucheng Wang, Zhiyao Xu, Nan Cheng, Wenchao Xu, Jun-Jie Zhang
Date: 2024-08-18 00:00:00
Tasks: Distributed Optimization, Efficient Neural Network, Management, Trajectory Planning

Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior information, still faces challenges related to poor planning performance and low distributed execution. These challenges arise when UAVs rely solely on their own observation information and the information from other UAVs within their communicable range, without access to global information. To address these challenges, this paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. The framework utilizes GNNs to extract information from UAVs, users within the observable range, and other UAVs within the communicable range, thereby enabling effective UAV trajectory planning. Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize distributed partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users. By modeling the UAV network optimization problem in terms of AoI and applying the Kolmogorov-Arnold representation theorem, the Qedgix framework achieves efficient neural network training through parameter sharing based on permutation invariance. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users. The code is available at https://github.com/UNIC-Lab/Qedgix.