pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
Authors: Ivania Donoso-Guzmán, Jeroen Ooge, Denis Parra, Katrien Verbert
Date: 2023-07-31 00:00:00
Tasks: Recommendation Systems

While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not assess XAI methods holistically in the sense that they do not treat explanations' effects on humans as a complex user experience. To tackle this challenge, we propose to adapt the User-Centric Evaluation Framework used in recommender systems: we integrate explanation aspects, summarise explanation properties, indicate relations between them, and categorise metrics that measure these properties. With this comprehensive evaluation framework, we hope to contribute to the human-centred standardisation of XAI evaluation.
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse
Authors: Vinay Chamola, Gaurang Bansal, Tridib Kumar Das, Vikas Hassija, Naga Siva Sai Reddy, Jiacheng Wang, Sherali Zeadally, Amir Hussain, F. Richard Yu, Mohsen Guizani, Dusit Niyato
Date: 2023-07-28 00:00:00
Tasks: Image Generation, Text Generation

Imagine stepping into a virtual world that's as rich, dynamic, and interactive as our physical one. This is the promise of the Metaverse, and it's being brought to life by the transformative power of Generative Artificial Intelligence (AI). This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We explore the role of image generation models such as DALL-E and MidJourney in creating visually stunning and diverse content. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects that enrich the Metaverse experience. But the journey doesn't stop there. We also address the challenges and ethical considerations of implementing these technologies in the Metaverse, offering insights into the balance between user control and AI automation. This paper is not just a study, but a guide to the future of the Metaverse, offering readers a roadmap to harnessing the power of generative AI in creating immersive virtual worlds.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
Authors: Xingyu Zhou, Le Liang, Jing Zhang, Chao-Kai Wen, Shi Jin
Date: 2023-08-12 00:00:00
Tasks: Bayesian Inference

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov's gradient method. Nesterov's GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov's GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.
VoxBlink: A Large Scale Speaker Verification Dataset on Camera
Authors: Yuke Lin, Xiaoyi Qin, Guoqing Zhao, Ming Cheng, Ning Jiang, Haiyang Wu, Ming Li
Date: 2023-08-14 00:00:00
Tasks: Speaker Recognition, Speaker Verification

In this paper, we introduce a large-scale and high-quality audio-visual speaker verification dataset, named VoxBlink. We propose an innovative and robust automatic audio-visual data mining pipeline to curate this dataset, which contains 1.45M utterances from 38K speakers. Due to the inherent nature of automated data collection, introducing noisy data is inevitable. Therefore, we also utilize a multi-modal purification step to generate a cleaner version of the VoxBlink, named VoxBlink-clean, comprising 18K identities and 1.02M utterances. In contrast to the VoxCeleb, the VoxBlink sources from short videos of ordinary users, and the covered scenarios can better align with real-life situations. To our best knowledge, the VoxBlink dataset is one of the largest publicly available speaker verification datasets. Leveraging the VoxCeleb and VoxBlink-clean datasets together, we employ diverse speaker verification models with multiple architectural backbones to conduct comprehensive evaluations on the VoxCeleb test sets. Experimental results indicate a substantial enhancement in performance,ranging from 12% to 30% relatively, across various backbone architectures upon incorporating the VoxBlink-clean into the training process. The details of the dataset can be found on http://voxblink.github.io
Localization of DOA trajectories -- Beyond the grid
Authors: Ruchi Pandey, Santosh Nannuru
Date: 2023-08-14 00:00:00
Tasks: Computational Efficiency

The direction of arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper we propose two trajectory models, namely the polynomial and bandlimited trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, we adopt two gridless algorithms: i) Sliding Frank-Wolfe (SFW), which solves the Beurling LASSO problem and ii) Newtonized Orthogonal Matching Pursuit (NOMP), which improves over OMP using cyclic refinement. Furthermore, we extend our analysis to include wideband processing. The simulation results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency.
Compositional nonlinear audio signal processing with Volterra series
Authors: Jake Araujo-Simon
Date: 2023-08-14 00:00:00
Tasks: Audio Signal Processing

We present a compositional theory of nonlinear audio signal processing based on a categorification of the Volterra series. We begin by augmenting the classical definition of the Volterra series so that it is functorial with respect to a base category whose objects are temperate distributions and whose morphisms are certain linear transformations. This motivates the derivation of formulae describing how the outcomes of nonlinear transformations are affected if their input signals are linearly processed--e.g., translated, modulated, sampled, or periodized. We then consider how nonlinear systems, themselves, change, and introduce as a model thereof the notion of morphism of Volterra series, which we exhibit as both a type of lens map and natural transformation. We show how morphisms can be parameterized and used to generate indexed families of Volterra series, which are well-suited to model nonstationary or time-varying nonlinear phenomena. We then describe how Volterra series and their morphisms organize into a category, which we call Volt. We exhibit the operations of sum, product, and series composition of Volterra series as monoidal products on Volt, and identify, for each in turn, its corresponding universal property. In particular, we show that the series composition of Volterra series is associative. We then bridge between our framework and the subject at the heart of audio signal processing: time-frequency analysis. Specifically, we show that a known equivalence, between a class of second-order Volterra series and the bilinear time-frequency distributions, can be extended to one between certain higher-order Volterra series and the so-called polynomial TFDs. We end by outlining potential avenues for future work, including the incorporation of system identification techniques and the potential extension of our theory to the settings of graph and topological audio signal processing.
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
Authors: Wen Wu, Chao Zhang, Philip C. Woodland
Date: 2023-08-14 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, speech-recognition, Speech Recognition

Although automatic emotion recognition (AER) has recently drawn significant research interest, most current AER studies use manually segmented utterances, which are usually unavailable for dialogue systems. This paper proposes integrating AER with automatic speech recognition (ASR) and speaker diarisation (SD) in a jointly-trained system. Distinct output layers are built for four sub-tasks including AER, ASR, voice activity detection and speaker classification based on a shared encoder. Taking the audio of a conversation as input, the integrated system finds all speech segments and transcribes the corresponding emotion classes, word sequences, and speaker identities. Two metrics are proposed to evaluate AER performance with automatic segmentation based on time-weighted emotion and speaker classification errors. Results on the IEMOCAP dataset show that the proposed system consistently outperforms two baselines with separately trained single-task systems on AER, ASR and SD.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
Authors: Stefan Uhlich, Giorgio Fabbro, Masato Hirano, Shusuke Takahashi, Gordon Wichern, Jonathan Le Roux, Dipam Chakraborty, Sharada Mohanty, Kai Li, Yi Luo, Jianwei Yu, Rongzhi Gu, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Mikhail Sukhovei, Yuki Mitsufuji
Date: 2023-08-14 00:00:00

This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the structure of the competition and the datasets used. Especially, we detail CDXDB23, a new hidden dataset constructed from real movies that was used to rank the submissions. The paper also offers insights into the most successful approaches employed by participants. Compared to the cocktail-fork baseline, the best-performing system trained exclusively on the simulated Divide and Remaster (DnR) dataset achieved an improvement of 1.8 dB in SDR, whereas the top-performing system on the open leaderboard, where any data could be used for training, saw a significant improvement of 5.7 dB. A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
Authors: Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco Martínez-Ramírez, WeiHsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada Mohanty, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji
Date: 2023-08-14 00:00:00
Tasks: Music Source Separation

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding. We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers and musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.
Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface
Authors: Wenqiang Lai, Qihan Yang, Ye Mao, Endong Sun, Jiangnan Ye
Date: 2023-08-07 00:00:00

Voice disorders affect millions of people worldwide. Surface electromyography-based Silent Speech Interfaces (sEMG-based SSIs) have been explored as a potential solution for decades. However, previous works were limited by small vocabularies and manually extracted features from raw data. To address these limitations, we propose a lightweight deep learning knowledge-distilled ensemble model for sEMG-based SSI (KDE-SSI). Our model can classify a 26 NATO phonetic alphabets dataset with 3900 data samples, enabling the unambiguous generation of any English word through spelling. Extensive experiments validate the effectiveness of KDE-SSI, achieving a test accuracy of 85.9\%. Our findings also shed light on an end-to-end system for portable, practical equipment.
Immersive Human-Machine Teleoperation Framework for Precision Agriculture: Integrating UAV-based Digital Mapping and Virtual Reality Control
Authors: Tao Liu, Baohua Zhang, Qianqiu Tan, Jun Zhou, Shuwan Yu, Qingzhen Zhu, Yifan Bian
Date: 2023-08-14 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

In agricultural settings, the unstructured nature of certain production environments, along with the high complexity and inherent risks of production tasks, poses significant challenges to achieving full automation and effective on-site machine control. Remote control technology, which leverages human intelligence and precise machine movements, ensures operator safety and boosts productivity. Recently, virtual reality (VR) has shown promise in remote control applications by overcoming single-view limitations and providing three-dimensional information, yet most studies have not focused on agricultural settings. Therefore, to bridge the gap, this study proposes a large-scale digital mapping and immersive human-machine teleoperation framework specifically designed for precision agriculture. In this research, a DJI unmanned aerial vehicle (UAV) was utilized for data collection, and a novel video segmentation approach based on feature points was introduced. To accommodate the variability of complex textures, this method proposes an enhanced Structure from Motion (SfM) approach. It integrates the open Multiple View Geometry (OpenMVG) framework with Local Features from Transformers (LoFTR). The enhanced SfM produces a point cloud map, which is further processed through Multi-View Stereo (MVS) to generate a complete map model. For control, a closed-loop system utilizing TCP/IP for VR control and positioning of agricultural machinery was introduced. This system offers a fully visual-based method for immersive control, allowing operators to utilize VR technology for remote operations. The experimental results demonstrate that the user-friendly remote control method also showcases its advantages over traditional video streaming-based remote operations, providing operators with a more comprehensive and immersive experience and a higher level of situational awareness.
Forward completeness does not imply bounded reachability sets and global asymptotic stability is not necessarily uniform for time-delay systems
Authors: Jose L. Mancilla-Aguilar, Hernan Haimovich
Date: 2023-08-14 00:00:00

An example of a time-invariant time-delay system that is uniformly globally attractive and exponentially stable, hence forward complete, but whose reachability sets from bounded initial conditions are not bounded over compact time intervals is provided. This gives a negative answer to two current conjectures by showing that (i) forward completeness is not equivalent to robust forward completeness (i.e. boundedness of reachability sets) and (ii) global asymptotic stability is not equivalent to uniform global asymptotic stability. In addition, a novel characterization of robust forward completeness for systems having a finite number of discrete delays is provided. This characterization relates robust forward completeness of the time-delay system with the forward completeness of an associated nondelayed finite-dimensional system.
A Lightweight Sensor Scheduler Based on AoI Function for Remote State Estimation over Lossy Wireless Channels
Authors: Taige Chang, Xianghui Cao, Wei Xing Zheng
Date: 2023-08-14 00:00:00
Tasks: Scheduling, State Estimation

This paper investigates the problem of sensor scheduling for remotely estimating the states of heterogeneous dynamical systems over resource-limited and lossy wireless channels. Considering the low time complexity and high versatility requirements of schedulers deployed on the transport layer, we propose a lightweight scheduler based on an Age of Information (AoI) function built with the tight scalar upper bound of the remote estimation error. We show that the proposed scheduler is indexable and sub-optimal. We derive an upper and a lower bound of the proposed scheduler and give stability conditions for estimation error. Numerical simulations demonstrate that, compared to existing policies, the proposed scheduler achieves estimation performance very close to the optimal at a much lower computation time.
Hydrogen Supply Infrastructure Network Planning Approach towards Chicken-egg Conundrum
Authors: Haoran Deng, Bo Yang, Mo-Yuen Chow, Gang Yao, Cailian Chen, Xinping Guan
Date: 2023-08-14 00:00:00

In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), reasonable hydrogen supply infrastructure (HSI) planning decisions is a premise for promoting the popularization of HFCVs. However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is also known as the ``chicken and egg'' conundrum. Meanwhile, the hydrogen demand is uncertain with insufficient prior knowledge, and thus there is a decision-dependent uncertainty (DDU) in the planning issue. This poses great challenges to solving the optimization problem. To this end, this work establishes a multi-network HSI planning model coordinating hydrogen, power, and transportation networks. Then, to reflect the causal relationship between HFCVs and HRSs effectively without sufficient historical data, a distributionally robust optimization framework with decision-dependent uncertainty is developed. The uncertainty of hydrogen demand is modeled as a Wasserstein ambiguity set with a decision-dependent empirical probability distribution. Subsequently, to reduce the computational complexity caused by the introduction of a large number of scenarios and high-dimensional nonlinear constraints, we developed an improved distribution shaping method and techniques of scenario and variable reduction to derive the solvable form with less computing burden. Finally, the simulation results demonstrate that this method can reduce costs by at least 10.4% compared with traditional methods and will be more effective in large-scale HSI planning issues. Further, we put forward effective suggestions for the policymakers and investors to formulate relevant policies and decisions.
A Frequency-Domain Path-Following Method for Discrete Data-Based Paths
Authors: Zirui Chen, Zongyu Zuo
Date: 2023-08-14 00:00:00

This paper presents a novel frequency-domain approach for path following problem, specifically designed to handle paths described by discrete data. The proposed algorithm utilizes the fast Fourier Transform (FFT) to process the discrete path data, enabling the construction of a non-singular guiding vector field. This vector field serves as a reference direction for the controlled robot, offering the ability to adapt to different levels of precision. Additionally, the frequency-domain nature of the vector field allows for the reduction of computational complexity and effective noise suppression. The efficacy of the proposed approach is demonstrated through a numerical simulation, and theoretical analysis provides an upper bound for the ultimate mean-square path-following error.
Non-singular Cooperative Guiding Vector Field Under a Homotopy Equivalence Transformation
Authors: Zirui Chen, Zongyu Zuo
Date: 2023-08-14 00:00:00
Tasks: Navigate

The present article advances the concept of a non-singular cooperative guiding vector field under a homotopy equivalence transformation. Firstly, the derivation of a guiding vector field, based on a non-singular vector field, is elaborated to navigate a transformed path from another frame. The existence of such vector fields is also deliberated herein. Subsequently, a coordination vector field derived from the guiding vector field is presented, incorporating an in-depth analysis concerning the impact of the vector field parameters. Lastly, the practical implementation of this novel vector field is demonstrated by its applications to 2-D and 3-D cooperative moving path following issues, establishing its efficacy.