pwc-archive/papers-with-abstracts huggingface dataset:
Combining Automatic Coding and Instructor Input to Generate ENA Visualizations for Asynchronous Online Discussion
Authors: Marcia Moraes, Sadaf Ghaffari, Yanye Luther, James Folkestad
Date: 2023-08-22 00:00:00

Asynchronous online discussions are a common fundamental tool to facilitate social interaction in hybrid and online courses. However, instructors lack the tools to accomplish the overwhelming task of evaluating asynchronous online discussion activities. In this paper we present an approach that uses Latent Dirichlet Analysis (LDA) and the instructor's keywords to automatically extract codes from a relatively small dataset. We use the generated codes to build an Epistemic Network Analysis (ENA) model and compare this model with a previous ENA model built by human coders. The results show that there is no statistical difference between the two models. We present an analysis of these models and discuss the potential use of ENA as a visualization to help instructors evaluating asynchronous online discussions.
A Transmit-Receive Parameter Separable Electromagnetic Channel Model for LoS Holographic MIMO
Authors: Tierui Gong, Chongwen Huang, Jiguang He, Marco Di Renzo, Mérouane Debbah, Chau Yuen
Date: 2023-08-28 00:00:00

To support the extremely high spectral efficiency and energy efficiency requirements, and emerging applications of future wireless communications, holographic multiple-input multiple-output (H-MIMO) technology is envisioned as one of the most promising enablers. It can potentially bring extra degrees-of-freedom for communications and signal processing, including spatial multiplexing in line-of-sight (LoS) channels and electromagnetic (EM) field processing performed using specialized devices, to attain the fundamental limits of wireless communications. In this context, EM-domain channel modeling is critical to harvest the benefits offered by H-MIMO. Existing EM-domain channel models are built based on the tensor Green function, which require prior knowledge of the global position and/or the relative distances and directions of the transmit/receive antenna elements. Such knowledge may be difficult to acquire in real-world applications due to extensive measurements needed for obtaining this data. To overcome this limitation, we propose a transmit-receive parameter separable channel model methodology in which the EM-domain (or holographic) channel can be simply acquired from the distance/direction measured between the center-points between the transmit and receive surfaces, and the local positions between the transmit and receive elements, thus avoiding extensive global parameter measurements. Analysis and numerical results showcase the effectiveness of the proposed channel modeling approach in approximating the H-MIMO channel, and achieving the theoretical channel capacity.
Research on the Influence of Underwater Environment on the Dynamic Performance of the Mechanical Leg of a Deep-sea Crawling and Swimming Robot
Authors: Lihui Liao, Baoren Li, Dijia Zhang, Luping Gao, Mboulé Ngwa, Jingmin Du
Date: 2023-08-28 00:00:00

The performance of underwater crawling and adjustment of the body posture for underwater manipulating of the deep-sea crawling and swimming robot (DCSR) is directly influenced by the dynamic performance of the underwater mechanical legs (UWML), as it serves as the executive mechanism of the DCSR. Compared with the mechanical legs of legged robots working on land, the UWML of the DCSR not only possesses the characteristics of the land used mechanical legs, but is also affected by the influence of the deep-sea underwater working environment (i.e., the hydrodynamic force, viscous resistance and dynamic seal resistance). To reduce these influence, firstly, the hydrodynamic force of the UWML were researched based on theory and experiment, and the hydrodynamic model was established with the fitted hydrodynamic parameters. Secondly, the oil viscous resistance and the dynamic seal resistance were studied experimentally, and the change laws of both with respect to the joint speed and the ambient pressure (depth of operation) were obtained. The results provide a basis for the subsequent research on the structure optimization and high performance control of the UWML and DCSR.
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge
Authors: Ruoyu Wang, Maokui He, Jun Du, Hengshun Zhou, Shutong Niu, Hang Chen, Yanyan Yue, Gaobin Yang, Shilong Wu, Lei Sun, Yanhui Tu, Haitao Tang, Shuangqing Qian, Tian Gao, Mengzhi Wang, Genshun Wan, Jia Pan, Jianqing Gao, Chin-Hui Lee
Date: 2023-08-28 00:00:00
Tasks: speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

This technical report details our submission system to the CHiME-7 DASR Challenge, which focuses on speaker diarization and speech recognition under complex multi-speaker scenarios. Additionally, it also evaluates the efficiency of systems in handling diverse array devices. To address these issues, we implemented an end-to-end speaker diarization system and introduced a rectification strategy based on multi-channel spatial information. This approach significantly diminished the word error rates (WER). In terms of recognition, we utilized publicly available pre-trained models as the foundational models to train our end-to-end speech recognition models. Our system attained a Macro-averaged diarization-attributed WER (DA-WER) of 21.01% on the CHiME-7 evaluation set, which signifies a relative improvement of 62.04% over the official baseline system.
Data-driven 3D Room Geometry Inference with a Linear Loudspeaker Array and a Single Microphone
Authors: Cagdas Tuna, Altan Akat, H. Nazim Bicer, Andreas Walther, Emanuël A. P. Habets
Date: 2023-08-28 00:00:00
Tasks: Sound Source Localization

Knowing the room geometry may be very beneficial for many audio applications, including sound reproduction, acoustic scene analysis, and sound source localization. Room geometry inference (RGI) deals with the problem of reflector localization (RL) based on a set of room impulse responses (RIRs). Motivated by the increasing popularity of commercially available soundbars, this article presents a data-driven 3D RGI method using RIRs measured from a linear loudspeaker array to a single microphone. A convolutional recurrent neural network (CRNN) is trained using simulated RIRs in a supervised fashion for RL. The Radon transform, which is equivalent to delay-and-sum beamforming, is applied to multi-channel RIRs, and the resulting time-domain acoustic beamforming map is fed into the CRNN. The room geometry is inferred from the microphone position and the reflector locations estimated by the network. The results obtained using measured RIRs show that the proposed data-driven approach generalizes well to unseen RIRs and achieves an accuracy level comparable to a baseline model-driven RGI method that involves intermediate semi-supervised steps, thereby offering a unified and fully automated RGI framework.
Rep2wav: Noise Robust text-to-speech Using self-supervised representations
Authors: Qiushi Zhu, Yu Gu, Rilin Chen, Chao Weng, Yuchen Hu, LiRong Dai, Jie Zhang
Date: 2023-08-28 00:00:00
Tasks: Speech Enhancement, text-to-speech, Text to Speech

Benefiting from the development of deep learning, text-to-speech (TTS) techniques using clean speech have achieved significant performance improvements. The data collected from real scenes often contains noise and generally needs to be denoised by speech enhancement models. Noise-robust TTS models are often trained using the enhanced speech, which thus suffer from speech distortion and background noise that affect the quality of the synthesized speech. Meanwhile, it was shown that self-supervised pre-trained models exhibit excellent noise robustness on many speech tasks, implying that the learned representation has a better tolerance for noise perturbations. In this work, we therefore explore pre-trained models to improve the noise robustness of TTS models. Based on HiFi-GAN, we first propose a representation-to-waveform vocoder, which aims to learn to map the representation of pre-trained models to the waveform. We then propose a text-to-representation FastSpeech2 model, which aims to learn to map text to pre-trained model representations. Experimental results on the LJSpeech and LibriTTS datasets show that our method outperforms those using speech enhancement methods in both subjective and objective metrics. Audio samples are available at: https://zqs01.github.io/rep2wav.
Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0
Authors: Oubaida Chouchane, Michele Panariello, Chiara Galdi, Massimiliano Todisco, Nicholas Evans
Date: 2023-08-27 00:00:00
Tasks: Decision Making, Fairness, Speaker Verification

This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems. We adopt an approach that involves the fine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluating potential gender-related privacy vulnerabilities in the process. Gender influences during the fine-tuning process were employed to enhance fairness and privacy in order to emphasise or obscure gender information within the speakers' embeddings. Results from VoxCeleb datasets indicate our adversarial model increases privacy against uninformed attacks, yet slightly diminishes speaker verification performance compared to the non-adversarial model. However, the model's efficacy reduces against informed attacks. Analysis of system performance was conducted to identify potential gender biases, thus highlighting the need for further research to understand and improve the delicate interplay between utility, privacy, and equity in voice biometric systems.
A Stochastic Surveillance Stackelberg Game: Co-Optimizing Defense Placement and Patrol Strategy
Authors: Yohan John, Gilberto Diaz-Garcia, Xiaoming Duan, Jason R. Marden, Francesco Bullo
Date: 2023-08-28 00:00:00

Stochastic patrol routing is known to be advantageous in adversarial settings; however, the optimal choice of stochastic routing strategy is dependent on a model of the adversary. We adopt a worst-case omniscient adversary model from the literature and extend the formulation to accommodate heterogeneous defenses at the various nodes of the graph. Introducing this heterogeneity leads to interesting new patrol strategies. We identify efficient methods for computing these strategies in certain classes of graphs. We assess the effectiveness of these strategies via comparison to an upper bound on the value of the game. Finally, we leverage the heterogeneous defense formulation to develop novel defense placement algorithms that complement the patrol strategies.
Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks
Authors: Xuan Wang, Jorge Cortes
Date: 2023-08-27 00:00:00

This paper studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly non-convex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, non-convex objective function. We prove that the minimizer of the objective function is unique and establish that the solution of the optimization problem leads to the identification of all the desired system parameters. These results are the basis to introduce an algorithm to find the optimizer by searching the different regions corresponding to the domain of definition of the objective function. To deal with measurement noise in sampled data, we propose a modification of the original algorithm whose identification error is linearly bounded by the magnitude of the measurement noise. We demonstrate the effectiveness of the proposed algorithms through simulations on synthetic and experimental data.
Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation
Authors: Angela Chen, Konstantinos Mitsopoulos, Raffaele Romagnoli
Date: 2023-08-27 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Unmanned autonomous vehicles (UAVs) rely on effective path planning and tracking control to accomplish complex tasks in various domains. Reinforcement Learning (RL) methods are becoming increasingly popular in control applications, as they can learn from data and deal with unmodelled dynamics. Cyber-physical systems (CPSs), such as UAVs, integrate sensing, network communication, control, and computation to solve challenging problems. In this context, Software Rejuvenation (SR) is a protection mechanism that refreshes the control software to mitigate cyber-attacks, but it can affect the tracking controller's performance due to discrepancies between the control software and the physical system state. Traditional approaches to mitigate this effect are conservative, hindering the overall system performance. In this paper, we propose a novel approach that incorporates Deep Reinforcement Learning (Deep RL) into SR to design a safe and high-performing tracking controller. Our approach optimizes safety and performance, and we demonstrate its effectiveness during UAV simulations. We compare our approach with traditional methods and show that it improves the system's performance while maintaining safety constraints.
Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning
Authors: Jinhao Li, Yu Hui Yuan, Qiushi Cui, Hao Wang
Date: 2023-08-27 00:00:00
Tasks: Decision Making

Pure electric vehicles (PEVs) are increasingly adopted to decarbonize the transport sector and mitigate global warming. However, the inadequate PEV charging infrastructure may hinder the further adoption of PEVs in the large-scale traffic network, which calls for effective planning solutions for the charging station (CS) placement. The deployment of charging infrastructure inevitably increases the load on the associated power distribution network. Therefore, we are motivated to develop a comprehensive multi-objective framework for optimal CS placement in a traffic network overlaid by a distribution network, considering multiple stakeholders' interested factors, such as traffic flow, PEV charging time cost, PEV travel distance, and the reliability of the distribution network. We leverage a cross-entropy-based method to solve the optimal CS placement and evaluate our method in a real-world 183-node traffic network in Chengdu, China, overlaid by a 26-region distribution network. It is demonstrated that our work provides various viable planning options favoring different objectives for the stakeholders' decision-making in practice.
Simulator-Driven Deceptive Control via Path Integral Approach
Authors: Apurva Patil, Mustafa O. Karabag, Takashi Tanaka, Ufuk Topcu
Date: 2023-08-27 00:00:00

We consider a setting where a supervisor delegates an agent to perform a certain control task, while the agent is incentivized to deviate from the given policy to achieve its own goal. In this work, we synthesize the optimal deceptive policies for an agent who attempts to hide its deviations from the supervisor's policy. We study the deception problem in the continuous-state discrete-time stochastic dynamics setting and, using motivations from hypothesis testing theory, formulate a Kullback-Leibler control problem for the synthesis of deceptive policies. This problem can be solved using backward dynamic programming in principle, which suffers from the curse of dimensionality. However, under the assumption of deterministic state dynamics, we show that the optimal deceptive actions can be generated using path integral control. This allows the agent to numerically compute the deceptive actions via Monte Carlo simulations. Since Monte Carlo simulations can be efficiently parallelized, our approach allows the agent to generate deceptive control actions online. We show that the proposed simulation-driven control approach asymptotically converges to the optimal control distribution.
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation
Authors: Xinhong Feng, Yongzhi Zhang, Rui Xiong, Chun Wang
Date: 2023-08-27 00:00:00
Tasks: GPR, Management, Transfer Learning

Battery state of health (SOH), which informs the maximal available capacity of the battery, is a key indicator of battery aging failure. Accurately estimating battery SOH is a vital function of the battery management system that remains to be addressed. In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods. The method performance is validated based on 58826 cycling data units of 118 cells. Experimental results show that the physics-driven ML generally estimates more accurate SOH than other non-physical features under different scenarios. The physical features-based GPR predicts battery SOH with the errors being less than 1.1% based on 10 to 20 mins' relaxation data. And the high robustness and generalization capability of the methodology is also validated against different ratios of training and test data under unseen conditions. Results also highlight the more effective capability of knowledge transfer between different types of batteries with the physical features and GPR. This study demonstrates the excellence of physical features in indicating the state evolution of complex systems, and the improved indication performance of these features by combining a suitable ML method.
Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement
Authors: Jinfeng Chen, Zhiqiang Gao, Qin Lin
Date: 2023-08-26 00:00:00
Tasks: State Estimation

Control barrier functions-based quadratic programming (CBF-QP) is gaining popularity as an effective controller synthesis tool for safe control. However, the provable safety is established on an accurate dynamic model and access to all states. To address such a limitation, this paper proposes a novel design combining an extended state observer (ESO) with a CBF for safe control of a system with model uncertainty and external disturbances only using output measurement. Our approach provides a less conservative estimation error bound than other disturbance observer-based CBFs. Moreover, only output measurements are needed to estimate the disturbances instead of access to the full state. The bounds of state estimation error and disturbance estimation error are obtained in a unified manner and then used for robust safe control under uncertainty. We validate our approach's efficacy in simulations of an adaptive cruise control system and a Segway self-balancing scooter.
Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks
Authors: Wallace Tan Gian Yion, Zhe Wu
Date: 2023-08-26 00:00:00
Tasks: Chemical Process, Model Predictive Control, Sensitivity

Neural networks (NNs) have emerged as a state-of-the-art method for modeling nonlinear systems in model predictive control (MPC). However, the robustness of NNs, in terms of sensitivity to small input perturbations, remains a critical challenge for practical applications. To address this, we develop Lipschitz-Constrained Neural Networks (LCNNs) for modeling nonlinear systems and derive rigorous theoretical results to demonstrate their effectiveness in approximating Lipschitz functions, reducing input sensitivity, and preventing over-fitting. Specifically, we first prove a universal approximation theorem to show that LCNNs using SpectralDense layers can approximate any 1-Lipschitz target function. Then, we prove a probabilistic generalization error bound for LCNNs using SpectralDense layers by using their empirical Rademacher complexity. Finally, the LCNNs are incorporated into the MPC scheme, and a chemical process example is utilized to show that LCNN-based MPC outperforms MPC using conventional feedforward NNs in the presence of training data noise.
Field Testing of Residential Bidirectional Electric Vehicle Charger for Power System Applications
Authors: Shivam Saxena, Hany Farag, Khunsha Nasr, Leigh St. Hilaire
Date: 2023-08-25 00:00:00

Bidirectional electric vehicle (EV) charging is a technology that is gaining rapid popularity due to its ability to provide economic and environmental benefits to both EV owners and power system operators (PSOs). Using the EV as a flexible source of energy, an EV owner can provide power to homes/buildings, or even participate in grid services such as demand response and frequency regulation. However, there is a lack of real-world testing and validation for bidirectional charging technology, particularly in the residential segment. As such, this paper presents real-world field testing of a bidirectional EV charger deployed in a home. Control software is developed to dispatch the EV according to static setpoints, as well as automated load following, and its accuracy and responsiveness is reported on. The results of the testing with the charger and 2019 Nissan Leaf combination indicates a responsiveness of 6-8 seconds and accuracy of over 99%, which suggests feasible participation for applications such as load following, arbitrage, and demand response.
EEATC: A Novel Calibration Approach for Low-cost Sensors
Authors: M V Narayana, Devendra Jalihal, Shiva Nagendra
Date: 2023-08-25 00:00:00
Tasks: regression

Low-cost sensors (LCS) are affordable, compact, and often portable devices designed to measure various environmental parameters, including air quality. These sensors are intended to provide accessible and cost-effective solutions for monitoring pollution levels in different settings, such as indoor, outdoor and moving vehicles. However, the data produced by LCS is prone to various sources of error that can affect accuracy. Calibration is a well-known procedure to improve the reliability of the data produced by LCS, and several developments and efforts have been made to calibrate the LCS. This work proposes a novel Estimated Error Augmented Two-phase Calibration (\textit{EEATC}) approach to calibrate the LCS in stationary and mobile deployments. In contrast to the existing approaches, the \textit{EEATC} calibrates the LCS in two phases, where the error estimated in the first phase calibration is augmented with the input to the second phase, which helps the second phase to learn the distributional features better to produce more accurate results. We show that the \textit{EEATC} outperforms well-known single-phase calibration models such as linear regression models (single variable linear regression (SLR) and multiple variable linear regression (MLR)) and Random forest (RF) in stationary and mobile deployments. To test the \textit{EEATC} in stationary deployments, we have used the Community Air Sensor Network (CAIRSENSE) data set approved by the United States Environmental Protection Agency (USEPA), and the mobile deployments are tested with the real-time data obtained from SensurAir, an LCS device developed and deployed on moving vehicle in Chennai, India.