pwc-archive/papers-with-abstracts huggingface dataset:
Suppressing Noise Disparity in Training Data for Automatic Pathological Speech Detection
Authors: Mahdi Amiri, Ina Kodrasi
Date: 2024-09-02 00:00:00

Although automatic pathological speech detection approaches show promising results when clean recordings are available, they are vulnerable to additive noise. Recently it has been shown that databases commonly used to develop and evaluate such approaches are noisy, with the noise characteristics between healthy and pathological recordings being different. Consequently, automatic approaches trained on these databases often learn to discriminate noise rather than speech pathology. This paper introduces a method to mitigate this noise disparity in training data. Using noise estimates from recordings from one group of speakers to augment recordings from the other group, the noise characteristics become consistent across all recordings. Experimental results demonstrate the efficacy of this approach in mitigating noise disparity in training data, thereby enabling automatic pathological speech detection to focus on pathology-discriminant cues rather than noise-discriminant ones.
Multi-label Zero-Shot Audio Classification with Temporal Attention
Authors: Duygu Dogan, Huang Xie, Toni Heittola, Tuomas Virtanen
Date: 2024-08-31 00:00:00
Tasks: Audio Classification, Classification, Zero-shot Audio Classification, zero-shot-classification, Zero-Shot Learning

Zero-shot learning models are capable of classifying new classes by transferring knowledge from the seen classes using auxiliary information. While most of the existing zero-shot learning methods focused on single-label classification tasks, the present study introduces a method to perform multi-label zero-shot audio classification. To address the challenge of classifying multi-label sounds while generalizing to unseen classes, we adapt temporal attention. The temporal attention mechanism assigns importance weights to different audio segments based on their acoustic and semantic compatibility, thus enabling the model to capture the varying dominance of different sound classes within an audio sample by focusing on the segments most relevant for each class. This leads to more accurate multi-label zero-shot classification than methods employing temporally aggregated acoustic features without weighting, which treat all audio segments equally. We evaluate our approach on a subset of AudioSet against a zero-shot model using uniformly aggregated acoustic features, a zero-rule baseline, and the proposed method in the supervised scenario. Our results show that temporal attention enhances the zero-shot audio classification performance in multi-label scenario.
Progressive Residual Extraction based Pre-training for Speech Representation Learning
Authors: Tianrui Wang, Jin Li, Ziyang Ma, Rui Cao, Xie Chen, Longbiao Wang, Meng Ge, Xiaobao Wang, Yuguang Wang, Jianwu Dang, Nyima Tashi
Date: 2024-08-31 00:00:00
Tasks: Emotion Recognition, Representation Learning, Self-Supervised Learning, Speaker Identification, Speech Enhancement, speech-recognition, Speech Recognition, Speech Representation Learning, Voice Conversion

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named ProgRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM.
Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning
Authors: Zhenyu Wang, Shuyu Kong, Li Wan, Biqiao Zhang, Yiteng Huang, Mumin Jin, Ming Sun, Xin Lei, Zhaojun Yang
Date: 2024-08-27 00:00:00
Tasks: Keyword Spotting, Multi-Task Learning

Existing keyword spotting (KWS) systems primarily rely on predefined keyword phrases. However, the ability to recognize customized keywords is crucial for tailoring interactions with intelligent devices. In this paper, we present a novel Query-by-Example (QbyE) KWS system that employs spectral-temporal graph attentive pooling and multi-task learning. This framework aims to effectively learn speaker-invariant and linguistic-informative embeddings for QbyE KWS tasks. Within this framework, we investigate three distinct network architectures for encoder modeling: LiCoNet, Conformer and ECAPA_TDNN. The experimental results on a substantial internal dataset of $629$ speakers have demonstrated the effectiveness of the proposed QbyE framework in maximizing the potential of simpler models such as LiCoNet. Particularly, LiCoNet, which is 13x more efficient, achieves comparable performance to the computationally intensive Conformer model (1.98% vs. 1.63\% FRR at 0.3 FAs/Hr).
Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples
Authors: Qi Fan, Yutong Li, Yi Xin, Xinyu Cheng, Guanglai Gao, Miao Ma
Date: 2024-08-23 00:00:00
Tasks: Contrastive Learning, Emotion Recognition, Multimodal Emotion Recognition

The Multimodal Emotion Recognition challenge MER2024 focuses on recognizing emotions using audio, language, and visual signals. In this paper, we present our submission solutions for the Semi-Supervised Learning Sub-Challenge (MER2024-SEMI), which tackles the issue of limited annotated data in emotion recognition. Firstly, to address the class imbalance, we adopt an oversampling strategy. Secondly, we propose a modality representation combinatorial contrastive learning (MR-CCL) framework on the trimodal input data to establish robust initial models. Thirdly, we explore a self-training approach to expand the training set. Finally, we enhance prediction robustness through a multi-classifier weighted soft voting strategy. Our proposed method is validated to be effective on the MER2024-SEMI Challenge, achieving a weighted average F-score of 88.25% and ranking 6th on the leaderboard. Our project is available at https://github.com/WooyoohL/MER2024-SEMI.
A Hetero-functional Graph Resilience Analysis for Convergent Systems-of-Systems
Authors: Amro M. Farid
Date: 2024-09-08 00:00:00
Tasks: Language Modelling

Our modern life has grown to depend on many and nearly ubiquitous large complex engineering systems. Many disciplines now seemingly ask the same question: ``In the face of assumed disruption, to what degree will these systems continue to perform and when will they be able to bounce back to normal operation"? Furthermore, there is a growing recognition that the greatest societal challenges of the Anthropocene era are intertwined, necessitating a convergent systems-of-systems modeling and analysis framework based upon reconciled ontologies, data, and theoretical methods. Consequently, this paper develops a methodology for hetero-functional graph resilience analysis and demonstrates it on a convergent system-of-systems. It uses the Systems Modeling Language, model-based systems engineering and Hetero-Functional Graph Theory (HFGT) to overcome the convergence research challenges when constructing models and measures from multiple disciplines for systems resilience. The paper includes both the ``survival" as well as ``recovery" components of resilience. It also strikes a middle ground between two disparate approaches to resilience measurement: structural measurement of formal graphs and detailed behavioral simulation. This paper also generalizes a previous resilience measure based on HFGT and benefits from recent theoretical and computational developments in HFGT. To demonstrate the methodological developments, the resilience analysis is conducted on a hypothetical energy-water nexus system of moderate size as a type of system-of-systems.
Improving Initial Transients of Online Learning Echo State Network Control System with Feedback Adjustments
Authors: Junyi Shen
Date: 2024-09-12 00:00:00

Echo state networks (ESNs) have become increasingly popular in online learning control systems due to their ease of training. However, online learning ESN controllers often suffer from slow convergence during the initial transient phase. Existing solutions, such as prior training, control mode switching, and incorporating plant dynamic approximations, have notable drawbacks, including undermining the system's online learning property or relying on prior knowledge of the controlled system. This work proposes a simple yet effective approach to address the slow initial convergence of online learning ESN control systems by integrating a feedback proportional-derivative (P-D) controller. Simulation results demonstrate that the proposed control system achieves rapid convergence during the initial transient phase and shows strong robustness against changes in the controlled system's dynamics and variations in the online learning model's hyperparameters. We show that the feedback controller accelerates convergence by guiding the online learning ESN to operate within a data range well-suited for learning. This study offers practical benefits for engineers aiming to implement online learning ESN control systems with fast convergence and easy deployment.
Optimal Operation of Distribution System Operator and the Impact of Peer-to-Peer Transactions
Authors: Hanyang Lin, Ye Guo, Firdous Ul Nazir, Jianguo Zhou, Chi Yung Chung, Nikos Hatziargyriou
Date: 2024-09-12 00:00:00
Tasks: energy trading

Peer-to-peer (P2P) energy trading, commonly recognized as a decentralized approach, has emerged as a popular way to better utilize distributed energy resources (DERs). In order to better manage this user-side decentralized approach from a system operator's point of view, this paper proposes an optimal operation approach for distribution system operators (DSO), comprising internal prosumers who engage in P2P transactions. The DSO is assumed to be a financial neutral entity, holding the responsibility of aggregating the surplus energy and deficit demand of prosumers after their P2P transactions while dispatching DERs and considering network integrity. Impacts of P2P transactions on DSO's optimal operation have been studied. Results indicate that energy matching P2P trading where only the total amount of energy over a given period of time is defined may affect quantities of energy exchanged between the DSO and the wholesale market, but not internal dispatch decisions of the DSO. Different levels of real-time power consistency may lead to different total surpluses in the distribution network. For the real-time power matching P2P trading, as a special case of energy matching P2P trading, the provided energy and total surplus are not affected. In other words, DSO can safely ignore P2P transactions if they follow the format defined in this paper. Case studies verify these conclusions and further demonstrate that P2P trading will not affect physical power flow of the whole system, but the financial distribution between the DSO and prosumers.
Disinfectant Control in Drinking Water Networks: Integrating Advection-Dispersion-Reaction Models and Byproduct Constraints
Authors: Salma M. Elsherif, Ahmad F. Taha, Ahmed A. Abokifa
Date: 2024-09-12 00:00:00

Effective disinfection is essential for maintaining water quality standards in distribution networks. Chlorination, as the most used technique, ensures safe water by maintaining sufficient chlorine residuals but also leads to the formation of disinfection byproducts (DBPs). These DBPs pose health risks, highlighting the need for chlorine injection control (CIC) by booster stations to balance safety and DBPs formation. Prior studies have followed various approaches to address this research problem. However, most of these studies overlook the changing flow conditions and their influence on the evolution of the chlorine and DBPs concentrations by integrating simplified transport-reaction models into CIC. In contrast, this paper proposes a novel CIC method that: (i) integrates multi-species dynamics, (ii) allows for a more accurate representation of the reaction dynamics of chlorine, other substances, and the resulting DBPs formation, and (iii) optimizes for the regulation of chlorine concentrations subject to EPA mandates thereby mitigating network-wide DBPs formation. The novelty of this study lies in its incorporation of time-dependent controllability analysis that captures the control coverage of each booster station. The effectiveness of the proposed CIC method is demonstrated through its application and validation via numerical case studies on different water networks with varying scales, initial conditions, and parameters.
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning
Authors: Xiang Huo, Boming Liu, Jin Dong, Jianming Lian, Mingxi Liu
Date: 2024-09-12 00:00:00
Tasks: Decision Making, Management, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.
Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems
Authors: Michael Nestor, Fei Teng
Date: 2024-09-12 00:00:00

Communication topology is a crucial part of a distributed control implementation for cyber-physical systems, yet is typically treated as a constraint within control design problems rather than a design variable. We propose a data-driven method for designing an optimal topology for the purpose of distributed control when a system model is unavailable or unaffordable, via a mixed-integer second-order conic program. The approach demonstrates improved control performance over random topologies in simulations and efficiently drops links which have a small effect on predictor accuracy, which we show correlates well with closed-loop control cost.
Impacts of EPA Power Plant Emissions Regulations on the US Electricity Sector
Authors: Qian Luo, Jesse Jenkins
Date: 2024-09-12 00:00:00

Taking aim at one of the largest greenhouse gas emitting sectors, the US Environmental Protection Agency (EPA) finalized new regulations on power plant greenhouse gas emissions in May 2024. These rules take the form of different emissions performance standards for different classes of power plant technologies, creating a complex set of regulations that make it difficult to understand their consequential impacts on power system capacity, operations, and emissions without dedicated and sophisticated modeling. Here, we enhance a state-of-the-art power system capacity expansion model by incorporating new detailed operational constraints tailored to different technologies to represent the EPA's rules. Our results show that adopting these new regulations could reduce US power sector emissions in 2040 to 51% below the 2022 level (vs 26% without the rules). Regulations on coal-fired power plants drive the largest share of reductions. Regulations on new gas turbines incrementally reduce emissions but lower overall efficiency of the gas fleet, increasing the average cost of carbon mitigation. Therefore, we explore several alternative emission mitigation strategies. By comparing these alternatives with regulations finalized by EPA, we highlight the importance of accelerating the retirement of inefficient fossil fuel-fired generators and applying consistent and strict emissions regulations to all gas generators, regardless of their vintage, to cost-effectively achieve deep decarbonization and avoid biasing investment decisions towards less efficient generators.
Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints
Authors: Md Umar Hashmi, Dirk Van Hertem, Aleen van der Meer, Andrew Keane
Date: 2024-09-12 00:00:00
Tasks: Benchmarking

The power networks are evolving with increased active components such as energy storage and flexibility derived from loads such as electric vehicles, heat pumps, industrial processes, etc. Better models are needed to accurately represent these assets; otherwise, their true capabilities might be over or under-estimated. In this work, we propose a new energy storage and flexibility arbitrage model that accounts for both ramp (power) and capacity (energy) limits, while accurately modelling the ramp rate constraint. The proposed models are linear in structure and efficiently solved using off-the-shelf solvers as a linear programming problem. We also provide an online repository for wider application and benchmarking. Finally, numerical case studies are performed to quantify the sensitivity of ramp rate constraint on the operational goal of profit maximization for energy storage and flexibility. The results are encouraging for assets with a slow ramp rate limit. We observe that for resources with a ramp rate limit of 10% of the maximum ramp limit, the marginal value of performing energy arbitrage using such resources exceeds 65% and up to 90% of the maximum profit compared to the case with no ramp rate limitations.
Learning Communities from Equilibria of Nonlinear Opinion Dynamics
Authors: Yu Xing, Anastasia Bizyaeva, Karl H. Johansson
Date: 2024-09-12 00:00:00
Tasks: Community Detection, Stochastic Block Model

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the inter-community connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms.
Covariance Intersection-based Invariant Kalman Filtering(DInCIKF) for Distributed Pose Estimation
Authors: Haoying Li, Xinghan Li, Shuaiting Huang, Chao Yang, Junfeng Wu
Date: 2024-09-12 00:00:00
Tasks: Pose Estimation

This paper presents a novel approach to distributed pose estimation in the multi-agent system based on an invariant Kalman filter with covariance intersection. Our method models uncertainties using Lie algebra and applies object-level observations within Lie groups, which have practical application value. We integrate covariance intersection to handle estimates that are correlated and use the invariant Kalman filter for merging independent data sources. This strategy allows us to effectively tackle the complex correlations of cooperative localization among agents, ensuring our estimates are neither too conservative nor overly confident. Additionally, we examine the consistency and stability of our algorithm, providing evidence of its reliability and effectiveness in managing multi-agent systems.
Critical link identification of power system vulnerability based on modified graph attention network
Authors: Changgang Wang, Xianwei Wang, Yu Cao, Yang Li, Qi Lv, Yaoxin Zhang
Date: 2024-09-12 00:00:00
Tasks: Graph Attention

With the expansion of the power grid and the increase of the proportion of new energy sources, the uncertainty and random factors of the power grid increase, endangering the safe operation of the system. It is particularly important to find out the critical links of vulnerability in the power grid to ensure the reliability of the power grid operation. Aiming at the problem that the identification speed of the traditional critical link of vulnerability identification methods is slow and difficult to meet the actual operation requirements of the power grid, the improved graph attention network (IGAT) based identification method of the critical link is proposed. First, the evaluation index set is established by combining the complex network theory and the actual operation data of power grid. Secondly, IGAT is used to dig out the mapping relationship between various indicators and critical links of vulnerability during the operation of the power grid, establish the identification model of critical links of vulnerability, and optimize the original graph attention network considering the training accuracy and efficiency. Thirdly, the original data set is obtained through simulation, and the identification model is trained, verified and tested. Finally, the model is applied to the improved IEEE 30-node system and the actual power grid, and the results show that the proposed method is feasible, and the accuracy and speed are better than that of traditional methods. It has certain engineering utilization value.
Resilient Learning-Based Control Under Denial-of-Service Attacks
Authors: Sayan Chakraborty, Weinan Gao, Kyriakos G. Vamvoudakis, Zhong-Ping Jiang
Date: 2024-09-12 00:00:00

In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart.