pwc-archive/papers-with-abstracts huggingface dataset:
XANE: eXplainable Acoustic Neural Embeddings
Authors: Sri Harsha Dumpala, Dushyant Sharma, Chandramouli Shama Sastri, Stanislav Kruchinin, James Fosburgh, Patrick A. Naylor
Date: 2024-06-07 00:00:00

We present a novel method for extracting neural embeddings that model the background acoustics of a speech signal. The extracted embeddings are used to estimate specific parameters related to the background acoustic properties of the signal in a non-intrusive manner, which allows the embeddings to be explainable in terms of those parameters. We illustrate the value of these embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 95.2\% for three different tasks, outperforming significantly the WavLM based signal embeddings. We also show that the proposed method can explain the embeddings by estimating 14 acoustic parameters characterizing the background acoustics, including reverberation and noise levels, overlapped speech detection, CODEC type detection and noise type detection with high accuracy and a real-time factor 17 times lower than an external baseline method.
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement
Authors: Wangyou Zhang, Robin Scheibler, Kohei Saijo, Samuele Cornell, Chenda Li, Zhaoheng Ni, Anurag Kumar, Jan Pirklbauer, Marvin Sach, Shinji Watanabe, Tim Fingscheidt, Yanmin Qian
Date: 2024-06-07 00:00:00
Tasks: Bandwidth Extension, Denoising, Diversity, Speech Enhancement

The last decade has witnessed significant advancements in deep learning-based speech enhancement (SE). However, most existing SE research has limitations on the coverage of SE sub-tasks, data diversity and amount, and evaluation metrics. To fill this gap and promote research toward universal SE, we establish a new SE challenge, named URGENT, to focus on the universality, robustness, and generalizability of SE. We aim to extend the SE definition to cover different sub-tasks to explore the limits of SE models, starting from denoising, dereverberation, bandwidth extension, and declipping. A novel framework is proposed to unify all these sub-tasks in a single model, allowing the use of all existing SE approaches. We collected public speech and noise data from different domains to construct diverse evaluation data. Finally, we discuss the insights gained from our preliminary baseline experiments based on both generative and discriminative SE methods with 12 curated metrics.
Neural Codec-based Adversarial Sample Detection for Speaker Verification
Authors: Xuanjun Chen, Jiawei Du, Haibin Wu, Jyh-Shing Roger Jang, Hung-Yi Lee
Date: 2024-06-07 00:00:00
Tasks: Speaker Verification

Automatic Speaker Verification (ASV), increasingly used in security-critical applications, faces vulnerabilities from rising adversarial attacks, with few effective defenses available. In this paper, we propose a neural codec-based adversarial sample detection method for ASV. The approach leverages the codec's ability to discard redundant perturbations and retain essential information. Specifically, we distinguish between genuine and adversarial samples by comparing ASV score differences between original and re-synthesized audio (by codec models). This comprehensive study explores all open-source neural codecs and their variant models for experiments. The Descript-audio-codec model stands out by delivering the highest detection rate among 15 neural codecs and surpassing seven prior state-of-the-art (SOTA) detection methods. Note that, our single-model method even outperforms a SOTA ensemble method by a large margin.
A Human-in-the-Loop Approach to Improving Cross-Text Prosody Transfer
Authors: Himanshu Maurya, Atli Sigurgeirsson
Date: 2024-06-06 00:00:00
Tasks: text-to-speech, Text to Speech

Text-To-Speech (TTS) prosody transfer models can generate varied prosodic renditions, for the same text, by conditioning on a reference utterance. These models are trained with a reference that is identical to the target utterance. But when the reference utterance differs from the target text, as in cross-text prosody transfer, these models struggle to separate prosody from text, resulting in reduced perceived naturalness. To address this, we propose a Human-in-the-Loop (HitL) approach. HitL users adjust salient correlates of prosody to make the prosody more appropriate for the target text, while maintaining the overall reference prosodic effect. Human adjusted renditions maintain the reference prosody while being rated as more appropriate for the target text $57.8\%$ of the time. Our analysis suggests that limited user effort suffices for these improvements, and that closeness in the latent reference space is not a reliable prosodic similarity metric for the cross-text condition.
VerilogReader: LLM-Aided Hardware Test Generation
Authors: Ruiyang Ma, Yuxin Yang, Ziqian Liu, Jiaxi Zhang, Min Li, Junhua Huang, Guojie Luo
Date: 2024-06-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Prompt Engineering

Test generation has been a critical and labor-intensive process in hardware design verification. Recently, the emergence of Large Language Model (LLM) with their advanced understanding and inference capabilities, has introduced a novel approach. In this work, we investigate the integration of LLM into the Coverage Directed Test Generation (CDG) process, where the LLM functions as a Verilog Reader. It accurately grasps the code logic, thereby generating stimuli that can reach unexplored code branches. We compare our framework with random testing, using our self-designed Verilog benchmark suite. Experiments demonstrate that our framework outperforms random testing on designs within the LLM's comprehension scope. Our work also proposes prompt engineering optimizations to augment LLM's understanding scope and accuracy.
RAG Does Not Work for Enterprises
Authors: Tilmann Bruckhaus
Date: 2024-05-31 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval. However, implementing RAG in enterprises poses challenges around data security, accuracy, scalability, and integration. This paper explores the unique requirements for enterprise RAG, surveys current approaches and limitations, and discusses potential advances in semantic search, hybrid queries, and optimized retrieval. It proposes an evaluation framework to validate enterprise RAG solutions, including quantitative testing, qualitative analysis, ablation studies, and industry case studies. This framework aims to help demonstrate the ability of purpose-built RAG architectures to deliver accuracy and relevance improvements with enterprise-grade security, compliance and integration. The paper concludes with implications for enterprise deployments, limitations, and future research directions. Close collaboration between researchers and industry partners may accelerate progress in developing and deploying retrieval-augmented generation technology.
Naming the Pain in Machine Learning-Enabled Systems Engineering
Authors: Marcos Kalinowski, Daniel Mendez, Görkem Giray, Antonio Pedro Santos Alves, Kelly Azevedo, Tatiana Escovedo, Hugo Villamizar, Helio Lopes, Teresa Baldassarre, Stefan Wagner, Stefan Biffl, Jürgen Musil, Michael Felderer, Niklas Lavesson, Tony Gorschek
Date: 2024-05-20 00:00:00
Tasks: Survey

Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic research. Method: We conducted an international survey to collect insights from practitioners on the current practices and problems in engineering ML-enabled systems. We received 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems using open and axial coding procedures. Results: Our survey results reinforce and extend existing empirical evidence on engineering ML-enabled systems, providing additional insights into typical ML-enabled systems project contexts, the perceived relevance and complexity of ML life cycle phases, and current practices related to problem understanding, model deployment, and model monitoring. Furthermore, the qualitative analysis provides a detailed map of the problems practitioners face within each ML life cycle phase and the problems causing overall project failure. Conclusions: The results contribute to a better understanding of the status quo and problems in practical environments. We advocate for the further adaptation and dissemination of software engineering practices to enhance the engineering of ML-enabled systems.
BugBlitz-AI: An Intelligent QA Assistant
Authors: Yi Yao, Jun Wang, Yabai Hu, LiFeng Wang, Yi Zhou, Jack Chen, Xuming Gai, Zhenming Wang, Wenjun Liu
Date: 2024-05-17 00:00:00
Tasks: software testing

The evolution of software testing from manual to automated methods has significantly influenced quality assurance (QA) practices. However, challenges persist in post-execution phases, particularly in result analysis and reporting. Traditional post-execution validation phases require manual intervention for result analysis and report generation, leading to inefficiencies and potential development cycle delays. This paper introduces BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end test automation by automating result analysis and bug reporting processes. BugBlitz-AI leverages recent advancements in artificial intelligence to reduce the time-intensive tasks of manual result analysis and report generation, allowing QA teams to focus more on crucial aspects of product quality. By adopting BugBlitz-AI, organizations can advance automated testing practices and integrate AI into QA processes, ensuring higher product quality and faster time-to-market. The paper outlines BugBlitz-AI's architecture, discusses related work, details its quality enhancement strategies, and presents results demonstrating its effectiveness in real-world scenarios.
Multimodal Approach for Harmonized System Code Prediction
Authors: Otmane Amel, Sedrick Stassin, Sidi Ahmed Mahmoudi, Xavier Siebert
Date: 2024-05-08 00:00:00
Tasks: Prediction

The rapid growth of e-commerce has placed considerable pressure on customs representatives, prompting advanced methods. In tackling this, Artificial intelligence (AI) systems have emerged as a promising approach to minimize the risks faced. Given that the Harmonized System (HS) code is a crucial element for an accurate customs declaration, we propose a novel multimodal HS code prediction approach using deep learning models exploiting both image and text features obtained through the customs declaration combined with e-commerce platform information. We evaluated two early fusion methods and introduced our MultConcat fusion method. To the best of our knowledge, few studies analyze the featurelevel combination of text and image in the state-of-the-art for HS code prediction, which heightens interest in our paper and its findings. The experimental results prove the effectiveness of our approach and fusion method with a top-3 and top-5 accuracy of 93.5% and 98.2% respectively
Process Variant Analysis Across Continuous Features: A Novel Framework
Authors: Ali Norouzifar, Majid Rafiei, Marcus Dees, Wil van der Aalst
Date: 2024-05-06 00:00:00

Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other dimensions that may play a role in the performance of the process. This research addresses the challenge of effectively segmenting cases within operational processes based on continuous features, such as duration of cases, and evaluated risk score of cases, which are often overlooked in traditional process analysis. We present a novel approach employing a sliding window technique combined with the earth mover's distance to detect changes in control flow behavior over continuous dimensions. This approach enables case segmentation, hierarchical merging of similar segments, and pairwise comparison of them, providing a comprehensive perspective on process behavior. We validate our methodology through a real-life case study in collaboration with UWV, the Dutch employee insurance agency, demonstrating its practical applicability. This research contributes to the field by aiding organizations in improving process efficiency, pinpointing abnormal behaviors, and providing valuable inputs for process comparison, and outcome prediction.
Collaborative Team Recognition: A Core Plus Extension Structure
Authors: Shuo Yu, Fayez Alqahtani, Amr Tolba, Ivan Lee, Tao Jia, Feng Xia
Date: 2024-06-07 00:00:00

Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods.
Accurate Current Sharing in a DC Microgrid Using Modified Droop Control Algorithm
Authors: Naser Souri, Ali Mehrizi-Sani
Date: 2024-06-11 00:00:00

Due to the increasing popularity of DC loads and the potential for higher efficiency, DC microgrids are gaining significant attention. DC microgrids utilize multiple parallel converters to deliver sufficient power to the load. However, a key challenge arises when connecting these converters to a common DC bus: maintaining voltage regulation and accurate current sharing. Unequal cable resistances can cause uneven power sharing and lead to power losses. Conventional droop control methods, which employ a virtual resistor to address this issue, have limitations in achieving good performance across the entire converter operating range. This paper proposes a modified droop control algorithm to address this issue. This method modifies the virtual resistor in a way that ensures power sharing aligns with each converter-rated capacity. The algorithm is simple to implement and uses local measurements to update the droop gain. This paper presents simulation studies and experimental tests to analyze the performance of the proposed method, considering scenarios with equal and unequal converter ratings. The results successfully validate the accuracy and effectiveness of this innovative approach.
Hybrid Machine Learning Approach for Cyberattack Mitigation of Parallel Converters in a DC Microgrid
Authors: Naser Souri, Ali Mehrizi-Sani
Date: 2024-06-11 00:00:00
Tasks: Hybrid Machine Learning

Cyberattack susceptibilities are introduced as the communication requirement increases with the incorporation of more renewable energy sources into DC microgrids. Parallel DC-DC converters are utilized to provide high current and supply the load. Nevertheless, these systems are susceptible to cyberattacks that have the potential to disrupt operations and jeopardize stability. Voltage instability may result from the manipulation of communication commands and low-layer control signals. Therefore, in this paper, a cyberattack that specifically targets parallel DC-DC converters is examined in a DC microgrid. A hybrid machine learning-based detection and mitigation strategy is suggested as a means to counteract this threat. The false data injection (FDI) attack targeting the converters is investigated within a DC microgrid. The efficacy of the suggested approach is verified via simulations executed for various scenarios within the MATLAB/Simulink environment. The technique successfully identifies and blocks FDI attacks, preventing cyberattacks and ensuring the safe operation of the DC microgrid.
Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control
Authors: Jacob Thrän, Jakub Mareček, Robert N. Shorten, Timothy C. Green
Date: 2024-06-11 00:00:00
Tasks: Model Predictive Control

Controlled charging of electric vehicles, EVs, is a major potential source of flexibility to facilitate the integration of variable renewable energy and reduce the need for stationary energy storage. To offer system services from EVs, fleet aggregators must address the uncertainty of individual driving and charging behaviour. This paper introduces a means of forecasting the service volume available from EVs by considering several EV batteries as one conceptual battery with aggregate power and energy boundaries. Aggregation avoids the difficult prediction of individual driving behaviour. The predictability of the boundaries is demonstrated using a multiple linear regression model which achieves a normalised root mean square error of 20% - 40% for a fleet of 1,000 EVs. A two-stage stochastic model predictive control algorithm is used to schedule reserve services on a day-ahead basis addressing risk trade-offs by including Conditional Value-at-Risk in the objective function. A case study with 1.2 million domestic EV charge records from Great Britain illustrates that increasing fleet size improves prediction accuracy, thereby increasing reserve revenues and decreasing an aggregator's operational costs. For fleet sizes of 400 or above, cost reductions plateau at 60% compared to uncontrolled charging, with an average of 1.8kW of reserve provided per vehicle.
ADDOPT: An Additive Manufacturing Optimal Control Framework Demonstrated in Minimizing Layer-Level Thermal Variance in Electron Beam Powder Bed Fusion
Authors: Mikhail Khrenov, William Frieden Templeton, Sneha Prabha Narra
Date: 2024-06-11 00:00:00

Additive manufacturing (AM) techniques hold promise but face significant challenges in process planning and optimization. The large temporal and spatial variations in temperature that can occur in layer-wise AM lead to thermal excursions, resulting in property variations and defects. These variations cannot always be fully mitigated by simple static parameter search. To address this challenge, we propose a general approach based on modeling AM processes on the part-scale in state-space and framing AM process planning as a numerical optimal control problem. We demonstrate this approach on the problem of minimizing thermal variation in a given layer in the electron beam powder bed fusion (EB-PBF) AM process, and are able to compute globally optimal dynamic process plans. These optimized process plans are then evaluated in simulation, achieving an 87% and 86% reduction in cumulative variance compared to random spot melting and a uniform power field respectively, and are further validated in experiment. This one-shot feedforward planning approach expands the capabilities of AM technology by minimizing the need for experimentation and iteration to achieve process optimization. Further, this work opens the possibility for the application of optimal control theory to part-scale optimization and control in AM.
Capacity Credit Evaluation of Generalized Energy Storage Considering Strategic Capacity Withholding and Decision-Dependent Uncertainty
Authors: Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Yingrui Zhuang, Yifan Su, Feng Liu
Date: 2024-06-11 00:00:00
Tasks: Decision Making, Scheduling

This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The proposed method is sequentially implemented along with the Monte Carlo simulation process, coordinating the pre-dispatched price arbitrage and capacity withholding in the energy market with adequacy-oriented re-dispatch during capacity market calls. In addition to decision-independent uncertainties in operational states and baseline behavior, we explicitly address the inherent DDU of GES (i.e., the uncertainty of available discharge capacity affected by the incentives and accumulated discomfort) during the re-dispatch stage using the proposed data-driven distributional robust chance-constrained approach. Furthermore, a capacity credit metric called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of uncertain GES. Simulations on the modified IEEE RTS-79 benchmark system with 20 years real-world data from Elia demonstrate that the proposed method yields accurate capacity credit and improved economic performance. We show that the capacity credit of GES increases with more strategic capacity withholding but decreases with more DDU levels. Key factors, such as capacity withholding and DDU structure impacting GES's capacity credit are analyzed with insights into capacity market decision-making.
Lyapunov equations: a (fixed) point of view
Authors: Richard Pates
Date: 2024-06-11 00:00:00

The Lyapunov equation is the gateway drug of nonlinear control theory. In these notes we revisit an elegant statement connecting the concepts of asymptotic stability and observability, to the solvability of Lyapunov equations, and discuss how this statement can be proved using the Brouwer fixed-point theorem.
Optimal Scheduling of Battery Storage Systems in the Swedish Multi-FCR Market Incorporating Battery Degradation and Technical Requirements
Authors: Nima Mirzaei Alavijeh, Rahmat Khezri, Mohammadreza Mazidi, David Steen, Le Anh Tuan
Date: 2024-06-11 00:00:00
Tasks: Scheduling

This paper develops a novel mixed-integer linear programming (MILP) model for optimal participation of battery energy storage systems (BESSs) in the Swedish frequency containment reserve (FCR) markets. The developed model aims to maximize the battery owner's potential profit by considering battery degradation and participation in multiple FCR markets, i.e., FCR in normal operation (FCR-N), and FCR in disturbances (FCR-D) for up- and down-regulations. Accordingly, a precise formulation of a detailed battery degradation model and adherence to the technical requirements of the Swedish FCR markets are incorporated into the developed model. To achieve more practical results, simulations are conducted based on one minute time step realistic data for the whole year 2022. The results show a potential profit of 708 thousand Euros for a 1MW/1MWh BESS by participating in multi-FCR market. Analyzing the impact of considering degradation in the optimization problem has shown that the annual battery aging cost could decrease by 5%-29% without a significant effect on profit. The proposed model can be practically used by flexibility asset owners to achieve profitable and sustainable operation strategies that reduce battery degradation.