pwc-archive/papers-with-abstracts huggingface dataset:
sDAC -- Semantic Digital Analog Converter for Semantic Communications
Authors: Zhicheng Bao, Chen Dong, Xiaodong Xu
Date: 2024-04-26 00:00:00
Tasks: Quantization, Semantic Communication

In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility of semantic communications and digital communications. Most of the current semantic communication systems are based on the analog modulations, ignoring their incorporation with digital communication systems, which are more common in practice. In fact, quantization methods in traditional communication systems are not appropriate for use in the era of semantic communication as these methods do not consider the semantic information inside symbols. In this case, any bit flip caused by channel noise can lead to a great performance drop. To address this challenge, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. On the transmitter side, continuous values from the encoder are converted to binary bits and then can be modulated by any existing methods. After transmitting through the noisy channel, these bits get demodulated by paired methods and converted back to continuous values for further semantic decoding. The whole progress does not depend on any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness.
Enhancing Channel Estimation in Quantized Systems with a Generative Prior
Authors: Benedikt Fesl, Aziz Banna, Wolfgang Utschick
Date: 2024-04-26 00:00:00
Tasks: Quantization

Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.
Adaptive Semantic Token Selection for AI-native Goal-oriented Communications
Authors: Alessio Devoto, Simone Petruzzi, Jary Pomponi, Paolo Di Lorenzo, Simone Scardapane
Date: 2024-04-25 00:00:00

In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems.
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning
Authors: Pengcheng Sun, Erwu Liu, Rui Wang
Date: 2024-04-20 00:00:00
Tasks: Federated Learning, Quantization

The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.
Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection
Authors: Abhinav Lalwani, Tasha Kim, Lovish Chopra, Christopher Hahn, Zhijing Jin, Mrinmaya Sachan
Date: 2024-04-18 00:00:00
Tasks: Logical Fallacies, Logical Fallacy Detection, Misinformation, Sentence, valid

Translating natural language into formal language such as First-Order Logic (FOL) is a foundational challenge in NLP with wide-ranging applications in automated reasoning, misinformation tracking, and knowledge validation. In this paper, we introduce Natural Language to First-Order Logic (NL2FOL), a framework to autoformalize natural language to FOL step by step using Large Language Models (LLMs). Our approach addresses key challenges in this translation process, including the integration of implicit background knowledge. By leveraging structured representations generated by NL2FOL, we use Satisfiability Modulo Theory (SMT) solvers to reason about the logical validity of natural language statements. We present logical fallacy detection as a case study to evaluate the efficacy of NL2FOL. Being neurosymbolic, our approach also provides interpretable insights into the reasoning process and demonstrates robustness without requiring model fine-tuning or labeled training data. Our framework achieves strong performance on multiple datasets. On the LOGIC dataset, NL2FOL achieves an F1-score of 78%, while generalizing effectively to the LOGICCLIMATE dataset with an F1-score of 80%.
Gradient-enhanced sparse Hermite polynomial expansions for pricing and hedging high-dimensional American options
Authors: Jiefei Yang, Guanglian Li
Date: 2024-05-04 00:00:00

We propose an efficient and easy-to-implement gradient-enhanced least squares Monte Carlo method for computing price and Greeks (i.e., derivatives of the price function) of high-dimensional American options. It employs the sparse Hermite polynomial expansion as a surrogate model for the continuation value function, and essentially exploits the fast evaluation of gradients. The expansion coefficients are computed by solving a linear least squares problem that is enhanced by gradient information of simulated paths. We analyze the convergence of the proposed method, and establish an error estimate in terms of the best approximation error in the weighted $H^1$ space, the statistical error of solving discrete least squares problems, and the time step size. We present comprehensive numerical experiments to illustrate the performance of the proposed method. The results show that it outperforms the state-of-the-art least squares Monte Carlo method with more accurate price, Greeks, and optimal exercise strategies in high dimensions but with nearly identical computational cost, and it can deliver comparable results with recent neural network-based methods up to dimension 100.
Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios
Authors: Nils L. Westhausen, Hendrik Kayser, Theresa Jansen, Bernd T. Meyer
Date: 2024-05-03 00:00:00
Tasks: Deep Learning, Speech Enhancement

Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids. Deep models suited for real-world application should feature a low computational complexity and low processing delay of only a few milliseconds. In this paper, we explore deep speech enhancement that matches these requirements and contrast monaural and binaural processing algorithms in two complex acoustic scenes. Both algorithms are evaluated with objective metrics and in experiments with hearing-impaired listeners performing a speech-in-noise test. Results are compared to two traditional enhancement strategies, i.e., adaptive differential microphone processing and binaural beamforming. While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers. Through a post-analysis, this can be attributed to improvements at low SNRs and to precise spatial filtering.
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model
Authors: Zongyang Du, Junchen Lu, Kun Zhou, Lakshmish Kaushik, Berrak Sisman
Date: 2024-05-02 00:00:00
Tasks: Denoising, Emotion Recognition, Speaker Verification, Speech Emotion Recognition, Voice Conversion

Expressive voice conversion (VC) conducts speaker identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Emotional style modeling for arbitrary speakers in expressive VC has not been extensively explored. Previous approaches have relied on vocoders for speech reconstruction, which makes speech quality heavily dependent on the performance of vocoders. A major challenge of expressive VC lies in emotion prosody modeling. To address these challenges, this paper proposes a fully end-to-end expressive VC framework based on a conditional denoising diffusion probabilistic model (DDPM). We utilize speech units derived from self-supervised speech models as content conditioning, along with deep features extracted from speech emotion recognition and speaker verification systems to model emotional style and speaker identity. Objective and subjective evaluations show the effectiveness of our framework. Codes and samples are publicly available.
Speech Technology Services for Oral History Research
Authors: Christoph Draxler, Henk van den Heuvel, Arjan van Hessen, Pavel Ircing, Jan Lehečka
Date: 2024-04-26 00:00:00

Oral history is about oral sources of witnesses and commentors on historical events. Speech technology is an important instrument to process such recordings in order to obtain transcription and further enhancements to structure the oral account In this contribution we address the transcription portal and the webservices associated with speech processing at BAS, speech solutions developed at LINDAT, how to do it yourself with Whisper, remaining challenges, and future developments.
On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation
Authors: Atharva Naik
Date: 2024-04-26 00:00:00
Tasks: Code Generation, HumanEval

The task of code generation from natural language (NL2Code) has become extremely popular, especially with the advent of Large Language Models (LLMs). However, efforts to quantify and track this progress have suffered due to a lack of reliable metrics for functional correctness. While popular benchmarks like HumanEval have test cases to enable reliable evaluation of correctness, it is time-consuming and requires human effort to collect test cases. As an alternative several reference-based evaluation metrics have been proposed, with embedding-based metrics like CodeBERTScore being touted as having a high correlation with human preferences and functional correctness. In our work, we analyze the ability of embedding-based metrics like CodeBERTScore to measure functional correctness and other helpful constructs like editing effort by analyzing outputs of ten models over two popular code generation benchmarks. Our results show that while they have a weak correlation with functional correctness (0.16), they are strongly correlated (0.72) with editing effort.
Mining patterns in syntax trees to automate code reviews of student solutions for programming exercises
Authors: Charlotte Van Petegem, Kasper Demeyere, Rien Maertens, Niko Strijbol, Bram De Wever, Bart Mesuere, Peter Dawyndt
Date: 2024-04-26 00:00:00

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings.
Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024
Authors: Thuy Nguyen Thi, Anh Nguyen Viet, Thin Dang Van, Ngan Nguyen Luu Thuy
Date: 2024-04-23 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Sentence Classification, XLM-R

This paper describes our systems for the sub-task I in the Software Mention Detection in Scholarly Publications shared-task. We propose three approaches leveraging different pre-trained language models (BERT, SciBERT, and XLM-R) to tackle this challenge. Our bestperforming system addresses the named entity recognition (NER) problem through a three-stage framework. (1) Entity Sentence Classification - classifies sentences containing potential software mentions; (2) Entity Extraction - detects mentions within classified sentences; (3) Entity Type Classification - categorizes detected mentions into specific software types. Experiments on the official dataset demonstrate that our three-stage framework achieves competitive performance, surpassing both other participating teams and our alternative approaches. As a result, our framework based on the XLM-R-based model achieves a weighted F1-score of 67.80%, delivering our team the 3rd rank in Sub-task I for the Software Mention Recognition task.
On Using Agent-based Modeling and Simulation for Studying Blockchain Systems
Authors: Önder Gürcan
Date: 2024-04-23 00:00:00
Tasks: Management

There is a need for a simulation framework, which is develop as a software using modern engineering approaches (e.g., modularity --i.e., model reuse--, testing, continuous development and continuous integration, automated management of builds, dependencies and documentation) and agile principles, (1) to make rapid prototyping of industrial cases and (2) to carry out their feasibility analysis in a realistic manner (i.e., to test hypothesis by simulating complex experiments involving large numbers of participants of different types acting in one or several blockchain systems).
Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository
Authors: Ajinkya Deshpande, Anmol Agarwal, Shashank Shet, Arun Iyer, Aditya Kanade, Ramakrishna Bairi, Suresh Parthasarathy
Date: 2024-04-22 00:00:00
Tasks: Class-level Code Generation, Code Generation, Navigate

LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the complexities associated with creating code artifacts like classes, particularly within the context of real-world software repositories, remain underexplored. Prior research treats class-level generation as an isolated task, neglecting the intricate dependencies & interactions that characterize real-world software environments. To address this gap, we introduce RepoClassBench, a comprehensive benchmark designed to rigorously evaluate LLMs in generating complex, class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We ensure that each class in our dataset not only has cross-file dependencies within the repository but also includes corresponding test cases to verify its functionality. We find that current models struggle with the realistic challenges posed by our benchmark, primarily due to their limited exposure to relevant repository contexts. To address this shortcoming, we introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context in an agent-based framework. Our experiments demonstrate that RRR significantly outperforms existing baselines on RepoClassBench, showcasing its effectiveness across programming languages & under various settings. Our findings emphasize the critical need for code-generation benchmarks to incorporate repo-level dependencies to more accurately reflect the complexities of software development. Our work shows the benefits of leveraging specialized tools to enhance LLMs' understanding of repository context. We plan to make our dataset & evaluation harness public.
Configuration-Constrained Tube MPC for Tracking
Authors: Filippo Badalamenti, Sampath Kumar Mulagaleti, Alberto Bemporad, Boris Houska, Mario Eduardo Villanueva
Date: 2024-05-06 00:00:00
Tasks: Model Predictive Control

This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative uncertainties. The MPC controllers designed using this framework exhibit recursively feasible for changing references, and robust asymptotic stability for piecewise constant references. The framework leverages configuration-constrained polytopes to parameterize the tubes, offering flexibility to optimize their shape. The efficacy of the approach is demonstrated through two numerical examples. The first example illustrates the theoretical results, and the second uses the framework to design a lane-change controller for an autonomous vehicle.
A Valuation Framework for Customers Impacted by Extreme Temperature-Related Outages
Authors: Min Gyung Yu, Monish Mukherjee, Shiva Poudela, Sadie R. Bender, Sarmad Hanif, Trevor D. Hardy, Hayden M. Reeve
Date: 2024-05-06 00:00:00
Tasks: Specificity

Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI) due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. These findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.
Human-Variability-Respecting Optimal Control for Physical Human-Machine Interaction
Authors: Sean Kille, Paul Leibold, Philipp Karg, Balint Varga, Sören Hohmann
Date: 2024-05-06 00:00:00

Physical Human-Machine Interaction plays a pivotal role in facilitating collaboration across various domains. When designing appropriate model-based controllers to assist a human in the interaction, the accuracy of the human model is crucial for the resulting overall behavior of the coupled system. When looking at state-of-the-art control approaches, most methods rely on a deterministic model or no model at all of the human behavior. This poses a gap to the current neuroscientific standard regarding human movement modeling, which uses stochastic optimal control models that include signal-dependent noise processes and therefore describe the human behavior much more accurate than the deterministic counterparts. To close this gap by including these stochastic human models in the control design, we introduce a novel design methodology resulting in a Human-Variability-Respecting Optimal Control that explicitly incorporates the human noise processes and their influence on the mean and variability behavior of a physically coupled human-machine system. Our approach results in an improved overall system performance, i.e. higher accuracy and lower variability in target point reaching, while allowing to shape the joint variability, for example to preserve human natural variability patterns.