pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models
Authors: Tobias Morocutti, Florian Schmid, Jonathan Greif, Francesco Foscarin, Gerhard Widmer
Date: 2025-03-14 00:00:00
Tasks: Audio Tagging, Event Detection, Knowledge Distillation, Sound Event Detection

We target the problem of developing new low-complexity networks for the sound event detection task. Our goal is to meticulously analyze the performance-complexity trade-off, aiming to be competitive with the large state-of-the-art models, at a fraction of the computational requirements. We find that low-complexity convolutional models previously proposed for audio tagging can be effectively adapted for event detection (which requires frame-wise prediction) by adjusting convolutional strides, removing the global pooling, and, importantly, adding a sequence model before the (now frame-wise) classification heads. Systematic experiments reveal that the best choice for the sequence model type depends on which complexity metric is most important for the given application. We also investigate the impact of enhanced training strategies such as knowledge distillation. In the end, we show that combined with an optimized training strategy, we can reach event detection performance comparable to state-of-the-art transformers while requiring only around 5% of the parameters. We release all our pre-trained models and the code for reproducing this work to support future research in low-complexity sound event detection at https://github.com/theMoro/EfficientSED.
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification
Authors: Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer
Date: 2025-03-14 00:00:00
Tasks: Acoustic Scene Classification, Knowledge Distillation, Scene Classification

Knowledge Distillation (KD) is a widespread technique for compressing the knowledge of large models into more compact and efficient models. KD has proved to be highly effective in building well-performing low-complexity Acoustic Scene Classification (ASC) systems and was used in all the top-ranked submissions to this task of the annual DCASE challenge in the past three years. There is extensive research available on establishing the KD process, designing efficient student models, and forming well-performing teacher ensembles. However, less research has been conducted on investigating which teacher model attributes are beneficial for low-complexity students. In this work, we try to close this gap by studying the effects on the student's performance when using different teacher network architectures, varying the teacher model size, training them with different device generalization methods, and applying different ensembling strategies. The results show that teacher model sizes, device generalization methods, the ensembling strategy and the ensemble size are key factors for a well-performing student network.
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets
Authors: Juan Antonio De Rus, Mario Montagud, Jesus Lopez-Ballester, Francesc J. Ferri, Maximo Cobos
Date: 2025-03-14 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Precise elevation perception in binaural audio remains a challenge, despite extensive research on head-related transfer functions (HRTFs) and spectral cues. While prior studies have advanced our understanding of sound localization cues, the interplay between spectral features and elevation perception is still not fully understood. This paper presents a comprehensive analysis of over 600 subjects from 11 diverse public HRTF datasets, employing a convolutional neural network (CNN) model combined with explainable artificial intelligence (XAI) techniques to investigate elevation cues. In addition to testing various HRTF pre-processing methods, we focus on both within-dataset and inter-dataset generalization and explainability, assessing the model's robustness across different HRTF variations stemming from subjects and measurement setups. By leveraging class activation mapping (CAM) saliency maps, we identify key frequency bands that may contribute to elevation perception, providing deeper insights into the spectral features that drive elevation-specific classification. This study offers new perspectives on HRTF modeling and elevation perception by analyzing diverse datasets and pre-processing techniques, expanding our understanding of these cues across a wide range of conditions.
Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment
Authors: Ke Wang, Lei He, Kun Liu, Yan Deng, Wenning Wei, Sheng Zhao
Date: 2025-03-14 00:00:00

Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with a particular focus on evaluating the capabilities of the Generative Pre-trained Transformer (GPT) model, specifically GPT-4o. Our study investigates its ability to process speech and audio for pronunciation assessment across multiple levels of granularity and dimensions, with an emphasis on feedback generation and scoring. For our experiments, we use the publicly available Speechocean762 dataset. The evaluation focuses on two key aspects: multi-level scoring and the practicality of the generated feedback. Scoring results are compared against the manual scores provided in the Speechocean762 dataset, while feedback quality is assessed using Large Language Models (LLMs). The findings highlight the effectiveness of integrating LMMs with traditional methods for pronunciation assessment, offering insights into the model's strengths and identifying areas for further improvement.
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
Authors: Wuwei Huang, Renren Jin, Wen Zhang, Jian Luan, Bin Wang, Deyi Xiong
Date: 2025-03-14 00:00:00
Tasks: Decoder, Transfer Learning, Translation

Recent studies on end-to-end speech translation(ST) have facilitated the exploration of multilingual end-to-end ST and end-to-end simultaneous ST. In this paper, we investigate end-to-end simultaneous speech translation in a one-to-many multilingual setting which is closer to applications in real scenarios. We explore a separate decoder architecture and a unified architecture for joint synchronous training in this scenario. To further explore knowledge transfer across languages, we propose an asynchronous training strategy on the proposed unified decoder architecture. A multi-way aligned multilingual end-to-end ST dataset was curated as a benchmark testbed to evaluate our methods. Experimental results demonstrate the effectiveness of our models on the collected dataset. Our codes and data are available at: https://github.com/XiaoMi/TED-MMST.
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning
Authors: Xinyi Wang, Jiashui Wang, Jinbo Su, Ke Wang, Peng Chen, Yanming Liu, Long Liu, Xiang Li, Yangdong Wang, Qiyuan Chen, Rongze Chen, Chunfu Jia
Date: 2025-03-14 00:00:00
Tasks: Code Generation, Decoder, Instruction Following, Language Modeling, Language Modelling, Masked Language Modeling

Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lack of explicit syntactic structures. While traditional masked language modeling (MLM) approaches do not explicitly focus on natural language interaction, emerging decoder-focused large language models (LLMs) demonstrate partial success in binary analysis yet remain underexplored for holistic comprehension. We present Assembly Augmented Tuning, an end-to-end structural-semantic instruction tuning framework that synergizes encoder architecture with decoder-based LLMs through a projector module, where the assembly encoder extracts hardware-level structural features, the projector bridges representations with the semantic space, and the instruction-tuned LLM preserves natural language capabilities. Experimental results demonstrate three key advantages: (1) State-of-the-art performance in assembly comprehension with +39.7% Recall@1 and +17.8% MRR improvements over GPT-4-Turbo, (2) Consistent enhancements across base models (24.6-107.4% Recall@1 and 15.2-106.3% MRR on Qwen2.5-Coder, Deepseek-Coder and CodeLlama variants), and (3) Superior instruction-following capabilities (41.5%-118% improvements) with controlled code generation degradation (-8.9% to -35% across architectures).
Cloud2BIM: An open-source automatic pipeline for efficient conversion of large-scale point clouds into IFC format
Authors: Slávek Zbirovský, Václav Nežerka
Date: 2025-03-14 00:00:00

Building Information Modeling (BIM) is an essential component in the sustainable reconstruction and revitalization of ageing structures. However, model creation usually relies on laborious manual transformation of the unstructured point cloud data provided by laser scans or photogrammetry. This paper presents Cloud2BIM, an open-source software tool designed to automate the conversion of point clouds into BIM models compliant with the Industry Foundation Classes (IFC) standard. Cloud2BIM integrates advanced algorithms for wall and slab segmentation, opening detection, and room zoning based on real wall surfaces, resulting in a comprehensive and fully automated workflow. Unlike existing tools, it avoids computationally- and calibration-intensive techniques such as RANSAC, supports non-orthogonal geometries, and provides unprecedented processing speed-achieving results up to seven times faster than fastest competing solutions. Systematic validation using benchmark datasets confirms that Cloud2BIM is an easy-to-use, efficient, and scalable solution for generating accurate BIM models, capable of converting extensive point cloud datasets for entire buildings into IFC format with minimal user input.
Collaboration is all you need: LLM Assisted Safe Code Translation
Authors: Rabimba Karanjai, Sam Blackshear, Lei Xu, Weidong Shi
Date: 2025-03-14 00:00:00
Tasks: All, Code Translation, Natural Language Inference, Translation

This paper introduces UniTranslator, a visionary framework that re-imagines code translation as a collaborative endeavor among multiple, compact LLMs. By orchestrating the interaction of specialized agents, each focused on different aspects of the translation process and grounded in a deep understanding of programming concepts, UniTranslator achieves a level of accuracy and efficiency that rivals larger, monolithic models. Our preliminary evaluation demonstrates the potential of UniTranslator to overcome the limitations of existing approaches and unlock the power of smaller LLMs for complex code translation tasks. We explore the effectiveness of this dynamic multi-agent paradigm in handling diverse language pairs, including low-resource languages, and in mitigating common issues such as code artifacts and hallucinations through the use of Natural Language Inference (NLI) grounding and iterative feedback mechanisms
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms
Authors: Camilo Chacón Sartori, Christian Blum
Date: 2025-03-14 00:00:00
Tasks: All, Code Generation, Combinatorial Optimization

Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather than creating algorithms from scratch, can improve existing ones without the need for specialized expertise. To explore this potential, we selected 10 baseline optimization algorithms from various domains (metaheuristics, reinforcement learning, deterministic, and exact methods) to solve the classic Travelling Salesman Problem. The results show that our simple methodology often results in LLM-generated algorithm variants that improve over the baseline algorithms in terms of solution quality, reduction in computational time, and simplification of code complexity, all without requiring specialized optimization knowledge or advanced algorithmic implementation skills.
Empirical Computation
Authors: Eric Tang, Marcel Böhme
Date: 2025-03-13 00:00:00
Tasks: Large Language Model

In this vision paper, we explore the challenges and opportunities of a form of computation that employs an empirical (rather than a formal) approach, where the solution of a computational problem is returned as empirically most likely (rather than necessarily correct). We call this approach as *empirical computation* and observe that its capabilities and limits *cannot* be understood within the classic, rationalist framework of computation. While we take a very broad view of "computational problem", a classic, well-studied example is *sorting*: Given a set of $n$ numbers, return these numbers sorted in ascending order. * To run a classical, *formal computation*, we might first think about a *specific algorithm* (e.g., merge sort) before developing a *specific* program that implements it. The program will expect the input to be given in a *specific* format, type, or data structure (e.g., unsigned 32-bit integers). In software engineering, we have many approaches to analyze the correctness of such programs. From complexity theory, we know that there exists no correct program that can solve the average instance of the sorting problem faster than $O(n\log n)$. * To run an *empirical computation*, we might directly ask a large language model (LLM) to solve *any* computational problem (which can be stated informally in natural language) and provide the input in *any* format (e.g., negative numbers written as Chinese characters). There is no (problem-specific) program that could be analyzed for correctness. Also, the time it takes an LLM to return an answer is entirely *independent* of the computational complexity of the problem that is solved. What are the capabilities or limits of empirical computation in the general, in the problem-, or in the instance-specific? Our purpose is to establish empirical computation as a field in SE that is timely and rich with interesting problems.
Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview
Authors: Norbert Tihanyi, Tamas Bisztray, Mohamed Amine Ferrag, Bilel Cherif, Richard A. Dubniczky, Ridhi Jain, Lucas C. Cordeiro
Date: 2025-03-13 00:00:00
Tasks: Automated Theorem Proving, software testing, Vulnerability Detection

Software testing and verification are critical for ensuring the reliability and security of modern software systems. Traditionally, formal verification techniques, such as model checking and theorem proving, have provided rigorous frameworks for detecting bugs and vulnerabilities. However, these methods often face scalability challenges when applied to complex, real-world programs. Recently, the advent of Large Language Models (LLMs) has introduced a new paradigm for software analysis, leveraging their ability to understand insecure coding practices. Although LLMs demonstrate promising capabilities in tasks such as bug prediction and invariant generation, they lack the formal guarantees of classical methods. This paper presents a comprehensive study of state-of-the-art software testing and verification, focusing on three key approaches: classical formal methods, LLM-based analysis, and emerging hybrid techniques, which combine their strengths. We explore each approach's strengths, limitations, and practical applications, highlighting the potential of hybrid systems to address the weaknesses of standalone methods. We analyze whether integrating formal rigor with LLM-driven insights can enhance the effectiveness and scalability of software verification, exploring their viability as a pathway toward more robust and adaptive testing frameworks.
Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarization
Authors: Weisong Sun, Yiran Zhang, Jie Zhu, Zhihui Wang, Chunrong Fang, Yonglong Zhang, Yebo Feng, JiangPing Huang, Xingya Wang, Zhi Jin, Yang Liu
Date: 2025-03-13 00:00:00
Tasks: Code Summarization

Commenting code is a crucial activity in software development, as it aids in facilitating future maintenance and updates. To enhance the efficiency of writing comments and reduce developers' workload, researchers has proposed various automated code summarization (ACS) techniques to automatically generate comments/summaries for given code units. However, these ACS techniques primarily focus on generating summaries for code units at the method level. There is a significant lack of research on summarizing higher-level code units, such as file-level and module-level code units, despite the fact that summaries of these higher-level code units are highly useful for quickly gaining a macro-level understanding of software components and architecture. To fill this gap, in this paper, we conduct a systematic study on how to use LLMs for commenting higher-level code units, including file level and module level. These higher-level units are significantly larger than method-level ones, which poses challenges in handling long code inputs within LLM constraints and maintaining efficiency. To address these issues, we explore various summarization strategies for ACS of higher-level code units, which can be divided into three types: full code summarization, reduced code summarization, and hierarchical code summarization. The experimental results suggest that for summarizing file-level code units, using the full code is the most effective approach, with reduced code serving as a cost-efficient alternative. However, for summarizing module-level code units, hierarchical code summarization becomes the most promising strategy. In addition, inspired by the research on method-level ACS, we also investigate using the LLM as an evaluator to evaluate the quality of summaries of higher-level code units. The experimental results demonstrate that the LLM's evaluation results strongly correlate with human evaluations.
From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution
Authors: Zhe Zhao, Haibin Wen, Pengkun Wang, Ye Wei, Zaixi Zhang, Xi Lin, Fei Liu, Bo An, Hui Xiong, Yang Wang, Qingfu Zhang
Date: 2025-03-13 00:00:00
Tasks: Code Generation, global-optimization

Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization. However, current methods such as EoH and FunSearch mainly rely on predefined templates and expert-specified functions that focus solely on the local evolution of key functionalities. Consequently, they fail to fully leverage the synergistic benefits of the overall architecture and the potential of global optimization. In this paper, we introduce an end-to-end algorithm generation and optimization framework based on LLMs. Our approach utilizes the deep semantic understanding of LLMs to convert natural language requirements or human-authored papers into code solutions, and employs a two-dimensional co-evolution strategy to optimize both functional and structural aspects. This closed-loop process spans problem analysis, code generation, and global optimization, automatically identifying key algorithm modules for multi-level joint optimization and continually enhancing performance and design innovation. Extensive experiments demonstrate that our method outperforms traditional local optimization approaches in both performance and innovation, while also exhibiting strong adaptability to unknown environments and breakthrough potential in structural design. By building on human research, our framework generates and optimizes novel algorithms that surpass those designed by human experts, broadening the applicability of LLMs for algorithm design and providing a novel solution pathway for automated algorithm development.
Fault-tolerant control of nonlinear systems: An inductive synthesis approach
Authors: Daniele Masti, Davide Grande, Andrea Peruffo, Filippo Fabiani
Date: 2025-03-14 00:00:00

Actuator faults heavily affect the performance and stability of control systems, an issue that is even more critical for systems required to operate autonomously under adverse environmental conditions, such as unmanned vehicles. To this end, passive fault-tolerant control (PFTC) systems can be employed, namely fixed-gain control laws that guarantee stability both in the nominal case and in the event of faults. In this paper, we propose a counterexample guided inductive synthesis (CEGIS)-based approach to design reliable PFTC policies for nonlinear control systems affected by partial, or total, actuator faults. Our approach enjoys finite-time convergence guarantees and extends available techniques by considering nonlinear dynamics with possible fault conditions. Extensive numerical simulations illustrate how the proposed method can be applied to realistic operational scenarios involving the velocity and heading control of autonomous underwater vehicles (AUVs). Our PFTC technique exhibits comparatively low synthesis time (i.e. minutes) and minimal computational requirements, which render it is suitable for embedded applications with limited availability of energy and onboard power resources.
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
Authors: Jens Engel, Andrea Castellani, Patricia Wollstadt, Felix Lanfermann, Thomas Schmitt, Sebastian Schmitt, Lydia Fischer, Steffen Limmer, David Luttropp, Florian Jomrich, René Unger, Tobias Rodemann
Date: 2025-03-14 00:00:00
Tasks: energy management, Management

We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed throughout the facility are organized in a hierarchical metering structure with multiple sub-metering levels, which is reflected in the dataset. The dataset contains measurement data from 72 energy meters, 9 heat meters and a weather station. Both raw and processed data at different processing levels, including labeled issues, is available. In this paper, we describe the data acquisition and post-processing employed to create the dataset. The dataset enables the application of a wide range of methods in the domain of energy management, including optimization, modeling, and machine learning to optimize building operations and reduce costs and carbon emissions.
Feasibility of Randomized Detector Tuning for Attack Impact Mitigation
Authors: Sribalaji C. Anand, Kamil Hassan, Henrik Sandberg
Date: 2025-03-14 00:00:00

This paper considers the problem of detector tuning against false data injection attacks. In particular, we consider an adversary injecting false sensor data to maximize the state deviation of the plant, referred to as impact, whilst being stealthy. To minimize the impact of stealthy attacks, inspired by moving target defense, the operator randomly switches the detector thresholds. In this paper, we theoretically derive the sufficient (and in some cases necessary) conditions under which the impact of stealthy attacks can be made smaller with randomized switching of detector thresholds compared to static thresholds. We establish the conditions for the stateless ($\chi^2$) and the stateful (CUSUM) detectors. The results are illustrated through numerical examples.