pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions
Authors: Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, Milos Skotak
Date: 2021-12-13 00:00:00
Proceeding: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) 2021 12
Tasks: Anomaly Detection, Defect Detection

Anomaly detection (AD) plays a key role in automated quality analysis in industrial production. Recent AD methods have shown great potential for the detection of visual defects in several real-world applications. Most of the datatsets used in AD research (e.g. MVTec AD) are composed mainly of images from the laboratory environment with a monochromatic background. Each image contains only one object, which is centred, and its distance and spatial orientation to the camera do not change significantly. However, these conditions cannot be achieved in many realworld manufacturing processes and production lines. In order to test the performance of state-of-the-art (SOTA) AD methods under conditions of variable spatial orientation, position and distance of multiple objects concerning the camera at different light intensities and with a non-homogeneous background, it is necessary to create a new dataset. In this paper, we introduce a new dataset focused specifically on the issue of defect detection during painted metal parts fabrication. Next, we evaluate the performance of current SOTA AD methods on the proposed dataset. Our results show that some SOTA AD methods, which perform well on the standard industrial anomaly detection datatset – MVTec AD, show significantly different performance on our dataset. AUROC image-level difference is up to 23.12%. If we average the scores for all methods on each dataset, we observe the difference of 15.24%. Our experiment shows that for further development and improvement of AD methods, it is necessary to test these methods on datasets based on specific real-world applications.
CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation
Authors: Mingwei Liu, Tianyong Yang, Yiling Lou, Xueying Du, Ying Wang, Xin Peng
Date: 2023-09-11 00:00:00
Proceeding: IEEE/ACM International Conference on Automated Software Engineering 2023 9
Tasks: Code Generation, Library-Oriented Code Generation

Automated code generation has been extensively studied in recent literature. In this work, we first survey 66 participants to motivate a more pragmatic code generation scenario, i.e., library-oriented code generation, where the generated code should implement the functionally of the natural language query with the given library. We then revisit existing learning- based code generation techniques and find they have limited effectiveness in such a library-oriented code generation scenario. To address this limitation, we propose a novel library-oriented code generation technique, CodeGen4Libs, which incorporates two stages: import generation and code generation. The import generation stage generates import statements for the natural language query with the given third-party libraries, while the code generation stage generates concrete code based on the generated imports and the query. To evaluate the effectiveness of our approach, we conduct extensive experiments on a dataset of 403,780 data items. Our results demonstrate that CodeGen4Libs outperforms baseline models in both import generation and code generation stages, achieving improvements of up to 97.4% on EM (Exact Match), 54.5% on BLEU, and 53.5% on Hit@All. Overall, our proposed CodeGen4Libs approach shows promising results in generating high-quality code with specific third-party libraries, which can improve the efficiency and effectiveness of software development.
Incremental Constrained Clustering by Minimal Weighted Modification
Authors: Aymeric Beauchamp, Thi-Bich-Hanh Dao, Samir Loudni, Christel Vrain
Date: 2023-09-22 00:00:00
Proceeding: 29th International Conference on Principles and Practice of Constraint Programming 2023 9
Tasks: Clustering, Constrained Clustering, Incremental Constrained Clustering, Time Series

Clustering is a well-known task in Data Mining that aims at grouping data instances according to their similarity. It is an exploratory and unsupervised task whose results depend on many parameters, often requiring the expert to iterate several times before satisfaction. Constrained clustering has been introduced for better modeling the expectations of the expert. Nevertheless constrained clustering is not yet sufficient since it usually requires the constraints to be given before the clustering process. In this paper we address a more general problem that aims at modeling the exploratory clustering process, through a sequence of clustering modifications where expert constraints are added on the fly. We present an incremental constrained clustering framework integrating active query strategies and a Constraint Programming model to fit the expert expectations while preserving the stability of the partition, so that the expert can understand the process and apprehend its impact. Our model supports instance and group-level constraints, which can be relaxed. Experiments on reference datasets and a case study related to the analysis of satellite image time series show the relevance of our framework.
Failure of R0 near the epidemic threshold in the classical SIS model
Authors: Mozzamil Mohammed
Date: 2023-09-18 00:00:00
Proceeding: medRxiv 2023 9

The primary predictor of a disease outbreak and severity is the basic reproduction number R0, which represents the average number of secondary cases produced by introducing an infected individual into an entirely susceptible population. According to the classical SIS model, a disease with R0 less than one will eventually die out and persist if R0 is greater than one. Using the pair-approximation method, we reconstruct the classical SIS model by explicitly accounting for neighbourhood interactions between susceptible and infected individuals. Specifically, the disease can only be transmitted, with some transmission probability, if a susceptible individual is surrounded by at least one infected individual within its direct neighborhoods. Despite the simplicity of the SIS model present here, results produced by the pair-approximation model deviates significantly from predictions by the mean-field approximation model, particularly near the epidemic threshold. Contrasting the standard SIS model based on the mean-field approach, we find scenarios where the disease dies out even if R0 is greater than one. We suggest a crucial need for redefining the basic reproduction number on a smaller spatial scale and taking the average R0 over a global scale, rather than applying it globally to an entire population. However, in the realm of more intricate models of infectious diseases, it remains an open question to what extent mean-field approximation predictions diverge from predictions produced by models that consider neighborhood interactions.
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry
Authors: Xia Chen, Ruiji Sun, Ueli Saluz, Stefano Schiavon, Philipp Geyer
Date: 2023-09-11 00:00:00
Tasks: Causal Inference, Decision Making, feature selection

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.
Brief Architectural Survey of Biopotential Recording Front-Ends since the 1970s
Authors: Taeju Lee, Minkyu Je
Date: 2023-09-20 00:00:00
Tasks: Brain Computer Interface

Measuring the bioelectric signals is one of the key functions in wearable healthcare devices and implantable medical devices. The use of wearable healthcare devices has made continuous and immediate monitoring of personal health status possible. Implantable medical devices have played an important role throughout the fields of neuroscience, brain-machine (or brain-computer) interface, and rehabilitation technology. Over the last five decades, the bioelectric signals have been observed through a variety of biopotential recording front-ends, along with advances in semiconductor technology scaling and circuit techniques. Also, for reliable and continuous signal acquisition, the front-end architectures have evolved while maintaining low power and low noise performance. In this article, the architecture history of the biopotential recording front-ends developed since the 1970s is surveyed, and overall key circuit techniques are discussed. Depending on the bioelectric signals being measured, appropriate front-end architecture needs to be chosen, and the characteristics and challenges of each architecture are also covered in this article.
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Authors: Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdoğmuş, Mathew Yarossi
Date: 2023-09-13 00:00:00
Tasks: Gesture Recognition, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Synthetic Data Generation

Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomechanically independent components - a set of wrist directions and a set of finger modifiers. To maintain fast calibration time, we train models for each component using only individual gestures, and extrapolate to the full product space of combination gestures by generating synthetic data. We collected a supervised dataset with high-confidence ground truth labels in which subjects performed combination gestures while holding a joystick, and conducted experiments to analyze the impact of model architectures, classifier algorithms, and synthetic data generation strategies on the performance of the proposed approach. Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time. Significance: sEMG-based gesture recognition has applications in human-computer interaction, virtual reality, and the control of robotic and prosthetic devices. Existing approaches require exhaustive model calibration. The proposed approach increases expressivity without requiring users to demonstrate all combination gesture classes. Our results may be extended to larger gesture vocabularies and more complicated model architectures.
Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off
Authors: Min Fu, Weidong Mei, Rui Zhang
Date: 2023-09-21 00:00:00
Tasks: Combinatorial Optimization

Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.
Semi-Supervised Variational Inference over Nonlinear Channels
Authors: David Burshtein, Eli Bery
Date: 2023-09-21 00:00:00
Tasks: Meta-Learning, Variational Inference

Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inference, for decoding unknown nonlinear channels. These methods, which include Monte Carlo expectation maximization and a variational autoencoder, make efficient use of few pilot symbols and the payload data. The best semi-supervised learning results are achieved with a variational autoencoder. For sufficiently many payload symbols, the variational autoencoder also has lower error rate compared to meta learning that uses the pilot data of the present as well as previous transmission blocks.
RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet
Authors: Shamik Sarkar, Dongning Guo, Danijela Cabric
Date: 2023-09-21 00:00:00
Tasks: parameter estimation

Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS). In this paper, we propose a supervised deep learning-based spectrum sensing approach called RadYOLOLet that can detect low-power radar signals in the presence of interference and estimate the radar signal parameters. The core of RadYOLOLet is two different convolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trained independently. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet. However, it suffers from low radar detection accuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNN specifically to deal with this limitation of RadYOLO. Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLet using different experiments corresponding to different types of interference signals. Based on our evaluations, we find that RadYOLOLet can achieve 100% radar detection accuracy for our considered radar types up to 16 dB SNR, which cannot be guaranteed by other comparable methods. RadYOLOLet can also function accurately under interference up to 16 dB SINR.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Authors: Jia He, Ziye Jia, Chao Dong, Junyu Liu, Qihui Wu, Jingxian Liu
Date: 2023-09-21 00:00:00
Tasks: Q-Learning

Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability. However, the ability of a single UAV is limited, and as for the large-scale three-dimensional (3D) scenario, UAV swarms can establish seamless wireless communication services. Hence, in this work, we consider a scenario of UAV swarm deployment and trajectory to satisfy 3D coverage considering the effects of obstacles. In detail, we propose a hierarchical swarm framework to efficiently serve the large-area users. Then, the problem is formulated to minimize the total trajectory loss of the UAV swarm. However, the problem is intractable due to the non-convex property, and we decompose it into smaller issues of users clustering, UAV swarm hovering points selection, and swarm trajectory determination. Moreover, we design a Q-learning based algorithm to accelerate the solution efficiency. Finally, we conduct extensive simulations to verify the proposed mechanisms, and the designed algorithm outperforms other referred methods.
Reachability Analysis of ARMAX Models
Authors: Laura Lützow, Matthias Althoff
Date: 2023-09-21 00:00:00
Tasks: State Space Models, valid

Reachability analysis is a powerful tool for computing the set of states or outputs reachable for a system. While previous work has focused on systems described by state-space models, we present the first methods to compute reachable sets of ARMAX models - one of the most common input-output models originating from data-driven system identification. The first approach we propose can only be used with dependency-preserving set representations such as symbolic zonotopes, while the second one is valid for arbitrary set representations but relies on a reformulation of the ARMAX model. By analyzing the computational complexities, we show that both approaches scale quadratically with respect to the time horizon of the reachability problem when using symbolic zonotopes. To reduce the computational complexity, we propose a third approach that scales linearly with respect to the time horizon when using set representations that are closed under Minkowski addition and linear transformation and that satisfy that the computational complexity of the Minkowski sum is independent of the representation size of the operands. Our numerical experiments demonstrate that the reachable sets of ARMAX models are tighter than the reachable sets of equivalent state space models in case of unknown initial states. Therefore, this methodology has the potential to significantly reduce the conservatism of various verification techniques.
A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement
Authors: Bengt J. Borgstrom, Michael S. Brandstein
Date: 2023-09-21 00:00:00
Tasks: Automatic Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Neural network approaches to single-channel speech enhancement have received much recent attention. In particular, mask-based architectures have achieved significant performance improvements over conventional methods. This paper proposes a multiscale autoencoder (MSAE) for mask-based end-to-end neural network speech enhancement. The MSAE performs spectral decomposition of an input waveform within separate band-limited branches, each operating with a different rate and scale, to extract a sequence of multiscale embeddings. The proposed framework features intuitive parameterization of the autoencoder, including a flexible spectral band design based on the Constant-Q transform. Additionally, the MSAE is constructed entirely of differentiable operators, allowing it to be implemented within an end-to-end neural network, and be discriminatively trained. The MSAE draws motivation both from recent multiscale network topologies and from traditional multiresolution transforms in speech processing. Experimental results show the MSAE to provide clear performance benefits relative to conventional single-branch autoencoders. Additionally, the proposed framework is shown to outperform a variety of state-of-the-art enhancement systems, both in terms of objective speech quality metrics and automatic speech recognition accuracy.
Is the Ideal Ratio Mask Really the Best? -- Exploring the Best Extraction Performance and Optimal Mask of Mask-based Beamformers
Authors: Atsuo Hiroe, Katsutoshi Itoyama, Kazuhiro Nakadai
Date: 2023-09-21 00:00:00

This study investigates mask-based beamformers (BFs), which estimate filters to extract target speech using time-frequency masks. Although several BF methods have been proposed, the following aspects are yet to be comprehensively investigated. 1) Which BF can provide the best extraction performance in terms of the closeness of the BF output to the target speech? 2) Is the optimal mask for the best performance common for all BFs? 3) Is the ideal ratio mask (IRM) identical to the optimal mask? Accordingly, we investigate these issues considering four mask-based BFs: the maximum signal-to-noise ratio BF, two variants of this, and the multichannel Wiener filter (MWF) BF. To obtain the optimal mask corresponding to the peak performance for each BF, we employ an approach that minimizes the mean square error between the BF output and target speech for each utterance. Via the experiments with the CHiME-3 dataset, we verify that the four BFs have the same peak performance as the upper bound provided by the ideal MWF BF, whereas the optimal mask depends on the adopted BF and differs from the IRM. These observations differ from the conventional idea that the optimal mask is common for all BFs and that peak performance differs for each BF. Hence, this study contributes to the design of mask-based BFs.
Multi-Channel MOSRA: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and a Teacher Model
Authors: Jozef Coldenhoff, Andrew Harper, Paul Kendrick, Tijana Stojkovic, Milos Cernak
Date: 2023-09-21 00:00:00
Tasks: Descriptive, Prediction

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device selection for rooms with multiple recording devices may benefit from a multi-channel approach where the descriptive metrics are predicted for multiple devices in parallel. Following our hypothesis that a model may benefit from multi-channel training, we develop a multi-channel model for joint MOS and room acoustics prediction (MOSRA) for five channels in parallel. The lack of multi-channel audio data with ground truth labels necessitated the creation of simulated data using an acoustic simulator with room acoustic labels extracted from the generated impulse responses and labels for MOS generated in a student-teacher setup using a wav2vec2-based MOS prediction model. Our experiments show that the multi-channel model improves the prediction of the direct-to-reverberation ratio, clarity, and speech transmission index over the single-channel model with roughly 5$\times$ less computation while suffering minimal losses in the performance of the other metrics.