pwc-archive/papers-with-abstracts huggingface dataset:
PRODIS - a speech database and a phoneme-based language model for the study of predictability effects in Polish
Authors: Zofia Malisz, Jan Foremski, Małgorzata Kul
Date: 2024-04-15 00:00:00
Tasks: Language Modeling, Language Modelling

We present a speech database and a phoneme-level language model of Polish. The database and model are designed for the analysis of prosodic and discourse factors and their impact on acoustic parameters in interaction with predictability effects. The database is also the first large, publicly available Polish speech corpus of excellent acoustic quality that can be used for phonetic analysis and training of multi-speaker speech technology systems. The speech in the database is processed in a pipeline that achieves a 90% degree of automation. It incorporates state-of-the-art, freely available tools enabling database expansion or adaptation to additional languages.
Towards free-response paradigm: a theory on decision-making in spiking neural networks
Authors: Zhichao Zhu, Yang Qi, Wenlian Lu, Zhigang Wang, Lu Cao, Jianfeng Feng
Date: 2024-04-16 00:00:00
Tasks: Decision Making

The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing. One prevalent challenge encountered by SNNs is the trade-off between inference speed and accuracy, which requires sufficient time to achieve the desired level of performance. Drawing inspiration from animal behavior experiments that demonstrate a connection between decision-making reaction times, task complexity, and confidence levels, this study seeks to apply these insights to SNNs. The focus is on understanding how SNNs make inferences, with a particular emphasis on untangling the interplay between signal and noise in decision-making processes. The proposed theoretical framework introduces a new optimization objective for SNN training, highlighting the importance of not only the accuracy of decisions but also the development of predictive confidence through learning from past experiences. Experimental results demonstrate that SNNs trained according to this framework exhibit improved confidence expression, leading to better decision-making outcomes. In addition, a strategy is introduced for efficient decision-making during inference, which allows SNNs to complete tasks more quickly and can use stopping times as indicators of decision confidence. By integrating neuroscience insights with neuromorphic computing, this study opens up new possibilities to explore the capabilities of SNNs and advance their application in complex decision-making scenarios.
An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems
Authors: Maojiang Tian, Mingke Chen, Wei Du, Yang Tang, Yaochu Jin
Date: 2024-04-16 00:00:00

Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based cooperative coevolution (CC) algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However, current CC frameworks designed for overlapping problems rely on grouping methods for the identification of overlapping problem structures and the current grouping methods for large-scale overlapping problems fail to consider both accuracy and efficiency simultaneously. In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption. In the first stage, OEDG employs a grouping method based on the finite differences principle to identify all subcomponents and shared variables. In the second stage, we propose two grouping refinement methods, called subcomponent union detection (SUD) and subcomponent detection (SD), to enhance and refine the grouping results. SUD examines the information of the subcomponents and shared variables obtained in the previous stage, and SD corrects inaccurate grouping results. To better verify the performance of the proposed OEDG, we propose a series of novel benchmarks that consider various properties of large-scale overlapping problems, including the topology structure, overlapping degree, and separability. Extensive experimental results demonstrate that OEDG is capable of accurately grouping different types of large-scale overlapping problems while consuming fewer computational resources. Finally, we empirically verify that the proposed OEDG can effectively improve the optimization performance of diverse large-scale overlapping problems.
Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
Authors: Jiyuan Pei, Jialin Liu, Yi Mei
Date: 2024-04-16 00:00:00

In many practical applications, usually, similar optimisation problems or scenarios repeatedly appear. Learning from previous problem-solving experiences can help adjust algorithm components of meta-heuristics, e.g., adaptively selecting promising search operators, to achieve better optimisation performance. However, those experiences obtained from previously solved problems, namely offline experiences, may sometimes provide misleading perceptions when solving a new problem, if the characteristics of previous problems and the new one are relatively different. Learning from online experiences obtained during the ongoing problem-solving process is more instructive but highly restricted by limited computational resources. This paper focuses on the effective combination of offline and online experiences. A novel hybrid framework that learns to dynamically and adaptively select promising search operators is proposed. Two adaptive operator selection modules with complementary paradigms cooperate in the framework to learn from offline and online experiences and make decisions. An adaptive decision policy is maintained to balance the use of those two modules in an online manner. Extensive experiments on 170 widely studied real-value benchmark optimisation problems and a benchmark set with 34 instances for combinatorial optimisation show that the proposed hybrid framework outperforms the state-of-the-art methods. Ablation study verifies the effectiveness of each component of the framework.
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
Authors: Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, Yingfei Sun
Date: 2024-04-16 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Open-Domain Question Answering, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Open Domain Question Answering (ODQA) task as a point of entry, our findings reveal a potential digital "Spiral of Silence" effect, with LLM-generated text consistently outperforming human-authored content in search rankings, thereby diminishing the presence and impact of human contributions online. This trend risks creating an imbalanced information ecosystem, where the unchecked proliferation of erroneous LLM-generated content may result in the marginalization of accurate information. We urge the academic community to take heed of this potential issue, ensuring a diverse and authentic digital information landscape.
Promoting the linguistic diversity of TEI in the Maghreb and the Arab region
Authors: Henri Hudrisier, Rachid Zghibi, Sihem Zghidi, Mokhtar Ben Henda
Date: 2024-04-16 00:00:00
Tasks: Diversity

The project targets both oral corpus and the rich text resources written in the Maghreb region. It focuses particularly on the continuity, for more than 12 centuries, of a classical still alive Arabic language and on the extreme hybridization of vernacular languages sustained by the rich Libyan, Roman, Hebrew and Ottoman influences and by the more recent French, Spanish and Italian linguistic interference. In short, the Maghreb is a place of extremely abundant, but much unexploited, textual studies.
Exact and Efficient Unlearning for Large Language Model-based Recommendation
Authors: Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang, Fuli Feng, Xiangnan He
Date: 2024-04-16 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, parameter-efficient fine-tuning

The evolving paradigm of Large Language Model-based Recom- mendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommenda- tion data. The inclusion of user data in LLMs raises privacy concerns. To protect users, the unlearning process in LLMRec, specifically removing unusable data (e.g., historical behaviors) from established LLMRec models, becomes crucial. However, existing unlearning methods are insufficient for the unique characteristics of LLM- Rec, mainly due to high computational costs or incomplete data erasure. In this study, we introduce the Adapter Partition and Ag- gregation (APA) framework for exact and efficient unlearning while maintaining recommendation performance. APA achieves this by establishing distinct adapters for partitioned training data shards and retraining only the adapters impacted by unusable data for un- learning. To preserve recommendation performance and mitigate considerable inference costs, APA employs parameter-level adapter aggregation with sample-adaptive attention for individual testing samples. Extensive experiments substantiate the effectiveness and efficiency of our proposed framework
Cluster-based Graph Collaborative Filtering
Authors: Fan Liu, Shuai Zhao, Zhiyong Cheng, Liqiang Nie, Mohan Kankanhalli
Date: 2024-04-16 00:00:00
Tasks: Clustering, Collaborative Filtering, Node Clustering, Recommendation Systems, Representation Learning

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. Thus, the noisy information from unreliable neighbor nodes (e.g., users with dissimilar interests) negatively impacts the representation learning of the target node. Additionally, conducting graph convolution operations without differentiating high-order neighbors suffers the over-smoothing issue when stacking more layers, resulting in performance degradation. In this paper, we aim to capture more valuable information from high-order neighboring nodes while avoiding noise for better representation learning of the target node. To achieve this goal, we propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF). This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them. Specifically, we design an unsupervised and optimizable soft node clustering approach to classify user and item nodes into multiple clusters. Based on the soft node clustering results and the topology of the user-item interaction graph, we assign the nodes with probabilities for different clusters to construct the cluster-specific graphs. To evaluate the effectiveness of ClusterGCF, we conducted extensive experiments on four publicly available datasets. Experimental results demonstrate that our model can significantly improve recommendation performance.
Contrastive Learning based Deep Latent Masking for Music Source Separation
Authors: Jihyun Kim, Hong-Goo Kang
Date: 2023-08-20 00:00:00
Proceeding: Interspeech 2023 8
Tasks: Contrastive Learning, Music Source Separation

Recent studies on music source separation have extended their applicability to generic audio signals. Real-time applications for music source separation are necessary to provide services such as custom equalizers or to improve the sound of live streaming with diverse effects. However, most prior methods are unsuitable for real-time applications due to their high computational complexity, large memory usage, or long latency. To overcome these problems, we propose a Wave-U-Net type of music source separation network that utilizes high-dimensional masking for the deep latent domain features. We also introduce a contrastive learning technique to estimate the salient latent space embedding of each target source using a masking-based approach. The performance of our proposed model is evaluated on the MUSDB18HQ dataset in comparison with several baselines. The experiments confirm that our proposed model is capable of real-time processing and outperforms existing models.
Intergenerational Insurance
Authors: Francesco Lancia, Alessia Russo, Tim Worrall
Date: 2024-04-15 00:00:00

How should successive generations insure each other when the young can default on previously promised transfers to the old? This paper studies intergenerational insurance that maximizes the expected discounted utility of all generations subject to participation constraints for each generation. If complete insurance is unattainable, the optimal intergenerational insurance is history-dependent even when the environment is stationary. The risk from a generational shock is spread into the future, with periodic resetting. Interpreting intergenerational insurance in terms of debt, the fiscal reaction function is nonlinear and the risk premium on debt is lower than the risk premium with complete insurance.
LatentPaint: Image Inpainting in Latent Space with Diffusion Models
Authors: Ciprian Corneanu, Raghudeep Gadde, Aleix M Martinez
Date: 2024-01-30 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024 1
Tasks: Image Inpainting

Image inpainting is generally done using either a domain-specific (preconditioned) model or a generic model that is postconditioned at inference time. Preconditioned models are fast at inference time but extremely costly to train, requiring training on each domain they are applied to. Postconditioned models do not require any domain-specific training but are slow during inference, requiring multiple forward and backward passes to converge to a desirable solution. Here, we derive an approach that does not require any domain specific training, yet is fast at inference time. To solve the costly inference computational time, we perform the forward-backward fusion step on a latent space rather than the image space. This is solved with a newly proposed propagation module in the diffusion process. Experiments on a number of domains demonstrate our approach attains or improves state-of-the-art results with the advantages of preconditioned and postconditioned models and none of their disadvantages.
Iterated Invariant Extended Kalman Filter (IterIEKF)
Authors: Sven Goffin, Axel Barrau, Silvère Bonnabel, Olivier Brüls, Pierre Sacré
Date: 2024-04-16 00:00:00
Tasks: Benchmarking

We study the mathematical properties of the Invariant Extended Kalman Filter (IEKF) when iterating on the measurement update step, following the principles of the well-known Iterated Extended Kalman Filter. This iterative variant of the IEKF (IterIEKF) systematically improves its accuracy through Gauss-Newton-based relinearization, and exhibits additional theoretical properties, particularly in the low-noise regime, that resemble those of the linear Kalman filter. We apply the proposed approach to the problem of estimating the extended pose of a crane payload using an inertial measurement unit. Our results suggest that the IterIEKF significantly outperforms the IEKF when measurements are highly accurate.
A Robust Road Vanishing Point Detection Adapted to the Real-World Driving Scenes
Authors: Cuong Nguyen Khac, Yeongyu Choi, Ju H. Park and Ho-Youl Jung
Date: 2021-03-18 00:00:00
Proceeding: Sensors 2021 3
Tasks: Autonomous Vehicles, Motion Detection

Vanishing point (VP) provides extremely useful information related to roads in driving
scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP
detection methods for driving scenes still have not achieved sufficiently high accuracy and robustness
to apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection
method to compensate for the deficiencies. For such purposes, three main processing steps often
used in the existing road VP detection methods are carefully examined. Based on the analysis,
stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC
(RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including
various objects and illuminations is used to verify the robustness and real-time capability of the
proposed method. The experimental results show that the proposed method outperforms the existing
motion-based and edge-based road VP detection methods for various illumination conditioned
driving scenes.
Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset
Authors: Rabie El Kharoua, Xiaoming Jiang
Date: 2024-03-11 00:00:00
Proceeding: Journal of Computer and Communications 2024 3
Tasks: Deep Learning, Image Classification, Position, Sign Language Recognition

This paper introduces a Convolutional Neural Network (CNN) model for Arabic Sign Language (AASL) recognition, using the AASL dataset. Recognizing the fundamental importance of communication for the hearing-impaired, especially within the Arabic-speaking deaf community, the study emphasizes the critical role of sign language recognition systems. The proposed methodology achieves outstanding accuracy, with the CNN model reaching 99.9% accuracy on the training set and a validation accuracy of 97.4%. This study not only establishes a high-accuracy AASL recognition model but also provides insights into effective dropout strategies. The achieved high accuracy rates position the proposed model as a significant advancement in the field, holding promise for improved communication accessibility for the Arabic-speaking deaf community.
McQueen : Mixed Precision Quantization of Early Exit Networks
Authors: Utkarsh Saxena; Kaushik Roy
Date: 2023-11-20 00:00:00
Proceeding: British Machine Vision Conference (BMVC) 2023 11
Tasks: Quantization

Mixed precision quantization offers a promising way of obtaining the optimal tradeoff between model complexity and accuracy. However, most quantization techniques do not support input adaptive execution of neural networks resulting in a fixed computational cost for all the instances in a dataset. On the other hand, early exit networks augment traditional architectures with multiple exit classifiers and spend varied computational effort depending on dataset instance complexity, reducing the computational cost. In this work, we propose McQueen, a mixed precision quantization technique for early exit networks. Specifically, we develop a Parametric Differentiable Quantizer (PDQ) which learns the quantizer precision, threshold, and scaling factor during training. Further, we propose a gradient masking technique that facilitates the joint optimization of exit and final classifiers to learn PDQ and network parameters. Extensive experiments on a variety of datasets demonstrate that our method can achieve significant reduction in BitOperations (BOPs) while maintaining the top-1 accuracy of the original floating-point model. Specifically, McQueen is able to reduce BOPs by 109x compared to floatingpoint baseline without accuracy degradation on ResNet-18 trained on ImageNet.
Tao: Re-Thinking DL-based Microarchitecture Simulation
Authors: Santosh Pandey, Amir Yazdanbakhsh, Hang Liu
Date: 2024-04-16 00:00:00
Tasks: Transfer Learning

Microarchitecture simulators are indispensable tools for microarchitecture designers to validate, estimate, and optimize new hardware that meets specific design requirements. While the quest for a fast, accurate and detailed microarchitecture simulation has been ongoing for decades, existing simulators excel and fall short at different aspects: (i) Although execution-driven simulation is accurate and detailed, it is extremely slow and requires expert-level experience to design. (ii) Trace-driven simulation reuses the execution traces in pursuit of fast simulation but faces accuracy concerns and fails to achieve significant speedup. (iii) Emerging deep learning (DL)-based simulations are remarkably fast and have acceptable accuracy but fail to provide adequate low-level microarchitectural performance metrics crucial for microarchitectural bottleneck analysis. Additionally, they introduce substantial overheads from trace regeneration and model re-training when simulating a new microarchitecture. Re-thinking the advantages and limitations of the aforementioned simulation paradigms, this paper introduces TAO that redesigns the DL-based simulation with three primary contributions: First, we propose a new training dataset design such that the subsequent simulation only needs functional trace as inputs, which can be rapidly generated and reused across microarchitectures. Second, we redesign the input features and the DL model using self-attention to support predicting various performance metrics. Third, we propose techniques to train a microarchitecture agnostic embedding layer that enables fast transfer learning between different microarchitectural configurations and reduces the re-training overhead of conventional DL-based simulators. Our extensive evaluation shows TAO can reduce the overall training and simulation time by 18.06x over the state-of-the-art DL-based endeavors.
Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network
Authors: Yongkai Ma, Shili Liang, Zekun Chen
Date: 2024-04-17 00:00:00
Tasks: Action Recognition, feature selection

Surface electromyographic (sEMG) signal serve as a signal source commonly used for lower limb movement recognition, reflecting the intent of human movement. However, it has been a challenge to improve the movements recognition rate while using fewer features in this area of research area. In this paper, a method for lower limb movements recognition based on recursive feature elimination and backpropagation neural network of support vector machine is proposed. First, the sEMG signal of five subjects performing eight different lower limb movements was recorded using a BIOPAC collector. The optimal feature subset consists of 25 feature vectors, determined using a Recursive Feature Elimination based on Support Vector Machine (SVM-RFE). Finally, this study used five supervised classification algorithms to recognize these eight different lower limb movements. The results of the experimental study show that the combination of the BPNN classifier and the SVM-RFE feature selection algorithm is able to achieve an excellent action recognition accuracy of 95\%, which provides sufficient support for the feasibility of this approach.