pwc-archive/papers-with-abstracts huggingface dataset:
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy
Authors: Rishit Kapoor, Jesher Joshua, Muralidharan Vijayarangan, Natarajan B
Date: 2024-11-15 00:00:00
Tasks: Classification, Continual Learning, Ensemble Learning, Federated Learning, Transfer Learning

This research work introduces a novel approach to the classification of Alzheimer's disease by using the advanced deep learning techniques combined with secure data processing methods. This research work primary uses transfer learning models such as ResNet, ImageNet, and VNet to extract high-level features from medical image data. Thereafter, these pre-trained models were fine-tuned for Alzheimer's related subtle patterns such that the model is capable of robust feature extraction over varying data sources. Further, the federated learning approaches were incorporated to tackle a few other challenges related to classification, aimed to provide better prediction performance and protect data privacy. The proposed model was built using federated learning without sharing sensitive patient data. This way, the decentralized model benefits from the large and diversified dataset that it is trained upon while ensuring confidentiality. The cipher-based encryption mechanism is added that allows us to secure the transportation of data and further ensure the privacy and integrity of patient information throughout training and classification. The results of the experiments not only help to improve the accuracy of the classification of Alzheimer's but at the same time provides a framework for secure and collaborative analysis of health care data.
Deep Insights into Noisy Pseudo Labeling on Graph Data
Authors: Botao Wang, Jia Li, Yang Liu, Jiashun Cheng, Yu Rong, Wenjia Wang, Fugee Tsung
Date: 2023-10-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Learning, Link Prediction, Node Classification, Pseudo Label

Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process. Several works have shown that it can improve the graph learning model performance in general. However, we notice that the incorrect labels can be fatal to the graph training process. Inappropriate PL may result in the performance degrading, especially on graph data where the noise can propagate. Surprisingly, the corresponding error is seldom theoretically analyzed in the literature. In this paper, we aim to give deep insights of PL on graph learning models. We first present the error analysis of PL strategy by showing that the error is bounded by the confidence of PL threshold and consistency of multi-view prediction. Then, we theoretically illustrate the effect of PL on convergence property. Based on the analysis, we propose a cautious pseudo labeling methodology in which we pseudo label the samples with highest confidence and multi-view consistency. Finally, extensive experiments demonstrate that the proposed strategy improves graph learning process and outperforms other PL strategies on link prediction and node classification tasks.
Attenuation of Seismic Random Noise With Unknown Distribution: A Gaussianization Framework
Authors: Chuangji Meng, Jinghuai Gao, Yajun Tian, Liang Zhao, Haoqi Zhao, Zhiqiang Wang.
Date: 2023-07-25 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 7
Tasks: Denoising, Geophysics

Random noise attenuation is a critical step in seismic data processing. Since the distribution of field noise is complex and unknown, this poses a challenge to noise attenuation methods where the default noise distribution is known, such as a Gaussian distribution. To address this issue, we propose a method called seismic random noise Gaussianization framework (SRNGF) to attenuate random noise with unknown distribution. Specifically, SRNGF couples the Gaussianization subproblem and Gaussian denoising subproblem based on the plug-and-play (PaP) framework. The Gaussianization submodule with learnable parameters maps seismic data with the noise of unknown distribution to the one corrupted by Gaussian noise. The learnable parameters can be updated through unsupervised online training according to the noise of the unknown distribution on a single field data. The Gaussian denoising submodule, which can be replaced by seismic denoiser, aims only for Gaussian noise removal, making SRNGF incorporate the denoiser prior to seismic data. Thus, we incorporate different kinds of seismic [nondeep/deep learning (DL)] denoisers into SRNGF and give their corresponding implementations of SRNGF. Experiments on noise with unknown distribution qualitatively and quantitatively validate the superiority of SRNGF. SRNGF improves the results of non-DL/DL seismic denoiser by a large margin.
Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach
Authors: Elizaveta Stavinova, Ilyas Varshavskiy, Petr Chunaev, Ivan Derevitskii, Alexander Boukhanovsky
Date: 2023-06-09 00:00:00
Proceeding: Smart Cities 2023 6

Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict the impact of dynamic pricing strategies. Publications in the field lack a common quality evaluation methodology, public data, and source code, making them difficult to reproduce. In this paper, a data-driven method, DPRank, for evaluating dynamic pricing systems is proposed. DPRank first builds a surrogate price elasticity of demand model using public data generated by a hidden dynamic pricing model, and then applies the surrogate model to build an exposed dynamic pricing model. The hidden and exposed dynamic pricing models were then systematically compared in terms of quality using a Monte Carlo simulation in terms of a company’s revenue. The effectiveness of the proposed method was tested on the dataset collected from the website of a Russian railway passenger carrier company. Depending on the train type, the quality difference between the hidden and exposed models can vary by several dozen percent on average, indicating the potential for improving the existing (hidden) company’s dynamic pricing model.
Solar photovoltaic power prediction using different machine learning methods
Authors: Bouchaib Zazoum
Date: 2021-11-26 00:00:00
Proceeding: 2021 8th International Conference on Power and Energy Systems Engineering (CPESE 2021),  10–12 September 2021, Fukuoka, Japan 2021 11
Tasks: GPR

The main aim of the present study is to explore the relationship between numerous input parameters and the solar
 photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine
 (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV
 panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the
 solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal
 performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement
 with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of
 different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting
 solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error
 (MAE) criteria.
Discrete Cosine Transform-Based Joint Spectral-Spatial Information Compression and Band Correlation Calculation for Hyperspectral Feature Extraction
Authors: Ziqi Zhao, Changbao Yang, Zhongjun Qiu, Qiong Wu
Date: 2024-11-16 00:00:00
Proceeding: Remote Sensing 2024 11
Tasks: Hyperspectral Image Classification

Prediction tasks over pixels in hyperspectral images (HSI) require careful effort to engineer the features used for learning a classifier. However, the generated classification map may suffer from an over-smoothing problem, which is manifested in significant differences from the original image in terms of object boundaries and details. To address this over-smoothing problem, we designed a method for extracting spectral-spatial-band correlation (SSBC) features. In SSBC features, joint spectral-spatial feature extraction is considered a discrete cosine transform-based information compression, where a flattening operation is used to avoid the high computational cost induced by the requirement of distillation from 3D images for joint spectral-spatial information. However, this process can yield extracted features with lost spectral information. We argue that increasing the spectral information in the extracted features is the key to addressing the over-smoothing problem in the classification map. Consequently, the normalized difference vegetation index and iron oxide are improved for HSI data in extracting band correlation features as added spectral information, because their calculations involving two spectral bands are not appropriate for the abundant spectral bands of HSI. Experimental results on four real HSI datasets show that the proposed features can significantly mitigate the over-smoothing problem, and the classification performance is comparable to that of state-of-the-art deep features.
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural Network
Authors: Chuangji Meng, Jinghuai Gao, Yajun Tian, Zhen Li.
Date: 2023-08-23 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 8
Tasks: Denoising, Geophysics, Image Generation, Seismic Interpretation

Recovering the useful signal from seismic field data is critical in seismic data processing. Seismic field data are usually coupled by a useful signal and field noise (random noise with unknown distribution), making them difficult to decouple. Unfortunately, subject to the assumption biases of data prior and noise prior, different random noise attenuation methods may have different performance biases. Suppose data-driven supervised deep learning methods have plenty of labeled [real noisy (field) and clean (useful)] data pairs. In that case, they will learn useful information from the labeled dataset and relax the biases of the data-prior and noise-prior assumptions. To this end, we first use the invertible neural network (INN) to disentangle the field data in observational space into the latent variable in latent space. Then, by manipulating the latent variable’s partitions encoding high- and low-frequency information, INN can generate quality-controlled fake field data and decouple useful signal and field noise parts from field data in its backward pass. To gain decoupling and generative capabilities, the training of our INN only requires a relatively small labeled dataset containing field–useful data pairs. Sampling in latent space, the trained INN can generate an infinite number of paired (fake field and useful) samples. Experiments show that our method can effectively decouple useful signal and field noise, and the noise of the fake field data is close to field noise. The generated paired dataset can benefit downstream tasks, such as field noise attenuation.
Handling Missing Values in Local Post-hoc Explainability
Authors: Martina Cinquini, Fosca Giannotti, Riccardo Guidotti, Andrea Mattei
Date: 2023-10-21 00:00:00
Proceeding: World Conference on Explainable Artificial Intelligence 2023 10
Tasks: Decision Making, Feature Importance, Imputation, Missing Values

Missing data are quite common in real scenarios when using Artificial Intelligence (AI) systems for decision-making with tabular data and effectively handling them poses a significant challenge for such systems. While some machine learning models used by AI systems can tackle this problem, the existing literature lacks post-hoc explainability approaches able to deal with predictors that encounter missing data. In this paper, we extend a widely used local model-agnostic post-hoc explanation approach that enables explainability in the presence of missing values by incorporating state-of-the-art imputation methods within the explanation process. Since our proposal returns explanations in the form of feature importance, the user will be aware also of the importance of a missing value in a given record for a particular prediction. Extensive experiments show the effectiveness of the proposed method with respect to some baseline solutions relying on traditional data imputation.
Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership
Authors: Lars Fluri, A. Ege Yilmaz, Denis Bieri, Thomas Ankenbrand, Aurelio Perucca
Date: 2024-11-20 00:00:00
Tasks: Data Augmentation

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data.
RHM: Robot House Multi-view Human Activity Recognition Dataset
Authors: Mohammad Hossein Bamorovat Abadi, Mohamad Reza Shahabian Alashti, Patrick Holthaus, Catherine Menon, Farshid Amirabdollahian
Date: 2023-04-24 00:00:00
Proceeding: ACHI, The Sixteenth International Conference on Advances in Computer-Human Interactions // 2023 4
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition, Temporal Action Localization

With the recent increased development of deep neural networks and dataset capabilities, the Human Action Recognition (HAR) domain is growing rapidly in terms of both the available datasets and deep models. Despite this, there are some lacks of datasets specifically covering the Robotics field and Human-Robot interaction. We prepare and introduce a new multi-view dataset to address this. The Robot House Multi-View (RHM) dataset contains four views: Front, Back, Ceiling (Omni), and robot-views. There are 14 classes with 6701 video clips for each view, making a total of 26804 video clips for the four views. The lengths of the video clips are between 1 to 5 seconds. The videos with the same number and the same classes are synchronized in different views. In the second part of this paper, we consider how single streams afford activity recognition using established state-of-the-art models. We then assess the affordance for each view based on information theoretic modelling and mutual information concept. Furthermore, we benchmark the performance of different views, thus establishing the strengths and weaknesses of each view relevant to their information content and performance of the benchmark. Our results lead us to conclude that multi-view and multi-stream activity recognition has the added potential to improve activity recognition results. The RHM dataset is available at {href{https://robothouse-dev.herts.ac.uk/datasets/RHM/HAR-1/}{Robot House}}.
Robotic Vision and Multi-View Synergy: Action and activity recognition in assisted living scenarios
Authors: Mohammad Hossein Bamorovat Abadi, Mohamad Reza Shahabian Alashti, Patrick Holthaus, Catherine Menon, Farshid Amirabdollahian
Date: 2024-09-01 00:00:00
Proceeding: 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) 2024 9
Tasks: Activity Recognition, Human Activity Recognition

The significance of Human-Robot Interaction (HRI) is increasingly evident when integrating robotics within human-centric settings. A crucial component of effective HRI is Human Activity Recognition (HAR), which is instrumental in enabling robots to respond aptly in human presence, especially within Ambient Assisted Living (AAL) environments. Since robots are generally mobile and their visual perception is often compromised by motion and noise, this paper evaluates methods by merging the robot's mobile perspective with a static viewpoint utilising multi-view deep learning models. We introduce a dual-stream Convolutional 3D (C3D) model to improve vision-based HAR accuracy for robotic applications. Utilising the Robot House Multiview (RHM) dataset, which encompasses a robotic perspective along with three static views (Front, Back, Top), we examine the efficacy of our model and conduct comparisons with the dual-stream ConvNet and Slow-Fast models. The primary objective of this study is to enhance the accuracy of robot viewpoints by integrating them with static views using dual-stream models. The metrics for evaluation include Top-1 and Top-5 accuracy. Our findings reveal that the integration of static views with robotic perspectives significantly boosts HAR accuracy in both Top-1 and Top-5 metrics across all models tested. Moreover, the proposed dual-stream C3D model demonstrates superior performance compared to the other contemporary models in our evaluations.
Multipath Mitigation Technology-integrated GNSS Direct Position Estimation Plug-in Module
Authors: Sergio Vicenzo, Bing Xu
Date: 2024-11-20 00:00:00
Tasks: Position

Direct position estimation (DPE) is an effective solution to the MP issue at the signal processing level. Unlike two-step positioning (2SP) receivers, DPE directly solves for the receiver position, velocity, and time (PVT) in the navigation domain, without the estimation of intermediate measurements, thus allowing it to provide more robust and accurate PVT estimates in the presence of multipath (MP) and weak signals. But GNSS positioning with DPE is mostly left unapplied commercially, and continuing research into DPE has remained relatively stagnant over the past few years. To encourage further research on DPE by the GNSS community, we propose a DPE plug-in module that can be integrated into the conventional 2SP software-defined receivers (SDRs). Programmed in MATLAB, the proposed DPE plug-in module is aimed for better understanding and familiarity of a practical implementation of DPE. Its plug-in module architecture allows it to be incorporated with 2SP MATLAB SDRs, both vector tracking and scalar tracking with minimum changes, making it easy to use, and provides greater flexibility for researchers using various 2SP SDRs. We propose to further improve the performance of DPE against MP by incorporating Multipath Mitigation Technology (MMT) into DPE. Referred to as MMT-DPE, it is proposed as a variant of DPE that adds the MP components into DPE signal model by integrating MMT cost function into DPE, with the aim to better suit DPE for urban environment applications. Results show that while in MP-only conditions, an MMT-integrated 2SP (MMT-2SP) has similar performance with MMT-DPE, the proposed MMT-DPE manages to show great superiority against NLOS, making it the preferable option for applications in urban environments.
Automatically Improving LLM-based Verilog Generation using EDA Tool Feedback
Authors: Jason Blocklove, Shailja Thakur, Benjamin Tan, Hammond Pearce, Siddharth Garg, Ramesh Karri
Date: 2024-11-01 00:00:00

Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a potential tool to help generate fully functioning HDL code, but most works have focused on generation in the single-shot capacity: i.e., run and evaluate, a process that does not leverage debugging and, as such, does not adequately reflect a realistic development process. In this work, we evaluate the ability of LLMs to leverage feedback from electronic design automation (EDA) tools to fix mistakes in their own generated Verilog. To accomplish this, we present an open-source, highly customizable framework, AutoChip, which combines conversational LLMs with the output from Verilog compilers and simulations to iteratively generate and repair Verilog. To determine the success of these LLMs we leverage the VerilogEval benchmark set. We evaluate four state-of-the-art conversational LLMs, focusing on readily accessible commercial models. EDA tool feedback proved to be consistently more effective than zero-shot prompting only with GPT-4o, the most computationally complex model we evaluated. In the best case, we observed a 5.8% increase in the number of successful designs with a 34.2% decrease in cost over the best zero-shot results. Mixing smaller models with this larger model at the end of the feedback iterations resulted in equally as much success as with GPT-4o using feedback, but incurred 41.9% lower cost (corresponding to an overall decrease in cost over zero-shot by 89.6%).
LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference
Authors: Yanyue Xie, Zhengang Li, Dana Diaconu, Suranga Handagala, Miriam Leeser, Xue Lin
Date: 2024-11-01 00:00:00
Tasks: Efficient Neural Network

For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplications. This paper introduces LUTMUL, which harnesses the potential of look-up tables (LUTs) for performing multiplications. The availability of LUTs typically outnumbers that of DSPs by a factor of 100, offering a significant computational advantage. By exploiting this advantage of LUTs, our method demonstrates a potential boost in the performance of FPGA-based neural network accelerators with a reconfigurable dataflow architecture. Our approach challenges the conventional peak performance on DSP-based accelerators and sets a new benchmark for efficient neural network inference on FPGAs. Experimental results demonstrate that our design achieves the best inference speed among all FPGA-based accelerators, achieving a throughput of 1627 images per second and maintaining a top-1 accuracy of 70.95% on the ImageNet dataset.
A Survey of Financial AI: Architectures, Advances and Open Challenges
Authors: Junhua Liu
Date: 2024-11-01 00:00:00
Tasks: Decision Making, Portfolio Optimization, Survey, Time Series

Financial AI empowers sophisticated approaches to financial market forecasting, portfolio optimization, and automated trading. This survey provides a systematic analysis of these developments across three primary dimensions: predictive models that capture complex market dynamics, decision-making frameworks that optimize trading and investment strategies, and knowledge augmentation systems that leverage unstructured financial information. We examine significant innovations including foundation models for financial time series, graph-based architectures for market relationship modeling, and hierarchical frameworks for portfolio optimization. Analysis reveals crucial trade-offs between model sophistication and practical constraints, particularly in high-frequency trading applications. We identify critical gaps and open challenges between theoretical advances and industrial implementation, outlining open challenges and opportunities for improving both model performance and practical applicability.