pwc-archive/papers-with-abstracts huggingface dataset:
Price Comparison Website for Online Shopping
Authors: Sanika.V.Chaudhari1, Rimpa.R.Singh2, Sneha.S.Sarkar3, Pratiksha.M.Rajpurohit4, Tejal.S.Bobade5  Dr. Dhananjay Dumbere
Date: 2023-06-01 00:00:00
Proceeding: International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal 2023 6

Price comparison sites are designed to compare the price of goods and services from a range of 
providers, which will help consumers in making decision to choose products that will save their money 
through online. Considering the customers’ busy lifestyle especially those who are living in the city area, 
most of the consumers prefer to buy their needs through the internet because it saves their time. Besides, 
consumers always go for the cheaper price in purchasing products therefore by using price comparison 
website, customers do not have to travel from shop to shop only to survey the price offered by different shops 
for the same product. They can just check it from the price comparison website itself and decide where they 
should buy the products they need. Price Compare is a dynamic price comparison online shopping site 
developed using the Flask framework, aimed at providing users with a seamless and efficient platform for 
comparing prices across a wide range of products. With the rapid growth of e-commerce, consumers face the 
challenge of finding the best deals amidst countless online retailers. Price Compare addresses this issue by 
offering a user-friendly interface and powerful functionality, allowing users to easily search and compare 
prices, ultimately saving time and money.
Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control
Authors: On Tai Wu, Frodo Kin Sun Chan, Zunhao Zhang, Yan Nei Law, Benny Drescher, Edmond Shiao Bun Lai
Date: 2024-03-11 00:00:00
Tasks: Code Generation, Diversity, GSM8K, Language Modeling, Language Modelling, Large Language Model, Mathematical Reasoning

Few-shot prompting and step-by-step reasoning have enhanced the capabilities of Large Language Models (LLMs) in tackling complex tasks including code generation. In this paper, we introduce a prompt selection and augmentation algorithm aimed at improving mathematical reasoning and robot arm operations. Our approach incorporates a multi-stage example augmentation scheme combined with an example selection scheme. This algorithm improves LLM performance by selecting a set of examples that increase diversity, minimize redundancy, and increase relevance to the question. When combined with the Program-of-Thought prompting, our algorithm demonstrates an improvement in performance on the GSM8K and SVAMP benchmarks, with increases of 0.3% and 1.1% respectively. Furthermore, in simulated tabletop environments, our algorithm surpasses the Code-as-Policies approach by achieving a 3.4% increase in successful task completions and a decrease of over 70% in the number of examples used. Its ability to discard examples that contribute little to solving the problem reduces the inferencing time of an LLM-powered robotics system. This algorithm also offers important benefits for industrial process automation by streamlining the development and deployment process, reducing manual programming effort, and enhancing code reusability.
Transductive Active Learning with Application to Safe Bayesian Optimization
Authors: Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause
Date: 2024-07-12 00:00:00
Proceeding: ICML Workshop on Aligning Reinforcement Learning Experimentalists and Theorists 2024 7
Tasks: Active Learning, Bayesian Optimization, Prediction, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning, Transductive Learning

Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization of active learning with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We study a family of policies that sample adaptively to minimize uncertainty about prediction targets. We are the first to show, under general regularity assumptions, that such policies converge uniformly to the smallest possible uncertainty obtainable from the accessible data. Leveraging this result, we apply our framework to Safe BO and demonstrate that our policies improve substantially upon the state-of-the-art.
Diffusion Model-Based Video Editing: A Survey
Authors: Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, DaCheng Tao
Date: 2024-06-26 00:00:00
Tasks: model, Survey, Video Editing

The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making "what you want is what you see" a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain's key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research.
Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review
Authors: Oresti Banos, Zhoe Comas-González, Javier Medina, Aurora Polo-Rodríguez, David Gil, Jesús Peral, Sandra Amador, Claudia Villalonga
Date: 2024-05-15 00:00:00
Tasks: Articles, Emotion Recognition

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models.
RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model
Authors: Khaled Alomar, Halil Ibrahim Aysel, Xiaohao Cai
Date: 2024-06-02 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Video Retrieval

Human Action Recognition (HAR) encompasses the task of monitoring human activities across various domains, including but not limited to medical, educational, entertainment, visual surveillance, video retrieval, and the identification of anomalous activities. Over the past decade, the field of HAR has witnessed substantial progress by leveraging Convolutional Neural Networks (CNNs) to effectively extract and comprehend intricate information, thereby enhancing the overall performance of HAR systems. Recently, the domain of computer vision has witnessed the emergence of Vision Transformers (ViTs) as a potent solution. The efficacy of transformer architecture has been validated beyond the confines of image analysis, extending their applicability to diverse video-related tasks. Notably, within this landscape, the research community has shown keen interest in HAR, acknowledging its manifold utility and widespread adoption across various domains. This article aims to present an encompassing survey that focuses on CNNs and the evolution of Recurrent Neural Networks (RNNs) to ViTs given their importance in the domain of HAR. By conducting a thorough examination of existing literature and exploring emerging trends, this study undertakes a critical analysis and synthesis of the accumulated knowledge in this field. Additionally, it investigates the ongoing efforts to develop hybrid approaches. Following this direction, this article presents a novel hybrid model that seeks to integrate the inherent strengths of CNNs and ViTs.
Analysis of extremum seeking control for wind turbine torque controller optimization by aerodynamic and generator power objectives
Authors: Sebastiaan P. Mulders, Alex J. Gallo, Mario A. Rotea
Date: 2024-07-10 00:00:00

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the $K\omega^2$-type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power.
An Enhanced Fake News Detection System With Fuzzy Deep Learning
Authors: Cheng Xu, M-Tahar Kechadi
Date: 2024-06-24 00:00:00
Proceeding: IEEE Access 2024 6
Tasks: Deep Learning, Fact Checking, Fake News Detection, Navigate

Addressing the intricate challenge of fake news detection, traditionally reliant on the expertise of professional fact-checkers due to the inherent uncertainty in fact-checking processes, this research leverages advancements in language models to propose a novel fuzzy logic-based network. The proposed model is specifically tailored to navigate the uncertainty inherent in the fake news detection task. The evaluation is conducted on the well-established LIAR dataset, a prominent benchmark for fake news detection research, yielding state-of-the-art results. Moreover, recognizing the limitations of the LIAR dataset, we introduce LIAR2 as a new benchmark, incorporating valuable insights from the academic community. Our study presents detailed comparisons and ablation experiments on both LIAR and LIAR2 datasets and establishes our results as the baseline for LIAR2. The proposed approach aims to enhance our understanding of dataset characteristics, contributing to refining and improving fake news detection methodologies.
CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique
Authors: T. Kesornsri et al.
Date: 2024-06-07 00:00:00
Proceeding: IEEE Access 2024 6

Automatic cranial implant design aims to design a patient-specific implant where various machine-learning-based skull reconstruction techniques have been introduced to predict the implant. Despite the significant progress made in the previous research, the existing techniques often struggle to generalize to diverse clinical cases and may not fully leverage the latest advancements in deep learning architectures. Moreover, the limited availability of large-scale clinical datasets hinders the development of the models. In this paper, we represent a novel skull reconstruction model, CraNeXt, which utilizes a ConvNeXt backbone to achieve a 5.8x reduction in size when compared to 3DUNetCNN without sacrificing reconstruction quality. In addition, we introduce a novel method, skull categorization, to classify unlabeled skulls and determine the location of defects and the distribution of skull areas. We expand the training dataset by incorporating a larger collection of 328 in-house clinical cases, enabling the model to better capture the diversity of real-world cranial defects. CraNeXt demonstrates superior results with the skull categorization technique, achieving a dice score of 0.7969±0.13 on both public and in-house data. We perform a qualitative assessment of the predicted implants and discuss potential improvements to the skull reconstruction toward clinical use cases.
Multi-branch CNN and grouping cascade attention for medical image classification
Authors: Shiwei Liu, Wenwen Yue, Zhiqing Guo, Liejun Wang
Date: 2024-07-01 00:00:00
Proceeding: Sci Rep 14, 15013 2024 7
Tasks: Classification, image-classification, Image Classification, Medical Image Analysis, Medical Image Classification

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.
The differentials and determinants of perinatal mortality in rural Bangladesh
Authors: W. Bari, R. I. Chowdhury, M. A. Islam, N. Chakraborty, H. A. H. Akhter
Date: 2003-01-01 00:00:00
Proceeding: journal 2003 1

Objective In Bangladesh, the perinatal mortality is very high. This study examined the
differentials and determinants of perinatal mortality in rural Bangladesh.
Methods The study was based on the prospective data on maternal morbidity collected by
the Bangladesh Institute of Research for Promotion of Essential and Reproductive Health
and Technologies (BIRPERHT). This prospective study was conducted during the
period November 1992 to December 1993. In this study, the factors associated with
perinatal deaths were examined, employing differential and multivariate analyses.
Results It was found that assisted delivery caused higher risks of stillbirth and death among
live births during the first week of life. It was also observed that five ormore pregnancies, prior
to the index pregnancy, were positively associated with perinatal death.
Conclusion If the delivery is assisted or there are complications in delivery, then it is likely
that the incidence of perinatal mortality will increase sharply. If the newborn baby was given
colostrum, then perinatal mortality decreased to a great extent.
Distortion-aware super-resolution for planetary exploration images
Authors: Nicola Landro, Ignazio Gallo, Filippo Pelosi, Riccardo La Grassa, Anwar Ur Rehman
Date: 2024-04-03 00:00:00
Proceeding: ACNP 2024 4
Tasks: distortion correction, SSIM, Super-Resolution

Super-resolution is crucial in computer vision and digital image processing, aiming to enhance low-quality images’ resolution and visual quality. This paper focuses on correcting the distortion introduced by fisheye lenses and improving the resolution of images for better detail representation. Specifically, we propose an evaluation approach that benchmarks three state-of-the-art models in different categories: Real-ESRGAN (convolutions), SwinIR (transformers), and SR3 (diffusion). We evaluate their performance in super-resolution and distortion correction tasks using metrics such as PSNR and SSIM. To facilitate this evaluation, we create and release a new dataset of lunar surface images with fisheye distortion applied. Our experiments demonstrate the effectiveness of each model in handling distortion and improving image resolution. The results show that large models generally outperform medium models, and PSNR models achieve higher PSNR and SSIM scores than GAN models. Additionally, we evaluate the distortion correction by comparing the corrected images with ground truth. Our findings contribute to understanding different model categories and their performance in super-resolution and distortion correction tasks. The proposed dataset and evaluation approach can be valuable resources for future research.
Enhancing crop segmentation in satellite image time-series with transformer networks
Authors: Ignazio Gallo, Mattia Gatti, Nicola Landro, C. Loschiavo, M. Boschetti, Riccardo La Grassa, A. U. Rehman
Date: 2024-04-03 00:00:00
Proceeding: ACNP 2024 4
Tasks: Segmentation, Semantic Segmentation, Time Series, UNET Segmentation

Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time-Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in crop segmentation of SITS. This paper presents a revised version of the Transformer-based Swin UNETR model adapted specifically for crop segmentation of SITS. The proposed model demonstrates significant advancements, achieving a validation accuracy of 96.14% and a test accuracy of 95.26% on the Munich dataset, surpassing the previous best results of 93.55% for validation and 92.94% for the test. Additionally, the model’s performance on the Lombardia dataset is comparable to UNet3D and superior to FPN and DeepLabV3. Experiments of this study indicate that the model will likely achieve comparable or superior accuracy to CNNs while requiring significantly less training time. These findings highlight the potential of transformer-based architectures for crop segmentation in SITS, opening new avenues for remote sensing applications.
Optimal Sensor and Actuator Selection for Factored Markov Decision Processes: Complexity, Approximability and Algorithms
Authors: Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram
Date: 2024-07-10 00:00:00

Factored Markov Decision Processes (fMDPs) are a class of Markov Decision Processes (MDPs) in which the states (and actions) can be factored into a set of state (and action) variables and can be encoded compactly using a factored representation. In this paper, we consider a setting where the state of the fMDP is not directly observable, and the agent relies on a set of potential sensors to gather information. Each sensor has a selection cost and the designer must select a subset of sensors under a limited budget. We formulate the problem of selecting a set of sensors for fMDPs (under a budget) to maximize the infinite-horizon discounted return provided by the optimal policy. We show the fundamental result that it is NP-hard to approximate this problem to within any non-trivial factor. Our inapproximability results for optimal sensor selection also extend to a general class of Partially Observable MDPs (POMDPs). We then study the dual problem of budgeted actuator selection (at design-time) to maximize the expected return under the optimal policy. Again, we show that it is NP-hard to approximate this problem to within any non-trivial factor. Furthermore, with explicit examples, we show the failure of greedy algorithms for both the sensor and actuator selection problems and provide insights into the factors that cause these problems to be challenging. Despite this, through extensive simulations, we show the practical effectiveness and near-optimal performance of the greedy algorithm for actuator and sensor selection in many real-world and randomly generated instances.
C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
Authors: Noah J. Bagazinski, Faez Ahmed
Date: 2024-05-10 00:00:00

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.
TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks
Authors: Sander Noels, Sébastien Viaene, Tijl De Bie
Date: 2024-04-19 00:00:00
Tasks: Data Augmentation, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding

This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical information from the charts of accounts into the sentence embedding process, aiming to accurately capture both the semantic similarity and the hierarchical structure of the ledger accounts. In addition, we introduce a data augmentation strategy that enriches the training data and, as a result, increases the performance of our proposed model. Compared to benchmark methods, TopoLedgerBERT demonstrates superior performance in terms of accuracy and mean reciprocal rank.