pwc-archive/papers-with-abstracts huggingface dataset:
A GEN AI Framework for Medical Note Generation
Authors: Hui Yi Leong, Yi Fan Gao, Shuai Ji, Bora Kalaycioglu, Uktu Pamuksuz
Date: 2024-09-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), parameter-efficient fine-tuning, RAG, Retrieval, Retrieval-augmented Generation, speech-recognition, Speech Recognition

The increasing administrative burden of medical documentation, particularly through Electronic Health Records (EHR), significantly reduces the time available for direct patient care and contributes to physician burnout. To address this issue, we propose MediNotes, an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio, generating structured and contextually accurate medical notes. The framework also incorporates advanced techniques like Quantized Low-Rank Adaptation (QLoRA) and Parameter-Efficient Fine-Tuning (PEFT) for efficient model fine-tuning in resource-constrained environments. Additionally, MediNotes offers a query-based retrieval system, allowing healthcare providers and patients to access relevant medical information quickly and accurately. Evaluations using the ACI-BENCH dataset demonstrate that MediNotes significantly improves the accuracy, efficiency, and usability of automated medical documentation, offering a robust solution to reduce the administrative burden on healthcare professionals while improving the quality of clinical workflows.
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks
Authors: Wei Lu, Si-Bao Chen, Qing-Ling Shu, Jin Tang, and Bin Luo
Date: 2024-09-23 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 9
Tasks: ARC, Computational Efficiency, Image Classification, Object, object-detection, Object Detection In Aerial Images, Oriented Object Detection, Semantic Segmentation

In the realm of computer vision (CV), balancing speed and accuracy remains a significant challenge. Recent efforts have focused on developing lightweight networks that optimize computational efficiency and feature extraction. However, in remote sensing (RS) imagery, where small and multiscale object detection is critical, these networks often fall short in performance. To address these challenges, DecoupleNet is proposed, an innovative lightweight backbone network specifically designed for RS visual tasks in resource-constrained environments. DecoupleNet incorporates two key modules: the feature integration downsampling (FID) module and the multibranch feature decoupling (MBFD) module. The FID module preserves small object features during downsampling, while the MBFD module enhances small and multiscale object feature representation through a novel decoupling approach. Comprehensive evaluations on three RS visual tasks demonstrate DecoupleNet’s superior balance of accuracy and computational efficiency compared to existing lightweight networks. On the NWPU-RESISC45 classification dataset, DecoupleNet achieves a top-1 accuracy of 95.30%, surpassing FasterNet by 2%, with fewer parameters and lower computational overhead. In object detection tasks using the DOTA 1.0 test set, DecoupleNet records an accuracy of 78.04%, outperforming ARC-R50 by 0.69%. For semantic segmentation on the LoveDA test set, DecoupleNet achieves 53.1% accuracy, surpassing UnetFormer by 0.70%. These findings open new avenues for advancing RS image analysis on resource-constrained devices, addressing a pivotal gap in the field. The code and pretrained models are publicly available at https://github.com/lwCVer/DecoupleNet.
2D Semantic-Guided Semantic Scene Completion
Authors: Xianzhu Liu, Haozhe Xie, Shengping Zhang, Hongxun Yao, Rongrong Ji, Liqiang Nie, DaCheng Tao
Date: 2024-09-12 00:00:00
Proceeding: International Journal of Computer Vision (IJCV) 2024 9
Tasks: 2D Semantic Segmentation, 3D Semantic Scene Completion, Missing Values, Semantic Segmentation

Semantic scene completion (SSC) aims to simultaneously perform scene completion (SC) and predict semantic categories
of a 3D scene from a single depth and/or RGB image. Most existing SSC methods struggle to handle complex regions with
multiple objects close to each other, especially for objects with reflective or dark surfaces. This primarily stems from two
challenges: (1) the loss of geometric information due to the unreliability of depth values from sensors, and (2) the potential for
semantic confusion when simultaneously predicting 3D shapes and semantic labels. To address these problems, we propose a
Semantic-guided Semantic Scene Completion framework, dubbed SG-SSC, which involves Semantic-guided Fusion (SGF)
and Volume-guided Semantic Predictor (VGSP). Guided by 2D semantic segmentation maps, SGF adaptively fuses RGB
and depth features to compensate for the missing geometric information caused by the missing values in depth images, thus
performing more robustly to unreliable depth information. VGSP exploits the mutual benefit between SC and SSC tasks,
making SSC more focused on predicting the categories of voxels with high occupancy probabilities and also allowing SC
to utilize semantic priors to better predict voxel occupancy. Experimental results show that SG-SSC outperforms existing
state-of-the-art methods on the NYU, NYUCAD, and SemanticKITTI datasets. Models and code are available at https://
github.com/aipixel/SG-SSC.
The effect of moving heat source in DPL model on viscoelastic biological tissues during during thermal treatment applications
Authors: Abdelrahman A. Abbas, Ibrahim A. Abbas, Aboelnour N. Abdalla
Date: 2024-09-15 00:00:00

This study utilizes mathematical modeling to offer computational insights into biological heat transfer within elastic skin tissues. This heat transfer is driven by a moving heat source that relies on various relaxation times. Time delay parameters were incorporated based on these relaxation times, highlighting the importance of understanding heat transfer mechanisms and thermo-mechanical interactions to ensure the effectiveness of thermal treatment procedures. Given the significant impact of different variables on thermal relaxation times, their effects were analyzed on the distributions of displacement, thermal stress, and temperature in living tissues. By applying advanced mathematical techniques such as Laplace transforms, precise evaluations of these distributions were achieved. The results are presented graphically, with a focus on enhancing treatment outcomes by deepening our understanding of the behavior of living tissues in thermal environments.
Adeosun Marvelous Olamilekan
Authors: Adeosun Marvelous Olamilekan
Date: 2005-06-13 00:00:00
Proceeding: https://adeosunmarvelousolamilekan.my.canva.site/ 2005 6
Tasks: Marketing, Unity

He is a professional network marketer and freelancer been in an organization called FHG An Manager from iragbiji Osun state Nigeria.

My Name is Adeosun Marvelous Olamilekan is a young guy in the town of Iragbiji, Osun State Nigeria and name of he parents is Mr. & Mrs. Adeosun. He went to Legacy Nursery and secondary school in Iragbiji, when he is in pry 3 he left Legacy Nursery and secondary school and went to Olasunbomi Nursery and secondary school,Ororuwo when he is in J.S.S 3 he left Olasunbomi Nursery and secondary school and went to Unity High School, Iragbiji and finish his S.S.S 3 there and go to Adikoko comprehensive School in Iragbiji to do he Neco after Olamilekan Finish he Neco in 2022.

After graduating from secondary school, he was introduced to 2 in 1 business (network marketing and Online business),Since joining the organization, he has grown into a skilled freelancer who works on Fiverr, Upwork, kwork and other freelance platforms with a range of projects (such as Social Media marketer and Digital Marketing| AND MANY MORE), though he occasionally specializes in Digital Marketing services such as Google knowledge panel, Tiktok promotion, build killer Shopify store and Generate Unlimited Sales for shopify store| AND MANY MORe.
Unsupervised domain adaptation using feature aligned maximum classifier discrepancy
Authors: Prasanna Reddy Pulakurthi, Sohail A. Dianat, Majid Rabbani, Suya You, Raghuveer M. Rao
Date: 2022-10-03 00:00:00
Proceeding: Applications of Machine Learning, SPIE 2022 10
Tasks: Domain Adaptation, image-classification, Unsupervised Domain Adaptation

The maximum classifier discrepancy method has achieved great success in solving unsupervised domain adaptation tasks for image classification in recent years. Its basic structure consists of a feature generator and two classifiers that aim to maximize the classifier discrepancy while minimizing the generator discrepancy of the target samples. This method improves the performance of the existing adversarial training methods by employing task-specific classifiers that remove the ambiguity in classifying the target samples near the class boundaries. In this paper, we propose a modified network architecture and two training objectives to further boost the performance of the maximum classifier discrepancy method. The first training objective minimizes the feature level discrepancy and forces the generator to generate domain invariant features. This training objective is particularly beneficial when the source and the target domain distributions are vastly different. The second training objective that works at the mini-batch level aims at creating a uniform distribution of the target class predictions by maximizing the entropy of the expectation of the target class predictions. We show through extensive empirical evaluations that the proposed architecture and training objectives significantly improve the performance of the original algorithm. Furthermore, this method also outperforms the state-of-the-art techniques in most unsupervised domain adaptation tasks.
Enhancing human action recognition with GAN-based data augmentation
Authors: Prasanna Reddy Pulakurthi, Celso M. de Melo, Raghuveer Rao, Majid Rabbani
Date: 2024-06-07 00:00:00
Proceeding: Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, SPIE 2024 6
Tasks: Action Recognition, Data Augmentation, Synthetic Data Generation

Deep Neural Networks (DNNs) have emerged as a powerful tool for human action recognition, yet their reliance on vast amounts of high-quality labeled data poses significant challenges. A promising alternative is to train the network on generated synthetic data. However, existing synthetic data generation pipelines require complex simulation environments. Our novel solution bypasses this requirement by employing Generative Adversarial Networks (GANs) to generate synthetic data from only a small existing real-world dataset. Our training pipeline extracts the motion from each training video and augments it across various subject appearances within the training set. This approach increases the diversity in both motion and subject representations, thus significantly enhancing the model's performance. A rigorous evaluation of the model's performance is presented under diverse scenarios, including ground and aerial views. Moreover, an insightful analysis of critical factors influencing human action recognition performance, such as gesture motion diversity and subject appearance, is presented.
A globally optimal fast iterative linear maximum likelihood classifier
Authors: Prasanna Reddy Pulakurthi, Sohail A Dianat, Majid Rabbani, Suya You, Raghuveer M Rao
Date: 2023-01-16 00:00:00
Proceeding: Electronic Imaging 2023 1
Tasks: Classification

A novel iterative linear classification algorithm is developed from a maximum likelihood (ML) linear classifier. The main contribution of this paper is the discovery that a well-known maximum likelihood linear classifier with regularization is the solution to a contraction mapping for an acceptable range of values of the regularization parameter. Hence, a novel iterative scheme is proposed that converges to a fixed point, the globally optimum solution. To the best of our knowledge, this formulation has not been discovered before. Furthermore, the proposed iterative solution converges to a fixed point at a rate faster than the traditional gradient descent technique. The performance of the proposed iterative solution is compared to conventional gradient descent methods on linear and non-linearly separable data in terms of both convergence speed and overall classification performance.
Reinforcenment Learning-Aided NOMA Random Access: An AoI-Based Timeliness Perspective
Authors: Felippe Moraes Pereira, Jamil de Araujo Farhat, João Luiz Rebelatto, Glauber Brante, Richard Demo Souza
Date: 2024-10-04 00:00:00
Tasks: Q-Learning

In this paper, we investigate the age-of-information (AoI) of a power domain non-orthogonal multiple access (NOMA) network, where multiple internet-of-things (IoT) devices transmit to a common gateway in a grant-free random fashion. More specifically, we consider a framed setup composed of multiple time slots, and resort to the $Q$-learning algorithm to properly define, in a distributed manner, the time slot and the power level each IoT device transmits within a frame. In the proposed AoI-QL-NOMA scheme, the $Q$-learning reward is adapted with the aim of minimizing the average AoI of the network, while only requiring a single feedback bit per time slot, in a frame basis. Our results show that AoI-QL-NOMA significantly improves the AoI performance compared to some recently proposed schemes, without significantly reducing the network throughput.
Entanglement-induced provable and robust quantum learning advantages
Authors: Haimeng Zhao, Dong-Ling Deng
Date: 2024-10-04 00:00:00

Quantum computing holds the unparalleled potentials to enhance, speed up or innovate machine learning. However, an unambiguous demonstration of quantum learning advantage has not been achieved so far. Here, we rigorously establish a noise-robust, unconditional quantum learning advantage in terms of expressivity, inference speed, and training efficiency, compared to commonly-used classical machine learning models. Our proof is information-theoretic and pinpoints the origin of this advantage: quantum entanglement can be used to reduce the communication required by non-local machine learning tasks. In particular, we design a fully classical task that can be solved with unit accuracy by a quantum model with a constant number of variational parameters using entanglement resources, whereas commonly-used classical models must scale at least linearly with the size of the task to achieve a larger-than-exponentially-small accuracy. We further show that the quantum model can be trained with constant time and a number of samples inversely proportional to the problem size. We prove that this advantage is robust against constant depolarization noise. We show through numerical simulations that even though the classical models can have improved performance as their sizes are increased, they would suffer from overfitting. The constant-versus-linear separation, bolstered by the overfitting problem, makes it possible to demonstrate the quantum advantage with relatively small system sizes. We demonstrate, through both numerical simulations and trapped-ion experiments on IonQ Aria, the desired quantum-classical learning separation. Our results provide a valuable guide for demonstrating quantum learning advantages in practical applications with current noisy intermediate-scale quantum devices.
Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL
Authors: Naoaki Kanazawa, Kento Kawaharazuka, Yoshiki Obinata, Kei Okada, Masayuki Inaba
Date: 2024-10-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Although there is a growing demand for cooking behaviours as one of the expected tasks for robots, a series of cooking behaviours based on new recipe descriptions by robots in the real world has not yet been realised. In this study, we propose a robot system that integrates real-world executable robot cooking behaviour planning using the Large Language Model (LLM) and classical planning of PDDL descriptions, and food ingredient state recognition learning from a small number of data using the Vision-Language model (VLM). We succeeded in experiments in which PR2, a dual-armed wheeled robot, performed cooking from arranged new recipes in a real-world environment, and confirmed the effectiveness of the proposed system.
FedCert: Federated Accuracy Certification
Authors: Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen
Date: 2024-10-04 00:00:00
Tasks: Federated Learning

Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data perturbations on clients remains a significant challenge. Previous studies have assessed the effectiveness of models in centralized training based on certified accuracy, which guarantees that a certain percentage of the model's predictions will remain correct even if the input data is perturbed. However, the challenge of extending these evaluations to FL remains unresolved due to the unknown client's local data. To tackle this challenge, this study proposed a method named FedCert to take the first step toward evaluating the robustness of FL systems. The proposed method is designed to approximate the certified accuracy of a global model based on the certified accuracy and class distribution of each client. Additionally, considering the Non-Independent and Identically Distributed (Non-IID) nature of data in real-world scenarios, we introduce the client grouping algorithm to ensure reliable certified accuracy during the aggregation step of the approximation algorithm. Through theoretical analysis, we demonstrate the effectiveness of FedCert in assessing the robustness and reliability of FL systems. Moreover, experimental results on the CIFAR-10 and CIFAR-100 datasets under various scenarios show that FedCert consistently reduces the estimation error compared to baseline methods. This study offers a solution for evaluating the robustness of FL systems and lays the groundwork for future research to enhance the dependability of decentralized learning. The source code is available at https://github.com/thanhhff/FedCert/.
Autoregressive Action Sequence Learning for Robotic Manipulation
Authors: Xinyu Zhang, YuHan Liu, Haonan Chang, Liam Schramm, Abdeslam Boularias
Date: 2024-10-04 00:00:00
Tasks: Chunking, Language Modeling, Language Modelling, Robot Manipulation

Designing a universal policy architecture that performs well across diverse robots and task configurations remains a key challenge. In this work, we address this by representing robot actions as sequential data and generating actions through autoregressive sequence modeling. Existing autoregressive architectures generate end-effector waypoints sequentially as word tokens in language modeling, which are limited to low-frequency control tasks. Unlike language, robot actions are heterogeneous and often include continuous values -- such as joint positions, 2D pixel coordinates, and end-effector poses -- which are not easily suited for language-based modeling. Based on this insight, we introduce a straightforward enhancement: we extend causal transformers' single-token prediction to support predicting a variable number of tokens in a single step through our Chunking Causal Transformer (CCT). This enhancement enables robust performance across diverse tasks of various control frequencies, greater efficiency by having fewer autoregression steps, and lead to a hybrid action sequence design by mixing different types of actions and using a different chunk size for each action type. Based on CCT, we propose the Autoregressive Policy (ARP) architecture, which solves manipulation tasks by generating hybrid action sequences. We evaluate ARP across diverse robotic manipulation environments, including Push-T, ALOHA, and RLBench, and show that ARP, as a universal architecture, matches or outperforms the environment-specific state-of-the-art in all tested benchmarks, while being more efficient in computation and parameter sizes. Videos of our real robot demonstrations, all source code and the pretrained models of ARP can be found at http://github.com/mlzxy/arp.
Action Selection Learning for Multi-label Multi-view Action Recognition
Authors: Trung Thanh Nguyen, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide
Date: 2024-10-04 00:00:00
Tasks: Action Recognition

Multi-label multi-view action recognition aims to recognize multiple concurrent or sequential actions from untrimmed videos captured by multiple cameras. Existing work has focused on multi-view action recognition in a narrow area with strong labels available, where the onset and offset of each action are labeled at the frame-level. This study focuses on real-world scenarios where cameras are distributed to capture a wide-range area with only weak labels available at the video-level. We propose the method named Multi-view Action Selection Learning (MultiASL), which leverages action selection learning to enhance view fusion by selecting the most useful information from different viewpoints. The proposed method includes a Multi-view Spatial-Temporal Transformer video encoder to extract spatial and temporal features from multi-viewpoint videos. Action Selection Learning is employed at the frame-level, using pseudo ground-truth obtained from weak labels at the video-level, to identify the most relevant frames for action recognition. Experiments in a real-world office environment using the MM-Office dataset demonstrate the superior performance of the proposed method compared to existing methods. The source code is available at https://github.com/thanhhff/MultiASL/.
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach
Authors: Tuan-Cuong Vuong, Cong Chi Nguyen, Van-Cuong Pham, Thi-Thanh-Huyen Le, Xuan-Nam Tran, Thien Van Luong
Date: 2024-10-01 00:00:00
Tasks: feature selection, Intrusion Detection, Multi-class Classification

This paper proposes a novel intrusion detection method for unmanned aerial vehicles (UAV) in the presence of recent actual UAV intrusion dataset. In particular, in the first stage of our method, we design an autoencoder architecture for effectively extracting important features, which are then fed into various machine learning models in the second stage for detecting and classifying attack types. To the best of our knowledge, this is the first attempt to propose such the autoencoder-based machine learning intrusion detection method for UAVs using actual dataset, while most of existing works only consider either simulated datasets or datasets irrelevant to UAV communications. Our experiment results show that the proposed method outperforms the baselines such as feature selection schemes in both binary and multi-class classification tasks.