pwc-archive/papers-with-abstracts huggingface dataset:
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
Authors: Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabijańska
Date: 2025-02-28 00:00:00
Proceeding: International Conference on Agents and Artificial Intelligence 2025 2
Tasks: Classification, Hyperspectral Image-Based Fruit Ripeness Prediction, Hyperspectral Image Classification, image-classification, Image Classification

Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques. However, challenges including the limited availability of labeled data and the lack of robust methods generalizable to various hyperspectral cameras and fruit types can compromise the effectiveness of hyperspectral image-based FRP. Addressing these challenges, this paper introduces Fruit-HSNet, a machine learning architecture specifically designed for hyperspectral classification of fruit ripeness. Fruit-HSNet incorporates a spatio-spectral feature extraction module based on Fourier Transform and central pixel spectral signature followed by learnable feature fusion and a classifier optimized for ripeness classification. The proposed architecture was evaluated using the DeepHS Fruit dataset, the largest publicly available labeled real-world hyperspectral dataset for predicting fruit ripeness, which includes five different types of fruits—avocado, kiwi, mango, kaki, and papaya—captured with three distinct hyperspectral cameras at various stages of ripeness. Experimental results highlight that Fruit-HSNet substantially outperforms existing deep learning methods, from baseline to state-of-the-art models, with improvements of 12%, achieving a new state-of-the-art overall accuracy of 70.73%.
Reward Learning from Multiple Feedback Types
Authors: Yannick Metz, András Geiszl, Raphaël Baur, Mennatallah El-Assady
Date: 2025-02-28 00:00:00

Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison between multiple completions, is an established method to acquire large-scale human feedback. However, human feedback in other contexts is often much more diverse. Such diverse feedback can better support the goals of a human annotator, and the simultaneous use of multiple sources might be mutually informative for the learning process or carry type-dependent biases for the reward learning process. Despite these potential benefits, learning from different feedback types has yet to be explored extensively. In this paper, we bridge this gap by enabling experimentation and evaluating multi-type feedback in a broad set of environments. We present a process to generate high-quality simulated feedback of six different types. Then, we implement reward models and downstream RL training for all six feedback types. Based on the simulated feedback, we investigate the use of types of feedback across ten RL environments and compare them to pure preference-based baselines. We show empirically that diverse types of feedback can be utilized and lead to strong reward modeling performance. This work is the first strong indicator of the potential of multi-type feedback for RLHF.
Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis
Authors: Ahmad Hafez, Alireza Naderi Akhormeh, Amr Hegazy, Amr Alanwar
Date: 2025-03-05 00:00:00
Tasks: Autonomous Navigation, Navigate, Task Planning, Zero-Shot Learning

The deployment of Large Language Models (LLMs) in robotic systems presents unique safety challenges, particularly in unpredictable environments. Although LLMs, leveraging zero-shot learning, enhance human-robot interaction and decision-making capabilities, their inherent probabilistic nature and lack of formal guarantees raise significant concerns for safety-critical applications. Traditional model-based verification approaches often rely on precise system models, which are difficult to obtain for real-world robotic systems and may not be fully trusted due to modeling inaccuracies, unmodeled dynamics, or environmental uncertainties. To address these challenges, this paper introduces a safety assurance framework for LLM-controlled robots based on data-driven reachability analysis, a formal verification technique that ensures all possible system trajectories remain within safe operational limits. Our framework specifically investigates the problem of instructing an LLM to navigate the robot to a specified goal and assesses its ability to generate low-level control actions that successfully guide the robot safely toward that goal. By leveraging historical data to construct reachable sets of states for the robot-LLM system, our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models. We validate the framework through experimental case studies in autonomous navigation and task planning, demonstrating its effectiveness in mitigating risks associated with LLM-generated commands. This work advances the integration of formal methods into LLM-based robotics, offering a principled and practical approach to ensuring safety in next-generation autonomous systems.
Weighted Combination and Singular Spectrum Analysis Based Remote Photoplethysmography Pulse Extraction in Low-light Environments
Authors: Lin Xi, Xingming Wu, Weihai Chen, Jianhua Wang, Changchen Zhao
Date: 2025-03-04 00:00:00
Proceeding: Medical Engineering & Physics 2022 6
Tasks: Heart rate estimation

Camera-based vital signs monitoring in recent years has attracted more and more researchers and the results are promising. However, a few research works focus on heart rate extraction under extremely low illumination environments. In this paper, we propose a novel framework for remote heart rate estimation under low-light conditions. This method uses singular spectrum analysis (SSA) to decompose the filtered signal into several reconstructed components. A spectral masking algorithm is utilized to refine the preliminary candidate components on the basis of a reference heart rate. The contributive components are fused into the final pulse signal. To evaluate the performance of our framework in low-light conditions, the proposed approach is tested on a large-scale multi-illumination HR dataset (named MIHR). The test results verify that the proposed method has stronger robustness to low illumination than state-of-the-art methods, effectively improving the signal-to-noise ratio and heart rate estimation precision. We further perform experiments on the PUlse RatE detection (PURE) dataset which is recorded under normal light conditions to demonstrate the generalization of our method. The experiment results show that our method can stably detect pulse rate and achieve comparative results. The proposed method pioneers a new solution to the remote heart rate estimation in low-light conditions.
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward
Authors: Eyad Elyan1, Pattaramon Vuttipittayamongkol2, Pamela Johnston1, Kyle Martin1, Kyle McPherson1, Carlos Francisco Moreno-García1, Chrisina Jayne3, Md. Mostafa Kamal Sarker4
Date: 2022-03-21 00:00:00
Proceeding: Art Int Surg 2022 3
Tasks: image-classification, Image Classification, Medical Image Analysis, Medical Image Classification, Object Recognition

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions.
IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement
Authors: Zhihao Shi, Dong Huo, Yuhongze Zhou, Kejia Yin, Yan Min, Juwei Lu, Xinxin Zuo
Date: 2025-03-06 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Inpainting, Image Inpainting, Test-time Adaptation

Current 3D inpainting and object removal methods are largely limited to front-facing scenes, facing substantial challenges when applied to diverse, "unconstrained" scenes where the camera orientation and trajectory are unrestricted. To bridge this gap, we introduce a novel approach that produces inpainted 3D scenes with consistent visual quality and coherent underlying geometry across both front-facing and unconstrained scenes. Specifically, we propose a robust 3D inpainting pipeline that incorporates geometric priors and a multi-view refinement network trained via test-time adaptation, building on a pre-trained image inpainting model. Additionally, we develop a novel inpainting mask detection technique to derive targeted inpainting masks from object masks, boosting the performance in handling unconstrained scenes. To validate the efficacy of our approach, we create a challenging and diverse benchmark that spans a wide range of scenes. Comprehensive experiments demonstrate that our proposed method substantially outperforms existing state-of-the-art approaches.
A Network Screening Method for Short-Term Safety Performance
Authors: Maroa Mumtarin, Jonathan Wood
Date: 2025-02-25 00:00:00
Proceeding: SSRN 2025 2

Network screening is crucial to transportation safety management, enabling practitioners to identify high-risk locations and implement countermeasures to reduce crashes. Traditional methods focus on long-term crash trends using multi-year data. Short-term network screening—analyzing crash patterns over weeks to months—remains underexplored. This gap may lead to missed opportunities by overlooking locations with short-term crash hotspots that could be mitigated through enforcement or other low-cost interventions. Additionally, distinguishing between long-term and short-term hotspots is essential for effective safety mitigation. To address this gap, this study developed a short-term network screening framework integrating observed crashes and predictive modeling to identify crash hotspots. Using weekly crash data from the Iowa Interstate network, the study employs a binary logistic regression model to estimate predicted crash probabilities. Then, historically observed crash data is combined with the predicted crash probabilities to estimate the final crash likelihood of a particular interstate segment. The framework was then validated with a 12-week validation period data to identify the performance of the network screening process. Despite using a highly skewed dataset where 98% of records indicated no crashes, the proposed framework demonstrated effective performance compared to the validation data, proving its usefulness for highly skewed datasets. Results also show that the proposed framework successfully identifies the short-term crash hotspots, with ranking correlation values larger than 0.8 across different analysis periods, further indicating the framework's reliability and consistency.
Fabio Paul
Authors: Fabio Paul
Date: 2025-03-08 00:00:00
Proceeding: 03/08 2025 3

Fabio Paul is a musician from Cyprus, born on January 29, 1990. Passionate about music from a young age, he has developed a unique sound that blends various genres, captivating audiences with his creativity and talent. Through his website, paulfabio.com, he shares his latest projects, music releases, and updates about his musical journey. Whether performing live or working on new compositions, Fabio Paul continues to make an impact in the music scene with his distinctive style and artistic vision.
Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Authors: Guanghao Li, Wenhao Jiang, Li Shen, Ming Tang, Chun Yuan
Date: 2025-02-17 00:00:00

Resource limitations often constrain the parameter counts of Large Language Models (LLMs), hindering their performance. While existing methods employ parameter sharing to reuse the same parameter set under fixed budgets, such approaches typically force each layer to assume multiple roles with a predetermined number of iterations, restricting efficiency and adaptability. In this work, we propose the Zero Token Transformer (ZTT), which features a head-tail decoupled parameter cycling method. We disentangle the first (head) and last (tail) layers from parameter cycling and iteratively refine only the intermediate layers. Furthermore, we introduce a Zero-Token Mechanism, an internal architectural component rather than an input token, to guide layer-specific computation. At each cycle, the model retrieves a zero token (with trainable key values) from a Zero-Token Pool, integrating it alongside regular tokens in the attention mechanism. The corresponding attention scores not only reflect each layer's computational importance but also enable dynamic early exits without sacrificing overall model accuracy. Our approach achieves superior performance under tight parameter budgets, effectively reduces computational overhead via early exits, and can be readily applied to fine-tune existing pre-trained models for enhanced efficiency and adaptability.
Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
Authors: Pawel Renc, Michal K. Grzeszczyk, Nassim Oufattole, Deirdre Goode, Yugang Jia, Szymon Bieganski, Matthew B. A. McDermott, Jaroslaw Was, Anthony E. Samir, Jonathan W. Cunningham, David W. Bates, Arkadiusz Sitek
Date: 2025-02-10 00:00:00
Tasks: Benchmarking

We developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines (PHTs) from EHRs. ETHOS predicts future PHTs using transformer-based architectures. The Adaptive Risk Estimation System (ARES) employs ETHOS to compute dynamic and personalized risk probabilities for clinician-defined critical events. ARES incorporates a personalized explainability module that identifies key clinical factors influencing risk estimates for individual patients. ARES was evaluated on the MIMIC-IV v2.2 dataset in emergency department (ED) settings, benchmarking its performance against traditional early warning systems and machine learning models. We processed 299,721 unique patients from MIMIC-IV into 285,622 PHTs, with 60% including hospital admissions. The dataset contained over 357 million tokens. ETHOS outperformed benchmark models in predicting hospital admissions, ICU admissions, and prolonged hospital stays, achieving superior AUC scores. ETHOS-based risk estimates demonstrated robustness across demographic subgroups with strong model reliability, confirmed via calibration curves. The personalized explainability module provides insights into patient-specific factors contributing to risk. ARES, powered by ETHOS, advances predictive healthcare AI by providing dynamic, real-time, and personalized risk estimation with patient-specific explainability to enhance clinician trust. Its adaptability and superior accuracy position it as a transformative tool for clinical decision-making, potentially improving patient outcomes and resource allocation in emergency and inpatient settings. We release the full code at github.com/ipolharvard/ethos-ares to facilitate future research.
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained Model
Authors: Peihao Xiang, Kaida Wu, Ou Bai
Date: 2025-03-03 00:00:00
Proceeding: Preprints.org 2025 3
Tasks: Classification, Dimensionality Reduction, Human Activity Recognition, Image Classification, Multi-Label Learning, Position, Semantic Similarity

This paper investigates the impact of randomly initialized unique encoding of classification label position on the visual masked self-supervised pre-trained model when fine-tuning downstream classification tasks. Our findings indicate that different random initializations lead to significant variations in fine-tuned results, even when using the same allocation strategy for classification datasets. The accuracy gap between these results suggests that the visual masked self-supervised pre-trained model has an inherent preference for classification label positions. To investigate this, we compare it with the non-self-supervised visual pre-trained model and hypothesize that the masked self-supervised model exhibits a self-aware bias toward certain label positions. To mitigate the instability caused by random encoding, we propose a classification label position ranking algorithm, Label Ranker. It is based on 1-D dimensionality reduction of feature maps using Linear Discriminant Analysis and position-rank encoding of them by unsupervised feature clustering using the similarity property of Euclidean distance. This algorithm ensures that label position encoding align with the model’s inherent preference. Extensive ablation experiments using ImageMAE and VideoMAE models on the CIFAR-100, UCF101, and HMDB51 classification datasets validate our approach. Results demonstrate that our method effectively stabilizes classification label position encoding, improving fine-tuned performance for visual masked self-supervised models.
Soccer management system report project
Authors: Kamal Acharya, Acharya Kamal
Date: 2024-11-20 00:00:00
Proceeding: Authorea 2024 11
Tasks: Management

This web application comes with a simple interface where a person can simply login or
register using some basic requirement. That login welcome’s him to the main interface which
held with some data fields. The main fields are Players, Store, Management, Sponsors, Events.
The user can simply visit this fields and give support to his favourite team and if a person who is
dreams to join with this team at any of the field can apply his request through this web
application. The live updates about the team should be released through the event field in the
web application.
Student feedback management system project report
Authors: Kamal Acharya
Date: 2024-11-20 00:00:00
Proceeding: Authorea 2024 11
Tasks: Management

The aim of the study was to develop a World Wide Web-based course feedback
system serving both students and teachers. The system includes generation and
analysis of course feedback pages, provision of feedback, and a summary. Student
feedback system is developed for the engineering college students. Students give
opinion about their faculty members and vice versa.
The purpose of this feedback system is to gather information on learning
experience as well as the about lecturer(s) performance in the class. Student after
login into the system, then only is possible to give feedback He has to login with
registration number not with the name. He will give the feedback to the faculty
member by subject wise. He has five options Excellent, Very good etc. and student
has to select one of the five options about faculty in his feedback form. There is a
different areas he has to cover individually while giving the feedback like voice
strength, Teaching style, Subject knowledge, Behavior and Timing sense. Then He has
to submit his feedback with the system. Student can view the Semester results in this
system.
Administrator module to avail the facilities college has to register with the
system. College user has to append details of the courses offering and branches (EEE,
CSE etc). College has to provide Branch name, Branch Code, Course Name and no of
Seats. College has to add Faculty members by subject wise, and his/ her full Contact
details. They can view details of subjects by branch, Course, Year, Semester and
regulation wise. College has to register the students in bulk then they will divide into
sections. Student can view the details of results in way of promotion and demotion.
Rethinking IC layout vulnerability: Simulation-based hardware Trojan threat assessment with high fidelity
Authors: Xinming Wei, Jiaxi Zhang, Guojie Luo
Date: 2024-05-19 00:00:00
Proceeding: IEEE Symposium on Security and Privacy (SP) 2024 5
Tasks: Side Channel Analysis

Due to the escalating complexity of chip design and the exorbitant cost of building cutting-edge manufacturing facilities, outsourcing the fabrication of Integrated Circuits (ICs) is prevalent in modern semiconductor industry. However, significant security risks may arise because untrustworthy foundries can conduct insidious attacks without close supervision. Since prior works show the feasibility of implementing practical foundry-level Trojan attacks that circumvent post-fabrication detection, IC designers should protect their IC layouts before sending them to a third-party foundry, and such protections are known as design-time defenses. To this end, security metrics for layout vulnerability assessment are crucial to test the effectiveness of the proposed defenses. However, existing metrics are geometric-only and Trojan-oblivious, failing to capture the fundamental aspects of foundry-level Trojan insertion and the associated side effects.To bridge the gap between real attacks and threat prediction, we present SiliconCritic, a simulation-based, extensible framework that leverages design-time techniques to simulate the blackbox foundry-level Trojan attacks and post-fabrication analysis. SiliconCritic encodes the difficulty of inserting a specific Trojan into a finalized physical layout by measuring the variation of side-channel parameters (timing, power) after the simulated Trojan insertion, where larger deviations denote better detectability and thus enhanced security. SiliconCritic allows IC designers to interactively refine defensive strategies against the objective Trojan based on the feedback of side-channel analysis. Through evaluations on real-world ASIC designs and reported hardware Trojans, SiliconCritic demonstrates the limitations of existing layout-level defenses and highlights the influence of Trojan properties on defensive efficacy. Our work refreshes the understanding of Trojan prevention and suggests future directions for defenses against untrustworthy foundries.