pwc-archive/papers-with-abstracts huggingface dataset:
AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Authors: Jan Carreras Boada, Rao Muhammad Umer, Carsten Marr
Date: 2025-07-07 00:00:00
Tasks: Image Generation, Medical Diagnosis

Biomedical datasets often contain a large sample imbalance and are subject to strict privacy constraints, which together hinder the development of accurate machine learning models. One potential solution is to generate synthetic images, as this can improve data availability while preserving patient privacy. However, it remains difficult to generate synthetic images of sufficient quality for training robust classifiers. In this work, we focus on the classification of single white blood cells, a key component in the diagnosis of hematological diseases such as acute myeloid leukemia (AML), a severe blood cancer. We demonstrate how synthetic images generated with a fine-tuned stable diffusion model using LoRA weights when guided by real few-shot samples of the target white blood cell classes, can enhance classifier performance for limited data. When training a ResNet classifier, accuracy increased from 27.3\% to 78.4\% (+51.1\%) by adding 5000 synthetic images per class to a small and highly imbalanced real dataset. For a CLIP-based classifier, the accuracy improved from 61.8\% to 76.8\% (+15.0\%). The synthetic images are highly similar to real images, and they can help overcome dataset limitations, enhancing model generalization. Our results establish synthetic images as a tool in biomedical research, improving machine learning models, and facilitating medical diagnosis and research.
DESIGN AND IMPLEMENTATION OF ONLINE CLEARANCE REPORT.
Authors: Kamal Acharya
Date: 2025-07-07 00:00:00
Proceeding: Zenodo 2025 7
Tasks: All, Management

Online clearance system is a research work that will help build an effective 
information management for schools. It is aimed at developing a system for 
making clearance after graduation . The designed software will serve as a 
more reliable and effective means of undertaking students clearance, remove 
all forms of delay and stress as well as enable you understand the procedures 
involved as well as how to do your clearance online. This project work made 
use of data collected from the University, materials and journals from 
various authors and software was developed to effectively achieve the aims 
of this project. In this project, the implementation of the computer-based 
system was carried out using PHP, JAVASCRIPT, CSS, APACHE and 
MYSQL for the database. In conclusion, the work met all the objectives 
intended. It is, however, recommended for use by all tertiary institutions.
ANTI-PHISHING IN ANDROID PHONE PROJECT REPORT.
Authors: Kamal Acharya
Date: 2025-07-07 00:00:00
Proceeding: Zenodo 2025 7

Phishing is a new word produced from 'fishing', it refers to the act that the attacker allure users 
to visit a faked Web site by sending them faked e-mails (or instant messages), and stealthily 
get victim's personal information such as user name, password, and national security ID, etc. 
This information then can be used for future target advertisements or even identity theft attacks 
(e.g., transfer money from victims' bank account). The frequently used attack method is to send 
e-mails to potential victims, which seemed to be sent by banks, online organizations, or ISPs. 
In these e-mails, they will make up some causes, e.g. the password of your credit card had been 
mis-entered for many times, or they are providing upgrading services, to allure you visit their 
Web site to conform or modify your account number and password through the hyperlink 
provided in the e-mail (Leon, 2008).
Digital clock and calender management system project report.
Authors: Kamal Acharya
Date: 2025-07-07 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Decoder, Management

The design and operational principle of the digital calendar and digital clock is 
the subject of this thesis. The system as realized in this book is made up of six 
units.
The power supply unit supplies the required regulated voltage to the appropriate 
connections of the overall circuit. The micro controller controls the general 
operation of the circuit; all the other units are interfaced to it. The input unit is 
activated by pressing the keypad (buttons) unit to call up the subroutine program 
that controls the set date, month, year and time with alarm instructions. The 
decoder with the aid of the micro controller executes the instruction codes, 
which are transmitted to the light emitting diode that corresponds to the actual 
day that is to display. The display unit consist of a twelve seven segment display 
in which their common anode is driven by a NPN switching transistor; displaying 
digits in a multiplex mode.
ONLINE BIDDING MANAGEMENT SYSTEM PROJECT.
Authors: Kamal Acharya
Date: 2025-07-07 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Management

The online bidding system is a flexible solution for supporting lot- based online 
bidding. The thesis explains the construction of a bidding website. The system has been 
designed to be highly scalable and capable of supporting large numbers of bidders in 
inactive bidding. The online bidding system lets you easily browse lots and place bids 
using a secure server. All cost of mailing lots will be paid by the buyer. The objective 
is to develop a user-friendly bidding site where any kind of product can be bid and 
provide value added services to the bidders and the sellers. The products will be 
authenticated, and the site provides a safe environment for online users.
Dental clinic management system project report.
Authors: Kamal Acharya
Date: 2025-07-07 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Management

Dental Clinics, Dental department or other specialty department in a general 
hospital can use this software. Though this software was designed primarily with the 
inputs from dental Clinics, it could be adoptable in other specialty hospitals also, 
with some little changes in the medical terms, database etc.
DCMS software has been developed to provide comprehensive software 
solution for the clinics. But there are clinics that cannot afford to run such 
comprehensive system or may not be required due to the volume of work handled. 
Still to encourage such clinics to use computers for generating useful information to 
run the organization efficiently, we provide the following Software from which one 
can choose according to their requirement.
It is a system which will help dentist to keep track patient dental problems, 
from time to time. This system allow dentist to help patient to improve their 
awareness and take care about their oral health. The data regarding the patient dental 
information will help the patient in order to apply for the next treatment and also to 
be use for the future.
DCMS can analysis the data that had been captured and come out with the 
analysis report to summarize the dental score for each patient. The reports able to 
summarize the patient dental healthcare performance from time to time according to 
the treatment made. Other than help the patient to upgrade their awareness regarding 
the oral health, these reports will also give benefits to patients as they can view their 
dental health performance.
Force-IMU Fusion-Based Sensing Acupuncture Needle and Quantitative Analysis System for Acupuncture Manipulations
Authors: Peng Tian, Kang Yu, Tianyun Jiang, Yuqi Wang, Haiying Zhang, Hao Yang, Yunfeng Wang, Jun Zhang, Shuo Gao, Junhong Gao
Date: 2025-07-07 00:00:00

Acupuncture, one of the key therapeutic methods in Traditional Chinese Medicine (TCM), has been widely adopted in various clinical fields. Quantitative research on acupuncture manipulation parameters is critical to achieve standardized techniques. However, quantitative mechanical detection of acupuncture parameters remains limited. This study establishes a kinematic and dynamic model of acupuncture, identifying key parameters such as lifting-thrusting force, acceleration, velocity, displacement, as well as twirling-rotating angular velocity and angle. To measure these critical parameters, we propose a quantitative system comprising a sensing needle equipped with a force sensor and an inertial measurement unit (IMU), as well as an external camera module to capture image information. By fusing visual and IMU data, we accurately identify the stationary or motion states of the needle, enabling segmented computation of lifting-thrusting velocity and displacement. The experimental results demonstrate that the sensing needle achieves comprehensive detection with high precision, featuring a nonlinearity error of 0.45% in force measurement and an RMSE of 1.2 mm in displacement. The extracted parameters provide an objective description of the operational characteristics and motion patterns of the four basic acupuncture manipulations. These findings provide valuable tools and methods for research in acupuncture standardization.
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
Authors: Eduardo Fernandes Montesuma, Adel El Habazi, Fred Ngole Mboula
Date: 2025-02-18 00:00:00
Tasks: Anomaly Detection, Fault Detection, Unsupervised Anomaly Detection

Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods.
Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
Authors: Vanja Stojanović, Bor Pangeršič
Date: 2025-07-01 00:00:00

The NP-complete mutual-visibility (MV) problem currently lacks empirical analysis on its practical behaviour despite theoretical studies. This paper addresses this gap by implementing and evaluating three distinct algorithms - a direct greedy heuristic, a hypergraph-based approximation, and a genetic algorithm - on diverse synthetic graph datasets, including those with analytically known $\mu(G)$ values and general graph models. Our results demonstrate that for smaller graphs, the algorithms consistently achieve MV set sizes aligning with theoretical bounds. However, for larger instances, achieved solution sizes notably diverge from theoretical limits; this, combined with the absence of tight bounds, complicates absolute quality assessment. Nevertheless, validation on known optimal graphs showed the Genetic Algorithm and other heuristics empirically performing best among tested methods.
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Authors: Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang
Date: 2025-07-07 00:00:00
Tasks: Depth Estimation, Vision-Language-Action

Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, their generalization remains limited when applied to novel objects or unfamiliar environments that lie outside the training distribution. To address this, many existing approaches integrate additional components such as depth estimation, segmentation, or even diffusion to improve generalization, at the cost of adding significant computation overhead, resulting in low efficiency. This motivates the exploration of efficient action prediction methods, which are independent of additional high-level visual representations or diffusion techniques. In this work, we propose VOTE, an efficient and general framework for the optimization and acceleration of VLA models. In details, we propose a novel tokenizer-free fine-tuning approach for parallel accurate action prediction, which reduces computational overhead and accelerates inference speed. Additionally, we adopt an ensemble voting strategy for the action sampling, which significantly improves model performance and enhances generalization. Experimental results show that our method achieves state-of-the-art performance with 35$\times$ faster inference and 145 Hz throughput. All the details and codes will be open-sourced.
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky
Authors: Ashutosh Hathidara, Julien Yu, Sebastian Schreiber
Date: 2025-07-04 00:00:00
Tasks: Response Generation

Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the same user intent or when required arguments are left underspecified. We introduce DiaFORGE (Dialogue Framework for Organic Response Generation & Evaluation), a disambiguation-centric, three-stage pipeline that (i) synthesizes persona-driven, multi-turn dialogues in which the assistant must distinguish among highly similar tools, (ii) performs supervised fine-tuning of open-source models with reasoning traces across 3B - 70B parameters, and (iii) evaluates real-world readiness via a dynamic suite that redeploys each model in a live agentic loop and reports end-to-end goal completion alongside conventional static metrics. On our dynamic benchmark DiaBENCH, models trained with DiaFORGE raise tool-invocation success by 27 pp over GPT-4o and by 49 pp over Claude-3.5-Sonnet, both under optimized prompting. To spur further research, we release an open corpus of 5000 production-grade enterprise API specifications paired with rigorously validated, disambiguation-focused dialogues, offering a practical blueprint for building reliable, enterprise-ready tool-calling agents.
Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Authors: Tianyi Ding, Hongli Chen, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun
Date: 2025-07-04 00:00:00
Tasks: Magnetic Resonance Fingerprinting, Mamba, SSIM, State Space Models

Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a dual Mamba-based encoder with a Gate-Aware Spatial-Temporal (GAST) processor. Built on structured state-space models, our architecture efficiently captures long-range spatial dependencies with linear complexity. On 5 times accelerated simulated MRF data (200 frames), GAST-Mamba achieved a T1 PSNR of 33.12~dB, outperforming SCQ (31.69~dB). For T2 mapping, it reached a PSNR of 30.62~dB and SSIM of 0.9124. In vivo experiments further demonstrated improved anatomical detail and reduced artifacts. Ablation studies confirmed that each component contributes to performance, with the GAST module being particularly important under strong undersampling. These results demonstrate the effectiveness of GAST-Mamba for accurate and robust reconstruction from highly undersampled MRF acquisitions, offering a scalable alternative to traditional DM-based methods.
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
Authors: Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur, Tuhin Sahai, Susmit Jha
Date: 2025-07-05 00:00:00

We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to the ODE over a given set of parameters, initial conditions, and range of times. Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for learning such models that combine data-driven deep learning with symbolic physics models in a principled manner. However, the accuracy of PINNs degrade when they are used to solve an entire family of initial value problems characterized by varying parameters and initial conditions. In this paper, we combine symbolic differentiation and Taylor series methods to propose a class of higher-order models for capturing the solutions to ODEs. These models combine neural networks and symbolic terms: they use higher order Lie derivatives and a Taylor series expansion obtained symbolically, with the remainder term modeled as a neural network. The key insight is that the remainder term can itself be modeled as a solution to a first-order ODE. We show how the use of these higher order PINNs can improve accuracy using interesting, but challenging ODE benchmarks. We also show that the resulting model can be quite useful for situations such as controlling uncertain physical systems modeled as ODEs.
Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization
Authors: Xuesong, Zhou, Taehooie Kim, Mostafa Ameli, Henan, Zhu, Yu- dai Honma, Ram M. Pendyala
Date: 2025-06-30 00:00:00
Tasks: Tensor Decomposition

Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation. This paper introduces Flow Through Tensors (FTT), a unified computational graph architecture that connects origin destination flows, path probabilities, and link travel times as interconnected tensors. Our framework makes three key contributions: first, it establishes a consistent mathematical structure that enables gradient-based optimization across previously separate modeling elements; second, it supports multidimensional analysis of traffic patterns over time, space, and user groups with precise quantification of system efficiency; third, it implements tensor decomposition techniques that maintain computational tractability for large scale applications. These innovations collectively enable real time control strategies, efficient coordination between multiple transportation modes and operators, and rigorous enforcement of physical network constraints. The FTT framework bridges the gap between theoretical transportation models and practical deployment needs, providing a foundation for next generation integrated mobility systems.
Fuzzy Classification Aggregation for a Continuum of Agents
Authors: Zijun Meng
Date: 2025-07-06 00:00:00
Tasks: Classification

We prove that any optimal, independent, and zero unanimous fuzzy classification aggregation function of a continuum of individual classifications of $m\ge 3$ objects into $2\le p\le m$ types must be a weighted arithmetic mean.
Multiple Integration Model for Single-source Domain Generalizable Person Re-identification
Authors: Jia Sun, Yanfeng Li, Luyifu Chen, Houjin Chen, Wanru Peng
Date: 2024-02-01 00:00:00
Proceeding: JVCIR 2024 2
Tasks: Continual Learning, Generalizable Person Re-identification, Person Re-Identification

Domain generalizable (DG) person re-identification (re-ID) aims to train a model on labeled source domains which can perform well on invisible target domains. Because of the distribution shifts between different domains, it is a challenging task. Existing methods address this challenge by using multiple source domains to train a model which requires more data, manual labor, and computation. In contrast, we pay attention to the single-source DG re-ID task, that is, only one source domain data is accessible for training. However, due to the limited availability of training data, this task is more difficult. In this paper, a novel MulTiple Integration (MTI) model is introduced for single-source DG person re-ID. By integrating multiple reliable perturbations, the generalization performance can be improved. Specifically, MTI model contains two types of integration modules, one is shallow-level compensation (SLC) and the other is deep-level integration (DLI). For SLC, according to the idea of continual learning, the shallow-level information of the ImageNet pre-trained ResNet-50 branch is introduced and fused with the shallow-level information of our backbone network. In this way, massive information in ImageNet can be used to prevent the disastrous forgetting of the pre-trained information, and information compensation can be provided for backbone network. Additionally, we propose a hybrid integrated normalization layer to fuse information and improve the model’s generalization performance. For DLI, a wave transformer block is introduced in the deep layer of the backbone, which can integrate the information of a batch images and contain reliable disturbance, so that the robustness of the model can be promoted. Extensive experimental results demonstrate the superiority of our model.
Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems
Authors: Sandeep Mishra, Anubhab Mandal, Bishal Santra, Tushar Abhishek, Pawan Goyal, Manish Gupta
Date: 2025-07-08 00:00:00
Tasks: Deep Learning

Ghosting, the ability to predict a user's intended text input for inline query auto-completion, is an invaluable feature for modern search engines and chat interfaces, greatly enhancing user experience. By suggesting completions to incomplete queries (or prefixes), ghosting aids users with slow typing speeds, disabilities, or limited language proficiency. Ghosting is a challenging problem and has become more important with the ubiquitousness of chat-based systems like ChatGPT, Copilot, etc. Despite the increasing prominence of chat-based systems utilizing ghosting, this challenging problem of Chat-Ghosting has received little attention from the NLP/ML research community. There is a lack of standardized benchmarks and relative performance analysis of deep learning and non-deep learning methods. We address this through an open and thorough study of this problem using four publicly available dialog datasets: two human-human (DailyDialog and DSTC7-Ubuntu) and two human-bot (Open Assistant and ShareGPT). We experiment with various existing query auto-completion methods (using tries), n-gram methods and deep learning methods, with and without dialog context. We also propose a novel entropy-based dynamic early stopping strategy. Our analysis finds that statistical n-gram models and tries outperform deep learning based models in terms of both model performance and inference efficiency for seen prefixes. For unseen queries, neural models like T5 and Phi-2 lead to better results. Adding conversational context leads to significant improvements in ghosting quality, especially for Open-Assistant and ShareGPT. We make code and data publicly available