pwc-archive/papers-with-abstracts huggingface dataset:
VOICE CONTROL ROBOT USING ARDUINO MANAGEMENT SYSTEM PROJECT.
Authors: Kamal Acharya
Date: 2025-06-25 00:00:00
Proceeding: Zenodo 2025 6
Tasks: Management, speech-recognition, Speech Recognition

This robotic is designed to control vehicle by using human voice command through Bluetooth module. Voice Control Robot is used to complete specific commands like Forward, Backward, Stop, Left, Right and dancing (or rotation of robot) etc. Voice Control Robot is based on Speech Recognition. The commands are given to robot using Android application. The Android application (AMR-Voice) is connected to Bluetooth Module (HC-05), which is directly connected to Arduino Uno R3. We give command to the robot and it performs work according to the given command. Voice Control Robot is much useful for those areas where humans can't reached. Robot can work in all type of situations like toxic area, in fire situations, polluted area and also on hills. This robot is very useful for those who is physically handicapped. This robot is very small in size so we can use this project for spying or espial. If we implement in this project so we can use this robot in military application, agriculture purpose, and industrial purpose and also for surveillance device.
Digital watermarking system project report.
Authors: Kamal Acharya
Date: 2025-06-25 00:00:00
Proceeding: Zenodo 2025 6

Data security is the essential in the today’s world of internet and networking. In 
any organization information is critical. In today’s world people are ready to spent 
thousands and lacks of money in order to ensure high level of information security. In 
spite of spending such a huge amount, still the objective of securing the information is
not achieved as the data some how gets in the hands of hacker. As the technology for 
securing the data is advancing, hackers are also keeping pace with this technology. 
Hackers now make use of certain algorithm or other techniques to decode the data 
encoded by the senders. One of the ways to ensure security is to ensure that data is not 
visible to the hacker. This can be done by hiding the message itself behind some other 
objects. Here we are achieving this data security concept through the technique of 
Steganography.
Cakeshop management system project report.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Management

A cake shop management system is a computerized management system. This system provides 
the Records of hardware assets beside software of the organization. the proposed system keep a 
track of different types of cakes available in the system.
The main objective of cake shop management system is to provide solution for the customers to 
manage their work using computerized process. this software application will help to handle the 
customer information details about the products, payment detailed information etc. detailed 
explanation about modules and design are provided in the documentation.
Online discussion project management system report.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Articles, Management

The project titled “Online Discuss-forum” is designed using Active Server Pages 
.NET with Microsoft Visual Studio.Net 2008 as front end and Microsoft SQL Server 
2000 as back end which works in .Net framework version 3.5. The coding language used 
is C# .Net.
This project is aimed at developing online form for the group discussion. This is a 
web-based tool. Any user can post the doubts topics and can reply for the other user 
doubts. The user can invites others for Discussion and submit query. This is useful for a 
small office, school or a department or for that matter any group who is interested to 
organize it effectively. Facility to share the resource and post articles that can be viewed 
by registered user.
Detection of Cyber Attack in Network using Machine Learning Techniques.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Intrusion Detection

Contrasted with the past, improvements in PC and correspondence 
innovations have given broad and propelled changes. The use of new 
innovations give incredible advantages to people, organizations, and 
governments, be that as it may, messes some up against them. For instance, 
the protection of significant data, security of put away information stages, 
accessibility of information and so forth. Contingent upon these issues, 
digital fear based oppression is one of the most significant issues in this day 
and age. Digital fear, which made a great deal of issues people and 
establishments, has arrived at a level that could undermine open and nation 
security by different gatherings, for example, criminal association, 
proficient people and digital activists. Along these lines, Intrusion Detection 
Systems (IDS) has been created to maintain a strategic distance from digital 
assaults. Right now, learning the bolster support vector machine (SVM) 
calculations were utilized to recognize port sweep endeavors dependent on 
the new CICIDS2017 dataset with 97.80%, 69.79% precision rates were 
accomplished individually.
Face mask detection project report.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Face Detection, Face Recognition, Occluded Face Detection

Recognition from faces is a popular and significant technology in recent years. In the 
real-world, when a person is uncooperative with the systems such as in video surveillance 
then masking is further common scenarios. For these masks, current face recognition 
performance degrades. Still, difficulties created by masks are usually disregarded. Face 
recognition is a promising area of applied computer vision. This technique is used to 
recognize a face or identify a person automatically from given images. In our daily life 
activates like, in a passport checking, smart door, access control, voter verification, criminal 
investigation, and many other purposes face recognition is widely used to authenticate a 
person correctly and automatically. Face recognition has gained much attention as a unique, 
reliable biometric recognition technology that makes it most popular than any other 
biometric technique likes password, pin, fingerprint, etc.
The primary concern to this work is about facial masks, and especially to enhance 
the recognition accuracy of different masked faces. A feasible approach has been proposed 
that consists of first detecting the facial regions. The occluded face detection problem has 
been approached using Cascaded Convolutional Neural Network (CNN). Besides, its 
performance has been also evaluated within excessive facial masks and found attractive 
outcomes. Finally, a correlative study also made here for a better understanding.
Entrance seat allotment system project report.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7

This project Entrance Seat Allotment System is windows application in which students can 
register with their rank number for the entrance examination and the administrator can allot the 
seats for the students. Administrator can add the college details and he batch details. Using this 
software the entrance seat allotment became easier and can be implemented using system. The 
main advantage of the project is the computerization of the entrance seat allotment process. 
Administrator has the power for the allotment. He can add the allotted seats into a file and the 
details are saved into the system. The total time for the entrance allotment became lesser and the 
allotment process became faster.
HOUSE TAX BILLING MANAGEMENT SYSTEM REPORT.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Management

Main aim of this project is to implement an application which deals with maintaining house tax 
activities like generating house tax bill, Customer personal records and other Administration 
activities. Initially, all the information about residence and the address will be entered and maintained, 
which in turn helps to generate an House Tax bill .This system will reduce manual work for 
maintaining records in files. 
This system provides new mechanism in maintaining house tax records effectively. Regular 
transactions which include bill generation, payment etc. and exceptional transactions that are related 
to, change of customer’s address, not clearing bills within due date etc. also will have to be handled 
by the system.
DESIGN AND DEVELOPMENT OF AN SMART ELECTRICITY BILL PAYMENT MANAGEMENT SYSTEM.
Authors: Kamal Acharya
Date: 2025-07-02 00:00:00
Proceeding: Zenodo 2025 7
Tasks: Management

The project is a web based application where users can get instant electricity bill and pay 
them online via credit card. The system automates the conventional process of paying electricity 
bill by visiting the place. Users have to stand in queue for paying bill and wait for their turn. The 
process is tiresome and time consuming. They even have to wait for the bill being delivered to 
their place which sometimes can be delivered late by the delivery boy. Hence the system is 
developed to automate the electricity bill calculation and payment for user convenience.
The system would be having two logins admin and user login. Admin can view user 
account details and can even add or updates things in their account. Admin has to feed the system 
with electricity usage data into respective users account. The system then calculates the electricity 
bill for every user and updates the information into their account every month. User can view their 
electricity bill and then the user can get the information like how voltages are used in individual 
home appliances. Then the system is used to calculate the voltage level of particular home 
appliance and how much of cost is used in that particular appliance and also individually pay on 
the spot before month end. If user is incapable of paying the bill before month end, it then 
calculates fine for each day.
IndianBailJudgments-1200: A Multi-Attribute Dataset for Legal NLP on Indian Bail Orders
Authors: Sneha Deshmukh, Prathmesh Kamble
Date: 2025-07-03 00:00:00
Tasks: Attribute, Fairness, Jurisprudence, Legal Reasoning

Legal NLP remains underdeveloped in regions like India due to the scarcity of structured datasets. We introduce IndianBailJudgments-1200, a new benchmark dataset comprising 1200 Indian court judgments on bail decisions, annotated across 20+ attributes including bail outcome, IPC sections, crime type, and legal reasoning. Annotations were generated using a prompt-engineered GPT-4o pipeline and verified for consistency. This resource supports a wide range of legal NLP tasks such as outcome prediction, summarization, and fairness analysis, and is the first publicly available dataset focused specifically on Indian bail jurisprudence.
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
Authors: Abhishek Verma, Nallarasan V, Balaraman Ravindran
Date: 2025-06-17 00:00:00
Tasks: Atari Games, Board Games, Computational Efficiency, Decision Making, Deep Learning, Deep Reinforcement Learning, Multi-Armed Bandits, Reinforcement Learning, Reinforcement Learning (Atari Games), Sequential Decision Making

Deep Reinforcement Learning (DRL) has achieved remarkable success in complex sequential decision-making tasks, such as playing Atari 2600 games and mastering board games. A critical yet underexplored aspect of DRL is the temporal scale of action execution. We propose a novel paradigm that integrates contextual bandits with DRL to adaptively select action durations, enhancing policy flexibility and computational efficiency. Our approach augments a Deep Q-Network (DQN) with a contextual bandit module that learns to choose optimal action repetition rates based on state contexts. Experiments on Atari 2600 games demonstrate significant performance improvements over static duration baselines, highlighting the efficacy of adaptive temporal abstractions in DRL. This paradigm offers a scalable solution for real-time applications like gaming and robotics, where dynamic action durations are critical.
ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions
Authors: Quang-Manh Hoang, Van Nam Nguyen, Taehyung Kim, Guilherme Vieira Hollweg, Wencong Su, Van-Hai Bui
Date: 2025-06-29 00:00:00
Tasks: Scheduling

In contrast to grid-following inverters, Virtual Synchronous Generators (VSGs) perform well under weak grid conditions but may become unstable when the grid is strong. Grid strength depends on grid impedance, which unfortunately varies over time. In this paper, we propose a novel adaptive gain-scheduling control scheme for VSGs. First, an Artificial Neural Network (ANN) estimates the fundamental-frequency grid impedance; then these estimates are fed into an adaptive gain-scheduling function to recalculate controller parameters under varying grid conditions. The proposed method is validated in Simulink and compared with a conventional VSG employing fixed controller gains. The results demonstrate that settling times and overshoot percentages remain consistent across different grid conditions. Additionally, previously unseen grid impedance values are estimated with high accuracy and minimal time delay, making the approach well suited for real-time gain-scheduling control.
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
Authors: Jingkai Li
Date: 2025-06-26 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), Interpretable Machine Learning, Language Modeling, Language Modelling, Large Language Model

Integrated Information Theory (IIT) provides a quantitative framework for explaining consciousness phenomenon, positing that conscious systems comprise elements integrated through causal properties. We apply IIT 3.0 and 4.0 -- the latest iterations of this framework -- to sequences of Large Language Model (LLM) representations, analyzing data derived from existing Theory of Mind (ToM) test results. Our study systematically investigates whether the differences of ToM test performances, when presented in the LLM representations, can be revealed by IIT estimates, i.e., $\Phi^{\max}$ (IIT 3.0), $\Phi$ (IIT 4.0), Conceptual Information (IIT 3.0), and $\Phi$-structure (IIT 4.0). Furthermore, we compare these metrics with the Span Representations independent of any estimate for consciousness. This additional effort aims to differentiate between potential "consciousness" phenomena and inherent separations within LLM representational space. We conduct comprehensive experiments examining variations across LLM transformer layers and linguistic spans from stimuli. Our results suggest that sequences of contemporary Transformer-based LLM representations lack statistically significant indicators of observed "consciousness" phenomena but exhibit intriguing patterns under $\textit{spatio}$-permutational analyses. The Appendix and code are available as Supplementary Materials at: https://doi.org/10.1016/j.nlp.2025.100163.
No time to train! Training-Free Reference-Based Instance Segmentation
Authors: Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh, Elliot J. Crowley
Date: 2025-07-03 00:00:00
Tasks: Cross-Domain Few-Shot Object Detection, Few-Shot Object Detection, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a promptable, semantics-agnostic, segmentation paradigm and yet still requires manual visual-prompts or complex domain-dependent prompt-generation rules to process a new image. Towards reducing this new burden, our work investigates the task of object segmentation when provided with, alternatively, only a small set of reference images. Our key insight is to leverage strong semantic priors, as learned by foundation models, to identify corresponding regions between a reference and a target image. We find that correspondences enable automatic generation of instance-level segmentation masks for downstream tasks and instantiate our ideas via a multi-stage, training-free method incorporating (1) memory bank construction; (2) representation aggregation and (3) semantic-aware feature matching. Our experiments show significant improvements on segmentation metrics, leading to state-of-the-art performance on COCO FSOD (36.8% nAP), PASCAL VOC Few-Shot (71.2% nAP50) and outperforming existing training-free approaches on the Cross-Domain FSOD benchmark (22.4% nAP).
Seeding neural network quantum states with tensor network states
Authors: Ryui Kaneko, Shimpei Goto
Date: 2025-06-30 00:00:00

We find an efficient approach to approximately convert matrix product states (MPSs) into restricted Boltzmann machine wave functions consisting of a multinomial hidden unit through a canonical polyadic (CP) decomposition of the MPSs. This method allows us to generate well-behaved initial neural network quantum states for quantum many-body ground-state calculations in polynomial time of the number of variational parameters and systematically shorten the distance between the initial states and the ground states with increasing the rank of the CP decomposition. We demonstrate the efficiency of our method by taking the transverse-field Ising model as an example and discuss possible applications of our method to more general quantum many-body systems in which the ground-state wave functions possess complex nodal structures.
Sentence Fusion for Multidocument News Summarization
Authors: Regina Barzilay, Kathleen R. McKeown
Date: 2005-01-01 00:00:00
Proceeding: Computational Linguistics 2005 1
Tasks: News Summarization, Sentence, Sentence Fusion, Text Generation, Text Summarization

A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents. Sentence fusion involves bottom-up local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources.
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
Authors: François Rozet, Ruben Ohana, Michael McCabe, Gilles Louppe, François Lanusse, Shirley Ho
Date: 2025-07-03 00:00:00
Tasks: Diversity, Video Generation

The steep computational cost of diffusion models at inference hinders their use as fast physics emulators. In the context of image and video generation, this computational drawback has been addressed by generating in the latent space of an autoencoder instead of the pixel space. In this work, we investigate whether a similar strategy can be effectively applied to the emulation of dynamical systems and at what cost. We find that the accuracy of latent-space emulation is surprisingly robust to a wide range of compression rates (up to 1000x). We also show that diffusion-based emulators are consistently more accurate than non-generative counterparts and compensate for uncertainty in their predictions with greater diversity. Finally, we cover practical design choices, spanning from architectures to optimizers, that we found critical to train latent-space emulators.
A Diagrammatic Calculus for a Functional Model of Natural Language Semantics
Authors: Matthieu Pierre Boyer
Date: 2025-07-01 00:00:00

In this paper, we study a functional programming approach to natural language semantics, allowing us to increase the expressivity of a more traditional denotation style. We will formalize a category based type and effect system, and construct a diagrammatic calculus to model parsing and handling of effects, and use it to efficiently compute the denotations for sentences.
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Authors: Geonwoo Cho, Jaemoon Lee, Jaegyun Im, Subi Lee, JIhwan Lee, Sundong Kim
Date: 2025-06-06 00:00:00
Tasks: Diversity

Skill-based reinforcement learning (SBRL) enables rapid adaptation in environments with sparse rewards by pretraining a skill-conditioned policy. Effective skill learning requires jointly maximizing both exploration and skill diversity. However, existing methods often face challenges in simultaneously optimizing for these two conflicting objectives. In this work, we propose a new method, Adaptive Multi-objective Projection for balancing Exploration and skill Diversification (AMPED), which explicitly addresses both exploration and skill diversification. We begin by conducting extensive ablation studies to identify and define a set of objectives that effectively capture the aspects of exploration and skill diversity, respectively. During the skill pretraining phase, AMPED introduces a gradient surgery technique to balance the objectives of exploration and skill diversity, mitigating conflicts and reducing reliance on heuristic tuning. In the subsequent fine-tuning phase, AMPED incorporates a skill selector module that dynamically selects suitable skills for downstream tasks, based on task-specific performance signals. Our approach achieves performance that surpasses SBRL baselines across various benchmarks. These results highlight the importance of explicitly harmonizing exploration and diversity and demonstrate the effectiveness of AMPED in enabling robust and generalizable skill learning. Project Page: https://geonwoo.me/amped/
Graph Style Transfer for Counterfactual Explainability
Authors: Bardh Prenkaj, Efstratios Zaradoukas, Gjergji Kasneci
Date: 2025-05-23 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Graph Classification, Style Transfer, valid

Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted outcome. This task becomes particularly challenging for graph data due to preserving structural integrity and semantic meaning. Unlike prior approaches that rely on forward perturbation mechanisms, we introduce Graph Inverse Style Transfer (GIST), the first framework to re-imagine graph counterfactual generation as a backtracking process, leveraging spectral style transfer. By aligning the global structure with the original input spectrum and preserving local content faithfulness, GIST produces valid counterfactuals as interpolations between the input style and counterfactual content. Tested on 8 binary and multi-class graph classification benchmarks, GIST achieves a remarkable +7.6% improvement in the validity of produced counterfactuals and significant gains (+45.5%) in faithfully explaining the true class distribution. Additionally, GIST's backtracking mechanism effectively mitigates overshooting the underlying predictor's decision boundary, minimizing the spectral differences between the input and the counterfactuals. These results challenge traditional forward perturbation methods, offering a novel perspective that advances graph explainability.