pwc-archive/papers-with-abstracts huggingface dataset:
Online voting system project report.
Authors: Kamal Acharya
Date: 2024-08-07 00:00:00
Proceeding: Authorea 2024 8

This is a small scale project for Online voting system. The basic idea is that
the citizens of the country can vote for the candidates during election in the online. It
consists of voter details, security system, status and exits. The administrator can enter
the name and password and generate the reports and can perform operation like add
citizens, search, delete the citizens in the database. In Online voting system we can get
the result of the election based on polling.
 In the voter details module various details like Card No, Name,
Password, and Validation of the user are obtained. In the Display module the total
database is displayed. In the User Login module, the user first select whether it is
polling or admin login then by using the username and password the user can login and
the database is displayed which shows the Card No, Name, Password and Validation of
the particular user when it is polling login or otherwise the list of administrator
operation like Add Citizens and Generate Report are displayed. By using the report
Generation module we can get the result of the election and reset database after the
report is generated.
Scientific calculator project report.
Authors: Acharya Kamal
Date: 2024-08-05 00:00:00
Proceeding: Authorea 2024 8

The project Scientific Calculator is designed to automate the calculation procedure. Making less
paper work and automate the process of calculation.
Water supply management system project report.
Authors: Kamal Acharya
Date: 2024-08-05 00:00:00
Proceeding: Authorea 2024 8
Tasks: Management

The project entitled as “WATER SUPPLY MANAGEMENT SYSTEM” for
Mineral Water Supply agency. The maintenance of each and every detail manually is a tough
and tedious process. The main objective of this project is to computerize the activities
performed in the concern. In this system viewing and updating of regular customer details,
water bottle supplier details, stock details, sales details and employee details is easy because
it reduces manual work.
This system is helpful in maintaining the record of customers. This project is
developed in order to help the management to keep their overall records such as staff details
and area office details etc., the details are maintained by the secured login.
ACCIDENT DETECTION SYSTEM PROJECT REPORT.
Authors: Kamal Acharya
Date: 2024-08-05 00:00:00
Proceeding: Authorea 2024 8

The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Cricket Management System Project Report.
Authors: Kamal Acharya
Date: 2024-08-05 00:00:00
Proceeding: Authorea 2024 8
Tasks: Management

The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium Orchids
Authors: Yingshu Peng, Yuxia Zhou, Li Zhang, Hongyan Fu, Guimei Tang, Guolin Huang, Weidong Li
Date: 2024-08-13 00:00:00
Proceeding: Plant Methods 2024 8
Tasks: Fine-Grained Image Classification, Image Classification, parameter-efficient fine-tuning

The authors dedicated over a year to collecting a cultivar image dataset for Chinese Cymbidium orchids named Orchid2024. This dataset contains over 150,000 images spanning 1,275 different categories, involving visits to 20 cities across 12 provincial administrative regions in China to gather pertinent data. Subsequently, we introduced various visual parameter-efficient fine-tuning (PEFT) methods to expedite model development, achieving the highest top-1 accuracy of 86.14% and top-5 accuracy of 95.44%.
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection
Authors: Parth Ganeriwala, Siddhartha Bhattacharyya, Raja Muthalagu
Date: 2024-09-10 00:00:00
Tasks: Autonomous Vehicles, Lane Detection, Transfer Learning

Transfer Learning has become one of the standard methods to solve problems to overcome the isolated learning paradigm by utilizing knowledge acquired for one task to solve another related one. However, research needs to be done, to identify the initial steps before inducing transfer learning to applications for further verification and explainablity. In this research, we have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. Lane detection is an important aspect of autonomous vehicles driving assistance system. In most circumstances, modern deep-learning-based lane recognition systems are successful, but they struggle with lanes with complex topologies. The proposed architecture, ERFCondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies like dense, curved and fork lines. The newly proposed technique was tested on two common lane detecting benchmarks, CULane and CurveLanes respectively, and two different backbones, ResNet and ERFNet. The researched technique with ERFCondLaneNet, exhibited similar performance in comparison to ResnetCondLaneNet, while using 33% less features, resulting in a reduction of model size by 46%.
Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections
Authors: Qing Li, Jinxing Hua, Qiuxia Sun
Date: 2024-09-11 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Q-Learning

In this paper, we propose a Q-learning based decision-making framework to improve the safety and efficiency of Autonomous Vehicles when they encounter other maliciously behaving vehicles while passing through unsignalized intersections. In Autonomous Vehicles, conventional reward signals are set as regular rewards regarding feedback factors such as safety and efficiency. In this paper, safety gains are modulated by variable weighting parameters to ensure that safety can be emphasized more in emergency situations. The framework proposed in this paper introduces first-order theory of mind inferences on top of conventional rewards, using first-order beliefs as additional reward signals. The decision framework enables Autonomous Vehicles to make informed decisions when encountering vehicles with potentially malicious behaviors at unsignalized intersections, thereby improving the overall safety and efficiency of Autonomous Vehicle transportation systems. In order to verify the performance of the decision framework, this paper uses Prescan/Simulink co-simulations for simulation, and the results show that the performance of the decision framework can meet the set requirements.
What Would You Ask When You First Saw $a^2+b^2=c^2$? Evaluating LLM on Curiosity-Driven Questioning
Authors: Shashidhar Reddy Javaji, Zining Zhu
Date: 2024-09-19 00:00:00
Tasks: General Knowledge

Large language models (LLMs) can store a massive amount of knowledge, yet their potential to acquire new knowledge remains unknown. We propose a novel evaluation framework that evaluates this capability. This framework prompts LLMs to generate questions about a statement introducing scientific knowledge, simulating a curious person when facing the statement for the first time. We score the qualities of the generated questions, thereby evaluating the knowledge acquisition potential of the LLM. We apply controlled ablation studies to validate our scoring procedures. Additionally, we created a synthetic dataset consisting of 1101 statements in physics, chemistry, and maths with distinct levels of difficulties, 300 general knowledge statements, and 567 incorrect statements. Human evaluations were conducted to validate our model assessments, achieving an approximate weighted Cohen's kappa of 0.7 on all three metrics considered. We find that while large models like GPT-4 and Mistral 8x7b are adept at generating coherent and relevant questions, the smaller Phi-2 model is equally or more effective. This indicates that size does not solely determine a model's knowledge acquisition potential. The proposed framework quantifies a critical model capability that was commonly overlooked and opens up research opportunities for developing more knowledgeable AI systems
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Authors: Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis, Pierre Bonnet, Hervé Goeau, Alexis Joly, W. Daniel Kissling, César Leblanc, André S. J. van Proosdij, Konstantinos P. Panousis
Date: 2024-09-23 00:00:00

Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, massive amounts of information about species descriptions is available online in the form of text, although the lack of structure makes this source of data impossible to use at scale. To overcome this, we propose to leverage recent advances in large language models (LLMs) and devise a mechanism for gathering and processing information on plant traits in the form of unstructured textual descriptions, without manual curation. We evaluate our approach by automatically replicating three manually created species-trait matrices. Our method managed to find values for over half of all species-trait pairs, with an F1-score of over 75%. Our results suggest that large-scale creation of structured trait databases from unstructured online text is currently feasible thanks to the information extraction capabilities of LLMs, being limited by the availability of textual descriptions covering all the traits of interest.
Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection
Authors: Yacouba Kaloga, Shakeel A. Sheikh, Ina Kodrasi
Date: 2024-09-13 00:00:00
Tasks: Dimensionality Reduction

Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology irrelevant uncorrelated information, such as changing phonetic content or variations in speaking style across time, which can adversely affect classification performance. To address this issue, we propose to use Multiview Canonical Correlation Analysis (MCCA) on these input representations prior to automatic pathological speech detection. Our results demonstrate that unlike other dimensionality reduction techniques, the use of MCCA leads to a considerable improvement in pathological speech detection performance by eliminating uncorrelated information present in the input representations. Employing MCCA with traditional classifiers yields a comparable or higher performance than using sophisticated architectures, while preserving the representation structure and providing interpretability.
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
Authors: Cui Zhang, Wenjun Zhang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
Date: 2024-09-20 00:00:00
Tasks: Data Compression, Data Interaction

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies.
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
Authors: Bingyao Liu, Iris Li, Jianhua Yao, Yuan Chen, Guanming Huang, Jiajing Wang
Date: 2024-09-23 00:00:00
Tasks: Graph Neural Network, Representation Learning

This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly, based on the experience of predecessors, we selected 29 enterprise financial data indicators, abstracted each indicator as a vertex, deeply analyzed the relationships between the indicators, constructed a similarity matrix of indicators, and used the maximum spanning tree algorithm to achieve the graph structure mapping of enterprises; secondly, in the representation learning phase of the mapped graph, a graph neural network model was built to obtain its embedded representation. The feature vector of each node was expanded to 32 dimensions, and three GraphSAGE operations were performed on the graph, with the results pooled using the Pool operation, and the final output of three feature vectors was averaged to obtain the graph's embedded representation; finally, a classifier was constructed using a two-layer fully connected network to complete the prediction task. Experimental results on real enterprise data show that the model proposed in this paper can well complete the multi-level credit level estimation of enterprises. Furthermore, the tree-structured graph mapping deeply portrays the intrinsic connections of various indicator data of the company, and according to the ROC and other evaluation criteria, the model's classification effect is significant and has good "robustness".
An Art-centric perspective on AI-based content moderation of nudity
Authors: Piera Riccio, Georgina Curto, Thomas Hofmann, Nuria Oliver
Date: 2024-09-10 00:00:00
Tasks: zero-shot-classification, Zero-Shot Learning

At a time when the influence of generative Artificial Intelligence on visual arts is a highly debated topic, we raise the attention towards a more subtle phenomenon: the algorithmic censorship of artistic nudity online. We analyze the performance of three "Not-Safe-For-Work'' image classifiers on artistic nudity, and empirically uncover the existence of a gender and a stylistic bias, as well as evident technical limitations, especially when only considering visual information. Hence, we propose a multi-modal zero-shot classification approach that improves artistic nudity classification. From our research, we draw several implications that we hope will inform future research on this topic.
MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations
Authors: Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange
Date: 2024-09-23 00:00:00
Tasks: Diversity, Pupil Tracking, Sensitivity

Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modeling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several datasets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations.
Multi-scale decomposition of sea surface height snapshots using machine learning
Authors: Jingwen Lyu, Yue Wang, Christian Pedersen, Spencer Jones, Dhruv Balwada
Date: 2024-09-11 00:00:00
Tasks: Data Augmentation, Image-to-Image Translation

Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales.
BERTScoreVisualizer: A Web Tool for Understanding Simplified Text Evaluation with BERTScore
Authors: Sebastian Jaskowski, Sahasra Chava, Agam Shah
Date: 2024-09-10 00:00:00
Tasks: Text Simplification

The BERTScore metric is commonly used to evaluate automatic text simplification systems. However, current implementations of the metric fail to provide complete visibility into all information the metric can produce. Notably, the specific token matchings can be incredibly useful in generating clause-level insight into the quality of simplified text. We address this by introducing BERTScoreVisualizer, a web application that goes beyond reporting precision, recall, and F1 score and provides a visualization of the matching between tokens. We believe that our software can help improve the analysis of text simplification systems by specifically showing where generated, simplified text deviates from reference text. We host our code and demo on GitHub.
REAL: Response Embedding-based Alignment for LLMs
Authors: Honggen Zhang, Xufeng Zhao, Igor Molybog, June Zhang
Date: 2024-09-17 00:00:00

Aligning large language models (LLMs) to human preferences is a crucial step in building helpful and safe AI tools, which usually involve training on supervised datasets. Popular algorithms such as Direct Preference Optimization rely on pairs of AI-generated responses ranked according to human feedback. The response pair annotation process is the most labor-intensive and costly part of the alignment pipeline, and improving its efficiency and annotation quality would have a meaningful impact on AI development. We propose REAL: Response Embedding-based Alignment for LLMs, a strategy for constructing a high-quality training dataset that focuses on acquiring the most informative response pairs for labeling out of a set of response candidates. Our selection process is based on embedding responses independently of prompts. Experimental results on real-world dataset SHP2 and synthetic HH-RLHF benchmarks indicate that choosing dissimilar response pairs enhances the direct alignment of LLMs while reducing inherited labeling errors. The model aligned on dissimilar response pairs obtained a better margin and win rate on the dialogue task. Our findings suggest that focusing on distinct pairs can reduce the label error to improve the efficiency of LLM alignment, saving up to 65% of annotators' work.