pwc-archive/papers-with-abstracts huggingface dataset:
Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making
Authors: Alessandro Castelnovo
Date: 2024-01-13 00:00:00
Tasks: Decision Making, Fairness

In an era characterized by the pervasive integration of artificial intelligence into decision-making processes across diverse industries, the demand for trust has never been more pronounced. This thesis embarks on a comprehensive exploration of bias and fairness, with a particular emphasis on their ramifications within the banking sector, where AI-driven decisions bear substantial societal consequences. In this context, the seamless integration of fairness, explainability, and human oversight is of utmost importance, culminating in the establishment of what is commonly referred to as "Responsible AI". This emphasizes the critical nature of addressing biases within the development of a corporate culture that aligns seamlessly with both AI regulations and universal human rights standards, particularly in the realm of automated decision-making systems. Nowadays, embedding ethical principles into the development, training, and deployment of AI models is crucial for compliance with forthcoming European regulations and for promoting societal good. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. These contributions are validated through their practical application in real-world scenarios, in collaboration with Intesa Sanpaolo. This collaborative effort not only contributes to our understanding of fairness but also provides practical tools for the responsible implementation of AI-based decision-making systems. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages, further promoting progress in the field of AI fairness.
Foundations of Vector Retrieval
Authors: Sebastian Bruch
Date: 2024-01-17 00:00:00
Tasks: Retrieval

Vectors are universal mathematical objects that can represent text, images, speech, or a mix of these data modalities. That happens regardless of whether data is represented by hand-crafted features or learnt embeddings. Collect a large enough quantity of such vectors and the question of retrieval becomes urgently relevant: Finding vectors that are more similar to a query vector. This monograph is concerned with the question above and covers fundamental concepts along with advanced data structures and algorithms for vector retrieval. In doing so, it recaps this fascinating topic and lowers barriers of entry into this rich area of research.
BibSonomy Meets ChatLLMs for Publication Management: From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs
Authors: Tom Völker, Jan Pfister, Tobias Koopmann, Andreas Hotho
Date: 2024-01-17 00:00:00
Tasks: Hallucination, Management, Retrieval, Retrieval-augmented Generation

The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search & Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing & Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.
Algorithmic amplification of biases on Google Search
Authors: Hussam Habib, Ryan Stoldt, Andrew High, Brian Ekdale, Ashley Peterson, Katy Biddle, Javie Ssozi, Rishab Nithyanand
Date: 2024-01-17 00:00:00

The evolution of information-seeking processes, driven by search engines like Google, has transformed the access to information people have. This paper investigates how individuals' preexisting attitudes influence the modern information-seeking process, specifically the results presented by Google Search. Through a comprehensive study involving surveys and information-seeking tasks focusing on the topic of abortion, the paper provides four crucial insights: 1) Individuals with opposing attitudes on abortion receive different search results. 2) Individuals express their beliefs in their choice of vocabulary used in formulating the search queries, shaping the outcome of the search. 3) Additionally, the user's search history contributes to divergent results among those with opposing attitudes. 4) Google Search engine reinforces preexisting beliefs in search results. Overall, this study provides insights into the interplay between human biases and algorithmic processes, highlighting the potential for information polarization in modern information-seeking processes.
Estimating Gender Completeness in Wikipedia
Authors: Hrishikesh Patel, Tianwa Chen, Ivano Bongiovanni, Gianluca Demartini
Date: 2024-01-17 00:00:00
Tasks: Attribute

Gender imbalance in Wikipedia content is a known challenge which the editor community is actively addressing. The aim of this paper is to provide the Wikipedia community with instruments to estimate the magnitude of the problem for different entity types (also known as classes) in Wikipedia. To this end, we apply class completeness estimation methods based on the gender attribute. Our results show not only which gender for different sub-classes of Person is more prevalent in Wikipedia, but also an idea of how complete the coverage is for difference genders and sub-classes of Person.
Exploring Content-Based and Meta-Data Analysis for Detecting Fake News Infodemic: A case study on COVID-19
Authors: Oluwaseun Ajao, Ashish Garg, Marjory Da Costa-Abreu
Date: 2024-01-16 00:00:00
Tasks: Misinformation

The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was spreading, infecting people and claiming thousands of lives so was the spread and propagation of fake news, misinformation and disinformation about the event. These included the spread of unconfirmed health advice and remedies on social media. In this paper, false information about the pandemic is identified using a content-based approach and metadata curated from messages posted to online social networks. A content-based approach combined with metadata as well as an initial feature analysis is used and then several supervised learning models are tested for identifying and predicting misleading posts. Our approach shows up to 93% accuracy in the detection of fake news related posts about the COVID-19 pandemic
Automated Answer Validation using Text Similarity
Authors: Balaji Ganesan, Arjun Ravikumar, Lakshay Piplani, Rini Bhaumik, Dhivya Padmanaban, Shwetha Narasimhamurthy, Chetan Adhikary, Subhash Deshapogu
Date: 2024-01-13 00:00:00
Tasks: Information Retrieval, Multiple-choice, Question Answering, Retrieval, Science Question Answering, text similarity

Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform neural methods, especially in the multiple choice version of this problem. We implement Siamese neural network models and produce a generalised solution to this problem. We compare our supervised model with other text similarity based solutions.
Rendering Music Performance With Interpretation Variations Using Conditional Variational RNN
Authors: Akira Maezawa, Kazuhiko Yamamoto, Takuya Fujishima
Date: 2019-11-04 00:00:00
Proceeding: ISMIR 2019 11
Tasks: Decoder, Music Performance Rendering

Capturing and generating a wide variety of musical expression is important in music performance rendering, but current methods fail to model such a variation. This paper presents a music performance rendering method that could explicitly model differences in interpretations for a given piece of music. Conditional variational auto-encoder is used to jointly train, conditioned on the music score, an encoder from performance to a latent code and a decoder from the latent code to music performance. Evaluation demonstrates the method is capable of generating a wide variety of human-like expressive music performances as the latent code is varied.
Dynamic spectrum allocation based on fuzzy logic in cognitive radio networks
Authors: Anil Kumar Dulichand Vishwakarma, Girish Ashok Kulkarni
Date: 2022-12-01 00:00:00
Proceeding: AIP Conference Proceedings 2576(1):030003 2022 12
Tasks: Management

Cognitive radio has developed as a novelapproach that enables efficient use of unexploited or underutilized parts of frequency bands. Wireless cognitive radio networks aim to speculativelyfeat the empty or underutilized parts of the spectrum by listening to the frequency bands of their secondary users with various sensing techniques. For this purpose, the process of switching from a frequency channel used by secondary users for various reasons to another free channel is defined as spectrum allocation. At the beginning of the cases where the spectrum allocation takes place, the frequency channels allocated to secondary users are put back into the service of primary users. Several techniques are utilized for the spectrum allocation process decided by the access points. Artificial intelligence techniques are used to make the decision to realize the spectrum allocation transaction. In this paper, we present a fuzzy logic based spectrum allocation technique and thus ensure better management. The simulation results shows the performance in terms of received signal strength and signal to noise power.
A Joint Multi-Gradient Algorithm for Demosaicing Bayer Images
Authors: Di wu, Zhihui Xin, Chao Zhang
Date: 2023-12-21 00:00:00
Proceeding: International Conference on Communication, Image and Signal Processing (CCISP) 2023 12
Tasks: Demosaicking, SSIM

In image sensors such as CMOS and CCD, demosaicing is usually required to replace Bayer-format images with RGB-format images. Poor de-mosaicing algorithms can produce zipper effects and pseudo-colors, etc. In view of this situation and the hardware requirements of image devices, the original de-mosaicing technology gradually fails to meet the practical needs. On the other hand, although the deep learning-based de-mosaicing technology is more effective, the computational volume is too large, occupies too much hardware resources, and is difficult to be realized on the hardware platform. Therefore, this paper proposes a de-mosaicing algorithm based on multi-gradient union, which improves the traditional gradient-based first-order differentiation method, designs a new gradient operator, and ultimately judges the image edges by means of four gradient direction decisions to interpolate along the image edges. Experiments show that the algorithm in this paper has better recovery in image edges as well as texture complex regions with higher PSNR and SSIM values and better subjective visual perception compared to the traditional gradient algorithms such as BI, Cok, Hibbard, Laroche, Hamiton, while the algorithm involves only the add-subtract and shift operations, which is suitable to be implemented on the hardware platform.
Spectral Distribution Complexity of the Surface Fibrillatory Waves Predicts Post-Catheter Ablation Relapse in Persistent Atrial Fibrillation
Authors: Pilar Escribano, Juan Rodenas, Manuel Garcia, Miguel A Arias, Jose J Rieta, Raul Alcaraz
Date: 2024-01-17 00:00:00
Tasks: Rhythm, Specificity

As for most of cardiac arrhythmias, atrial fibrillation (AF) is primarily treated by catheter ablation (CA). However, the mid-term recurrence rate of this procedure in persistent AF patients is still limited and the preoperative prediction of its outcome is clinically interesting to select candidates who could benefit the most from the intervention. This context encouraged the study of C0 complexity as a novel predictor, because it estimates organization of the power spectral distribution (PSD) of the fibrillatory waves (f-waves). For that purpose, the PSD was divided into two divergent components using a threshold, theta, which was considered by multiplying the mean value of the PSD by a factor, alpha, ranging between 1.5 and 2.5. On a database of 74 patients, the values of C0 complexity computed for all alpha factors reported statistically significant differences between the patients who maintained sinus rhythm and those who relapsed to AF after a follow-up of 9 months. They also showed higher values of sensitivity (Se), specificity (Sp), and accuracy (Acc) than the well known predictors of the dominant frequency (DF) and f-wave amplitude. Moreover, the combination of the DF and the C0 complexity computed with alpha = 2, via a decision tree, improved classification until values of Se, Sp and Acc of 75.33, 77.33 and 76.58%, respectively. These results manifests the relevance of the f-wave PSD distribution to anticipate CA outcome in persistent AF patients.
RI-LIO: Reflectivity Image Assisted Tightly-Coupled LiDAR-Inertial Odometry
Authors: Yanfeng Zhang, Yunong Tian, Wanguo Wang, Guodong Yang, Zhishuo Li, Fengshui Jing, Min Tan
Date: 2023-02-09 00:00:00
Proceeding: IEEE ROBOTICS AND AUTOMATION LETTERS 2023 2

In this letter, we propose RI-LIO, a new reflectivity image assisted tightly-coupled LiDAR-inertial odometry (LIO) framework that introduces additional reflectivity texture information to efficiently reduce the drift of geometric-only methods. To achieve this, we construct an iterated extended Kalman filter framework by blending the point-to-plane geometric measurement and the reflectivity image measurement. Specifically, the geometric measurement is defined as the distance from the raw point of a new scan to its nearest neighbor plane in the global incremental kd-tree map. The searched nearest neighbor point is used to render a sparse reflectivity image after LiDAR motion distortion information is given by its corresponding raw point. Then, the reflectivity measurement is built to align the sparse reflectivity image with the dense reflectivity image of the current scan by minimizing the photometric errors directly. In addition, based on the mechanism of high-resolution LiDAR, a corrected spherical projection model is proposed to project spatial points into the image frame. Finally, extensive experiments are conducted in structured, unstructured and challenging open field scenarios. The results demonstrate that the proposed method outperforms existing geometric-only methods in terms of robustness and accuracy, especially in the rotation direction.
Neutral TTS Female Voice Corpus in Brazilian Portuguese
Authors: Pedro H. L. Leite, Edmundo Hoyle, Álvaro Antelo, Luiz F. Kruszielski, Luiz W. P. Biscainho
Date: 2023-10-08 00:00:00
Proceeding: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023) 2023 10
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Transfer Learning

This paper introduces a new dataset designed to address the limitations in high-quality, diverse and representative datasets for training text-to-speech (TTS) models, specifically for female voices in Brazilian Portuguese. The dataset features a female voice recorded in a professional and controlled environment with neutral emotion and comprises more than 20 hours of recordings. The goal is to facilitate transfer learning and enable the development of more natural-sounding, high-quality, and gender-balanced TTS systems. Alongside the dataset, gender-aware voice transfer experiments are performed to understand the impact of utilizing gender-specific pretrained models for speech synthesis. The results obtained show that same-gender voice transfer yields better speech similarity and intelligibility when compared to cross-gender transfer, emphasizing the importance of gender-aware training procedures and highlighting the need for balanced gender data.
A Corpus of Neutral Voice Speech in Brazilian Portuguese
Authors: Pedro H. L. Leite, Edmundo Hoyle, Álvaro Antelo, Luiz F. Kruszielski, Luiz W. P. Biscainho
Date: 2021-05-21 00:00:00
Proceeding: International Conference on Computational Processing of the Portuguese Language 2021 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech

This work presents a new database containing high sampling rate recordings of a single male speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for synthesis and other speech-oriented applications, the dataset contains text scripts extracted from a popular Brazilian news TV program, read out loud by a trained individual in a controlled environment, resulting in roughly 20 h of audio data. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. To illustrate the potential benefits of having this data available, text-to-speech experiments were conducted using state-of-the-art models for speech synthesis (Tacotron 2 and Waveglow). As a result, we obtained intelligible and natural sounding voices from as few as 8 min of audio samples coming from an unseen target speaker, after having trained over our data; moreover, by increasing the target recording time to 75 min, we have noticeably improved accuracy in pronunciation.
Assessing the impact of forced and voluntary behavioral changes on economic-epidemiological co-dynamics: A comparative case study between Belgium and Sweden during the 2020 COVID-19 pandemic
Authors: Tijs W. Alleman, Jan M. Baetens
Date: 2024-01-16 00:00:00

During the COVID-19 pandemic, governments faced the challenge of managing population behavior to prevent their healthcare systems from collapsing. Sweden adopted a strategy centered on voluntary sanitary recommendations while Belgium resorted to mandatory measures. Their consequences on pandemic progression and associated economic impacts remain insufficiently understood. This study leverages the divergent policies of Belgium and Sweden during the COVID-19 pandemic to relax the unrealistic -- but persistently used -- assumption that social contacts are not influenced by an epidemic's dynamics. We develop an epidemiological-economic co-simulation model where pandemic-induced behavioral changes are a superposition of voluntary actions driven by fear, prosocial behavior or social pressure, and compulsory compliance with government directives. Our findings emphasize the importance of early responses, which reduce the stringency of measures necessary to safeguard healthcare systems and minimize ensuing economic damage. Voluntary behavioral changes lead to a pattern of recurring epidemics, which should be regarded as the natural long-term course of pandemics. Governments should carefully consider prolonging lockdown longer than necessary because this leads to higher economic damage and a potentially higher second surge when measures are released. Our model can aid policymakers in the selection of an appropriate long-term strategy that minimizes economic damage.
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Authors: Sunwoong Yang, Hojin Kim, Yoonpyo Hong, Kwanjung Yee, Romit Maulik, Namwoo Kang
Date: 2024-01-05 00:00:00
Tasks: Uncertainty Quantification

This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives. First, various adaptive sampling approaches for collocation points are investigated to verify their effectiveness in the mesh-free framework of PINNs, which allows automated construction of virtual representation without manual mesh generation. Then, the overall performance of the data-driven PINNs (DD-PINNs) framework is examined, which can utilize the acquired datasets in DT scenarios. Its scalability to more general physics is validated within parametric Navier-Stokes equations, where PINNs do not need to be retrained as the Reynolds number varies. In addition, since datasets can be often collected from different fidelity/sparsity in practice, multi-fidelity DD-PINNs are also proposed and evaluated. They show remarkable prediction performance even in the extrapolation tasks, with $42\sim62\%$ improvement over the single-fidelity approach. Finally, the uncertainty quantification performance of multi-fidelity DD-PINNs is investigated by the ensemble method to verify their potential in DT, where an accurate measure of predictive uncertainty is critical. The DD-PINN frameworks explored in this study are found to be more suitable for DT scenarios than traditional PINNs from the above perspectives, bringing engineers one step closer to seamless DT realization.
Creating Visual Effects with Neural Radiance Fields
Authors: Cyrus Vachha
Date: 2023-12-11 00:00:00
Tasks: NeRF

We present a pipeline for integrating NeRFs into traditional compositing VFX pipelines using Nerfstudio, an open-source framework for training and rendering NeRFs. Our approach involves using Blender, a widely used open-source 3D creation software, to align camera paths and composite NeRF renders with meshes and other NeRFs, allowing for seamless integration of NeRFs into traditional VFX pipelines. Our NeRF Blender add-on allows for more controlled camera trajectories of photorealistic scenes, compositing meshes and other environmental effects with NeRFs, and compositing multiple NeRFs in a single scene.This approach of generating NeRF aligned camera paths can be adapted to other 3D tool sets and workflows, enabling a more seamless integration of NeRFs into visual effects and film production. Documentation can be found here: https://docs.nerf.studio/extensions/blender_addon.html