pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic Lyrics Detection Across Languages and Genres
Authors: Yanis Labrak, Markus Frohmann, Gabriel Meseguer-Brocal, Elena V. Epure
Date: 2024-06-21 00:00:00
Tasks: Text Detection

In recent years, the use of large language models (LLMs) to generate music content, particularly lyrics, has gained in popularity. These advances provide valuable tools for artists and enhance their creative processes, but they also raise concerns about copyright violations, consumer satisfaction, and content spamming. Previous research has explored content detection in various domains. However, no work has focused on the modality of lyrics in music. To address this gap, we curated a diverse dataset of real and synthetic lyrics from multiple languages, music genres, and artists. The generation pipeline was validated using both humans and automated methods. We conducted a comprehensive evaluation of existing synthetic text detection features on this novel data type. Additionally, we explored strategies to adjust the best feature for lyrics using unsupervised adaptation. Adhering to constraints of our application domain, we investigated cross-lingual generalization, data scalability, robustness to language combinations, and the impact of genre novelty in a few-shot detection scenario. Our findings show promising results within language families and similar genres, yet challenges persist with lyrics in languages that exhibit distinct semantic structures.
Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
Authors: Jiyong Oh, Syed M. Raza, Lusungu J. Mwasinga, Moonseong Kim, Hyunseung Choo
Date: 2024-06-21 00:00:00
Tasks: Deep Reinforcement Learning, Unity

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.
Backdooring Bias into Text-to-Image Models
Authors: Ali Naseh, Jaechul Roh, Eugene Bagdasaryan, Amir Houmansadr
Date: 2024-06-21 00:00:00
Tasks: Backdoor Attack, Image Generation, Text-to-Image Generation

Text-conditional diffusion models, i.e. text-to-image, produce eye-catching images that represent descriptions given by a user. These images often depict benign concepts but could also carry other purposes. Specifically, visual information is easy to comprehend and could be weaponized for propaganda -- a serious challenge given widespread usage and deployment of generative models. In this paper, we show that an adversary can add an arbitrary bias through a backdoor attack that would affect even benign users generating images. While a user could inspect a generated image to comply with the given text description, our attack remains stealthy as it preserves semantic information given in the text prompt. Instead, a compromised model modifies other unspecified features of the image to add desired biases (that increase by 4-8x). Furthermore, we show how the current state-of-the-art generative models make this attack both cheap and feasible for any adversary, with costs ranging between $12-$18. We evaluate our attack over various types of triggers, adversary objectives, and biases and discuss mitigations and future work. Our code is available at https://github.com/jrohsc/Backdororing_Bias.
How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?
Authors: Subhankar Maity, Aniket Deroy, Sudeshna Sarkar
Date: 2024-06-21 00:00:00
Tasks: Question Generation, Question-Generation

We evaluate the effectiveness of GPT-4 Turbo in generating educational questions from NCERT textbooks in zero-shot mode. Our study highlights GPT-4 Turbo's ability to generate questions that require higher-order thinking skills, especially at the "understanding" level according to Bloom's Revised Taxonomy. While we find a notable consistency between questions generated by GPT-4 Turbo and those assessed by humans in terms of complexity, there are occasional differences. Our evaluation also uncovers variations in how humans and machines evaluate question quality, with a trend inversely related to Bloom's Revised Taxonomy levels. These findings suggest that while GPT-4 Turbo is a promising tool for educational question generation, its efficacy varies across different cognitive levels, indicating a need for further refinement to fully meet educational standards.
Exploring the Efficacy of Robotic Assistants with ChatGPT and Claude in Enhancing ADHD Therapy: Innovating Treatment Paradigms
Authors: Santiago Berrezueta-Guzman, Mohanad Kandil, María-Luisa Martín-Ruiz, Iván Pau-de-la-Cruz, Stephan Krusche
Date: 2024-06-21 00:00:00

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity, which can significantly impact an individual's daily functioning and quality of life. Occupational therapy plays a crucial role in managing ADHD by fostering the development of skills needed for daily living and enhancing an individual's ability to participate fully in school, home, and social situations. Recent studies highlight the potential of integrating Large Language Models (LLMs) like ChatGPT and Socially Assistive Robots (SAR) to improve psychological treatments. This integration aims to overcome existing limitations in mental health therapy by providing tailored support and adapting to the unique needs of this sensitive group. However, there remains a significant gap in research exploring the combined use of these advanced technologies in ADHD therapy, suggesting an opportunity for novel therapeutic approaches. Thus, we integrated two advanced language models, ChatGPT-4 Turbo and Claude-3 Opus, into a robotic assistant to explore how well each model performs in robot-assisted interactions. Additionally, we have compared their performance in a simulated therapy scenario to gauge their effectiveness against a clinically validated customized model. The results of this study show that ChatGPT-4 Turbo excelled in performance and responsiveness, making it suitable for time-sensitive applications. Claude-3 Opus, on the other hand, showed strengths in understanding, coherence, and ethical considerations, prioritizing safe and engaging interactions. Both models demonstrated innovation and adaptability, but ChatGPT-4 Turbo offered greater ease of integration and broader language support. The selection between them hinges on the specific demands of ADHD therapy.
UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis
Authors: Yulong Hui, Yao Lu, Huanchen Zhang
Date: 2024-06-21 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

The use of Retrieval-Augmented Generation (RAG) has improved Large Language Models (LLMs) in collaborating with external data, yet significant challenges exist in real-world scenarios. In areas such as academic literature and finance question answering, data are often found in raw text and tables in HTML or PDF formats, which can be lengthy and highly unstructured. In this paper, we introduce a benchmark suite, namely Unstructured Document Analysis (UDA), that involves 2,965 real-world documents and 29,590 expert-annotated Q&A pairs. We revisit popular LLM- and RAG-based solutions for document analysis and evaluate the design choices and answer qualities across multiple document domains and diverse query types. Our evaluation yields interesting findings and highlights the importance of data parsing and retrieval. We hope our benchmark can shed light and better serve real-world document analysis applications. The benchmark suite and code can be found at https://github.com/qinchuanhui/UDA-Benchmark.
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
Authors: wei he, Marco Idiart, Carolina Scarton, Aline Villavicencio
Date: 2024-06-21 00:00:00
Tasks: Contrastive Learning, Machine Translation, Triplet

Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not derive their meanings solely from their constituent words, but also due to the scarcity of relevant data resources, and their impact on the performance of downstream tasks such as machine translation and simplification. In this paper we propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning for training language models by using adaptive contrastive learning and resampling miners to build an idiomatic-aware learning objective. Our proposed method is evaluated on a SemEval challenge and outperforms previous alternatives significantly in many metrics.
Évaluation des capacités de réponse de larges modèles de langage (LLM) pour des questions d'historiens
Authors: Mathieu Chartier, Nabil Dakkoune, Guillaume Bourgeois, Stéphane Jean
Date: 2024-06-21 00:00:00
Tasks: Information Retrieval, Retrieval

Large Language Models (LLMs) like ChatGPT or Bard have revolutionized information retrieval and captivated the audience with their ability to generate custom responses in record time, regardless of the topic. In this article, we assess the capabilities of various LLMs in producing reliable, comprehensive, and sufficiently relevant responses about historical facts in French. To achieve this, we constructed a testbed comprising numerous history-related questions of varying types, themes, and levels of difficulty. Our evaluation of responses from ten selected LLMs reveals numerous shortcomings in both substance and form. Beyond an overall insufficient accuracy rate, we highlight uneven treatment of the French language, as well as issues related to verbosity and inconsistency in the responses provided by LLMs.
This actually looks like that: Proto-BagNets for local and global interpretability-by-design
Authors: Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein, Philipp Berens, Lisa Koch
Date: 2024-06-21 00:00:00
Tasks: image-classification, Image Classification, Medical Diagnosis

Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on post-hoc methods that do not faithfully reflect the model's behavior. As a remedy, prototype-based networks have been proposed, but their interpretability is limited as they have been shown to provide coarse, unreliable, and imprecise explanations. In this work, we introduce Proto-BagNets, an interpretable-by-design prototype-based model that combines the advantages of bag-of-local feature models and prototype learning to provide meaningful, coherent, and relevant prototypical parts needed for accurate and interpretable image classification tasks. We evaluated the Proto-BagNet for drusen detection on publicly available retinal OCT data. The Proto-BagNet performed comparably to the state-of-the-art interpretable and non-interpretable models while providing faithful, accurate, and clinically meaningful local and global explanations. The code is available at https://github.com/kdjoumessi/Proto-BagNets.
Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
Authors: Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini
Date: 2024-06-21 00:00:00
Tasks: Informativeness, Out-of-Distribution Generalization

As Graph Neural Networks (GNNs) become more pervasive, it becomes paramount to build robust tools for computing explanations of their predictions. A key desideratum is that these explanations are faithful, i.e., that they portray an accurate picture of the GNN's reasoning process. A number of different faithfulness metrics exist, begging the question of what faithfulness is exactly, and what its properties are. We begin by showing that existing metrics are not interchangeable -- i.e., explanations attaining high faithfulness according to one metric may be unfaithful according to others -- and can be systematically insensitive to important properties of the explanation, and suggest how to address these issues. We proceed to show that, surprisingly, optimizing for faithfulness is not always a sensible design goal. Specifically, we show that for injective regular GNN architectures, perfectly faithful explanations are completely uninformative. The situation is different for modular GNNs, such as self-explainable and domain-invariant architectures, where optimizing faithfulness does not compromise informativeness, and is also unexpectedly tied to out-of-distribution generalization.
Generative Topological Networks
Authors: Alona Levy-Jurgenson, Zohar Yakhini
Date: 2024-06-21 00:00:00

Generative methods have recently seen significant improvements by generating in a lower-dimensional latent representation of the data. However, many of the generative methods applied in the latent space remain complex and difficult to train. Further, it is not entirely clear why transitioning to a lower-dimensional latent space can improve generative quality. In this work, we introduce a new and simple generative method grounded in topology theory -- Generative Topological Networks (GTNs) -- which also provides insights into why lower-dimensional latent-space representations might be better-suited for data generation. GTNs are simple to train -- they employ a standard supervised learning approach and do not suffer from common generative pitfalls such as mode collapse, posterior collapse or the need to pose constraints on the neural network architecture. We demonstrate the use of GTNs on several datasets, including MNIST, CelebA, CIFAR-10 and the Hands and Palm Images dataset by training GTNs on a lower-dimensional latent representation of the data. We show that GTNs can improve upon VAEs and that they are quick to converge, generating realistic samples in early epochs. Further, we use the topological considerations behind the development of GTNs to offer insights into why generative models may benefit from operating on a lower-dimensional latent space, highlighting the important link between the intrinsic dimension of the data and the dimension in which the data is generated. Particularly, we demonstrate that generating in high dimensional ambient spaces may be a contributing factor to out-of-distribution samples generated by diffusion models. We also highlight other topological properties that are important to consider when using and designing generative models. Our code is available at: https://github.com/alonalj/GTN
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks
Authors: Alex Quach, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
Date: 2024-06-21 00:00:00
Tasks: Imitation Learning

Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavior learned from simulation data into the real world proves to be difficult, usually mitigated with compute-heavy domain randomization methods or further model fine-tuning. We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. To this end, we first build a simulator by integrating Gaussian Splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, crafty programming of expert demonstration training data, and the task understanding capabilities of Liquid networks. Through a series of quantitative flight tests, we demonstrate the robust transfer of navigation skills learned in a single simulation scene directly to the real world. We further show the ability to maintain performance beyond the training environment under drastic distribution and physical environment changes. Our learned Liquid policies, trained on single target manoeuvres curated from a photorealistic simulated indoor flight only, generalize to multi-step hikes onboard a real hardware platform outdoors.
Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks
Authors: Victor Hugo Nascimento Rocha, Igor Cataneo Silveira, Paulo Pirozelli, Denis Deratani Mauá, Fabio Gagliardi Cozman
Date: 2024-06-21 00:00:00
Tasks: Misinformation

The recent success of Large Language Models (LLMs) has sparked concerns about their potential to spread misinformation. As a result, there is a pressing need for tools to identify ``fake arguments'' generated by such models. To create these tools, examples of texts generated by LLMs are needed. This paper introduces a methodology to obtain good, bad and ugly arguments from argumentative essays produced by ChatGPT, OpenAI's LLM. We then describe a novel dataset containing a set of diverse arguments, ArGPT. We assess the effectiveness of our dataset and establish baselines for several argumentation-related tasks. Finally, we show that the artificially generated data relates well to human argumentation and thus is useful as a tool to train and test systems for the defined tasks.
A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
Authors: Ayush Roy, Sujan Sarkar, Sohom Ghosal, Dmitrii Kaplun, Asya Lyanova, Ram Sarkar
Date: 2024-06-21 00:00:00
Tasks: Cancer Classification, Skin Cancer Classification

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17\% F1-score and 90.75\% accuracy. The code is made available at: https://github.com/AyushRoy2001/WAGF-Fusion.
Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation
Authors: Yi Chang, Zhao Ren, Zhonghao Zhao, Thanh Tam Nguyen, Kun Qian, Tanja Schultz, Björn W. Schuller
Date: 2024-06-21 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and computational resources. Moreover, emotional speech data often contains private information, raising concerns about privacy leakage during the deployment of SER models. To address these challenges, we propose a data distillation framework to facilitate efficient development of SER models in IoT applications using a synthesised, smaller, and distilled dataset. Our experiments demonstrate that the distilled dataset can be effectively utilised to train SER models with fixed initialisation, achieving performances comparable to those developed using the original full emotional speech dataset.
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays
Authors: Ayush Roy, Anurag Bhattacharjee, Diego Oliva, Oscar Ramos-Soto, Francisco J. Alvarez-Padilla, Ram Sarkar
Date: 2024-06-21 00:00:00
Tasks: Multi-class Classification, Pneumonia Detection

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15\% and 79.79\% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://github.com/AyushRoy2001/FA-Net.
A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images
Authors: Soham Chakraborty, Ayush Roy, Payel Pramanik, Daria Valenkova, Ram Sarkar
Date: 2024-06-21 00:00:00

Deep learning and computer vision methods are nowadays predominantly used in the field of ophthalmology. In this paper, we present an attention-aided DenseNet-121 for classifying normal and glaucomatous eyes from fundus images. It involves the convolutional block attention module to highlight relevant spatial and channel features extracted by DenseNet-121. The channel recalibration module further enriches the features by utilizing edge information along with the statistical features of the spatial dimension. For the experiments, two standard datasets, namely RIM-ONE and ACRIMA, have been used. Our method has shown superior results than state-of-the-art models. An ablation study has also been conducted to show the effectiveness of each of the components. The code of the proposed work is available at: https://github.com/Soham2004GitHub/DADGC.