pwc-archive/papers-with-abstracts huggingface dataset:
Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models
Authors: Zitao Chen, Karthik Pattabiraman
Date: 2024-09-10 00:00:00
Tasks: Memorization

The rise of deep learning (DL) has led to a surging demand for training data, which incentivizes the creators of DL models to trawl through the Internet for training materials. Meanwhile, users often have limited control over whether their data (e.g., facial images) are used to train DL models without their consent, which has engendered pressing concerns. This work proposes MembershipTracker, a practical data auditing tool that can empower ordinary users to reliably detect the unauthorized use of their data in training DL models. We view data auditing through the lens of membership inference (MI). MembershipTracker consists of a lightweight data marking component to mark the target data with small and targeted changes, which can be strongly memorized by the model trained on them; and a specialized MI-based verification process to audit whether the model exhibits strong memorization on the target samples. MembershipTracker only requires the users to mark a small fraction of data (0.005% to 0.1% in proportion to the training set), and it enables the users to reliably detect the unauthorized use of their data (average 0% FPR@100% TPR). We show that MembershipTracker is highly effective across various settings, including industry-scale training on the full-size ImageNet-1k dataset. We finally evaluate MembershipTracker under multiple classes of countermeasures.
Towards Robust Uncertainty-Aware Incomplete Multi-View Classification
Authors: Mulin Chen, Haojian Huang, Qiang Li
Date: 2024-09-10 00:00:00
Tasks: Classification, Decision Making, Imputation

Handling incomplete data in multi-view classification is challenging, especially when traditional imputation methods introduce biases that compromise uncertainty estimation. Existing Evidential Deep Learning (EDL) based approaches attempt to address these issues, but they often struggle with conflicting evidence due to the limitations of the Dempster-Shafer combination rule, leading to unreliable decisions. To address these challenges, we propose the Alternating Progressive Learning Network (APLN), specifically designed to enhance EDL-based methods in incomplete MVC scenarios. Our approach mitigates bias from corrupted observed data by first applying coarse imputation, followed by mapping the data to a latent space. In this latent space, we progressively learn an evidence distribution aligned with the target domain, incorporating uncertainty considerations through EDL. Additionally, we introduce a conflict-aware Dempster-Shafer combination rule (DSCR) to better handle conflicting evidence. By sampling from the learned distribution, we optimize the latent representations of missing views, reducing bias and enhancing decision-making robustness. Extensive experiments demonstrate that APLN, combined with DSCR, significantly outperforms traditional methods, particularly in environments characterized by high uncertainty and conflicting evidence, establishing it as a promising solution for incomplete multi-view classification.
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking
Authors: Jihyun Lee, Solee Im, Wonjun Lee, Gary Geunbae Lee
Date: 2024-09-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Dialogue State Tracking, speech-recognition, Speech Recognition, Task-Oriented Dialogue Systems

Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Speech Recognition (ASR) systems. We introduce a simple yet effective data augmentation method that targets those entities to improve the robustness of DST model. Our novel method can control the placement of errors using keyword-highlighted prompts while introducing phonetically similar errors. As a result, our method generated sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.
DiPT: Enhancing LLM reasoning through diversified perspective-taking
Authors: Hoang Anh Just, Mahavir Dabas, Lifu Huang, Ming Jin, Ruoxi Jia
Date: 2024-09-10 00:00:00
Tasks: Language Modeling, Language Modelling

Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this paper introduces DiPT, a novel approach that complements current reasoning methods by explicitly incorporating diversified viewpoints. This approach allows the model to gain a deeper understanding of the problem's context and identify the most effective solution path during the inference stage. Additionally, it provides a general data-centric AI recipe for augmenting existing data to improve their quality for fine-tuning. Our empirical results demonstrate that DiPT can be flexibly integrated into existing methods that focus on a single reasoning approach, enhancing their reasoning performance and stability when presented with paraphrased problems. Furthermore, we illustrate improved context understanding by maintaining the model's safe outputs against "jailbreaking" prompts intentionally designed to bypass safeguards built into deployed models. Lastly, we show that fine-tuning with data enriched with diverse perspectives can boost the reasoning capabilities of the model compared to fine-tuning with raw data alone.
CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities
Authors: Rashik Shahriar Akash, Radiful Islam, S. M. Saiful Islam Badhon, K. S. M. Tozammel Hossain
Date: 2024-09-10 00:00:00
Tasks: Computational Efficiency, Diagnostic

Cervical cancer is a major cause of cancer-related mortality among women worldwide, and its survival rate improves significantly with early detection. Traditional diagnostic methods such as Pap smears and cervical biopsies rely heavily on cytologist expertise, making the process prone to human error. This study introduces CerviXpert, a multi-structural convolutional neural network model designed to efficiently classify cervix types and detect cervical cell abnormalities. CerviXpert is built as a computationally efficient model that classifies cervical cancer using images from the publicly available SiPaKMeD dataset. The model architecture emphasizes simplicity, using a limited number of convolutional layers followed by max pooling and dense layers, trained from scratch. We assessed the performance of CerviXpert against other state of the art convolutional neural network models including ResNet50, VGG16, MobileNetV2, and InceptionV3, evaluating them on accuracy, computational efficiency, and robustness using five fold cross validation. CerviXpert achieved an accuracy of 98.04 percent in classifying cervical cell abnormalities into three classes and 98.60 percent for five class cervix type classification, outperforming MobileNetV2 and InceptionV3 in both accuracy and computational requirements. It showed comparable results to ResNet50 and VGG16 while reducing computational complexity and resource needs. CerviXpert provides an effective solution for cervical cancer screening and diagnosis, balancing accuracy with computational efficiency. Its streamlined design enables deployment in resource constrained environments, potentially enhancing early detection and management of cervical cancer.
Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis
Authors: Xin Zhang, Deval Mehta, Yanan Hu, Chao Zhu, David Darby, Zhen Yu, Daniel Merlo, Melissa Gresle, Anneke Van Der Walt, Helmut Butzkueven, ZongYuan Ge
Date: 2024-09-10 00:00:00
Tasks: Survival Analysis

Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv. Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods.
NOVI : Chatbot System for University Novice with BERT and LLMs
Authors: Yoonji Nam, TaeWoong Seo, Gyeongcheol Shin, Sangji Lee, JaeEun Im
Date: 2024-09-10 00:00:00
Tasks: Chatbot

To mitigate the difficulties of university freshmen in adapting to university life, we developed NOVI, a chatbot system based on GPT-4o. This system utilizes post and comment data from SKKU 'Everytime', a university community site. Developed using LangChain, NOVI's performance has been evaluated with a BLEU score, Perplexity score, ROUGE-1 score, ROUGE-2 score, ROUGE-L score and METEOR score. This approach is not only limited to help university freshmen but is also expected to help various people adapting to new environments with different data. This research explores the development and potential application of new educational technology tools, contributing to easier social adaptation for beginners and settling a foundation for future advancement in LLM studies.
Can Large Language Models Unlock Novel Scientific Research Ideas?
Authors: Sandeep Kumar, Tirthankar Ghosal, Vinayak Goyal, Asif Ekbal
Date: 2024-09-10 00:00:00

"An idea is nothing more nor less than a new combination of old elements" (Young, J.W.). The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people's everyday lives. This study explores the capability of LLMs in generating novel research ideas based on information from research papers. We conduct a thorough examination of 4 LLMs in five domains (e.g., Chemistry, Computer, Economics, Medical, and Physics). We found that the future research ideas generated by Claude-2 and GPT-4 are more aligned with the author's perspective than GPT-3.5 and Gemini. We also found that Claude-2 generates more diverse future research ideas than GPT-4, GPT-3.5, and Gemini 1.0. We further performed a human evaluation of the novelty, relevancy, and feasibility of the generated future research ideas. This investigation offers insights into the evolving role of LLMs in idea generation, highlighting both its capability and limitations. Our work contributes to the ongoing efforts in evaluating and utilizing language models for generating future research ideas. We make our datasets and codes publicly available.
Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks
Authors: Georgios Chochlakis, Niyantha Maruthu Pandiyan, Kristina Lerman, Shrikanth Narayanan
Date: 2024-09-10 00:00:00
Tasks: In-Context Learning

In-Context Learning (ICL) in Large Language Models (LLM) has emerged as the dominant technique for performing natural language tasks, as it does not require updating the model parameters with gradient-based methods. ICL promises to "adapt" the LLM to perform the present task at a competitive or state-of-the-art level at a fraction of the computational cost. ICL can be augmented by incorporating the reasoning process to arrive at the final label explicitly in the prompt, a technique called Chain-of-Thought (CoT) prompting. However, recent work has found that ICL relies mostly on the retrieval of task priors and less so on "learning" to perform tasks, especially for complex subjective domains like emotion and morality, where priors ossify posterior predictions. In this work, we examine whether "enabling" reasoning also creates the same behavior in LLMs, wherein the format of CoT retrieves reasoning priors that remain relatively unchanged despite the evidence in the prompt. We find that, surprisingly, CoT indeed suffers from the same posterior collapse as ICL for larger language models. Code is avalaible at https://github.com/gchochla/cot-priors.
MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder
Authors: Peng Wang, Xin Wen, Ruochen Cao, Chengxin Gao, Yanrong Hao, Rui Cao
Date: 2024-09-10 00:00:00
Tasks: Functional Connectivity, Graph Learning

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challenges posed by network noise. To address these gaps, we introduce the Masked Connection-based Dynamic Graph Learning Network (MCDGLN). Our approach first segments BOLD signals using sliding temporal windows to capture dynamic brain characteristics. We then employ a specialized weighted edge aggregation (WEA) module, which uses the cross convolution with channel-wise element-wise convolutional kernel, to integrate dynamic functional connectivity and to isolating task-relevant connections. This is followed by topological feature extraction via a hierarchical graph convolutional network (HGCN), with key attributes highlighted by a self-attention module. Crucially, we refine static functional connections using a customized task-specific mask, reducing noise and pruning irrelevant links. The attention-based connection encoder (ACE) then enhances critical connections and compresses static features. The combined features are subsequently used for classification. Applied to the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, our framework achieves a 73.3\% classification accuracy between ASD and Typical Control (TC) groups among 1,035 subjects. The pivotal roles of WEA and ACE in refining connectivity and enhancing classification accuracy underscore their importance in capturing ASD-specific features, offering new insights into the disorder.
Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
Authors: Dong Han, Jihye Moon, Luís Roberto Mercado Díaz, DarRen Chen, Devan Williams, Eric Y. Ding, Khanh-Van Tran, David D. McManus, Ki H. Chon
Date: 2024-09-10 00:00:00
Tasks: Sensitivity

Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).
Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis
Authors: Qi Yang, Binjie Mao, Zili Wang, Xing Nie, Pengfei Gao, Ying Guo, Cheng Zhen, Pengfei Yan, Shiming Xiang
Date: 2024-09-10 00:00:00
Tasks: Audio Synthesis, Audio-Visual Synchronization

Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A), as a particular type of automatic foley task, presents inherent challenges related to audio-visual synchronization. These challenges encompass maintaining the content consistency between the input video and the generated audio, as well as the alignment of temporal and loudness properties within the video. To address these issues, we construct a controllable video-to-audio synthesis model, termed Draw an Audio, which supports multiple input instructions through drawn masks and loudness signals. To ensure content consistency between the synthesized audio and target video, we introduce the Mask-Attention Module (MAM), which employs masked video instruction to enable the model to focus on regions of interest. Additionally, we implement the Time-Loudness Module (TLM), which uses an auxiliary loudness signal to ensure the synthesis of sound that aligns with the video in both loudness and temporal dimensions. Furthermore, we have extended a large-scale V2A dataset, named VGGSound-Caption, by annotating caption prompts. Extensive experiments on challenging benchmarks across two large-scale V2A datasets verify Draw an Audio achieves the state-of-the-art. Project page: https://yannqi.github.io/Draw-an-Audio/.
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
Authors: Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh, Jason Cong
Date: 2024-09-10 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Question Answering

Traditional Large Language Model (LLM) pretraining relies on autoregressive language modeling with randomly sampled data from web-scale datasets. Inspired by human learning techniques like spaced repetition, we hypothesize that random sampling leads to high training costs, lower-quality models, and significant data forgetting. To address these inefficiencies, we propose the Learn-Focus-Review (LFR) paradigm -- a dynamic training approach that adapts to the model's learning progress. LFR tracks the model's learning performance across data blocks (sequences of tokens) and prioritizes revisiting challenging regions of the dataset that are more prone to being forgotten, enabling better retention and more efficient learning. Using the LFR paradigm, we pretrained Llama and GPT models on the SlimPajama and OpenWebText datasets, respectively. These models were evaluated on downstream tasks across various domains, including question answering, problem-solving, commonsense reasoning, language modeling, and translation. Compared to baseline models trained on the full datasets, LFR consistently achieved lower perplexity and higher accuracy, while using only 5%--19% of the training tokens. Furthermore, LFR matched the performance of industry-standard Pythia models with up to 2$\times$ the parameter count, using just 3.2% of the training tokens, demonstrating its effectiveness and efficiency.
On the Weaknesses of Backdoor-based Model Watermarking: An Information-theoretic Perspective
Authors: Aoting Hu, Yanzhi Chen, Renjie Xie, Adrian Weller
Date: 2024-09-10 00:00:00

Safeguarding the intellectual property of machine learning models has emerged as a pressing concern in AI security. Model watermarking is a powerful technique for protecting ownership of machine learning models, yet its reliability has been recently challenged by recent watermark removal attacks. In this work, we investigate why existing watermark embedding techniques particularly those based on backdooring are vulnerable. Through an information-theoretic analysis, we show that the resilience of watermarking against erasure attacks hinges on the choice of trigger-set samples, where current uses of out-distribution trigger-set are inherently vulnerable to white-box adversaries. Based on this discovery, we propose a novel model watermarking scheme, In-distribution Watermark Embedding (IWE), to overcome the limitations of existing method. To further minimise the gap to clean models, we analyze the role of logits as watermark information carriers and propose a new approach to better conceal watermark information within the logits. Experiments on real-world datasets including CIFAR-100 and Caltech-101 demonstrate that our method robustly defends against various adversaries with negligible accuracy loss (< 0.1%).
Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
Authors: E. Wes Bethel, Vianna Cramer, Alexander del Rio, Lothar Narins, Chris Pestano, Satvik Verma, Erick Arias, Nicola Bertelli, Talita Perciano, Syun'ichi Shiraiwa, Álvaro Sánchez Villar, Greg Wallace, John C. Wright
Date: 2024-09-10 00:00:00

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.
Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty
Authors: Sara Pohland, Claire Tomlin
Date: 2024-09-09 00:00:00

Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the navigation system is able to recognize the predictive uncertainty of the perception model and respond safely and effectively in the face of uncertainty. In an effort to enable safe navigation under perception uncertainty, we develop a probabilistic and reconstruction-based competency estimation (PaRCE) method to estimate the model's level of familiarity with an input image as a whole and with specific regions in the image. We find that the overall competency score can correctly predict correctly classified, misclassified, and out-of-distribution (OOD) samples. We also confirm that the regional competency maps can accurately distinguish between familiar and unfamiliar regions across images. We then use this competency information to develop a planning and control scheme that enables effective navigation while maintaining a low probability of error. We find that the competency-aware scheme greatly reduces the number of collisions with unfamiliar obstacles, compared to a baseline controller with no competency awareness. Furthermore, the regional competency information is very valuable in enabling efficient navigation.
Doppelgänger's Watch: A Split Objective Approach to Large Language Models
Authors: Shervin Ghasemlou, Ashish Katiyar, Aparajita Saraf, Seungwhan Moon, Mangesh Pujari, Pinar Donmez, Babak Damavandi, Anuj Kumar
Date: 2024-09-09 00:00:00
Tasks: Language Modeling, Language Modelling

In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from their core capability, helpfulness. Doppelg\"anger, a new module parallel to the underlying language model, supervises the generation of each token, and learns to concurrently predict the supervision score(s) of the sequences up to and including each token. In this work, we present the theoretical findings, and leave the report on experimental results to a forthcoming publication.