pwc-archive/papers-with-abstracts huggingface dataset:
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN
Authors: Chang Yu, Yixin Jin, Qianwen Xing, Ye Zhang, Shaobo Guo, Shuchen Meng
Date: 2024-08-07 00:00:00
Tasks: Dimensionality Reduction

Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucial for the profitability of a bank'sbank's credit card business. In the past, screening applicants'applicants' conditions often required a significant amount of manual labor, which was time-consuming and labor-intensive. Although the accuracy and reliability of previously used ML models have been continuously improving, the pursuit of more reliable and powerful AI intelligent models is undoubtedly the unremitting pursuit by major banks in the financial industry. In this study, we used a dataset of over 40,000 records provided by a commercial bank as the research object. We compared various dimensionality reduction techniques such as PCA and T-SNE for preprocessing high-dimensional datasets and performed in-depth adaptation and tuning of distributed models such as LightGBM and XGBoost, as well as deep models like Tabnet. After a series of research and processing, we obtained excellent research results by combining SMOTEENN with these techniques. The experiments demonstrated that LightGBM combined with PCA and SMOTEENN techniques can assist banks in accurately predicting potential high-quality customers, showing relatively outstanding performance compared to other models.
Automated Theorem Provers Help Improve Large Language Model Reasoning
Authors: Lachlan McGinness, Peter Baumgartner
Date: 2024-08-07 00:00:00
Tasks: Formal Logic, Language Modeling, Language Modelling, Large Language Model, Logical Reasoning, Translation

In this paper we demonstrate how logic programming systems and Automated first-order logic Theorem Provers (ATPs) can improve the accuracy of Large Language Models (LLMs) for logical reasoning tasks where the baseline performance is given by direct LLM solutions. We first evaluate LLM reasoning on steamroller problems using the PRONTOQA benchmark. We show how accuracy can be improved with a neuro-symbolic architecture where the LLM acts solely as a front-end for translating a given problem into a formal logic language and an automated reasoning engine is called for solving it. However, this approach critically hinges on the correctness of the LLM translation. To assess this translation correctness, we secondly define a framework of syntactic and semantic error categories. We implemented the framework and used it to identify errors that LLMs make in the benchmark domain. Based on these findings, we thirdly extended our method with capabilities for automatically correcting syntactic and semantic errors. For semantic error correction we integrate first-order logic ATPs, which is our main and novel contribution. We demonstrate that this approach reduces semantic errors significantly and further increases the accurracy of LLM logical reasoning.
Harnessing the Power of LLMs in Source Code Vulnerability Detection
Authors: Andrew A Mahyari
Date: 2024-08-07 00:00:00
Tasks: Vulnerability Detection

Software vulnerabilities, caused by unintentional flaws in source code, are a primary root cause of cyberattacks. Static analysis of source code has been widely used to detect these unintentional defects introduced by software developers. Large Language Models (LLMs) have demonstrated human-like conversational abilities due to their capacity to capture complex patterns in sequential data, such as natural languages. In this paper, we harness LLMs' capabilities to analyze source code and detect known vulnerabilities. To ensure the proposed vulnerability detection method is universal across multiple programming languages, we convert source code to LLVM IR and train LLMs on these intermediate representations. We conduct extensive experiments on various LLM architectures and compare their accuracy. Our comprehensive experiments on real-world and synthetic codes from NVD and SARD demonstrate high accuracy in identifying source code vulnerabilities.
Can LLMs Serve As Time Series Anomaly Detectors?
Authors: Manqing Dong, Hao Huang, Longbing Cao
Date: 2024-08-06 00:00:00
Tasks: Anomaly Detection, In-Context Learning, Time Series, Time Series Anomaly Detection, Time Series Forecasting

An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.
Identifying treatment response subgroups in observational time-to-event data
Authors: Vincent Jeanselme, Chang Ho Yoon, Fabian Falck, Brian Tom, Jessica Barrett
Date: 2024-08-06 00:00:00

Identifying patient subgroups with different treatment responses is an important task to inform medical recommendations, guidelines, and the design of future clinical trials. Existing approaches for treatment effect estimation primarily rely on Randomised Controlled Trials (RCTs), which are often limited by insufficient power, multiple comparisons, and unbalanced covariates. In addition, RCTs tend to feature more homogeneous patient groups, making them less relevant for uncovering subgroups in the population encountered in real-world clinical practice. Subgroup analyses established for RCTs suffer from significant statistical biases when applied to observational studies, which benefit from larger and more representative populations. Our work introduces a novel, outcome-guided, subgroup analysis strategy for identifying subgroups of treatment response in both RCTs and observational studies alike. It hence positions itself in-between individualised and average treatment effect estimation to uncover patient subgroups with distinct treatment responses, critical for actionable insights that may influence treatment guidelines. In experiments, our approach significantly outperforms the current state-of-the-art method for subgroup analysis in both randomised and observational treatment regimes.
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures
Authors: Teng Liang, Andrews Damoah
Date: 2024-08-06 00:00:00
Tasks: Brain Computer Interface, EEG, Gaze Prediction, Knowledge Distillation, Position, regression

Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal processing techniques must be fast, accurate, and resource-conscious to deliver low-latency neural analytics. This study presents a model that leverages a pre-trained MobileViT alongside Knowledge Distillation (KD) for EEG regression tasks. Our results showcase that this model is capable of performing at a level comparable (only 3% lower) to the previous State-Of-The-Art (SOTA) on the EEGEyeNet Absolute Position Task while being 33% faster and 60% smaller. Our research presents a cost-effective model applicable to resource-constrained devices and contributes to expanding future research on lightweight, mobile-friendly models for EEG regression.
Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles
Authors: Nazish Tahir, Ramviyas Parasuraman, Haijian Sun
Date: 2024-08-06 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Reinforcement Learning

Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency.
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey
Authors: Vu Tuan Truong, Luan Ba Dang, Long Bao Le
Date: 2024-08-06 00:00:00
Tasks: Denoising, Image Generation, Survey

Diffusion models (DMs) have achieved state-of-the-art performance on various generative tasks such as image synthesis, text-to-image, and text-guided image-to-image generation. However, the more powerful the DMs, the more harmful they potentially are. Recent studies have shown that DMs are prone to a wide range of attacks, including adversarial attacks, membership inference, backdoor injection, and various multi-modal threats. Since numerous pre-trained DMs are published widely on the Internet, potential threats from these attacks are especially detrimental to the society, making DM-related security a worth investigating topic. Therefore, in this paper, we conduct a comprehensive survey on the security aspect of DMs, focusing on various attack and defense methods for DMs. First, we present crucial knowledge of DMs with five main types of DMs, including denoising diffusion probabilistic models, denoising diffusion implicit models, noise conditioned score networks, stochastic differential equations, and multi-modal conditional DMs. We further survey a variety of recent studies investigating different types of attacks that exploit the vulnerabilities of DMs. Then, we thoroughly review potential countermeasures to mitigate each of the presented threats. Finally, we discuss open challenges of DM-related security and envision certain research directions for this topic.
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
Authors: Luis Roque, Carlos Soares, Luís Torgo
Date: 2024-08-06 00:00:00
Tasks: Time Series, Time Series Forecasting

We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.
A Non-negative VAE:the Generalized Gamma Belief Network
Authors: Zhibin Duan, Tiansheng Wen, Muyao Wang, Bo Chen, Mingyuan Zhou
Date: 2024-08-06 00:00:00
Tasks: Representation Learning, Variational Inference

The gamma belief network (GBN), often regarded as a deep topic model, has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data. Its notable capability to acquire interpretable latent factors is partially attributed to sparse and non-negative gamma-distributed latent variables. However, the existing GBN and its variations are constrained by the linear generative model, thereby limiting their expressiveness and applicability. To address this limitation, we introduce the generalized gamma belief network (Generalized GBN) in this paper, which extends the original linear generative model to a more expressive non-linear generative model. Since the parameters of the Generalized GBN no longer possess an analytic conditional posterior, we further propose an upward-downward Weibull inference network to approximate the posterior distribution of the latent variables. The parameters of both the generative model and the inference network are jointly trained within the variational inference framework. Finally, we conduct comprehensive experiments on both expressivity and disentangled representation learning tasks to evaluate the performance of the Generalized GBN against state-of-the-art Gaussian variational autoencoders serving as baselines.
Prioritize Alignment in Dataset Distillation
Authors: Zekai Li, Ziyao Guo, Wangbo Zhao, Tianle Zhang, Zhi-Qi Cheng, Samir Khaki, Kaipeng Zhang, Ahmad Sajedi, Konstantinos N Plataniotis, Kai Wang, Yang You
Date: 2024-08-06 00:00:00
Tasks: Dataset Distillation

Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the target dataset and embed it into the distilled dataset. Consequently, the quality of extracted and embedded information determines the quality of the distilled dataset. In this work, we find that existing methods introduce misaligned information in both information extraction and embedding stages. To alleviate this, we propose Prioritize Alignment in Dataset Distillation (PAD), which aligns information from the following two perspectives. 1) We prune the target dataset according to the compressing ratio to filter the information that can be extracted by the agent model. 2) We use only deep layers of the agent model to perform the distillation to avoid excessively introducing low-level information. This simple strategy effectively filters out misaligned information and brings non-trivial improvement for mainstream matching-based distillation algorithms. Furthermore, built on trajectory matching, \textbf{PAD} achieves remarkable improvements on various benchmarks, achieving state-of-the-art performance.
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
Authors: Zhen Qin, Junru Wu, Jiaming Shen, Tianqi Liu, Xuanhui Wang
Date: 2024-08-06 00:00:00
Tasks: Hate Speech Detection, Ordinal Classification

We introduce LAMPO, a novel paradigm that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks. Unlike conventional methods, which concatenate all demonstration examples with the test instance and prompt LLMs to produce the pointwise prediction, our framework uses the LLM as a preference machine that makes a relative comparative decision between the test instance and each demonstration. A self-supervised method is then introduced to aggregate these binary comparisons into the final ordinal decision. LAMPO addresses several limitations inherent in previous methods, including context length constraints, ordering biases, and challenges associated with absolute point-wise estimation. Extensive experiments on seven public datasets demonstrate LAMPO's remarkably competitive performance across a diverse spectrum of applications (e.g., movie review analysis and hate speech detection). Notably, in certain applications, the improvement can be substantial, exceeding 20% in an absolute term. Moreover, we believe LAMPO represents an interesting addition to the non-parametric application layered on top of LLMs, as it supports black-box LLMs without necessitating the outputting of LLM's internal states (e.g., embeddings), as seen in previous approaches.
MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis
Authors: Wenqi Zhu, Yinghua Fu, Ze Wang
Date: 2024-08-06 00:00:00
Tasks: Functional Connectivity, Graph Generation, Graph Neural Network

Alzheimer's Disease (AD) is a currently incurable neurodegeneartive disease. Accurately detecting AD, especially in the early stage, represents a high research priority. AD is characterized by progressive cognitive impairments that are related to alterations in brain functional connectivity (FC). Based on this association, many studies have been published over the decades using FC and machine learning to differentiate AD from healthy aging. The most recent development in this detection method highlights the use of graph neural network (GNN) as the brain functionality analysis. In this paper, we proposed a stack of spatio-temporal feature extraction and graph generation based AD classification model using resting state fMRI. The proposed multi-level generated connectome (MLC) based graph convolutional network (GCN) (MLC-GCN) contains a multi-graph generation block and a GCN prediction block. The multi-graph generation block consists of a hierarchy of spatio-temporal feature extraction layers for extracting spatio-temporal rsfMRI features at different depths and building the corresponding connectomes. The GCN prediction block takes the learned multi-level connectomes to build and optimize GCNs at each level and concatenates the learned graphical features as the final predicting features for AD classification. Through independent cohort validations, MLC-GCN shows better performance for differentiating MCI, AD, and normal aging than state-of-art GCN and rsfMRI based AD classifiers. The proposed MLC-GCN also showed high explainability in terms of learning clinically reasonable connectome node and connectivity features from two independent datasets. While we only tested MLC-GCN on AD, the basic rsfMRI-based multi-level learned GCN based outcome prediction strategy is valid for other diseases or clinical outcomes.
The Use of Large Language Models (LLM) for Cyber Threat Intelligence (CTI) in Cybercrime Forums
Authors: Vanessa Clairoux-Trepanier, Isa-May Beauchamp, Estelle Ruellan, Masarah Paquet-Clouston, Serge-Olivier Paquette, Eric Clay
Date: 2024-08-06 00:00:00

Large language models (LLMs) can be used to analyze cyber threat intelligence (CTI) data from cybercrime forums, which contain extensive information and key discussions about emerging cyber threats. However, to date, the level of accuracy and efficiency of LLMs for such critical tasks has yet to be thoroughly evaluated. Hence, this study assesses the performance of an LLM system built on the OpenAI GPT-3.5-turbo model [8] to extract CTI information. To do so, a random sample of more than 700 daily conversations from three cybercrime forums - XSS, Exploit_in, and RAMP - was extracted, and the LLM system was instructed to summarize the conversations and predict 10 key CTI variables, such as whether a large organization and/or a critical infrastructure is being targeted, with only simple human-language instructions. Then, two coders reviewed each conversation and evaluated whether the information extracted by the LLM was accurate. The LLM system performed well, with an average accuracy score of 96.23%, an average precision of 90% and an average recall of 88.2%. Various ways to enhance the model were uncovered, such as the need to help the LLM distinguish between stories and past events, as well as being careful with verb tenses in prompts. Nevertheless, the results of this study highlight the relevance of using LLMs for cyber threat intelligence.
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning
Authors: Xiaozhou Ye, Kevin I-Kai Wang
Date: 2024-08-06 00:00:00
Tasks: Activity Recognition, Denoising, Diversity, Domain Adaptation, Human Activity Recognition, Robust classification

Human Activity Recognition (HAR) plays a crucial role in various applications such as human-computer interaction and healthcare monitoring. However, challenges persist in HAR models due to the data distribution differences between training and real-world data distributions, particularly evident in cross-user scenarios. This paper introduces a novel framework, termed Diffusion-based Noise-centered Adversarial Learning Domain Adaptation (Diff-Noise-Adv-DA), designed to address these challenges by leveraging generative diffusion modeling and adversarial learning techniques. Traditional HAR models often struggle with the diversity of user behaviors and sensor data distributions. Diff-Noise-Adv-DA innovatively integrates the inherent noise within diffusion models, harnessing its latent information to enhance domain adaptation. Specifically, the framework transforms noise into a critical carrier of activity and domain class information, facilitating robust classification across different user domains. Experimental evaluations demonstrate the effectiveness of Diff-Noise-Adv-DA in improving HAR model performance across different users, surpassing traditional domain adaptation methods. The framework not only mitigates distribution mismatches but also enhances data quality through noise-based denoising techniques.
miniCTX: Neural Theorem Proving with (Long-)Contexts
Authors: Jiewen Hu, Thomas Zhu, Sean Welleck
Date: 2024-08-05 00:00:00
Tasks: Automated Theorem Proving

Real-world formal theorem proving often depends on a wealth of context, including definitions, lemmas, comments, file structure, and other information. We introduce miniCTX, which tests a model's ability to prove formal mathematical theorems that depend on new context that is not seen during training. miniCTX contains theorems sourced from real Lean projects and textbooks, each associated with a context that can span tens of thousands of tokens. Models are tasked with proving a theorem given access to code from the theorem's repository, which contains context that is needed for the proof. As a baseline for miniCTX, we tested fine-tuning and prompting methods that condition theorem proving on preceding context. Both approaches substantially outperform traditional methods that rely solely on state information. We found that this ability to use context is not captured by previous benchmarks such as miniF2F. Alongside miniCTX, we offer ntp-toolkit for automatically extracting and annotating theorem proving data, making it easy to add new projects into miniCTX to ensure that contexts are not seen during training. miniCTX offers a challenging and realistic evaluation of neural theorem provers.