pwc-archive/papers-with-abstracts huggingface dataset:
Visual Fourier Prompt Tuning
Authors: Runjia Zeng, Cheng Han, Qifan Wang, Chunshu Wu, Tong Geng, Lifu Huang, Ying Nian Wu, Dongfang Liu
Date: 2024-11-02 00:00:00
Tasks: Visual Prompt Tuning

With the scale of vision Transformer-based models continuing to grow, finetuning these large-scale pretrained models for new tasks has become increasingly parameter-intensive. Visual prompt tuning is introduced as a parameter-efficient finetuning (PEFT) method to this trend. Despite its successes, a notable research challenge persists within almost all PEFT approaches: significant performance degradation is observed when there is a substantial disparity between the datasets applied in pretraining and finetuning phases. To address this challenge, we draw inspiration from human visual cognition, and propose the Visual Fourier Prompt Tuning (VFPT) method as a general and effective solution for adapting large-scale transformer-based models. Our approach innovatively incorporates the Fast Fourier Transform into prompt embeddings and harmoniously considers both spatial and frequency domain information. Apart from its inherent simplicity and intuitiveness, VFPT exhibits superior performance across all datasets, offering a general solution to dataset challenges, irrespective of data disparities. Empirical results demonstrate that our approach outperforms current state-of-the-art baselines on two benchmarks, with low parameter usage (e.g., 0.57% of model parameters on VTAB-1k) and notable performance enhancements (e.g., 73.20% of mean accuracy on VTAB-1k). Our code is avaliable at https://github.com/runtsang/VFPT.
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization
Authors: Kai Jiang, Chen Zhao, Haoliang Wang, Feng Chen
Date: 2024-11-02 00:00:00
Tasks: Disentanglement, Domain Generalization, Fairness, Meta-Learning

Generalizing to out-of-distribution data while being aware of model fairness is a significant and challenging problem in meta-learning. The goal of this problem is to find a set of fairness-aware invariant parameters of classifier that is trained using data drawn from a family of related training domains with distribution shift on non-sensitive features as well as different levels of dependence between model predictions and sensitive features so that the classifier can achieve good generalization performance on unknown but distinct test domains. To tackle this challenge, existing state-of-the-art methods either address the domain generalization problem but completely ignore learning with fairness or solely specify shifted domains with various fairness levels. This paper introduces an approach to fairness-aware meta-learning that significantly enhances domain generalization capabilities. Our framework, Fairness-Enhanced Meta-Learning for Domain Generalization (FEED), disentangles latent data representations into content, style, and sensitive vectors. This disentanglement facilitates the robust generalization of machine learning models across diverse domains while adhering to fairness constraints. Unlike traditional methods that focus primarily on domain invariance or sensitivity to shifts, our model integrates a fairness-aware invariance criterion directly into the meta-learning process. This integration ensures that the learned parameters uphold fairness consistently, even when domain characteristics vary widely. We validate our approach through extensive experiments across multiple benchmarks, demonstrating not only superior performance in maintaining high accuracy and fairness but also significant improvements over existing state-of-the-art methods in domain generalization tasks.
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
Authors: Md Raihan Uddin, Ratun Rahman, Dinh C. Nguyen
Date: 2024-11-02 00:00:00
Tasks: Edge-computing, Federated Learning

Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) methods have shown immense benefits in detecting FDI attacks via data learning and prediction abilities. Literature works have mostly focused on centralized learning and deploying FDI attack detection models at the control center, which requires data collection from local utilities like meters and transformers. However, this data sharing may raise privacy concerns due to the potential disclosure of household information like energy usage patterns. This paper proposes a new privacy-preserved FDI attack detection by developing an efficient federated learning (FL) framework in the smart meter network with edge computing. Distributed edge servers located at the network edge run an ML-based FDI attack detection model and share the trained model with the grid operator, aiming to build a strong FDI attack detection model without data sharing. Simulation results demonstrate the efficiency of our proposed FL method over the conventional method without collaboration.
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
Authors: Vu Khanh Quy, Nguyen Minh Quy, Tran Thi Hoai, Shaba Shaon, Md Raihan Uddin, Tien Nguyen, Dinh C. Nguyen, Aryan Kaushik, Periklis Chatzimisios
Date: 2024-11-02 00:00:00
Tasks: Federated Learning

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted.
Marginal Causal Flows for Validation and Inference
Authors: Daniel de Vassimon Manela, Laura Battaglia, Robin J. Evans
Date: 2024-11-02 00:00:00
Tasks: Normalising Flows

Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which often fail to reproduce intricate real-world data patterns. In this paper we introduce Frugal Flows, a novel likelihood-based machine learning model that uses normalising flows to flexibly learn the data-generating process, while also directly inferring the marginal causal quantities from observational data. We propose that these models are exceptionally well suited for generating synthetic data to validate causal methods. They can create synthetic datasets that closely resemble the empirical dataset, while automatically and exactly satisfying a user-defined average treatment effect. To our knowledge, Frugal Flows are the first generative model to both learn flexible data representations and also exactly parameterise quantities such as the average treatment effect and the degree of unobserved confounding. We demonstrate the above with experiments on both simulated and real-world datasets.
Causal reasoning in difference graphs
Authors: Charles K. Assaad
Date: 2024-11-02 00:00:00
Tasks: Causal Discovery, Epidemiology

Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a tool to visually represent causal variations between two distinct populations. While there has been progress in inferring these graphs from data through causal discovery methods, there remains a gap in systematically leveraging their potential to enhance causal reasoning. This paper addresses that gap by establishing conditions for identifying causal changes and effects using difference graphs. It specifically focuses on identifying total causal changes and total effects in a nonparametric setting, as well as direct causal changes and direct effects in a linear setting. In doing so, it provides a novel approach to causal reasoning that holds potential for various public health applications.
Varco Arena: A Tournament Approach to Reference-Free Benchmarking Large Language Models
Authors: Seonil Son, Ju-Min Oh, Heegon Jin, Cheolhun Jang, Jeongbeom Jeong, Kuntae Kim
Date: 2024-11-02 00:00:00
Tasks: Benchmarking

The rapid advancement of Large Language Models (LLMs) necessitates robust evaluation methodologies. Current benchmarking approaches often rely on comparing model outputs against predefined prompts and reference outputs. Relying on predefined reference outputs hinders flexible adaptation of benchmarks to the rapidly evolving capabilities of LLMs. This limitation necessitates periodic efforts to prepare new benchmarks. To keep pace with rapidly evolving LLM capabilities, we propose a more flexible benchmarking approach. Our method, \textit{\textbf{Varco Arena}}, provides reference-free benchmarking of LLMs in tournament style. \textit{\textbf{Varco Arena}} directly compares LLM outputs across a diverse set of prompts, determining model rankings through a single-elimination tournament structure. This direct pairwise comparison offers two key advantages: (1) Direct comparison, unmediated by reference text, more effectively orders competing LLMs, resulting in more reliable rankings, and (2) reference-free approach to benchmarking adds flexibility in updating benchmark prompts by eliminating the need for quality references. Our empirical results, supported by simulation experiments, demonstrate that the \textit{\textbf{Varco Arena}} tournament approach aligns better with the current Elo model for benchmarking LLMs. The alignment is measured in terms of Spearman correlation, showing improvement over current practice of benchmarking that use reference outputs as comparison \textit{anchor}s.
Improving Energy Efficiency in Manufacturing: A Novel Expert System Shell
Authors: Borys Ioshchikhes, Michael Frank, Tresa Maria Joseph, Matthias Weigold
Date: 2024-11-02 00:00:00

Expert systems are effective tools for automatically identifying energy efficiency potentials in manufacturing, thereby contributing significantly to global climate targets. These systems analyze energy data, pinpoint inefficiencies, and recommend optimizations to reduce energy consumption. Beyond systematic approaches for developing expert systems, there is a pressing need for simple and rapid software implementation solutions. Expert system shells, which facilitate the swift development and deployment of expert systems, are crucial tools in this process. They provide a template that simplifies the creation and integration of expert systems into existing manufacturing processes. This paper provides a comprehensive comparison of existing expert system shells regarding their suitability for improving energy efficiency, highlighting significant gaps and limitations. To address these deficiencies, we introduce a novel expert system shell, implemented in Jupyter Notebook, that provides a flexible and easily integrable solution for expert system development.
Interacting Large Language Model Agents. Interpretable Models and Social Learning
Authors: Adit Jain, Vikram Krishnamurthy
Date: 2024-11-02 00:00:00
Tasks: Bayesian Inference, Language Modeling, Language Modelling, Large Language Model, Sentiment Analysis, State Estimation

This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both fields are mature, their application to decision-making involving interacting LLMAs remains unexplored. Motivated by Bayesian sentiment analysis on online platforms, we construct interpretable models and algorithms that enable LLMAs to interact and perform Bayesian inference. Because interacting LLMAs learn from both prior decisions and external inputs, they can exhibit bias and herding behavior. Thus, developing interpretable models and stochastic control algorithms is essential to understand and mitigate these behaviors. This paper has three main results. First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. Our proposed models capture the herding behavior exhibited by interacting LLMAs. Third, we propose a stochastic control framework to delay herding and improve state estimation accuracy under 2 settings: (a) centrally controlled LLMAs (b) autonomous LLMAs with incentives. We demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and closed-source models like ChatGPT. The main takeaway of this paper, based on empirical analysis and mathematical formalism, is that LLMAs act as rationally bounded Bayesian agents that exhibit social learning when interacting.
Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection
Authors: Andreas Lutz, Gabriele Steidl, Karsten Müller, Wojciech Samek
Date: 2024-11-02 00:00:00
Tasks: Federated Learning

Deep learning is an emerging field revolutionizing various industries, including natural language processing, computer vision, and many more. These domains typically require an extensive amount of data for optimal performance, potentially utilizing huge centralized data repositories. However, such centralization could raise privacy issues concerning the storage of sensitive data. To address this issue, federated learning was developed. It is a newly distributed learning technique that enables to collaboratively train a deep learning model on decentralized devices, referred to as clients, without compromising their data privacy. Traditional federated learning methods often suffer from severe performance degradation when the data distribution among clients differs significantly. This becomes especially problematic in the case of label distribution skew, where the distribution of labels varies across clients. To address this, a novel method called FedEntOpt is proposed. FedEntOpt is designed to mitigate performance issues caused by label distribution skew by maximizing the entropy of the global label distribution of the selected client subset in each federated learning round. This ensures that the aggregated model parameters from the clients were exhibited to data from all available labels, which improves the accuracy of the global model. Extensive experiments on multiple benchmark datasets show that the proposed method outperforms several state-of-the-art algorithms by up to 6\% in classification accuracy under standard settings regardless of the model size. Moreover, it exhibits robust and superior performance in scenarios with low participation rates and client dropout, achieving increases in classification accuracy of over 30\%. In addition, FedEntOpt offers the flexibility to be combined with existing algorithms, enhancing their performance by over 40\%.
AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?
Authors: Benlong Wu, Guoqiang Chen, Kejiang Chen, Xiuwei Shang, Jiapeng Han, Yanru He, Weiming Zhang, Nenghai Yu
Date: 2024-11-02 00:00:00

Penetration testing is essential to ensure Web security, which can detect and fix vulnerabilities in advance, and prevent data leakage and serious consequences. The powerful inference capabilities of large language models (LLMs) have made significant progress in various fields, and the development potential of LLM-based agents can revolutionize the cybersecurity penetration testing industry. In this work, we establish a comprehensive end-to-end penetration testing benchmark using a real-world penetration testing environment to explore the capabilities of LLM-based agents in this domain. Our results reveal that the agents are familiar with the framework of penetration testing tasks, but they still face limitations in generating accurate commands and executing complete processes. Accordingly, we summarize the current challenges, including the difficulty of maintaining the entire message history and the tendency for the agent to become stuck. Based on the above insights, we propose a Penetration testing State Machine (PSM) that utilizes the Finite State Machine (FSM) methodology to address these limitations. Then, we introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs, which utilizes the inherent inference ability of LLM and the constraint framework of state machines. Our evaluation results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model and improves the task completion rate from 22% to 41% on the benchmark target. Compared with the baseline framework and manual work, AutoPT also reduces time and economic costs further. Hence, our AutoPT has facilitated the development of automated penetration testing and significantly impacted both academia and industry.
The Interaction Layer: An Exploration for Co-Designing User-LLM Interactions in Parental Wellbeing Support Systems
Authors: Sruthi Viswanathan, Seray Ibrahim, Ravi Shankar, Reuben Binns, Max Van Kleek, Petr Slovak
Date: 2024-11-02 00:00:00

Parenting brings emotional and physical challenges, from balancing work, childcare, and finances to coping with exhaustion and limited personal time. Yet, one in three parents never seek support. AI systems potentially offer stigma-free, accessible, and affordable solutions. Yet, user adoption often fails due to issues with explainability and reliability. To see if these issues could be solved using a co-design approach, we developed and tested NurtureBot, a wellbeing support assistant for new parents. 32 parents co-designed the system through Asynchronous Remote Communities method, identifying the key challenge as achieving a "successful chat." As part of co-design, parents role-played as NurtureBot, rewriting its dialogues to improve user understanding, control, and outcomes. The refined prototype, featuring an Interaction Layer, was evaluated by 32 initial and 46 new parents, showing improved user experience and usability, with final CUQ score of 91.3/100, demonstrating successful interaction patterns. Our process revealed useful interaction design lessons for effective AI parenting support.
Infinite-Resolution Integral Noise Warping for Diffusion Models
Authors: Yitong Deng, Winnie Lin, Lingxiao Li, Dmitriy Smirnov, Ryan Burgert, Ning Yu, Vincent Dedun, Mohammad H. Taghavi
Date: 2024-11-02 00:00:00

Adapting pretrained image-based diffusion models to generate temporally consistent videos has become an impactful generative modeling research direction. Training-free noise-space manipulation has proven to be an effective technique, where the challenge is to preserve the Gaussian white noise distribution while adding in temporal consistency. Recently, Chang et al. (2024) formulated this problem using an integral noise representation with distribution-preserving guarantees, and proposed an upsampling-based algorithm to compute it. However, while their mathematical formulation is advantageous, the algorithm incurs a high computational cost. Through analyzing the limiting-case behavior of their algorithm as the upsampling resolution goes to infinity, we develop an alternative algorithm that, by gathering increments of multiple Brownian bridges, achieves their infinite-resolution accuracy while simultaneously reducing the computational cost by orders of magnitude. We prove and experimentally validate our theoretical claims, and demonstrate our method's effectiveness in real-world applications. We further show that our method readily extends to the 3-dimensional space.
Spatial Transformers for Radio Map Estimation
Authors: Pham Q. Viet, Daniel Romero
Date: 2024-11-02 00:00:00
Tasks: Spatial Interpolation

Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations to a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a great number of parameters. The first contribution of this paper addresses these limitations by means of an attention-based estimator named Spatial TransfOrmer for Radio Map estimation (STORM). This scheme not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, by which the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real-measurement datasets.
PRIMO: Progressive Induction for Multi-hop Open Rule Generation
Authors: Jianyu Liu, Sheng Bi, Guilin Qi
Date: 2024-11-02 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Relation Extraction

Open rule refer to the implication from premise atoms to hypothesis atoms, which captures various relations between instances in the real world. Injecting open rule knowledge into the machine helps to improve the performance of downstream tasks such as dialogue and relation extraction. Existing approaches focus on single-hop open rule generation, ignoring multi-hop scenarios, leading to logical inconsistencies between premise and hypothesis atoms, as well as semantic duplication of generated rule atoms. To address these issues, we propose a progressive multi-stage open rule generation method called PRIMO. We introduce ontology information during the rule generation stage to reduce ambiguity and improve rule accuracy. PRIMO constructs a multi-stage structure consisting of generation, extraction, and ranking modules to fully leverage the latent knowledge within the language model across multiple dimensions. Furthermore, we employ reinforcement learning from human feedback to further optimize model, enhancing the model's understanding of commonsense knowledge. Experiments show that compared to baseline models, PRIMO significantly improves rule quality and diversity while reducing the repetition rate of rule atoms.