pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Privacy Leakage in Federated Large Language Models
Authors: Minh N. Vu, Truc Nguyen, Tre' R. Jeter, My T. Thai
Date: 2024-03-02 00:00:00
Tasks: Federated Learning

With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs. While substantial adjustments to the protocol have been introduced as a response, comprehensive privacy analysis for the adapted FL protocol is currently lacking. To address this gap, our work delves into an extensive examination of the privacy analysis of FL when used for training LLMs, both from theoretical and practical perspectives. In particular, we design two active membership inference attacks with guaranteed theoretical success rates to assess the privacy leakages of various adapted FL configurations. Our theoretical findings are translated into practical attacks, revealing substantial privacy vulnerabilities in popular LLMs, including BERT, RoBERTa, DistilBERT, and OpenAI's GPTs, across multiple real-world language datasets. Additionally, we conduct thorough experiments to evaluate the privacy leakage of these models when data is protected by state-of-the-art differential privacy (DP) mechanisms.
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Authors: Yifan Zeng, Yiran Wu, Xiao Zhang, Huazheng Wang, Qingyun Wu
Date: 2024-03-02 00:00:00
Tasks: Instruction Following, LLM real-life tasks, Multi-agent Integration

Despite extensive pre-training in moral alignment to prevent generating harmful information, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we propose AutoDefense, a multi-agent defense framework that filters harmful responses from LLMs. With the response-filtering mechanism, our framework is robust against different jailbreak attack prompts, and can be used to defend different victim models. AutoDefense assigns different roles to LLM agents and employs them to complete the defense task collaboratively. The division in tasks enhances the overall instruction-following of LLMs and enables the integration of other defense components as tools. With AutoDefense, small open-source LMs can serve as agents and defend larger models against jailbreak attacks. Our experiments show that AutoDefense can effectively defense against different jailbreak attacks, while maintaining the performance at normal user request. For example, we reduce the attack success rate on GPT-3.5 from 55.74% to 7.95% using LLaMA-2-13b with a 3-agent system. Our code and data are publicly available at https://github.com/XHMY/AutoDefense.
Selective Encryption using Segmentation Mask with Chaotic Henon Map for Multidimensional Medical Images
Authors: S Arut Prakash, Aditya Ganesh Kumar, Prabhu Shankar K. C., Lithicka Anandavel, Aditya Lakshmi Narayanan
Date: 2024-03-02 00:00:00
Tasks: Retrieval

A user-centric design and resource optimization should be at the center of any technology or innovation. The user-centric perspective gives the developer the opportunity to develop with task-based optimization. The user in the medical image field is a medical professional who analyzes the medical images and gives their diagnosis results to the patient. This scheme, having the medical professional user's perspective, innovates in the area of Medical Image storage and security. The architecture is designed with three main segments, namely: Segmentation, Storage, and Retrieval. This architecture was designed owing to the fact that the number of retrieval operations done by medical professionals was toweringly higher when compared to the storage operations done for some handful number of times for a particular medical image. This gives room for our innovation to segment out the medically indispensable part of the medical image, encrypt it, and store it. By encrypting the vital parts of the image using a strong encryption algorithm like the chaotic Henon map, we are able to keep the security intact. Now retrieving the medical image demands only the computationally less stressing decryption of the segmented region of interest. The decryption of the segmented region of interest results in the full recovery of the medical image which can be viewed on demand by the medical professionals for various diagnosis purposes. In this scheme, we were able to achieve a retrieval speed improvement of around 47% when compared to a full image encryption of brain medical CT images.
(Un)paired signal-to-signal translation with 1D conditional GANs
Authors: Eric Easthope
Date: 2024-03-05 00:00:00
Tasks: Audio Generation, Generative Adversarial Network, Image Generation, Image-to-Image Translation, Translation

I show that a one-dimensional (1D) conditional generative adversarial network (cGAN) with an adversarial training architecture is capable of unpaired signal-to-signal ("sig2sig") translation. Using a simplified CycleGAN model with 1D layers and wider convolutional kernels, mirroring WaveGAN to reframe two-dimensional (2D) image generation as 1D audio generation, I show that recasting the 2D image-to-image translation task to a 1D signal-to-signal translation task with deep convolutional GANs is possible without substantial modification to the conventional U-Net model and adversarial architecture developed as CycleGAN. With this I show for a small tunable dataset that noisy test signals unseen by the 1D CycleGAN model and without paired training transform from the source domain to signals similar to paired test signals in the translated domain, especially in terms of frequency, and I quantify these differences in terms of correlation and error.
Information Theory in a Darwinian Evolution Population Dynamics Model
Authors: Eddy Kwessi
Date: 2024-03-08 00:00:00

Using information theory, we propose an estimation method for traits parameters in a Darwinian evolution model for species with on trait or multiple traits. We use the Fisher's information to obtain the errors on the estimation for one species with one or multiple traits. We perform simulations to illustrate the method.
Superposition with Delayed Unification
Authors: Ahmed Bhayat, Johannes Schoisswohl, Michael Rawson
Date: 2024-02-29 00:00:00

Classically, in saturation-based proof systems, unification has been considered atomic. However, it is also possible to move unification to the calculus level, turning the steps of the unification algorithm into inferences. For calculi that rely on unification procedures returning large or even infinite sets of unifiers, integrating unification into the calculus is an attractive method of dovetailing unification and inference. This applies, for example, to AC-superposition and higher-order superposition. We show that first-order superposition remains complete when moving unification rules to the calculus level. We discuss some of the benefits this has even for standard first-order superposition and provide an experimental evaluation.
Representing Pedagogic Content Knowledge Through Rough Sets
Authors: A Mani
Date: 2024-02-26 00:00:00

A teacher's knowledge base consists of knowledge of mathematics content, knowledge of student epistemology, and pedagogical knowledge. It has severe implications on the understanding of student's knowledge of content, and the learning context in general. The necessity to formalize the different content knowledge in approximate senses is recognized in the education research literature. A related problem is that of coherent formalizability. Existing responsive or smart AI-based software systems do not concern themselves with meaning, and trained ones are replete with their own issues. In the present research, many issues in modeling teachers' understanding of content are identified, and a two-tier rough set-based model is proposed by the present author for the purpose of developing software that can aid the varied tasks of a teacher. The main advantage of the proposed approach is in its ability to coherently handle vagueness, granularity and multi-modality. An extended example to equational reasoning is used to demonstrate these. The paper is meant for rough set researchers intending to build logical models or develop meaning-aware AI-software to aid teachers, and education research experts.
AttentionStitch: How Attention Solves the Speech Editing Problem
Authors: Antonios Alexos, Pierre Baldi
Date: 2024-03-05 00:00:00
Tasks: text-to-speech, Text to Speech

The generation of natural and high-quality speech from text is a challenging problem in the field of natural language processing. In addition to speech generation, speech editing is also a crucial task, which requires the seamless and unnoticeable integration of edited speech into synthesized speech. We propose a novel approach to speech editing by leveraging a pre-trained text-to-speech (TTS) model, such as FastSpeech 2, and incorporating a double attention block network on top of it to automatically merge the synthesized mel-spectrogram with the mel-spectrogram of the edited text. We refer to this model as AttentionStitch, as it harnesses attention to stitch audio samples together. We evaluate the proposed AttentionStitch model against state-of-the-art baselines on both single and multi-speaker datasets, namely LJSpeech and VCTK. We demonstrate its superior performance through an objective and a subjective evaluation test involving 15 human participants. AttentionStitch is capable of producing high-quality speech, even for words not seen during training, while operating automatically without the need for human intervention. Moreover, AttentionStitch is fast during both training and inference and is able to generate human-sounding edited speech.
Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian Manifolds
Authors: Jake Welde, Vijay Kumar
Date: 2024-03-07 00:00:00

In this work, we address the design of tracking controllers that drive a mechanical system's state asymptotically towards a reference trajectory. Motivated by aerospace and robotics applications, we consider fully-actuated systems evolving on the broad class of homogeneous spaces (encompassing all vector spaces, Lie groups, and spheres of any finite dimension). In this setting, the transitive action of a Lie group on the configuration manifold enables an intrinsic description of the tracking error as an element of the state space, even in the absence of a group structure on the configuration manifold itself (e.g., for $\mathbb{S}^2$). Such an error state facilitates the design of a generalized control policy depending smoothly on state and time, which drives the geometric tracking error to a designated origin from almost every initial condition, thereby guaranteeing almost global convergence to the reference trajectory. Moreover, the proposed controller simplifies elegantly when specialized to a Lie group or the n-sphere. In summary, we propose a unified, intrinsic controller guaranteeing almost global asymptotic trajectory tracking for fully-actuated mechanical systems evolving on a broad class of manifolds. We apply the method to an axisymmetric satellite and an omnidirectional aerial robot.
Sparse dynamic network reconstruction through L1-regularization of a Lyapunov equation
Authors: Ian Xul Belaustegui, Marcela Ordorica Arango, Román Rossi-Pool, Naomi Ehrich Leonard, Alessio Franci
Date: 2024-03-08 00:00:00
Tasks: Time Series

An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes. A common approach is to use the elements of the correlation matrix or its inverse as proxies of the interaction strengths, but the reconstructed networks are necessarily undirected. Transfer entropy methods have been proposed to reconstruct directed networks but the reconstructed network lacks information about interaction strengths. We propose a network reconstruction method that inherits the best of the two approaches by reconstructing a directed weighted network from noisy data under the assumption that the network is sparse and the dynamics are governed by a linear (or weakly-nonlinear) stochastic dynamical system. The two steps of our method are i) constructing an (infinite) family of candidate networks by solving the covariance matrix Lyapunov equation for the state matrix and ii) using L1-regularization to select a sparse solution. We further show how to use prior information on the (non)existence of a few directed edges to drastically improve the quality of the reconstruction.
Control-Oriented Identification for the Linear Quadratic Regulator: Technical Report
Authors: Sean Anderson, João Pedro Hespanha
Date: 2024-03-08 00:00:00

Data-driven control benefits from rich datasets, but constructing such datasets becomes challenging when gathering data is limited. We consider an offline experiment design approach to gathering data where we design a control input to collect data that will most improve the performance of a feedback controller. We show how such a control-oriented approach can be used in a setting with linear dynamics and quadratic objective and, through design of a gradient estimator, solve the problem via stochastic gradient descent. We show our formulation numerically outperforms an A- and L-optimal experiment design approach as well as a robust dual control approach.
Exploiting polar symmetry in designing equivariant observers for vision-based motion estimation
Authors: Tarek Bouazza, Robert Mahony, Tarek Hamel
Date: 2024-03-08 00:00:00
Tasks: Motion Estimation, Pose Estimation

Accurately estimating camera motion from image sequences poses a significant challenge in computer vision and robotics. Many computer vision methods first compute the essential matrix associated with a motion and then extract orientation and normalized translation as inputs to pose estimation, reconstructing the scene scale (that is unobservable in the epipolar construction) from separate information. In this paper, we design a continuous-time filter that exploits the same perspective by using the epipolar constraint to define pseudo-measurements. We propose a novel polar symmetry on the pose of the camera that makes these measurements equivariant. This allows us to apply recent results from equivariant systems theory to estimating pose. We provide a novel explicit persistence of excitation condition to characterize observability of the full pose, ensuring reconstruction of the scale parameter that is not directly observable in the epipolar construction.
An Overview of Automated Vehicle Longitudinal Platoon Formation Strategies
Authors: M Sabbir Salek, Mugdha Basu Thakur, Pardha Sai Krishna Ala, Mashrur Chowdhury, Matthias Schmid, Pamela Murray-Tuite, Sakib Mahmud Khan, Venkat Krovi
Date: 2024-03-08 00:00:00

Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energy efficiency of surface transportation systems by limiting or eliminating human involvement in the driving tasks. The theoretical validity of the AV platooning strategies has been established and practical applications are being tested under real-world conditions. The emergence of sensors, communication, and control strategies has resulted in rapid and constant evolution of AV platooning strategies. In this paper, we review the state-of-the-art knowledge in AV longitudinal platoon formation using a five-component platooning framework, which includes vehicle model, information-receiving process, information flow topology, spacing policy, and controller and discuss the advantages and limitations of the components. Based on the discussion about existing strategies and associated limitations, potential future research directions are presented.
Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Authors: Lorenzo Sforni, Guido Carnevale, Ivano Notarnicola, Giuseppe Notarstefano
Date: 2024-03-08 00:00:00

In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing stability certificates for the overall learning and control scheme. Specifically, in the proposed on-policy learning framework, the control input is applied to the actual (unknown) linear system while iteratively optimized. We propose a learning and control procedure, termed Relearn LQR, that combines a recursive least squares method with a direct policy search based on the gradient method. The resulting scheme is analyzed by modeling it as a feedback-interconnected nonlinear dynamical system. A Lyapunov-based approach, exploiting averaging and timescale separation theories for nonlinear systems, allows us to provide formal stability guarantees for the whole interconnected scheme. The effectiveness of the proposed strategy is corroborated by numerical simulations, where Relearn LQR is deployed on an aircraft control problem, with both static and drifting parameters.
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation
Authors: Zhi Zhang, Chenyu Ma, Saleh Soudijani, Sadegh Soudjani
Date: 2024-03-08 00:00:00

A novel data-driven method for formal verification is proposed to study complex systems operating in safety-critical domains. The proposed approach is able to formally verify discrete-time stochastic dynamical systems against temporal logic specifications only using observation samples and without the knowledge of the model, and provide a probabilistic guarantee on the satisfaction of the specification. We first propose the theoretical results for using non-parametric estimation to estimate an asymptotic upper bound for the \emph{Lipschitz constant} of the stochastic system, which can determine a finite abstraction of the system. Our results prove that the asymptotic convergence rate of the estimation is $O(n^{-\frac{1}{3+d}})$, where $d$ is the dimension of the system and $n$ is the data scale. We then construct interval Markov decision processes using two different data-driven methods, namely non-parametric estimation and empirical estimation of transition probabilities, to perform formal verification against a given temporal logic specification. Multiple case studies are presented to validate the effectiveness of the proposed methods.
Modeling Fault Recovery and Transient Stability of Grid-Forming Converters Equipped With Current Reference Limitation
Authors: Ali Arjomandi-Nezhad, Yifei Guo, Bikash C. Pal, Guangya Yang
Date: 2024-03-08 00:00:00

When grid-forming (GFM) inverter-based resources (IBRs) face severe grid disturbances (e.g., short-circuit faults), the current limitation mechanism may be triggered. Consequently, the GFM IBRs enter the current-saturation mode, inducing nonlinear dynamical behaviors and posing great challenges to the post-disturbance transient angle stability. This paper presents a systematic study to reveal the fault recovery behaviors of a GFM IBR and identify the risk of instability. A closed-form expression for the necessary condition that a GFM IBR returns from the current-saturation mode to the normal operation mode is presented. Based on these analyses, it is inferred that the angle of the magnitude-saturated current significantly affects the post-fault recovery and transient stability; with different angle selection, the system may follow multiple post-fault trajectories depending on those conditions: 1) Convergence to a normal stable equilibrium point (SEP), 2) convergence to a saturated stable equilibrium point (satSEP), or 3) divergence (instability). In this paper, the circumstances under which a GFM IBR cannot escape from the current-saturation mode are thoroughly investigated. The theoretical analyses are verified by dynamic simulations.
Device Fault Prediction Model based on LSTM and Random Forest
Authors: Jing Xu, Yongbo Zhang
Date: 2024-03-08 00:00:00

The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a comprehensive control system and effective control measures, it is not possible to trace the execution process of business operations afterward. This inability to trace hampers the investigation of testing issues and risk control, as it lacks effective data support. Additionally, a significant amount of original record information for key parameters in the testing process, which is based on sampling operation standards, has not been effectively utilized. To address these issues, we conduct researches on key monitoring technologies in the typical material inspection process based on the Internet of Things (IoT) and analyze the key parameters in inspection results. For purpose of complete the above tasks, this paper investigates the use of Long Short-Term Memory (LSTM) algorithms for quality prediction in material equipment based on key inspection parameters. In summary, this paper aims to provide professional and reliable quality data support for various business processes within the company, including material procurement, engineering construction, and equipment operation.
Sampling Model for Grid Material Inspection Based on Analytic Hierarchy Process with Absolute Measurement
Authors: Jing Xu, Yongbo Zhang
Date: 2024-03-08 00:00:00

The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a comprehensive control system and effective control measures, it is not possible to trace the execution process of business operations afterward. This inability to trace hampers the investigation of testing issues and risk control, as it lacks effective data support. Additionally, a significant amount of original record information for key parameters in the testing process, which is based on sampling operation standards, has not been effectively utilized. To address these issues, we conduct researches on key monitoring technologies in the typical material inspection process based on the Internet of Things (IoT) and analyze the key parameters in inspection results. For purpose of complete the above tasks, this paper investigates the use of Long Short-Term Memory (LSTM) algorithms for quality prediction in material equipment based on key inspection parameters. In summary, this paper aims to provide professional and reliable quality data support for various business processes within the company, including material procurement, engineering construction, and equipment operation.