pwc-archive/papers-with-abstracts huggingface dataset:
Vulnerability Detection Using Two-Stage Deep Learning Models
Authors: Mohamed Mjd Alhafi, Mohammad Hammade, Khloud Al Jallad
Date: 2023-05-08 00:00:00
Tasks: Binary Classification, Deep Learning, Vulnerability Detection

Application security is an essential part of developing modern software, as lots of attacks depend on vulnerabilities in software. The number of attacks is increasing globally due to technological advancements. Companies must include security in every stage of developing, testing, and deploying their software in order to prevent data breaches. There are several methods to detect software vulnerability Non-AI-based such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). However, these approaches have substantial false-positive and false-negative rates. On the other side, researchers have been interested in developing an AI-based vulnerability detection system employing deep learning models like BERT, BLSTM, etc. In this paper, we proposed a two-stage solution, two deep learning models were proposed for vulnerability detection in C/C++ source codes, the first stage is CNN which detects if the source code contains any vulnerability (binary classification model) and the second stage is CNN-LTSM that classifies this vulnerability into a class of 50 different types of vulnerabilities (multiclass classification model). Experiments were done on SySeVR dataset. Results show an accuracy of 99% for the first and 98% for the second stage.
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks
Authors: Nils Lukas, Florian Kerschbaum
Date: 2023-05-07 00:00:00
Tasks: Data Poisoning, image-classification, Image Classification

Deep image classification models trained on vast amounts of web-scraped data are susceptible to data poisoning - a mechanism for backdooring models. A small number of poisoned samples seen during training can severely undermine a model's integrity during inference. Existing work considers an effective defense as one that either (i) restores a model's integrity through repair or (ii) detects an attack. We argue that this approach overlooks a crucial trade-off: Attackers can increase robustness at the expense of detectability (over-poisoning) or decrease detectability at the cost of robustness (under-poisoning). In practice, attacks should remain both undetectable and robust. Detectable but robust attacks draw human attention and rigorous model evaluation or cause the model to be re-trained or discarded. In contrast, attacks that are undetectable but lack robustness can be repaired with minimal impact on model accuracy. Our research points to intrinsic flaws in current attack evaluation methods and raises the bar for all data poisoning attackers who must delicately balance this trade-off to remain robust and undetectable. To demonstrate the existence of more potent defenders, we propose defenses designed to (i) detect or (ii) repair poisoned models using a limited amount of trusted image-label pairs. Our results show that an attacker who needs to be robust and undetectable is substantially less threatening. Our defenses mitigate all tested attacks with a maximum accuracy decline of 2% using only 1% of clean data on CIFAR-10 and 2.5% on ImageNet. We demonstrate the scalability of our defenses by evaluating large vision-language models, such as CLIP. Attackers who can manipulate the model's parameters pose an elevated risk as they can achieve higher robustness at low detectability compared to data poisoning attackers.
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free
Authors: Syomantak Chaudhuri, Thomas A. Courtade
Date: 2023-04-27 00:00:00

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm. Traditional DP formulations impose a uniform privacy requirement for all users, which is often inconsistent with real-world scenarios in which users dictate their privacy preferences individually. This work considers the problem of mean estimation under heterogeneous DP constraints, where each user can impose their own distinct privacy level. The algorithm we propose is shown to be minimax optimal when there are two groups of users with distinct privacy levels. Our results elicit an interesting saturation phenomenon that occurs as one group's privacy level is relaxed, while the other group's privacy level remains constant. Namely, after a certain point, further relaxing the privacy requirement of the former group does not improve the performance of the minimax optimal mean estimator. Thus, the central server can offer a certain degree of privacy without any sacrifice in performance.
NUANCE: Near Ultrasound Attack On Networked Communication Environments
Authors: Forrest McKee, David Noever
Date: 2023-04-25 00:00:00

This study investigates a primary inaudible attack vector on Amazon Alexa voice services using near ultrasound trojans and focuses on characterizing the attack surface and examining the practical implications of issuing inaudible voice commands. The research maps each attack vector to a tactic or technique from the MITRE ATT&CK matrix, covering enterprise, mobile, and Industrial Control System (ICS) frameworks. The experiment involved generating and surveying fifty near-ultrasonic audios to assess the attacks' effectiveness, with unprocessed commands having a 100% success rate and processed ones achieving a 58% overall success rate. This systematic approach stimulates previously unaddressed attack surfaces, ensuring comprehensive detection and attack design while pairing each ATT&CK Identifier with a tested defensive method, providing attack and defense tactics for prompt-response options. The main findings reveal that the attack method employs Single Upper Sideband Amplitude Modulation (SUSBAM) to generate near-ultrasonic audio from audible sources, transforming spoken commands into a frequency range beyond human-adult hearing. By eliminating the lower sideband, the design achieves a 6 kHz minimum from 16-22 kHz while remaining inaudible after transformation. The research investigates the one-to-many attack surface where a single device simultaneously triggers multiple actions or devices. Additionally, the study demonstrates the reversibility or demodulation of the inaudible signal, suggesting potential alerting methods and the possibility of embedding secret messages like audio steganography.
Coordinating Charitable Donations
Authors: Felix Brandt, Matthias Greger, Erel Segal-haLevi, Warut Suksompong
Date: 2023-05-17 00:00:00

Charity is typically carried out by individual donors, who donate money to charities they support, or by centralized organizations such as governments or municipalities, which collect individual contributions and distribute them among a set of charities. Individual charity respects the will of the donors, but may be inefficient due to a lack of coordination; centralized charity is potentially more efficient, but may ignore the will of individual donors. We present a mechanism that combines the advantages of both methods for donors with Leontief preferences (i.e., each donor seeks to maximize an individually weighted minimum of all contributions across the charities). The mechanism distributes the contribution of each donor efficiently such that no subset of donors has an incentive to redistribute their donations. Moreover, it is group-strategyproof, satisfies desirable monotonicity properties, maximizes Nash welfare, returns a unique Lindahl equilibrium, can be computed efficiently, and implemented via natural best-response spending dynamics.
Data-Driven Modeling of Directly-Modulated Lasers
Authors: Sergio Hernandez Fernandez, Christophe Peucheret, Ognjen Jovanovic, Francesco Da Ros, Darko Zibar
Date: 2023-05-15 00:00:00

The end-to-end optimization of links based on directly-modulated lasers may require an analytically differentiable channel. We overcome this problem by developing and comparing differentiable laser models based on machine learning techniques.
On the Difficulty of Intersection Checking with Polynomial Zonotopes
Authors: Yushen Huang, Ertai Luo, Stanley Bak, Yifan Sun
Date: 2023-05-17 00:00:00
Tasks: Motion Planning

Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with polynomial zonotopes is intersection checking. Determining whether the reachable set, represented as a polynomial zonotope, intersects an unsafe set is not straightforward. In fact, we show that this fundamental operation is NP-hard, even for a simple class of polynomial zonotopes. The standard method for intersection checking with polynomial zonotopes is a two-part algorithm that overapproximates a polynomial zonotope with a regular zonotope and then, if the overapproximation error is deemed too large, splits the set and recursively tries again. Beyond the possible need for a large number of splits, we identify two sources of concern related to this algorithm: (1) overapproximating a polynomial zonotope with a zonotope has unbounded error, and (2) after splitting a polynomial zonotope, the overapproximation error can actually increase. Taken together, this implies there may be a possibility that the algorithm does not always terminate.We perform a rigorous analysis of the method and detail necessary conditions for the union of overapproximations to provably converge to the original polynomial zonotope.
An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients
Authors: Ke Ma, Hongkai Chen, Shan Lin
Date: 2023-05-11 00:00:00
Tasks: Ensemble Learning

Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset with 12 diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
Authors: Batool Salehi, Utku Demir, Debashri Roy, Suyash Pradhan, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury
Date: 2023-05-10 00:00:00
Tasks: Decision Making, Self-Learning

Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consider a twin of a wireless millimeter-wave band radio that is mounted on a vehicle and show how it speeds up directional beam selection in mobile environments. To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity. Towards this goal, this paper describes (i) a decision strategy at the vehicle that determines which twin must be used given the computational and latency limitations, and (ii) a self-learning scheme that uses the Multiverse-guided beam outcomes to enhance DL-based decision-making in the real world over time. Our work is distinguished from prior works as follows: First, we use a publicly available RF dataset collected from an autonomous car for creating different twins. Second, we present a framework with continuous interaction between the real world and Multiverse of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that Multiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe 52.72-85.07% improvement in beam selection time compared to 802.11ad standard.
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Authors: Thomas Beckers, Tom Z. Jiahao, George J. Pappas
Date: 2023-05-15 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification

Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dynamics and switching conditions are often hard to obtain or even inaccessible in case of a-priori unknown environments and nonlinear components. Black-box neural networks can learn to approximately represent switching dynamics, but typically require a large amount of data, neglect the underlying axioms of physics, and lack of uncertainty quantification. We propose a Gaussian process based learning approach enhanced by switching Port-Hamiltonian systems (GP-SPHS) to learn physical plausible system dynamics and identify the switching condition. The Bayesian nature of Gaussian processes uses collected data to form a distribution over all possible switching policies and dynamics that allows for uncertainty quantification. Furthermore, the proposed approach preserves the compositional nature of Port-Hamiltonian systems. A simulation with a hopping robot validates the effectiveness of the proposed approach.
Continual Learning for End-to-End ASR by Averaging Domain Experts
Authors: Peter Plantinga, Jaekwon Yoo, Chandra Dhir
Date: 2023-05-12 00:00:00
Tasks: Automatic Speech Recognition, Continual Learning, speech-recognition, Speech Recognition

Continual learning for end-to-end automatic speech recognition has to contend with a number of difficulties. Fine-tuning strategies tend to lose performance on data already seen, a process known as catastrophic forgetting. On the other hand, strategies that freeze parameters and append tunable parameters must maintain multiple models. We suggest a strategy that maintains only a single model for inference and avoids catastrophic forgetting. Our experiments show that a simple linear interpolation of several models' parameters, each fine-tuned from the same generalist model, results in a single model that performs well on all tested data. For our experiments we selected two open-source end-to-end speech recognition models pre-trained on large datasets and fine-tuned them on 3 separate datasets: SGPISpeech, CORAAL, and DiPCo. The proposed average of domain experts model performs well on all tested data, and has almost no loss in performance on data from the domain of original training.
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Authors: Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros
Date: 2023-05-17 00:00:00
Tasks: Distributed Optimization, Model Predictive Control

Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions. While the asymptotic stability of such algorithms is well studied, their finite-time performance has not received much attention. In this work, we quantify the performance of suboptimal linear model predictive control in terms of the additional closed-loop cost incurred due to performing only a finite number of optimization iterations. Leveraging this novel analysis framework, we propose a novel suboptimal MPC algorithm with a diminishing horizon length and finite-time closed-loop performance guarantees. This analysis allows the designer to plan a limited computational power budget distribution to achieve a desired performance level. We provide numerical examples to illustrate the algorithm's transient behavior and computational complexity.
Infinite-dimensional observers for high order boundary-controlled port-Hamiltonian systems
Authors: Jesus-Pablo Toledo-Zucco, Yongxin Wu, Hector Ramirez, Yann Le Gorrec
Date: 2023-05-17 00:00:00

This letter investigates the design of a class of infinite-dimensional observers for one dimensional (1D) boundary controlled port-Hamiltonian systems (BC-PHS) defined by differential operators of order $N \geq 1$. The convergence of the proposed observer depends on the number and location of available boundary measurements. \textcolor{hector}{Asymptotic convergence is assured for $N\geq 1$, and provided that enough boundary measurements are available, exponential convergence can be assured for the cases $N=1$ and $N=2$.} Furthermore, in the case of partitioned BC-PHS with $N=2$, such as the Euler-Bernoulli beam, \textcolor{hector}{it is shown} that exponential convergence can be assured considering less available measurements. The Euler-Bernoulli beam model is used to illustrate the design of the proposed observers and to perform numerical simulations.
Recursive Dynamic State Estimation for Power Systems with an Incomplete Nonlinear DAE Model
Authors: Milos Katanic, John Lygeros, Gabriela Hug
Date: 2023-05-17 00:00:00
Tasks: State Estimation

Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This paper proposes a novel, centralized dynamic state estimator for power systems that lack models of some components. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, we apply the least squares criterion to estimate all dynamic and algebraic states recursively. The approach results in an algorithm that generalizes the iterated extended Kalman filter and does not require static network observability. We further derive a graph theoretic condition for placing phasor measurement units that guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature within computational times that are feasible for real-time application.
Dynamic event-triggered control for multi-agent systems with adjustable inter-event time: a moving average approach
Authors: Zeyuan Wang, Mohammed Chadli
Date: 2023-05-17 00:00:00

This extended abstract presents our recent work on the leader-following consensus control for generic linear multi-agent systems. An improved dynamic event-triggered control framework are proposed, based on a moving average approach. The proposed methods involve model-based estimation and clock-like auxiliary dynamic variables to increase the inter-event time as long as possible eventually. Compared to the static event-triggered strategy and the existing state-of-the-art dynamic event-triggered mechanism, the proposed approach significantly reduces the communication frequency while still guaranteeing asymptotic convergence. Numerical simulations demonstrate the validity of the proposed theoretical results.
Angle-based formation stabilization and maneuvers in port-Hamiltonian form with bearing and velocity measurements
Authors: Ningbo Li, Pablo Borja, Arjan van der Schaft, Jacquelien M. A. Scherpen
Date: 2023-05-17 00:00:00
Tasks: Form

This paper proposes a port-Hamiltonian framework for angle-based formation stabilization and maneuvers using bearing and velocity measurements with an underlying triangulated Laman graph. The corresponding port-Hamiltonian controller is designed using virtual couplings on the errors of angle constraints in angle space and then the angle constraints and agent actuators are mapped by the constraint Jacobian, which can be applied to other formation constraints. In addition, due to the fact that the port-Hamiltonian model allows for complex and heterogeneous agent dynamics, our framework can be extended to networks with different agent dynamics and formation constraints. To avoid unavailable distance terms in the control law, an estimator is designed based on port-Hamiltonian theory and the property that energy is coordinate-free for different sensor modalities using bearing and velocity measurements, which permits our framework to inject damping for the formation maneuvers. Furthermore, several maneuvers are analyzed under both considerations of stabilization and transient performance. Simulations are performed to illustrate the effectiveness of the approach.
A port-Hamiltonian framework for displacement-based and rigid formation tracking
Authors: Ningbo Li, Zhiyong Sun, Arjan van der Schaft, Jacquelien M. A. Scherpen
Date: 2023-05-17 00:00:00

This paper proposes a passivity-based port-Hamiltonian (pH) framework for multi-agent displacement-based and rigid formation control and velocity tracking. The control law consists of two parts, where the internal feedback is to track the velocity and the external feedback is to achieve formation stabilization by steering variables of neighboring agents that prescribe the desired geometric shape. Regarding the external feedback, a general framework is proposed for all kinds of formations by means of the advantage that the pH model is energy-based and coordinate-free. To solve the issue that the incidence matrix is not of full column rank over cyclic graphs, the matrix property is used to prove the convergence to the target sets for the displacement-based formation, while for rigid formations, the algebraic conditions of infinitesimal rigidity are investigated to achieve asymptotic local stability. Furthermore, the rigid formation with heterogeneous constraints is further investigated under this framework and the asymptotic local stability is proved under a mild assumption. Simulations are performed to illustrate the effectiveness of the framework.
Distributionally Robust Differential Dynamic Programming with Wasserstein Distance
Authors: Astghik Hakobyan, Insoon Yang
Date: 2023-05-16 00:00:00

Differential dynamic programming (DDP) is a popular technique for solving nonlinear optimal control problems with locally quadratic approximations. However, existing DDP methods are not designed for stochastic systems with unknown disturbance distributions. To address this limitation, we propose a novel DDP method that approximately solves the Wasserstein distributionally robust control (WDRC) problem, where the true disturbance distribution is unknown but a disturbance sample dataset is given. Our approach aims to develop a practical and computationally efficient DDP solution. To achieve this, we use the Kantrovich duality principle to decompose the value function in a novel way and derive closed-form expressions of the distributionally robust control and worst-case distribution policies to be used in each iteration of our DDP algorithm. This characterization makes our method tractable and scalable without the need for numerically solving any minimax optimization problems. The superior out-of-sample performance and scalability of our algorithm are demonstrated through kinematic car navigation and coupled oscillator problems.