pwc-archive/papers-with-abstracts huggingface dataset:
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Authors: Marc Vucovich, Devin Quinn, Kevin Choi, Christopher Redino, Abdul Rahman, Edward Bowen
Date: 2023-12-04 00:00:00
Tasks: Data Poisoning, Federated Learning

Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing to the training of the global model. However, the main advantage of privacy in federated learning is also the easiest aspect to exploit. Without being able to see the clients' data, it is difficult to determine the quality of the data. By utilizing data poisoning methods, such as backdoor or label-flipping attacks, or by sending manipulated information about their data back to the server, malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights given to the prior model's weights using Bayesian statistics. Our results show that this approach negates the effects of malicious clients and protects the overall federation.
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
Authors: Mingyan Zhu, Yiming Li, Junfeng Guo, Tao Wei, Shu-Tao Xia, Zhan Qin
Date: 2023-12-03 00:00:00
Tasks: Attribute, Backdoor Attack

Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and malicious methods since they can easily circumvent most of the current backdoor defenses. In this paper, we reveal that SSBAs are not sufficiently stealthy due to their poisoned-label nature, where users can discover anomalies if they check the image-label relationship. In particular, we demonstrate that it is ineffective to directly generalize existing SSBAs to their clean-label variants by poisoning samples solely from the target class. We reveal that it is primarily due to two reasons, including \textbf{(1)} the `antagonistic effects' of ground-truth features and \textbf{(2)} the learning difficulty of sample-specific features. Accordingly, trigger-related features of existing SSBAs cannot be effectively learned under the clean-label setting due to their mild trigger intensity required for ensuring stealthiness. We argue that the intensity constraint of existing SSBAs is mostly because their trigger patterns are `content-irrelevant' and therefore act as `noises' for both humans and DNNs. Motivated by this understanding, we propose to exploit content-relevant features, $a.k.a.$ (human-relied) attributes, as the trigger patterns to design clean-label SSBAs. This new attack paradigm is dubbed backdoor attack with attribute trigger (BAAT). Extensive experiments are conducted on benchmark datasets, which verify the effectiveness of our BAAT and its resistance to existing defenses.
zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof
Authors: Mojtaba Ahmadi, Reza Nourmohammadi
Date: 2023-12-01 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.
The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics
Authors: Jaroslaw Szumega, Lamine Bougueroua, Blerina Gkotse, Pierre Jouvelot, Federico Ravotti
Date: 2023-11-29 00:00:00

With the Open Science approach becoming important for research, the evolution towards open scientific-paper reviews is making an impact on the scientific community. However, there is a lack of publicly available resources for conducting research activities related to this subject, as only a limited number of journals and conferences currently allow access to their review process for interested parties. In this paper, we introduce the new comprehensive Open Review-Based dataset (ORB); it includes a curated list of more than 36,000 scientific papers with their more than 89,000 reviews and final decisions. We gather this information from two sources: the OpenReview.net and SciPost.org websites. However, given the volatile nature of this domain, the software infrastructure that we introduce to supplement the ORB dataset is designed to accommodate additional resources in the future. The ORB deliverables include (1) Python code (interfaces and implementations) to translate document data and metadata into a structured and high-level representation, (2) an ETL process (Extract, Transform, Load) to facilitate the automatic updates from defined sources and (3) data files representing the structured data. The paper presents our data architecture and an overview of the collected data along with relevant statistics. For illustration purposes, we also discuss preliminary Natural-Language-Processing-based experiments that aim to predict (1) papers' acceptance based on their textual embeddings, and (2) grading statistics inferred from embeddings as well. We believe ORB provides a valuable resource for researchers interested in open science and review, with our implementation easing the use of this data for further analysis and experimentation. We plan to update ORB as the field matures as well as introduce new resources even more fitted to dedicated scientific domains such as High-Energy Physics.
Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion
Authors: Thomas Feys, Liesbet Van der Perre, François Rottenberg
Date: 2023-12-05 00:00:00
Tasks: Graph Neural Network

Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximizes the sum rate affected by non-linear distortion, using a high-order polynomial PA model. In the distortion-limited regime, this GNN-based precoder outperforms zero forcing (ZF), ZF plus digital pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from the state-of-the-art. At an input back-off of -3 dB the proposed precoder compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two and four users respectively. Radiation patterns show that these gains are achieved by transmitting the non-linear distortion in non-user directions. In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and ZF plus DPD respectively. A complexity analysis shows six orders of magnitude reduction compared to DAB precoding. This opens perspectives to operate PAs closer to saturation, which drastically increases their energy efficiency.
Binaural multichannel blind speaker separation with a causal low-latency and low-complexity approach
Authors: Nils L. Westhausen, Bernd T. Meyer
Date: 2023-12-08 00:00:00
Tasks: Speaker Separation

In this paper, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83 % and the complexity up to 73 % compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.
Online Model-free Safety Verification for Markov Decision Processes Without Safety Violation
Authors: Abhijit Mazumdar, Rafal Wisniewski, Manuela L. Bujorianu
Date: 2023-12-08 00:00:00

In this paper, we consider the problem of safety assessment for Markov decision processes without explicit knowledge of the model. We aim to learn probabilistic safety specifications associated with a given policy without compromising the safety of the process. To accomplish our goal, we characterize a subset of the state-space called proxy set, which contains the states that are near in a probabilistic sense to the forbidden set consisting of all unsafe states. We compute the safety function using the single-step temporal difference method. To this end, we relate the safety function computation to that of the value function estimation using temporal difference learning. Since the given control policy could be unsafe, we use a safe baseline subpolicy to generate data for learning. We then use an off-policy temporal difference learning method with importance sampling to learn the safety function corresponding to the given policy. Finally, we demonstrate our results using a numerical example
A Privacy-Preserving Framework for Cloud-Based HVAC Control
Authors: Zhenan Feng, Ehsan Nekouei
Date: 2023-12-08 00:00:00
Tasks: Model Predictive Control, Privacy Preserving

The objective of this work is (i) to develop an encrypted cloud-based HVAC control framework to ensure the privacy of occupancy information, (ii) to reduce the communication and computation costs of encrypted HVAC control,(iii) to reduce the leakage of private information via the triggering time instances. Occupancy of a building is sensitive and private information that can be accurately inferred by cloud-based HVAC controllers. To ensure the privacy of the privacy information, in our framework, the measurements of an HVAC system are encrypted by a fully homomorphic encryption prior to communication with the cloud controller. We first develop an encrypted algorithm that allows the cloud controller to regulate the indoor temperature and CO_2 of a building. We next develop an event-triggered control policy to reduce the communication and computation costs of the encrypted HVAC control. We cast the optimal design of the event-triggered policy as an optimal control problem. Using Bellman's optimality principle, we study the structural properties of the optimal event-triggered policy and show the necessary information for optimal triggering policy. We also show that the optimal design of the event-triggered policy can be transformed into a Markov decision process by introducing new states. As the triggering time instances are not encrypted, there is a risk that the cloud may use them to deduce sensitive information. To mitigate this risk, we introduce two randomized triggering strategies. We finally study the performance of the developed encrypted HVAC control framework using the TRNSYS simulator. Our numerical results show that the proposed framework not only ensures efficient control of the indoor temperature and CO$_2$ but also reduces the computation and communication costs of encrypted HVAC control by at least 60%.
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator
Authors: Samuel Mallick, Filippo Airaldi, Azita Dabiri, Bart De Schutter
Date: 2023-12-08 00:00:00
Tasks: Model Predictive Control, Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use of model predictive control (MPC) as a function approximator for the policy and value functions. The current paper is the first work to extend this idea to the multi-agent setting. We propose the use of a distributed MPC scheme as a function approximator, with a structure allowing for distributed learning and deployment. We then show that Q-learning updates can be performed distributively without introducing nonstationarity, by reconstructing a centralized learning update. The effectiveness of the approach is demonstrated on two numerical examples.
Unlocking Energy Flexibility From Thermal Inertia of Buildings: A Robust Optimization Approach
Authors: Yun Li, Neil Yorke-Smith, Tamas Keviczky
Date: 2023-12-08 00:00:00
Tasks: Management

Towards integrating renewable electricity generation sources into the grid, an important facilitator is the energy flexibility provided by buildings' thermal inertia. Most of the existing research follows a single-step price- or incentive-based scheme for unlocking the flexibility potential of buildings. In contrast, this paper proposes a novel two-step design approach for better harnessing buildings' energy flexibility. In a first step, a robust optimization model is formulated for assessing the energy flexibility of buildings in the presence of uncertain predictions of external conditions, such as ambient temperature, solar irradiation, etc. In a second step, energy flexibility is activated in response to a feasible demand response (DR) request from grid operators without violating indoor temperature constraints, even in the presence of uncertain external conditions. The proposed approach is tested on a high-fidelity Modelica simulator to evaluate its effectiveness. Simulation results show that, compared with price-based demand-side management, the proposed approach achieves greater energy reduction during peak hours.
Estimating the Faults/Attacks using a Fast Adaptive Unknown Input Observer: An Enhanced LMI Approach
Authors: Shivaraj Mohite, Adil Sheikh, S. R. Wagh
Date: 2023-12-08 00:00:00

This paper deals with the problem of robust fault estimation for the Lipschitz nonlinear systems under the influence of sensor faults and actuator faults. In the proposed methodology, a descriptor system is formulated by augmenting sensor fault vectors with the states of the system. A novel fast adaptive unknown input observer (FAUIO) structure is proposed for the simultaneous estimation of both faults and states of a class of nonlinear systems. A new LMI condition is established by utilizing the $\mathcal{H}_\infty$ criterion to ensure the asymptotic convergence of the estimation error of the developed observer. This derived LMI condition is deduced by incorporating the reformulated Lipschitz property, a new variant of Young inequality, and the well-known linear parameter varying (LPV) approach. It is less conservative than the existing ones in the literature, and its effectiveness is compared with the other proposed approaches. Further, the proposed observer methodology is extended for the disturbance-affected nonlinear system for the purpose of the reconstruction of states and faults with optimal noise attenuation. Later on, both designed approaches are validated through an application of a single-link robotic arm manipulator in MATLAB Simulink.
Time- and Behavior-Preserving Execution of Determinate Supervisory Control
Authors: Alireza Mohamadkhani, Marc Geilen, Jeroen Voeten, Twan Basten
Date: 2023-12-08 00:00:00

The activity framework is a promising model-based design approach for Flexible Manufacturing Systems (FMS). It is used in industry for specification and analysis of FMS. It provides an intuitive specification language with a hierarchical view of the system's actions and events, activities built from them, and an automaton that captures the overall behavior of the system in terms of sequences of activities corresponding to its accepted words. It also provides a scalable timing analysis method using max-plus linear systems theory. The framework currently requires manual implementation of the supervisory controller that governs the system behavior. This is labor-intensive and error-prone. In this article, we turn the framework into a model-driven approach by introducing an execution architecture and execution engine that allow a specification to be executed in a time- and behavior-preserving fashion. We prove that the architecture and engine preserve the specified ordering of actions and events in the specification as well as the timing thereof up to a specified bound. We validate our approach on a prototype production system.
Data-Driven Identification of Attack-free Sensors in Networked Control Systems
Authors: Sribalaji C. Anand, Michelle S. Chong, André M. H. Teixeira
Date: 2023-12-08 00:00:00

This paper proposes a data-driven framework to identify the attack-free sensors in a networked control system when some of the sensors are corrupted by an adversary. An operator with access to offline input-output attack-free trajectories of the plant is considered. Then, a data-driven algorithm is proposed to identify the attack-free sensors when the plant is controlled online. We also provide necessary conditions, based on the properties of the plant, under which the algorithm is feasible. An extension of the algorithm is presented to identify the sensors completely online against certain classes of attacks. The efficacy of our algorithm is depicted through numerical examples.
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
Authors: Mi Zhou, Jiazhi Li, Masood Mortazavi, Ning Yan, Chaouki Abdallah
Date: 2023-12-08 00:00:00
Tasks: Multi-agent Reinforcement Learning

In this article, a \underline{S}tate-dependent \underline{M}ulti-\underline{A}gent \underline{D}eep \underline{D}eterministic \underline{P}olicy \underline{G}radient (\textbf{SMADDPG}) method is proposed in order to learn an optimal control policy for regionally switched systems. We observe good performance of this method and explain it in a rigorous mathematical language using some simplifying assumptions in order to motivate the ideas and to apply them to some canonical examples. Using reinforcement learning, the performance of the switched learning-based multi-agent method is compared with the vanilla DDPG in two customized demonstrative environments with one and two-dimensional state spaces.
A mathematical model for the within-host (re)infection dynamics of SARS-CoV-2
Authors: Lea Schuh, Peter V. Markov, Vladimir M. Veliov, Nikolaos I. Stilianakis
Date: 2023-12-07 00:00:00

Interactions between SARS-CoV-2 and the immune system during infection are complex. However, understanding the within-host SARS-CoV-2 dynamics is of enormous importance, especially when it comes to assessing treatment options. Mathematical models have been developed to describe the within-host SARS-CoV-2 dynamics and to dissect the mechanisms underlying COVID-19 pathogenesis. Current mathematical models focus on the acute infection phase, thereby ignoring important post-acute infection effects. We present a mathematical model, which not only describes the SARS-CoV-2 infection dynamics during the acute infection phase, but also reflects the recovery of the number of susceptible epithelial cells to an initial pre-infection homeostatic level, shows clearance of the infection within the individual, immune waning, and the formation of long-term immune response levels after infection. Moreover, the model accommodates reinfection events assuming a new virus variant with either increased infectivity or immune escape. Together, the model provides an improved reflection of the SARS-CoV-2 infection dynamics within humans, particularly important when using mathematical models to develop or optimize treatment options.
Developers' Leverage, Capital Market Financing, and Fire Sale Externalities Evidence from the Thai Condominium Market
Authors: Kanis Saengchote
Date: 2023-12-08 00:00:00
Tasks: Attribute

Leveraged developers facing rollover risk are more likely to engage in fire sales. Using COVID-19 as a natural experiment, we find evidence of fire sale externalities in the Thai condominium market. Resales in properties whose developers have higher leverage ratios have lower listing prices for listed developers (who have access to capital market financing) but not unlisted developers (who primarily use bank financing). We attribute this difference to the flexibility of bank loan renegotiation versus the rigidity of debt capital market repayments and highlight the role of commercial banks in financial intermediation in the presence of information asymmetry.
Foreign Capital and Economic Growth: Evidence from Bangladesh
Authors: Ummya Salma, Md. Fazlul Huq Khan, Md. Masum Billah
Date: 2023-12-07 00:00:00
Tasks: Time Series

This study aims to examine the relationship between Foreign Direct Investment (FDI), personal remittances received, and official development assistance (ODA) in the economic growth of Bangladesh. The study utilizes time series data on Bangladesh from 1976 to 2021. Additionally, this research contributes to the existing literature by introducing the Foreign Capital Depthless Index (FCDI) and exploring its impact on Bangladesh's economic growth. The results of the Vector Error Correction Model (VECM) suggest that the economic growth of Bangladesh depends on FDI, remittances, and aid in the long run. However, these variables do not exhibit a causal relationship with GDP in the short run. The relationship between FCDI and economic growth is positive in the long run. Nevertheless, the presence of these three variables has a more significant impact on the economic growth of Bangladesh
Independence of Irrelevant Decisions in Stochastic Choice
Authors: Fedor Sandomirskiy, Po Hyun Sung, Omer Tamuz, Ben Wincelberg
Date: 2023-12-08 00:00:00

We investigate stochasticity in choice behavior across diverse decisions. Each decision is modeled as a menu of actions with associated outcomes, and a stochastic choice rule assigns probabilities to actions based on the outcome profile. We characterize rules whose predictions are not affected by whether or not additional, irrelevant decisions are included in the model. Our main result is that such rules form the parametric family of mixed-logit rules.