pwc-archive/papers-with-abstracts huggingface dataset:
Secure Decentralized IoT Service Platform
Authors: Ruipeng Zhang, Chen Xu, Mengjun Xie
Date: 2022-10-26 00:00:00
Proceeding: Sensors 2022 10
Tasks: Management

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform’s transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.
Secure Decentralized IoT Service Platform Using Consortium
Authors: Ruipeng Zhang, Chen Xu, Mengjun Xie
Date: 2022-10-26 00:00:00
Proceeding: Sensors 2022 10
Tasks: Management

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform’s transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.
The NoXi database: multimodal recordings of mediated novice-expert interactions
Authors: Cafaro A, Wagner J, Baur T, et al.
Date: 2017-11-03 00:00:00
Proceeding: Proceedings of the 19th ACM International Conference on Multimodal Interaction 2017 11
Tasks: Retrieval

We present a novel multi-lingual database of natural dyadic novice-expert interactions, named NoXi, featuring screen-mediated dyadic human interactions in the context of information exchange and retrieval. NoXi is designed to provide spontaneous interactions with emphasis on adaptive behaviors and unexpected situations (e.g. conversational interruptions). A rich set of audio-visual data, as well as continuous and discrete annotations are publicly available through a web interface. Descriptors include low level social signals (e.g. gestures, smiles), functional descriptors (e.g. turn-taking, dialogue acts) and interaction descriptors (e.g. engagement, interest, and fluidity).
Resilient Set-based State Estimation for Linear Time-Invariant Systems Using Zonotopes
Authors: Muhammad Umar B. Niazi, Amr Alanwar, Michelle S. Chong, Karl Henrik Johansson
Date: 2022-11-15 00:00:00
Tasks: Computational Efficiency, State Estimation

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm's worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system.
Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints
Authors: Zhichao Wang, Xinsheng Wang, Lei Xie, Yuanzhe Chen, Qiao Tian, Yuping Wang
Date: 2022-11-16 00:00:00
Tasks: Voice Conversion

Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC). However, in a low-resource situation, where only limited utterances from the target speaker are accessible, existing VC methods are hard to meet this requirement and capture the target speaker's timber. In this work, a novel VC model, referred to as MFC-StyleVC, is proposed for the low-resource VC task. Specifically, speaker timbre constraint generated by clustering method is newly proposed to guide target speaker timbre learning in different stages. Meanwhile, to prevent over-fitting to the target speaker's limited data, perceptual regularization constraints explicitly maintain model performance on specific aspects, including speaking style, linguistic content, and speech quality. Besides, a simulation mode is introduced to simulate the inference process to alleviate the mismatch between training and inference. Extensive experiments performed on highly expressive speech demonstrate the superiority of the proposed method in low-resource VC.
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence
Authors: Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Date: 2022-11-16 00:00:00
Tasks: Speech Enhancement

Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues. Compared with single channel approaches, multichannel PSE systems can be more effective in adverse acoustic conditions by leveraging the spatial information in microphone signals. However, the implementation of multichannel PSEs to accommodate a wide range of array topology in household applications can be challenging. To develop an array configuration agnostic PSE system, we define a spatial feature termed the long short term spatial coherence as the input feature to a convolutional recurrent network to monitor the voice activity of the target speaker. As another refinement, an equivalent rectangular bandwidth scaled LSTSC feature can be used to reduce the computational cost. Experiments were conducted to compare the proposed PSE systems, including the complete and the simplified versions with two baselines using unseen room responses and array configurations in the presence of TV noise and competing speakers. The results demonstrated that the proposed multichannel PSE network trained with the LSTSC feature achieved superior enhancement performance without precise knowledge of the array configurations and room responses.
Hybrid Transformers for Music Source Separation
Authors: Simon Rouard, Francisco Massa, Alexandre Défossez
Date: 2022-11-15 00:00:00
Tasks: Music Source Separation, Speech Enhancement

A natural question arising in Music Source Separation (MSS) is whether long range contextual information is useful, or whether local acoustic features are sufficient. In other fields, attention based Transformers have shown their ability to integrate information over long sequences. In this work, we introduce Hybrid Transformer Demucs (HT Demucs), an hybrid temporal/spectral bi-U-Net based on Hybrid Demucs, where the innermost layers are replaced by a cross-domain Transformer Encoder, using self-attention within one domain, and cross-attention across domains. While it performs poorly when trained only on MUSDB, we show that it outperforms Hybrid Demucs (trained on the same data) by 0.45 dB of SDR when using 800 extra training songs. Using sparse attention kernels to extend its receptive field, and per source fine-tuning, we achieve state-of-the-art results on MUSDB with extra training data, with 9.20 dB of SDR.
Online convex optimization for constrained control of linear systems using a reference governor
Authors: Marko Nonhoff, Johannes Köhler, Matthias A. Müller
Date: 2022-11-16 00:00:00

In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a $\lambda$-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
Programming Wireless Security through Learning-Aided Spatiotemporal Digital Coding Metamaterial Antenna
Authors: Alireza Nooraiepour, Shaghayegh Vosoughitabar, Chung-Tse Michael Wu, Waheed U. Bajwa, Narayan B. Mandayam
Date: 2022-11-16 00:00:00
Tasks: Management

The advancement of future large-scale wireless networks necessitates the development of cost-effective and scalable security solutions. Conventional cryptographic methods, due to their computational and key management complexity, are unable to fulfill the low-latency and scalability requirements of these networks. Physical layer (PHY) security has been put forth as a cost-effective alternative to cryptographic mechanisms that can circumvent the need for explicit key exchange between communication devices, owing to the fact that PHY security relies on the physics of the signal transmission for providing security. In this work, a space-time-modulated digitally-coded metamaterial (MTM) leaky wave antenna (LWA) is proposed that can enable PHY security by achieving the functionalities of directional modulation (DM) using a machine learning-aided branch and bound (B&B) optimized coding sequence. From the theoretical perspective, it is first shown that the proposed space-time MTM antenna architecture can achieve DM through both the spatial and spectral manipulation of the orthogonal frequency division multiplexing (OFDM) signal received by a user equipment. Simulation results are then provided as proof-of-principle, demonstrating the applicability of our approach for achieving DM in various communication settings. To further validate our simulation results, a prototype of the proposed architecture controlled by a field-programmable gate array (FPGA) is realized, which achieves DM via an optimized coding sequence carried out by the learning-aided branch-and-bound algorithm corresponding to the states of the MTM LWA's unit cells. Experimental results confirm the theory behind the space-time-modulated MTM LWA in achieving DM, which is observed via both the spectral harmonic patterns and bit error rate (BER) measurements.
A moving horizon state and parameter estimation scheme with guaranteed robust convergence
Authors: Julian D. Schiller, Matthias A. Müller
Date: 2022-11-16 00:00:00
Tasks: parameter estimation

We propose a moving horizon estimation scheme for joint state and parameter estimation for nonlinear uncertain discrete-time systems. We establish robust exponential convergence of the combined estimation error subject to process disturbances and measurement noise. We employ a joint incremental input/output-to-state stability ($\delta$-IOSS) Lyapunov function to characterize nonlinear detectability for the states and (constant) parameters of the system. Sufficient conditions for the construction of a joint $\delta$-IOSS Lyapunov function are provided for a special class of nonlinear systems using a persistence of excitation condition. The theoretical results are illustrated by a numerical example.
Energy Smart Buildings: Parallel Uniform Cost-Search with Energy Storage and Generation
Authors: Brian Setz, Kawsar Haghshenas, Marco Aiello
Date: 2022-11-16 00:00:00
Tasks: Management, Scheduling

The amalgamation of Internet of Things and the smart grid enables the energy optimal scheduling of appliances based on user needs and dynamic energy prices. Additionally, progress in local storage technology calls for exploiting additional sources of flexibility. In this paper, we propose a scheduling approach for building operation management, considering factors such as energy storage, local energy generation, and dynamic energy prices. In addition, we propose a new optimization strategy to discover the optimal scheduling of devices. Our approach utilizes parallel uniform cost-search to explore the complex search space and to find the optimal schedule within a user-acceptable amount of time. The evaluation utilizes real-world data for the devices, and the price signals, while the architecture is designed following a micro-service approach, enabling modularity and loose-coupling. The evaluation shows that including local energy storage as part of the optimization problem further reduces overall costs by up to 22.64\% when compared to schedules without energy storage. Parallel uniform cost-search decreases the time to find the optimal schedule by a factor of 4.7 with respect to the traditional uniform cost-search algorithm.
Bi-directional Digital Twin and Edge Computing in the Metaverse
Authors: Jiadong Yu, Ahmad Alhilal, Pan Hui, Danny H. K. Tsang
Date: 2022-11-16 00:00:00
Tasks: Continual Learning, Edge-computing

The Metaverse has emerged to extend our lifestyle beyond physical limitations. As essential components in the Metaverse, digital twins (DTs) are the real-time digital replicas of physical items. Multi-access edge computing (MEC) provides responsive services to the end users, ensuring an immersive and interactive Metaverse experience. While the digital representation (DT) of physical objects, end users, and edge computing systems is crucial in the Metaverse, the construction of these DTs and the interplay between them have not been well-investigated. In this paper, we discuss the bidirectional reliance between the DT and the MEC system and investigate the creation of DTs of objects and users on the MEC servers and DT-assisted edge computing (DTEC). To ensure seamless handover among MEC servers and to avoid intermittent Metaverse services, we also explore the interaction between local DTECs on local MEC servers and the global DTEC on the cloud server due to the dynamic nature of network states (e.g., channel state and users' mobility). We investigate a continual learning framework for resource allocation strategy in local DTEC through a case study. Our strategy mitigates the desynchronization between physical-digital twins, ensures higher learning outcomes, and provides a satisfactory Metaverse experience.
Compositional Approximately Bisimilar Abstractions of Interconnected Systems
Authors: Belamfedel Alaoui Sadek, Saharsh, Pushpak Jagtap, Adnane Saoud
Date: 2022-11-16 00:00:00

This paper formulates and studies the concepts of approximate (alternating) bisimulation relations characterizing equivalence relations between interconnected systems and their abstractions. These equivalence relations guarantee that the symbolic model conserves the original model's dynamics. We develop a compositional approach for abstraction-based controller synthesis by relying on the notions of approximate composition and incremental input-to-state stability. In particular, given a large-scale system consisting of interconnected components, we provide conditions under which the concept of approximate (alternating) simulation relation is preserved when going from the subsystems to the large-scale interconnected system. The engineering relevance of the theoretical results has been evaluated through an application in traffic congestion control.
Cyber-Attack Event Analysis for EV Charging Stations
Authors: Mansi Girdhar, Junho Hong, Yongsik You, Tai-jin Song, Manimaran Govindarasu
Date: 2022-11-15 00:00:00
Tasks: Anomaly Detection

Safe and secure electric vehicle charging stations (EVCSs) are important in smart transportation infrastructure. The prevalence of EVCSs has rapidly increased over time in response to the rising demand for EV charging. However, developments in information and communication technologies (ICT) have made the cyber-physical system (CPS) of EVCSs susceptible to cyber-attacks, which might destabilize the infrastructure of the electric grid as well as the environment for charging. This study suggests a 5Ws \& 1H-based investigation approach to deal with cyber-attack-related incidents due to the incapacity of the current investigation frameworks to comprehend and handle these mishaps. Also, a stochastic anomaly detection system (ADS) is proposed to identify the anomalies, abnormal activities, and unusual operations of the station entities as a post cyber event analysis.
Parameter-Covariance Maximum Likelihood Estimation
Authors: Alex Nguyen-Le, Victor M. Preciado
Date: 2022-11-15 00:00:00
Tasks: Time Series, Time Series Analysis

Linear time series modelling is dominated by the use of purely autoregressive models even though incorporating moving average components can greatly improve parsimony. We present a convex formulation for vector-ARMA system identification which respects this fundamental property, thus granting access to the nice properties afforded by convex programming. The identification procedure is done purely in the time domain which can accommodate non-stationarity through regime switching. As a proof of concept, we present experimental results demonstrating this convex program in action. Next, we show how to adapt the expectation-maximization algorithm to support regime switching behavior.
Characterizing and Utilizing the Interplay between Quantum Technologies and Non-Terrestrial Networks
Authors: Hayder Al-Hraishawi, Junaid ur Rehman, Mohsen Razavi, Symeon Chatzinotas
Date: 2022-11-15 00:00:00

Quantum technologies are increasingly recognized as groundbreaking advancements set to redefine the landscape of computing, communications, and sensing by leveraging quantum phenomena, like entanglement and teleportation. Quantum technologies offer an interesting set of advantages such as unconditional security, large communications capacity, unparalleled computational speed, and ultra-precise sensing capabilities. However, their global deployment faces challenges related to communication ranges and geographical boundaries. Non-terrestrial networks (NTNs) have emerged as a potential remedy for these challenges through providing free-space quantum links to circumvent the exponential losses inherent in fiber optics. This paper delves into the dynamic interplay between quantum technologies and NTNs to unveil their synergistic potential. Specifically, we investigate their integration challenges and the potential solutions to foster a symbiotic convergence of quantum and NTN functionalities while identifying avenues for enhanced interoperability. This paper not only offers useful insights into the mutual advantages but also presents future research directions, aiming to inspire additional studies and advance this interdisciplinary collaboration.
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems
Authors: Mingjian Tuo, Xingpeng Li
Date: 2022-11-15 00:00:00
Tasks: Computational Efficiency

Conventional synchronous generators are gradually being replaced by inverter-based resources, such transition introduces more complicated operation conditions. And the reduction in system inertia imposes challenges for system operators on maintaining system rate-of-change-of-frequency (RoCoF) security. This paper presents a selectively linearized neural network (SNLNN) based RoCoF-constrained unit commitment (SLNN-RCUC) model. A RoCoF predictor is first trained to predict the system wide highest locational RoCoF based on a high-fidelity simulation dataset. Instead of incorporating the complete neural network into unit commitment, a ReLU linearization method is implemented on active selected neurons to improve the algorithm computational efficiency. The effectiveness of proposed SLNN-RCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulation on PSS/E
ECCO: Equivalent Circuit Controlled Optimization
Authors: Aayushya Agarwal, Carmel Fiscko, Soummya Kar, Larry Pileggi, Bruno Sinopoli
Date: 2022-11-15 00:00:00

We propose an adaptive optimization algorithm for solving unconstrained scaled gradient flow problems that achieves fast convergence by controlling the optimization trajectory shape and the discretization step sizes. Under a broad class of scaling functions, we establish convergence of the proposed approach to critical points of smooth objective functions, while demonstrating its flexibility and robustness with respect to hyperparameter tuning. First, we prove convergence of component-wise scaled gradient flow to a critical point under regularity conditions. We show that this controlled gradient flow dynamics is equivalent to the transient response of an electrical circuit, allowing for circuit theory concepts to solve the problem. Based on this equivalence, we develop two optimization trajectory control schemes based on minimizing the charge stored in the circuit: a second order method that uses the true Hessian and an alternate first order method that approximates the optimization trajectory with only gradient information. While the control schemes are derived from circuit concepts, no circuit knowledge is needed to implement the algorithms. To find the value of the critical point, we propose a time step search routine for Forward Euler discretization that controls the local truncation error, a method adapted from circuit simulation ideas. In simulation we find that the trajectory control outperforms uncontrolled gradient flow, and the error-aware discretization out-performs line search with the Armijo condition. Our algorithms are evaluated on convex and non-convex test functions, including neural networks, with convergence speeds comparable to or exceeding Adam.