pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Bayesian Persuasion: Importance of Vice Presidents
Authors: Majid Mahzoon
Date: 2022-04-11 00:00:00

We study strategic information transmission in a hierarchical setting where information gets transmitted through a chain of agents up to a decision maker whose action is of importance to every agent. This situation could arise whenever an agent can communicate to the decision maker only through a chain of intermediaries, for example, an entry-level worker and the CEO in a firm, or an official in the bottom of the chain of command and the president in a government. Each agent can decide to conceal part or all the information she receives. Proving we can focus on simple equilibria, where the only player who conceals information is the first one, we provide a tractable recursive characterization of the equilibrium outcome, and show that it could be inefficient. Interestingly, in the binary-action case, regardless of the number of intermediaries, there are a few pivotal ones who determine the amount of information communicated to the decision maker. In this case, our results underscore the importance of choosing a pivotal vice president for maximizing the payoff of the CEO or president.
MmWave 6D Radio Localization with a Snapshot Observation from a Single BS
Authors: Mohammad A. Nazari, Gonzalo Seco-Granados, Pontus Johannisson, Henk Wymeersch
Date: 2022-04-11 00:00:00
Tasks: Position

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional non-convex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cram\'er-Rao bound (CRB) is tightened using the ML estimation.
Dual-Function Radar-Communication System Aided by Intelligent Reflecting Surfaces
Authors: Yikai Li, Athina Petropulu
Date: 2022-04-10 00:00:00

We propose a novel design of a dual-function radar communication (DFRC) system aided by an Intelligent Reflecting Surface (IRS). We consider a scenario with one target and multiple communication receivers, where there is no line-of-sight between the radar and the target. The radar precoding matrix and the IRS weights are optimally designed to maximize the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and the SNR at the communication receivers subject to power constraints and constant modulus constraints on the IRS weights. The problem is decoupled into two sub-problems, namely, waveform design and IRS weight design, and is solved via alternating optimization. The former subproblem is solved via linear programming, and the latter via manifold optimization with a quartic polynomial objective. The key contribution of this paper lies in solving the IRS weight design sub-problem that is based on the optimization of a quartic objective function in the IRS weights, and is subject to unit modulus-constraint on the IRS weights. Simulation results are provided to show the convergence behavior of the proposed algorithm under different system configurations, and the effectiveness of using IRS to improve radar and communication performance.
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization
Authors: Mingxin Zhang, Zipei Fan, Ryosuke Shibasaki, Xuan Song
Date: 2022-04-06 00:00:00
Tasks: Indoor Localization

In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile communication devices. However, many existing methods for constructing fingerprint datasets rely on labor-intensive and time-consuming processes of collecting large amounts of data. Additionally, these methods often focus on ideal laboratory environments, rather than considering the practical challenges of large multi-floor buildings. To address these issues, we present a novel WiDAGCN model that can be trained using a small number of labeled site survey data and large amounts of unlabeled crowdsensed WiFi fingerprints. By constructing heterogeneous graphs based on received signal strength indicators (RSSIs) between waypoints and WiFi access points (APs), our model is able to effectively capture the topological structure of the data. We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies. To deal with the challenges of large amounts of unlabeled data and multiple data domains, we employ a semi-supervised domain adversarial training scheme to effectively utilize unlabeled data and align the data distributions across domains. Our system is evaluated using a public indoor localization dataset that includes multiple buildings, and the results show that it performs competitively in terms of localization accuracy in large buildings.
Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model
Authors: Rongbo Fan, Bochuan Hou, Jinbao Liu, Jianhua Yang, Zenglin Hong
Date: 2020-11-19 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020 11
Tasks: Attribute, Change Detection, Image Registration, Image Stitching

The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve subpixel registration. The relative registration accuracy improved by 1.6%-7.5%, whereas the overall performance improved by 4.5%-14.1%.
Invariant Smoothing with low process noise
Authors: Paul Chauchat, Silvere Bonnabel, Axel Barrau
Date: 2022-04-05 00:00:00

In this paper we address smoothing-that is, optimisation-based-estimation techniques for localisation problems in the case where motion sensors are very accurate. Our mathematical analysis focuses on the difficult limit case where motion sensors are infinitely precise, resulting in the absence of process noise. Then the formulation degenerates, as the dynamical model that serves as a soft constraint becomes an equality constraint, and conventional smoothing methods are not able to fully respect it. By contrast, once an appropriate Lie group embedding has been found, we prove theoretically that invariant smoothing gracefully accommodates this limit case in that the estimates tend to be consistent with the induced constraints when the noise tends to zero. Simulations on the important problem of initial alignement in inertial navigation show that, in a low noise setting, invariant smoothing may favorably compare to state-of-the-art smoothers when using precise inertial measurements units (IMU).
Listen only to me! How well can target speech extraction handle false alarms?
Authors: Marc Delcroix, Keisuke Kinoshita, Tsubasa Ochiai, Katerina Zmolikova, Hiroshi Sato, Tomohiro Nakatani
Date: 2022-04-11 00:00:00
Tasks: Speaker Identification, Speaker Verification, Speech Enhancement, Speech Extraction

Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses thus the challenging problem of simultaneously performing separation and speaker identification. There has been much progress in extraction performance following the recent development of neural networks for speech enhancement and separation. Most studies have focused on processing mixtures where the target speaker is actively speaking. However, the target speaker is sometimes silent in practice, i.e., inactive speaker (IS). A typical TSE system will tend to output a signal in IS cases, causing false alarms. It is a severe problem for the practical deployment of TSE systems. This paper aims at understanding better how well TSE systems can handle IS cases. We consider two approaches to deal with IS, (1) training a system to directly output zero signals or (2) detecting IS with an extra speaker verification module. We perform an extensive experimental comparison of these schemes in terms of extraction performance and IS detection using the LibriMix dataset and reveal their pros and cons.
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction
Authors: Zehai Tu, Ning Ma, Jon Barker
Date: 2022-04-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access. The construction of many non-intrusive predictors require either ground truth intelligibility labels or clean reference signals for supervised learning. In this work, we leverage an unsupervised uncertainty estimation method for predicting speech intelligibility, which does not require intelligibility labels or reference signals to train the predictor. Our experiments demonstrate that the uncertainty from state-of-the-art end-to-end automatic speech recognition (ASR) models is highly correlated with speech intelligibility. The proposed method is evaluated on two databases and the results show that the unsupervised uncertainty measures of ASR models are more correlated with speech intelligibility from listening results than the predictions made by widely used intrusive methods.
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners
Authors: Zehai Tu, Ning Ma, Jon Barker
Date: 2022-04-08 00:00:00
Tasks: Prediction, Speech Enhancement, speech-recognition, Speech Recognition

An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids. Most algorithms measures the signal-to-noise ratios or correlations between the acoustic features of clean reference signals and degraded signals. However, these hand-picked acoustic features are usually not explicitly correlated with recognition. Meanwhile, deep neural network (DNN) based automatic speech recogniser (ASR) is approaching human performance in some speech recognition tasks. This work leverages the hidden representations from DNN-based ASR as features for speech intelligibility prediction in hearing-impaired listeners. The experiments based on a hearing aid intelligibility database show that the proposed method could make better prediction than a widely used short-time objective intelligibility (STOI) based binaural measure.
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition
Authors: Zehai Tu, Jack Deadman, Ning Ma, Jon Barker
Date: 2022-04-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

End-to-end models have achieved significant improvement on automatic speech recognition. One common method to improve performance of these models is expanding the data-space through data augmentation. Meanwhile, human auditory inspired front-ends have also demonstrated improvement for automatic speech recognisers. In this work, a well-verified auditory-based model, which can simulate various hearing abilities, is investigated for the purpose of data augmentation for end-to-end speech recognition. By introducing the auditory model into the data augmentation process, end-to-end systems are encouraged to ignore variation from the signal that cannot be heard and thereby focus on robust features for speech recognition. Two mechanisms in the auditory model, spectral smearing and loudness recruitment, are studied on the LibriSpeech dataset with a transformer-based end-to-end model. The results show that the proposed augmentation methods can bring statistically significant improvement on the performance of the state-of-the-art SpecAugment.
A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks
Authors: Jingyi Lu, Daniel E. Quevedo
Date: 2021-03-10 00:00:00
Tasks: Q-Learning, Scheduling

We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems. A sensor takes measurements of the system output and forwards its dynamic state estimates to a remote controller over a packet-dropping link. The controller determines the optimal control law for the process using the estimates it receives. An attacker aims at degrading the control performance by increasing the packet-dropout rate with a DoS attack towards the sensor-controller channel. We assume both the controller and the attacker are rational in a game-theoretic sense and establish a partially observable stochastic game to derive the optimal joint design of scheduling and control. Using dynamic programming we prove that the control and scheduling policies can be designed separately without sacrificing optimality, making the problem equivalent to a complete information game. We employ Nash Q-learning to solve the problem and prove that the solution is guaranteed to constitute an $\epsilon$-Nash equilibrium. Numerical examples are provided to illustrate the tradeoffs between control performance and communication cost.
Privacy-Preserving Distributed Optimal Power Flow with Partially Homomorphic Encryption
Authors: Tong Wu, Changhong Zhao, Ying-Jun Angela Zhang
Date: 2021-01-21 00:00:00
Tasks: Privacy Preserving

Distribution grid agents are obliged to exchange and disclose their states explicitly to neighboring regions to enable distributed optimal power flow dispatch. However, the states contain sensitive information of individual agents, such as voltage and current measurements. These measurements can be inferred by adversaries, such as other participating agents or eavesdroppers. To address the issue, we propose a privacy-preserving distributed optimal power flow (OPF) algorithm based on partially homomorphic encryption (PHE). First of all, we exploit the alternating direction method of multipliers (ADMM) to solve the OPF in a distributed fashion. In this way, the dual update of ADMM can be encrypted by PHE. We further relax the augmented term of the primal update of ADMM with the $\ell_1$-norm regularization. In addition, we transform the relaxed ADMM with the $\ell_1$-norm regularization to a semidefinite program (SDP), and prove that this transformation is exact. The SDP can be solved locally with only the sign messages from neighboring agents, which preserves the privacy of the primal update. At last, we strictly prove the privacy preservation guarantee of the proposed algorithm. Numerical case studies validate the effectiveness and exactness of the proposed approach.
Approximation-free control based on the bioinspired reference model for suspension systems with uncertainty and unknown nonlinearity
Authors: Xiaoyan Hu, Guilin Wen, Shan Yin, Zhao Tan, Zebang Pan
Date: 2022-04-09 00:00:00

Uncertainty and unknown nonlinearity are often inevitable in the suspension systems, which were often solved using fuzzy logic system (FLS) or neural networks (NNs). However, these methods are restricted by the structural complexity of the controller and the huge computing cost. Meanwhile, the estimation error of such approximators is affected by adopted adaptive laws and learning gains. Thus, in view of the above problem, this paper proposes the approximation-free control based on the bioinspired reference model for a class of uncertain suspension systems with unknown nonlinearity. The proposed method integrates the superior vibration suppression of the bioinspired reference model and the structural advantage of the prescribed performance function (PPF) in approximation-free control. Then, the vibration suppression performance is improved, the calculation burden is relieved, and the transient performance is improved, which is analyzed theoretically in this paper. Finally, the simulation results validate the approach, and the comparisons show the advantages of the proposed control method in terms of good vibration suppression, fast convergence, and less calculation burden.
CMOS Circuit Implementation of Spiking Neural Network for Pattern Recognition Using On-chip Unsupervised STDP Learning
Authors: Sahibia Kaur Vohra, Sherin A Thomas, Mahendra Sakare, Devarshi Mrinal Das
Date: 2022-04-09 00:00:00

Computation on a large volume of data at high speed and low power requires energy-efficient computing architectures. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising solution for energy-efficient neuromorphic systems than conventional artificial neural network (ANN). Previous works on SNN with STDP learning primarily uses memristive devices which are difficult to fabricate. Some reported works on SNN makes use of memristor macro models, which are software-based and cannot give complete insight into circuit implementation challenges. This article presents for the first time, a full circuit-level implementation of the SNN system featuring on-chip unsupervised STDP learning in standard CMOS technology. It does not involve the use of FPGAs, CPUs or GPUs for training the neural network. We demonstrated the complete circuit-level design, implementation and simulation of SNN with on-chip training and inference for pattern classification using 180 nm CMOS technology. A comprehensive comparison of the proposed SNN circuit with the previous related work is also presented. To demonstrate the versatility of the CMOS synapse circuit for application scenarios requiring rate-based learning, we have tuned the pair-based STDP circuit to obtain Bienenstock-Cooper-Munro (BCM) characteristics and applied it to heart rate classification.
Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals
Authors: Solomon Gudeta, Ali Karimoddini, Mohammadreza Davoodi
Date: 2022-04-09 00:00:00

This paper presents continuous dynamic average consensus (DAC) algorithms for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose consensus algorithms that are robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithms are edge-based protocols with smooth functions in their internal structure to avoid the chattering effect. Furthermore, each agent is only capable of performing local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the agents' estimate of their average to asymptotically converge to the average of the time-varying reference signals of the agents. We provide simulation results to validate the proposed consensus algorithms and to perform a performance comparison of the proposed algorithms to existing algorithms in the literature.
A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies
Authors: Ramin Vakili, Mojdeh Khorsand
Date: 2022-04-09 00:00:00

Modeling protective relays is crucial for performing accurate stability studies as they play a critical role in defining the dynamic responses of power systems during disturbances. Nevertheless, due to the current limitations of stability software and the challenges of keeping track of the changes in the settings information of thousands of protective relays, modeling all the protective relays in bulk power systems is a challenging task. Distance relays are among the critical protection schemes, which are not properly modeled in current practices of stability studies. This paper proposes a machine learning-based method that uses the results of early-terminated stability studies to identify the critical distance relays required to be modeled in those studies. The algorithm used is the random forest (RF) classifier. GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The model is trained and tested on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer peak load under different operating conditions and topologies of the system. The results show the great performance of the method in identifying the critical distance relays. The results also show that only modeling the identified critical distance relays suffices to perform accurate stability studies.
Small-Gain Theorem for Safety Verification under High-Relative-Degree Constraints
Authors: Ziliang Lyu, Xiangru Xu, Yiguang Hong
Date: 2022-04-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper develops a small-gain technique for the safety analysis and verification of interconnected systems with high-relative-degree safety constraints. In this technique, input-to-state safety (ISSf) is used to characterize how the safety of a subsystem is influenced by the external input, and ISSf-barrier functions (ISSf-BFs) with high relative degree are employed to capture the safety of subsystems. With a coordination transform, the relationship between ISSf-BFs and the existing high-relative-degree (or high-order) barrier functions is established in order to simplify the ISSf analysis. With the help of high-relative-degree ISSf-BFs, a small-gain theorem is proposed for safety verification. It is shown that, under the small-gain condition, i) the interconnection of ISSf subsystems is still ISSf; and ii) the overall interconnected system is input-to-state stable (ISS) with respect to the compositional safe set. The effectiveness of the proposed small-gain theorem is illustrated on the output-constrained decentralized control of two inverted pendulums connected by a spring mounted on two carts.