pwc-archive/papers-with-abstracts huggingface dataset:
PRISM: Privacy Preserving Healthcare Internet of Things Security Management
Authors: Savvas Hadjixenophontos, Anna Maria Mandalari, Yuchen Zhao, Hamed Haddadi
Date: 2022-12-27 00:00:00
Tasks: Anomaly Detection, Edge-computing, Management, Privacy Preserving

Consumer healthcare Internet of Things (IoT) devices are gaining popularity in our homes and hospitals. These devices provide continuous monitoring at a low cost and can be used to augment high-precision medical equipment. However, major challenges remain in applying pre-trained global models for anomaly detection on smart health monitoring, for a diverse set of individuals that they provide care for. In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices. We develop a rigorous methodology that relies on automated IoT experimentation. We use a rich real-world dataset from in-home patient monitoring from 44 households of People Living With Dementia (PLWD) over two years. Our results indicate that anomalies can be identified with accuracy up to 99% and mean training times as low as 0.88 seconds. While all models achieve high accuracy when trained on the same patient, their accuracy degrades when evaluated on different patients.
Joint User Association and Bandwidth Allocation in Semantic Communication Networks
Authors: Le Xia, Yao Sun, Dusit Niyato, Xiaoqian Li, Muhammad Ali Imran
Date: 2022-12-29 00:00:00
Tasks: Management, Semantic Communication

Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.
Multisensor Multiobject Tracking with Improved Sampling Efficiency
Authors: Wenyu Zhang, Florian Meyer
Date: 2022-12-30 00:00:00
Tasks: Object, object-detection, Object Detection, Single Particle Analysis, Vocal Bursts Intensity Prediction

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or "particles". The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.
Active Planning for Cooperative Localization: A Fisher Information Approach
Authors: Wenyu Zhang, Bryan Teague, Florian Meyer
Date: 2022-12-30 00:00:00

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.
StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models
Authors: Yinghao Aaron Li, Cong Han, Nima Mesgarani
Date: 2022-12-29 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech, Transfer Learning, Voice Conversion

One-shot voice conversion (VC) aims to convert speech from any source speaker to an arbitrary target speaker with only a few seconds of reference speech from the target speaker. This relies heavily on disentangling the speaker's identity and speech content, a task that still remains challenging. Here, we propose a novel approach to learning disentangled speech representation by transfer learning from style-based text-to-speech (TTS) models. With cycle consistent and adversarial training, the style-based TTS models can perform transcription-guided one-shot VC with high fidelity and similarity. By learning an additional mel-spectrogram encoder through a teacher-student knowledge transfer and novel data augmentation scheme, our approach results in disentangled speech representation without needing the input text. The subjective evaluation shows that our approach can significantly outperform the previous state-of-the-art one-shot voice conversion models in both naturalness and similarity.
Secure Fusion Estimation Against FDI Sensor Attacks in Cyber-Physical Systems
Authors: Bo Chen, Pindi Weng, Daniel W. C. Ho, Li Yu
Date: 2022-12-30 00:00:00

This paper is concerned with the problem of secure multi-sensors fusion estimation for cyber-physical systems, where sensor measurements may be tampered with by false data injection (FDI) attacks. In this work, it is considered that the adversary may not be able to attack all sensors. That is, several sensors remain not being attacked. In this case, new local reorganized subsystems including the FDI attack signals and un-attacked sensor measurements are constructed by the augmentation method. Then, a joint Kalman fusion estimator is designed under linear minimum variance sense to estimate the system state and FDI attack signals simultaneously. Finally, illustrative examples are employed to show the effectiveness and advantages of the proposed methods.
Sizing Grid-Connected Wind Power Generation and Energy Storage with Wake Effect and Endogenous Uncertainty: A Distributionally Robust Method
Authors: Rui Xie, Wei Wei, Yue Chen
Date: 2022-12-30 00:00:00

Wind power, as a green energy resource, is growing rapidly worldwide, along with energy storage systems (ESSs) to mitigate its volatility. Sizing of wind power generation and ESSs has become an important problem to be addressed. Wake effect in a wind farm can cause wind speed deficits and a drop in downstream wind turbine power generation, which however was rarely considered in the sizing problem in power systems. In this paper, a bi-objective distributionally robust optimization (DRO) model is proposed to determine the capacities of wind power generation and ESSs considering the wake effect. An ambiguity set based on Wasserstein metric is established to characterize the wind power and demand uncertainties. In particular, wind power uncertainty is affected by the wind power generation capacity which is determined in the first stage. Thus, the proposed model is a DRO problem with endogenous uncertainty (or decision-dependent uncertainty). To solve the proposed model, a stochastic programming approximation method based on minimum Lipschitz constants is developed to turn the DRO model into a linear program. Then, an iterative algorithm is built, embedded with methods for evaluating the minimum Lipschitz constants. Case studies demonstrate the necessity of considering wake effect and the effectiveness of the proposed method.
Similarity-Based Predictive Maintenance Framework for Rotating Machinery
Authors: Sulaiman Aburakhia, Tareq Tayeh, Ryan Myers, Abdallah Shami
Date: 2022-12-30 00:00:00
Tasks: Fault Diagnosis

Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to predict or classify different operational states of the machine. However, in most industrial applications, labeled data is limited in terms of its size and type. Hence, it cannot serve the training purpose. In this paper, this problem is tackled by addressing the classification task as a similarity measure to a reference sample rather than a supervised classification task. Similarity-based approaches require a limited amount of labeled data and hence, meet the requirements of real-world industrial applications. Accordingly, the paper introduces a similarity-based framework for predictive maintenance (PdM) of rotating machinery. For each operational state of the machine, a reference vibration signal is generated and labeled according to the machine's operational condition. Consequentially, statistical time analysis, fast Fourier transform (FFT), and short-time Fourier transform (STFT) are used to extract features from the captured vibration signals. For each feature type, three similarity metrics, namely structural similarity measure (SSM), cosine similarity, and Euclidean distance are used to measure the similarity between test signals and reference signals in the feature space. Hence, nine settings in terms of feature type-similarity measure combinations are evaluated. Experimental results confirm the effectiveness of similarity-based approaches in achieving very high accuracy with moderate computational requirements compared to machine learning (ML)-based methods. Further, the results indicate that using FFT features with cosine similarity would lead to better performance compared to the other settings.
Passivity-based sliding mode control for mechanical port-Hamiltonian systems
Authors: Naoki Sakata, Kenji Fujimoto, Ichiro Maruta
Date: 2022-12-30 00:00:00

In this work, we propose a new passivity-based sliding mode control method for mechanical port-Hamiltonian systems. Passivity-based sliding mode control (PBSMC) is unification of sliding mode control and passivity-based control. It achieves sliding mode control and Lyapunov stability simultaneously by employing an energy based Lyapunov function. The proposed method gives a family of stabilizing controllers which smoothly interpolates passivity-based control and sliding mode control with free parameters. The freedom is useful to adjust the trade-off between robustness against external disturbances and undesired chattering vibration. In addition, this paper relaxes the restrictive condition which is required in the authors' former result. As a result, we can apply the proposed PBSMC method to trajectory tracking control problems. Furthermore, the robustness of the proposed controller against matched and unmatched disturbances is investigated. Numerical examples demonstrate the effectiveness of the proposed method.
Heterogeneous Computing Systems
Authors: Dimple P. Khatri, Guanqun Song, Ting Zhu
Date: 2022-12-29 00:00:00

This survey of heterogeneous computing systems will help in analyzing the technological trends that will be at the basis of heterogeneous computing systems, highlighting the major opportunities and challenges such technologies will bring with them. This will help to understand the importance of heterogeneous computing systems, which are becoming common architectural elements of not only the modern data centers but also highly integrated devices (IoT). Identify problems related to it, such as the resource allocation problem, middleware, processing architectures, programming challenges, etc. from the perspective of heterogeneous resources.
Analysis of Inter-Event Times in Linear Systems under Region-Based Self-Triggered Control
Authors: Anusree Rajan, Pavankumar Tallapragada
Date: 2022-12-29 00:00:00

This paper analyzes the evolution of inter-event times (IETs) in linear systems under region-based self-triggered control (RBSTC). In this control method, the state space is partitioned into a finite number of conic regions and each region is associated with a fixed IET. In this framework, studying the steady state behavior of the IETs is equivalent to studying the existence of a conic subregion that is positively invariant under the map that gives the evolution of the state from one event to the next. We provide necessary conditions and sufficient conditions for the existence of a positively invariant subregion (PIS). We also provide necessary and sufficient conditions for a PIS to be asymptotically stable. Indirectly, they provide necessary and sufficient conditions for local convergence of IETs to a constant or to a given periodic sequence. We illustrate the proposed method of analysis and results through numerical simulations.
Efficient Strategy Synthesis for Switched Stochastic Systems with Distributional Uncertainty
Authors: Ibon Gracia, Dimitris Boskos, Morteza Lahijanian, Luca Laurenti, Manuel Mazo Jr
Date: 2022-12-29 00:00:00

We introduce a framework for the control of discrete-time switched stochastic systems with uncertain distributions. In particular, we consider stochastic dynamics with additive noise whose distribution lies in an ambiguity set of distributions that are $\varepsilon-$close, in the Wasserstein distance sense, to a nominal one. We propose algorithms for the efficient synthesis of distributionally robust control strategies that maximize the satisfaction probability of reach-avoid specifications with either a given or an arbitrary (not specified) time horizon, i.e., unbounded-time reachability. The framework consists of two main steps: finite abstraction and control synthesis. First, we construct a finite abstraction of the switched stochastic system as a \emph{robust Markov decision process} (robust MDP) that encompasses both the stochasticity of the system and the uncertainty in the noise distribution. Then, we synthesize a strategy that is robust to the distributional uncertainty on the resulting robust MDP. We employ techniques from optimal transport and stochastic programming to reduce the strategy synthesis problem to a set of linear programs, and propose a tailored and efficient algorithm to solve them. The resulting strategies are correctly refined into switching strategies for the original stochastic system. We illustrate the efficacy of our framework on various case studies comprising both linear and non-linear switched stochastic systems.
Scheduling of Software-Defined Microgrids for Optimal Frequency Regulation
Authors: Zhongda Chu, Guoxuan Cui, Fei Teng
Date: 2022-12-29 00:00:00
Tasks: Scheduling

Integrated with a high share of Inverter-Based Resources (IBRs), microgrids face increasing complexity of frequency dynamics, especially after unintentional islanding from the maingrid. These IBRs, on the other hand, provide more control flexibility to shape the frequency dynamics of microgrid and together with advanced communication infrastructure offer new opportunities in the future software-defined microgrids. To enhance the frequency stability of microgrids with high IBR penetration, this paper proposes an optimal scheduling framework for software-defined microgrids to maintain frequency stability by utilizing the non-essential load shedding and dynamical optimization of the virtual inertia and virtual damping from IBRs. Moreover, side effects of these services, namely, the time delay associated with non-essential load shedding and potential IBR control parameter update failure are explicitly modeled to avoid underestimations of frequency deviation and over-optimistic results. The effectiveness and significant economic value of the proposed simultaneous and dynamic virtual inertia and damping provision strategy are demonstrated based on case studies in the modified IEEE 33-bus system.
How to Share: Balancing Layer and Chain Sharing in Industrial Microservice Deployment
Authors: Yuxiang Liu, Bo Yang, Yu Wu, Cailian Chen, Xinping Guan
Date: 2022-12-29 00:00:00
Tasks: Edge-computing

With the rapid development of smart manufacturing, edge computing-oriented microservice platforms are emerging as an important part of production control. In the containerized deployment of microservices, layer sharing can reduce the huge bandwidth consumption caused by image pulling, and chain sharing can reduce communication overhead caused by communication between microservices. The two sharing methods use the characteristics of each microservice to share resources during deployment. However, due to the limited resources of edge servers, it is difficult to meet the optimization goals of the two methods at the same time. Therefore, it is of critical importance to realize the improvement of service response efficiency by balancing the two sharing methods. This paper studies the optimal microservice deployment strategy that can balance layer sharing and chain sharing of microservices. We build a problem that minimizes microservice image pull delay and communication overhead and transform the problem into a linearly constrained integer quadratic programming problem through model reconstruction. A deployment strategy is obtained through the successive convex approximation (SCA) method. Experimental results show that the proposed deployment strategy can balance the two resource sharing methods. When the two sharing methods are equally considered, the average image pull delay can be reduced to 65% of the baseline, and the average communication overhead can be reduced to 30% of the baseline.
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network
Authors: Chen Feng, Andrew L. Lu, Yihsu Chen
Date: 2022-12-29 00:00:00
Tasks: energy trading, Management, Multi-agent Reinforcement Learning

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system's resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers' bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.
SARS-CoV-2 Virus-Like Particles with Plasmonic Au Cores and S1-Spike Protein Coronas
Authors: Weronika Andrzejewska, Barbara Peplińska, Jagoda Litowczenko, Patryk Obstarczyk, Joanna Olesiak-Bańska, Stefan Jurga, Mikołaj Lewandowski
Date: 2022-12-27 00:00:00

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies, aimed at the development of quick and safe ways of detecting viruses in human body, studying the virus-antibody and virus-cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. One of the groups of such species are the so-called virus-like particles (VLPs). Instead of capsids with viral DNA/RNA, VLPs have synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by "coronas" of S1 domains of the virus's Spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. Within our studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.
Dual-aptamer Drift Cancelling Techniques to Improve Long-term Stability of Real-Time Structure-Switching Aptasensors
Authors: Ya-Chen Tsai, Wei-Yang Weng, Yu-Tong Yeh, Jun-Chau Chien
Date: 2022-12-30 00:00:00

This paper presents a dual-aptamer scheme to cancel the signal drifts from structure-switching aptamers during long-term monitoring. Electrochemical aptamer-based (E-AB) biosensors recently demonstrated their great potential for in vivo continuous monitoring. Nevertheless, the detection accuracy is often limited by the signaling drifts. Conventionally, these drifts are removed by the kinetic differential measurements (KDM) when coupled with square-wave voltammetry. Yet we discover that KDM does not apply to every aptamer as the responses at different SWV frequencies heavily depend on its structure-switching characteristics and the redox reporters' electron transfer (ET) kinetics. To this end, we present a "dual-aptamer" scheme that uses two aptamers responding differentially to the same molecular target for drift cancellation. We identify these paired aptamers through (1) screening from the existing aptamers pool and (2) engineering the signaling behavior of the redox reporters. We demonstrate their differential signaling to ampicillin and ATP molecules and show that the aptamer pair bears common drifts in undilute goat serum. Through cancellation, sensor drift is reduced by 370-fold. Benefiting from the "differential" signaling, the recording throughput is also doubled using differential readout electronics. The authors believe the proposed technique is beneficial for long-term in vivo monitoring.