pwc-archive/papers-with-abstracts huggingface dataset:
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection
Authors: Yufan Zeng, Jiashan Tang
Date: 2021-06-18 00:00:00
Proceeding: Applied Sciences 2021 6
Tasks: Fraud Detection, Graph Neural Network, Node Classification

Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.
A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world
Authors: Nicola Bellomo, Richard Bingham, Mark A. J. Chaplain, Giovanni Dosi, Guido Forni, Damian A. Knopoff, John Lowengrub, Reidun Twarock, Maria Enrica Virgillito
Date: 2020-06-06 00:00:00
Tasks: Epidemiology, Virology

This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called \textit{SARS--CoV--2 Severe Acute Respiratory Syndrome, corona virus n.2}. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focused also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.
Quantifying Software Reliability via Model-Counting
Authors: Samuel Teuber, Alexander Weigl
Date: 2021-08-19 00:00:00
Proceeding: International Conference on Quantitative Evaluation of Systems 2021 8
Tasks: model

Critical software should be verified. But how to handle the situation when a proof for the functional correctness could not be established? In this case, an assessment of the software is required to estimate the risk of using the software.

In this paper, we contribute to the assessment of critical software with a formal approach to measure the reliability of the software against its functional specification. We support bounded C-programs precisely where the functional specification is given as assumptions and assertions within the source code. We count and categorize the various program runs to compute the reliability as the ratio of failing program runs (violating an assertion) to all terminating runs. Our approach consists of a preparing program translation, the reduction of C-program into SAT instances via software-bounded model-checker (cbmc), and precise or approximate model-counting providing a reliable assessment. We evaluate our prototype implementation on over 24 examples with different model-counters. We show the feasibility of our pipeline and benefits against competitors.
A Deep Learning Approach for COVID-19 & Viral Pneumonia Screening with X-ray Images
Authors: FAIZAN AHMED, SYED AHMAD CHAN BUKHARI, FAZEL KESHTKAR
Date: 2021-01-02 00:00:00
Proceeding: Digital Government: Research and Practice 2021 1
Tasks: Diagnostic

Beginning in December 2019, the spread of the novel Coronavirus (COVID-19) has exposed weaknesses in healthcare systems across the world. To sufficiently contain the virus, countries have had to carry out a set of extraordinary measures, including exhaustive testing and screening for positive cases of the disease. It is crucial to detect and isolate those who are infected as soon as possible to keep the virus contained. However, in countries and areas where there are limited COVID-19 testing kits, there is an urgent need for alternative diagnostic measures. The standard screening method currently used for detecting COVID-19 cases is RT-PCR testing, which is a very time-consuming, laborious, and complicated manual process. Given that nearly all hospitals have X-ray imaging machines, it is possible to use X-rays to screen for COVID-19 without the dedicated test kits and separate those who are infected and those who are not. In this study, we applied deep convolutional neural networks on chest X-rays to determine this phenomena. The proposed deep learning model produced an average classification accuracy of 90.64% and F1-Score of 89.8% after performing 5-fold cross-validation on a multi-class dataset consisting of COVID-19, Viral Pneumonia, and normal X-ray images.
Latency-Aware Multi-antenna SWIPT System with Battery-Constrained Receivers
Authors: Dileep Kumar, Onel L. Alcaraz López, Satya Krishna Joshi, Antti Tölli
Date: 2021-12-10 00:00:00

Power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) is considered in a multi-user multiple-input-single-output broadcast scenario. Specifically, we focus on jointly configuring the transmit beamforming vectors and receive PS ratios to minimize the total transmit energy of the base station under the user-specific latency and energy harvesting (EH) requirements. The battery depletion phenomenon is avoided by preemptively incorporating information regarding the receivers' battery state and EH fluctuations into the resource allocation design. The resulting time-average sum-power minimization problem is temporally correlated, non-convex (including mutually coupled latency-battery queue dynamics), and in general intractable. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the original problem into a sequence of deterministic and independent subproblems, which are then solved via two alternative approaches: i) semidefinite relaxation combined with fractional programming, and ii) successive convex approximation. Furthermore, we design a low-complexity closed-form iterative algorithm exploiting the Karush-Kuhn-Tucker optimality conditions for a specific scenario with delay bounded batteryless receivers. Numerical results provide insights on the robustness of the proposed design to realize an energy-efficient SWIPT system while ensuring latency and EH requirements in a time dynamic mobile access network.
System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks
Authors: Manan Gupta, Ian P. Roberts, Jeffrey G. Andrews
Date: 2021-12-10 00:00:00

Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave(mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both FD-IAB and half-duplex(HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB,FD-IAB can improve throughput by 8x and reduce latency by 4x for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.
An Adaptive Observer for Uncertain Linear Time-Varying Systems with Unknown Additive Perturbations
Authors: Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Alberto Isidori
Date: 2021-12-10 00:00:00

In this paper we are interested in the problem of adaptive state observation of linear time-varying (LTV) systems where the system and the input matrices depend on unknown time-varying parameters. It is assumed that these parameters satisfy some known LTV dynamics, but with unknown initial conditions. Moreover, the state equation is perturbed by an additive signal generated from an exosystem with uncertain constant parameters. Our main contribution is to propose a globally convergent state observer that requires only a weak excitation assumption on the system.
An Improved Fractional-Order Active Disturbance Rejection Control: Performance Analysis and Experiment Verification
Authors: Bolin Li, Lijun Zhu
Date: 2021-12-10 00:00:00

This paper presents an improved active disturbance rejection control scheme (IFO-ADRC) with an improved fractional-order extended state observer (IFO-ESO). The structural information of the system is utilized in IFO-ESO rather than buried as in the typical fractional-order extended state observer (FO-ESO) and help significantly improve the performance of IFO-ESO and closed-loop system. Compared with the integer-order active disturbance rejection controller (IO-ADRC), the auxiliary tracking controller of IFO-ADRC has a simpler form and fewer parameters need to be tuned. Frequency-domain analysis shows that IFO-ESO has better performance over the larger frequency band than FO-ESO, and time-domain simulation shows that IFO-ADRC has better transient performance and is more robust against the parameter variations than traditional fractional-order active disturbance rejection controller (FO-ADRC) and IO-ADRC. The IFO-ADRC is applied to permanent magnet synchronous motor (PMSM) servo control system and demonstrates its capability in the real-world application.
Mean-Square Stability and Stabilizability Analyses of LTI Systems Under Spatially Correlated Multiplicative Perturbations
Authors: Jianqi Chen, Tian Qi, Jie Chen
Date: 2021-12-10 00:00:00

In this paper, we investigate the mean-square stability and stabilizability problems for linear time-invariant systems under stochastic spatially correlated multiplicative uncertainties.
Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems
Authors: Guangchun Ruan, Zekuan Yu, Shutong Pu, Songtao Zhou, Haiwang Zhong, Le Xie, Qing Xia, Chongqing Kang
Date: 2021-12-10 00:00:00

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, public policy, and educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Typical methods are reformulated and standardized in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Here the fluctuation index and probabilistic baseline are proposed for the first time to consider data fluctuation and estimation uncertainty. Based on these, we conduct three empirical studies on the U.S. power systems, and share new solutions and unexpected findings to address the issues of public concerns. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.
High Voltage Shore Connection Systems: Grounding Resistance Selection and Short Circuit Currents Evaluation
Authors: Fabio D'Agostino, Samuele Grillo, Roberto Infantino, Enrico Pons
Date: 2021-12-09 00:00:00

Cold ironing represents an effective solution to remove air polluting emissions from ports. The high voltage shore connection system is the key enabling facility that allows to provide power from the shore side electrical system to the ship. The design of the shore connection needs a comprehensive assessment of the fault currents in different operating scenarios. International standards require the neutral point of the shore connection transformer be equipped with a neutral grounding resistor. Its value has to be defined to guarantee safety and protection of equipment and personnel in case of single phase-to-ground faults. Moreover, three-phase short circuits need to be considered to size equipment and protection devices. A crucial role is played by the frequency converter control system, required to adapt the mains frequency to the frequency of the ship. In this work, a complete electro-magnetic dynamic model of the high voltage shore connection and of the on-board power system has been developed, including frequency converter, shore-side transformer, connection MV cables and power system of the ship, to analyze in detail the behavior of the system in case of single phase-to-ground fault and three-phase short circuit, taking into account relevant standards and best practices.
Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm
Authors: Pablo Krupa, Ignacio Alvarado, Daniel Limon, Teodoro Alamo
Date: 2020-08-20 00:00:00
Tasks: Model Predictive Control

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature.
Using Machine Learning to Find New Density Functionals
Authors: Bhupalee Kalita, Kieron Burke
Date: 2021-12-04 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning has now become an integral part of research and innovation. The field of machine learning density functional theory has continuously expanded over the years while making several noticeable advances. We briefly discuss the status of this field and point out some current and future challenges. We also talk about how state-of-the-art science and technology tools can help overcome these challenges. This draft is a part of the "Roadmap on Machine Learning in Electronic Structure" to be published in Electronic Structure (EST).
Option Pricing with State-dependent Pricing Kernel
Authors: Chen Tong, Peter Reinhard Hansen, Zhuo Huang
Date: 2021-12-10 00:00:00

We introduce a new volatility model for option pricing that combines Markov switching with the Realized GARCH framework. This leads to a novel pricing kernel with a state-dependent variance risk premium and a pricing formula for European options, which is derived with an analytical approximation method. We apply the Markov switching Realized GARCH model to S&P 500 index options from 1990 to 2019 and find that investors' aversion to volatility-specific risk is time-varying. The proposed framework outperforms competing models and reduces (in-sample and out-of-sample) option pricing errors by 15% or more.
Realized GARCH, CBOE VIX, and the Volatility Risk Premium
Authors: Peter Reinhard Hansen, Zhuo Huang, Chen Tong, Tianyi Wang
Date: 2021-12-10 00:00:00

We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models.
A New Approach to Image Compression in Industrial Internet of Things
Authors: Nahid Hajizadeh, Pirooz Shamsinejad, Reza Javidan
Date: 2021-12-10 00:00:00
Tasks: Image Compression

Applying image sensors in automation of Industrial Internet of Things (IIoT) technology is on the rise, day by day. In such companies, a large number of high volume images are transmitted at any moment; therefore, a significant challenge is reducing the amount of transmitted information and consequently bandwidth without reducing the quality of images. Image compression in sensors, in this regard, will save bandwidth and speed up data transmitting. There are several pieces of research in image compression for sensor networks, but, according to the nature of image transfer in IIoT, there is no study in this particular field. In this paper, it is for the first time that a new reusable technique to improve productivity in image compression is introduced and applied. To do this, a new adaptive lossy compression technique to compact sensor-generated images in IIoT by using K- Means++ and Intelligent Embedded Coding (IEC) as our novel approach, is presented. The new method is based on the colour of pixels so that pixels with the same or nearly the same colours are clustered around a centroid and finally, the colour of the pixels will be encoded. The experiments are based on a reputable image dataset from a real smart greenhouse; i.e. KOMATSUNA. The evaluation results reveal that, with the same compression rate, our approach compresses images with higher quality in comparison with other methods such as K-means, fuzzy C-means and fuzzy C-means++ clustering.
Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation
Authors: Huiping Huang, Hing Cheung So, Abdelhak M. Zoubir
Date: 2021-12-10 00:00:00
Tasks: Direction of Arrival Estimation

The problem of off-grid direction-of-arrival (DOA) estimation is investigated. We develop a grid-based method to jointly estimate the closest spatial frequency (the sine of DOA) grids, and the gaps between the estimated grids and the corresponding frequencies. By using a second-order Taylor approximation, the data model under the framework of joint-sparse representation is formulated. We point out an important property of the signals of interest in the model, namely the proportionality relationship, which is empirically demonstrated to be useful in the sense that it increases the probability of the mixing matrix satisfying the block restricted isometry property. Simulation examples demonstrate the effectiveness and superiority of the proposed method against several state-of-the-art grid-based approaches.
A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method
Authors: Sen Zhao, Yong Zhang, Shang Wang, Beitong Zhou, Cheng Cheng
Date: 2021-12-10 00:00:00
Tasks: Prediction

Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features. This paper proposes a method for RUL prediction which depends on a trend feature representing the overall time sequence of degradation. Complete ensemble empirical mode decomposition, followed by a reconstruction procedure, is created to build the trend features. The probability distribution of sensors' measurement learned by conditional neural processes is used to evaluate the trend features. With the best trend feature, a data-driven model using long short-term memory is developed to predict the RUL. To prove the effectiveness of the proposed method, experiments on a benchmark C-MAPSS dataset are carried out and compared with other state-of-the-art methods. Comparison results show that the proposed method achieves the smallest root mean square values in prediction of all RUL.
On the Dilution of Precision for Time Difference of Arrival with Station Deployment
Authors: Fengyun Zhang, Hao Li, Yulong Ding, Shuang-Hua Yang, Li Yang
Date: 2021-12-10 00:00:00
Tasks: Object Tracking, TAG

The paper aims to reveal the relationship between the performance of moving object tracking algorithms and the tracking anchors (station) deployment. The Dilution of Precision (DoP) for Time difference of arrival (TDoA) technique with respect to anchor deployment is studied. Linear estimator and non-linear estimator are used for TDoA algorithms. The research findings are: for the linear estimator, the DoP attain a lower value when other anchors are scattered around a central anchor; for the nonlinear estimator, the DoP is optimal when the anchors are scattered around the target tag. Experiments on both of the algorithms are conducted, targeting the location precision related to the anchors' deployment, with practical situations for tracking moving objects integrated with a Kalman Filter (KF) in an Ultra-Wide Band (UWB) based real-time localization system. The work provides a guideline for deploying anchors in UWB-based tracking systems.