pwc-archive/papers-with-abstracts huggingface dataset:
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Authors: Andrew Churcher, Rehmat Ullah, Jawad Ahmad, Sadaqat ur Rehman, Fawad Masood, Mandar Gogate, Fehaid Alqahtani, Boubakr Nour, William J. Buchanan
Date: 2021-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, General Classification, Intrusion Detection, Multi-class Classification

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.
Discovering dependencies in complex physical systems using Neural Networks
Authors: Sachin Kasture
Date: 2021-01-27 00:00:00
Tasks: Weather Forecasting

In todays age of data, discovering relationships between different variables is an interesting and a challenging problem. This problem becomes even more critical with regards to complex dynamical systems like weather forecasting and econometric models, which can show highly non-linear behavior. A method based on mutual information and deep neural networks is proposed as a versatile framework for discovering non-linear relationships ranging from functional dependencies to causality. We demonstrate the application of this method to actual multivariable non-linear dynamical systems. We also show that this method can find relationships even for datasets with small number of datapoints, as is often the case with empirical data.
Line Graph for Weighted Networks toward Overlapping Community Discovery
Authors: Tetsuya Y oshida
Date: 2012-01-01 00:00:00

We propose generalized line graph for weighted networks toward over-
lapping community discovery from the networks. Community discovery from a net-
work has often been conducted by assigning each node in a network only to one
community. However, in real world networks, a node (e.g., user) might belong to
several communities. For undirected networks without self-loops, we propose to
generalize line graph by defining the weights in the line graph based on the weights
in the original network. Based on the line graph representation, a node can be as-
signed to more than one community by assigning the links adjacent to the node to
the corresponding communities. V arious properties of the proposed generalized line
graph are clarified, and the properties indicate that our proposal is a natural exten-
sion of the conventional line graph. Preliminary experiments are conducted over
several real-world networks, and the results indicate that the proposed generalized
line graph can improve the quality of the discovered overlapping communities.
Surface Defect Saliency of Magnetic Tile
Authors: Yibin Huang, Congying Qiu, Yue Guo, Xiaonan Wang, and Kui Yuan
Date: 2018-08-24 00:00:00
Tasks: Anomaly Detection, Defect Detection, Saliency Detection

Vision-based detection on surface defects has long postulated in the magnetic tile automation process. In this work, we introduce a real-time and multi-module neural network model called MCuePush U-Net, specifically designed for the image saliency detection of magnetic tile. We show that the model exceeds the state-of-the-art, in which it both effectively and explicitly maps multiple surface defects from low-contrast images. Our model significantly reduces time cost of machinery from 0.5s per image to 0.07s, and enhances saliency accuracy on surface defect detection.
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning
Authors: Fanfei Chen, Shi Bai, Tixiao Shan, Brendan Englot
Date: 2019-01-06 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Self-Learning

Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors uses mutual information as the basis for a cost metric, and reasons about how much information gain is associated with each action a robot can take while constructing an occupancy map from its range measurements. However, the computational cost of such an optimization scales poorly as the number of potential robot actions increases. We propose a novel approach to utilize the local structure of the environment while predicting a robot's optimal sensing action using Deep Reinforcement Learning (DRL). The learned exploration policy can select an optimal or near-optimal exploratory sensing action with improved computational efficiency. Our computational results demonstrate that the proposed method provides both efficiency and accuracy in choosing informative sensing actions.
Quarter and Full Car Models Optimisation of Passive and Active Suspension System Using Genetic Algorithm
Authors: Srikanth Raju Mudduluru, Mahmoud Chizari
Date: 2021-01-29 00:00:00

This study evaluates a suspension design of a passenger car to obtain maximum rider's comfort when the vehicle is subjected to different road profile or road surface condition. The challenge will be on finding a balance between the rider's comfort and vehicle handling to optimize design parameters. The study uses a simple passive suspension system and an active suspension model integrated with a pneumatic actuator controlled by proportional integral derivative (PID) controller in both quarter car and full car models having a different degree of freedoms (DOF) and increasing degrees of complexities. The quarter car considered as a 2-DOF model, while the full car model is a 7-DOF model. The design process set to optimise the spring stiffnesses, damping coefficients and actuator PID controller gains. For optimisation, the research featured genetic algorithm optimisation technique to obtain a balanced response of the vehicle as evaluated from the displacement, velocity and acceleration of sprung and unsprung masses along with different human comfort and vehicle performance criteria. The results revealed that the active suspension system with optimised spring stiffness, damping coefficients and PID gains demonstrated the superior riding comfort and road holding compared to a passive suspension system.
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions
Authors: D. Calvetti, B. Johnson, A. Pascarella, F. Pitolli, E. Somersalo, B. Vantaggi
Date: 2021-01-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Meditation practices have been claimed to have a positive effect on the regulation of mood and emotion for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the changes induced by meditation on human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as evidence for effectiveness of it beyond the subjective self reporting. Evidence based on real time monitoring of meditating brain by functional imaging modalities such as MEG or EEG remains a challenge. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data is first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas, and further by bootstrapping and spectral analysis to data matrices representing a random sample of power spectral densities over bandwidths corresponding to $\alpha$, $\beta$, $\gamma$, and $\theta$ bands in the spectral range. We demonstrate using linear discriminant analysis (LDA) that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that cingulate cortex, insular cortex and some of the internal structures, most notably accumbens, caudate and putamen nuclei, thalamus and amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.
Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images
Authors: Neeraj Pandey, Shobha Sundar Ram
Date: 2021-01-29 00:00:00
Tasks: Classification, General Classification, Transfer Learning

We present a framework for simulating realistic inverse synthetic aperture radar images of automotive targets at millimeter wave frequencies. The model incorporates radar scattering phenomenology of commonly found vehicles along with range-Doppler based clutter and receiver noise. These images provide insights into the physical dimensions of the target, the number of wheels and the trajectory undertaken by the target. The model is experimentally validated with measurement data gathered from an automotive radar. The images from the simulation database are subsequently classified using both traditional machine learning techniques as well as deep neural networks based on transfer learning. We show that the ISAR images offer a classification accuracy above 90% and are robust to both noise and clutter.
Cramér-Rao Bound Optimization for Joint Radar-Communication Design
Authors: Fan Liu, Ya-Feng Liu, Ang Li, Christos Masouros, Yonina C. Eldar
Date: 2021-01-29 00:00:00
Tasks: Joint Radar-Communication

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.
The Behavioral Economics of Intrapersonal Conflict: A Critical Assessment
Authors: Sebastian Krügel, Matthias Uhl
Date: 2021-01-29 00:00:00
Tasks: Decision Making

Preferences often change -- even in short time intervals -- due to either the mere passage of time (present-biased preferences) or changes in environmental conditions (state-dependent preferences). On the basis of the empirical findings in the context of state-dependent preferences, we critically discuss the Aristotelian view of unitary decision makers in economics and urge a more Heraclitean perspective on human decision-making. We illustrate that the conceptualization of preferences as present-biased or state-dependent has very different normative implications under the Aristotelian view, although both concepts are empirically hard to distinguish. This is highly problematic, as it renders almost any paternalistic intervention justifiable.
On the Space-Time Statistics of Motion Pictures
Authors: Dae Yeol Lee, Hyunsuk Ko, Jongho Kim, Alan C. Bovik
Date: 2021-01-29 00:00:00
Tasks: Optical Flow Estimation

It is well-known that natural images possess statistical regularities that can be captured by bandpass decomposition and divisive normalization processes that approximate early neural processing in the human visual system. We expand on these studies and present new findings on the properties of space-time natural statistics that are inherent in motion pictures. Our model relies on the concept of temporal bandpass (e.g. lag) filtering in LGN and area V1, which is similar to smoothed frame differencing of video frames. Specifically, we model the statistics of the differences between adjacent or neighboring video frames that have been slightly spatially displaced relative to one another. We find that when these space-time differences are further subjected to locally pooled divisive normalization, statistical regularities (or lack thereof) arise that depend on the local motion trajectory. We find that bandpass and divisively normalized frame-differences that are displaced along the motion direction exhibit stronger statistical regularities than for other displacements. Conversely, the direction-dependent regularities of displaced frame differences can be used to estimate the image motion (optical flow) by finding the space-time displacement paths that best preserve statistical regularity.
One-parameter robust global frequency estimator for slowly varying amplitude and noisy oscillations
Authors: Michael Ruderman
Date: 2021-01-29 00:00:00

Robust online estimation of oscillation frequency belongs to classical problems of system identification and adaptive control. The given harmonic signal can be noisy and with varying amplitude at the same time, as in the case of damped vibrations. A novel robust frequency-estimation algorithm is proposed here, motivated by the existing globally convergent frequency estimator. The advantage of the proposed estimator is in requiring one design parameter only and being robust against measurement noise and initial conditions. The proven global convergence also allows for slowly varying amplitudes, which is useful for applications with damped oscillations or additionally shaped harmonic signals. The proposed analysis is simple and relies on an averaging theory of the periodic signals. Our results show an exponential convergence rate, which depends, analytically, on the sought frequency, adaptation gain and oscillation amplitude. Numerical and experimental examples demonstrate the robustness and efficiency of the proposed estimator for signals with slowly varying amplitude and noise.
Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids
Authors: Thom S. Badings, Arnd Hartmanns, Nils Jansen, Marnix Suilen
Date: 2021-01-29 00:00:00

We study a smart grid with wind power and battery storage. Traditionally, day-ahead planning aims to balance demand and wind power, yet actual wind conditions often deviate from forecasts. Short-term flexibility in storage and generation fills potential gaps, planned on a minutes time scale for 30-60 minute horizons. Finding the optimal flexibility deployment requires solving a semi-infinite non-convex stochastic program, which is generally intractable to do exactly. Previous approaches rely on sampling, yet such critical problems call for rigorous approaches with stronger guarantees. Our method employs probabilistic model checking techniques. First, we cast the problem as a continuous-space Markov decision process with discretized control, for which an optimal deployment strategy minimizes the expected grid frequency deviation. To mitigate state space explosion, we exploit specific structural properties of the model to implement an iterative exploration method that reuses pre-computed values as wind data is updated. Our experiments show the method's feasibility and versatility across grid configurations and time scales.
Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities
Authors: Matthias Pezzutto, Luca Schenato, Subhrakanti Dey
Date: 2021-01-29 00:00:00

In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e.g. with the latest wireless technologies such as 5G and WiFi. To this end we introduce a general model where packet arrival probabilities are determined based on the received Signal-to-Interference-and-Noise Ratio and with two different receivers design schemes, one implementing standard multi-packet reception technique and one implementing Successive Interference Cancellation decoding algorithm in addition. Then we cast the power allocation problem as an optimization task where the mean error covariance at the remote estimator is minimized, while penalizing the mean transmission power consumption. For the infinite-horizon problem we show the existence of a stationary optimal policy, while for the finite-horizon case we derive some structural properties under the special scenario where the overall system to be estimated can be seen as a set of independent subsystems. Numerical simulations illustrate the improvement given by the proposed receivers over orthogonal schemes that schedules only one sensor transmission at a time in order to avoid interference.
Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems
Authors: Ashkan Jasour, Allen Wang, Brian C. Williams
Date: 2021-01-29 00:00:00

In this paper, we address the problem of uncertainty propagation through nonlinear stochastic dynamical systems. More precisely, given a discrete-time continuous-state probabilistic nonlinear dynamical system, we aim at finding the sequence of the moments of the probability distributions of the system states up to any desired order over the given planning horizon. Moments of uncertain states can be used in estimation, planning, control, and safety analysis of stochastic dynamical systems. Existing approaches to address moment propagation problems provide approximate descriptions of the moments and are mainly limited to particular set of uncertainties, e.g., Gaussian disturbances. In this paper, to describe the moments of uncertain states, we introduce trigonometric and also mixed-trigonometric-polynomial moments. Such moments allow us to obtain closed deterministic dynamical systems that describe the exact time evolution of the moments of uncertain states of an important class of autonomous and robotic systems including underwater, ground, and aerial vehicles, robotic arms and walking robots. Such obtained deterministic dynamical systems can be used, in a receding horizon fashion, to propagate the uncertainties over the planning horizon in real-time. To illustrate the performance of the proposed method, we benchmark our method against existing approaches including linear, unscented transformation, and sampling based uncertainty propagation methods that are widely used in estimation, prediction, planning, and control problems.
Robustness of the international oil trade network under targeted attacks to economies
Authors: N. Wei, W. -J. Xie, W. -X. Zhou
Date: 2021-01-26 00:00:00

In the international oil trade network (iOTN), trade shocks triggered by extreme events may spread over the entire network along the trade links of the central economies and even lead to the collapse of the whole system. In this study, we focus on the concept of "too central to fail" and use traditional centrality indicators as strategic indicators for simulating attacks on economic nodes, and simulates various situations in which the structure and function of the global oil trade network are lost when the economies suffer extreme trade shocks. The simulation results show that the global oil trade system has become more vulnerable in recent years. The regional aggregation of oil trade is an essential source of iOTN's vulnerability. Maintaining global oil trade stability and security requires a focus on economies with greater influence within the network module of the iOTN. International organizations such as OPEC and OECD established more trade links around the world, but their influence on the iOTN is declining. We improve the framework of oil security and trade risk assessment based on the topological index of iOTN, and provide a reference for finding methods to maintain network robustness and trade stability.
Newtonalized Orthogonal Matching Pursuit for Linear Frequency Modulated Pulse Frequency Agile Radar
Authors: Jiang Zhu, Honghui Guo, Ning Zhang, Chunyi Song, Zhiwei Xu
Date: 2021-01-29 00:00:00

The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow. In this way, high range resolution profiles (HRRP) can be obtained without increasing the hardware system cost. Furthermore, the agility provides improved both robustness to jamming and spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP-FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, a postprocessing algorithm is then proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP-FAR.
Sparsity Based Autoencoders for Denoising Cluttered Radar Signatures
Authors: Shobha Sundar Ram, Shelly Vishwakarma, Akanksha Sneh, Kainat Yasmeen
Date: 2021-01-29 00:00:00
Tasks: Denoising

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target dependent and independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating wall clutter in indoor radar images. The algorithm relies on the availability of clean images and corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband RF imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Further, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal to noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of -10dB and label mismatch error of 50%.