pwc-archive/papers-with-abstracts huggingface dataset:
A novel method for recommendation systems using invasive weed optimization
Authors: Fahimeh Soltaninejad, Amir Jalaly Bidgoly
Date: 2021-06-05 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

One of the popular approaches in recommendation systems is Collaborative Filtering (CF). The most significant step in CF is choosing the appropriate set of users. For this purpose, similarity measures are usually used for computing the similarity between a specific user and the other users. This paper proposes a new invasive weed optimization (IWO) based CF approach that uses users' context to identify important and effective users set. By using a newly defined similarity measure based on both rating values and a measure values called confidence, the proposed approach calculates the similarity between users and thus identifies and filters the most similar users to a specific user. It then uses IWO to calculate the importance degree of users and finally, by using the identified important users and their importance degrees it predicts unknown ratings. To evaluate the proposed method, several experiments have been performed on two known real world datasets and the results show that the proposed method improves the state of the art results up to 15% in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
Auditing Source Diversity Bias in Video Search Results Using Virtual Agents
Authors: Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa
Date: 2021-06-04 00:00:00
Tasks: Diversity

We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube's major competitors such as Vimeo or Dailymotion do not appear in the sampled Google's video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google's search results.
Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees
Authors: YiXuan Wang, Chao Huang, Zhaoran Wang, Zhilu Wang, Qi Zhu
Date: 2021-06-06 00:00:00

In the current control design of safety-critical autonomous systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the required properties (e.g., safety) are satisfied. However, due to the increasing system complexity and the fundamental hardness of designing a controller with formal guarantees, such an open-loop process of design-then-verify often results in many iterations and fails to provide the necessary guarantees. In this paper, we propose a correct-by-construction control learning framework that integrates the verification into the control design process in a closed-loop manner, i.e., design-while-verify. Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching. We formulate an optimization problem based on such metrics for tuning the controller parameters, and develop an approximated gradient descent algorithm with a difference method to solve the optimization problem and learn the controller. The learned controller is formally guaranteed to meet the required reach-avoid property. By treating verifiability as a first-class objective and effectively leveraging the verification results during the control learning process, our approach can significantly improve the chance of finding a control design with formal property guarantees. This is demonstrated via a set of experiments on both linear and non-linear systems that use model-based or neural network based controllers.
Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior
Authors: Zheng Wang, Satoshi Suga, Edric John Cruz Nacpil, Bo Yang, Kimihiko Nakano
Date: 2021-06-07 00:00:00
Tasks: Steering Control

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the physiological status of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane changes. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.
Drone-based AI and 3D Reconstruction for Digital Twin Augmentation
Authors: Alex To, Maican Liu, Muhammad Hazeeq Bin Muhammad Hairul, Joseph G. Davis, Jeannie S. A. Lee, Henrik Hesse, Hoang D. Nguyen
Date: 2021-05-20 00:00:00
Tasks: 3D Reconstruction, Defect Detection

Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.
Closed-Loop Wireless Power Transfer with Adaptive Waveform and Beamforming: Design, Prototype, and Experiment
Authors: Shanpu Shen, JungHoon Kim, Bruno Clerckx
Date: 2021-06-07 00:00:00

In this paper, we design, prototype, and experiment a closed-loop radiative wireless power transfer (WPT) system with adaptive waveform and beamforming using limited feedback. Spatial and frequency domains are exploited by jointly utilizing multi-sine waveform and multi-antenna beamforming at the transmitter in WPT system to adapt to the multipath fading channel and boost the output dc power. A closed-loop architecture based on a codebook design and a low complexity over-the-air limited feedback using an IEEE 802.15.4 RF interface is proposed. The codebook consists of multiple codewords where each codeword represents particular waveform and beamforming. The transmitter sweeps through the codebook and then the receiver feeds back the index of the optimal codeword, so that the waveform and beamforming can be adapted to the multipath fading channel to maximize the output dc power without requiring explicit channel estimation and the knowledge of accurate Channel State Information. The proposed closed-loop WPT with adaptive waveform and beamforming using limited feedback is prototyped using a Software Defined Radio equipment and measured in a real indoor environment. The measurement results show that the proposed closed-loop WPT with adaptive waveform and beamforming can increase the output dc power by up to 14.7 dB compared with the conventional single-tone and single-antenna WPT system.
Task-driven Semantic Coding via Reinforcement Learning
Authors: Xin Li, Jun Shi, Zhibo Chen
Date: 2021-06-07 00:00:00
Tasks: Face Detection, License Plate Detection, Medical Diagnosis, Quantization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have been studied for this purpose due to their inherent end-to-end optimization mechanism. However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process. Therefore, it is still attractive and challenging to implement task-driven semantic coding with the traditional hybrid coding framework, which should still be widely used in practical industry for a long time. To solve this challenge, we design semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Instead of directly integrating the semantic fidelity metric into traditional hybrid coding framework, we implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL). We formulate the semantic bit allocation problem as a Markov decision process (MDP) and utilize one RL agent to automatically determine the quantization parameters (QPs) for different coding units (CUs) according to the task-driven semantic fidelity metric. Extensive experiments on different tasks, such as classification, detection and segmentation, have demonstrated the superior performance of our approach by achieving an average bitrate saving of 34.39% to 52.62% over the High Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related semantic fidelity.
Improved Method for Dealing with Discontinuities in Power System Transient Simulation Based on Frequency Response Optimized Integrators Considering Second Order Derivative
Authors: Sheng Lei, Alexander Flueck
Date: 2021-06-07 00:00:00

Potential disagreement in the result induced by discontinuities is revealed in this paper between a novel power system transient simulation scheme using numerical integrators considering second order derivative and conventional ones using numerical integrators considering first order derivative. The disagreement is due to the formula of the different numerical integrators. An improved method for dealing with discontinuities in the novel transient simulation scheme is proposed to resolve the disagreement. The effectiveness of the improved method is demonstrated and verified via numerical case studies. Although the disagreement is studied on and the improved method is proposed for a particular transient simulation scheme, similar conclusions also apply to other ones using numerical integrators considering high order derivative.
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
Authors: Daniel Korzekwa, Roberto Barra-Chicote, Szymon Zaporowski, Grzegorz Beringer, Jaime Lorenzo-Trueba, Alicja Serafinowicz, Jasha Droppo, Thomas Drugman, Bozena Kostek
Date: 2020-12-29 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech

This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically derives optimal syllable-level representation from frame-level and phoneme-level audio features. Training this model is challenging because of the limited amount of incorrect stress patterns. To solve this problem, we propose to augment the training set with incorrectly stressed words generated with Neural TTS. Combining both techniques achieves 94.8% precision and 49.2% recall for the detection of incorrectly stressed words in L2 English speech of Slavic and Baltic speakers.
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios
Authors: Emiru Tsunoo, Kentaro Shibata, Chaitanya Narisetty, Yosuke Kashiwagi, Shinji Watanabe
Date: 2021-06-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Generative Adversarial Network, Pseudo Label, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Although end-to-end automatic speech recognition (E2E ASR) has achieved great performance in tasks that have numerous paired data, it is still challenging to make E2E ASR robust against noisy and low-resource conditions. In this study, we investigated data augmentation methods for E2E ASR in distant-talk scenarios. E2E ASR models are trained on the series of CHiME challenge datasets, which are suitable tasks for studying robustness against noisy and spontaneous speech. We propose to use three augmentation methods and thier combinations: 1) data augmentation using text-to-speech (TTS) data, 2) cycle-consistent generative adversarial network (Cycle-GAN) augmentation trained to map two different audio characteristics, the one of clean speech and of noisy recordings, to match the testing condition, and 3) pseudo-label augmentation provided by the pretrained ASR module for smoothing label distributions. Experimental results using the CHiME-6/CHiME-4 datasets show that each augmentation method individually improves the accuracy on top of the conventional SpecAugment; further improvements are obtained by combining these approaches. We achieved 4.3\% word error rate (WER) reduction, which was more significant than that of the SpecAugment, when we combine all three augmentations for the CHiME-6 task.
Multi-agent Battery Storage Management using MPC-based Reinforcement Learning
Authors: A. Bahari Kordabad, W. Cai, S. Gros
Date: 2021-06-07 00:00:00
Tasks: Management, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present the use of Model Predictive Control (MPC) based on Reinforcement Learning (RL) to find the optimal policy for a multi-agent battery storage system. A time-varying prediction of the power price and production-demand uncertainty are considered. We focus on optimizing an economic objective cost while avoiding very low or very high state of charge, which can damage the battery. We consider the bounded power provided by the main grid and the constraints on the power input and state of each agent. A parametrized MPC-scheme is used as a function approximator for the deterministic policy gradient method and RL optimizes the closed-loop performance by updating the parameters. Simulation results demonstrate that the proposed method is able to tackle the constraints and deliver the optimal policy.
Performance assessment of Synchronous Condensers vs Voltage Source Converters providing grid-forming functions
Authors: Dorsan Lepour, Mario Paolone, Guillaume Denis, Carmen Cardozo, Thibault Prevost, Emeline Guiu
Date: 2021-06-07 00:00:00

Having sufficient grid-forming sources is one of the necessary conditions to guarantee the stability in a power system hosting a very large share of inverter-based generation. The grid-forming function has been historically fulfilled by synchronous machines. However, with the appropriate control, it can also be provided by voltage source converters (VSC). This work presents a comparison between two technologies with grid-forming capability: the VSC with a grid-forming control coupled with an adequate energy storage system, and the synchronous condensers (SC). Both devices are compared regarding their inertial response, as well as their contribution to the system strength and short-circuit current for an equivalent capacity expressed in terms of apparent power and inertial reserve. Their behaviour following grid disturbances is assessed through time-domain simulations based on detailed electromagnetic transient (EMT) models. The results show that both devices achieve similar performance in the time-scale of seconds. For shorter time-windows, however, they present a different behavior: the SC ensures a better stiffness in the first tens of ms following the disturbance, while the VSC offers a faster resynchronization.
Set-Estimation based Networked Model Predictive Control for Energy Management of Faulty Microgrids
Authors: Quanwei Qiu, Fuwen Yang, Yong Zhu
Date: 2021-06-07 00:00:00
Tasks: energy management, Management, Model Predictive Control

This paper addresses the issue of power flow control for partially faulty microgrids. In microgrid control systems, faults may occur in both electrical and communication layers. This may have severe effects on the operation of microgrids. In addition, disturbances always coexist with faults in microgrids, which may further deteriorate system performance. To address the faults and disturbances simultaneously, a model predictive control (MPC) method based on set-membership estimation (SME) that transmits information via a communication network is proposed. When electrical devices are nonfunctional or communication failures occur, the corresponding system states will become unavailable. To this end, the SME method is employed to estimate the states with the existence of unknown-but-bounded process and measurement disturbances. The networked MPC method is designed to schedule the power dispatch by using the forecasts of photovoltaic (PV) generation and load demand. With these two methods, the fault-tolerant control can be achieved. Further, a deviation compensation method is proposed to compensate for the forecast errors. The effectiveness of the proposed control strategy is demonstrated through wireless communication tests using Raspberry Pis.
Power System Transient Modeling and Simulation using Integrated Circuit
Authors: Xiang Zhang, Renchang Dai, Peng Wei, Yijing Liu, Guangyi Liu, Zhiwei Wang
Date: 2021-06-06 00:00:00
Tasks: Numerical Integration

Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system. Traditionally, transient stability analysis methods have been developed using time domain simulation by means of numerical integration method. In this paper, a new approach is proposed to model power systems as an integrated circuit and simulate the power system dynamic behavior by integrated circuit simulator. The proposed method modeled power grid, generator, governor, and exciter with high fidelity. The power system dynamic simulation accuracy and efficiency of the proposed approach are verified and demonstrated by case study on an IEEE standard system.
Efficient Secure State Estimation against Sparse Integrity Attack for System with Non-derogatory Dynamics
Authors: Zishuo Li, Yilin Mo
Date: 2021-06-06 00:00:00
Tasks: State Estimation

We consider the problem of estimating the state of a time-invariant linear Gaussian system in the presence of integrity attacks. The attacker can compromise $p$ out of $m$ sensors, the set of which is fixed over time and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that all the unstable eigenvalues of system matrix $A$ have geometric multiplicity 1 (unstable part of $A$ is non-derogatory), we propose a secure estimation scheme that is resilient to integrity attack as long as the system is $2p$-sparse detectable, which is proved to be the fundamental limit of secure dynamic estimation. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted to trade-off between performance with and without attack. Furthermore, the detectability condition checking in the designing phase and the estimation computing in the online operating phase are both computationally efficient. A numerical example is provided to corroborate the results and illustrate the performance of the proposed estimator.
Consensus Analysis over Clustered Networks of Multi-Agent Systems under External Disturbances
Authors: Thiem V. Pham, Quynh T. T. Nguyen
Date: 2021-06-05 00:00:00

This paper studies a consensus problem of multi-agent systems subjected to external disturbances over the clustered network. It considers that the agents are divided into several clusters. They are almost all the time isolated one from another, which has a directed spanning tree. The goal of agents achieves a common value. To support interaction between clusters with a minimum exchange of information, we consider that each cluster has an agent, who can exchange information to any agents outside of its cluster at some discrete instants of time. Our main contribution proposes a consensus protocol, which takes into account the continuous-time communications among agents inside the clusters and discrete-time communication information across clusters. Accordingly, the consensus and the robust $\mathcal{H}_{\infty}$ consensus over the clustered network are respectively analyzed. Thanks to results from matrix theory and algebraic graph theory, we show that the proposed control protocols can solve the problems mentioned above. Finally, a numerical example is given to show the effectiveness of the proposed theoretical results.
Electricity-Aware Bid Format for Heat Commitment and Dispatch
Authors: Lesia Mitridati, Pascal Van Hentenryck, Jalal Kazempour
Date: 2020-05-06 00:00:00
Tasks: valid

Coordination between heat and electricity markets is essential to achieve a cost-effective and efficient operation of the overall energy system. In the current sequential market practice, the heat market is cleared before the electricity market, and has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an ``electricity-aware'' bid format for the coordination of heat and electricity systems. This novel market mechanism defines heat bids conditionally on day-ahead electricity prices. Prior to clearing heat and electricity markets, the proposed bid selection mechanism selects the ``valid'' bids which minimize the operating cost of the heat system, while anticipating heat and electricity market clearing.} This mechanism is modeled as a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish electricity and heat system, and show that the proposed bid selection mechanism yields a 4.5\% reduction in total operating cost of heat and electricity systems compared to the existing market-clearing procedure, while reducing the financial losses of combined heat and power plants and heat pumps due to invalid bids by up to 20.3 million euros.