pwc-archive/papers-with-abstracts huggingface dataset:
Addressing the Selection Bias in Voice Assistance: Training Voice Assistance Model in Python with Equal Data Selection
Authors: Kashav Piya, Srijal Shrestha, Cameran Frank, Estephanos Jebessa, Tauheed Khan Mohd
Date: 2022-12-20 00:00:00
Tasks: Information Retrieval, Retrieval, Selection bias

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.
Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown
Authors: Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Date: 2022-12-12 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning

Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception of different view points, namely cooperative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total volume of information received from others, to limit communication costs and message processing time. For this, we propose a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different generative models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm, and compared them to a broadcasting policy and a policy randomly selecting areas. In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL. Experiments were conducted in the driving simulator CARLA. Our best models reached on average a gain of 25% of the total complementary information, while only requesting about 5% of the ego-vehicle's perceptual field. This trade-off is adjustable through the interpretable hyperparameters of our reward function.
Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos
Authors: Khalid Alnajjar, Mika Hämäläinen, Shuo Zhang
Date: 2022-12-15 00:00:00

We present the first openly available multimodal metaphor annotated corpus. The corpus consists of videos including audio and subtitles that have been annotated by experts. Furthermore, we present a method for detecting metaphors in the new dataset based on the textual content of the videos. The method achieves a high F1-score (62\%) for metaphorical labels. We also experiment with other modalities and multimodal methods; however, these methods did not out-perform the text-based model. In our error analysis, we do identify that there are cases where video could help in disambiguating metaphors, however, the visual cues are too subtle for our model to capture. The data is available on Zenodo.
A gallery of diagonal stability conditions with structured matrices (and review papers)
Authors: Zhiyong Sun
Date: 2023-01-01 00:00:00

This note presents a summary and review of various conditions and characterizations for matrix stability (in particular diagonal matrix stability) and matrix stabilizability.
Deep R Programming
Authors: Marek Gagolewski
Date: 2022-12-29 00:00:00

Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning. This textbook is a non-profit project. Its online and PDF versions are freely available at <https://deepr.gagolewski.com/>.
Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems
Authors: Hyunsoo Lee, Soohyun Park, Won Joon Yun, Soyi Jung, Joongheon Kim
Date: 2022-12-31 00:00:00
Tasks: Deep Reinforcement Learning, Unity

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.
Hyperuniform disordered parametric loudspeaker array
Authors: Kun Tang, Yuqi Wang, Shaobo Wang, Da Gao, Haojie Li, Xindong Liang, Patrick Sebbah, Yibin Li, Jin Zhang, Junhui Shi
Date: 2023-01-03 00:00:00

A steerable parametric loudspeaker array is known for its directivity and narrow beam width. However, it often suffers from the grating lobes due to periodic array distributions. Here we propose the array configuration of hyperuniform disorder, which is short-range random while correlated at large scales, as a promising alternative distribution of acoustic antennas in phased arrays. Angle-resolved measurements reveal that the proposed array suppresses grating lobes and maintains a minimal radiation region in the vicinity of the main lobe for the primary frequency waves. These distinctive emission features benefit the secondary frequency wave in canceling the grating lobes regardless of the frequencies of the primary waves. Besides that, the hyperuniform disordered array is duplicatable, which facilitates extra-large array design without any additional computational efforts.
Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions
Authors: David Theodor Machacek, Stijn van Dooren, Thomas Huber, Christopher Onder
Date: 2023-01-03 00:00:00
Tasks: energy management, Management, Model Predictive Control

This paper presents an online-capable controller for the energy management system of a parallel hybrid electric vehicle based on model predictive control. Its task is to minimize the vehicle's fuel consumption along a predicted driving mission by calculating the distribution of the driver's power request between the electrical and the combustive part of the powertrain, and by choosing the driving mode, which depends on the vehicle's clutch state. The inclusion of the clutch state in a model predictive control structure is not trivial because the underlying optimization problem becomes a mixed-integer program as a consequence. Using Pontryagin's Minimum Principle and a simplified vehicle model, it is possible to prove that a drive cycle-dependent critical power request Pcrit exists, which uniquely separates the optimal driving mode. Based on this result, a learning algorithm is proposed to determine Pcrit during the operation of the vehicle. The learning algorithm is incorporated into a multi-level controller structure and the working principle of the resulting multi-level learning-based model predictive controller is analyzed in detail using two realistic driving missions. A comparison to the solution obtained by Dynamic Programming reveals that the proposed controller achieves close-to-optimal performance.
Consensus-based Distributed Intentional Controlled Islanding of Power Grids
Authors: Francesco Lo Iudice, Ricardo Cardona-Rivera, Antonio Grotta, Marco Coraggio, Mario di Bernardo
Date: 2023-01-03 00:00:00

The problem of partitioning a power grid into a set of islands can be a solution to restore power dispatchment in sections of a grid affected by an extreme failure. Current solutions to this problem usually involve finding the partition of the grid into islands that minimizes the sum of their absolute power imbalances. This combinatorial problem is often solved through heuristic offline methods. In this paper, we propose instead a distributed online algorithm through which nodes can migrate among islands, self-organizing the network into a suitable partition. We prove that, under a set of appropriate assumptions, the proposed solution yields a partition whose absolute power imbalance falls within a given bound of the optimal solution. We validate our analytical results by testing our partitioning strategy on the IEEE 118 and 300 benchmark problems.
Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN
Authors: Xiaoxuan Zhang, Zengfu Wang, Kun Lu, Quan Pan
Date: 2023-01-03 00:00:00
Tasks: Classification, Data Augmentation, Generative Adversarial Network

In the sea-land clutter classification of sky-wave over-the-horizon-radar (OTHR), the imbalanced and scarce data leads to a poor performance of the deep learning-based classification model. To solve this problem, this paper proposes an improved auxiliary classifier generative adversarial network~(AC-GAN) architecture, namely auxiliary classifier variational autoencoder generative adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality sea-land clutter samples than AC-GAN and serve as an effective tool for data augmentation. Specifically, a 1-dimensional convolutional AC-VAEGAN architecture is designed to synthesize sea-land clutter samples. Additionally, an evaluation method combining both traditional evaluation of GAN domain and statistical evaluation of signal domain is proposed to evaluate the quality of synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified. Further, the effect of AC-VAEGAN as a data augmentation method on the classification performance of imbalanced and scarce sea-land clutter samples is validated. The experiment results show that the quality of samples synthesized by AC-VAEGAN is better than that of AC-GAN, and the data augmentation method with AC-VAEGAN is able to improve the classification performance in the case of imbalanced and scarce sea-land clutter samples.
A Survey of Feedback Particle Filter and related Controlled Interacting Particle Systems (CIPS)
Authors: Amirhossein Taghvaei, Prashant G. Mehta
Date: 2023-01-03 00:00:00
Tasks: Survey

In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems. Part I of the survey is focussed on the feedback particle filter (FPF) algorithm, its derivation based on optimal transportation theory, and its relationship to the ensemble Kalman filter (EnKF) and the conventional sequential importance sampling-resampling (SIR) particle filters. The central numerical problem of FPF -- to approximate the solution of the Poisson equation -- is described together with the main solution approaches. An analytical and numerical comparison with the SIR particle filter is given to illustrate the advantages of the CIPS approach. Part II of the survey is focussed on adapting these algorithms for the problem of reinforcement learning. The survey includes several remarks that describe extensions as well as open problems in this subject.
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control
Authors: Habtamu Hailemichael, Beshah Ayalew, Lindsey Kerbel, Andrej Ivanco, Keith Loiselle
Date: 2023-01-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy efficiency and traffic flow. However, the application of RL in safety critical systems such as ACC requires strong safety guarantees which are difficult to achieve with learning agents that have a fundamental need to explore. In this paper, we derive control barrier functions as safety filters that allow an RL-based ACC controller to explore freely within a collision safe set. Specifically, we derive control barrier functions for high relative degree nonlinear systems to take into account inertia effects relevant to commercial vehicles. We also outline an algorithm for accommodating actuation saturation with these barrier functions. While any RL algorithm can be used as the performance ACC controller together with these filters, we implement the Maximum A Posteriori Policy Optimization (MPO) algorithm with a hybrid action space that learns fuel optimal gear selection and torque control policies. The safety filtering RL approach is contrasted with a reward shaping RL approach that only learns to avoid collisions after sufficient training. Evaluations on different drive cycles demonstrate significant improvements in fuel economy with the proposed approach compared to baseline ACC algorithms.
Sparse Array Design for Dual-Function Radar-Communications System
Authors: Huiping Huang, Linlong Wu, Bhavani Shankar, Abdelhak M. Zoubir
Date: 2023-01-02 00:00:00

The problem of sparse array design for dual-function radar-communications is investigated. Our goal is to design a sparse array which can simultaneously shape desired beam responses and serve multiple downlink users with the required signal-to-interference-plus-noise ratio levels. Besides, we also take into account the limitation of the radiated power by each antenna. The problem is formulated as a quadratically constrained quadratic program with a joint-sparsity-promoting regularization, which is NP-hard. The resulting problem is solved by the consensus alternating direction method of multipliers, which enjoys parallel implementation. Numerical simulations exhibit the effectiveness and superiority of the proposed method which leads to a more power-efficient solution.
Fixed-time synchronization for quaternion-valued memristor-based neural networks with mixed delays
Authors: Yanlin Zhang, Liqiao Yang, Kit Ian Kou, Yang Liu
Date: 2023-01-02 00:00:00

In this paper, the fixed-time synchronization (FXTSYN) of unilateral coefficients quaternion-valued memristor-based neural networks (UCQVMNNs) with mixed delays is investigated. Instead of decomposition, a direct analytical method is proposed to achieve FXTSYN of UCQVMNNs using one-norm smoothly. Then apply the set-valued map and the differential inclusion theorem to handle discontinuity problems of drive-response systems. The novel nonlinear controllers together with the Lyapunov function are designed to achieve the control goal. Using the FXTSYN theory and inequality techniques, some criteria of FXTSYN for UCQVMNNs are given. Furthermore, the estimated settling time is obtained explicitly. Finally, numerical simulations are presented to demonstrate the correctness, effectiveness and practicability of the obtained theoretical results.
A map-based model predictive control approach for train operation
Authors: Michael Hauck, Patrick Schmidt, Alexander Kobelski, Stefan Streif
Date: 2023-01-02 00:00:00
Tasks: Model Predictive Control

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.
Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF
Authors: Juan S. Giraldo, Nataly Banol Arias, Pedro P. Vergara, Maria Vlasiou, Gerwin Hoogsteen, Johann L. Hurink
Date: 2023-01-02 00:00:00
Tasks: Management

This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.
High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios
Authors: Yiran Luo, Li-Ta Hsu, Yang Jiang, Baoyu Liu, Zhetao Zhang, Yan Xiang, Naser El-Sheimy
Date: 2022-12-31 00:00:00
Tasks: Position

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desiring way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Besides, inertial navigation systems (INSs) have been used as a well-complementary dead reckoning (DR) sensor for GNSS receivers in kinematic scenarios resisting various interferences since early. But little work focuses on the case of whether the INS can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information, and integrated solution of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Real-world experiments demonstrate the proposed SDR regarding estimating time-of-arrival (TOA) and positioning accuracy.
A Survey about Acquisition System Design for Myoelectric Prosthesis
Authors: Ahmed Naguib, Dina Reda Eldamak
Date: 2022-12-31 00:00:00
Tasks: Survey

According to the World Health Organization (WHO), 30 million people are in need of prosthetic and orthotic devices. Some people are born with this limb loss, while others lose limbs due to diseases such as Cancer, diabetes, and work accidents. Additionally, limb amputation is among the most severe and heavily reported injuries among veterans during war. The medical applications of integrated circuit technology have recently made significant advances, thus improving human quality of life. Moreover, the use of microelectronics integration dominates a lot of medical applications, especially portable and wearable battery-operated devices. Thus, the objective of this report is to provide the reader with the basic understanding of integrated solutions for controlling prosthetic limbs.
robust synergistic hybrid feedback
Authors: Pedro Casau, Ricardo G. Sanfelice, Carlos Silvestre
Date: 2022-12-31 00:00:00

Synergistic hybrid feedback refers to a collection of feedback laws that allow for global asymptotic stabilization of a compact set through the following switching logic: given a collection of Lyapunov functions that are indexed by a logic variable, whenever the currently selected Lyapunov function exceeds the value of another function in the collection by a given margin, then a switch to the corresponding feedback law is triggered. This kind of feedback has been under development over the past decade and it has led to multiple solutions for global asymptotic stabilization on compact manifolds. The contributions of this paper include a synergistic controller design in which the logic variable is not necessarily constant between jumps, a synergistic hybrid feedback that is able to tackle the presence of parametric uncertainty, backstepping of adaptive synergistic hybrid feedbacks, and a demonstration of the proposed solutions to the problem of global obstacle avoidance.