pwc-archive/papers-with-abstracts huggingface dataset:
Towards capturing fine phonetic variation in speech using articulatory features
Authors: Odette Scharenborg, Vincent Wan, Roger K. Moore
Date: 2007-01-11 00:00:00
Proceeding: Speech communication 2007 1
Tasks: speech-recognition, Speech Recognition

The ultimate goal of our research is to develop a computational model of human speech recognition that is able to capture the effects of fine-grained acoustic variation on speech recognition behaviour. As part of this work we are investigating automatic feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. In the experiments reported here, we analysed the classification results from support vector machines (SVMs) and multilayer perceptrons (MLPs). MLPs have been widely and successfully used for the task of multi-value articulatory feature classification, while (to the best of our knowledge) SVMs have not. This paper compares the performance of the two classifiers and analyses the results in order to better understand the articulatory representations. It was found that the SVMs outperformed the MLPs for five out of the seven articulatory feature classes we investigated while using only 8.8–44.2% of the training material used for training the MLPs. The structure in the misclassifications of the SVMs and MLPs suggested that there might be a mismatch between the characteristics of the classification sys- tems and the characteristics of the description of the AF values themselves. The analyses showed that some of the misclassified features are inherently confusable given the acoustic space. We concluded that in order to come to a feature set that can be used for a reliable and accurate automatic description of the speech signal; it could be beneficial to move away from quantised representations.
Open Source German Distant Speech Recognition: Corpus and Acoustic Model
Authors: Stephan Radeck-Arneth, Benjamin Milde, Arvid Lange, Evandro Gouvea, Stefan Radomski, Max Mühlhäuser, and Chris Biemann
Date: 2015-12-11 00:00:00
Proceeding: International Conference on Text, Speech, and Dialogue 2015 12
Tasks: Distant Speech Recognition, speech-recognition, Speech Recognition

We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus. The corpus has been recorded in a controlled environment with three different microphones at a distance of one meter. It comprises 180 different speakers with a total of 36 hours of audio recordings. We show recognition results with the open source toolkit Kaldi (20.5% WER) and PocketSphinx (39.6% WER) and make a complete open source solution for German distant speech recognition possible.
Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic
Authors: Zhiming Zhang, Yongming Liu
Date: 2021-10-15 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data. Existing approaches, however, still lack robustness, especially when the measured data contain a large level of noise. The lack of robustness is mainly attributed to the insufficient representativeness of used features. As a result, the intrinsic mechanism governing the observed system cannot be accurately identified. In this study, we use an efficient topological descriptor for complex data, i.e., the Euler characteristics (ECs), as features to characterize the spatiotemporal data collected from dynamical systems and discover the underlying physics. Unsupervised manifold learning and supervised classification results show that EC can be used to efficiently distinguish systems with different while similar governing models. We also demonstrate that the machine learning approaches using EC can improve the confidence level of sparse regression methods of physics discovery.
Bidders' Responses to Auction Format Change in Internet Display Advertising Auctions
Authors: Shumpei Goke, Gabriel Y. Weintraub, Ralph Mastromonaco, Sam Seljan
Date: 2021-10-20 00:00:00

We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by different publishers (sellers) of first-price auctions (FPAs), instead of the traditional second-price auctions (SPAs). Event study regression estimates indicate that, immediately after the auction format change, the revenue per sold impression (price) jumped considerably for the treated publishers relative to the control publishers, ranging from 35% to 75% of the pre-treatment price level of the treatment group. Further, we observe that in later auction format changes the increase in the price levels under FPAs relative to price levels under SPAs dissipates over time, reminiscent of the celebrated revenue equivalence theorem. We take this as evidence of initially insufficient bid shading after the format change rather than an immediate shift to a new Bayesian Nash equilibrium. Prices then went down as bidders learned to shade their bids. We also show that bidders' sophistication impacted their response to the auction format change. Our work constitutes one of the first field studies on bidders' responses to auction format changes, providing an important complement to theoretical model predictions. As such, it provides valuable information to auction designers when considering the implementation of different formats.
LOS Coverage Area in Vehicular Networks with Cox distributed Roadside Units and Relays
Authors: Chang-Sik Choi, François Baccelli
Date: 2021-10-26 00:00:00
Tasks: Point Processes

We develop an analytical framework to examine the line-of-sight (LOS) coverage area in vehicular networks with roadside units (RSU) and vehicle relays. In practical deployment scenarios, RSUs and vehicle relays are spatially correlated and we characterize this by employing Cox point processes to model the locations of RSUs and vehicle relays simultaneously. Leveraging the random blockage model, we model the LOS coverage area as Boolean models on these Cox point processes. The LOS coverage area is then evaluated by its area fraction. We show that relays can increase the area fraction of LOS coverage by nearly 50\% even when RSUs and relays are spatially correlated. By presenting a stochastic geometry model for a vehicular network with RSUs and relays and then by providing a tool to capture its LOS coverage, our work assesses the viability of vehicle relays for modern vehicular networks exploiting LOS coverage.
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning
Authors: Suraj Kothawade, Vinaya Khandelwal, Kinjal Basu, Huaduo Wang, Gopal Gupta
Date: 2021-10-17 00:00:00
Tasks: Autonomous Driving, BIG-bench Machine Learning, Common Sense Reasoning, Decision Making, Ethics

Driving an automobile involves the tasks of observing surroundings, then making a driving decision based on these observations (steer, brake, coast, etc.). In autonomous driving, all these tasks have to be automated. Autonomous driving technology thus far has relied primarily on machine learning techniques. We argue that appropriate technology should be used for the appropriate task. That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning. In this paper, we discuss (i) how commonsense reasoning can be automated using answer set programming (ASP) and the goal-directed s(CASP) ASP system, and (ii) develop the AUTO-DISCERN system using this technology for automating decision-making in driving. The goal of our research, described in this paper, is to develop an autonomous driving system that works by simulating the mind of a human driver. Since driving decisions are based on human-style reasoning, they are explainable, their ethics can be ensured, and they will always be correct, provided the system modeling and system inputs are correct.
Event-triggered Consensus of Matrix-weighted Networks Subject to Actuator Saturation
Authors: Lulu Pan, Haibin Shao, Yuanlong Li, Dewei Li, Yugeng Xi
Date: 2021-10-26 00:00:00

The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global consensus of matrix-weighted networks subject to actuator saturation. Specifically, a distributed dynamic event-triggered coordination strategy, whose design involves sampled state of agents, saturation constraint and auxiliary systems, is proposed for this category of generalized network to guarantee its global consensus. Under the proposed event-triggered coordination strategy, sufficient conditions are derived to guarantee the leaderless and leader-follower global consensus of the multi-agent systems on matrix-weighted networks, respectively. The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy. It turns out that the spectral properties of matrix-valued weights are crucial in event-triggered mechanism design for matrix-weighted networks with actuator saturation constraint. Finally, simulations are provided to demonstrate the effectiveness of proposed event-triggered coordination strategy. This work provides a more general design framework compared with existing results that are only applicable to scalar-weighted networks.
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning
Authors: Jakob Abeßer, Meinard Müller
Date: 2021-10-26 00:00:00
Tasks: Acoustic Scene Classification, Disentanglement, Domain Adaptation, Scene Classification, Unsupervised Domain Adaptation

The deployment of machine listening algorithms in real-life applications is often impeded by a domain shift caused for instance by different microphone characteristics. In this paper, we propose a novel domain adaptation strategy based on disentanglement learning. The goal is to disentangle task-specific and domain-specific characteristics in the analyzed audio recordings. In particular, we combine two strategies: First, we apply different binary masks to internal embedding representations and, second, we suggest a novel combination of categorical cross-entropy and variance-based losses. Our results confirm the disentanglement of both tasks on an embedding level but show only minor improvement in the acoustic scene classification performance, when training data from both domains can be used. As a second finding, we can confirm the effectiveness of a state-of-the-art unsupervised domain adaptation strategy, which performs across-domain adaptation on a feature-level instead.
Vaccine skepticism detection by network embedding
Authors: Ferenc Béres, Rita Csoma, Tamás Vilmos Michaletzky, András A. Benczúr
Date: 2021-10-20 00:00:00
Tasks: Community Detection, Network Embedding

We demonstrate the applicability of network embedding to vaccine skepticism, a controversial topic of long-past history. With the Covid-19 pandemic outbreak at the end of 2019, the topic is more important than ever. Only a year after the first international cases were registered, multiple vaccines were developed and passed clinical testing. Besides the challenges of development, testing, and logistics, another factor that might play a significant role in the fight against the pandemic are people who are hesitant to get vaccinated, or even state that they will refuse any vaccine offered to them. Two groups of people commonly referred to as a) pro-vaxxer, those who support vaccinating people b) vax-skeptic, those who question vaccine efficacy or the need for general vaccination against Covid-19. It is very difficult to tell exactly how many people share each of these views. It is even more difficult to understand all the reasoning why vax-skeptic opinions are getting more popular. In this work, our intention was to develop techniques that are able to efficiently differentiate between pro-vaxxer and vax-skeptic content. After multiple data preprocessing steps, we analyzed the tweet text as well as the structure of user interactions on Twitter. We deployed several node embedding and community detection models that scale well for graphs with millions of edges.
As long as you talk about me: The importance of family firm brands and the contingent role of family-firm identity
Authors: P. Rovelli, C. Benedetti, A. Fronzetti Colladon, A. De Massis
Date: 2021-10-19 00:00:00
Tasks: Articles, Diversity

This study explores the role of external audiences in determining the importance of family firm brands and the relationship with firm performance. Drawing on text mining and social network analysis techniques, and considering the brand prevalence, diversity, and connectivity dimensions, we use the semantic brand score to measure the importance the media give to family firm brands. The analysis of a sample of 52,555 news articles published in 2017 about 63 Italian entrepreneurial families reveals that brand importance is positively associated with family firm revenues, and this relationship is stronger when there is identity match between the family and the firm. This study advances current literature by offering a rich and multifaceted perspective on how external audiences perceptions of the brand shape family firm performance.
Multilevel Inverter Real-Time Simulation and Optimization Through Hybrid GA/PSO Algorithm
Authors: Hussein Zolfaghari, Hossein Karimi, Hamidreza Momeni
Date: 2021-10-26 00:00:00

This paper presents a new real-time intelligent optimization algorithm to minimize the voltage harmonics of a multilevel inverter. Hybrid Genetic algorithm /Particle swarm optimization algorithm is employed in a real-time simulation to identify the best fire angels of the multilevel inverter to eliminate any destructive effect, such as dc voltage variations and changes in line and dc-link resistors. The dual objective function of harmonic minimization and voltage regulation is considered in this real-time simulation. This approach can be applied to any multilevel inverter with various numbers of levels. The validity of the proposed algorithm is proven by real-time simulation of seven and an eleven-level inverter.
Malicious Mode Attack on EV Coordinated Charging Load and MIADRC Defense Strategy
Authors: Yichen Zhou, Weidong Liu, Jing Ma, Xinghao Zhen, Yonggang Li
Date: 2021-10-26 00:00:00

The Internet of Things (IoT) provides a salient communication environment to facilitate the coordinated charging of electric vehicle (EV) load. However, as IoT is connected with the public network, the coordinated charging system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyberattack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations could be stimulated by MMA, which seriously threats the power system stability. First, the potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. And then, based on the attack process, an MMA model is established for impact analysis where expressions of the mean and stochastic responses of the MMA forced oscillation are derived to discover main impact factors. Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation. Simulations are conducted to verify the existence and characteristics of MMA threats, and the efficiency of the proposed defense strategy is also validated.
A Non-linear Differentiable Model for Stormwater-based Irrigation of a Green Roof in Toronto
Authors: Chia-Hui Yeh, Margaret P. Chapman
Date: 2021-10-26 00:00:00

Green infrastructure has potential to alleviate the environmental impact of rapidly growing cities. This potential has inspired laws in Toronto that require the inclusion of rooftops with large vegetation beds, called green roofs, into sufficiently sized construction projects. We study the problem of reusing stormwater to irrigate a green roof in Toronto, where potable water is the current irrigation source. The vision is that widespread reuse of stormwater runoff for irrigation of green roofs and other purposes can reduce sewer overflow volumes without over-building (with the added benefit of conserving potable water). Towards this vision, our goal is to develop and evaluate two pump controllers for transporting stormwater to the green roof of interest in simulation. A key contribution is our development of a site-specific non-linear model for stormwater flow using smoothing techniques that permits linearization and a standard model predictive controller (MPC). We compare the efficacy of the MPC, which anticipates the weather, and an on/off controller, which is reactive rather than anticipative, for the site in simulation. With further study, we are hopeful that this research will advance control systems technology to improve the performance of green and stormwater infrastructure in growing urban areas.
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
Authors: Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Date: 2021-10-26 00:00:00
Tasks: Model Predictive Control

We provide a functional view of distributional robustness motivated by robust statistics and functional analysis. This results in two practical computational approaches for approximate distributionally robust nonlinear optimization based on gradient norms and reproducing kernel Hilbert spaces. Our method can be applied to the settings of statistical learning with small sample size and test distribution shift. As a case study, we robustify scenario-based stochastic model predictive control with general nonlinear constraints. In particular, we demonstrate constraint satisfaction with only a small number of scenarios under distribution shift.
Multi-scale Deterministic Optimisation of Blended Composite Structures: Case Study of a Box-Wing
Authors: M. Picchi Scardaoni, M. I. Izzi, M. Montemurro, E. Panettieri, V. Cipolla, V. Binante
Date: 2021-10-26 00:00:00

This work presents a multi-scale design methodology for the deterministic optimisation of thin-walled composite structures integrating a global-local approach for the assessment of the buckling strength and a dedicated strategy to recover blended stacking sequences. The methodology is based on the multi-scale two-level optimisation strategy for anisotropic materials and structures. In the first step, focused on the macroscopic scale, several design requirements are included in the problem formulation: lightness, feasibility, manufacturing, blending, buckling failure, static failure and stiffness. The second step, which focuses on the laminate mesoscopic scale, deals with the recovery of blended stacking sequences, for the structure at hand, matching the optimal geometric and elastic properties determined in the first step. As a case study, the unconventional PrandtlPlane box-wing system is used to show the effectiveness of the proposed design methodology.
Modeling of Frequency Security Constraints and Quantification of Frequency Control Reserve Requirements for Unit Commitment
Authors: Likai Liu, Zechun Hu
Date: 2021-10-26 00:00:00

The high penetration of converter-based renewable energy sources has brought challenges to the power system frequency control. It is essential to consider the frequency security constraints and frequency control reserve requirements in unit commitment (UC). Considering that the risk of frequency insecurity varies under the changeable operational condition, we propose to optimize the PFC droop gains and reserve capacities in the UC model to provide diverse control efforts in different risk levels adaptively. Copula theory is used to establish the joint distribution model among frequency control performance, secondary frequency control (SFC) reserve capacities, and power fluctuations. Then the distributionally robust optimization technique is utilized in the SFC reserve requirement determination to handle the possible error in the probability model. The UC simulation is conducted on IEEE 118-bus system to test the proposed optimal PFC droop gain strategy and SFC reserve requirement quantification method. Simulation results show that the proposed optimal PFC droop gain strategy is better than the traditional fixed PFC droop gain setting on economic efficiency and operational flexibility. Besides, the SFC reserve requirement calculated by the proposed method is more appropriate than the actual SFC reserve capacity in the historical operation.
Meter-Range Wireless Motor Drive for Pipeline Transportation
Authors: Wei Liu, K. T. Chau, Hui Wang, Tengbo Yang
Date: 2021-10-26 00:00:00

This paper proposes and implements a meter-range wireless motor drive (WMD) system for promising applications of underground pipeline transportations or in-pipe robots. To power a pipeline network beneath the earth, both the power grid and the control system are usually required to be deployed deep underground, thus increasing the construction cost, maintenance difficulty and system complexity. The proposed system newly develops a hybrid repeater to enable the desired meter-range wireless power and drive transfer, which can offer a fault-tolerant network with a robust structure for the underground sensor-free WMD while maintaining a high transmission efficiency. Hence, this wireless pipeline network can reduce the maintenance requirement and regulate the flow rate effectively. A full-scale prototype has been built for practical verification, and the system efficiency can reach 88.8% at a long transfer distance of 150 cm. Theoretical analysis, software simulation and hardware experimentation are given to verify the feasibility of proposed meter-range WMD for underground pipeline transportations.
Newtonian Mechanics Based Transient Stability PART IV: Equivalent Machine
Authors: Songyan Wang, Jilai Yu, Aoife Foley, Jingrui Zhang
Date: 2021-10-26 00:00:00

This paper analyzes the mechanisms of the equivalent machine and also its advantages in TSA. Based on the two group separations, an equivalent machine is modeled through the equivalence of the motions of all machines inside each group. This "motion equivalence" fully ensures the modeling of the two-machine system and the corresponding Newtonian energy conversion. Against this background, the original system becomes the equivalent system. It is clarified that the equivalent machine strictly follows the machine paradigms. These strict followings bring the two advantages in the equivalent-machine based TSA: (i) the stability of the equivalent machine is characterized precisely, and (ii) the equivalent-machine trajectory variance is depicted clearly. The two advantages are fully reflected in the precise definitions of the equivalent-machine based transient stability concepts. In particular, the equivalent machine swing is clearly depicted through the EDSP or EDLP of the machine, and the critical stability of the equivalent system is strictly defined as the critical stability of the equivalent machine. Simulation results show that the effectiveness of the equivalent-machine in TSA.