pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Lingual Text-to-Speech Using Multi-Task Learning and Speaker Classifier Joint Training
Authors: J. Yang, Lei He
Date: 2022-01-20 00:00:00
Tasks: Multi-Task Learning, Speech Synthesis, text-to-speech, Text to Speech

In cross-lingual speech synthesis, the speech in various languages can be synthesized for a monoglot speaker. Normally, only the data of monoglot speakers are available for model training, thus the speaker similarity is relatively low between the synthesized cross-lingual speech and the native language recordings. Based on the multilingual transformer text-to-speech model, this paper studies a multi-task learning framework to improve the cross-lingual speaker similarity. To further improve the speaker similarity, joint training with a speaker classifier is proposed. Here, a scheme similar to parallel scheduled sampling is proposed to train the transformer model efficiently to avoid breaking the parallel training mechanism when introducing joint training. By using multi-task learning and speaker classifier joint training, in subjective and objective evaluations, the cross-lingual speaker similarity can be consistently improved for both the seen and unseen speakers in the training set.
Chatbot Based Solution for Supporting Software Incident Management Process
Authors: Nagib Sabbag Filho, Rogerio Rossi
Date: 2022-01-15 00:00:00
Tasks: Chatbot, Decision Making, Management

A set of steps for implementing a chatbot, to support decision-making activities in the software incident management process is proposed and discussed in this article. Each step is presented independently of the platform used for the construction of chatbots and are detailed with their respective activities. The proposed steps can be carried out in a continuous and adaptable way, favoring the constant training of a chatbot and allowing the increasingly cohesive interpretatin of the intentions of the specialists who work in the Software Incident Management Process. The software incident resolution process accordingly to the ITIL framework, is considered for the experiment. The results of the work present the steps for the chatbot construction, the solution based on DialogFlow platform and some conclusions based on the experiment.
"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint
Authors: Bart van Oort, Luís Cruz, Babak Loni, Arie van Deursen
Date: 2022-01-20 00:00:00
Tasks: Management

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user.
Invasion Dynamics in the Biased Voter Process
Authors: Loke Durocher, Panagiotis Karras, Andreas Pavlogiannis, Josef Tkadlec
Date: 2022-01-20 00:00:00

The voter process is a classic stochastic process that models the invasion of a mutant trait $A$ (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait $B$, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias $r\in(0,\infty)$ quantifies the stochastic preference towards ($r>1$) or against ($r<1$) adopting $A$ over $B$. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait $A$. In this paper we study the problem of fixation probability maximization under this model: given a budget $k$, find a set of $k$ agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both $r>1$ and $r<1$, while the latter case is also inapproximable within any multiplicative factor. On the positive side, we show that when $r>1$, the optimization function is submodular and thus can be greedily approximated within a factor $1-1/e$. An experimental evaluation of some proposed heuristics corroborates our results.
Allocation of locally generated electricity in renewable energy communities
Authors: Miguel Manuel de Villena, Samy Aittahar, Sebastien Mathieu, Ioannis Boukas, Eric Vermeulen, Damien Ernst
Date: 2020-09-09 00:00:00

Local electricity markets represent a way of supplementing traditional retailing contracts for end consumers -- among these markets, the renewable energy community has gained momentum over the last few years. This paper proposes a practical and readily to be adopted modelling solution for these communities, one that allows their members to share the economic benefits derived from them. The proposed solution relies on an \emph{ex-post} allocation of the electricity that is generated within energy communities (i.e., local electricity) based on the optimisation of \emph{repartition keys}. Repartition keys are therefore optimally computed to represent the proportion of total local electricity to be allocated to each community member, and aim to minimise the sum of electricity bills of all community members. Since the optimisation takes place \emph{ex-post} the repartition keys do not modify the actual electricity flows, but rather the financial flows of the community members. Then, the billing process of the community will take these keys into account to correctly send the electricity bills to each member. Building on this concept, we also introduce two additions to the basic algorithm to enhance the stability of the community, which a global bill minimisation may fail to ensure (e.g., very asymmetrical solutions between members may lead to some of them opting out).
THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-Offs
Authors: M. Mahdi Azari, Sourabh Solanki, Symeon Chatzinotas, Mehdi Bennis
Date: 2022-01-13 00:00:00

Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few. If such requirements are satisfactorily met, it can create novel applications and enable highly reliable and harmonized integration of UAVs in the 6G network ecosystem. Towards this, terahertz (THz) bands are perceived as a prospective technological enabler for various improved functionalities such as ultra-high throughput and enhanced sensing capabilities. This paper focuses on THzempowered UAVs with the following capabilities: communication, sensing, localization, imaging, and control. We review the potential opportunities and use cases of THz-empowered UAVs, corresponding novel design challenges, and resulting trade-offs. Furthermore, we overview recent advances in UAV deployments regulations, THz standardization, and health aspects related to THz bands. Finally, we take UAV to UAV (U2U) communication as a case-study to provide numerical insights into the impact of various system design parameters and environment factors.
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing
Authors: Junyan Yang, Yang Jiang, Shuoyao Wang
Date: 2022-01-20 00:00:00
Tasks: Deep Reinforcement Learning, Super-Resolution, Video Compression, Video Enhancement

The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled video streaming network under a time-varying wireless network and limited computation capacity. "Enhancement" means that the client can improve the quality of the downloaded video segments via image processing modules. We aim to design a joint bitrate adaptation and client-side enhancement algorithm toward maximizing the quality of experience (QoE). We formulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL)-based framework, named ENAVS. As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming. Ultimately, we implement ENAVS and demonstrate extensive testbed results under real-world bandwidth traces and videos. The simulation shows that ENAVS is capable of delivering 5%-14% more QoE under the same bandwidth and computing power conditions as conventional ABR streaming.
GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors
Authors: Nazareno Bruschi, Germain Haugou, Giuseppe Tagliavini, Francesco Conti, Luca Benini, Davide Rossi
Date: 2022-01-20 00:00:00

The last few years have seen the emergence of IoT processors: ultra-low power systems-on-chips (SoCs) combining lightweight and flexible micro-controller units (MCUs), often based on open-ISA RISC-V cores, with application-specific accelerators to maximize performance and energy efficiency. Overall, this heterogeneity level requires complex hardware and a full-fledged software stack to orchestrate the execution and exploit platform features. For this reason, enabling agile design space exploration becomes a crucial asset for this new class of low-power SoCs. In this scenario, high-level simulators play an essential role in breaking the speed and design effort bottlenecks of cycle-accurate simulators and FPGA prototypes, respectively, while preserving functional and timing accuracy. We present GVSoC, a highly configurable and timing-accurate event-driven simulator that combines the efficiency of C++ models with the flexibility of Python configuration scripts. GVSoC is fully open-sourced, with the intent to drive future research in the area of highly parallel and heterogeneous RISC-V based IoT processors, leveraging three foundational features: Python-based modular configuration of the hardware description, easy calibration of platform parameters for accurate performance estimation, and high-speed simulation. Experimental results show that GVSoC enables practical functional and performance analysis and design exploration at the full-platform level (processors, memory, peripherals and IOs) with a speed-up of 2500x with respect to cycle-accurate simulation with errors typically below 10% for performance analysis.
Comparative Study on Reliability Estimation Using Monte Carlo Simulation with Application to Cylindrical Pressure Vessel
Authors: Abolfazl Zolfaghari, Maryam Khoshkhoo
Date: 2022-01-20 00:00:00
Tasks: Sensitivity

One of the methods to design products is reliability-based design, in which failure probability is usually used instead of safety factors. In the technique, it should not be less than a predetermined value. Choosing the proper design criterion is a challenging problem for designers who are dealing with the technique, particularly, when there are various criteria. One of these kinds of products is a cylindrical pressure vessel which has diverse criteria proposed in the literature to calculate the burst pressure as a start point of the design. In this paper, we are going to evaluate and compare the performances of various burst pressure criteria in estimating failure probability which is used for a sample pressure vessel. For each criterion, Monte Carlo simulation has been employed to calculate the probability of failure due to variations related to major design variables. The design parameters include material properties and operating pressure. First, the effects of variations in standard deviations of the design variables on the calculated burst probabilities have been determined by standard deviation analysis. Then, sensitivity analyses were carried out to assess the sensitivity of each burst pressure criterion against changes in the magnitude of design variables.
Distributed Stochastic Model Predictive Control for an Urban Traffic Network
Authors: Viet Hoang Pham, Hyo-Sung Ahn
Date: 2022-01-20 00:00:00
Tasks: Model Predictive Control, Traffic Signal Control

In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.
FAHP-based Mathematical Model for Exercise Rehabilitation Management of Diabetes Mellitus
Authors: Daoyan Pan, Kewei Wang, Zhiheng Zhou, Xingran Liu, Jie Shen
Date: 2022-01-07 00:00:00
Tasks: Management

Exercise rehabilitation is an important part in the comprehensive management of patients with diabetes and there is a need to conduct comprehensively evaluation of several factors such as the physical fitness, cardiovascular risk and diabetic disease factors. However, special disease features of diabetes and its wide heterogeneity make it difficult to apply individualized approaches. In this study, a novel framework was established based on the Fuzzy Analytic Hierarchy Process (FAHP) approach to calculate various physiological factors weights when developing a diabetic exercise prescription. Proposed factors were investigated with respect to three groups which contains 12 different aspects. The relative weights were assessed by a database which established through a questionnaire survey. It is concluded that the physical fitness factors and cardiovascular risk factors need to be paid more attention to considered in the formulation of exercise rehabilitation programs than disease factors. And the cardiopulmonary function of physical fitness factors accounts for the highest importance. Furthermore, it was found that blood lipids have the lowest importance among studied factors. The mathematical model of exercise rehabilitation program for diabetes patients was established, which provided the theoretical basis for individualized guidance of exercise rehabilitation program.
Estimation of Conditional Random Coefficient Models using Machine Learning Techniques
Authors: Stephan Martin
Date: 2022-01-20 00:00:00
Tasks: BIG-bench Machine Learning, Density Estimation

Nonparametric random coefficient (RC)-density estimation has mostly been considered in the marginal density case under strict independence of RCs and covariates. This paper deals with the estimation of RC-densities conditional on a (large-dimensional) set of control variables using machine learning techniques. The conditional RC-density allows to disentangle observable from unobservable heterogeneity in partial effects of continuous treatments adding to a growing literature on heterogeneous effect estimation using machine learning. %It is also informative of the conditional potential outcome distribution. This paper proposes a two-stage sieve estimation procedure. First a closed-form sieve approximation of the conditional RC density is derived where each sieve coefficient can be expressed as conditional expectation function varying with controls. Second, sieve coefficients are estimated with generic machine learning procedures and under appropriate sample splitting rules. The $L_2$-convergence rate of the conditional RC-density estimator is derived. The rate is slower by a factor then typical rates of mean regression machine learning estimators which is due to the ill-posedness of the RC density estimation problem. The performance and applicability of the estimator is illustrated using random forest algorithms over a range of Monte Carlo simulations and with real data from the SOEP-IS. Here behavioral heterogeneity in an economic experiment on portfolio choice is studied. The method reveals two types of behavior in the population, one type complying with economic theory and one not. The assignment to types appears largely based on unobservables not available in the data.
Identification of Direct Socio-Geographical Price Discrimination: An Empirical Study on iPhones
Authors: Davidson Cheng
Date: 2022-01-19 00:00:00

Price discrimination is a practice where firms utilize varying sensitivities to prices among consumers to increase profits. The welfare effects of price discrimination are not agreed on among economists, but identification of such actions may contribute to our standing of firms' pricing behaviors. In this letter, I use econometric tools to analyze whether Apple Inc, one of the largest companies in the globe, is practicing price discrimination on the basis of socio-economical and geographical factors. My results indicate that iPhones are significantly (p $<$ 0.01) more expensive in markets where competitions are weak or where Apple has a strong market presence. Furthermore, iPhone prices are likely to increase (p $<$ 0.01) in developing countries/regions or markets with high income inequality.
Deep self-consistent learning of local volatility
Authors: Zhe Wang, Ameir Shaa, Nicolas Privault, Claude Guet
Date: 2021-12-09 00:00:00

We present an algorithm for the calibration of local volatility from market option prices through deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks. Our method uses the initial-boundary value problem of the underlying Dupire's partial differential equation solved by the parameterized option prices to bring corrections to the parameterization in a self-consistent way. By exploiting the differentiability of neural networks, we can evaluate Dupire's equation locally at each strike-maturity pair; while by exploiting their continuity, we sample strike-maturity pairs uniformly from a given domain, going beyond the discrete points where the options are quoted. Moreover, the absence of arbitrage opportunities are imposed by penalizing an associated loss function as a soft constraint. For comparison with existing approaches, the proposed method is tested on both synthetic and market option prices, which shows an improved performance in terms of reduced interpolation and reprice errors, as well as the smoothness of the calibrated local volatility. An ablation study has been performed, asserting the robustness and significance of the proposed method.
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion
Authors: Charul Paliwal, Pravesh Biyani, Ketan Rajawat
Date: 2022-01-20 00:00:00
Tasks: Imputation, Low-Rank Matrix Completion, Matrix Completion

Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sampling and noisy sampling, a case present in many real-world problems in the field of traffic sensing and environment monitoring, etc. However, jointly exploiting the spatiotemporal and periodic structure, which is generally not captured by classical matrix completion approaches, can improve the imputation performance of sensor data in such real-world conditions. We present a Bayesian approach towards spatiotemporal matrix completion wherein we estimate the underlying temporarily varying subspace using a Variational Bayesian technique. We jointly couple the low-rank matrix completion with the state space autoregressive framework along with a penalty function on the slowly varying subspace to model the temporal and periodic evolution in the data. A major advantage of our method is that a critical parameter like the rank of the model is automatically tuned using the automatic relevance determination (ARD) approach, unlike most matrix/tensor completion techniques. We also propose a robust version of the above formulation, which improves the performance of imputation in the presence of outliers. We evaluate the proposed Variational Bayesian Filtering with Subspace Information (VBFSI) method to impute matrices in real-world traffic and air pollution data. Simulation results demonstrate that the proposed method outperforms the recent state-of-the-art methods and provides a sufficiently accurate imputation for different sampling rates. In particular, we demonstrate that fusing the subspace evolution over days can improve the imputation performance with even 15% of the data sampling.
Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning
Authors: Timothy Flavin, Bhaskar Mitra, Vidhyashree Nagaraju, Rounak Meyur
Date: 2022-01-20 00:00:00
Tasks: Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).