pwc-archive/papers-with-abstracts huggingface dataset:
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness
Authors: McKane Andrus, Sarah Villeneuve
Date: 2022-04-18 00:00:00
Tasks: Attribute, Fairness

Most proposed algorithmic fairness techniques require access to data on a "sensitive attribute" or "protected category" (such as race, ethnicity, gender, or sexuality) in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of algorithmic fairness. Through this paper, we consider calls to collect more data on demographics to enable algorithmic fairness and challenge the notion that discrimination can be overcome with smart enough technical methods and sufficient data alone. We show how these techniques largely ignore broader questions of data governance and systemic oppression when categorizing individuals for the purpose of fairer algorithmic processing. In this work, we explore under what conditions demographic data should be collected and used to enable algorithmic fairness methods by characterizing a range of social risks to individuals and communities. For the risks to individuals we consider the unique privacy risks associated with the sharing of sensitive attributes likely to be the target of fairness analysis, the possible harms stemming from miscategorizing and misrepresenting individuals in the data collection process, and the use of sensitive data beyond data subjects' expectations. Looking more broadly, the risks to entire groups and communities include the expansion of surveillance infrastructure in the name of fairness, misrepresenting and mischaracterizing what it means to be part of a demographic group or to hold a certain identity, and ceding the ability to define for themselves what constitutes biased or unfair treatment. We argue that, by confronting these questions before and during the collection of demographic data, algorithmic fairness methods are more likely to actually mitigate harmful treatment disparities without reinforcing systems of oppression.
Data Justice in Practice: A Guide for Developers
Authors: David Leslie, Michael Katell, Mhairi Aitken, Jatinder Singh, Morgan Briggs, Rosamund Powell, Cami Rincón, Antonella Perini, Smera Jayadeva, Christopher Burr
Date: 2022-04-12 00:00:00
Tasks: Fairness

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for developers and organisations, which are producing, procuring, or using data-intensive technologies.In the first section, we introduce the field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes a description of the six pillars of data justice around which this guidance revolves. Next, to support developers in designing, developing, and deploying responsible and equitable data-intensive and AI/ML systems, we outline the AI/ML project lifecycle through a sociotechnical lens. To support the operationalisation data justice throughout the entirety of the AI/ML lifecycle and within data innovation ecosystems, we then present five overarching principles of responsible, equitable, and trustworthy data research and innovation practices, the SAFE-D principles-Safety, Accountability, Fairness, Explainability, and Data Quality, Integrity, Protection, and Privacy. The final section presents guiding questions that will help developers both address data justice issues throughout the AI/ML lifecycle and engage in reflective innovation practices that ensure the design, development, and deployment of responsible and equitable data-intensive and AI/ML systems.
Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review
Authors: Abdulla All Noman, Umma Habiba Akter, Tahmid Hasan Pranto, AKM Bahalul Haque
Date: 2022-04-01 00:00:00
Tasks: Management, Systematic Literature Review

With unorganized, unplanned and improper use of limited raw materials, an abundant amount of waste is being produced, which is harmful to our environment and ecosystem. While traditional linear production lines fail to address far-reaching issues like waste production and a shorter product life cycle, a prospective concept, namely circular economy (CE), has shown promising prospects to be adopted at industrial and governmental levels. CE aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. Innovative technologies like artificial intelligence (AI) and machine learning(ML) provide vital assistance in effectively adopting and implementing CE in real-world practices. This study explores the adoption and integration of applied AI techniques in CE. First, we conducted bibliometric analysis on a collection of 104 SCOPUS indexed documents exploring the critical research criteria in AI and CE. Forty papers were picked to conduct a systematic literature review from these documents. The selected documents were further divided into six categories: sustainable development, reverse logistics, waste management, supply chain management, recycle & reuse, and manufacturing development. Comprehensive research insights and trends have been extracted and delineated. Finally, the research gap needing further attention has been identified and the future research directions have also been discussed.
Decisions with Uncertain Consequences -- A Total Ordering on Loss-Distributions
Authors: Stefan Rass, Sandra König, Stefan Schauer
Date: 2022-05-02 00:00:00

Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible total ordering on random loss variables, which enables decisions under uncertainty of consequences. Using data obtained from simulations, we demonstrate the practical applicability of our approach.
Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements
Authors: Devansh Jalota, Yinyu Ye
Date: 2022-04-27 00:00:00

Fisher markets are one of the most fundamental models for resource allocation. However, the problem of computing equilibrium prices in Fisher markets typically relies on complete knowledge of users' budgets and utility functions and requires transactions to happen in a static market where all users are present simultaneously. Motivated by these practical considerations, we study an online variant of Fisher markets, wherein users with privately known utility and budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this setting, we first study the limitations of static pricing algorithms, which set uniform prices for all users, along two performance metrics: (i) regret, i.e., the optimality gap in the objective of the Eisenberg-Gale program between an online algorithm and an oracle with complete information, and (ii) capacity violations, i.e., the over-consumption of goods relative to their capacities. Given the limitations of static pricing, we design adaptive posted-pricing algorithms, one with knowledge of the distribution of users' budget and utility parameters and another that adjusts prices solely based on past observations of user consumption, i.e., revealed preference feedback, with improved performance guarantees. Finally, we present numerical experiments to compare our revealed preference algorithm's performance to several benchmarks.
Receiver Design for MIMO Unsourced Random Access with SKP Coding
Authors: Zeyu Han, Xiaojun Yuan, Chongbin Xu, Xin Wang
Date: 2022-04-30 00:00:00

In this letter, we extend the sparse Kronecker-product (SKP) coding scheme, originally designed for the additive white Gaussian noise (AWGN) channel, to multiple input multiple output (MIMO) unsourced random access (URA). With the SKP coding adopted for MIMO transmission, we develop an efficient Bayesian iterative receiver design to solve the intended challenging trilinear factorization problem. Numerical results show that the proposed design outperforms the existing counterparts, and that it performs well in all simulated settings with various antenna sizes and active-user numbers.
Optimal preference satisfaction for conflict-free joint decisions
Authors: Hiroaki Shinkawa, Nicolas Chauvet, Guillaume Bachelier, André Röhm, Ryoichi Horisaki, Makoto Naruse
Date: 2022-05-02 00:00:00
Tasks: Decision Making

We all have preferences when multiple choices are available. If we insist on satisfying our preferences only, we may suffer a loss due to conflicts with other people's identical selections. Such a case applies when the choice cannot be divided into multiple pieces due to the intrinsic nature of the resources. Former studies, such as the top trading cycle, examined how to conduct fair joint decision-making while avoiding decision conflicts from the perspective of game theory when multiple players have their own deterministic preference profiles. However, in reality, probabilistic preferences can naturally appear in relation to the stochastic decision-making of humans. Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players. More specifically, we mathematically prove the conditions wherein the deviation of the resultant chance of obtaining each choice from the individual preference profile, which we call the loss, becomes zero, meaning that all players' satisfaction is perfectly appreciated while avoiding decision conflicts. Furthermore, even in situations where zero-loss conflict-free joint decision-making is unachievable, we show how to derive joint decision-making that accomplishes the theoretical minimum loss while ensuring conflict-free choices. Numerical demonstrations are also shown with several benchmarks.
Local Topology Inference of Mobile Robotic Networks under Formation Control
Authors: Yushan Li, Jianping He, Lin Cai, Xinping Guan
Date: 2022-04-30 00:00:00

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings.
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network
Authors: Tobias Gburrek, Christoph Boeddeker, Thilo von Neumann, Tobias Cord-Landwehr, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Date: 2022-05-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Position, Speech Enhancement, speech-recognition, Speech Recognition

We propose a system that transcribes the conversation of a typical meeting scenario that is captured by a set of initially unsynchronized microphone arrays at unknown positions. It consists of subsystems for signal synchronization, including both sampling rate and sampling time offset estimation, diarization based on speaker and microphone array position estimation, multi-channel speech enhancement, and automatic speech recognition. With the estimated diarization information, a spatial mixture model is initialized that is used to estimate beamformer coefficients for source separation. Simulations show that the speech recognition accuracy can be improved by synchronizing and combining multiple distributed microphone arrays compared to a single compact microphone array. Furthermore, the proposed informed initialization of the spatial mixture model delivers a clear performance advantage over random initialization.
Baselines and Protocols for Household Speaker Recognition
Authors: Alexey Sholokhov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen
Date: 2022-04-30 00:00:00
Tasks: Speaker Recognition

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist, there is less published research and no publicly-available evaluation protocols or open-source baselines. Our work serves to bridge this gap by providing an accessible evaluation benchmark derived from public resources (VoxCeleb and ASVspoof 2019 data) along with a preliminary pool of open-source baselines. This includes four algorithms for active enrollment (speaker labels available) and one algorithm for passive enrollment.
Flood Risk Mitigation and Valve Control in Stormwater Systems: State-Space Modeling, Control Algorithms, and Case Studies
Authors: Marcus N. Gomes Junior, Marcio H. Giacomoni, Ahmad F. Taha, Eduardo M. Mendiondo
Date: 2022-05-02 00:00:00
Tasks: Management, Model Predictive Control

The increasing access to non-expensive sensors, computing power, and more accurate forecasting of storm events provides unique opportunities to shift flood management practices from static approaches to an optimization-based real-time control (RTC) of urban drainage systems. Recent studies have addressed a plethora of strategies for flood control in stormwater reservoirs; however, advanced control theoretic techniques are not yet fully investigated and applied to these systems. In addition, there is an absence of a coupled integrated control model for systems composed of watersheds, reservoirs, and channels for flood mitigation. To this end, we develop a novel state-space model of hydrologic and hydrodynamic processes in reservoirs and one-dimensional channels. The model is tested under different types of reservoir control strategies based on real-time measurements (reactive control), and based on predictions of the future behavior of the system (predictive control) using rainfall forecastings. We apply the modeling approach in a system composed by a single watershed, reservoir, and a channel connected in series, respectively, for the San Antonio observed rainfall data. Results indicate that for flood mitigation, the predictive control strategy outperforms the reactive controls not only when applied for synthetic design storm events, but also for a continuous simulation. Moreover, the predictive control strategy requires smaller valve operations, while still guaranteeing efficient hydrological performance. From the results, we recommend the use of the model predictive control strategy to control stormwater systems due to the ability to handle different objective functions, which can be altered according to rainfall forecasting and shift the reservoir operation from flood-based control to strategies focused on increasing detention times, depending on the forecasting.
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
Authors: Adrien Banse, Zheming Wang, Raphaël M. Jungers
Date: 2022-05-02 00:00:00

We present a framework based on multiple Lyapunov functions to find probabilistic data-driven guarantees on the stability of unknown constrained switching linear systems (CSLS), which are switching linear systems whose switching signal is constrained by an automaton. The stability of a CSLS is characterized by its constrained joint spectral radius (CJSR). Inspired by the scenario approach and previous work on unconstrained switching systems, we characterize the number of observations needed to find sufficient conditions on the (in-)stability of a CSLS using the notion of CJSR. More precisely, our contribution is the following: we derive a probabilistic upper bound on the CJSR of an unknown CSLS from a finite number of observations. We also derive a deterministic lower bound on the CJSR. From this we obtain a probabilistic method to characterize the stability of an unknown CSLS.
Learning stability guarantees for data-driven constrained switching linear systems
Authors: Adrien Banse, Zheming Wang, Raphaël M. Jungers
Date: 2022-05-02 00:00:00

We consider stability analysis of constrained switching linear systems in which the dynamics is unknown and whose switching signal is constrained by an automaton. We propose a data-driven Lyapunov framework for providing probabilistic stability guarantees based on data harvested from observations of the system. By generalizing previous results on arbitrary switching linear systems, we show that, by sampling a finite number of observations, we are able to construct an approximate Lyapunov function for the underlying system. Moreover, we show that the entropy of the language accepted by the automaton allows to bound the number of samples needed in order to reach some pre-specified accuracy.
Rational social distancing policy during epidemics with limited healthcare capacity
Authors: Simon K. Schnyder, John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
Date: 2022-05-02 00:00:00
Tasks: Decision Making

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be `overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.
Variation-cognizant Probabilistic Power Flow Analysis via Multi-task Learning
Authors: Kejun Chen, Yu Zhang
Date: 2022-05-02 00:00:00
Tasks: Multi-Task Learning, regression

With an increasing high penetration of solar photovoltaic generation in electric power grids, voltage phasors and branch power flows experience more severe fluctuations. In this context, probabilistic power flow (PPF) study aims at characterizing the statistical properties of the state of the system with respect to the random power injections. To avoid repeated power flow calculations involved in PPF study, the present paper leverages regression algorithms and neural networks to improve the estimation performance and speed up the computation. Specifically, based on the variation level of the voltage magnitude at each bus, we develop either a linear regression or a fully connected neural network to approximate the inverse AC power flow mappings. The proposed multi-task learning technique further improves the accuracy of branch flow estimation by incorporating the errors of voltage angle differences into the loss function design. Tested on IEEE-300 and IEEE-1354 bus systems with real data, the proposed methods achieve better performance in estimating voltage phasors and branch flows.
Controlled Power System Separation Using Generator PMU Data and System Kinetic Energy
Authors: Ilya Tyuryukanov, Marjan Popov, Jorrit A. Bos, Mart A. M. M. van der Meijden, Vladimir Terzija
Date: 2022-05-01 00:00:00

With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well-known and interrelated problems: "when to split?" and "where to split?". Multiple previous publications consider these problems separately, and even those pursuing a combined approach propose solutions with limited applicability. In this paper, we are proposing a novel PMU-based detector of loss of synchronism that utilizes generator rotor angles and speeds to promptly detect rotor angle instabilities over a wide area. Moreover, we are showing how our loss of synchronism detection principle can be coupled with the known controlled splitting techniques to form an integrated defense scheme against unintentional loss of synchronism. The performance of this wide-area SIPS is demonstrated on the IEEE 39-bus test power system for various types of unstable conditions.
Controller Hardware-in-the-loop (C-HIL) Testing of Decentralized EV-Grid Integration
Authors: Calvin Flack, Emin Ucer, Charles Parker Smith, Mithat Kisacikoglu
Date: 2022-05-01 00:00:00

Present grid infrastructure is unprepared for the wide-scale integration of electric vehicles (EVs). Real-time grid simulation and hardware testing are necessary for the fast and accurate development of EV charging control strategies that are applicable in the field to mitigate their adverse effects. To help facilitate this goal, in this study, we develop a controller verification testbed using a baseline bidirectional AC/DC converter, commonly used in on-board EV chargers, within a hardware-in-the-loop (HIL) test system. The converter runs real-time on a very-fast time scale and integrates into an external grid simulator. We utilize the strength of the HIL device in simulating power electronics while simultaneously realizing a complex distribution grid operation in real-time. The whole system holistically provides an opportunity to see the implementation benefits of a decentralized charging algorithm on the voltage stability of the distribution grid. The study presents a step-by-step approach to developing and testing controllers, providing an essential contribution in the field demonstration of decentralized EV charger control strategies.