pwc-archive/papers-with-abstracts huggingface dataset:
Biometric recognition: why not massively adopted yet?
Authors: Marcos Faundez-Zanuy
Date: 2022-02-23 00:00:00
Tasks: Misconceptions

Although there has been a dramatically reduction on the prices of capturing devices and an increase on computing power in the last decade, it seems that biometric systems are still far from massive adoption for civilian applications. This paper deals with the causes of this phenomenon, as well as some misconceptions regarding biometric identification.
The Braess Paradox in Dynamic Traffic
Authors: Dingyi Zhuang, Yuzhu Huang, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu
Date: 2022-03-07 00:00:00

The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow. Previously, the existence of the BP is modeled using the static traffic assignment model, which solves for the user equilibrium subject to network flow conservation to find the equilibrium state and distributes all vehicles instantaneously. Such approach neglects the dynamic nature of real-world traffic, including vehicle behaviors and the interaction between vehicles and the infrastructure. As such, this article proposes a dynamic traffic network model and empirically validates the existence of the BP under dynamic traffic. In particular, we use microsimulation environment to study the impacts of an added path on a grid network. We explore how the network flow, vehicle travel time, and network capacity respond, as well as when the BP will occur.
Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning
Authors: Lu Dong, ZhenHua Ling, Qiang Ling, Zefeng Lai
Date: 2022-03-01 00:00:00
Tasks: cognitive diagnosis

Cognitive diagnosis is an essential task in many educational applications. Many solutions have been designed in the literature. The deterministic input, noisy "and" gate (DINA) model is a classical cognitive diagnosis model and can provide interpretable cognitive parameters, e.g., student vectors. However, the assumption of the probabilistic part of DINA is too strong, because it assumes that the slip and guess rates of questions are student-independent. Besides, the question matrix (i.e., Q-matrix) recording the skill distribution of the questions in the cognitive diagnosis domain often requires precise labels given by domain experts. Thus, we propose an explicit student vector estimation (ESVE) method to estimate the student vectors of DINA with a local self-consistent test, which does not rely on any assumptions for the probabilistic part of DINA. Then, based on the estimated student vectors, the probabilistic part of DINA can be modified to a student dependent model that the slip and guess rates are related to student vectors. Furthermore, we propose an unsupervised method called heuristic bidirectional calibration algorithm (HBCA) to label the Q-matrix automatically, which connects the question difficulty relation and the answer results for initialization and uses the fault tolerance of ESVE-DINA for calibration. The experimental results on two real-world datasets show that ESVE-DINA outperforms the DINA model on accuracy and that the Q-matrix labeled automatically by HBCA can achieve performance comparable to that obtained with the manually labeled Q-matrix when using the same model structure.
The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level
Authors: Ting Chang, Yingjie Hu, Dane Taylor, Brian M. Quigley
Date: 2022-02-26 00:00:00

Domestic violence (DV) is a serious public health issue, with 1 in 3 women and 1 in 4 men experiencing some form of partner-related violence every year. Existing research has shown a strong association between alcohol use and DV at the individual level. Accordingly, alcohol use could also be a predictor for DV at the neighborhood level, helping identify the neighborhoods where DV is more likely to happen. However, it is difficult and costly to collect data that can represent neighborhood-level alcohol use especially for a large geographic area. In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level. We use mobile phone data from the company SafeGraph, which is freely available to researchers and which contains information about how people visit various points-of-interest including alcohol outlets. In such data, a visit to an alcohol outlet is identified based on the GPS point location of the mobile phone and the building footprint (a polygon) of the alcohol outlet. We present our method for deriving neighborhood-level alcohol outlet visits, and experiment with four different statistical and machine learning models to investigate the role of the derived visits in enhancing DV prediction based on an empirical dataset about DV in Chicago. Our results reveal the effectiveness of the derived alcohol outlets visits in helping identify neighborhoods that are more likely to suffer from DV, and can inform policies related to DV intervention and alcohol outlet licensing.
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
Authors: Pengyuan Zhou, Benjamin Finley, Lik-Hang Lee, Yong Liao, Haiyong Xie, Pan Hui
Date: 2022-02-22 00:00:00
Tasks: Fairness

AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences. Thus, the trustworthiness of such AI systems is vital as failures in these systems can cause serious user harm. Although there are related works on exploring trustworthy AI (TAI) metrics in the current cyberspace, ecosystems towards user-centered services, such as the metaverse, are much more complicated in terms of system performance and user experience assessment, thus posing challenges for the applicability of existing approaches. Thus, we give an overlook on fairness, privacy and robustness, across the historical path from existing approaches. Eventually, we propose a research agenda towards systematic yet user-centered TAI in immersive ecosystems.
GPT-based Open-Ended Knowledge Tracing
Authors: Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew Lan
Date: 2022-02-21 00:00:00
Tasks: Code Generation, Knowledge Tracing, Navigate, Program Synthesis, Skill Mastery

In education applications, knowledge tracing refers to the problem of estimating students' time-varying concept/skill mastery level from their past responses to questions and predicting their future performance. One key limitation of most existing knowledge tracing methods is that they treat student responses to questions as binary-valued, i.e., whether they are correct or incorrect. Response correctness analysis/prediction ignores important information on student knowledge contained in the exact content of the responses, especially for open-ended questions. In this paper, we conduct the first exploration into open-ended knowledge tracing (OKT) by studying the new task of predicting students' exact open-ended responses to questions. Our work is grounded in the domain of computer science education with programming questions. We develop an initial solution to the OKT problem, a student knowledge-guided code generation approach, that combines program synthesis methods using language models with student knowledge tracing methods. We also conduct a series of quantitative and qualitative experiments on a real-world student code dataset to validate OKT and demonstrate its promise in educational applications.
Needs and Artificial Intelligence
Authors: Soheil Human, Ryan Watkins
Date: 2022-02-18 00:00:00

Throughout their history, homo sapiens have used technologies to better satisfy their needs. The relation between needs and technology is so fundamental that the US National Research Council defined the distinguishing characteristic of technology as its goal "to make modifications in the world to meet human needs". Artificial intelligence (AI) is one of the most promising emerging technologies of our time. Similar to other technologies, AI is expected "to meet [human] needs". In this article, we reflect on the relationship between needs and AI, and call for the realisation of needs-aware AI systems. We argue that re-thinking needs for, through, and by AI can be a very useful means towards the development of realistic approaches for Sustainable, Human-centric, Accountable, Lawful, and Ethical (HALE) AI systems. We discuss some of the most critical gaps, barriers, enablers, and drivers of co-creating future AI-based socio-technical systems in which [human] needs are well considered and met. Finally, we provide an overview of potential threats and HALE considerations that should be carefully taken into account, and call for joint, immediate, and interdisciplinary efforts and collaborations.
A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook
Authors: Ahmed Abd Elrahman, Taysir Hassan A Soliman, Ahmed I. Taloba, Mohammed F. Farghally
Date: 2022-02-16 00:00:00
Tasks: regression

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students' learning. With the careful analysis of this data, educators can gain useful insights into the performance of their students and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students' performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms on data from a data structures and algorithms course (CS2) offered in a large public research university. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification.
Trusted Data Forever: Is AI the Answer?
Authors: Emanuele Frontoni, Marina Paolanti, Tracey P. Lauriault, Michael Stiber, Luciana Duranti, Abdul-Mageed Muhammad
Date: 2022-02-16 00:00:00
Tasks: Management

Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical measures for the long-term preservation of digital assets in any medium and form - textual, visual, or aural. Public and private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records. This paper presents preliminary results of the InterPARES Trust AI (I Trust AI) international research partnership, which aims to (1) identify and develop specific AI technologies to address critical records and archives challenges; (2) determine the benefits and risks of employing AI technologies on records and archives; (3) ensure that archival concepts and principles inform the development of responsible AI; and (4) validate outcomes through a conglomerate of case studies and demonstrations.
Algorithmic audits of algorithms, and the law
Authors: Erwan Le Merrer, Ronan Pons, Gilles Trédan
Date: 2022-02-15 00:00:00
Tasks: Decision Making

Algorithmic decision making is now widespread, ranging from health care allocation to more common actions such as recommendation or information ranking. The aim to audit these algorithms has grown alongside. In this paper, we focus on external audits that are conducted by interacting with the user side of the target algorithm, hence considered as a black box. Yet, the legal framework in which these audits take place is mostly ambiguous to researchers developing them: on the one hand, the legal value of the audit outcome is uncertain; on the other hand the auditors' rights and obligations are unclear. The contribution of this paper is to articulate two canonical audit forms to law, to shed light on these aspects: 1) the first audit form (we coin the Bobby audit form) checks a predicate against the algorithm, while the second (Sherlock) is more loose and opens up to multiple investigations. We find that: Bobby audits are more amenable to prosecution, yet are delicate as operating on real user data. This can lead to reject by a court (notion of admissibility). Sherlock audits craft data for their operation, most notably to build surrogates of the audited algorithm. It is mostly used for acts for whistleblowing, as even if accepted as a proof, the evidential value will be low in practice. 2) these two forms require the prior respect of a proper right to audit, granted by law or by the platform being audited; otherwise the auditor will be also prone to prosecutions regardless of the audit outcome. This article thus highlights the relation of current audits with law, in order to structure the growing field of algorithm auditing.
Bayesian Persuasion with Mediators
Authors: Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy
Date: 2022-03-08 00:00:00

An informed sender communicates with an uninformed receiver through a sequence of uninformed mediators; agents' utilities depend on receiver's action and the state. For any number of mediators, the sender's optimal value is characterized. For one mediator, the characterization has a geometric meaning of constrained concavification of sender's utility, optimal persuasion requires the same number of signals as without mediators, and the presence of the mediator is never profitable for the sender. Surprisingly, the second mediator may improve the value but optimal persuasion may require more signals.
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for Cell-Free MIMO Systems
Authors: DaeSung Yu, Seok-Hwan Park, Osvaldo Simeone, Shlomo Shamai
Date: 2022-03-07 00:00:00
Tasks: Robust Design

Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by splitting the message for each user equipment (UE) into a private message and a set of common messages, which are simultaneously transmitted by means of superposition coding. The RSMA scheme can enhance throughput and connectivity as compared to conventional multiple access techniques by optimizing the rate-splitting ratios along with the corresponding downlink beamforming vectors. This work examines the impact of erroneous channel state information (CSI) on the performance of RSMA in cell-free multiple-input multiple-output (MIMO) systems. An efficient robust optimization algorithm is proposed by using closed-form lower bound expressions on the expected data rates. Extensive numerical results show the importance of robust design in the presence of CSI errors and how the performance gain of RSMA over conventional schemes is affected by CSI imperfection.
Extending life of Lithium-ion battery systems by embracing heterogeneities via an optimal control-based active balancing strategy
Authors: Vahid Azimi, Anirudh Allam, Simona Onori
Date: 2022-03-08 00:00:00

This paper formulates and solves a multi-objective fast charging-minimum degradation optimal control problem (OCP) for a lithium-ion battery module made of series-connected cells equipped with an active balancing circuitry. The cells in the module are subject to heterogeneity induced by manufacturing defects and non-uniform operating conditions. Each cell is expressed via a coupled nonlinear electrochemical, thermal, and aging model and the direct collocation approach is employed to transcribe the OCP into a nonlinear programming problem (NLP). The proposed OCP is formulated under two different schemes of charging operation: (i) same-charging-time (OCP-SCT) and (ii) different-charging-time (OCP-DCT). The former assumes simultaneous charging of all cells irrespective of their initial conditions, whereas the latter allows for different charging times of the cells to account for heterogeneous initial conditions. The problem is solved for a module with two series-connected cells with intrinsic heterogeneity among them in terms of state of charge and state of health. Results show that the OCP-DCT scheme provides more flexibility to deal with heterogeneity, boasting of lower temperature increase, charging current amplitudes, and degradation. Finally, comparison with the common practice of constant current (CC) charging over a long-term cycling operation shows that promising savings, in terms of retained capacity, are attainable under both the control (OCP-SCT and OCP-DCT) schemes.
Multi-agent consensus over time-invariant and time-varying signed digraphs via eventual positivity
Authors: Angela Fontan, Lingfei Wang, Yiguang Hong, Guodong Shi, Claudio Altafini
Date: 2022-03-08 00:00:00
Tasks: valid

Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called "repelling" signed Laplacians, the marginal stability property (needed to achieve consensus) is not guaranteed a priori and, even when it holds, it does not automatically lead to consensus, as these signed Laplacians may loose rank even in strongly connected digraphs. Furthermore, in the time-varying case, instability can occur even when switching in a family of systems each of which corresponds to a marginally stable signed Laplacian with the correct corank. In this paper we present conditions guaranteeing consensus of these signed Laplacians based on the property of eventual positivity, a Perron-Frobenius type of property for signed matrices. The conditions cover both time-invariant and time-varying cases. A particularly simple sufficient condition valid in both cases is that the Laplacians are normal matrices. Such condition can be relaxed in several ways. For instance in the time-invariant case it is enough that the Laplacian has this Perron-Frobenius property on the right but not on the left side (i.e., on the transpose). For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians. All conditions can be easily extended to bipartite consensus.
Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration
Authors: Xiangyu Zhang, Abinet Tesfaye Eseye, Bernard Knueven, Weijia Liu, Matthew Reynolds, Wesley Jones
Date: 2022-03-08 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.
Feedforward PID Control of Full-Car with Parallel Active Link Suspension for Improved Chassis Attitude Stabilization
Authors: Zilin Feng, Min Yu, Simos A. Evangelou, Imad M Jaimoukha, Daniele Dini
Date: 2022-03-08 00:00:00

PID control is commonly utilized in an active suspension system to achieve desirable chassis attitude, where, due to delays, feedback information has much difficulty regulating the roll and pitch behavior, and stabilizing the chassis attitude, which may result in roll over when the vehicle steers at a large longitudinal velocity. To address the problem of the feedback delays in chassis attitude stabilization, in this paper, a feedforward control strategy is proposed to combine with a previously developed PID control scheme in the recently introduced Parallel Active Link Suspension (PALS). Numerical simulations with a nonlinear multi-body vehicle model are performed, where a set of ISO driving maneuvers are tested. Results demonstrate the feedforward-based control scheme has improved suspension performance as compared to the conventional PID control, with faster speed of convergence in brake in a turn and step steer maneuvers, and surviving the fishhook maneuver (although displaying two-wheel lift-off) with 50 mph maneuver entrance speed at which conventional PID control rolls over.
Mu-synthesis PID Control of Full-Car with Parallel Active Link Suspension Under Variable Payload
Authors: Zilin Feng, Min Yu, Simos A. Evangelou, Imad M Jaimoukha, Daniele Dini
Date: 2022-03-08 00:00:00

This paper presents a combined mu-synthesis PID control scheme, employing a frequency separation paradigm, for a recently proposed novel active suspension, the Parallel Active Link Suspension (PALS). The developed mu-synthesis control scheme is superior to the conventional H-infinity control, previously designed for the PALS, in terms of ride comfort and road holding (higher frequency dynamics), with important realistic uncertainties, such as in vehicle payload, taken into account. The developed PID control method is applied to guarantee good chassis attitude control capabilities and minimization of pitch and roll motions (low frequency dynamics). A multi-objective control method, which merges the aforementioned PID and mu-synthesis-based controls is further introduced to achieve simultaneously the low frequency mitigation of attitude motions and the high frequency vibration suppression of the vehicle. A seven-degree-of-freedom Sport Utility Vehicle (SUV) full car model with PALS, is employed in this work to test the synthesized controller by nonlinear simulations with different ISO-defined road events and variable vehicle payload. The results demonstrate the control scheme's significant robustness and performance, as compared to the conventional passive suspension as well as the actively controlled PALS by conventional H-infinity control, achieved for a wide range of vehicle payload considered in the investigation.