pwc-archive/papers-with-abstracts huggingface dataset:
From Prompt Engineering to Prompt Science With Human in the Loop
Authors: Chirag Shah
Date: 2024-01-01 00:00:00
Tasks: Prompt Engineering

As LLMs make their way into many aspects of our lives, one place that warrants increased scrutiny with LLM usage is scientific research. Using LLMs for generating or analyzing data for research purposes is gaining popularity. But when such application is marred with ad-hoc decisions and engineering solutions, we need to be concerned about how it may affect that research, its findings, or any future works based on that research. We need a more scientific approach to using LLMs in our research. While there are several active efforts to support more systematic construction of prompts, they are often focused more on achieving desirable outcomes rather than producing replicable and generalizable knowledge with sufficient transparency, objectivity, or rigor. This article presents a new methodology inspired by codebook construction through qualitative methods to address that. Using humans in the loop and a multi-phase verification processes, this methodology lays a foundation for more systematic, objective, and trustworthy way of applying LLMs for analyzing data. Specifically, we show how a set of researchers can work through a rigorous process of labeling, deliberating, and documenting to remove subjectivity and bring transparency and replicability to prompt generation process. A set of experiments are presented to show how this methodology can be put in practice.
Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis
Authors: Yuki Yada, Tsuneo Matsumoto, Fuyuko Kido, Hayato Yamana
Date: 2023-12-30 00:00:00

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/why-darkpattern.
Semantic Draw Engineering for Text-to-Image Creation
Authors: Yang Li, Huaqiang Jiang, Yangkai Wu
Date: 2023-12-23 00:00:00
Tasks: Computational Efficiency, Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.
Timeline-based Process Discovery
Authors: Harleen Kaur, Jan Mendling, Christoffer Rubensson, Timotheus Kampik
Date: 2023-12-21 00:00:00

A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity to explicitly represent the time axis. In this paper, we present an approach for automatically constructing process models that explicitly align with a time axis. We exemplify our approach for directly-follows graphs. Our evaluation using two BPIC datasets and a proprietary dataset highlight the benefits of this representation in comparison to standard layout techniques.
Reconfigurable Intelligent Surface-Enabled Downlink NOMA
Authors: Ali Tugberk Dogukan, Emre Arslan, Ertugrul Basar
Date: 2024-01-09 00:00:00

Reconfigurable intelligent surfaces (RISs) bring great potential to the advancement of 6G and beyond wireless communication technologies. RISs introduce a great degree of flexibility, allowing some sort of virtual control over the wireless channel. Exploiting the flexibility introduced by RISs, we propose a novel RIS-enabled downlink (DL) non-orthogonal multiple access (NOMA) scheme where NOMA is enabled over-the-air rather than at the base station (BS) or the receiver (Rx). Here, the RIS is partitioned into distinctive groups where each part of the RIS serves a different user equipment (UE) to perform multiple accessing. The BS transmits an unmodulated signal to the RIS, and each partition modulates the impinging signal over-the-air by introducing a phase shift according to the incoming information bits to serve the corresponding UE. First, the end-to-end system model for the proposed system is presented. Furthermore, outage probability calculations, theoretical error probability analysis, and bit error rate (BER) derivations are discussed and reinforced with comprehensive computer simulation results.
Estimating an Executive Summary of a Time Series: The Tendency
Authors: Caio Alves, Juan M. Restrepo, Jorge M. Ramirez
Date: 2024-01-08 00:00:00
Tasks: Time Series

In this paper we revisit the problem of decomposing a signal into a tendency and a residual. The tendency describes an executive summary of a signal that encapsulates its notable characteristics while disregarding seemingly random, less interesting aspects. Building upon the Intrinsic Time Decomposition (ITD) and information-theoretical analysis, we introduce two alternative procedures for selecting the tendency from the ITD baselines. The first is based on the maximum extrema prominence, namely the maximum difference between extrema within each baseline. Specifically this method selects the tendency as the baseline from which an ITD step would produce the largest decline of the maximum prominence. The second method uses the rotations from the ITD and selects the tendency as the last baseline for which the associated rotation is statistically stationary. We delve into a comparative analysis of the information content and interpretability of the tendencies obtained by our proposed methods and those obtained through conventional low-pass filtering schemes, particularly the Hodrik-Prescott (HP) filter. Our findings underscore a fundamental distinction in the nature and interpretability of these tendencies, highlighting their context-dependent utility with emphasis in multi-scale signals. Through a series of real-world applications, we demonstrate the computational robustness and practical utility of our proposed tendencies, emphasizing their adaptability and relevance in diverse time series contexts.
EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure
Authors: Emmanuel Balogun, Elizabeth Buechler, Siddharth Bhela, Simona Onori, Ram Rajagopal
Date: 2024-01-09 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site.
Class-Incremental Learning for Multi-Label Audio Classification
Authors: Manjunath Mulimani, Annamaria Mesaros
Date: 2024-01-09 00:00:00
Tasks: Audio Classification, Classification, class-incremental learning, Class Incremental Learning, Incremental Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, we propose a method for class-incremental learning of potentially overlapping sounds for solving a sequence of multi-label audio classification tasks. We design an incremental learner that learns new classes independently of the old classes. To preserve knowledge about the old classes, we propose a cosine similarity-based distillation loss that minimizes discrepancy in the feature representations of subsequent learners, and use it along with a Kullback-Leibler divergence-based distillation loss that minimizes discrepancy in their respective outputs. Experiments are performed on a dataset with 50 sound classes, with an initial classification task containing 30 base classes and 4 incremental phases of 5 classes each. After each phase, the system is tested for multi-label classification with the entire set of classes learned so far. The proposed method obtains an average F1-score of 40.9% over the five phases, ranging from 45.2% in phase 0 on 30 classes, to 36.3% in phase 4 on 50 classes. Average performance degradation over incremental phases is only 0.7 percentage points from the initial F1-score of 45.2%.
FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation
Authors: Yang Liu, Li Wan, Yun Li, Yiteng Huang, Ming Sun, James Luan, Yangyang Shi, Xin Lei
Date: 2024-01-08 00:00:00
Tasks: Acoustic echo cancellation, Speech Enhancement

Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.
Using perceptive subbands analysis to perform audio scenes cartography
Authors: Laurent Millot, Gérard Pelé, Mohammed Elliq
Date: 2024-01-05 00:00:00

Audio scene cartography for real or simulated stereo recordings is presented. This audio scene analysis is performed doing successively: a perceptive 10-subbands analysis, calculation of temporal laws for relative delays and gains between both channels of each subband using a short-time cons\-tant scene assumption and channels inter-correlation which permit to follow a mobile source in its moves, calculation of global and subbands histograms whose peaks give the incidence information for fixed sources. Audio scenes composed of 2 to 4 fixed sources or with a fixed source and a mobile one have been already successfully tested. Further extensions and applications will be discussed. Audio illustrations of audio scenes, subband analysis and demonstration of real-time stereo recording simulations will be given.Paper 6340 presented at the 118th Convention of the Audio Engineering Society, Barcelona, 2005
Distributed Data-driven Unknown-input Observers for State Estimation
Authors: Yuzhou Wei, Giorgia Disarò, Wenjie Liu, Jian Sun, Maria Elena Valcher, Gang Wang
Date: 2024-01-09 00:00:00
Tasks: State Estimation

Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performance. To improve the performance, unknown-input observers (UIOs) have been proposed. Most of the results available to design UIOs are based on explicit system models, which can be difficult or impossible to obtain in real-world applications. Data-driven techniques, on the other hand, have become a viable alternative for the design and analysis of unknown systems using only data. In this context, a novel data-driven distributed unknown-input observer (D-DUIO) for unknown continuous-time linear time-invariant (LTI) systems is developed, which requires solely some data collected offline, without any prior knowledge of the system matrices. In the paper, first, a model-based approach to the design of a DUIO is presented. A sufficient condition for the existence of such a DUIO is recalled, and a new one is proposed, that is prone to a data-driven adaption. Moving to a data-driven approach, it is shown that under suitable assumptions on the input/output/state data collected from the continuous-time system, it is possible to both claim the existence of a D-DUIO and to derive its matrices in terms of the matrices of pre-collected data. Finally, the efficacy of the D-DUIO is illustrated by means of numerical examples.
Online convex optimization for robust control of constrained dynamical systems
Authors: Marko Nonhoff, Emiliano Dall'Anese, Matthias A. Müller
Date: 2024-01-09 00:00:00
Tasks: Model Predictive Control

This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances. We combine the online convex optimization framework with tools from robust model predictive control to propose an algorithm that is able to guarantee robust constraint satisfaction. The performance of the closed loop emerging from application of our framework is studied in terms of its dynamic regret, which is proven to be bounded linearly by the variation of the cost functions and the magnitude of the disturbances. We corroborate our theoretical findings and illustrate implementational aspects of the proposed algorithm by a numerical case study on a tracking control problem of an autonomous vehicle.
Quantum-Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures
Authors: Gabriel San Martin Silva, Enrique Lopez Droguett
Date: 2023-05-03 00:00:00
Tasks: Combinatorial Optimization, Structural Health Monitoring

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, Structural Health Monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the Optimal Sensor Placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of meta-heuristic optimization techniques. While approaches such as genetic algorithms (GA) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum-based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a Quadratic Unconstrained Binary Optimization (QUBO) model formulation is developed, taking as a starting point the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scale. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader.
The Required Spatial Resolution to Assess Imbalance using Plantar Pressure Mapping
Authors: Kelsey Detels, Shanni Zhou, Harrison Wilson, Jessica Rosendorf, Ghazal Shabestanipour, Elias Ben Mellouk, David Shin, Joseph Schwab, Hamid Ghaednia
Date: 2024-01-08 00:00:00

Roughly 1/3 of adults older than 65 fall each year, resulting in more than 3 million emergency room visits, thousands of deaths, and over $50 Billion in direct costs. The Centers for Disease Control and Prevention (CDC) estimate that 1/3 of falls are preventable with effective mitigation strategies, particularly for imbalance. Therefore, quantification of imbalance is being studied extensively in recent years. In this study we investigate the feasibility of plantar pressure mapping in balance assessment through a healthy human subject study. We used an in-house plantar pressure mapping device with high precision based on Frustrated Total Internal Reflection to measure subjects sway during the Romberg test. Through the measurements obtained from all subjects, we measured the minimum spatial resolution required for plantar pressure mapping devices in assessment of balance. We conclude that most of the current devices in the market lack the requirements for imbalance measurements.
Imágenes de Resonancia Magnética con Contraste en el Cáncer de Mama
Authors: Virginia del Campo, Iker Malaina
Date: 2024-01-08 00:00:00
Tasks: Prognosis

In this study, 529 variables extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of 922 breast cancer patients have been evaluated, focusing on distinguishing between recurrent and non-recurrent cases, as well as those with and without metastasis. Special emphasis is placed on the differences among invasive breast cancer subtypes (Luminal A, Luminal B, HER2 positive, and TNBC). The accurate identification of the subtype is crucial as it impacts both treatment and prognosis. The analysis is based on the dataset from Saha et al., highlighting key factors for predicting recurrences and metastases, providing valuable information for proper monitoring and the selection of effective treatments.
Robust Bayesian Method for Refutable Models
Authors: Moyu Liao
Date: 2024-01-09 00:00:00

We propose a robust Bayesian method for economic models that can be rejected by some data distributions. The econometrician starts with a refutable structural assumption which can be written as the intersection of several assumptions. To avoid the assumption refutable, the econometrician first takes a stance on which assumption $j$ will be relaxed and considers a function $m_j$ that measures the deviation from the assumption $j$. She then specifies a set of prior beliefs $\Pi_s$ whose elements share the same marginal distribution $\pi_{m_j}$ which measures the likelihood of deviations from assumption $j$. Compared to the standard Bayesian method that specifies a single prior, the robust Bayesian method allows the econometrician to take a stance only on the likeliness of violation of assumption $j$ while leaving other features of the model unspecified. We show that many frequentist approaches to relax refutable assumptions are equivalent to particular choices of robust Bayesian prior sets, and thus we give a Bayesian interpretation to the frequentist methods. We use the local average treatment effect ($LATE$) in the potential outcome framework as the leading illustrating example.
Teacher bias or measurement error?
Authors: Thomas van Huizen, Madelon Jacobs, Matthijs Oosterveen
Date: 2024-01-08 00:00:00

In many countries, teachers' track recommendations are used to allocate students to secondary school tracks. Previous studies have shown that students from families with low socioeconomic status (SES) receive lower track recommendations than their peers from high SES families, conditional on standardized test scores. It is often argued that this indicates teacher bias. However, this claim is invalid in the presence of measurement error in test scores. We discuss how measurement error in test scores generates a biased coefficient of the conditional SES gap, and consider three empirical strategies to address this bias. Using administrative data from the Netherlands, we find that measurement error explains 35 to 43% of the conditional SES gap in track recommendations.
World Wine Exports: What Determines the Success of New World Wine Producers?
Authors: Osiris Jorge Parcero, Emiliano Villanueva
Date: 2024-01-09 00:00:00

By using an econometric approach this paper looks at the evolution of the world wine industry in the period 1961-2005. A particular stylized fact is the appearance of nontraditional producing and exporting countries of wine from the beginning of the nineties. We show that the success of these new producing and exporting countries can be explained by the importance of the demand from non-producing countries with little or no tradition of wine consumption, relative to the world demand. This stylized fact is consistent with a testable implication of the switching cost literature and to the best of our knowledge this is the first time that this implication is tested.