pwc-archive/papers-with-abstracts huggingface dataset:
Coexistence of Competing Microbial Strains under Twofold Environmental Variability and Demographic Fluctuations
Authors: Matthew Asker, Lluís Hernández-Navarro, Alastair M. Rucklidge, Mauro Mobilia
Date: 2023-07-12 00:00:00

Microbial populations generally evolve in volatile environments, under conditions fluctuating between harsh and mild, e.g. as the result of sudden changes in toxin concentration or nutrient abundance. Environmental variability thus shapes the long-time population dynamics, notably by influencing the ability of different strains of microorganisms to coexist. Inspired by the evolution of antimicrobial resistance, we study the dynamics of a community consisting of two competing strains subject to twofold environmental variability. The level of toxin varies in time, favouring the growth of one strain under low drug concentration and the other strain when the toxin level is high. We also model time-changing resource abundance by a randomly switching carrying capacity that drives the fluctuating size of the community. While one strain dominates in a static environment, we show that species coexistence is possible in the presence of environmental variability. By computational and analytical means, we determine the environmental conditions under which long-lived coexistence is possible and when it is almost certain. Notably, we study the circumstances under which environmental and demographic fluctuations promote, or hinder, the strains coexistence. We also determine how the make-up of the coexistence phase and the average abundance of each strain depend on the environmental variability.
Responses of Unemployment to Productivity Changes for a General Matching Technology
Authors: Rich Ryan
Date: 2023-07-11 00:00:00

Workers separate from jobs, search for jobs, accept jobs, and fund consumption with their wages. Firms recruit workers to fill vacancies. Search frictions prevent firms from instantly hiring available workers. Unemployment persists. These features are described by the Diamond-Mortensen-Pissarides modeling framework. In this class of models, how unemployment responds to productivity changes depends on resources that can be allocated to job creation. Yet, this characterization has been made when matching is parameterized by a Cobb-Douglas technology. For a canonical DMP model, I (1) demonstrate that a unique steady-state equilibrium will exist as long as the initial vacancy yields a positive surplus; (2) characterize responses of unemployment to productivity changes for a general matching technology; and (3) show how a matching technology that is not Cobb-Douglas implies unemployment responds more to productivity changes, which is independent of resources available for job creation, a feature that will be of interest to business-cycle researchers.
Area, Delay, and Energy-Efficient Full Dadda Multiplier
Authors: Muteen Munawar, Zain Shabbir, Muhammad Akram
Date: 2023-07-11 00:00:00

The Dadda algorithm is a parallel structured multiplier, which is quite faster as compared to array multipliers, i.e., Booth, Braun, Baugh-Wooley, etc. However, it consumes more power and needs a larger number of gates for hardware implementation. In this paper, a modified-Dadda algorithm-based multiplier is designed using a proposed half-adder-based carry-select adder with a binary to excess-1 converter and an improved ripple-carry adder (RCA). The proposed design is simulated in different technologies, i.e., Taiwan Semiconductor Manufacturing Company (TSMC) 50nm, 90nm, and 120nm, and on different GHz frequencies, i.e., 0.5, 1, 2, and 3.33GHz. Specifically, the 4-bit circuit of the proposed design in TSMCs 50nm technology consumes 25uW of power at 3.33GHz with 76ps of delay. The simulation results reveal that the design is faster, more power-energy-efficient, and requires a smaller number of transistors for implementation as compared to some closely related works. The proposed design can be a promising candidate for low-power and low-cost digital controllers. In the end, the design has been compared with recent relevant works in the literature.
Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: a Lloyd's of London Case Study
Authors: Sedar Olmez, Akhil Ahmed, Keith Kam, Zhe Feng, Alan Tua
Date: 2023-07-10 00:00:00
Tasks: Management

This research presents a novel Discrete Event Simulation (DES) of the Lloyd's of London specialty insurance market, exploring complex market dynamics that have not been previously studied quantitatively. The proof-of-concept model allows for the simulation of various scenarios that capture important market phenomena such as the underwriting cycle, the impact of risk syndication, and the importance of appropriate exposure management. Despite minimal calibration, our model has shown that it is a valuable tool for understanding and analysing the Lloyd's of London specialty insurance market, particularly in terms of identifying areas for further investigation for regulators and participants of the market alike. The results generate the expected behaviours that, syndicates (insurers) are less likely to go insolvent if they adopt sophisticated exposure management practices, catastrophe events lead to more defined patterns of cyclicality and cause syndicates to substantially increase their premiums offered. Lastly, syndication enhances the accuracy of actuarial price estimates and narrows the divergence among syndicates. Overall, this research offers a new perspective on the Lloyd's of London market and demonstrates the potential of individual-based modelling (IBM) for understanding complex financial systems.
The Effects of Interaction Conflicts, Levels of Automation, and Frequency of Automation on Human Automation Trust and Acceptance
Authors: Hadi Halvachi, Ali Asghar Nazari Shirehjini, Zahra Kakavand, Niloofar Hashemi, Shervin Shirmohammadi
Date: 2023-07-05 00:00:00

In the presence of interaction conflicts, user trust in automation plays an important role in accepting intelligent environments such as smart homes. In this paper, a factorial research design is employed to investigate and compare the single and joint effects of Level of Automation (LoA), Frequency of Automated responses (FoA), and Conflict Intensity (CI) on human trust and acceptance of automation in the context of smart homes. To study these effects, we conducted web-based experiments to gather data from 324 online participants who experienced the system through a 3D simulation of a smart home. The findings show that the level and frequency of automation had an impact on user trust in smart environments. Furthermore, the results demonstrate that the users' acceptance of automated smart environments decreased in the presence of automation failures and interaction conflicts.
Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Authors: Doksoo Lee, Wei Wayne Chen, LiWei Wang, Yu-Chin Chan, Wei Chen
Date: 2023-07-01 00:00:00

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. In this review, we provide a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. We organize existing research into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. We further categorize the approaches within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.
Estimating See and Be Seen Performance with an Airborne Visual Acquisition Model
Authors: Ngaire Underhill, Evan Maki, Bilal Gill, Andrew Weinert
Date: 2023-06-29 00:00:00
Tasks: Collision Avoidance, Management

Separation provision and collision avoidance to avoid other air traffic are fundamental components of the layered conflict management system to ensure safe and efficient operations. Pilots have visual-based separation responsibilities to see and be seen to maintain separation between aircraft. To safely integrate into the airspace, drones should be required to have a minimum level of performance based on the safety achieved as baselined by crewed aircraft seen and be seen interactions. Drone interactions with crewed aircraft should not be more hazardous than interactions between traditional aviation aircraft. Accordingly, there is need for a methodology to design and evaluate detect and avoid systems, to be equipped by drones to mitigate the risk of a midair collision, where the methodology explicitly addresses, both semantically and mathematically, the appropriate operating rules associated with see and be seen. In response, we simulated how onboard pilots safely operate through see and be seen interactions using an updated visual acquisition model that was originally developed by J.W. Andrews decades ago. Monte Carlo simulations were representative two aircraft flying under visual flight rules and results were analyzed with respect to drone detect and avoid performance standards.
Keystroke Dynamics for User Identification
Authors: Atharva Sharma, Martin Jureček, Mark Stamp
Date: 2023-07-07 00:00:00
Tasks: User Identification

In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging multiclass user identification problem, based on free-text data. We experiment with a complex image-like feature that has previously been used to achieve state-of-the-art authentication results over free-text data. Using this image-like feature and multiclass Convolutional Neural Networks, we are able to obtain a classification (i.e., identification) accuracy of 0.78 over a set of 148 users. However, we find that a Random Forest classifier trained on a slightly modified version of this same feature yields an accuracy of 0.93.
The Ethical Implications of Generative Audio Models: A Systematic Literature Review
Authors: Julia Barnett
Date: 2023-07-07 00:00:00
Tasks: Systematic Literature Review

Generative audio models typically focus their applications in music and speech generation, with recent models having human-like quality in their audio output. This paper conducts a systematic literature review of 884 papers in the area of generative audio models in order to both quantify the degree to which researchers in the field are considering potential negative impacts and identify the types of ethical implications researchers in this area need to consider. Though 65% of generative audio research papers note positive potential impacts of their work, less than 10% discuss any negative impacts. This jarringly small percentage of papers considering negative impact is particularly worrying because the issues brought to light by the few papers doing so are raising serious ethical implications and concerns relevant to the broader field such as the potential for fraud, deep-fakes, and copyright infringement. By quantifying this lack of ethical consideration in generative audio research and identifying key areas of potential harm, this paper lays the groundwork for future work in the field at a critical point in time in order to guide more conscientious research as this field progresses.
Human in the AI loop via xAI and Active Learning for Visual Inspection
Authors: Jože M. Rožanec, Elias Montini, Vincenzo Cutrona, Dimitrios Papamartzivanos, Timotej Klemenčič, Blaž Fortuna, Dunja Mladenić, Entso Veliou, Thanassis Giannetsos, Christos Emmanouilidis
Date: 2023-07-03 00:00:00
Tasks: Active Learning, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity.
HIVA: Holographic Intellectual Voice Assistant
Authors: Ruslan Isaev, Radmir Gumerov, Gulzada Esenalieva, Remudin Reshid Mekuria, Ermek Doszhanov
Date: 2023-06-28 00:00:00
Tasks: Chatbot

Holographic Intellectual Voice Assistant (HIVA) aims to facilitate human computer interaction using audiovisual effects and 3D avatar. HIVA provides complete information about the university, including requests of various nature: admission, study issues, fees, departments, university structure and history, canteen, human resources, library, student life and events, information about the country and the city, etc. There are other ways for receiving the data listed above: the university's official website and other supporting apps, HEI (Higher Education Institution) official social media, directly asking the HEI staff, and other channels. However, HIVA provides the unique experience of "face-to-face" interaction with an animated 3D mascot, helping to get a sense of 'real-life' communication. The system includes many sub-modules and connects a family of applications such as mobile applications, Telegram chatbot, suggestion categorization, and entertainment services. The Voice assistant uses Russian language NLP models and tools, which are pipelined for the best user experience.
Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT
Authors: David James Woo, Kai Guo, Hengky Susanto
Date: 2023-06-19 00:00:00
Tasks: Chatbot, Prompt Engineering

ChatGPT is a state-of-the-art (SOTA) chatbot. Although it has potential to support English as a foreign language (EFL) students' writing, to effectively collaborate with it, a student must learn to engineer prompts, that is, the skill of crafting appropriate instructions so that ChatGPT produces desired outputs. However, writing an appropriate prompt for ChatGPT is not straightforward for non-technical users who suffer a trial-and-error process. This paper examines the content of EFL students' ChatGPT prompts when completing a writing task and explores patterns in the quality and quantity of the prompts. The data come from iPad screen recordings of secondary school EFL students who used ChatGPT and other SOTA chatbots for the first time to complete the same writing task. The paper presents a case study of four distinct pathways that illustrate the trial-and-error process and show different combinations of prompt content and quantity. The cases contribute evidence for the need to provide prompt engineering education in the context of the EFL writing classroom, if students are to move beyond an individual trial-and-error process, learning a greater variety of prompt content and more sophisticated prompts to support their writing.
GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study
Authors: Zachary Robertson
Date: 2023-06-16 00:00:00

In this pilot study, we investigate the use of GPT4 to assist in the peer-review process. Our key hypothesis was that GPT-generated reviews could achieve comparable helpfulness to human reviewers. By comparing reviews generated by both human reviewers and GPT models for academic papers submitted to a major machine learning conference, we provide initial evidence that artificial intelligence can contribute effectively to the peer-review process. We also perform robustness experiments with inserted errors to understand which parts of the paper the model tends to focus on. Our findings open new avenues for leveraging machine learning tools to address resource constraints in peer review. The results also shed light on potential enhancements to the review process and lay the groundwork for further research on scaling oversight in a domain where human-feedback is increasingly a scarce resource.
Channel Estimation for Beyond Diagonal Reconfigurable Intelligent Surfaces with Group-Connected Architectures
Authors: Hongyu Li, Yumeng Zhang, Bruno Clerckx
Date: 2023-07-12 00:00:00

We study channel estimation for a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided multiple input single output system. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimator, and formulate the BD-RIS design problem that minimizes the estimation MSE with unique constraints induced by group-connected architectures of BD-RIS. Then, we propose an efficient BD-RIS design which theoretically guarantees to achieve the MSE lower bound. Finally, we provide simulation results to verify the effectiveness of the proposed channel estimation scheme.
Analysis of Half-Duplex Two-Node Slotted ALOHA Network With Asynchronous Traffic
Authors: Seyed Ali Hashemian, Farid Ashtiani
Date: 2023-07-12 00:00:00

Despite the long history of research on slotted ALOHA, the exact analysis of the average delay is still in question as the performance of each node is coupled with the activity of other nodes. In this paper, we consider a network comprised of two half-duplex transmitter nodes with asynchronous arrival traffic that follow the slotted ALOHA protocol. We propose a new queueing theoretic model based on the state-dependent queues to analyze the network. In addition, we derive the exact values of delay and stability region for each node. The numerical results demonstrate the accuracy of our proposed model.
Learning Koopman Operators with Control Using Bi-level Optimization
Authors: Daning Huang, Muhammad Bayu Prasetyo, Yin Yu, Junyi Geng
Date: 2023-07-12 00:00:00

The accurate modeling and control of nonlinear dynamical effects are crucial for numerous robotic systems. The Koopman formalism emerges as a valuable tool for linear control design in nonlinear systems within unknown environments. However, it still remains a challenging task to learn the Koopman operator with control from data, and in particular, the simultaneous identification of the Koopman linear dynamics and the mapping between the physical and Koopman states. Conventionally, the simultaneous learning of the dynamics and mapping is achieved via single-level optimization based on one-step or multi-step discrete-time predictions, but the learned model may lack model robustness, training efficiency, and/or long-term predictive accuracy. This paper presents a bi-level optimization framework that jointly learns the Koopman embedding mapping and Koopman dynamics with exact long-term dynamical constraints. Our formulation allows back-propagation in standard learning framework and the use of state-of-the-art optimizers, yielding more accurate and stable system prediction in long-time horizon over various applications compared to conventional methods.
Global birdsong embeddings enable superior transfer learning for bioacoustic classification
Authors: Burooj Ghani, Tom Denton, Stefan Kahl, Holger Klinck
Date: 2023-07-12 00:00:00
Tasks: Audio Classification, Decision Making, Transfer Learning

Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on diverse datasets, including different bird calls and dialect types, bat calls, marine mammals calls, and amphibians calls. The embeddings extracted from the models trained on bird vocalization data consistently allowed higher quality classification than the embeddings trained on general audio datasets. The results of this study indicate that high-quality feature embeddings from large-scale acoustic bird classifiers can be harnessed for few-shot transfer learning, enabling the learning of new classes from a limited quantity of training data. Our findings reveal the potential for efficient analyses of novel bioacoustic tasks, even in scenarios where available training data is limited to a few samples.
Point to the Hidden: Exposing Speech Audio Splicing via Signal Pointer Nets
Authors: Denise Moussa, Germans Hirsch, Sebastian Wankerl, Christian Riess
Date: 2023-07-11 00:00:00

Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work. Here, one focus is on detecting deletion or insertion operations, so called audio splicing. While this is a rather easy approach to alter spoken statements, careful editing can yield quite convincing results. For difficult cases or big amounts of data, automated tools can support in detecting potential editing locations. To this end, several analytical and deep learning methods have been proposed by now. Still, few address unconstrained splicing scenarios as expected in practice. With SigPointer, we propose a pointer network framework for continuous input that uncovers splice locations naturally and more efficiently than existing works. Extensive experiments on forensically challenging data like strongly compressed and noisy signals quantify the benefit of the pointer mechanism with performance increases between about 6 to 10 percentage points.