pwc-archive/papers-with-abstracts huggingface dataset:
Retail Analytics in the New Normal: The Influence of Artificial Intelligence and the Covid-19 Pandemic
Authors: Yossiri Adulyasak, Maxime C. Cohen, Warut Khern-am-nuai, Michael Krause
Date: 2023-11-27 00:00:00

The COVID-19 pandemic has severely disrupted the retail landscape and has accelerated the adoption of innovative technologies. A striking example relates to the proliferation of online grocery orders and the technology deployed to facilitate such logistics. In fact, for many retailers, this disruption was a wake-up call after which they started recognizing the power of data analytics and artificial intelligence (AI). In this article, we discuss the opportunities that AI can offer to retailers in the new normal retail landscape. Some of the techniques described have been applied at scale to adapt previously deployed AI models, whereas in other instances, fresh solutions needed to be developed to help retailers cope with recent disruptions, such as unexpected panic buying, retraining predictive models, and leveraging online-offline synergies.
AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management
Authors: Olamide Oladeji, Seyed Shahabeddin Mousavi, Marc Roston
Date: 2023-11-26 00:00:00
Tasks: Decision Making, Knowledge Graphs, Management

While carbon accounting plays a fundamental role in our fight against climate change, it is not without its challenges. We begin the paper with a critique of the conventional carbon accounting practices, after which we proceed to introduce the E-liability carbon accounting methodology and Emissions Liability Management (ELM) originally proposed by Kaplan and Ramanna, highlighting their strengths. Recognizing the immense value of this novel approach for real-world carbon accounting improvement, we introduce a novel data-driven integrative framework that leverages AI and computation - the E-Liability Knowledge Graph framework - to achieve real-world implementation of the E-liability carbon accounting methodology. In addition to providing a path-to-implementation, our proposed framework brings clarity to the complex environmental interactions within supply chains, thus enabling better informed and more responsible decision-making. We analyze the implementation aspects of this framework and conclude with a discourse on the role of this AI-aided knowledge graph in ensuring the transparency and decarbonization of global supply chains.
Advancing AI Audits for Enhanced AI Governance
Authors: Arisa Ema, Ryo Sato, Tomoharu Hase, Masafumi Nakano, Shinji Kamimura, Hiromu Kitamura
Date: 2023-11-26 00:00:00

As artificial intelligence (AI) is integrated into various services and systems in society, many companies and organizations have proposed AI principles, policies, and made the related commitments. Conversely, some have proposed the need for independent audits, arguing that the voluntary principles adopted by the developers and providers of AI services and systems insufficiently address risk. This policy recommendation summarizes the issues related to the auditing of AI services and systems and presents three recommendations for promoting AI auditing that contribute to sound AI governance. Recommendation1.Development of institutional design for AI audits. Recommendation2.Training human resources for AI audits. Recommendation3. Updating AI audits in accordance with technological progress. In this policy recommendation, AI is assumed to be that which recognizes and predicts data with the last chapter outlining how generative AI should be audited.
Who is leading in AI? An analysis of industry AI research
Authors: Ben Cottier, Tamay Besiroglu, David Owen
Date: 2023-11-24 00:00:00

AI research is increasingly industry-driven, making it crucial to understand company contributions to this field. We compare leading AI companies by research publications, citations, size of training runs, and contributions to algorithmic innovations. Our analysis reveals the substantial role played by Google, OpenAI and Meta. We find that these three companies have been responsible for some of the largest training runs, developed a large fraction of the algorithmic innovations that underpin large language models, and led in various metrics of citation impact. In contrast, leading Chinese companies such as Tencent and Baidu had a lower impact on many of these metrics compared to US counterparts. We observe many industry labs are pursuing large training runs, and that training runs from relative newcomers -- such as OpenAI and Anthropic -- have matched or surpassed those of long-standing incumbents such as Google. The data reveals a diverse ecosystem of companies steering AI progress, though US labs such as Google, OpenAI and Meta lead across critical metrics.
Artificial Intelligence in Sustainable Vertical Farming
Authors: Hribhu Chowdhury, Debo Brata Paul Argha, Md Ashik Ahmed
Date: 2023-11-17 00:00:00
Tasks: Decision Making

As global challenges of population growth, climate change, and resource scarcity intensify, the agricultural landscape is at a critical juncture. Sustainable vertical farming emerges as a transformative solution to address these challenges by maximizing crop yields in controlled environments. This paradigm shift necessitates the integration of cutting-edge technologies, with Artificial Intelligence (AI) at the forefront. The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming, investigating its potential, challenges, and opportunities. The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics, in optimizing resource usage, automating tasks, and enhancing decision-making. It identifies gaps in research, emphasizing the need for optimized AI models, interdisciplinary collaboration, and the development of explainable AI in agriculture. The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security. The paper concludes by offering insights for stakeholders and suggesting avenues for future research, aiming to guide the integration of AI technologies in sustainable vertical farming for a resilient and sustainable future in agriculture.
Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning
Authors: Mounia Hamidouche, Eugeny Popko, Bassem Ouni
Date: 2023-11-20 00:00:00

This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack classification and device-type identification. Our approach involves training and evaluating a DL model using a range of diverse IoT-related datasets, allowing us to gain valuable insights into how adaptable and practical these models can be when confronted with various IoT configurations. We initially convert the unstructured network traffic data from IoT networks, stored in PCAP files, into images by processing the packet data. This conversion process adapts the data to meet the criteria of DL classification methods. The experiments showcase the ability of DL to surpass the constraints tied to manually engineered features, achieving superior results in attack detection and maintaining comparable outcomes in device-type identification. Additionally, a notable feature extraction time difference becomes evident in the experiments: traditional methods require around 29 milliseconds per data packet, while DL accomplishes the same task in just 2.9 milliseconds. The significant time gap, DL's superior performance, and the recognized limitations of manually engineered features, presents a compelling call to action within the IoT community. This encourages us to shift from exploring new IoT features for each dataset to addressing the challenges of integrating DL into IoT, making it a more efficient solution for real-world IoT scenarios.
LEAP: LLM-Generation of Egocentric Action Programs
Authors: Eadom Dessalene, Michael Maynord, Cornelia Fermüller, Yiannis Aloimonos
Date: 2023-11-29 00:00:00
Tasks: Action Recognition, Language Modeling, Language Modelling, Large Language Model

We introduce LEAP (illustrated in Figure 1), a novel method for generating video-grounded action programs through use of a Large Language Model (LLM). These action programs represent the motoric, perceptual, and structural aspects of action, and consist of sub-actions, pre- and post-conditions, and control flows. LEAP's action programs are centered on egocentric video and employ recent developments in LLMs both as a source for program knowledge and as an aggregator and assessor of multimodal video information. We apply LEAP over a majority (87\%) of the training set of the EPIC Kitchens dataset, and release the resulting action programs as a publicly available dataset here (https://drive.google.com/drive/folders/1Cpkw_TI1IIxXdzor0pOXG3rWJWuKU5Ex?usp=drive_link). We employ LEAP as a secondary source of supervision, using its action programs in a loss term applied to action recognition and anticipation networks. We demonstrate sizable improvements in performance in both tasks due to training with the LEAP dataset. Our method achieves 1st place on the EPIC Kitchens Action Recognition leaderboard as of November 17 among the networks restricted to RGB-input (see Supplementary Materials).
Modeling False Data Injection Attacks in Integrated Electricity-Gas Systems
Authors: Rong-Peng Liu, Xiaozhe Wang, Zuyi Li, Rawad Zgheib
Date: 2023-12-01 00:00:00
Tasks: State Estimation

This work studies the modeling of false data injection attacks (FDIAs) in integrated electricity-gas systems (IEGSs). First, we introduce a static state estimation model and bad data detection method for IEGSs. Then, we develop FDIAs on IEGSs with complete network topology and parameter information. Next, we develop FDIAs on IEGSs when intruders have only local network topology and parameter information of an IEGS. Lastly, we explore FDIAs on IEGSs when intruders have only local network topology information of an IEGS.
A Data-Driven Safety Preserving Control Architecture for Constrained Cyber-Physical Systems
Authors: Mehran Attar, Walter Lucia
Date: 2023-12-01 00:00:00

In this paper, we propose a data-driven networked control architecture for unknown and constrained cyber-physical systems capable of detecting networked false-data-injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data-driven outer approximation of the expected robust one-step reachable set. On the other hand, on the plant's side, we design a data-driven safety verification module, which resorts to worst-case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data-driven set-theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre-established safe configuration until an attack-free condition is recovered. Numerical simulations involving a two-tank water system illustrate the features and capabilities of the proposed control architecture.
Automated design space exploration for poultry processing systems using discrete-event simulation
Authors: Nick Paape, Joost van Eekelen, Michel Reniers
Date: 2023-12-01 00:00:00

The poultry processing industry struggles to keep up with new developments in meat consumption and livestock breeding. Designing poultry processing systems is becoming increasingly more complex, and an increasing number of iterations of (re)design are required to optimize the product flow in these systems. To address this issue, this study presents a discrete-event simulation-based method for design space exploration of production systems. This method is mostly automated, greatly reducing the time and effort required in the design process. The steps that are automated are iterating on the design, model construction, performing simulation experiments, and interpreting the simulation results. An industrial case study in which a poultry processing system is redesigned is used to validate the effectiveness of the proposed method. A detailed description of this case study is given to showcase the different ways in which this design space exploration method can be used.
DeepTreeGANv2: Iterative Pooling of Point Clouds
Authors: Moritz Alfons Wilhelm Scham, Dirk Krücker, Kerstin Borras
Date: 2023-11-24 00:00:00

In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the complex dependencies between the particles must be correctly modelled. Particle showers are inherently tree-based processes, as each particle is produced by the decay or detector interaction of a particle of the previous generation. In this work, we present a significant extension to DeepTreeGAN, featuring a critic, that is able to aggregate such point clouds iteratively in a tree-based manner. We show that this model can reproduce complex distributions, and we evaluate its performance on the public JetNet 150 dataset.
A Posteriori Evaluation of a Physics-Constrained Neural Ordinary Differential Equations Approach Coupled with CFD Solver for Modeling Stiff Chemical Kinetics
Authors: Tadbhagya Kumar, Anuj Kumar, Pinaki Pal
Date: 2023-11-22 00:00:00

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. These models often require solving a system of coupled stiff ordinary differential equations (ODEs). While deep learning techniques have been experimented with to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because deep learning methods optimize for training error without ensuring compatibility with ODE solvers, leading to accumulation of errors over time. Recently, NeuralODE-based techniques have offered a promising solution by effectively modeling chemical kinetics. In this study, we extend the NeuralODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass and the elemental mass are conserved, a critical requirement for reliable downstream integration with CFD solvers. Proof-of-concept studies are performed with physics-constrained neuralODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. Our results demonstrate that this enhancement not only improves the physical consistency with respect to mass conservation criteria but also ensures better robustness. Lastly, a posteriori studies are performed wherein the trained PC-NODE model is coupled with a 3D CFD solver for computing the chemical source terms. PC-NODE is shown to be more accurate relative to the purely data-driven neuralODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.
Inference on common trends in functional time series
Authors: Morten Ørregaard Nielsen, Won-Ki Seo, Dakyung Seong
Date: 2023-12-01 00:00:00
Tasks: Time Series, valid

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, i.e., the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time series of interest takes values in a subspace of possibly unknown dimension. This has wide applicability in practice; for example, to the case of cointegrated vector time series that are either high-dimensional or of finite dimension, to high-dimensional factor model that includes a finite number of nonstationary factors, to cointegrated curve-valued (or function-valued) time series, and to nonstationary dynamic functional factor models. We include two empirical illustrations to the term structure of interest rates and labor market indices, respectively.
GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications
Authors: Maria Elena Bontempi, Jan Ditzen
Date: 2023-12-01 00:00:00

We introduce a new estimator, CRE-GMM, which exploits the correlated random effects (CRE) approach within the generalised method of moments (GMM), specifically applied to level equations, GMM-lev. It has the advantage of estimating the effect of measurable time-invariant covariates using all available information. This is not possible with GMM-dif, applied to the equations of each period transformed into first differences, while GMM-sys uses little information as it adds the equation in levels for only one period. The GMM-lev, by implying a two-component error term containing individual heterogeneity and shock, exposes the explanatory variables to possible double endogeneity. For example, the estimation of actual persistence could suffer from bias if instruments were correlated with the unit-specific error component. The CRE-GMM deals with double endogeneity, captures initial conditions and enhance inference. Monte Carlo simulations for different panel types and under different double endogeneity assumptions show the advantage of our approach. The empirical applications on production and R&D contribute to clarify the advantages of using CRE-GMM.
Stochastic volatility models with skewness selection
Authors: Igor Ferreira Batista Martins, Hedibert Freitas Lopes
Date: 2023-12-01 00:00:00

This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically selects the skewness parameter as being dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening being partially explained by central banks' mandates. In an currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility which supports the idea of carry crashes being the result of volatility surges instead of dynamic skewness.
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
Authors: Martin Lnenicka, Anastasija Nikiforova, Mariusz Luterek, Petar Milic, Daniel Rudmark, Sebastian Neumaier, Caterina Santoro, Cesar Casiano Flores, Marijn Janssen, Manuel Pedro Rodríguez Bolívar
Date: 2023-12-01 00:00:00
Tasks: Benchmarking

Open government and open (government) data are seen as tools to create new opportunities, eliminate or at least reduce information inequalities and improve public services. More than a decade of these efforts has provided much experience, practices, and perspectives to learn how to better deal with them. This paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. To do this, we studied benchmarks and indices published over the last years (57 editions of 8 artifacts) and conducted a comparative case study of eight European countries, identifying patterns among them considering different potentially relevant contexts such as e-government, open government data, open data indices and rankings, and others relevant for the country under consideration. Using a Delphi method, we reached a consensus within a panel of experts and validated a final list of 94 patterns, including their frequency of occurrence among studied countries and their effects on the respective countries. Finally, we took a closer look at the developments in identified contexts over the years and defined 21 recommendations for more resilient and sustainable open government data initiatives and ecosystems and future steps in this area.
Causal propensity as an antecedent of entrepreneurial intentions in tourism students
Authors: Alicia Martin-Navarro, Felix Velicia-Martin, Jose Aurelio Medina-Garrido, Ricardo Gouveia Rodrigues
Date: 2023-12-01 00:00:00
Tasks: Decision Making

The tourism sector is a sector with many opportunities for business development. Entrepreneurship in this sector promotes economic growth and job creation. Knowing how entrepreneurial intention develops facilitates its transformation into entrepreneurial behaviour. Entrepreneurial behaviour can adopt a causal logic, an effectual logic or a combination of both. Considering the causal logic, decision-making is done through prediction. In this way, entrepreneurs try to increase their market share by planning strategies and analysing possible deviations from their plans. Previous literature studies causal entrepreneurial behaviour, as well as variables such as creative innovation, proactive decisions and entrepreneurship training when the entrepreneur has already created his or her firm. However, there is an obvious gap at a stage prior to the start of entrepreneurial activity when the entrepreneurial intention is formed. This paper analyses how creativity, proactivity, entrepreneurship education and the propensity for causal behaviour influence entrepreneurial intentions. To achieve the research objective, we analysed a sample of 464 undergraduate tourism students from two universities in southern Spain. We used SmartPLS 3 software to apply a structural equation methodology to the measurement model composed of nine hypotheses. The results show, among other relationships, that causal propensity, entrepreneurship learning programmes and proactivity are antecedents of entrepreneurial intentions. These findings have implications for theory, as they fill a gap in the field of entrepreneurial intentions. Considering propensity towards causal behaviour before setting up the firm is unprecedented. Furthermore, the results of this study have practical implications for the design of public education policies and the promotion of business creation in the tourism sector.