pwc-archive/papers-with-abstracts huggingface dataset:
Investigating social alignment via mirroring in a system of interacting language models
Authors: Harvey McGuinness, Tianyu Wang, Carey E. Priebe, Hayden Helm
Date: 2024-12-07 00:00:00
Tasks: Sociology

Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of the effect of mirroring on alignment have been limited due to the scalability of traditional experimental designs in sociology. In this paper, we introduce a simple computational framework that enables studying the effect of mirroring behavior on alignment in multi-agent systems. We simulate systems of interacting large language models in this framework and characterize overall system behavior and alignment with quantitative measures of agent dynamics. We find that system behavior is strongly influenced by the range of communication of each agent and that these effects are exacerbated by increased rates of mirroring. We discuss the observed simulated system behavior in the context of known human social dynamics.
Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor
Authors: Ashwin Baluja
Date: 2024-12-01 00:00:00
Tasks: All, Natural Language Understanding, Rhythm, text-to-speech, Text to Speech

While Large Language Models (LLMs) have demonstrated impressive natural language understanding capabilities across various text-based tasks, understanding humor has remained a persistent challenge. Humor is frequently multimodal, relying on phonetic ambiguity, rhythm and timing to convey meaning. In this study, we explore a simple multimodal prompting approach to humor understanding and explanation. We present an LLM with both the text and the spoken form of a joke, generated using an off-the-shelf text-to-speech (TTS) system. Using multimodal cues improves the explanations of humor compared to textual prompts across all tested datasets.
What AI evaluations for preventing catastrophic risks can and cannot do
Authors: Peter Barnett, Lisa Thiergart
Date: 2024-11-26 00:00:00

AI evaluations are an important component of the AI governance toolkit, underlying current approaches to safety cases for preventing catastrophic risks. Our paper examines what these evaluations can and cannot tell us. Evaluations can establish lower bounds on AI capabilities and assess certain misuse risks given sufficient effort from evaluators. Unfortunately, evaluations face fundamental limitations that cannot be overcome within the current paradigm. These include an inability to establish upper bounds on capabilities, reliably forecast future model capabilities, or robustly assess risks from autonomous AI systems. This means that while evaluations are valuable tools, we should not rely on them as our main way of ensuring AI systems are safe. We conclude with recommendations for incremental improvements to frontier AI safety, while acknowledging these fundamental limitations remain unsolved.
Digital Democracy in the Age of Artificial Intelligence
Authors: Claudio Novelli, Giulia Sandri
Date: 2024-11-26 00:00:00
Tasks: Misinformation

This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digital citizenship, the chapter examines how AI can improve online engagement and promote ethical behaviour while posing privacy risks and fostering identity stereotyping. Regarding political participation, it highlights AI's dual role in mobilising civic actions and spreading misinformation. Regarding representation, AI's involvement in electoral processes can enhance voter registration, e-voting, and the efficiency of result tabulation but raises concerns regarding privacy and public trust. Also, AI's predictive capabilities shift the dynamics of political competition, posing ethical questions about manipulation and the legitimacy of democracy. Finally, the chapter examines how integrating AI and digital technologies can facilitate democratic political advocacy and personalised communication. However, this also comes with higher risks of misinformation and targeted propaganda.
Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework
Authors: Sotiris Pelekis, Evangelos Karakolis, George Lampropoulos, Spiros Mouzakitis, Ourania Markaki, Christos Ntanos, Dimitris Askounis
Date: 2024-11-25 00:00:00
Tasks: Ethics

The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAI application framework, called E-TAI, tailored for energy applications, including smart grid and smart building systems. This framework draws inspiration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAI to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation
Authors: Linqi Lu, Xianshi Yu, Akhil Perumal Reddy
Date: 2024-11-22 00:00:00
Tasks: Denoising, Marketing

This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagement. Leveraging the CLIP model, we analyzed 42,743 images from Facebook (March 1 to August 31, 2021), with a focus on detecting food-related visuals and examining the influence of image attributes such as colorfulness and brightness on user interaction. For textual analysis, we utilized the BART model as a denoising autoencoder to classify ten topics derived from structural topic modeling, exploring their relationship with user engagement. Linear regression analysis identified significant positive correlations between food-related visuals (e.g., fruit, candy, and bakery) and user engagement scores, as well as between engagement and text topics such as cannabis legalization. In contrast, negative associations were observed with image colorfulness and certain textual themes. These findings offer actionable insights for policymakers and regulatory bodies in designing warning labels and marketing regulations to address potential risks associated with recreational cannabis edibles.
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems
Authors: Qian Yu, Zhen Xu, Zong Ke
Date: 2024-11-21 00:00:00
Tasks: Anomaly Detection, Contrastive Learning

In the context of globalization and the rapid expansion of the digital economy, anti-money laundering (AML) has become a crucial aspect of financial oversight, particularly in cross-border transactions. The rising complexity and scale of international financial flows necessitate more intelligent and adaptive AML systems to combat increasingly sophisticated money laundering techniques. This paper explores the application of unsupervised learning models in cross-border AML systems, focusing on rule optimization through contrastive learning techniques. Five deep learning models, ranging from basic convolutional neural networks (CNNs) to hybrid CNNGRU architectures, were designed and tested to assess their performance in detecting abnormal transactions. The results demonstrate that as model complexity increases, so does the system's detection accuracy and responsiveness. In particular, the self-developed hybrid Convolutional-Recurrent Neural Integration Model (CRNIM) model showed superior performance in terms of accuracy and area under the receiver operating characteristic curve (AUROC). These findings highlight the potential of unsupervised learning models to significantly improve the intelligence, flexibility, and real-time capabilities of AML systems. By optimizing detection rules and enhancing adaptability to emerging money laundering schemes, this research provides both theoretical and practical contributions to the advancement of AML technologies, which are essential for safeguarding the global financial system against illicit activities.
Dynamic data summarization for hierarchical spatial clustering
Authors: Kayumov Abduaziz, Min Sik Kim, Ji Sun Shin
Date: 2024-11-26 00:00:00
Tasks: Clustering, Data Summarization

Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static applications, so have recent studies focused on accelerating the algorithm for static applications using approximate or parallel methods. However, much less attention has been given to dynamic environments, where even a single point insertion or deletion can require recomputing the clustering hierarchy from scratch due to the need of maintaining the minimum spanning tree (MST) over a complete graph. This paper addresses the challenge of enhancing the clustering algorithm for dynamic data. We present an exact algorithm that maintains density information and updates the clustering hierarchy of HDBSCAN during point insertions and deletions. Considering the hardness of adapting the exact algorithm to dynamic data involving modern workloads, we propose an online-offline framework. The online component efficiently summarizes dynamic data using a tree structure, called Bubble-tree, while the offline step performs the static clustering. Experimental results demonstrate that the data summarization adapts well to fully dynamic environments, providing compression quality on par with existing techniques while significantly improving runtime performance of the clustering algorithm in dynamic data workloads.
Towards Agentic Schema Refinement
Authors: Agapi Rissaki, Ilias Fountalis, Nikolaos Vasiloglou, Wolfgang Gatterbauer
Date: 2024-11-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views, effectively acting as a refined version of the schema. To discover these views, we introduce a multi-agent Large Language Model (LLM) simulation where LLM agents collaborate to iteratively define and refine views with minimal input. Our approach paves the way for LLM-powered exploration of unwieldy databases.
Automatic answering of scientific questions using the FACTS-V1 framework: New methods in research to increase efficiency through the use of AI
Authors: Stefan Pietrusky
Date: 2024-12-01 00:00:00

The use of artificial intelligence (AI) offers various possibilities to expand and support educational research. Specifically, the implementation of AI can be used to develop new frameworks to establish new research tools that accelerate and meaningfully expand the efficiency of data evaluation and interpretation (Buckingham Shum et al., 2023). This article presents the prototype of the FACTS-V1 (Filtering and Analysis of Content in Textual Sources) framework. With the help of the application, numerous scientific papers can be automatically extracted, analyzed and interpreted from open access document servers without having to rely on proprietary applications and their limitations. The FACTS-V1 prototype consists of three building blocks. The first part deals with the extraction of texts, the second with filtering and interpretation, and the last with the actual statistical evaluation (topic modeling) using an interactive overview. The aim of the framework is to provide recommendations for future scientific questions based on existing data. The functionality is illustrated by asking how the use of AI will change the education sector. The data used to answer the question comes from 82 scientific papers on the topic of AI from 2024. The papers are publicly available on the peDOCS document server of the Leibniz Institute for Educational Research and Educational Information.
Publication Trends in Artificial Intelligence Conferences: The Rise of Super Prolific Authors
Authors: Ariful Azad, Afeefa Banu
Date: 2024-11-28 00:00:00

Papers published in top conferences contribute influential discoveries that are reshaping the landscape of modern Artificial Intelligence (AI). We analyzed 87,137 papers from 11 AI conferences to examine publication trends over the past decade. Our findings reveal a consistent increase in both the number of papers and authors, reflecting the growing interest in AI research. We also observed a rise in prolific researchers who publish dozens of papers at the same conference each year. In light of this analysis, the AI research community should consider revisiting authorship policies, addressing equity concerns, and evaluating the workload of junior researchers to foster a more sustainable and inclusive research environment.
Investigation of in vitro neuronal activity processing using a CMOS-integrated ZrO2-based memristive crossbar
Authors: Maria N. Koryazhkina, Albina V. Lebedeva, Darina D. Pakhomova, Ivan N. Antonov, Vyacheslav V. Razin, Elena D. Budylina, Alexey I. Belov, Alexey N. Mikhaylov, Anton A. Konakov
Date: 2024-12-10 00:00:00
Tasks: Hippocampus

The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was studied. Epileptiform neuronal activity was obtained in vitro in the hippocampus slices of laboratory mice using 4-aminopyridine experimental model. Synaptic plasticity of the memristive crossbar induced by epileptiform neuronal activity pulses was detected. Qualitatively, the results obtained in the case of normal (without pathologies) and epileptiform neuronal activity with and without noise coincide. For quantitative analysis, the value of the relative change in synaptic weight has been calculated for such important biological mechanisms of synapses as paired-pulse facilitation/depression, post-tetanic potentiation/depression, and long-term potentiation/depression. It has been shown that average value of the relative change in synaptic weight and its are smaller mainly in the case of epileptiform neuronal activity pulses. An effect of the influence of noise included in the neuronal activity was found, which consists in the fact that the current response of the memristive crossbar is smaller in the presence of noise. The results of this study can be used in the development of new generation hardware-implemented computing devices with high performance and energy efficiency for the tasks of restorative medicine and robotics. In particular, using these results, neurohybrid devices can be developed for processing epileptiform activity in real time and for its suppression.
Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis
Authors: Joseph Nyangon, Ruth Akintunde
Date: 2024-11-25 00:00:00
Tasks: Anomaly Detection, Management

Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex generation data and heteroskedasticity. Utilizing principal component analysis (PCA), we analyze CAISO's hourly electricity prices and demand from 2016-2021 to improve day-ahead forecasting accuracy. Initially, we apply traditional outlier analysis with the interquartile range method, followed by robust PCA (RPCA) for more effective outlier elimination. This approach improves data symmetry and reduces skewness. We then construct multiple linear regression models using both raw and PCA-transformed features. The model with transformed features, refined through traditional and SAS Sparse Matrix outlier removal methods, shows superior forecasting performance. The SAS Sparse Matrix method, in particular, significantly enhances model accuracy. Our findings demonstrate that PCA-based methods are key in advancing electricity price forecasting, supporting renewable integration and grid management in day-ahead markets. Keywords: Electricity price forecasting, principal component analysis (PCA), power system planning, heteroskedasticity, renewable energy integration.
A FinTech Clustering Framework: Technology, Model, and Stakeholder Perspectives
Authors: Pak-Lok Poon, Santoso Wibowo, Sau-Fun Tang
Date: 2024-11-21 00:00:00
Tasks: Clustering

Nowadays, the global booming of FinTech can be seen everywhere. FinTech has created innovative disruptions to traditional, long-established financial institutions (e.g., banks and insurance companies) in financial services markets. Despite of its popularity, there are many different definitions of FinTech. This problem occurs because many existing studies only focus on a particular aspect of FinTech without a comprehensive and in-depth analysis. This problem will hinder further development and industrial application of FinTech. In view of this problem, we perform a narrative review involving over 100 relevant studies or reports, with a view to developing a FinTech clustering framework for providing a more comprehensive and holistic view of FinTech. Furthermore, we use an Indian FinTech firm to illustrate how to apply our clustering framework for analysis.
Flexible Mesh Segmentation via Reeb Graph Representation of Geometrical and Topological Features
Authors: Florian Beguet, Sandrine Lanquetin, Romain Raffin
Date: 2024-12-05 00:00:00
Tasks: Computational Efficiency, Segmentation

This paper presents a new mesh segmentation method that integrates geometrical and topological features through a flexible Reeb graph representation. The algorithm consists of three phases: construction of the Reeb graph using the improved topological skeleton approach, topological simplification of the graph by cancelling critical points while preserving essential features, and generation of contiguous segments via an adaptive region-growth process that takes geometric and topological criteria into account. Operating with a computational complexity of O(n log(n)) for a mesh of n vertices, the method demonstrates both efficiency and scalability. An evaluation through case studies, including part-based decomposition with Shape Diameter Function and terrain analysis with Shape Index, validates the effectiveness of the method in completely different applications. The results establish this approach as a robust framework for advanced geometric analysis of meshes, connecting the geometric and topological features of shapes.
Learning to be Indifferent in Complex Decisions: A Coarse Payoff-Assessment Model
Authors: Philippe Jehiel, Aviman Satpathy
Date: 2024-12-12 00:00:00

We introduce the Coarse Payoff-Assessment Learning (CPAL) model, which captures reinforcement learning by boundedly rational decision-makers who focus on the aggregate outcomes of choosing among exogenously defined clusters of alternatives (similarity classes), rather than evaluating each alternative individually. Analyzing a smooth approximation of the model, we show that the learning dynamics exhibit steady-states corresponding to smooth Valuation Equilibria (Jehiel and Samet, 2007). We demonstrate the existence of multiple equilibria in decision trees with generic payoffs and establish the local asymptotic stability of pure equilibria when they occur. Conversely, when trivial choices featuring alternatives within the same similarity class yield sufficiently high payoffs, a unique mixed equilibrium emerges, characterized by indifferences between similarity classes, even under acute sensitivity to payoff differences. Finally, we prove that this unique mixed equilibrium is globally asymptotically stable under the CPAL dynamics.
A Joint Energy and Differentially-Private Smart Meter Data Market
Authors: Saurab Chhachhi, Fei Teng
Date: 2024-12-10 00:00:00

Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. This paper proposes a joint energy and data market focusing on the day-ahead retailer energy procurement problem with uncertain demand. The retailer can purchase differentially-private smart meter data from consumers to reduce uncertainty. The problem is modelled as an integrated forecasting and optimisation problem providing a means of valuing data directly rather than valuing forecasts or forecast accuracy. Value is determined by the Wasserstein distance, enabling privacy to be preserved during the valuation and procurement process. The value of joint energy and data clearing is highlighted through numerical case studies using both synthetic and real smart meter data.
Undergraduate Course Allocation through Competitive Markets
Authors: Daniel Kornbluth, Alexey Kushnir
Date: 2024-12-07 00:00:00
Tasks: Fairness

Prevailing methods of course allocation at undergraduate institutions involve reserving seats to give priority to designated groups of students. We introduce a competitive equilibrium-based mechanism that assigns course seats using student preferences and course priorities. This mechanism satisfies approximate notions of stability, efficiency, envy-freeness, and strategy-proofness. We evaluate its performance relative to a mechanism widely used in practice using preferences estimated from university data. Our empirical findings demonstrate an improvement in student satisfaction and allocation fairness. The number of students who envy another student of weakly lower priority declines by 8 percent, or roughly 500 students.
Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
Authors: Arend Hintze, Christoph Adami
Date: 2024-12-06 00:00:00

The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regulatory approaches to using AI as facilitators of cooperation. We investigate three scenarios: (1) Mandatory Cooperation Policy for AI Agents, where AI agents are institutionally mandated always to cooperate; (2) Player-Controlled Agent Cooperation Policy, where players evolve control over AI agents' likelihood to cooperate; and (3) Agents Mimic Players, where AI agents copy the behavior of players. Using a computational evolutionary model with a population of agents playing public goods games, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma. This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.