pwc-archive/papers-with-abstracts huggingface dataset:
Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and MCDM evaluation
Authors: Abdullah Hulusi Kökçam, Caner Erden, Alparslan Serhat Demir, Talas Fikret Kurnaz
Date: 2024-04-01 00:00:00
Proceeding: journal 2024 4
Tasks: Articles, Decision Making

The geotechnical phenomenon of soil liquefaction has serious implications for infrastructure and human safety, making it crucial to develop effective prediction and mitigation strategies as urbanization and infrastructure development expand. Recently, there has been significant interest in the potential of artificial intelligence (AI) techniques to address complex geotechnical issues, such as soil liquefaction. This study provides a bibliometric analysis of research literature on AI applications in predicting soil liquefaction. By systematically searching the Web of Science database, we identified 258 relevant articles published between 1994 and 2023 and applied bibliometric indicators to analyze publication trends, authorship patterns, affiliated institutions, publication venues, and citation patterns. This study presents a novel approach to evaluating the results obtained from bibliometric analysis. The MULTIMOORA method, a Multi-Criteria Decision Making (MCDM) technique, was employed to analyze further the journals that contributed to creating an academic knowledge inventory regarding AI techniques in soil liquefaction. This study demonstrates the utility of MCDM techniques as aggregators of bibliometric analysis results and their ability to facilitate decision-making. The interdisciplinary nature of this field, combining geotechnical engineering, computer science, and machine learning, is highlighted. The study also reveals a steady rise in publications on AI in liquefaction, with a notable increase in 2011 and 2019. The Soil Dynamics and Earthquake Engineering journal is shown to be particularly significant in studies on soil liquefaction prediction with AI techniques, followed by the Bulletin of Engineering Geology and the Environment and Environmental Earth Sciences journals.
Deep Learning Models for Atypical Serotonergic Cells Recognition
Authors: Daniele Corradetti, Alessandro Bernardi, Renato Corradetti
Date: 2024-05-09 00:00:00
Tasks: Deep Learning, Diversity

The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied. We propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials.
Some variation of COBRA in sequential learning setup
Authors: Aryan Bhambu, Arabin Kumar Dey
Date: 2024-04-07 00:00:00
Tasks: Bayesian Optimisation, Load Forecasting, Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.
Differentially Private Federated Learning without Noise Addition: When is it Possible?
Authors: Jiang Zhang, Konstantinos Psounis, Salman Avestimehr
Date: 2024-05-06 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the server from learning information about users' data from their individual encrypted model updates. Recent research has extended privacy guarantees of FL with SA by bounding the information leakage through the aggregate model over multiple training rounds thanks to leveraging the "noise" from other users' updates. However, the privacy metric used in that work (mutual information) measures the on-average privacy leakage, without providing any privacy guarantees for worse-case scenarios. To address this, in this work we study the conditions under which FL with SA can provide worst-case differential privacy guarantees. Specifically, we formally identify the necessary condition that SA can provide DP without addition noise. We then prove that when the randomness inside the aggregated model update is Gaussian with non-singular covariance matrix, SA can provide differential privacy guarantees with the level of privacy $\epsilon$ bounded by the reciprocal of the minimum eigenvalue of the covariance matrix. However, we further demonstrate that in practice, these conditions are almost unlikely to hold and hence additional noise added in model updates is still required in order for SA in FL to achieve DP. Lastly, we discuss the potential solution of leveraging inherent randomness inside aggregated model update to reduce the amount of addition noise required for DP guarantee.
CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
Authors: Kingsley Nweye, Kathryn Kaspar, Giacomo Buscemi, Tiago Fonseca, Giuseppe Pinto, Dipanjan Ghose, Satvik Duddukuru, Pavani Pratapa, Han Li, Javad Mohammadi, Luis Lino Ferreira, Tianzhen Hong, Mohamed Ouf, Alfonso Capozzoli, Zoltan Nagy
Date: 2024-05-02 00:00:00
Tasks: Benchmarking, Management, reinforcement-learning, Reinforcement Learning

As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation.
Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning
Authors: Ming Kuo, Shouvon Sarker, Lijun Qian, Yujian Fu, Xiangfang Li, Xishuang Dong
Date: 2024-04-25 00:00:00
Tasks: Knowledge Tracing

In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be made to meet individual needs. Recent advancements in deep learning have successfully enhanced knowledge tracking through Deep Knowledge Tracing (DKT). This paper introduces generative AI models to further enhance DKT. Generative AI models, rooted in deep learning, are trained to generate synthetic data, addressing data scarcity challenges in various applications across fields such as natural language processing (NLP) and computer vision (CV). This study aims to tackle data shortage issues in student learning records to enhance DKT performance for PAL. Specifically, it employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The proposed method's effectiveness is validated through extensive experiments on ASSISTments datasets. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance, particularly in scenarios with small data for training and large data for testing.
Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
Authors: Zhaoxing Li, Jujie Yang, Jindi Wang, Lei Shi, Sebastian Stein
Date: 2024-04-24 00:00:00
Tasks: Knowledge Tracing

The field of Knowledge Tracing aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed Knowledge Tracing models that use data from Intelligent Tutoring Systems to predict students' subsequent actions. However, with the development of Intelligent Tutoring Systems, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based Knowledge Tracing models face obstacles such as low efficiency, low accuracy, and low interpretability when dealing with large-scale datasets containing long-sequence data. To address these issues and promote the sustainable development of Intelligent Tutoring Systems, we propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings block to deal with different difficulty levels information and an LSTM block to process the sequential characteristic in students' actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC. Additionally, an ablation study is conducted to analyse the impact of each component of LBKT's overall performance. Moreover, we used t-SNE as the visualisation tool to demonstrate the model's embedding strategy. The results indicate that LBKT is faster, more interpretable, and has a lower memory cost than the traditional deep learning based Knowledge Tracing methods.
Ordinal Behavior Classification of Student Online Course Interactions
Authors: Thomas Trask
Date: 2024-04-20 00:00:00
Tasks: Classification

The study in interaction patterns between students in on-campus and MOOC-style online courses has been broadly studied for the last 11 years. Yet there remains a gap in the literature comparing the habits of students completing the same course offered in both on-campus and MOOC-style online formats. This study will look at browser-based usage patterns for students in the Georgia Tech CS1301 edx course for both the online course offered to on-campus students and the MOOCstyle course offered to anyone to determine what, if any, patterns exist between the two cohorts.
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
Authors: Alexander Scarlatos, Wanyong Feng, Digory Smith, Simon Woodhead, Andrew Lan
Date: 2024-04-19 00:00:00
Tasks: Distractor Generation, Math, Misconceptions, Multiple-choice

Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been challenging. In this work, we propose a novel method to enhance the quality of generated distractors through overgenerate-and-rank, training a ranking model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones.
Testing the Fairness-Accuracy Improvability of Algorithms
Authors: Eric Auerbach, Annie Liang, Kyohei Okumura, Max Tabord-Meehan
Date: 2024-05-08 00:00:00
Tasks: Fairness

Many organizations use algorithms that have a disparate impact, i.e., the benefits or harms of the algorithm fall disproportionately on certain social groups. Addressing an algorithm's disparate impact can be challenging, however, because it is often unclear whether it is possible to reduce this impact without sacrificing other objectives of the organization, such as accuracy or profit. Establishing the improvability of algorithms with respect to multiple criteria is of both conceptual and practical interest: in many settings, disparate impact that would otherwise be prohibited under US federal law is permissible if it is necessary to achieve a legitimate business interest. The question is how a policy-maker can formally substantiate, or refute, this "necessity" defense. In this paper, we provide an econometric framework for testing the hypothesis that it is possible to improve on the fairness of an algorithm without compromising on other pre-specified objectives. Our proposed test is simple to implement and can be applied under any exogenous constraint on the algorithm space. We establish the large-sample validity and consistency of our test, and microfound the test's robustness to manipulation based on a game between a policymaker and the analyst. Finally, we apply our approach to evaluate a healthcare algorithm originally considered by Obermeyer et al. (2019), and quantify the extent to which the algorithm's disparate impact can be reduced without compromising the accuracy of its predictions.
Small-Scale Testbed for Evaluating C-V2X Applications on 5G Cellular Networks
Authors: Kaj Munhoz Arfvidsson, Kleio Fragkedaki, Frank J. Jiang, Vandana Narri, Hans-Cristian Lindh, Karl H. Johansson, Jonas Mårtensson
Date: 2024-05-09 00:00:00

In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed's capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.
Optimizing Prosumer Policies in Periodic Double Auctions Inspired by Equilibrium Analysis (Extended Version)
Authors: Bharat Manvi, Sanjay Chandlekar, Easwar Subramanian
Date: 2024-05-07 00:00:00

We consider a periodic double auction (PDA) wherein the main participants are wholesale suppliers and brokers representing retailers. The suppliers are represented by a composite supply curve and the brokers are represented by individual bids. Additionally, the brokers can participate in small-scale selling by placing individual asks; hence, they act as prosumers. Specifically, in a PDA, the prosumers who are net buyers have multiple opportunities to buy or sell multiple units of a commodity with the aim of minimizing the cost of buying across multiple rounds of the PDA. Formulating optimal bidding strategies for such a PDA setting involves planning across current and future rounds while considering the bidding strategies of other agents. In this work, we propose Markov perfect Nash equilibrium (MPNE) policies for a setup where multiple prosumers with knowledge of the composite supply curve compete to procure commodities. Thereafter, the MPNE policies are used to develop an algorithm called MPNE-BBS for the case wherein the prosumers need to re-construct an approximate composite supply curve using past auction information. The efficacy of the proposed algorithm is demonstrated on the PowerTAC wholesale market simulator against several baselines and state-of-the-art bidding policies.
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence
Authors: Xiaomeng Chen, Wei Huo, Kemi Ding, Subhrakanti Dey, Ling Shi
Date: 2024-05-08 00:00:00
Tasks: Privacy Preserving

The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches often address these critical concerns in isolation. This work introduces a unified framework, named CDP-NES, designed to improve communication efficiency in the privacy-preserving NE seeking algorithm for distributed non-cooperative games over directed graphs. Leveraging both general compression operators and the noise adding mechanism, CDP-NES perturbs local states with Laplacian noise and applies difference compression prior to their exchange among neighbors. We prove that CDP-NES not only achieves linear convergence to a neighborhood of the NE in games with restricted monotone mappings but also guarantees $\epsilon$-differential privacy, addressing privacy and communication efficiency simultaneously. Finally, simulations are provided to illustrate the effectiveness of the proposed method.
Channel Estimation for Holographic MIMO: Wavenumber-Domain Sparsity Inspired Approaches
Authors: Yuqing Guo, Yuanbin Chen, Ying Wang
Date: 2024-05-09 00:00:00
Tasks: compressed sensing

This paper investigates the sparse channel estimation for holographic multiple-input multiple-output (HMIMO) systems. Given that the wavenumber-domain representation is based on a series of Fourier harmonics that are in essence a series of orthogonal basis functions, a novel wavenumber-domain sparsifying basis is designed to expose the sparsity inherent in HMIMO channels. Furthermore, by harnessing the beneficial sparsity in the wavenumber domain, the sparse estimation of HMIMO channels is structured as a compressed sensing problem, which can be efficiently solved by our proposed wavenumber-domain orthogonal matching pursuit (WD-OMP) algorithm. Finally, numerical results demonstrate that the proposed wavenumber-domain sparsifying basis maintains its detection accuracy regardless of the number of antenna elements and antenna spacing. Additionally, in the case of antenna spacing being much less than half a wavelength, the wavenumber-domain approach remains highly accurate in identifying the significant angular power of HMIMO channels.
Some Notes on the Sample Complexity of Approximate Channel Simulation
Authors: Gergely Flamich, Lennie Wells
Date: 2024-05-07 00:00:00
Tasks: Data Compression

Channel simulation algorithms can efficiently encode random samples from a prescribed target distribution $Q$ and find applications in machine learning-based lossy data compression. However, algorithms that encode exact samples usually have random runtime, limiting their applicability when a consistent encoding time is desirable. Thus, this paper considers approximate schemes with a fixed runtime instead. First, we strengthen a result of Agustsson and Theis and show that there is a class of pairs of target distribution $Q$ and coding distribution $P$, for which the runtime of any approximate scheme scales at least super-polynomially in $D_\infty[Q \Vert P]$. We then show, by contrast, that if we have access to an unnormalised Radon-Nikodym derivative $r \propto dQ/dP$ and knowledge of $D_{KL}[Q \Vert P]$, we can exploit global-bound, depth-limited A* coding to ensure $\mathrm{TV}[Q \Vert P] \leq \epsilon$ and maintain optimal coding performance with a sample complexity of only $\exp_2\big((D_{KL}[Q \Vert P] + o(1)) \big/ \epsilon\big)$.
Enhancing Holonic Architecture with Natural Language Processing for System of Systems
Authors: Muhammad Ashfaq, Ahmed R. Sadik, Tommi Mikkonen, Muhammad Waseem, Niko M akitalo
Date: 2024-05-08 00:00:00
Tasks: Position

The complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Conversational Generative Intelligence (CGI) techniques. Our approach leverages advancements in CGI, specifically Large Language Models (LLMs), to enable holons to understand and act on natural language instructions. This fosters more intuitive human-holon interactions, improving social intelligence and ultimately leading to better coordination among diverse systems. This position paper outlines a conceptual framework for CGI-enhanced holon interaction, discusses the potential impact on SoS adaptability, usability and efficiency, and sets the stage for future exploration and prototype implementation.
Resource Optimization in UAV-assisted IoT Networks: The Role of Generative AI
Authors: Sana Sharif, Sherali Zeadally, Waleed Ejaz
Date: 2024-05-06 00:00:00
Tasks: Decision Making, Management

We investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.