pwc-archive/papers-with-abstracts huggingface dataset:
Wastewater Treatment Plant Data for Nutrient Removal System
Authors: Esmaeel Mohammadi, Anju Rani, Mikkel Stokholm-Bjerregaard, Daniel Ortiz-Arroyo, Petar Durdevic
Date: 2024-07-07 00:00:00
Tasks: Deep Reinforcement Learning, Management

This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.
Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series
Authors: Lucija Žignić, Stjepan Begušić, Zvonko Kostanjčar
Date: 2024-07-04 00:00:00
Tasks: Clustering, Time Series

Estimation of high-dimensional covariance matrices in latent factor models is an important topic in many fields and especially in finance. Since the number of financial assets grows while the estimation window length remains of limited size, the often used sample estimator yields noisy estimates which are not even positive definite. Under the assumption of latent factor models, the covariance matrix is decomposed into a common low-rank component and a full-rank idiosyncratic component. In this paper we focus on the estimation of the idiosyncratic component, under the assumption of a grouped structure of the time series, which may arise due to specific factors such as industries, asset classes or countries. We propose a generalized methodology for estimation of the block-diagonal idiosyncratic component by clustering the residual series and applying shrinkage to the obtained blocks in order to ensure positive definiteness. We derive two different estimators based on different clustering methods and test their performance using simulation and historical data. The proposed methods are shown to provide reliable estimates and outperform other state-of-the-art estimators based on thresholding methods.
AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
Authors: Luka Grbcic, Minok Park, Juliane Müller, Vassilia Zorba, Wibe Albert de Jong
Date: 2024-06-20 00:00:00

Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.
Jacobi Set Simplification for Tracking Topological Features in Time-Varying Scalar Fields
Authors: Dhruv Meduri, Mohit Sharma, Vijay Natarajan
Date: 2024-06-07 00:00:00

The Jacobi set of a bivariate scalar field is the set of points where the gradients of the two constituent scalar fields align with each other. It captures the regions of topological changes in the bivariate field. The Jacobi set is a bivariate analog of critical points, and may correspond to features of interest. In the specific case of time-varying fields and when one of the scalar fields is time, the Jacobi set corresponds to temporal tracks of critical points, and serves as a feature-tracking graph. The Jacobi set of a bivariate field or a time-varying scalar field is complex, resulting in cluttered visualizations that are difficult to analyze. This paper addresses the problem of Jacobi set simplification. Specifically, we use the time-varying scalar field scenario to introduce a method that computes a reduced Jacobi set. The method is based on a stability measure called robustness that was originally developed for vector fields and helps capture the structural stability of critical points. We also present a mathematical analysis for the method, and describe an implementation for 2D time-varying scalar fields. Applications to both synthetic and real-world datasets demonstrate the effectiveness of the method for tracking features.
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques
Authors: Sudev Kumar Padhi, Sk. Subidh Ali
Date: 2024-07-09 00:00:00
Tasks: Adversarial Attack, Image Manipulation

Recent developments in Deep Neural Network (DNN) based watermarking techniques have shown remarkable performance. The state-of-the-art DNN-based techniques not only surpass the robustness of classical watermarking techniques but also show their robustness against many image manipulation techniques. In this paper, we performed a detailed security analysis of different DNN-based watermarking techniques. We propose a new class of attack called the Deep Learning-based OVErwriting (DLOVE) attack, which leverages adversarial machine learning and overwrites the original embedded watermark with a targeted watermark in a watermarked image. To the best of our knowledge, this attack is the first of its kind. We have considered scenarios where watermarks are used to devise and formulate an adversarial attack in white box and black box settings. To show adaptability and efficiency, we launch our DLOVE attack analysis on seven different watermarking techniques, HiDDeN, ReDMark, PIMoG, Stegastamp, Aparecium, Distortion Agostic Deep Watermarking and Hiding Images in an Image. All these techniques use different approaches to create imperceptible watermarked images. Our attack analysis on these watermarking techniques with various constraints highlights the vulnerabilities of DNN-based watermarking. Extensive experimental results validate the capabilities of DLOVE. We propose DLOVE as a benchmark security analysis tool to test the robustness of future deep learning-based watermarking techniques.
Hybrid Machine Learning Approach For Real-Time Malicious Url Detection Using Som-Rmo And Rbfn With Tabu Search Optimization
Authors: Swetha T, Seshaiah M, Hemalatha KL, ManjunathaKumar BH, Murthy SVN
Date: 2024-07-05 00:00:00
Tasks: Hybrid Machine Learning

The proliferation of malicious URLs has become a significant threat to internet security, encompassing SPAM, phishing, malware, and defacement attacks. Traditional detection methods struggle to keep pace with the evolving nature of these threats. Detecting malicious URLs in real-time requires advanced techniques capable of handling large datasets and identifying novel attack patterns. The challenge lies in developing a robust model that combines efficient feature extraction with accurate classification. We propose a hybrid machine learning approach combining Self-Organizing Map based Radial Movement Optimization (SOM-RMO) for feature extraction and Radial Basis Function Network (RBFN) based Tabu Search for classification. SOM-RMO effectively reduces dimensionality and highlights significant features, while RBFN, optimized with Tabu Search, classifies URLs with high precision. The proposed model demonstrates superior performance in detecting various malicious URL attacks. On a benchmark dataset, our approach achieved an accuracy of 96.5%, precision of 95.2%, recall of 94.8%, and an F1-score of 95.0%, outperforming traditional methods significantly.
PhishNet: A Phishing Website Detection Tool using XGBoost
Authors: Prashant Kumar, Kevin Antony, Deepakmoney Banga, Arshpreet Sohal
Date: 2024-06-29 00:00:00
Tasks: Phishing Website Detection

PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework. PhisNet utilizes Python to apply various machine learning algorithms and feature extraction techniques for high accuracy and efficiency. The project starts by collecting and preprocessing a comprehensive dataset of URLs, comprising both phishing and legitimate sites. Key features such as URL length, special characters, and domain age are extracted to effectively train the model. Multiple machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated to determine the best performance in phishing detection. The model is finely tuned to optimize metrics like accuracy, precision, recall, and the F1 score, ensuring reliable detection of both common and sophisticated phishing tactics. PhisNet's web application is developed using React.js, which allows for client-side rendering and smooth integration with backend services, creating a responsive and user-friendly interface. Users can input URLs and receive immediate predictions with confidence scores, thanks to a robust backend infrastructure that processes data and provides real-time results. The model is deployed using Google Colab and AWS EC2 for their computational power and scalability, ensuring the application remains accessible and functional under varying loads. In summary, PhisNet represents a significant advancement in cybersecurity, showcasing the effective use of machine learning and web development technologies to enhance user security. It empowers users to prevent phishing attacks and highlights AI's potential in transforming cybersecurity.
SideSeeing: A multimodal dataset and collection of tools for sidewalk assessment
Authors: R. J. P. Damaceno, L. Ferreira, F. Miranda, M. Hosseini, R. M. Cesar Jr
Date: 2024-07-09 00:00:00

This paper introduces SideSeeing, a novel initiative that provides tools and datasets for assessing the built environment. We present a framework for street-level data acquisition, loading, and analysis. Using the framework, we collected a novel dataset that integrates synchronized video footaged captured from chest-mounted mobile devices with sensor data (accelerometer, gyroscope, magnetometer, and GPS). Each data sample represents a path traversed by a user filming sidewalks near hospitals in Brazil and the USA. The dataset encompasses three hours of content covering 12 kilometers around nine hospitals, and includes 325,000 video frames with corresponding sensor data. Additionally, we present a novel 68-element taxonomy specifically created for sidewalk scene identification. SideSeeing is a step towards a suite of tools that urban experts can use to perform in-depth sidewalk accessibility evaluations. SideSeeing data and tools are publicly available at https://sites.usp.br/sideseeing/.
Synthetic data: How could it be used for infectious disease research?
Authors: Styliani-Christina Fragkouli, Dhwani Solanki, Leyla J Castro, Fotis E Psomopoulos, Núria Queralt-Rosinach, Davide Cirillo, Lisa C Crossman
Date: 2024-07-03 00:00:00
Tasks: Dataset Generation, Text Generation

Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.
ML Updates for OpenStreetMap: Analysis of Research Gaps and Future Directions
Authors: Lasith Niroshan, James D. Carswell
Date: 2024-06-28 00:00:00

Maintaining accurate, up-to-date maps is important in any dynamic urban landscape, supporting various aspects of modern society, such as urban planning, navigation, and emergency response. However, traditional (i.e. largely manual) map production and crowdsourced mapping methods still struggle to keep pace with rapid changes in the built environment. Such manual mapping workflows are time-consuming and prone to human errors, leading to early obsolescence and/or the need for extensive auditing. The current map updating process in OpenStreetMap provides an example of this limitation, relying on numerous manual steps in its online map updating workflow. To address this, there is a need to explore automating the entire end-to-end map up-dating process. Tech giants such as Google and Microsoft have already started investigating Machine Learning (ML) techniques to tackle this contemporary mapping problem. This paper offers an analysis of these ML approaches, focusing on their application to updating Open-StreetMap in particular. By analysing the current state-of-the-art in this field, this study identi-fies some key research gaps and introduces DeepMapper as a practical solution for advancing the automatic online map updating process in the future.
H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
Authors: Nikhil Abhyankar, Vivek Gupta, Dan Roth, Chandan K. Reddy
Date: 2024-06-29 00:00:00
Tasks: Fact Verification, Mathematical Reasoning, Natural Language Queries, Question Answering, Retrieval, Table Extraction

Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. H-STAR employs: (1) step-wise table extraction using `multi-view' column retrieval followed by row extraction, and (2) adaptive reasoning that adapts reasoning strategies based on question types, utilizing semantic reasoning for direct lookup and complex lexical queries while augmenting textual reasoning with symbolic reasoning support for quantitative and logical tasks. Our extensive experiments demonstrate that H-STAR significantly outperforms state-of-the-art methods across three tabular question-answering (QA) and fact-verification datasets, underscoring its effectiveness and efficiency.
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
Authors: Marco Cominelli, Francesco Gringoli, Lance M. Kaplan, Mani B. Srivastava, Trevor Bihl, Erik P. Blasch, Nandini Iyer, Federico Cerutti
Date: 2024-07-01 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Transfer Learning

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly.
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
Authors: Marco Cominelli, Francesco Gringoli, Lance M. Kaplan, Mani B. Srivastava, Federico Cerutti
Date: 2024-07-01 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data.
AgriLLM: Harnessing Transformers for Farmer Queries
Authors: Krish Didwania, Pratinav Seth, Aditya Kasliwal, Amit Agarwal
Date: 2024-06-21 00:00:00

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types.
Gaunt coefficients for complex and real spherical harmonics with applications to spherical array processing and Ambisonics
Authors: Archontis Politis
Date: 2024-07-09 00:00:00

Acoustical signal processing of directional representations of sound fields, including source, receiver, and scatterer transfer functions, are often expressed and modeled in the spherical harmonic domain (SHD). Certain such modeling operations, or applications of those models, involve multiplications of those directional quantities, which can also be expressed conveniently in the SHD through coupling coefficients known as Gaunt coefficients. Since the definition and notation of Gaunt coefficients varies across acoustical publications, this work defines them based on established conventions of complex and real spherical harmonics (SHs) along with a convenient matrix form for spherical multiplication of directionally band-limited spherical functions. Additionally, the report provides a derivation of the Gaunt coefficients for real SHs, which has been missing from the literature and can be used directly in spatial audio frameworks such as Ambisonics. Matlab code is provided that can compute all coefficients up to user specified SH orders. Finally, a number of relevant acoustical processing examples from the literature are presented, following the matrix formalism of coefficients introduced in the report.
Sketch-Guided Scene Image Generation
Authors: Tianyu Zhang, Xiaoxuan Xie, Xusheng Du, Haoran Xie
Date: 2024-07-09 00:00:00
Tasks: Image Generation, Object, Scene Generation

Text-to-image models are showcasing the impressive ability to create high-quality and diverse generative images. Nevertheless, the transition from freehand sketches to complex scene images remains challenging using diffusion models. In this study, we propose a novel sketch-guided scene image generation framework, decomposing the task of scene image scene generation from sketch inputs into object-level cross-domain generation and scene-level image construction. We employ pre-trained diffusion models to convert each single object drawing into an image of the object, inferring additional details while maintaining the sparse sketch structure. In order to maintain the conceptual fidelity of the foreground during scene generation, we invert the visual features of object images into identity embeddings for scene generation. In scene-level image construction, we generate the latent representation of the scene image using the separated background prompts, and then blend the generated foreground objects according to the layout of the sketch input. To ensure the foreground objects' details remain unchanged while naturally composing the scene image, we infer the scene image on the blended latent representation using a global prompt that includes the trained identity tokens. Through qualitative and quantitative experiments, we demonstrate the ability of the proposed approach to generate scene images from hand-drawn sketches surpasses the state-of-the-art approaches.