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arXiv 2024年

作者： Sinhababu, Nilanjan Parry, Andrew Ganguly, Debasis Samanta, Debasis Mitra, Pabitra Centre for Computational and Data Sciences IIT Kharagpur India School of Computing Science University of Glasgow United Kingdom Department of Computer Science and Engineering IIT Kharagpur India

A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that are capable of working in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline. © 2024, CC BY.

关键词： Zero-shot learning

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Comparative Analysis of Parametric and Non-Parametric Machine Learning Models for CO2 Solubility Estimation

Comparative Analysis of Parametric and Non-Parametric Machin...

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2025 International Petroleum Technology Conference, IPTC 2025

作者： Darkwah-Owusu, Victor Turkson, Joshua Nsiah Yusof, Muhammad Aslam Md Tackie-Otoo, Bennet Nii Sokama-Neuyam, Yen Adams Fjelde, Ingebret Gbillah, Louis Kwesi Otchere, Daniel Asante Petroleum Engineering Department Universiti Teknologi PETRONAS Malaysia Centre of Reservoir Dynamics Institute of Sustainable Energy & Resources Universiti Teknologi PETRONAS Malaysia Department of Petroleum Engineering King Fahd University of Petroleum & Minerals Saudi Arabia Department of Petroleum Engineering Kwame Nkrumah University of Science and Technology Ghana Department of Energy & Technology NORCE Norwegian Research Centre AS Stavanger Norway Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States

ISBN: (纸本)9781959025436

This study investigates the application of parametric and non-parametric supervised machine learning techniques for CO2 solubility estimation in brine. CO2 solubility in brine is crucial in estimating the CO2 storage capacity of geological formations, assessing CO2 surface mixing, and evaluating the performance of CO2-enhanced oil recovery projects. Hence this research aims to provide an alternative approach for accurate CO2 solubility prediction, critical for various applications in CO2 geo-storage and EOR projects. Independent variables including pressure (0.1-40 MPa), temperature (273-474 K), salinity (0-6 mol/kg), and salt type (NaCl, CaCl2, and MgCl2) were sourced from the credible scientific literature. To ensure data quality, a comprehensive data exploration process was conducted. Subsequently, the dataset was then split into training (70%) and testing (30%) sets for model development and evaluation. Hyperparameter tuning was employed to optimize model performance. Statistical metrics and visualizations were also used to evaluate model performance. The model reliability and the statistical validity of the dataset were assessed using William's plot. Sensitivity analysis and feature importance were also explored using correlation and model-intrinsic methods. The findings of this study showed that non-parametric models (random forest (RF) and decision tree (DT)) significantly outperformed the parametric model (multiple linear regression (MLR)). Specifically, RF and DT exhibited exceptional general behaviour and robustness, achieving an overall coefficient of determination>0.97, mean absolute error2/kg2 while MLR yielded 0.76, 0.272 mol/kg, and 0.155 mol2/kg2, respectively. This disparity is attributed to the inherent limitations of parametric models in capturing complex relationships between CO2 solubility and its influencing factors. The RF model was adjudged the best-performing model and demonstrated considerable prediction accuracy compared to the Søreide-Whi

关键词： Enhanced recovery

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Arunava Chaudhuri Shubhi Shukla Sarani Bhattacharya Debdeep Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges.

关键词： Surveys Deep learning Training Privacy data privacy Training data data models Stability analysis Security Hardware acceleration

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Logging Multi-Component Supply Chain Production in Blockchain 2021

Logging Multi-Component Supply Chain Production in Blockchai...

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4th International Conference on Computers in Management and Business, ICCMB 2021

作者： Madhwal, Yash Chistiakov, Ivan Yanovich, Yury Faculty of Computer Science National Research University Higher School of Economics Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Inst. of Sci. and Technol. and Lab. of Data Mining and Predictive Modelling Inst. for Info. Transmiss. Prob. Russia

ISBN: (纸本)9781450388610

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge. © 2021 ACM.

关键词： Smart contract

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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

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Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in semi-automated dataset for rice crop

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in ...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： Anamika Dey Somrita Sarkar Debajyoti Kumar Arijit Mondal Pabitra Mitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Accurate Normalized Difference Vegetation Index (NDVI) forecasting is crucial for effective agricultural planning. However, a good prediction of the same requires sufficient data, but structured data is not available in the public domain or open-source community. Also, most of the existing methods do not consider spatial information. This study presents a novel semi-automated dataset generation framework that utilizes Sentinel-2, POWER data Access Viewer, and Google Earth Engine to create a comprehensive time-series dataset. We propose a smoothed long short-term memory (LSTM) model considering time series, historical meteorological, and spatial information. The proposed Smoothed-LSTM-based model outperforms Traditional-LSTM models, demonstrating its effectiveness in NDVI prediction for agricultural applications.

关键词： Earth Time series analysis Predictive models Planning Internet Forecasting Long short term memory

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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Hyperparameter Tuning of SVM Using Metaheuristics: An Empirical Analysis

Hyperparameter Tuning of SVM Using Metaheuristics: An Empiri...

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Wireless Africa Conference (WAC)

作者： Sunday O. Oladejo Lateef A. Akinyemi Stephen O. Ekwe Taiwo G. Omomule Oluwaseyi P. Babalola School for Data Science and Computational Thinking Stellenbosch University South Africa Centre for Augmented Intelligence and Data Science University of South Africa South Africa Dept. of Electrical Electronic and Comp. Engineering Cape Peninsula University of Technology South Africa Dept of Mathematics and Statistics University of Guelph Ontario Canada

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

The Support Vector Machine (SVM) performance is highly dependent on the selection of optimal hyperparameterse Conventional optimisation methods, including grid search, gradient descent, randomized search, and various experimental approaches, have been extensively utilized in the literature to identify optimal hyperparameters. This paper provides an empirical analysis of SVM's hyperparameter tuning by use of metaheuristics. We compare 11 widely known metaheuristics, such as the Genetic Algorithm (GA), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Differential Evolution (DE), and employ three common datasets, namely Iris, Breast Cancer Wisconsin, and MNIST, due to their unique characteristics. Empirical analysis takes into account factors including cross-validation, number of agents, noise, and mis-labelled data insertion, with emphasis on performance metrics such as accuracy, number of support vectors, and computational time. Our findings reveal that the PSO outperforms other metaheuristics, followed by the DE and ACO. In addition, we conclude that the ACO has the least computational time.

关键词： Support vector machines Wireless communication Accuracy Reviews Metaheuristics Optimization methods Vectors Particle swarm optimization Tuning Genetic algorithms

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training Computer vision Accuracy Perturbation methods Neural networks Closed box Computer architecture Robustness Ensemble learning Standards

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Probing quasigeostrophic turbulence via complex networks

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Physical Review Fluids 2025年第5期10卷 054402-054402页

作者： V. R. Krishna Priya Snehal Sunil Patil Somnath Roy Konduri Aditya Rajaram Lakkaraju Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal-721302 India TuRbulent Interfaces And Dispersion (TRIAD) Department of Mechanical Engineering Indian Institute of Technology Kharagpur West Bengal-721302 India Department of Computational and Data Sciences Indian Institute of Science Bangalore-560012 India

Using statistical analysis and complex networks, we study the role of inverse Rossby deformation radius, λ, on freely decaying quasi-geostrophic turbulence dynamics. We find that the area fraction occupied by vortices remains constant over an extended duration for all values of λ. We establish an energy spectrum scaling based on a self-similar distribution of vortices of area A during this period. Our findings suggest that the vortex number density follows the relationship n(A)∼NvA−p, and the inertial range energy spectra scales as k−7+2p, where Nv represents the number of vortices and p depends on λ. To investigate vortical interactions, we construct a spatiotemporal weighted network. Our analysis uncovers two distinct strength distributions: (1) a briefly observed scale-free distribution resulting from nonlocal interactions when λ≪k0, and (2) a truncated scale-free distribution due to local interactions over an extended period when λ≫k0, where k0 denotes the initial characteristic wave number at which the total energy is at its maximum. Both networks are robust against random disturbances but are vulnerable to targeted attacks on their hubs. Additionally, we found that these network hubs contribute to the large-scale energy spectra, which can be reconstructed using only 1% of the total degrees of freedom in a quasi-geostrophic turbulent field.

关键词： Dynamics

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