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10th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2024

作者： Iacobelli, Emanuele Pelella, Davide Ponzi, Valerio Russo, Samuele Napoli, Christian Department of Computer Automatic and Management Engineering Sapienza University of Rome Rome00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Rome00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Eye-tracking technology has long been a valuable tool across various domains, and recent advancements in neural networks have significantly expanded its versatility and potential. However, real-world applications continue to face challenges such as accommodating users’ natural movements, variations in lighting, occlusions of the eyes, and the limited availability of large, open-source datasets for training models. To address these issues, we developed a comprehensive pipeline that produces a lightweight and efficient model, requiring only an RGB camera as external hardware, making it easily deployable on standard PCs. Key input features include facial images, eye regions, head pose angles, the Eye Aspect Ratio (EAR), and a face grid that determines the face’s location within the camera’s frame. The model was trained using a custom dataset, in which participants were instructed to fixate on both randomly positioned points and the standard 9-point grid commonly employed in eye-tracking calibration. The resulting system was integrated into a real-time application, offering fast and accessible gaze tracking, making it well-suited for studies requiring rapid gaze assessments across broad regions of the screen, such as psychometric research and Human-computer Interaction (HCI) tasks. Its design is particularly advantageous for gaze laterality studies, which explore hemispheric dominance and attentional bias in cognitive and emotional processing, key concepts relevant to ADHD and dyslexia. Moreover, the system’s capabilities naturally extend to emotional and decision-making tasks, where broad-area gaze tracking can support the analysis of preference formation and attentional patterns without the need for specialized hardware. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Open systems

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Improving neural network efficiency with multi-fidelity and dimensionality reduction techniques

Improving neural network efficiency with multi-fidelity and ...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Sella, Vignesh O’leary-Roseberry, Thomas Du, Xiaosong Guo, Mengwu Martins, Joaquim R. R. A. Ghattas, Omar Willcox, Karen Chaudhuri, Anirban Oden Institute for Computational Engineering and Sciences University of Texas at Austin TX78712 United States Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology MO65409 United States Centre for Mathematical Sciences Lund University SE-221 00 Sweden Department of Aerospace Engineering University of Michigan MI48109 United States Walker Department of Mechanical Engineering University of Texas at Austin TX78712 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Design problems in aerospace engineering often require numerous evaluations of expensive to-evaluate high-fidelity models, resulting in prohibitive computational costs. One way to address the computational cost is through building surrogates, such as deep neural networks (DNNs). However, DNNs may only be an effective surrogate when sufficient evaluations of the high-fidelity model are required such that the up-front training cost is amortized, or in situations that require real-time responses (such as interactive visualizations). Typically, the data requirements for adequately accurate training of DNNs are often impractical for engineering applications. To alleviate this issue, the proposed work utilizes output dimensionality reduction along with information from multiple models of varying fidelities and cost to develop accurate projection-enabled multi-fidelity neural networks (MF-NNs) with limited training samples. The dimensionality reduction leads to a more parsimonious network and the multi-fidelity aspect adds more training data from lower-cost, lower-fidelity models. Three approaches for MF-NNs that leverage proper orthogonal decomposition based projections are introduced: (i) pre-training method, (ii) additive method, and (iii) multi-step method. The MF-NN is applied to approximate the optimal design of 2D aerodynamic airfoils given the performance and design requirements. The MF-NN leads to ∼ 27%computational cost reduction compared to single-fidelity neural networks at the same accuracy (90%), with the multi-step approach performing the best for this application. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Neural Networks Proper Orthogonal Decomposition Break Even Point Aerospace engineering Uncertainty Quantification Aerodynamic Shape Optimization Reynolds Averaged Navier Stokes Aerodynamic Performance Multi Disciplinary Design Optimization High Speed Civil Transport

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The Geography of Temperature Space

The Geography of Temperature Space

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Wright, George A. Purver, Matthew School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

This paper takes as its basis, Gärdenfors’ [1] description of conceptual spaces and sketches an initial geography of the conceptual space for temperature. It highlights important differences between the space’s constituents, including: how category membership differs for concepts along different parts of the dimension;how comparatives and compounds are formed;how context affects concept location and behaviour;and how non-literal usage allows concepts to be used outside of their conceptual space or in a different way within their own conceptual space. The purpose of this tour of temperature space is to aid the practical implementation of semantic representations based upon conceptual spaces. © 2024 Copyright for this paper by its authors.

关键词： Semantics

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Quantum Cryptography with Classical Communication: Parallel Remote State Preparation for Copy-Protection, Verification, and More 50

Quantum Cryptography with Classical Communication: Parallel ...

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50th International Colloquium on Automata, Languages, and Programming, ICALP 2023

作者： Gheorghiu, Alexandru Metger, Tony Poremba, Alexander Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden Institute for Theoretical Studies ETH Zürich Switzerland Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States

ISBN: (纸本)9783959772785

Quantum mechanical effects have enabled the construction of cryptographic primitives that are impossible classically. For example, quantum copy-protection allows for a program to be encoded in a quantum state in such a way that the program can be evaluated, but not copied. Many of these cryptographic primitives are two-party protocols, where one party, Bob, has full quantum computational capabilities, and the other party, Alice, is only required to send random BB84 states to Bob. In this work, we show how such protocols can generically be converted to ones where Alice is fully classical, assuming that Bob cannot efficiently solve the LWE problem. In particular, this means that all communication between (classical) Alice and (quantum) Bob is classical, yet they can still make use of cryptographic primitives that would be impossible if both parties were classical. We apply this conversion procedure to obtain quantum cryptographic protocols with classical communication for unclonable encryption, copy-protection, computing on encrypted data, and verifiable blind delegated computation. The key technical ingredient for our result is a protocol for classically-instructed parallel remote state preparation of BB84 states. This is a multi-round protocol between (classical) Alice and (quantum polynomial-time) Bob that allows Alice to certify that Bob must have prepared n uniformly random BB84 states (up to a change of basis on his space). While previous approaches could only certify one- or two-qubit states, our protocol allows for the certification of an n-fold tensor product of BB84 states. Furthermore, Alice knows which specific BB84 states Bob has prepared, while Bob himself does not. Hence, the situation at the end of this protocol is (almost) equivalent to one where Alice sent n random BB84 states to Bob. This allows us to replace the step of preparing and sending BB84 states in existing protocols by our remote-state preparation protocol in a generic and modular way. © A

关键词： Quantum cryptography

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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A Consensus-Based Global Optimization Method with Adaptive Momentum Estimation

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Communications in computational Physics 2022年第4期31卷 1296-1316页

作者： Jingrun Chen Shi Jin Liyao Lyu School of Mathematical Sciences University of Science and Technology of ChinaHefeiAnhui 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhouJiangsu 215123China School of Mathematical Sciences Institute of Natural SciencesMOE-LSCShanghai Jiao Tong UniversityShanghai200240China Department of Computational Mathematics Scienceand EngineeringMichigan State UniversityEast LansingMI48824USA

Objective functions in large-scalemachine-learning and artificial intelligence applications often live in high dimensions with strong non-convexity and massive local ***-based methods,such as the stochastic gradient method and Adam[15],and gradient-freemethods,such as the consensus-based optimization(CBO)method,can be employed to find *** this work,based on the CBO method and Adam,we propose a consensus-based global optimization method with adaptive momentum estimation(Adam-CBO).Advantages of the Adam-CBO method include:It is capable of finding global minima of non-convex objective functions with high success rates and low *** is verified by finding the global minimizer of the 1000 dimensional Rastrigin function with 100%success rate at a cost only growing linearly with respect to the *** can handle non-differentiable activation functions and thus approximate lowregularity functions with better *** is confirmed by solving a machine learning task for partial differential equations with low-regularity solutions where the Adam-CBO method provides better results than *** is robust in the sense that its convergence is insensitive to the learning rate by a linear stability *** is confirmed by finding theminimizer of a quadratic function.

关键词： Consensus-based optimization global optimization machine learning curse of dimensionality

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Anand, Emile Karmarkar, Ishani Qu, Guannan School of Computer Science GeorgiaTech AtlantaGA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Institute of Computational and Mathematical Engineering Stanford University CA United States

Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially in the number of agents. These difficulties are exacerbated when balancing sequential global decision-making with local agent interactions. In this work, we propose a new algorithm SUBSAMPLE-MFQ (Subsample-Mean-Field-Q-learning) and a decentralized randomized policy for a system with n agents. For k ≤ n, our algorithm learns a policy for the system in time polynomial in k. We show that this learned policy converges to the optimal policy on the order of Õ(1/√k) as the number of subsampled agents k increases. We empirically validate our method in Gaussian squeeze and global exploration *** Codes 60J20, 68T99 © 2024, CC BY.

关键词： Reinforcement learning

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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