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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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The Fused Multiply-Add and Global Atmospheric Models: A Distributional Investigation into a Surprising Correctness Scenario

The Fused Multiply-Add and Global Atmospheric Models: A Dist...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Price-Broncucia, Teo Baker, Allison H. Duda, Michael University of Colorado Department of Computer Science BoulderCO80303 United States NSF National Center for Atmospheric Research Computational and Information Systems Lab BoulderCO80305 United States NSF National Center for Atmospheric Research Mesoscale and Microscale Meteorology Lab BoulderCO80305 United States

ISBN: (纸本)9798350355543

In a series of related works developing an ensemble consistency testing approach for multiple popular global climate models (GCMs), one test scenario has repeatedly stood out. Why does the use of the Fused Multiply-Add (FMA) operation result in model configurations getting flagged as failures, while changes to compiler choice, optimization level, processor type and number, etc. are passed as expected? This work explores the impacts of FMA on GCM simulation output from a distributional perspective and provides directions for future work to enable model developers and users to use numerical optimization techniques with confidence. © 2024 IEEE.

关键词： climate-models correctness ensemble-methods

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Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models

arXiv

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arXiv 2025年

作者： Kennington, Casey Lison, Pierre Schlangen, David DEPARTMENT OF COMPUTER SCIENCE BOISE STATE UNIVERSITY United States NORWEGIAN COMPUTING CENTER Norway COMPUTATIONAL LINGUISTICS UNIVERSITY OF POTSDAM Germany

Efforts towards endowing robots with the ability to speak have benefited from recent advancements in natural language processing, in particular large language models. However, current language models are not fully incremental, as their processing is inherently monotonic and thus lack the ability to revise their interpretations or output in light of newer observations. This monotonicity has important implications for the development of dialogue systems for human–robot interaction. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms in the age of large language models. © 2025, CC BY.

关键词： Social robots

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A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

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computers, Materials & Continua 2022年第12期73卷 4611-4626页

作者： Pramote Charongrattanasakul Wimonmas Bamrungsetthapong Poom Kumam Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Center of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research Laboratory Room SCL 802 Fixed Point LaboratoryScience Laboratory BuildingDepartments of MathematicsFaculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

The design of a new adaptive version of the multiple dependent state(AMDS)sampling plan is presented based on the time truncated life test under the Weibull *** achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans.A warning sign for acceptance number was proposed to increase the probability of current lot *** optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk.A simulation study was presented to support the proposed sampling plan.A comparison between the proposed and existing sampling plans,namely multiple dependent state(MDS)sampling plans and a modified multiple dependent state(MMDS)sampling plan,was considered under the average sampling number and operating characteristic curve *** addition,the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling *** results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.

关键词： Adaptive version of multiple dependent state sampling plan time truncated life test quality level weibull distribution mean lifetime

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Exploration of Trade-offs Between General-Purpose and Specialized Processing Elements in HPC-Oriented CGRA

Exploration of Trade-offs Between General-Purpose and Specia...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Emanuele Del Sozzo Xinyuan Wang Boma Adhi Carlos Cortes Jason Anderson Kentaro Sano Center for Computational Science (R-CCS) RIKEN Kobe Japan Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Coarse-Grained Reconfigurable Arrays (CGRAs) are a class of reconfigurable accelerators traditionally used in embedded computing. Recently, CGRA-like devices have gained traction for HPC and AI acceleration; however, typical HPC and AI workloads often require operations that current CGRAs cannot implement, such as complex mathematical calculations. In this work, we present a broad architectural study exploring potential heterogeneous computational resources in CGRA architectures for HPC, which are not commonly considered in typical CGRA architecture research. We first improved the general-purpose Processing Element (PE) of a baseline CGRA to optimize computational resources and then developed a new specialized PE for mathematical functions commonly found in HPC applications. Finally, we evaluated multiple CGRA configurations concerning floorplan, size, general-purpose/specialized PE ratio, and Power, Performance, and Area (PPA) results from hardware synthesis.

关键词： Embedded computing Distributed processing computer architecture Hardware Artificial intelligence

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Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

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2nd International Conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 IEEE.

关键词： Sensitive data

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Variational inference with Gaussian score matching 23

Variational inference with Gaussian score matching

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chirag Modi Charles C. Margossian Yuling Yao Robert M. Gower David M. Blei Lawrence K. Saul Center for Computational Astrophysics Center for Computational Mathematics Flatiron Institute New York Center for Computational Mathematics Flatiron Institute New York Department of Computer Science Statistics Columbia University New York

Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics. Typically, VI fits a simple parametric distribution to be close to the target posterior, optimizing an appropriate objective such as the evidence lower bound (ELBO). In this work, we present a new approach to VI. Our method is based on the principle of score matching—namely, that if two distributions are equal then their score functions (i.e., gradients of the log density) are equal at every point on their support. With this principle, we develop score-matching VI, an iterative algorithm that seeks to match the scores between the variational approximation and the exact posterior. At each iteration, score-matching VI solves an inner optimization, one that minimally adjusts the current variational estimate to match the scores at a newly sampled value of the latent variables. We show that when the variational family is a Gaussian, this inner optimization enjoys a closed-form solution, which we call Gaussian score matching VI (GSM-VI). GSM-VI is a "black box" variational algorithm in that it only requires a differentiable joint distribution, and as such it can be applied to a wide class of models. We compare GSM-VI to black box variational inference (BBVI), which has similar requirements but instead optimizes the ELBO. We first study how GSM-VI behaves as a function of the problem dimensionality, the condition number of the target covariance matrix (when the target is Gaussian), and the degree of mismatch between the approximating and exact posterior distribution. We then study GSM-VI on a collection of real-world Bayesian inference problems from the posteriorDB database of datasets and models. We find that GSM-VI is faster than BBVI and equally or more accurate. Specifically, over a wide range of target posteriors, GSM-VI requires 10-100x fewer gradient evaluations than BBVI to obtain a comparable quality of approximation. We

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Optimized Adaptive Based Method for MR Image Denoising

Optimized Adaptive Based Method for MR Image Denoising

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International Conference on computational Intelligence and Communication Networks (CICN)

作者： Noorbakhsh Amiri Golilarz Iman Dehzangi Keyan Rahimi Department of Computer Science and Engineering Mississippi State University Mississippi State MS USA Department of Computer Science Center for Computational and Integrative Biology (CCIB) Rutgers University Camden NJ USA Department of Computer Science Brown University Providence RI USA

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

In this paper, we aim to utilize the orca optimization algorithm (OOA) as a computational intelligence method combined with a non-linear data-driven function in the optimization process for brain image de-noising. This meta-heuristic population-based optimizer has recently been proposed to solve complex optimization problems and has achieved promising results in various domains. Here, it is used to obtain the optimum threshold value and other parameters for the activation function. We investigate the impact of this novel algorithm compared to other state-of-the-art optimizers and alternative approaches such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), Differential Evolution (DE), Thresholding Neural Network (TNN), Neighboring Wavelet Coefficients (Neishrink), Hard and Soft thresholds. Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are also used to measure the performance analysis of various de-noising approaches. The results show that the proposed method performs better than other alternative optimization techniques.

关键词： Brain PSNR Noise reduction Metaheuristics Particle measurements Performance analysis Particle swarm optimization computational intelligence Image denoising Wavelet coefficients

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Medical Personal Protective Equipment Detection Using YOLOV7

Medical Personal Protective Equipment Detection Using YOLOV7

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Ali, Md. Anas Haque, Mahmudul Alam, Saadia Binte Rahman, Rashedur Amin, Mashraful Kobashi, Syoji Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Independent University Department of Computer Science and Engineering Bangladesh Dhaka1299 Bangladesh Independent University Center for Computational & Data Sciences Bangladesh Dhaka1299 Bangladesh

ISBN: (纸本)9798350303780

In recent years, healthcare and safety have been a major focus of deep learning research. This paper focuses on the detection of Medical Personal Protective Equipment (MPPE) in the health-care sector using YOLOv7. Improper use of personal protective equipment (PPE) can result in the contamination and cross-contamination of infectious diseases, so it is crucial for healthcare professionals to use it correctly. The CPPE-5 dataset was used to train the model, which contains 1029 high-quality images divided into five categories: coveralls, face shield, gloves, masks, and goggles. The objective of this research is to create an accurate model for future applications and development using a suitable medical PPE dataset. The proposed model outperforms previous studies, with an optimal mAP of 90.93 %, indicating that it is a promising method for detecting MPPE in the healthcare sector. © 2023 IEEE.

关键词： Object detection

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