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arXiv 2024年

作者： Hicke, Rebecca M.M. Kristensen-McLachlan, Ross Deans Department of Computer Science Cornell University United States Center for Humanities Computing Aarhus University Denmark Department of Linguistics Cognitive Science and Semiotics Aarhus University Denmark

Metaphors are everywhere. They appear extensively across all domains of natural language, from the most sophisticated poetry to seemingly dry academic prose. A significant body of research in the cognitive science of language argues for the existence of conceptual metaphors, the systematic structuring of one domain of experience in the language of another. Conceptual metaphors are not simply rhetorical flourishes but are crucial evidence of the role of analogical reasoning in human cognition. In this paper, we ask whether Large Language Models (LLMs) can accurately identify and explain the presence of such conceptual metaphors in natural language data. Using a novel prompting technique based on metaphor annotation guidelines, we demonstrate that LLMs are a promising tool for large-scale computational research on conceptual metaphors. Further, we show that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Meta-HCI: First Workshop on Meta-Research in HCI

Meta-HCI: First Workshop on Meta-Research in HCI

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Oppenlaender, Jonas Malacria, Sylvain Fang, Xinrui van Berkel, Niels Chevalier, Fanny Yatani, Koji Hosio, Simo University of Oulu Oulu Finland Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL Lille France University of Tokyo Tokyo Japan Department of Computer Science Aalborg University Aalborg Denmark Department of Computer Science and Statistical Sciences University of Toronto Toronto ON Canada Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9798400713958

Human-computer Interaction (HCI) is a rapidly evolving field. It has undergone many changes, and several current challenges deserve more attention from the community. Meta-research – the study of research practices – offers insights into how a field can refine its methodological frameworks, enhance rigor, and address its challenges. We believe CHI deserves a dedicated space for meta-research. This workshop establishes an open space for HCI scholars in the top conference of the field to explore and discuss meta-research in HCI. We are equally focused on the past, present, and future: what we study, how we document it, how we evaluate, and how we distribute our work. Collateral effects such as mounting career pressures to publish always more are interesting, too. Short term results of this workshop include a research roadmap specifically for HCI meta-research. In the long term, we hope to see this workshop be the initial spark to establishing a permanent HCI meta-research community. © 2025 Copyright held by the owner/author(s).

关键词： CHI meta research meta science meta-HCI science of science

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Learning Survival Distribution with Implicit Survival Function

arXiv

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arXiv 2023年

作者： Ling, Yu Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization. In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions, and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision. Copyright © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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A computation of D(9) using FPGA Supercomputing

arXiv

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arXiv 2023年

作者： Van Hirtum, Lennart De Causmaecker, Patrick Goemaere, Jens Kenter, Tobias Riebler, Heinrich Lass, Michael Plessl, Christian KU Leuven Department of Computer Science KULAK Belgium Department of Computer Science Paderborn University Germany Paderborn Center for Parallel Computing Paderborn University Germany

This preprint makes the claim of having computed the 9th Dedekind Number. This was done by building an efficient FPGA Accelerator for the core operation of the process, and parallelizing it on the Noctua 2 Supercluster at Paderborn University. The resulting value is 286386577668298411128469151667598498812366 This value can be verified in two steps. We have made the data file containing the 490M results available, each of which can be verified separately on CPU, and the whole file sums to our proposed value. © 2023, CC BY-NC-SA.

关键词： Field programmable gate arrays (FPGA)

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoising

Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoisin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chenyu Huang Weimin Tan Jiaxing Shi Zhen Xing Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality.

关键词： Degradation visualization Uncertainty Art Noise reduction Signal processing Noise measurement

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Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders 41

Learning Latent Structures in Network Games via Data-Depende...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Xue Li, Muchen Leng, Yan Liao, Renjie Center for Applied Statistics School of Statistics Renmin University of China Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China Department of Computer Science University of British Columbia VancouverBC Canada McCombs School of Business The University of Texas at Austin AustinTX United States Vector Institute for AI Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)

关键词： Monte Carlo methods

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Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

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computers, Materials & Continua 2022年第10期73卷 801-815页

作者： Mahmoud Ragab Sultanah M.Alshammari Amer H.Asseri Waleed K.Almutiry Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics College of Science and Arts in Ar RassQassim UniversityBuryadah52571Saudi Arabia

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI ***,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

关键词： Brain cancer medical imaging deep learning fusion model metaheuristics feature extraction handcrafted features

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Microteaching: Binary Heaps, Side-Channel Attacks, Equitable Grading, Java Classes, Loops, and 3D Java 2024

Microteaching: Binary Heaps, Side-Channel Attacks, Equitable...

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55th ACM Technical Symposium on computer science Education, SIGCSE 2024

作者： Lewis, Colleen M. Bailey Lee, Cynthia Blank, Adam Camarena, Maria Hernandez, Manuel Vahid, Frank Computer Science Department Univ. of Illinois Urbana-Champaign UrbanaIL United States Computer Science Department Stanford University StanfordCA United States Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States Maywood Center for Enriched Studies Los Angeles Unified School District MaywoodCA United States Mathematics Department Bell Gardens High School Musd and Math for America - Los Angeles Bell GardensCA United States Computer Science and Engineering University of California Riverside RiversideCA United States

ISBN: (纸本)9798400704246

This microteaching session is like Nifty Assignments for instruction. Instead of having the presenters just talk about their teaching, they will simulate how they would actually teach something. Covering a range of topics and grade levels, six educators will demo how they would teach a specific topic. To help identify the pedagogical practices that cut across grade bands and topics, the moderator, Colleen Lewis, will describe how their pedagogical practices connect with education research. The goal of the session is to inspire SIGCSE attendees by highlighting innovative instruction by exceptional educators. Attendees can adopt the content and/or pedagogical practices from each microteaching example. © 2024 Owner/Author.

关键词： Side channel attack

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Allocation and Migration of Virtual Machines Using Machine Learning

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computers, Materials & Continua 2022年第2期70卷 3349-3364页

作者： Suruchi Talwani Khaled Alhazmi Jimmy Singla Hasan JAlyamani Ali Kashif Bashir School of Computer Science&Engg. Lovely Professional UniversityPunjabIndia National Center for Robotics and Internet of Things Technology Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computing and Mathematics ManchesterMetropolitan UniversityManchesterM156BHUK

Cloud computing promises the advent of a new era of service boosted by means of virtualization *** process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application *** extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to *** whole process is very challenging not only in terms of computation but also in terms of energy and *** research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data *** Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial *** most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto *** simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

关键词： Cloud computing VM allocation VM migration machine learning

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