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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ulmer, Dennis Frellsen, Jes Hardmeier, Christian Department of Computer Science IT University of Copenhagen Denmark Department of Applied Mathematics & Computer Science Technical University of Denmark Denmark

We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess the quality of estimates from a wide array of approaches and their dependence on the amount of available data. We find that while approaches based on pre-trained models and ensembles achieve the best results overall, the quality of uncertainty estimates can surprisingly suffer with more data. We also perform a qualitative analysis of uncertainties on sequences, discovering that a model's total uncertainty seems to be influenced to a large degree by its data uncertainty, not model uncertainty. All model implementations are open-sourced in a software package. © 2022 Association for Computational Linguistics.

关键词： Uncertainty analysis

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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Latent Representation Learning for Multimodal Brain Activity Translation

Latent Representation Learning for Multimodal Brain Activity...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Afrasiyabi, Arman Bhaskar, Dhananjay Busch, Erica L. Caplette, Laurent Singh, Rahul Lajoie, Guillaume Turk-Browne, Nicholas B. Krishnaswamy, Smita Department of Computer Science Department of Applied Mathematics Department of Psychology Department of Genetics Department of Mathematics and Statistics Mila - Quebec AI Institute Canada Université de Montréal Canada Wu Tsai Institute United States Yale University United States

ISBN: (纸本)9798350368741

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts. © 2025 IEEE.

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Novel Scheme for Robust Confusion Component Selection Based on Pythagorean Fuzzy Set

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computers, Materials & Continua 2023年第3期74卷 6523-6534页

作者： Nabilah Abughazalah Mohsin Iqbal Majid Khan Iqtadar Hussain Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics&Statistics Riphah International UniversityIslamabadPakistan Department of Applied Mathematics&Statistics Institute of Space TechnologyIslamabadPakistan Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar Statistical Consulting Unit College of Arts and ScienceQatar UniversityDohaQatar

The substitution box,often known as an S-box,is a nonlinear component that is a part of several block *** purpose is to protect cryptographic algorithms from a variety of cryptanalytic assaults.A Multi-Criteria Decision Making(MCDM)problem has a complex selection procedure because of having many options and criteria to choose *** of this,statistical methods are necessary to assess the performance score of each S-box and decide which option is the best one available based on this *** the Pythagorean Fuzzy-based Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)method,the major objective of this investigation is to select the optimal S-box to be implemented from a pool of twelve key *** the help of the Pythagorean fuzzy set(PFS),the purpose of this article is to evaluate whether this nonlinear component is suitable for use in a variety of encryption *** this article,we have considered various characteristics of S-boxes,including nonlinearity,algebraic degree,strict avalanche criterion(SAC),absolute indicator,bit independent criterion(BIC),sum of square indicator,algebraic immunity,transparency order,robustness to differential cryptanalysis,composite algebraic immunity,signal to noise ratio-differential power attack(SNR-DPA),and confusion coefficient variance on some standard S-boxes that are Advanced Encryption Following this,the findings of the investigation are changed into Pythagorean fuzzy numbers in the shape of a *** matrix is then subjected to an analysis using the TOPSIS method,which is dependent on the Pythagorean fuzzy set,to rank the most suitable S-box for use in encryption applications.

关键词： Decision making substitution box TOPSIS multi-criterion decision making fuzzy set Pythagorean fuzzy set

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Metric Basis of Four-Dimensional Klein Bottle

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computer Modeling in Engineering & sciences 2023年第9期136卷 3011-3024页

作者： Ali N.A.Koam Ali Ahmad Maryam Salem Alatawi Muhammad Azeem Muhammad Faisal Nadeem Department of Mathematics College of SciencesNew CampusJazan UniversityJazanSaudi Arabia College of Computer Science&Information Technology Jazan UniversityJazanSaudi Arabia Department of Mathematics Faculty of SciencesUniversity of TabukTabukSaudi Arabia Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahorePakistan Department of Mathematics COMSATS University IslamabadLahore CampusLahorePakistan

The Metric of a graph plays an essential role in the arrangement of different dimensional structures and finding their basis in various *** metric dimension of a graph is the selection of the minimum possible number of vertices so that each vertex of the graph is distinctively defined by its vector of distances to the set of selected *** set of selected vertices is known as the metric basis of a *** applied mathematics or computer science,the topic of metric basis is considered as locating number or locating set,and it has applications in robot navigation and finding a beacon set of a computer *** to the vast applications of this concept in computer science,optimization problems,and also in chemistry enormous research has been *** extend this research to a four-dimensional structure,we studied the metric basis of the Klein bottle and proved that the Klein bottle has a constant metric dimension for the variation of all its *** the metric basis is variying in 3 and 4 values when the values of its parameter change,it remains constant and unchanged concerning its order or number of *** methodology of determining the metric basis or locating set is based on the distances of a ***,we proved the main theorems in distance forms.

关键词： Klein bottle metric basis resolving set metric dimension

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Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

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Neural Computing and Applications 2024年第27期36卷 16727-16767页

作者： Paheding, Sidike Saleem, Ashraf Siddiqui, Mohammad Faridul Haque Rawashdeh, Nathir Essa, Almabrok Reyes, Abel A. Department of Computer Science and Engineering Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Computer Science West Texas AM University 2501 4th Ave CanyonTX79016 United States Department of Mathematics Computer Science and Data Science John Carroll University 1 John Carroll Blvd University HeightsOH44118 United States

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field. © The Author(s) 2024.

关键词： Remote sensing

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Sampling List Packings 16

Sampling List Packings

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16th Innovations in Theoretical computer science Conference, ITCS 2025

作者： Camrud, Evan Davies, Ewan Karduna, Alex Lee, Holden Department of Mathematics and Statistics Middlebury College VT United States Department of Computer Science Colorado State University Fort CollinsCO United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783959773614

We initiate the study of approximately counting the number of list packings of a graph. The analogous problem for usual vertex coloring and list coloring has attracted substantial attention. For list packing the setup is similar, but we seek a full decomposition of the lists of colors into pairwise-disjoint proper list colorings. The existence of a list packing implies the existence of a list coloring, but the converse is false. Recent works on list packing have focused on existence or extremal results of on the number of list packings, but here we turn to the algorithmic aspects of counting and sampling. In graphs of maximum degree ∆ and when the number of colors is at least Ω(∆2), we give a fully polynomial-time randomized approximation scheme (FPRAS) based on rapid mixing of a natural Markov chain (the Glauber dynamics) which we analyze with the path coupling technique. Some motivation for our work is the investigation of an atypical spin system, one where the number of spins for each vertex is much larger than the graph degree. © Evan Camrud, Ewan Davies, Alex Karduna, and Holden Lee.

关键词： Markov chains

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An Efficient Framework for Predicting Cancer Type Based on Microarray Gene Expressions Using CNN-BiLSTM Technique

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SN computer science 2023年第4期4卷 381页

作者： Metipatil, Prabhuraj Bhuvaneshwari, P. Basha, Syed Muzamil Patil, S.S. School of CSE REVA University Karnataka Bengaluru 560064 India Department of Computer Science & Applied Mathematics UAS Karnataka Bengaluru 560064 India

In recent years, a significant number of deaths worldwide have been due to cancer. Analysis of microarray gene expression data facilitates early cancer identification. The accurate discovery of information for thousands of genes is made possible by DNA microarray technology. In most biological study fields, gene expression analysis is crucial for obtaining the required information. It can be quite difficult to extract useful information from large datasets. Various optimizer outcomes are evaluated in this research on the RNA sequences dataset. The effectiveness of various optimizers which includes adaptive gradient optimization (AdaGrad), adaptive momentum, and stochastic gradient descent (SGD). AdaGrad and Adam are good. This paper presents an efficient framework for predicting cancer type based on the microarray gene expressions using the hybrid CNN + BiLSTM approach which can identify different forms of cancers. The Experimental Results got from the proposed CNN + BiLSTM outperforms the existing CNN and LSTM classifiers with several performance parameters like Classification Accuracy, Precision, Recall and F-measure in terms of 98.3%, 98.1%, 97.8% and 97.94%, respectively. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cancer diseases CNN + BiLSTM approach Hybrid framework Machine learning Micro array dataset

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An Iterative Thresholding Method for the Minimum Compliance Problem

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Communications in Computational Physics 2023年第4期33卷 1189-1216页

作者： Luyu Cen Wei Hu Dong Wang Xiaoping Wang Department of Mathematics Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina The Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA School of Science and Engineering The Chinese University of Hong KongShenzhenGuangdong 518172China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big DataGuangdong 518172China

In this paper,we propose a simple energy decaying iterative thresholding algorithm to solve the two-phase minimum compliance *** material domain is implicitly represented by its characteristic function,and the problem is formulated into a minimization problem by the principle of minimum complementary *** prove that the energy is decreasing in each *** effective continuation schemes are proposed to avoid trapping into the local *** results on 2D isotropic linear material demonstrate the effectiveness of the proposed methods.

关键词： Minimum compliance convolution thresholding linear elasticity

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The Influence of Workplace Environment on Employee Turnover at Delmon Poultry Company: Key Insights and Findings

The Influence of Workplace Environment on Employee Turnover ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Alaghbari, Mohammed Abdulrazzaq Ateeq, Ali Milhem, Marwan Garad, Askar Ahmed Yusuf, Ammar Yusuf College of Administrative Sciences Department of Business Administration Applied Science University Bahrain Bahrain College of Administrative and Financial Science Administrative Science Department Gulf University Sanad26489 Bahrain Universitas Muhammadyiah Yogyakarta Magister Program Yogyakarta Indonesia College of Arts and Science Department of Computer Science Applied Science University Bahrain Bahrain

ISBN: (纸本)9798350369106

The present study examines how organizational work environments affect employee turnover at Delmon Poultry Company. The researcher used a structured questionnaire and a quantitative approach to collect data from 340 employees. The results show that the organizational work environment has a greater impact on employee attrition than the social environment, accounting for 75.5% of the turnover variance. Although the social environment is substantial, it only accounts for 29.2% of the variance. The research establishes that attrition reduction necessitates the enhancement of organizational factors, including policy impartiality and management practices. In order to offer a more comprehensive understanding of attrition dynamics, future research should investigate these relationships across various industries and over time. The study contributes to the literature by emphasizing the significance of a supportive organizational environment for employee retention and providing practical recommendations for human resource management to improve employee retention strategies. © 2024 IEEE.

关键词： Employee Turnover Human Resource Management Organizational Environment Workplace Environment

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