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Multi-Granular and Multi-Criteria Large-Scale Group Decision-Making Method Using the Aggressive Ordered Aggregation Operator Created Using Sentiment Analysis

SSRN

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SSRN 2022年

作者： Morfeq, Ali Trillo, José Ramón Morente-Molinera, Juan Antonio Al-Hmouz, Rami Herrera-Viedma, Enrique Department of Electrical and Computer Engineering The Faculty of Engineering The King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Currently, human beings make different decisions daily, from choosing where to invest their money to decide how they can improve their business. Helping make the decision, Decision-Making processes can be used, in which several experts can participate. Nevertheless, when you want to make a Decision-Making process with large number of experts, Large-Scale Group Decision-Making methods are used, but several problems arise when using them. The first problem is how to manage all the information that is generated in the Group Decision-Making process, as the number of participants is larger than in a previous Group Decision-Making process. The second problem is knowing how to control the aggressiveness that the debate can generate when experts give their information. The third problem is the management of relationships in the debate, as differences can generate controversy and arguments. Solving these problems, this paper proposes a novel Large-Scale Group Decision-Making process applied to a multi-criteria environment and has a multi-granular fuzzy linguistic modelling that uses sentiment analysis to aggregate the criteria and optimises the consensus value. With this method, the experts can express themselves as they wish and it increases the relevance of the debate, as extra information is extracted and used during the process. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Depth Estimation of the Object of Interest in Challenging Cross-Domain Scenes

SSRN

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SSRN 2023年

作者： Xu, Chenglong Chen, Chu Xing, Wen Herrera-Viedma, Enrique College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Qingdao Innovation and Development Center Harbin Engineering University Qingdao266000 China Shenyang SIASUN Robot & Automation Co. LTD Shenyang110168 China Andalusian Research Institute of Data Science and Computational Intelligence Department of Computer Science and AI University of Granada Granada18071 Spain

Depth estimation plays an important role in 3D visual perception, autonomous vehicles, and near-field optical detection. At present, there are mainly learning-based methods and traditional geometric constraint reasoning based methods. However, most learning-based methods have poor cross-domain generalization, the accuracy will be greatly reduced when tested on new scenes. Although the traditional geometric methods have strong scene adaptability, they have more marginal noise in depth discontinuity areas, and require a large amount of memory at a high bandwidth. In order to solve the above problems, we present a disparity confidence propagation and edge smoothing framework, which combines robust semantic mask with tree diffusion model. Compared with those of state-of-the-art fast geometric constraint based methods and learning-based methods, the proposed method has a great improvement in cross-domain adaptability and efficiency. Experiments on multiple data sets verify the effectiveness of the method, and a good balance is achieved between generalization and accuracy. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

arXiv

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arXiv 2024年

作者： Karnik, Santhosh Veselovska, Anna Iwen, Mark Krahmer, Felix Department of Mathematics Northeastern University Boston United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing United States TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Garching Germany Munich Center for Machine Learning Munich Germany Department of Mathematics Michigan State University East Lansing United States

We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization strategies which provably lead to implicit regularization in gradient-descent methods. At the same time, it has been argued by [7] that more general classes of neural networks can be captured by considering tensor factorizations. However, in the tensor case, implicit regularization has only been rigorously established for gradient flow or in the lazy training regime. In this paper, we prove the first tensor result of its kind for gradient descent rather than gradient flow. We focus on the tubal tensor product and the associated notion of low tubal rank, encouraged by the relevance of this model for image data. We establish that gradient descent in an overparametrized tensor factorization model with a small random initialization exhibits an implicit bias towards solutions of low tubal rank. Our theoretical findings are illustrated in an extensive set of numerical simulations show-casing the dynamics predicted by our theory as well as the crucial role of using a small random initialization. © 2024, CC BY-SA.

关键词： Matrix factorization

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Duck Eggs Hatching Procedure with A Low Cost User Convenient Integrated Device for Developing Countries 9

Duck Eggs Hatching Procedure with A Low Cost User Convenient...

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Hossain, Eftakhar Zeyad, Mohammad Ahmed, S.M. Masum Tasrif Anubhove, Md Sadik Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Advanced Bioinformatics Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain Mons Belgium

ISBN: (纸本)9781665432405

Poultry agriculture is a form of livestock farming that involves raising chickens, turkeys, ducks, etc, and raises them to provide eggs or meat. However, this work sheds light on the method of hatching duck eggs which maintains a suitable environment around the eggs through an automated and low cost device by controlling temperature (T) and relative humidity (RH). This paper presents a control strategy for automatic temperature and relative humidity control systems for the egg/s hatching procedure for rural areas people or unprivileged communities in the developing countries. One of the best outcomes of this work is the proper hatching procedure steps shown from the beginning to the end. Moreover, for a better understanding and outcomes, the research work has been done under a poultry farm in Charsita village, Lakshmipur, Bangladesh for 6 consecutive months of 2020. The experiment (egg/s hatching) has been conducted under an ice-box. Furthermore, a microcontroller (ATmega328p) is used to monitor the temperature and relative humidity by receiving data from the sensor DHT22 and manipulating the appropriate temperature and humidity using a logic algorithm code. Egg/s need a constant temperature and relative humidity of 35.6 °C to 37.8 °C and 60% to 80%, respectively. Nonetheless, an LCD (16*2) with a matrix keypad (4*4) has been integrated into the unit, allowing farmers to easily set, run, and monitor the appropriate temperature and relative humidity levels within the ice-box. Finally, the results of sensor testing, system device management, and performance rates are recorded to ensure the research's success. © 2021 IEEE.

关键词： Microcontrollers

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To Trust or Not to Trust: Evolutionary Dynamics of an Asymmetric N-player Trust Game

arXiv

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arXiv 2023年

作者： Soo Lim, Ik. Masuda, Naoki School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom Department of Mathematics and Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States

Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g. to trust versus not to trust a trustee). According to the evolutionary game theory, the antisocial strategies (e.g. not to trust) evolve such that the investor and trustee end up with lower payoffs than those that they would get with the prosocial strategies (e.g. to trust). A generalisation of the TG to a multiplayer (i.e. more than two players) TG was recently proposed. However, its outcomes hinge upon two assumptions that various real situations may substantially deviate from: (i) investors are forced to trust trustees and (ii) investors can turn into trustees by imitation and vice versa. We propose an asymmetric multiplayer TG that allows investors not to trust and prohibits the imitation between players of different roles;instead, investors learn from other investors and the same for trustees. We show that the evolutionary game dynamics of the proposed TG qualitatively depends on the nonlinearity of the payoff function and the amount of incentives collected from and distributed to players through an institution. We also show that incentives given to trustees can be useful and sufficient to cost-effectively promote trust and trustworthiness among self-interested players. © 2023, CC BY.

关键词： Dynamics

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Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER

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Nature Biotechnology 2025年 1-13页

作者： Zheng, Wei Wuyun, Qiqige Li, Yang Liu, Quancheng Zhou, Xiaogen Peng, Chunxiang Zhu, Yiheng Freddolino, Lydia Zhang, Yang NITFID School of Statistics and Data Science AAIS LPMC and KLMDASR Nankai University Tianjin China Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Computer Science and Engineering Michigan State University East LansingMI United States Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Department of Biological Chemistry University of Michigan Ann ArborMI United States Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications. © The Author(s) 2025.

关键词： Deep learning

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initi...

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Supercomputing Conference

作者： Sambit Das Bikash Kanungo Vishal Subramanian Gourab Panigrahi Phani Motamarri David Rogers Paul M. Zimmerman Vikram Gavini Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Materials Science & Engineering University of Michigan Ann Arbor MI USA Department of Computational and Data Sciences Indian Institute of Science Bangalore Karnataka India Scientific Computing Group Oak Ridge National Laboratory Oak Ridge TN USA Department of Chemistry University of Michigan Ann Arbor MI USA

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

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IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

arXiv

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arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Hasan M. Mahmud S. Poudel K. Donthula K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computer Science Middle Tennessee State University Murfreesboro TN USA Senior Software Engineer Optum Inc Murfreesboro TN USA

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface.

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