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7th Information Systems International Conference, ISICO 2023

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Putra, Gustian Rama Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Factor analysis

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in Engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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EXTREMAL AND CONTINUA SOLUTIONS FOR STOCHASTIC FRACTIONAL DIFFERENTIAL EQUATIONS

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Journal of Mathematical sciences (United States) 2025年 1-15页

作者： Benhamida, Ouafaa Dahmani, Zoubir Ibrahim, Rabha W. Laboratory of Pure and Applied Maths Faculty of Sciences UMAB Mostaganem Algeria Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia /Mersin 10 99138 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Laboratory of LAMDA-RO Faculty of Sciences University of Blida 1 Blida 09000 Algeria

The aim of this work is to study two classes of stochastic fractional differential equations via the application of the method of upper and lower solutions combined with the Arzela-Ascoli theorem. We begin by proving an auxiliary result for the integral representation of an Airy-type stochastic problem. The specific symmetry features of an Airy-type stochastic problem depend on the form of the stochastic differential equations (SDE) and the relevant coefficients, it is vital to note. To comprehend each issue’s unique symmetries and their effects, a thorough investigation is necessary. Then, we prove an existence result for extremal solutions for the same problem. Another class of stochastic equations of higher-order type is also studied. We also present some examples to show the validity of the obtained results. At the end, a conclusion follows. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Airy equation Extremal solutions Fractional calculus Fractional differential operator Stochastic fractional differential equation Upper and lower method

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LEARNING CHEMOTHERAPY DRUG ACTION VIA UNIVERSAL PHYSICS-INFORMED NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Podina, Lena Ghodsi, Ali Kohandel, Mohammad Cheriton School of Computer Science University of Waterloo WaterlooON Canada Department of Applied Mathematics University of Waterloo WaterlooON Canada

Quantitative systems pharmacology (QSP) is widely used to assess drug effectsand toxicity before the drug goes to clinical trial. However, significantmanual distillation of the literature is needed in order to construct a QSPmodel. Parameters may need to be fit, and simplifying assumptions of the modelneed to be made. In this work, we apply Universal Physics-Informed NeuralNetworks (UPINNs) to learn unknown components of various differential equationsthat model chemotherapy pharmacodynamics. We learn three commonly employedchemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) fromsynthetic data. Then, we use the UPINN method to fit the parameters for severalsynthetic datasets simultaneously. Finally, we learn the net proliferation ratein a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these areonly toy examples, we highlight the usefulness of UPINNs in learning unknownterms in pharmacodynamic and pharmacokinetic models. © 2024, CC BY.

关键词： Chemotherapy

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Linux-Based End-Station Design for Seamless TSN Plug -and - Play

Linux-Based End-Station Design for Seamless TSN Plug -and - ...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Hamza Chahed Fredrik Hallström Héctor Blanco Alcaine Andreas Kassler Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Intel Deutschland GmbH Germany Faculty of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

In TSN networks, proper end-station configuration is essential to ensure the timely and reliable delivery of time-sensitive data, meeting strict end- to-end Quality of Service (QoS) criteria. However, the complexity of the configuration process requires a significant manual effort, which makes real-time application development on standard Operating Systems such as Linux a challenge. In this paper, we propose a sim-ple yet functional approach to automate the configuration of Linux-based TSN end-stations within TSN networks by adding a TSN layer on top of the networking system services and defining a configuration protocol tailored for the centralized network/distributed user configuration mode. Evaluation results demonstrate minimal overhead during stream addition, achieving hundreds-of-millisecond-Ievel configuration times and enabling a hassle-free Plug-and-Play mode of operation.

关键词： Protocols Linux Operating systems Quality of service Reliability engineering Software Real-time systems

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A Flow Artist for High-Dimensional Cellular Data

A Flow Artist for High-Dimensional Cellular Data

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IEEE Workshop on Machine Learning for Signal Processing

作者： Kincaid MacDonald Dhananjay Bhaskar Guy Thampakkul Nhi Nguyen Joia Zhang Michael Perlmutter Ian Adelstein Smita Krishnaswamy Department of Mathematics Yale University Department of Genetics Yale School of Medicine Department of Computer Science Yale University Department of Mathematics Pomona College Applied Mathematics Program Yale University Department of Statistics University of Washington Department of Mathematics Boise State University Computational Biology and Bioinformatics Program Yale University

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data.

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Adjoint-Free 4D-Var Methods Via Line Search Optimization For Non-Linear Data Assimilation

arXiv

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arXiv 2023年

作者： Nino-Ruiz, Elias D. Diaz-Rodriguez, Jairo Applied Math and Computer Science Laboratory Department of Computer Science Universidad del Norte Colombia Department of Mathematics and Statistics York University Canada

This paper proposes two practical implementations of Four-Dimensional Variational (4D-Var) Ensemble Kalman Filter (4D-EnKF) methods for non-linear data assimilation. Our formulations' main idea is to avoid the intrinsic need for adjoint models in the context of 4D-Var optimization and, even more, to handle non-linear observation operators during the assimilation of observations. The proposed methods work as follows: snapshots of an ensemble of model realizations are taken at observation times, these snapshots are employed to build control spaces onto which analysis increments can be estimated. Via the linearization of observation operators at observation times, a line-search based optimization method is proposed to estimate optimal analysis increments. The convergence of this method is theoretically proven as long as the dimension of control-spaces equals model one. In the first formulation, control spaces are given by full-rank square root approximations of background error covariance matrices via the Bickel and Levina precision matrix estimator. In this context, we propose an iterative Woodbury matrix formula to perform the optimization steps efficiently. The last formulation can be considered as an extension of the Maximum Likelihood Ensemble Filter to the 4D-Var context. This employs pseudo-square root approximations of prior error covariance matrices to build control spaces. Experimental tests are performed by using the Lorenz 96 model. The results reveal that, in terms of Root-Mean-Square-Error values, both methods can obtain reasonable estimates of posterior error modes in the 4D-Var optimization problem. Moreover, the accuracies of the proposed filter implementations can be improved as the ensemble sizes are increased. © 2023, CC BY.

关键词： Optimization

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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PROBABILISTIC BLOCK TERM DECOMPOSITION FOR THE MODELLING OF HIGHER-ORDER ARRAYS

arXiv

引用

arXiv 2023年

作者： Hinrich, Jesper Løve Mørup, Morten The Department of Food Science University of Copenhagen Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

Tensors are ubiquitous in science and engineering and tensor factorization approaches have become important tools for the characterization of higher order structure. Factorizations includes the outer-product rank Canonical Polyadic Decomposition (CPD) as well as the multi-linear rank Tucker decomposition in which the Block-Term Decomposition (BTD) is a structured intermediate interpolating between these two representations. Whereas CPD, Tucker, and BTD have traditionally relied on maximum-likelihood estimation, Bayesian inference has been use to form probabilistic CPD and Tucker. We propose, an efficient variational Bayesian probabilistic BTD, which uses the von-Mises Fisher matrix distribution to impose orthogonality in the multi-linear Tucker parts forming the BTD. On synthetic and two real datasets, we highlight the Bayesian inference procedure and demonstrate using the proposed pBTD on noisy data and for model order quantification. We find that the probabilistic BTD can quantify suitable multi-linear structures providing a means for robust inference of patterns in multi-linear data. © 2023, CC0.

关键词： Factorization

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Machine learning approach for studying the neural underpinnings of dyslexia on a phonological awareness task 35th

Machine learning approach for studying the neural underpinni...

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35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022

作者： Christoforou, Christoforos Papadopoulos, Timothy C. Theodorou, Maria Division of Computer Science Mathematics and Science St. John’s University NY United States Department of Psychology & Center for Applied Neuroscience University of Cyprus Nicosia Cyprus

Understanding the neural underpinnings of dyslexia is an open and fundamental question in developmental neuroscience. A widely agreed causal risk factor for dyslexia is phonological deficit (PD). However, the causal relationships between PD and dyslexia have been primarily investigated and theorized based on findings derived from behavioral measures. What is missing is evidence of the underlying neu-rophysiological origins of these relationships. The present study examined whether the performance on a phonological awareness task, namely phoneme elision (PE), differentiated children with dyslexia from their typically developing counterparts at a neural level. We proposed a novel machine-learning-based approach to extract neural activity from EEG to identify neural differences at the group level. Specifically, we formulated an optimization problem to first extract informative EEG components (termed phoneme-related neural-congruency components) by maximizing the congruency in neural activity among typically developing children during phoneme elision. Next, we utilized a machine-learning algorithm to optimally combine the resulting components to differentiate between children with dyslexia and controls. Results showed that the proposed phoneme-related neural-congruency components are predictive about the underlying neuronal differences amongst groups. These results provide empirical evidence towards the neural underpinnings of dyslexia and the potential neural origins of PD as a causal link to dyslexia. Notably, the proposed method could be used to study other behaviorally defined developmental disorders. © 2022, by the authors. All rights reserved.

关键词： Neurons

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