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Preprint ARTICLE | doi:10.20944/preprints202302.0275.v1

An Efficient Gaussian Mixture Model and It's Application to Neural Network

Weiguo Lu, Deng Ding, Fengyan Wu, Gangnan Yuan

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Neural Network, Uncertainties , GMM, Density approximation

Online: 16 February 2023 (06:59:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0051.v3

Duality Principles and Numerical Procedures for a Large Class of Non-convex Models in the Calculus of Variations

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Duality theory; non-convex analysis; numerical method for a non-smooth model

Online: 15 February 2023 (04:11:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0235.v1

Homotopic Quantum Fuzzy Adaptive Simulated Annealing [ HQF ASA ]

Hime A. e Oliveira Jr.

Subject: Mathematics & Computer Science, Numerical Analysis & Optimization Keywords: Quantum Annealing; Tunneling; Artificial Inference; Simulated Annealing; Global Learning

Online: 14 February 2023 (04:47:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0229.v1

Branching Random Walks with One Particle Generation Center and Possible Absorption at Every Point

Elena Filichkina, Elena Yarovaya

Subject: Mathematics & Computer Science, Probability And Statistics Keywords: branching random walks; moments of particle numbers; evolution operator; Green’s function

Online: 14 February 2023 (03:29:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0529.v2

SL(2, C) Scheme Processing of Singularities in Quantum Computing and Genetics

Michel Planat, Marcelo Amaral, David Chester, Klee Irwin

Subject: Mathematics & Computer Science, Geometry & Topology Keywords: Finitely generated group; SL(2,C) character variety; algebraic surfaces; schemes; exotic R4; topological quantum computing; microRNAs.

Online: 14 February 2023 (01:43:07 CET)

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Preprint SHORT NOTE | doi:10.20944/preprints202201.0269.v3

The Short Survey of ICT in Education in Japan

Takaaki Fujita

Subject: Mathematics & Computer Science, Other Keywords: ICT in Education; ICT Infrastructure; Cost Implications

Online: 13 February 2023 (15:22:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0222.v1

Gauss Quadrature for Integrals and Sums

Abdulaziz D. Alhaidari

Subject: Mathematics & Computer Science, Computational Mathematics Keywords: Gauss quadrature; integral approximation; continuous measure; discrete measure; mixed measure; orthogonal polynomials; recursion relation

Online: 13 February 2023 (15:08:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0220.v1

Intrusion Detection using Tripwire in UNIX Operating System

Muhammad Saqib Javed

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Message Digest; Index Node; Secure Hash Algorithm; Sandboxing

Online: 13 February 2023 (14:54:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0218.v1

Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN

Ghadah Alwakid, Walaa Gouda, Mamoona Humayun

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: diabetic retinopathy; Vision loss; Deep learning; CLAHE; ESRGAN

Online: 13 February 2023 (14:33:05 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0213.v1

dbs: Stata Command to Compute Double Bootstrap Confidence Intervals

Felix Bittmann

Subject: Mathematics & Computer Science, Probability And Statistics Keywords: bootstrapping; confidence intervals; dbs; resampling; variance estimation; heteroscedasticity

Online: 13 February 2023 (09:50:08 CET)

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Preprint REVIEW | doi:10.20944/preprints202302.0209.v1

Resilience and Resilient Systems of Artificial Intelligence: Tax-Onomy, Models and Methods

Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko, Borys Kuzikov

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: artificial intelligence system; resilience; robustness; fault tolerance; graceful degradation; do-main-adaptation; meat-learning; adversarial attack; fault injection; concept drift; resilience as-sessment

Online: 13 February 2023 (09:07:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0203.v1

Deep Learning for On-board Processing of Imaging Spectroscopy: A Survey

Nafiseh Ghasemi, Jens Nieke, Marco Celesti, Gianluigi Dicassimo

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Hyperpectral; Deep learning; Neural networks; image processing; classification; segmentation; hardware accelerators; CHIME mission

Online: 13 February 2023 (07:32:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0201.v1

Ramsey Theory and Geometry of Closed Loops

Edward Bormashenko, Nir Shvalb, Shraga Shoval, Mark Frenkel

Subject: Mathematics & Computer Science, General Mathematics Keywords: Ramsey theory; closed contour; Jordan theorem; complete graph.

Online: 13 February 2023 (06:56:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0196.v1

SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information

Devon Patterson, John Wilson, Michael Forrow, Tereza Wald

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Recommendation; GNN; Information; Feature; Structure

Online: 13 February 2023 (03:28:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0541.v2

A Solution of The Collatz Conjecture Problem

baoyuan duan

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: collatz conjecture

Online: 13 February 2023 (02:53:06 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202302.0189.v1

Infant Biometric Data Acquisitions: The Lessons Learnt

Norman Nelufule, Yaseen Moolla, Sthembile Ntshangase, Anton De Kock

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Infant Biometrics; Longitudinal data collection; infant fingerprint; infant iris; infant's ear

Online: 10 February 2023 (08:46:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0188.v1

CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction

Bumjin Park, Wonsang You

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Super resolution; Transformer; Deep Learning; Skin image; Dermoscopy

Online: 10 February 2023 (08:06:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0184.v1

Accelerating the Wheel Factoring Techniques

Alaa Zaki, M. Bakr, Arwa Alsahangiti, Saima Khosa, Khaled Fathy

Subject: Mathematics & Computer Science, Computational Mathematics Keywords: Integer factorization; Wheel factorization; Cryptography

Online: 10 February 2023 (07:43:56 CET)

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Preprint SHORT NOTE | doi:10.20944/preprints202302.0181.v1

Modular Dvoretzky, Type-Cotype, Khinchin-Kahane and Grothendieck Inequality Problems

K. MAHESH KRISHNA

Subject: Mathematics & Computer Science, Analysis Keywords: Dvoretzky Theorem; Type-Cotype; Khinchin-Kahane Inequality; Grothendieck Inequality; C*-algebra; Hilbert C*-module

Online: 10 February 2023 (06:19:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0178.v1

Blockchain-Based Patient-Driven Inter-Operable Model for Healthcare 5.0 System

Kaushal Shah, Lalit Garg, Divam Kachoria, Vaidehi Shah, Shristi Darbar, Andrea Visconti, Chintan Bhatt, Sudeep Tanwar

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: blockchain technology; trusted execution environment; attribute-based encryption; patient-driven inter- operability; Ethereum

Online: 10 February 2023 (03:48:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0170.v1

Research on Manufacturing Equipment Operation State Evaluation Technology Based on Fractional Calculus

Hua Cheng, Yan hong Zuo, Guoqing Geng, Shilong Xia, Chao Zhou

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Manufacturing equipment; Fractional order calculus; Effective evaluation; Operational status

Online: 9 February 2023 (15:02:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0166.v1

Computer-Aided Methods for Analysing Run of Speed Climbers

Adam Pieprzycki, Tomasz Mazur, Marcin Krawczyk, Daniel Król, Maciej Witek, Robert Rokowski

Subject: Mathematics & Computer Science, Other Keywords: speed sport climbing; video analysis; KLT algorithm; Convolutional Neural Network (CNN); OpenPose; AI; artificial intelligence

Online: 9 February 2023 (11:23:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0163.v1

Mathematical Root Simplification in Operational Amplifiers using an Ensemble Heuristic-Metaheuristic Algorithm

Navid Behmanesh-Fard, Hossein Yazdanjouei, Mohammad Shokouhifar, Frank Werner

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: operational transconductance amplifiers; symbolic circuit analysis; pole/zero extraction; root splitting; simplification; simulated annealing

Online: 9 February 2023 (11:08:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0051.v2

Duality Principles and Numerical Procedures for a Large Class of Non-convex Models in the Calculus of Variations

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Duality theory; non-convex analysis; numerical method for a non-smooth model

Online: 9 February 2023 (02:22:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0146.v1

Absolutely Summing Morphisms between Hilbert C*-Modules and Modular Pietsch Factorization Problem

K. MAHESH KRISHNA

Subject: Mathematics & Computer Science, Analysis Keywords: Absolutely summing operator; Commutative C*-algebra; Hilbert C*-module; Hilbert-Schmidt operator

Online: 8 February 2023 (10:58:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202110.0199.v2

The Suboptimal WMT Test Sets and Its Impact on Human Parity

Ahrii Kim, Yunju Bak, Jimin Sun, Sungwon Lyu, Changmin Lee

Subject: Mathematics & Computer Science, Analysis Keywords: human parity; NMT evaluation; WMT

Online: 7 February 2023 (12:12:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0097.v1

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Ghadah Alwakid, Walaa Gouda, Mamoona Humayun

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Vision loss; Diabetic retinopathy; Image enhancement; APTOS

Online: 6 February 2023 (09:50:58 CET)

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Preprint REVIEW | doi:10.20944/preprints202302.0096.v1

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Aurora Saibene, Mirko Caglioni, Silvia Corchs, Francesca Gasparini

Subject: Mathematics & Computer Science, Other Keywords: electroencephalogram (EEG); brain computer interface (BCI); motor imagery (MI); wearable devices

Online: 6 February 2023 (09:48:24 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0092.v1

Analysis of Adaptive Algorithms Based on Least-Mean-Square Applied to Hand Tremors Suppression Control

Rafael Silfarney Alves Araújo, Jessica Cristina Tironi, Wemerson Delcio Parreira, Renata Coelho Borges, Juan Francisco De Paz Santana, Valderi Reis Quietinho Leithardt

Subject: Mathematics & Computer Science, Other Keywords: Adaptive Algorithm; Tremor Suppression; LMS; Parkinson

Online: 6 February 2023 (09:09:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0082.v1

A Generalized Series Expansion of the Arctangent Function Based on the Enhanced Midpoint Integration

Sanjar Abrarov, Rehan Siddiqui, Rajinder Jagpal, Brendan Quine

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: arctangent function; midpoint integration; iterative algorithm; constant pi

Online: 6 February 2023 (07:11:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0078.v1

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-leveraged Transmission Models

Xiaoyan Li, Vyom Patel, Lujie Duan, Jalen Mikuliak, Jenny Basran, Nathaniel Osgood

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: COVID-19; Particle Filtering; Machine Learning; Epidemiologic Modeling; Compartmental Model; Projection and Intervention

Online: 6 February 2023 (02:55:38 CET)

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COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Preprint ARTICLE | doi:10.20944/preprints202302.0070.v1

Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

Javier Rodriguez-Vazquez, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina, Pascual Campoy

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: deep learning; aerial imagery; precision agriculture; plant detection; domain adaptation; unsupervised learning; self-supervision; adversarial learning; domain shift; tropical crops

Online: 3 February 2023 (10:14:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0066.v1

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Raniah Alsahafi, Ahmed Alzahrani, Rashid Mehmood

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Smart Tourism; Sustainable Tourism; Natural language Processing (NLP); Big Data Analytics; Deep Learning; Machine Learning; Unsupervised Learning; Bidirectional Encoder Representations from Transformers (BERT); Literature Review; Smart Societies

Online: 3 February 2023 (09:47:55 CET)

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The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media & Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

Preprint ARTICLE | doi:10.20944/preprints202302.0065.v1

Forecasting Geopolitical Conflicts Using GPT-3 AI: Reali-Ty-Check One Year into the 2022 Ukraine War

David Jungwirth, Daniela Haluza

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: OpenAI; ChatGPT; GPT-3; text-davinci-003; chatbots; prediction; scenario planning; scenario generation; future research; artificial intelligence; human-AI interaction; collaboration

Online: 3 February 2023 (09:34:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0051.v1

Duality Principles and Numerical Procedures for a Large Class of Non-convex Models in the Calculus of Variations

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Duality theory; non-convex analysis; numerical method for a non-smooth model

Online: 3 February 2023 (02:35:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0050.v1

A Concise Overview of the Vision-based Human Action Recognition Field

Fernando Camarena, Miguel Gonzalez-Mendoza, Leonardo Chang, Ricardo J Cuevas-Ascencio

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: video-based human action recognition; Action Recognition; Deep Learning Methods; handcrafted Methods; Human Action; Overview

Online: 3 February 2023 (01:17:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0032.v1

How to find Orchestrated Trolls?A Case Study on Identifying Polarized Twitter Echo Chambers

Nane Kratzke

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Social network; Twitter; Structural analysis; Echo chamber; Detection; Case study; German language; Disinformation

Online: 2 February 2023 (06:54:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0026.v1

Nearest Neighbours Graph Variational AutoEncoder

Lorenzo Arsini, Barbara Caccia, Andrea Ciardiello, Stefano Giagu, Carlo Mancini Terracciano

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Graph Neural Network; Variational Autoencoder; Pooling; Nearest Neighbours

Online: 2 February 2023 (03:36:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0561.v2

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

Lawrence Mdegela, Yorick De Bock, Esteban Municio, Edith Luhanga, Judith Leo, Steven Latré

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Sensors; data-set; Machine learning; river floods; river level

Online: 1 February 2023 (03:54:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0004.v1

Online Hybrid Neural Network for Stock Prices Prediction: A Case Study of High-frequency Stock Trading in China Market

Chengyu Li, Luyi Shen, Guoqi Qian

Subject: Mathematics & Computer Science, Other Keywords: High-frequency Limit Order Book; Online Fast Prediction; Hybrid Neural Network

Online: 1 February 2023 (02:37:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0582.v1

Gevrey Asymptotics for Logarithmic Type Solutions to Singularly Perturbed Problems with Nonlocal Nonlinearities

Stephane Malek

Subject: Mathematics & Computer Science, Analysis Keywords: Asymptotic expansion; Borel-Laplace transform; Fourier transform; initial value problem; formal power series; formal monodromy; singular perturbation

Online: 31 January 2023 (10:17:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0580.v1

Spyware Integrated with Prediction Models for Monitoring Corporate Computers

Darlan Noetzold, Anubis Graciela De Moraes Rossetto, Valderi Reis Quietinho Leithardt

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Electronic monitoring; hate speech; data leakage; prediction.

Online: 31 January 2023 (08:59:39 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0561.v1

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

Lawrence Mdegela, Yorick De Bock, Esteban Municio, Edith Luhanga, Judith Leo, Steven Latré

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Sensors; data-set; Machine learning; river floods; river level

Online: 31 January 2023 (02:13:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0558.v1

Extreme Rainfall Events Detection Using Machine Learning for Kikuletwa River Floods in Northern Tanzania

Lawrence Mdegela, Esteban Municio, Yorick De Bock, Steven Latré, Edith Luhanga

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Heavy rainfall; River floods; Machine learning

Online: 30 January 2023 (13:01:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0557.v1

PDF Malware Detection using Machine learning

Awss AlMahadeen, mouhammd alkasassbeh

Subject: Mathematics & Computer Science, Other Keywords: PDF; Malware; Machine Learning; Python; Random Forest

Online: 30 January 2023 (12:55:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0551.v1

CH-CC: A Chinese Multimodal Classroom Atmosphere Analysis Dataset Based on Teachers’ Behavior and Voice

Zicen Liao

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: multimodal dataset; sentiment analysis; classroom atmosphere; intelligent education

Online: 30 January 2023 (09:49:12 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202301.0335.v2

Safe Control of Autonomous Cloud Entities in Distributed Systems

Mostefa Kara

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Cloud Computing; Data Protection; Secure Communication; Middleware; Protocols

Online: 30 January 2023 (09:24:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0163.v4

Solve the 3x+1 Problem by the Multiplication and Division of Binary Numbers

Jishe Feng

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: The Collatz conjecture

Online: 30 January 2023 (09:23:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0541.v1

A Solution Of The Collatz Conjecture Problem

baoyuan duan

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: collatz conjecture

Online: 30 January 2023 (06:21:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0533.v1

Improving Deep Echo State Network with Neuronal Similarity-based Iterative Pruning Merging Algorithm

Qingyu Shen, Hanwen Zhang, Yao Mao

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: reservoir computing; deep echo state network; neuronal similarity-based iterative pruning merging algorithm; chaotic time series forecast

Online: 30 January 2023 (02:34:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0529.v1

SL(2, C) Scheme Processing of Singularities in Quantum Computing and Genetics

Michel Planat, Marcelo Amaral, David Chester, Klee Irwin

Subject: Mathematics & Computer Science, Geometry & Topology Keywords: Finitely generated group; SL(2,C) character variety; algebraic surfaces; schemes; exotic R4; topological quantum computing; microRNAs.

Online: 30 January 2023 (01:21:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0091.v12

Dual Variational Formulations for a Large Class of Non-Convex Models in the Calculus of Variations

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Duality principles; Generalized method of lines; Ginzburg-Landau type equations

Online: 29 January 2023 (09:58:00 CET)

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Preprint CASE REPORT | doi:10.20944/preprints202301.0427.v2

The SI4CARE Project: Using Wearable Devices for Assisting People with Dementia in Calabria

Alessandro Gallo, Salvatore Fregola, Margherita Menon, Filomena Talarico, Stella Fragkiadaki, Dyonisya Kontaxopoulou, Evangelia Stanitsa, Katarina Vukojevic, Danira Matijaca, Miodrag Milikovic, Srdjan Kozetinac, Alessio Merola, Vlado Dimovski, Anamarija Kejžar, Ivan Radevic, Danica Stevovic, Vlatika Martinovic, Katjia Matesan, Roberta Matkovic, Guido Piccoli, Elena Sodano, Amanda Gigliotti, Nicola Mayera, Elisabetta Pedace, Darjia Semrov, Pietro Hiram Guzzi

Subject: Mathematics & Computer Science, Other Keywords: Wearable Device; Dementia

Online: 29 January 2023 (09:29:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0524.v1

Statistical Inference for Finite Mixture of Matrix Variate t-distribution

Xinyu Yang, Leigang Dong, Zhichuan Zhu, Weisan Wu

Subject: Mathematics & Computer Science, Probability And Statistics Keywords: Matrix variate distribution; Mixture models; EM-algorithm; Penalized likelihood

Online: 28 January 2023 (08:53:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0521.v1

Feasibility Study on Utilization of the Artificial Intelligence GPT-3 in Public Health

David Jungwirth, Daniela Haluza

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: ChatGPT; GPT-3; OpenAI; chatbots; digital health; artificial intelligence; automation; technological advancement; human-AI interaction; collaboration; open science

Online: 28 January 2023 (07:56:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0163.v3

Solve the 3x+1 Problem by the Multiplication and Division of Binary Numbers

Jishe Feng

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: The Collatz conjecture

Online: 27 January 2023 (10:43:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0502.v1

Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity

Natalia S. Kovaleva, Valery V. Matrosov, Mikhail Mishchenko

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: working memory; STDP; short-term plasticity; spiking neural network; flexible cluster formation

Online: 27 January 2023 (10:32:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0484.v1

Ekeland Variational Principle and Some of Its Equivalents on a Weighted Graph, Completeness and the Osc Property

Ali Basit, Stefan Cobzas, Mokhwetha Daniel Mabula

Subject: Mathematics & Computer Science, Analysis Keywords: Ekeland variational principle; Takahashi minimization principle; Caristi fixed point theorem; weighted graph; partially ordered metric space; completeness; the OSC property

Online: 26 January 2023 (17:12:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0483.v1

Pulsed Thermography Dataset for Training Deep Learning Models

Ziang Wei, Ahmad Osman, Bernd Valeske, Xavier Maldague

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Pulsed thermography; Deep learning; Defect detection; Nondestructive evaluation

Online: 26 January 2023 (17:11:04 CET)

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Preprint DATA DESCRIPTOR | doi:10.20944/preprints202301.0473.v1

Prognosease: A Data Generator for Health Deterioration Prognosis

Tarek BERGHOUT, Mohamed Benbouzid

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Data generator; dataset; deep learning; health index; machine learning; prognosis and health management; remaining useful life

Online: 26 January 2023 (08:37:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0474.v1

Artificial Intelligence and Ten Societal Megatrends: A GPT-3 Case Study

Daniela Haluza, David Jungwirth

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: ChatGPT; GPT-3; global trends; OpenAI; chatbots; digital health; artificial intelligence; automation; technological advancement; human-AI interaction; collaboration

Online: 26 January 2023 (08:37:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0463.v1

Trainable Activations for Image Classification

Evgenii Pishchik

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Trainable Activations, Trainable Activation Functions, CosLU, DELU, LinComb, NormLinComb, ReLUN, ScaledSoftSign, ShiLU

Online: 26 January 2023 (02:59:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0091.v11

Dual Variational Formulations for a Large Class of Non-Convex Models in the Calculus of Variations

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Duality principles; Generalized method of lines; Ginzburg-Landau type equations

Online: 25 January 2023 (04:21:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0443.v1

Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study

Mostefa Kara, Abdelkader Laouid, Ahcène Bounceur, Omar Aldabbas

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Algerian dialect; Opinion mining; Sentiment analysis; Emotional detection; Social web

Online: 25 January 2023 (04:11:49 CET)

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Preprint CASE REPORT | doi:10.20944/preprints202301.0427.v1

The SI4CARE Project: Using Wearable Devices for Assisting People with Dementia in Calabria

Alessandro Gallo, Salvatore Fregola, Margherita Menon, Filomena Talarico, Stella Fragkiadaki, Dyonisya Kontaxopoulou, Katarina Vukojevic, Danira Matijaca, Miodrag Milikovic, Srdjan Kozetinac, Alessio Merola, Vlado Dimovski, Anamarija Kejžar, Ivan Radevic, Danica Stevovic, Vlatika Martinovic, Katjia Matesan, Roberta Matkovic, Guido Piccoli, Elena Sodano, Amanda Gigliotti, Nicola Mayera, Elisabetta Pedace, Darjia Semrov, Pietro Hiram Guzzi

Subject: Mathematics & Computer Science, Other Keywords: Wearable Device; Dementia

Online: 24 January 2023 (08:28:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0423.v1

Confidence Intervals and Regions for Proportions Under Various Three-Endmember Linear Mixture Models

Mark Berman

Subject: Mathematics & Computer Science, Probability And Statistics Keywords: proportion estimation; linear mixture model; confidence interval; confidence region; photosynthetic vegetation; non-photosynthetic vegetation; soil; Landsat Thematic Mapper

Online: 24 January 2023 (05:56:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0415.v1

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Sarah Alswedani, Rashid Mehmood, Iyad Katib, Saleh M. Altowaijri

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Psychological Health; Drugs; Twitter; Machine Learning; Big Data; Drug Abuse

Online: 23 January 2023 (13:16:45 CET)

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Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

Preprint ARTICLE | doi:10.20944/preprints202301.0406.v1

Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data

Ralf Gitzel, Marie Platenius-Mohr, Andreas Burger

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: AI; Sustainability; Energy efficiency; Deep learning; Neural networks

Online: 23 January 2023 (09:38:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0163.v2

Solve the 3x+1 Problem

Jishe Feng

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: The Collatz conjecture

Online: 23 January 2023 (09:33:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0402.v1

Separated Model for Stopping Point Prediction of Autoregressive Sequence

Tingzhen Liu, Shengxi Zhang, Qianqian Xiong

Subject: Mathematics & Computer Science, Numerical Analysis & Optimization Keywords: Sequence Encoder; Autoregressive Sequence; Separated Model; Statistical Test; Neural Network

Online: 23 January 2023 (08:30:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0396.v1

SuperHyperGirth Type-Results on extreme SuperHyperGirth theory and (Neutrosophic) SuperHyperGraphs Toward Cancer's extreme Recognition

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: extreme SuperHyperGraph; (extreme) SuperHyperGirth; Cancer's extreme Recognition

Online: 23 January 2023 (04:45:34 CET)

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In this research, the extreme SuperHyperNotion, namely, extreme SuperHyperGirth, is up. $E_1$ and $E_3$ are some empty extreme SuperHyperEdges but $E_2$ is a loop extreme SuperHyperEdge and $E_4$ is an extreme SuperHyperEdge. Thus in the terms of extreme SuperHyperNeighbor, there's only one extreme SuperHyperEdge, namely, $E_4.$ The extreme SuperHyperVertex, $V_3$ is extreme isolated means that there's no extreme SuperHyperEdge has it as an extreme endpoint. Thus the extreme SuperHyperVertex, $V_3,$ is excluded in every given extreme SuperHyperGirth. $ \mathcal{C}(NSHG)=\{E_i\}~\text{is an extreme SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^i~\text{is an extreme SuperHyperGirth SuperHyperPolynomial.} \ \ \mathcal{C}(NSHG)=\{V_i\}~\text{is an extreme R-SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^I~{\small\text{is an extreme R-SuperHyperGirth SuperHyperPolynomial.}} $ The following extreme SuperHyperSet of extreme SuperHyperEdges[SuperHyperVertices] is the extreme type-SuperHyperSet of the extreme SuperHyperGirth. The extreme SuperHyperSet of extreme SuperHyperEdges[SuperHyperVertices], is the extreme type-SuperHyperSet of the extreme SuperHyperGirth. The extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], is an extreme SuperHyperGirth $\mathcal{C}(ESHG)$ for an extreme SuperHyperGraph $ESHG:(V,E)$ is an extreme type-SuperHyperSet with the maximum extreme cardinality of an extreme SuperHyperSet $S$ of extreme SuperHyperEdges[SuperHyperVertices] such that there's only one extreme consecutive sequence of the extreme SuperHyperVertices and the extreme SuperHyperEdges form only one extreme SuperHyperCycle. There are not only four extreme SuperHyperVertices inside the intended extreme SuperHyperSet. Thus the non-obvious extreme SuperHyperGirth isn't up. The obvious simple extreme type-SuperHyperSet called the extreme SuperHyperGirth is an extreme SuperHyperSet includes only less than four extreme SuperHyperVertices. But the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], doesn't have less than four SuperHyperVertices inside the intended extreme SuperHyperSet. Thus the non-obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth isn't up. To sum them up, the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], isn't the non-obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth. Since the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], is an extreme SuperHyperGirth $\mathcal{C}(ESHG)$ for an extreme SuperHyperGraph $ESHG:(V,E)$ is the extreme SuperHyperSet $S$ of extreme SuperHyperVertices[SuperHyperEdges] such that there's only one extreme consecutive extreme sequence of extreme SuperHyperVertices and extreme SuperHyperEdges form only one extreme SuperHyperCycle given by that extreme type-SuperHyperSet called the extreme SuperHyperGirth and it's an extreme SuperHyperGirth . Since it 's the maximum extreme cardinality of an extreme SuperHyperSet $S$ of extreme SuperHyperEdges[SuperHyperVertices] such that there's only one extreme consecutive extreme sequence of extreme SuperHyperVertices and extreme SuperHyperEdges form only one extreme SuperHyperCycle. There are only less than four extreme SuperHyperVertices inside the intended extreme SuperHyperSet, thus the obvious extreme SuperHyperGirth, is up. The obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth, is: ,is the extreme SuperHyperSet, is: does includes only less than four SuperHyperVertices in a connected extreme SuperHyperGraph $ESHG:(V,E).$ It's interesting to mention that the only simple extreme type-SuperHyperSet called the extreme SuperHyperGirth amid those obvious[non-obvious] simple extreme type-SuperHyperSets called the neutrosophic SuperHyperGirth , is only and only. A basic familiarity with extreme SuperHyperGirth theory, SuperHyperGraphs, and extreme SuperHyperGraphs theory are proposed.

Preprint ARTICLE | doi:10.20944/preprints202301.0395.v1

Neutrosophic SuperHyperGraphs Warns Hyper Landmark of neutrosophic SuperHyperGirth In Super Type-Versions of Cancer's neutrosophic Recognition

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: neutrosophic SuperHyperGraph; (neutrosophic) SuperHyperGirth; Cancer's neutrosophic Recognition

Online: 23 January 2023 (04:44:12 CET)

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In this research, the neutrosophic SuperHyperNotion, namely, neutrosophic SuperHyperGirth, is up. $E_1$ and $E_3$ are some empty neutrosophic SuperHyperEdges but $E_2$ is a loop neutrosophic SuperHyperEdge and $E_4$ is an neutrosophic SuperHyperEdge. Thus in the terms of neutrosophic SuperHyperNeighbor, there's only one neutrosophic SuperHyperEdge, namely, $E_4.$ The neutrosophic SuperHyperVertex, $V_3$ is neutrosophic isolated means that there's no neutrosophic SuperHyperEdge has it as an neutrosophic endpoint. Thus the neutrosophic SuperHyperVertex, $V_3,$ is excluded in every given neutrosophic SuperHyperGirth. $ \mathcal{C}(NSHG)=\{E_i\}~\text{is an neutrosophic SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^i~\text{is an neutrosophic SuperHyperGirth SuperHyperPolynomial.} \ \ \mathcal{C}(NSHG)=\{V_i\}~\text{is an neutrosophic R-SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^I~{\small\text{is an neutrosophic R-SuperHyperGirth SuperHyperPolynomial.}} $ The following neutrosophic SuperHyperSet of neutrosophic SuperHyperEdges[SuperHyperVertices] is the neutrosophic type-SuperHyperSet of the neutrosophic SuperHyperGirth. The neutrosophic SuperHyperSet of neutrosophic SuperHyperEdges[SuperHyperVertices], is the neutrosophic type-SuperHyperSet of the neutrosophic SuperHyperGirth. The neutrosophic SuperHyperSet of the neutrosophic SuperHyperEdges[SuperHyperVertices], is an neutrosophic SuperHyperGirth $\mathcal{C}(ESHG)$ for an neutrosophic SuperHyperGraph $ESHG:(V,E)$ is an neutrosophic type-SuperHyperSet with the maximum neutrosophic cardinality of an neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperEdges[SuperHyperVertices] such that there's only one neutrosophic consecutive sequence of the neutrosophic SuperHyperVertices and the neutrosophic SuperHyperEdges form only one neutrosophic SuperHyperCycle. There are not only four neutrosophic SuperHyperVertices inside the intended neutrosophic SuperHyperSet. Thus the non-obvious neutrosophic SuperHyperGirth isn't up. The obvious simple neutrosophic type-SuperHyperSet called the neutrosophic SuperHyperGirth is an neutrosophic SuperHyperSet includes only less than four neutrosophic SuperHyperVertices. But the neutrosophic SuperHyperSet of the neutrosophic SuperHyperEdges[SuperHyperVertices], doesn't have less than four SuperHyperVertices inside the intended neutrosophic SuperHyperSet. Thus the non-obvious simple neutrosophic type-SuperHyperSet of the neutrosophic SuperHyperGirth isn't up. To sum them up, the neutrosophic SuperHyperSet of the neutrosophic SuperHyperEdges[SuperHyperVertices], isn't the non-obvious simple neutrosophic type-SuperHyperSet of the neutrosophic SuperHyperGirth. Since the neutrosophic SuperHyperSet of the neutrosophic SuperHyperEdges[SuperHyperVertices], is an neutrosophic SuperHyperGirth $\mathcal{C}(ESHG)$ for an neutrosophic SuperHyperGraph $ESHG:(V,E)$ is the neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperVertices[SuperHyperEdges] such that there's only one neutrosophic consecutive neutrosophic sequence of neutrosophic SuperHyperVertices and neutrosophic SuperHyperEdges form only one neutrosophic SuperHyperCycle given by that neutrosophic type-SuperHyperSet called the neutrosophic SuperHyperGirth and it's an neutrosophic SuperHyperGirth . Since it 's the maximum neutrosophic cardinality of an neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperEdges[SuperHyperVertices] such that there's only one neutrosophic consecutive neutrosophic sequence of neutrosophic SuperHyperVertices and neutrosophic SuperHyperEdges form only one neutrosophic SuperHyperCycle. There are only less than four neutrosophic SuperHyperVertices inside the intended neutrosophic SuperHyperSet, thus the obvious neutrosophic SuperHyperGirth, is up. The obvious simple neutrosophic type-SuperHyperSet of the neutrosophic SuperHyperGirth, is: ,is the neutrosophic SuperHyperSet, is: does includes only less than four SuperHyperVertices in a connected neutrosophic SuperHyperGraph $ESHG:(V,E).$ It's interesting to mention that the only simple neutrosophic type-SuperHyperSet called the neutrosophic SuperHyperGirth amid those obvious[non-obvious] simple neutrosophic type-SuperHyperSets called the neutrosophic SuperHyperGirth , is only and only. A basic familiarity with neutrosophic SuperHyperGirth theory, SuperHyperGraphs, and neutrosophic SuperHyperGraphs theory are proposed.

Preprint ARTICLE | doi:10.20944/preprints202301.0393.v1

The Spectrum of the Zariski Topology for Multiplication Krasner Hypermodules

Ergül Türkmen, Burcu Nisancı Türkmen, Öznur KULAK

Subject: Mathematics & Computer Science, Analysis Keywords: Pseudo-prime spectrum; Zariski topology; spectral hyperspace

Online: 23 January 2023 (02:25:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0385.v1

WEA: A Node Importance Algorithm in Weighted Networks

Na Zhao, Linjie Chen, Jie Li, Zhen Long, Ming Jing, Jian Wang

Subject: Mathematics & Computer Science, Geometry & Topology Keywords: Complex Network; Weighted Network; Node Importance; Dynamic Programming

Online: 23 January 2023 (01:45:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0382.v1

Approximation of the Mittag-Leffler functions by Elementary Functions with Physics Applications

Alvaro Salas

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: fractional oscillator, Caputo derivative, nonlinear fractional oscillator, Duffing equatio, fractional pendulum, fractional Van der Pol equation, Duffing.Mathieu fractional oscillator.

Online: 23 January 2023 (01:31:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202108.0146.v24

A Proof Of The Riemann Hypothesis Based On A New Expression Of The Completed Zeta Function

Weicun Zhang

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: Riemann Hypothesis (RH); Proof; Completed zeta function

Online: 20 January 2023 (15:09:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0372.v1

Traceability, Data Privacy, and Accountability in IoT using Blockchain

Abid Sultan, Yao Lin

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Blockchian; IoT; CoAP; MQTT; REST: Traceability

Online: 20 January 2023 (10:29:38 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202301.0335.v1

Secure Management of Autonomous Cloud Entities in Distributed Systems

Mostefa Kara

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Cloud Computing; Data Protection; Secure Communication; Middleware; Protocols

Online: 18 January 2023 (10:24:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0313.v1

PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Michal Brzus, Kevin Knoernschild, Jessica C Sieren, Hans J Johnson

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Minipig; Brain; Segmentation; Landmarks; Image Processing; Deep Learning; Pig

Online: 17 January 2023 (12:42:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0083.v2

Do Social Event Attendees Cluster Based on Socioeconomic Status?

Kerecsen Szabó, Gergő Pintér, Imre Felde

Subject: Mathematics & Computer Science, Other Keywords: mobile network data; call detail records; geospatial data; data analysis; human mobility; urban mobility; large social event; social sensing; socioeconomic status; machine learning; clustering

Online: 17 January 2023 (12:12:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0308.v1

Extreme SuperHyperClique as the Firm Scheme of Confrontation under Cancer's Recognition as the Model in the Setting of (Neutrosophic) SuperHyperGraphs

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: (Neutrosophic) SuperHyperGraph; Extreme SuperHyperClique; Cancer's Extreme Recognition

Online: 17 January 2023 (10:15:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0307.v1

Voronoi Diagrams Generated by the Archimedes Spiral

Mark Frenkel, Irina Legchenkova, Nir Shvalb, Shraga Shoval, Edward Bormashenko

Subject: Mathematics & Computer Science, General Mathematics Keywords: Archimedes Spiral; Voronoi tessellation; Voronoi entropy; surface patterns; aesthetic attraction; phyllotaxis; golden ratio.

Online: 17 January 2023 (10:07:18 CET)

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Preprint REVIEW | doi:10.20944/preprints202301.0291.v1

Towards Sustainable Virtual Reality: Gathering Design Guidelines for Intuitive Authoring Tools

Iolanda Lima Chamusca, Cristiano Vasconcellos Ferreira, Thiago Barros Murari, Antonio Lopes Apolinário Jr., Ingrid Winkler

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: virtual reality; authoring tools; intuitiveness; digitization; sustainability; user-centered design; human-computer interaction; metaverse

Online: 17 January 2023 (01:41:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0290.v1

On the 1st Level General Fractional Derivatives of Arbitrary Order

Yuri Luchko

Subject: Mathematics & Computer Science, Analysis Keywords: generalized Sonin condition; general fractional integral; general fractional derivative of arbitrary order; regularized general fractional derivative of arbitrary order; 1st level general fractional derivative; 1st fundamental theorem of fractional calculus; 2nd fundamental theorem of fractional calculus

Online: 17 January 2023 (01:34:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0282.v1

Uncertainty On The Act And Effect Of Cancer Alongside The Foggy Positions Of Cells Toward Neutrosophic Failed SuperHyperClique inside Neutrosophic SuperHyperGraphs Titled Cancer's Recognition

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Neutrosophic SuperHyperGraph; Neutrosophic Failed SuperHyperClique; Cancer's Neutrosophic Recognition

Online: 16 January 2023 (09:49:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0267.v1

Neutrosophic Version Of Separates Groups Of Cells In Cancer's Recognition On Neutrosophic SuperHyperGraphs

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Neutrosophic SuperHyperGraph, (Neutrosophic) SuperHyperMatching, Cancer's Neutrosophic Recognition

Online: 16 January 2023 (03:46:30 CET)

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In this research, assume a SuperHyperGraph. Then a neutrosophic SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperEdges such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge; a neutrosophic SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the neutrosophic SuperHyperPolynomial contains the neutrosophic coefficients defined as the neutrosophic number of the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperEdges such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge and the neutrosophic power is neutrosophicly corresponded to its neutrosophic coefficient; a neutrosophic R-SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge; a neutrosophic R-SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the neutrosophic SuperHyperPolynomial contains the neutrosophic coefficients defined as the neutrosophic number of the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge and the neutrosophic power is neutrosophicly corresponded to its neutrosophic coefficient. It's useful to define a ``neutrosophic'' version of a SuperHyperMatching . Since there's more ways to get type-results to make a SuperHyperMatching more understandable. For the sake of having neutrosophic SuperHyperMatching, there's a need to ``redefine'' the notion of a ``SuperHyperMatching ''. A basic familiarity with neutrosophic SuperHyperMatching theory, SuperHyperGraphs theory, and neutrosophic SuperHyperGraphs theory are proposed.

Preprint ARTICLE | doi:10.20944/preprints202301.0265.v1

The Shift Paradigm To Classify Separately The Cells and Affected Cells Toward The Totality Under Cancer's Recognition By New Multiple Definitions On the Sets Polynomials Alongside Numbers In The (Neutrosophic) SuperHyperMatching Theory Based on SuperHyperGraph and Neutrosophic SuperHyperGraph

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: SuperHyperGraph, (Neutrosophic) SuperHyperMatching, Cancer's Recognition

Online: 16 January 2023 (03:41:09 CET)

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In this research, assume a SuperHyperGraph. Then an extreme SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum cardinality of a SuperHyperSet $S$ of high cardinality SuperHyperEdges such that there's no SuperHyperVertex not to in a SuperHyperEdge and there's no SuperHyperEdge to have a SuperHyperVertex in a SuperHyperEdge; a neutrosophic SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperEdges such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge; an extreme SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the extreme SuperHyperPolynomial contains the coefficients defined as the number of the maximum cardinality of a SuperHyperSet $S$ of high cardinality SuperHyperEdges such that there's no SuperHyperVertex not to in a SuperHyperEdge and there's no SuperHyperEdge to have a SuperHyperVertex in a SuperHyperEdge and the power is corresponded to its coefficient; a neutrosophic SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the neutrosophic SuperHyperPolynomial contains the neutrosophic coefficients defined as the neutrosophic number of the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperEdges such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge and the neutrosophic power is neutrosophicly corresponded to its neutrosophic coefficient; an extreme R-SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum cardinality of a SuperHyperSet $S$ of high cardinality SuperHyperVertices such that there's no SuperHyperVertex not to in a SuperHyperEdge and there's no SuperHyperEdge to have a SuperHyperVertex in a SuperHyperEdge; a neutrosophic R-SuperHyperMatching $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge; an extreme R-SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the extreme SuperHyperPolynomial contains the coefficients defined as the number of the maximum cardinality of a SuperHyperSet $S$ of high cardinality SuperHyperVertices such that there's no SuperHyperVertex not to in a SuperHyperEdge and there's no SuperHyperEdge to have a SuperHyperVertex in a SuperHyperEdge and the power is corresponded to its coefficient; a neutrosophic R-SuperHyperMatching SuperHyperPolynomial $\mathcal{C}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the neutrosophic SuperHyperPolynomial contains the neutrosophic coefficients defined as the neutrosophic number of the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of high neutrosophic cardinality neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex not to in a neutrosophic SuperHyperEdge and there's no neutrosophic SuperHyperEdge to have a neutrosophic SuperHyperVertex in a neutrosophic SuperHyperEdge and the neutrosophic power is neutrosophicly corresponded to its neutrosophic coefficient. Assume a SuperHyperGraph. Then $\delta-$SuperHyperMatching is a maximal of SuperHyperVertices with a maximum cardinality such that either of the following expressions hold for the (neutrosophic) cardinalities of SuperHyperNeighbors of $s\in S:$ there are $|S\cap N(s)| > |S\cap (V\setminus N(s))|+\delta;$ and $ |S\cap N(s)| < |S\cap (V\setminus N(s))|+\delta.$ The first Expression, holds if $S$ is an $\delta-$SuperHyperOffensive. And the second Expression, holds if $S$ is an $\delta-$SuperHyperDefensive; a neutrosophic $\delta-$SuperHyperMatching is a maximal neutrosophic of SuperHyperVertices with maximum neutrosophic cardinality such that either of the following expressions hold for the neutrosophic cardinalities of SuperHyperNeighbors of $s\in S$ there are: $|S\cap N(s)|_{neutrosophic} > |S\cap (V\setminus N(s))|_{neutrosophic}+\delta;$ and $ |S\cap N(s)|_{neutrosophic} < |S\cap (V\setminus N(s))|_{neutrosophic}+\delta.$ The first Expression, holds if $S$ is a neutrosophic $\delta-$SuperHyperOffensive. And the second Expression, holds if $S$ is a neutrosophic $\delta-$SuperHyperDefensive It's useful to define a ``neutrosophic'' version of a SuperHyperMatching . Since there's more ways to get type-results to make a SuperHyperMatching more understandable. For the sake of having neutrosophic SuperHyperMatching, there's a need to ``redefine'' the notion of a ``SuperHyperMatching ''. A basic familiarity with Extreme SuperHyperMatching theory, SuperHyperGraphs, and Neutrosophic SuperHyperGraphs theory are proposed.

Preprint ARTICLE | doi:10.20944/preprints202301.0262.v1

Breaking the Continuity and Uniformity of Cancer In The Worst Case of Full Connections With Extreme Failed SuperHyperClique In Cancer's Recognition Applied in (Neutrosophic) SuperHyperGraphs

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: (Neutrosophic) SuperHyperGraph, Extreme Failed SuperHyperClique, Cancer's Extreme Recognition

Online: 16 January 2023 (03:10:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202203.0385.v2

About Adaptive Identification of Bouc-Wen Hysteresis

Nikolay Karabutov

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: hysteresis; Ben-Wien model; adaptive observer; exponential dissipativity; Lyapunov vector function; uncertainty

Online: 16 January 2023 (01:54:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0254.v1

Quickening Data-Aware Conformance Checking through Temporal Algebras

Giacomo Bergami, Samuel Appleby, Graham Morgan

Subject: Mathematics & Computer Science, General & Theoretical Computer Science Keywords: Logic Artificial Intelligence; Knowledge Bases; Query Plan; Temporal Logic; Conformance Checking; Temporal Data Mining; Intraquery Parallelism

Online: 13 January 2023 (11:07:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0240.v1

Neutrosophic Failed SuperHyperStable as the Survivors on the Cancer's Neutrosophic Recognition Based on Uncertainty to All Modes in Neutrosophic SuperHyperGraphs

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Neutrosophic SuperHyperGraph; Neutrosophic Failed SuperHyperStable; Cancer's Neutrosophic Recognition

Online: 13 January 2023 (07:45:33 CET)

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In this research, Assume a neutrosophic SuperHyperGraph. Then a ``Failed SuperHyperStable $\mathcal{I}(NSHG)$ for a SuperHyperGraph $NSHG:(V,E)$ is the maximum cardinality of a SuperHyperSet $S$ of SuperHyperVertices such that there's a SuperHyperVertex to have a SuperHyperEdge in common; a ``neutrosophic Failed SuperHyperStable'' $\mathcal{I}_n(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperVertices such that there's a neutrosophic SuperHyperVertex to have a neutrosophic SuperHyperEdge in common. Assume a SuperHyperGraph. Then an ``$\delta-$Failed SuperHyperStable'' is a \underline{maximal} Failed SuperHyperStable of SuperHyperVertices with \underline{maximum} cardinality such that either of the following expressions hold for the (neutrosophic) cardinalities of SuperHyperNeighbors of $s\in S:$ $~|S\cap N(s)| > |S\cap (V\setminus N(s))|+\delta,~|S\cap N(s)| < |S\cap (V\setminus N(s))|+\delta.$ The first Expression, holds if $S$ is an ``$\delta-$SuperHyperOffensive''. And the second Expression, holds if $S$ is an ``$\delta-$SuperHyperDefensive''; a``neutrosophic $\delta-$Failed SuperHyperStable'' is a \underline{maximal} neutrosophic Failed SuperHyperStable of SuperHyperVertices with \underline{maximum} neutrosophic cardinality such that either of the following expressions hold for the neutrosophic cardinalities of SuperHyperNeighbors of $s\in S:$ $~|S\cap N(s)|_{neutrosophic} > |S\cap (V\setminus N(s))|_{neutrosophic}+\delta,~ |S\cap N(s)|_{neutrosophic} < |S\cap (V\setminus N(s))|_{neutrosophic}+\delta.$ The first Expression, holds if $S$ is a ``neutrosophic $\delta-$SuperHyperOffensive''. And the second Expression, holds if $S$ is a ``neutrosophic $\delta-$SuperHyperDefensive''. A basic familiarity with Extreme Failed SuperHyperClique theory, Neutrosophic Failed SuperHyperClique theory, and (Neutrosophic) SuperHyperGraphs theory are proposed.

Preprint ARTICLE | doi:10.20944/preprints202301.0225.v1

On the Solution to Riemann Hypothesis

Michael Mark Anthony

Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: Gamma function; Zeta function; Bernoulli numbers

Online: 12 January 2023 (10:38:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0224.v1

Extremism of the Attacked Body Under the Cancer's Circumstances Where Cancer's Recognition Titled (Neutrosophic) SuperHyperGraphs

Henry Garrett

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: SuperHyperGraph; (Neutrosophic) Failed SuperHyperStable; Cancer's Recognition

Online: 12 January 2023 (09:49:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0221.v1

Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations

Hamed Ghazikhani, Gregory Butler

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Ion channels; Membrane proteins; Transmembrane proteins; Drug discovery; Protein language models; Convolutional Neural Network

Online: 12 January 2023 (09:21:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0219.v1

When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task

Tingzhen Liu, Qianqian Xiong, Shengxi Zhang

Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Large Language Model; Natural Language Processing; Reading Comprehension; Computational linguistics; Information Retrieval; BM25

Online: 12 January 2023 (08:58:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0116.v8

A Duality Principle and a Concerning Convex Dual Formulation Suitable for Non-convex Variational Optimization

Fabio Botelho

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Convex dual variational formulation; duality principle for non-convex optimization; model in non-linear elasticity

Online: 12 January 2023 (02:41:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0193.v1

A Different Geometric Approach to the Proof of Fermat’s Last Theorem

Elkhan Beylar Beylarov, Ilyas Ravan Hasanov

Subject: Mathematics & Computer Science, General Mathematics Keywords: Fermat's last theorem; triangle inequalities; Elba curves; ellipse; geometric proof; circle

Online: 11 January 2023 (03:38:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0190.v1

Reframed Design Thinking and Feasibility Analysis of UX Journey: Integrating User Experience and User Requirement for Solo Software Development

Wahyu Andhyka Kusuma, Azrul Hazri Jantan, Novia Indriaty Admodisastro, Noris Mohd Norowi

Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: User experience; user requirement; developer productivity; developer self-efficacy; solo software development; UX Journey

Online: 11 January 2023 (02:37:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0151.v2

Adaptive Parametric Identification of Systems with Multiple Nonlinearities

Nikolay Karabutov

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: adaptation; identification; identifiability; stability; excitation constancy; Lyapunov vector function; self-oscillation

Online: 10 January 2023 (03:12:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0169.v1

Cryptographic Tokens as an Incentive Mechanism for Patient-Reported Outcome Measures Surveys

Shuang Wu, Yao Jiang Galteland, Anton Hasselgren

Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Blockchain; smart contract; homomorphic encryption; IER detection

Online: 10 January 2023 (03:01:49 CET)

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