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Preprint ARTICLE | doi:10.20944/preprints202403.1311.v1

Research on Weed and Crop Identification System Using Pixel-Wise Segmentation

Xin-Zhi Hu, Wang-su Jeon, Sang-Yong Rhee

Subject: Computer Science And Mathematics, Computer Science Keywords: Semantic Segmentation; Weed Recognition; Deep Learning; Image Recognition; Machine Vision

Online: 22 March 2024 (08:17:47 CET)

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Preprint ESSAY | doi:10.20944/preprints202403.1119.v1

Black Swan Events: Mitigating Disruption to a Supply Chain Using Blockchain and Artificial Intelligence

Gordon Bowen, Hamid Jahankhani, Imad Yasir Nawaz

Subject: Computer Science And Mathematics, Computer Science Keywords: artificial intelligence; bblockchain; sustainable supply chain system; black swan events

Online: 22 March 2024 (07:40:26 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1127.v1

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multiomics Research – A Thorough Review on the Role of ChIP-Seq Data

Sarmistha Das, Shesh N Rai

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Super-enhancer; miRNA; ChIP-Seq; Gene regulatory network; Transcription factor; Multi-omics

Online: 22 March 2024 (07:38:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1110.v1

CCLR-DL: A Novel Statistics and Deep Learning Hybrid Method for Feature Selection and Forecasting Healthcare Demand

Guillem Hernández Guillamet, Francesc López Seguí, Josep Vidal Alaball, Beatriz López Ibáñez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; explainability; feature selection; Granger causality; health demand modelling; LSTM; multivariate time-series forecasting

Online: 22 March 2024 (07:31:07 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1289.v1

The Use of Graph Databases in Systems Biology: A Systematic Review

Ilya Mazein, Adrien Rougny, Alexander Mazein, Ron Henkel, Lea Gütebier, Lea Michaelis, Marek Ostaszewski, Reinhard Schneider, Venkata Satagopam, Lars Jensen, Dagmar Waltemath, Judith Wodke, Irina Balaur

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: graph databases; RDF; NoSQL databases; systems biology; network biology; ontology

Online: 21 March 2024 (13:52:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1314.v1

Finding All Efficient Solutions of an Interval Multiple Objective Linear Programming Problem

Ta Van Tu

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Interval multiple objective linear programming; the efficient basic set; the efficient set

Online: 21 March 2024 (13:47:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1273.v1

Rationality of Irrational Choice: The Logic of the Prisoner’s Dilemma

Eugene Kagan, Alexander Rybalov, Ronald Yager

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: multivalued logic; non-commutative algebra; decision-making; uncertainty; irrational decisions

Online: 21 March 2024 (11:55:46 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1272.v1

Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression

Chen-Hsiu Huang, Ja-Ling Wu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; image compression; video coding; neural compression; coding for machines

Online: 21 March 2024 (11:51:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1001.v2

ISM: A New Space-Learning Model for Heterogenous Multi-view Data Reduction, Visualization and Clustering

Paul Fogel, Galina Boldina, Franck Augé, Christophe Geissler, George Luta

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Principal Component Analysis; Non-negative Matrix Factorization; Non-negative Tensor Factorization; Multi-view Clustering; Canonical Correlation Analysis; Common Principal Components; Multi Dimensional Scaling

Online: 21 March 2024 (09:15:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1274.v1

ML-Enhanced Live-Video Streaming in Offline MANETs: An Applied Approach

Manuel Jesús-Azabal, Vasco N. G. J. Soares, Jaime Galán-Jiménez

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Mobile Adhoc Networks; Live-video streaming; Artificial Intelligence; Machine Learning; Bluetooth Low Energy; Offline streaming

Online: 21 March 2024 (08:20:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1214.v1

The Feit-Thompson Conjecture: An Example for Mathematical Research Based on Contradictory Sources

Carolin Hannusch

Subject: Computer Science And Mathematics, Mathematics Keywords: Feit-Thompson conjecture; prime numbers; mathematical research; research education; twin primes, generalized twin prime conjecture

Online: 21 March 2024 (01:53:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1211.v1

Reproducible Exploration of Disease Maps with Galaxy Workflows and the MINERVA Platform

Helena Rasche, Matti Hoch, Julia Scheel, Saskia Hiltemann, Martina Kutmon, Chris T. Evelo, Iacopo Cristoferi, Myrthe van Baardwijk, Andrew Stubbs, Marek Ostaszewski

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Galaxy workflows
disease maps
visual exploration
systems biomedicine

Online: 20 March 2024 (13:30:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1227.v1

DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8

Ji-Yuan Ding, Wang-su Jeon, Sang-Yong Rhee, Chang-Man Zou

Subject: Computer Science And Mathematics, Computer Science Keywords: component; yolov8s; MultiCat; C2fe; ADC2f; Pest detection

Online: 20 March 2024 (12:31:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0051.v62

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

Fabio Silva Botelho

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

Online: 20 March 2024 (12:24:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1205.v1

Persistence and Stochastic Extinction in a Lotka-Volterra Predator-Prey Stochastically Perturbed Model

Leonid Shaikhet, Andrei Korobeinikov

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Ito's stochastic differential equation; the Lyapunov functions method; stability in probability; stabilization by noise; stochastic extinction; persistence

Online: 20 March 2024 (10:23:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1106.v1

A Generic and Extendable Framework for Benchmarking and Assessing the Change Detection Models

Ahmed Alaa Abdelbaky Hassouna, Mohamed Badr Ismail, Ali Alqahtani, Nayef Alqahtani, Amany Shaban Hassan, Huthaifa I Ashqar, Anas M.R. AlSobeh, Abdallah A. Hassan, Mohammed Elhenawy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Change Detection; Remote Sensing; Aerial Images; Deep Learning; Convolution Neural Network (CNN); Recurrent Neural Network (RNN); Sustainable Development; Benchmarking; Generalization; Model Evaluation; Contour Analytics; Robustness Analysis

Online: 20 March 2024 (04:40:23 CET)

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Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

Preprint ARTICLE | doi:10.20944/preprints202403.1167.v1

Secure IoT communication: Implementing a One-Time-Pad Protocol with True Random Numbers and Secure Multiparty Sums

Julio Fenner, Patricio Galeas, Francisco Escobar, Rail Neira

Subject: Computer Science And Mathematics, Computer Science Keywords: One time pad cryptography; Internet of Things; Secure multiparty computation

Online: 19 March 2024 (16:13:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1173.v1

Solving Confirmation Time in Sharded Blockchain with PFQN

Junting Wu, Haotian Du, Jin Chen, Wei Ren

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: blockchain sharding; transaction confirmation time; cross-shard transactions; product-form queue network

Online: 19 March 2024 (16:06:53 CET)

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Preprint CONCEPT PAPER | doi:10.20944/preprints202307.0560.v18

Meaningfully Averaging Unbounded Functions

Bharath Krishnan

Subject: Computer Science And Mathematics, Analysis Keywords: expected value; hausdorff measure; (Exact) dimension function; function space; prevalent and shy sets; entropy; choice function

Online: 19 March 2024 (12:46:40 CET)

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In this paper we want meanignfully average an "infinite collection of objects covering an infinite expanse of space". We illustrate this quote with an explicit n-dimensional function, where the graph of the function is dense in ℝ^(n+1). The problem is non meaningful expected value of the function (e.g., w.r.t the Lebesgue or Hausodorff measure) has a finite value. In fact "almost no" Borel measurable functions have a finite and meaningful value. In rigorous terms, suppose for n∈ ℕ, set A⊆ℝⁿ and function f∶A→ℝⁿ. If set A is Borel; B* is the set of all Borel measurable function in ℝᴬ for all A⊆ℝⁿ, and B** is the set of all f∈B* with a finite expected value—w.r.t the Hausdorff measure—then B** is a shy subset of B*. To fix these issues, we wish to find a unique and "natural" extension of the expected value—w.r.t the Hausdorff measure—on bounded functions to unbounded/bounded f, which takes finite values only, so B** is a non-shy subset of B*. Note we haven't found evidence suggesting mathematicians have thought of this problem; however, it's assumed, in general, there's no meaningful way of averaging functions which cover an infinite expanse of space. Regardless, we'll attempt to solve the problem by defining a choice function—this shall choose a unique set of equivalent sequences of sets (Fₖ***) , where the set-theoretic limit of Fₖ*** is the graph of f; the measure Hʰ is the ℎ-Hausdorff measure, such for each k∈ℕ, 0 < Hʰ(Fₖ***) < +∞; and (fₖ*) is a sequence of functions where {(x₁,...,xₙ,fₖ*(x₁,...,xₙ))∶x∈ dom(Fₖ***)}=Fₖ***. Thus, if (Fₖ***) converges to A at a rate linear or super-linear to the rate non-equivelant sequences of sets converge, and the extended expected value of or E**[f,Fₖ***] in the eq. below: ∀(ε>0)∃(N∈ℕ)∀(k∈N)(k≥N⇒1/Hʰ(dom(Fₖ***))∫dom(Fₖ*** )fₖ* dHʰ - E**[f,Fₖ***]<ε ) exists, then E**[f,Fₖ***] is a unique and "natural" extension of the original expected value on bounded f, so B**—the set of all f∈B* with a finite E**[f,Fₖ***]—is a non-shy set in B*. Note we guessed the choice function using computer programming we redefine linear and super-linear convergence in terms of partitions of

Preprint ARTICLE | doi:10.20944/preprints202403.1140.v1

CheapSE: Improving Magnitude-Based Speech Enhancement Using Self-Reference

Benzhe Dai, Kaijun Tan, Huidong Xue, Huaxiang Lu

Subject: Computer Science And Mathematics, Security Systems Keywords: GRU; Self-Reference; speech enhancement

Online: 19 March 2024 (11:20:26 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1086.v1

Intention Recognition in Digital Forensics: Systematic Review

Yidnekachew Worku Kassa, Joshua Isaac James, Elefelious Getachew Belay

Subject: Computer Science And Mathematics, Security Systems Keywords: Digital Forensics; Intention Recognition; Goal Recognition; plan recognition; cyberattack; cybercrime; model

Online: 19 March 2024 (10:21:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.0913.v2

The Fourier Continuous Derivative: A New Approach to Fractional Differentiation

Eduardo Diedrich

Subject: Computer Science And Mathematics, Analysis Keywords: Fourier Continuous Derivative; Fractional Differentiation; Invariance Properties

Online: 19 March 2024 (10:15:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1102.v1

Classifying Violent Anti-government Conflicts in Mexico: A Machine Learning Framework

Vishal Subedi, Harsh Hemant Shah, Benjamin Bagozzi, Somya Sharma, Snigdhansu Chatterjee

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: spatial, conflict, machine learning. ;

Online: 19 March 2024 (10:10:20 CET)

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Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

Preprint ARTICLE | doi:10.20944/preprints202403.1029.v1

GeoFlood: Computational Model for Overland Flooding

Brian Kyanjo, Donna Calhoun, David L. George

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Adaptive Mesh Refinement; GeoFlood; p4est; Flood modeling

Online: 19 March 2024 (06:45:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1043.v1

Neural Information Organizing and Processing – Neural Machines

Iosif Iulian Petrila

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: neural information; neural machines; neural networks; sensors and actuators; nervous systems; artificial intelligence

Online: 18 March 2024 (16:14:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1060.v1

Leveraging Data Locality in Quantum Convolutional Classifiers

Mingyoung Jeng, Alvir Nobel, Vinayak Jha, David Levy, Dylan Kneidel, Manu Chaudhary, Ishraq Islam, Audrey Facer, Manish Singh, Evan Baumgartner, Eade Vanderhoof, Abina Arshad, Esam El-Araby

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: quantum computing; convolutional neural networks; quantum machine learning; variational quantum algorithms

Online: 18 March 2024 (15:28:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1030.v1

Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems

Do Jin Yoo, Cheong Jeong Seo

Subject: Computer Science And Mathematics, Computer Science Keywords: Cloud Resourece Optimization; AI-Based Automation in Cloud Computing; Performance Management in Cloud Environments; Cost Efficiency in Cloud Services; Turbonomic ARM

Online: 18 March 2024 (11:37:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1027.v1

Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis

Randa A. Makled, Weihu Cheng

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Energy Distance, Energy Statistic, Wishart distributions, Goodness of Fit, high-dimensional data.

Online: 18 March 2024 (11:36:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.0773.v14

The Collatz Conjecture: A New Proof using Algebraic Inverse Trees

Eduardo Diedrich

Subject: Computer Science And Mathematics, Mathematics Keywords: Discrete dynamical systems; Inverse modeling; Topological equivalence; Topological transport; Algebraic trees; Collatz Conjecture; Homeomorphic invariance

Online: 18 March 2024 (10:42:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1017.v1

Sampling Audit Evidence Using a Naive Bayes Classifier

Guang-Yih Sheu ---, Nai-Ru Liu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Sampling; audit evidence; representativeness index; Naive Bayes classifier

Online: 18 March 2024 (10:37:21 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1007.v1

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

Majid Hojati, Steven Roberts, Colin Robertson

Subject: Computer Science And Mathematics, Geometry And Topology Keywords: spatio-temporal indexing; temporal topology; query processing; IPFS; distributed systems; smart contracts; blockchain

Online: 18 March 2024 (10:29:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1013.v1

Using Physics-Informed Neural Networks (PINNs) for Tumor Cells Growth Modeling

José A. Rodrigues

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: physics-informed neural networks (PINNs); differential equation; loss function; activation function; deep learning; cancer cells; Montroll growth model; Verhulst growth model

Online: 18 March 2024 (10:29:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0843.v1

Brain-inspired agents for Quantum Reinforcement Learning

E. Andrés, Manuel Pegalajar Cuellar, Gabriel Navarro

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Quantum Reinforcement Learning; Quantum Neural Networks; Quantum Spiking Neural Network; Quantum Long Short-Term Memory; Brain-inspired models.

Online: 18 March 2024 (10:24:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0227.v20

Collatz Conjecture

Asset Durmagambetov, Aniyar Durmagambetova

Subject: Computer Science And Mathematics, Analysis Keywords: binary representation; Collatz conjecture

Online: 18 March 2024 (10:20:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0995.v1

Fractional Mathematical Models S.V. Dubovsky to Describe K-Waves in Economics

Danil Makarov, Roman Parovik

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: mathematical model; phase trajectory; oscillogram; limit cycle; fractional derivative; Adams-Bashforth-Moulton method; heredity

Online: 18 March 2024 (09:48:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1020.v1

A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects

Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Mohd Tahir Ismail

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: fractional cointegration, Residual-based test, panel data model, fixed effects, Asymptotic theory

Online: 18 March 2024 (09:46:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v11

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 18 March 2024 (08:29:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0982.v1

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Rodrigo Cuéllar-Hidalgo, Raúl Pinto-Elías, Juan-Manuel Torres-Moreno, Osslan Osiris Vergara Villegas, Gerardo Reyes-Salgado, Andrea Magadan-Salazar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Reference Mining; BiLSTM; Transformers; Byte-Pair Encoding; Conditional Random Fields

Online: 18 March 2024 (08:26:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0963.v1

The Generalized Eta Transformation Formulas as the Hecke Modular Relation

Nianliang Wang, Takato kuzumaki, Shigeru Kanemitsu

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: RHB correspondence; transformation formula for Lambert series; Hurwitz zeta-function; Lerch zeta-function; vector space structure

Online: 18 March 2024 (08:12:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0858.v6

Detecting CSV File Dialects by Table Uniformity Measurement and Data Type Inference

Wilfredo García

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: comma separated values; CSV dialect detection; data mining

Online: 18 March 2024 (07:25:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0703.v1

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

Mohammad AlKhanafseh, Ola Surakhi

Subject: Computer Science And Mathematics, Security Systems Keywords: Blockchain; Evidence Preservation, Forensics; Stenography

Online: 17 March 2024 (16:01:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0702.v1

Convergence in Discrete Inverse Dynamical Systems

Eduardo Diedrich

Subject: Computer Science And Mathematics, Security Systems Keywords: Discrete dynamical systems; Inverse modeling; Topological equivalence; Topological transport; Algebraic trees; Combinatorial explosions; Computational complexity; Universal convergence

Online: 17 March 2024 (15:58:52 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0919.v1

Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond

Zamara Mariam, Sarfaraz K. Niazi, Matthias Magoola

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: generative AI; drug development; digital twins; prospective analysis

Online: 15 March 2024 (19:11:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0925.v1

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

Gwo-Ching Chang, Bo-Han Zeng, Shih-Chiang Lin

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Eye State Detection; Driver Fatigue Monitoring; YOLOv7-tiny; Structured Pruning; Neural Network Optimization

Online: 15 March 2024 (19:10:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1675.v2

Mathematics of Epidemics: On the General Solution of SIRVD, SIRV, SIRD and SIR Compartment Models

Reinhard Schlickeiser, Martin Kröger

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: nonlinear differential equations; analytic solution; vaccination; pandemic spreading

Online: 15 March 2024 (15:03:36 CET)

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The susceptible-infected-recovered-vaccinated-deceased (SIRVD) epidemic compartment model extends the SIR model to include the effects of vaccination campaigns and time-dependent fatality rates on epidemic outbreaks. It encompasses the SIR, SIRV, SIRD, and SI models as special cases, with individual time-dependent rates governing transitions between different fractions. We investigate a special class of exact solutions and accurate analytical approximations for the SIRVD and SIRD compartment models. While the SIRVD and SIRD equations pose complex integro-differential equations for the rate of new infections and the fractions as a function of time, a simpler approach considers determining equations for the sum of ratios for given variations. This approach enables us to derive fully exact analytical solutions for the SIRVD and SIRD models. For nonlinear models with a high-dimensional parameter space, such as the SIRVD and SIRD models, analytical solutions, exact or accurately approximative, are of high importance and interest: not only as suitable benchmarks for numerical codes, but especially as they allow us to understand the critical behavior of epidemic outbursts as well as the decisive role of certain parameters. In the second part of our study, we apply a recently developed analytical approximation for the SIR and SIRV models to the more general SIRVD model. This approximation offers accurate analytical expressions for epidemic quantities, such as the rate of new infections and the fraction of infected persons, particularly when the cumulative fraction of infections is small. The distinction between recovered and deceased individuals in the SIRVD model affects the calculation of the death rate, which is proportional to the infected fraction in the SIRVD/SIRD cases but often proportional to the rate of new infections in many SIR models using a posteriori approach. We demonstrate that the temporal dependence of the infected fraction and the rate of new infections differs when considering the effects of vaccinations and when the real-time dependence of fatality and recovery rates diverge. These differences are highlighted for stationary ratios and gradually decreasing fatality rates. \Revised{The case of stationary ratios allows one to construct a new powerful diagnostics method to extract analytically all SIRVD model parameters from measured COVID-19 data of a completed pandemic wave.

Preprint ARTICLE | doi:10.20944/preprints202403.0933.v1

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis

Chengyuan Zha, Lei Li, Fangting Zhu, Yanzhe Zhao

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Breath detection; Sensor arrays; Convolutional Neural Network; Lightweight neural network; Cancer detection

Online: 15 March 2024 (14:42:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0844.v1

Web Application for Retrieval-Augmented Generation: Implementation and Testing

Irina Radeva, Ivan Popchev, Lyubka Doukovska, Miroslava Dimitrova

Subject: Computer Science And Mathematics, Software Keywords: Retrieval-augmented generation (RAG); open-source large language models (LLMs); 30 Antelope blockchain; IPFS; Ollama; LangChain; smart agriculture

Online: 15 March 2024 (14:38:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0910.v1

When Four Cyclic Antipodal Points Are Ordered Counterclockwise in Euclidean and Hyperbolic Geometry

Abraham A. Ungar

Subject: Computer Science And Mathematics, Geometry And Topology Keywords: cyclic antipodal points; relativistic model of hyperbolic geometry; gyrovector space; gyrotrigonometry

Online: 15 March 2024 (11:09:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0898.v1

Frilled Lizard Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Ibraheem Abu Falahah, Osama Al-Baik, Saleh Alomari, Gulnara Bektemyssova, Saikat Gochhait, Irina Leonova, Om Parkash Malik, Frank Werner, Mohammad Dehghani

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Optimization; bio-inspired; metaheuristic; frilled lizard optimization; exploration; exploitation

Online: 15 March 2024 (08:55:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0891.v1

Uttarakhand State Earthquake Early Warning System: A Case Study of Himalayan Environment

Pankaj Kumar, Kamal Kamal, Mukat Lal Sharma, Ravi Sankar Jakka, Pratibha Pratibha

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Himalaya; Uttarakhand; UEEWS; Instrumentation; Lead Time; Earthquake Early Warning Parameters; Mitigation Tool

Online: 15 March 2024 (08:54:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v10

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 14 March 2024 (10:11:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0810.v1

Extending Group Dynamics Models: Incorporating Heterogeneous Networks to Analyze Cooperative Behaviors and Collective Intelligence in Biological Groups

Yude Fu, Jing Zhu, Xiang Li, Xu Han, Wenhui Tan, Qizi Huangpeng, Xiaojun Duan

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: coordinated behavior; informed leaders; group consistency; biological interaction; power-law distribution; complex network

Online: 14 March 2024 (07:58:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0826.v1

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

Shuai You, Shijun Lin, Yujian Feng, Jianhua Fan, Zhenzheng Yan, Shangdong Liu, Yimu Ji

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: sauce-packet leakage segmentation; uneven illumination; multi-level feature fusion; attention mechanism

Online: 14 March 2024 (06:02:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0699.v1

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Mohammed Alhagyan

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Energy sector; GBM; GFBM; stochastic ; volatility

Online: 14 March 2024 (05:53:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0679.v1

Predicting and Analyzing Rainfall Patterns in Ethiopia Using Linear Regression Modeling

Moges Tsegaw Melesse, Gezahegn Mulusew Delele

Subject: Computer Science And Mathematics, Computer Science Keywords: Rainfall patterns; Ethiopia; Linear regression modeling; R-squared score; Mean squared error

Online: 14 March 2024 (05:28:48 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0585.v1

Customer Churn Prediction in Telecommunication Industry: A Literature Review

Mehdi Imani

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Customer churn prediction; machine learning; telecommunication industry; literature review; imbalanced data; sampling techniques; classification techniques; random forest; XGBoost; gradient boosting; ensemble techniques

Online: 14 March 2024 (05:27:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0747.v1

Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters

Simeon Karpuzov, Georgi Hristov Petkov, Silvia Ilieva, Alexander Georgiev Petkov, Stiliyan Nikolov Kalitzin

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: remote tracking; velocity flow reconstruction; target motion recognition

Online: 13 March 2024 (17:04:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0779.v1

Automatic Classification of All-sky Nighttime Cloud Images Based on Machine Learning

Xin Zhong, Fujia Du, Yi Hu, Xu Hou, Zonghong Zhu, Xiaogang Zheng, Kang Huang, Zhimin Ren, Yonghui Hou

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: nighttime cloud image; observational astronomy; all-sky cameras; PSO; XGBoost

Online: 13 March 2024 (12:40:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1551.v2

On Fuzzy Soft Beta-Continuity and Beta-Irresoluteness: Properties and Applications

Ibtesam Alshammari, Islam M. Taha

Subject: Computer Science And Mathematics, Geometry And Topology Keywords: fuzzy soft set; fuzzy soft topological space; r-fuzzy soft β -closed (β -open) set; fuzzy soft
β -closure (β -interior) operator; fuzzy soft β -continuity; fuzzy soft almost (weakly) β -continuity; fuzzy soft β -irresolute (strongly β -irresolute) function; connectedness

Online: 13 March 2024 (12:39:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0742.v1

Trends and Paradoxes of Competitive Evolution in the Predation Mechanism

Evariste Sanchez-Palencia, M.A. Aziz-Alaoui

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: predator– prey interactions; hopf bifurcation; limit cycle; evolutionary trend; evolutionary ecology; emergence of efficiency

Online: 13 March 2024 (11:35:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0743.v1

PointNet++ Network with Contextual Feature and Mutual Learning for Point Sets

Xi Hu, Xiaolan Xie

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: point cloud; part segmentation; deep learning; self-attention; object classification

Online: 13 March 2024 (10:18:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0775.v1

Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach

Ramin Mousa, Mitra Khezli, Saba Hesaraki

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Point Cloud Classification; Virtual Reality; Hybrid Model; GRULSTM;
GRU; LSTM

Online: 13 March 2024 (10:00:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0768.v1

Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval

Teng Ma, Daniel Organisciak, Wenbao Ma, Yang Long

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Visual Language Models; Zero-Shot Instance Retrieval; Cognition-Alignment

Online: 13 March 2024 (07:29:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0754.v1

Literacy in Artificial Intelligence as a Challenge for Teaching in Higher Education: A Case Study at Portalegre Polytechnic University

Eduardo Lérias, Cristina Guerra, Paulo Ferreira

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence; AI Literacy; AI in Education (AIED)

Online: 13 March 2024 (07:19:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0704.v1

Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News.

Jim Samuel, Tanya Khanna, Srinivasaraghavan Sundar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI phobia; Artificial Intelligence; AI; natural language processing; large language models; machine learning; topics; sentiment; emotion; fear; risk; threat; news; headlines; ML; NLP; LLMs

Online: 13 March 2024 (07:03:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0733.v1

SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection

Kyeong-Hwan Moon, Soo-Yol Ok, Suk-Hwan Lee

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deepfake Detection, Deepfake Unknown Domain, Meta Pseudo Labels, Supervised Contrastive Learning, Generative Misuse

Online: 12 March 2024 (16:39:39 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0732.v1

The Generating Functions and Goldbach Conjecture

Hui Yang

Subject: Computer Science And Mathematics, Discrete Mathematics And Combinatorics Keywords: generating functions; integer partitions; prime number; the Goldbach conjecture

Online: 12 March 2024 (15:43:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1615.v2

Exploring Cellular Automata Learning: An Innovative Approach for Secure and Imperceptible Digital Image Watermarking

Iram Khurshid Bhat, Fasel Qadir, Mehdi Neshat, Amir H. Gandomi

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Watermarking algortihms; Elementary Cellular Automata; Rule-30; Security; Copyright Protection; Authentication; Robustness

Online: 12 March 2024 (14:27:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0714.v1

Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning

Shereen Ismail, Hajar Moudoud, Diana Dawoud, Hassan Reza

Subject: Computer Science And Mathematics, Security Systems Keywords: Security; Zero Trust; Blockchain; Smart Contract; Supply Chain; Deep Reinforcement Learning; Cyberattacks

Online: 12 March 2024 (14:00:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0664.v1

The Jacobian Conjecture and Idempotent Ideals

Lukasz Matysiak

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: automorphism; idempotent ideal; Jacobian conjecture; polynomial mapping

Online: 12 March 2024 (13:46:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0687.v1

New Infinite Classes for Normal Trimagic Squares of Even Orders Using Row-Square Magic Rectangles

Can Hu, Fengchu Pan

Subject: Computer Science And Mathematics, Discrete Mathematics And Combinatorics Keywords: n/a; Trimagic square; row-square magic rectangle; trimagic pair; product construction; diagonal Latin square; hypergraph; magic labeling

Online: 12 March 2024 (08:27:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0685.v1

ML Based Software Defect Prediction in Embedded Software for Telecommunication Systems(Focusing on the Case of SAMSUNG ELECTRONICS)

Hongkoo Kang, Sungryong Do

Subject: Computer Science And Mathematics, Software Keywords: Software Defect Prediction; Machine Learning; Software Quality Metrics; Software Quality Management; Embedded Software for Telecommunication Systems

Online: 12 March 2024 (08:04:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0675.v1

Unexpected Uncertainty Principle for Disc Banach Spaces

K. MAHESH KRISHNA

Subject: Computer Science And Mathematics, Analysis Keywords: Uncertainty Principle, Frame, Banach space.

Online: 12 March 2024 (07:49:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0629.v1

Comparison of Drone-Assisted Maize Seedling Detection Models Based on Deep Learning

Zhijie Jia, Xinglong Zhang, Hongye Yang, Yuan Lu, Jiale Liu, Xun Yu, Dayun Feng, Kexing Gao, Jianfu Xue, Bo Ming, Chenwei Nie, Shaokun Li

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Maize Seedling Detection; Counting; Unmanned Aerial Vehicle

Online: 12 March 2024 (04:25:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0581.v1

A Mathematical Model of Stochastic Synaptic Noise Dynamics Based Spontaneous Action Potential in Non-neural Cell

Chitaranjan Mahapatra, Inna Samuilik

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: excitable cells; synaptic conductance; background synaptic noise dynamics; mathematical modeling; action potential

Online: 11 March 2024 (21:17:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0600.v1

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City

Miguel Medina-Pérez, Giovanni Guzmán, Magdalena Saldana-Pérez, Valeria Karina Legaria-Santiago

Subject: Computer Science And Mathematics, Computer Science Keywords: multiobjective optimization; mass casualty incidents; geographic information systems

Online: 11 March 2024 (21:14:51 CET)

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival in these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors such as insufficient prevention education, limited disaster assets, and inadequate coordination between public and private emergency services may complicate these situations. In large-scale emergencies, multiple hotspots are usually observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as an optimization problem, focusing on the location and allocation of resources in mass casualty incidents. The proposed case study is Mexico City in a post-earthquake scenario, using voluntary geographic information, open government data, and historical data from the September 19, 2017 earthquake. Additionally, it is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

Preprint ARTICLE | doi:10.20944/preprints202403.0661.v1

New Goodness-of-Fit Tests for the Kumaraswamy Distribution: A Simulation Study

David E. Giles

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: goodness-of-fit testing; empirical distribution function; Kumaraswamy distribution

Online: 11 March 2024 (18:09:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0654.v1

Design and Development of A Blockchain-Based Financial Aid Distribution System

Md. Raisul Hasan Shahrukh, Sifat Momen, Nafees Mansoor

Subject: Computer Science And Mathematics, Computer Science Keywords: Blockchain; Financial Distribution; Process Automation; Smart Contracts; Hyperledger

Online: 11 March 2024 (15:19:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0286.v2

DECLAREd: A Polytime LTL_f Fragment

Giacomo Bergami

Subject: Computer Science And Mathematics, Logic Keywords: LTLf; Declare; Verified Artificial Intelligence; Equational logic and rewriting

Online: 11 March 2024 (13:18:45 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0608.v1

Harnessing the Power of Artificial Intelligence for Enhanced Point-of-Care Quality Control in Healthcare

NGNOTOUOM NGNOKAM TANIA CYRIELLE, ANGYIBA ANDIGEMA, MAFO KAMGA Lethicia Danaëlle, EWANE Ekwelle

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence (AI); quality control; point-of-care diagnostics; predictive analytics; process automation

Online: 11 March 2024 (13:02:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0051.v61

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

Fabio Silva Botelho

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

Online: 11 March 2024 (09:04:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0557.v1

Information Data Length Theory of Human Emotions, How, What and Why

Ismail A Mageed

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Information data length; Psychological aspects of human emotions; emotional distribution

Online: 11 March 2024 (09:04:13 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0556.v1

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Peter Kokol

Subject: Computer Science And Mathematics, Software Keywords: Software engineering; Artificial Intelligence; Machine learning; Synthetic Knowledge Synthesis

Online: 11 March 2024 (08:58:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0518.v1

Avogadro Number’s-oriented HyperGeometric and ChebyshevT Functions for Black Hole Paradox Generalizations and Turing Machine Ruled Quantum Homeopathy Water Memory Entanglements for the Translation of COVID19 Homeopathy Remedies into the Neprilysin and ACE2/AT1R receptors targeted DRVYIHPFX- ligands.

Ioannis George Grigoriadis

Subject: Computer Science And Mathematics, Discrete Mathematics And Combinatorics Keywords: COVID19; ; Chern-Simons Topological; QED (Quantum Electrodynamics); Homeopathic medicine; Water Memory; AI-Quantum computing; Quantum-Inspired Evolutionary Algorithm Predictive Toxicology; QSAR Quantum gates; CS Supergravity Quantum Foam; Cheminformatics artificial intelligence; Phase Data Mining; Machine Learning; Euclid Chemical space exploration; (bosonic) Quantum fields Theory (QFT); Angiotensin Receptor Neprilysin targeted DRVYIHPFX mimetic Holomorphic Ligands; Hidden Entanglement Negativity Translations; Uncertainty Quantum Relationships; Quantum fields theory; Quantum information; HyperGeometric Functions; Chemical Block Systems; Black Hole Paradox Generalizations; SphericalHarmonicY-Supersymmetry breaking foundations; WhittakerM Functions; Hypergeometric1F1 Functions; Turing Machine Learning Ruled Calculations; Avogadro Number’ s oriented Quantum Homeopathy Hidden Entanglement Negativities; Quantum foam; non-commutative geometry; small molecule ligand engineering; prot

Online: 11 March 2024 (03:52:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202108.0146.v30

A Proof of the Riemann Hypothesis Based on a New Expression of the Completed Zeta Function

Weicun Zhang

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Riemann Hypothesis (RH); proof; completed zeta function

Online: 8 March 2024 (15:56:30 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0484.v1

Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review

Gabriel A. Carneiro, António Cunha, Joaquim Sousa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep Learning; Computer Vision; Grape Variety Classification; Grape Variety Identification; Artificial Intelligence; Explainable Artificial Intelligence; Precision Viticulture

Online: 8 March 2024 (15:31:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v9

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 8 March 2024 (11:06:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0511.v1

Should the Choice of BOIN Design Parameter p.tox only Depend on the Target DLT Rate?

Rong Lu

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: dose-finding; Bayesian optimal interval design; R package BOIN

Online: 8 March 2024 (10:01:35 CET)

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IMPORTANCE: On December 10, 2021, the FDA published a Determination Letter, along with a Statistical Review and Evaluation Report, and concluded that under the non-informative prior, the local Bayesian optimal interval design (BOIN) design, in its revised form, can be designated fit-for-purpose for identifying the maximum tolerated dose of a new drug, assuming that dose-toxicity relationship is monotonically increasing. Although setting the BOIN design parameter p.tox = 1.4 * target.DLT.rate is recommended in almost all BOIN methodology articles and is the default value in the R package BOIN, it's unclear if the choice of p.tox should only depend on the target DLT rate and if certain range of p.tox could produce the same BOIN boundary table. DESIGN: In this simulation study, following parameters were varied one at a time, using R package BOIN, to explore their effects on the equivalence intervals of p.saf and p.tox: 1) target DLT rate, 2) n.earlystop, 3) cutoff.eli, 4) cohortsize, and 5) ncohort. And a simple 3+3 design was used as an example to explore equivalent sets of BOIN design parameters that can generate the same boundary table. RESULTS: When the early stopping parameter n.earlystop is relatively small or the cohortsize value is not optimized via simulation, it might be better to use p.tox < 1.4 * target.DLT.rate, or try out different cohort sizes, or increase n.earlystop, whichever is both feasible and provides better operating characteristics. This is because if the cohortsize was not optimized via simulation, even when n.earlystop = 12 and cohortsize > 3, the BOIN escalation/de-escalation rules generated using p.tox = 1.4 * target.DLT.rate could be exactly the same as those calculated using p.tox > 3 * target.DLT.rate, which might not be acceptable for some pediatric trials targeting 10% DLT rate. The traditional 3+3 design stops the dose finding process when 3 patients have been treated at the current dose level, 0 DLT has been observed, and the next higher dose has already been eliminated. If additional 3 patients were required to be treated at the current dose in the situation described above, the decision rules of this commonly used 3+3 design could be generated using BOIN design with target DLT rates ranging from 18% to 29%, p.saf ranging from 8% to 26%, and different p.tox values ranging from 39% to 99%. These BOIN parameters also need to satisfy a set of conditions. Meaning: This study demonstrates the importance of interpreting BOIN design parameter p.tox as an interval of toxicity rates that are considered too toxic, rather than one prespecified value that corresponds to the lowest toxicity probability that is deemed overly toxic. When designing a dose-finding trial using BOIN, it is important to perform simulation studies to identify equivalent sets of BOIN design parameters that can generate the same boundary table so that we can better compare the safety properties of different boundary tables.

Preprint ARTICLE | doi:10.20944/preprints202403.0470.v1

A New Computational Algorithm for Assessing Overdispersion in Machine Learning Count Models with Python

Luiz Paulo Lopes Fávero, Alexandre Duarte, Helder Prado Santos

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Count data; Machine learning; Negative binomial regression; Overdispersion; Poisson regression; Python; Vuong Test; Zero inflation

Online: 8 March 2024 (09:30:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0492.v1

Vision Systems for a UR5 Cobot on a Quality Control Robotic Station

Piotr Kohut, Kamil Skop

Subject: Computer Science And Mathematics, Robotics Keywords: Cobot UR5; robotic station; quality control; image processing and analysis; object localization; vision-based measurements; camera-robot calibration; Python; OpenCV

Online: 8 March 2024 (05:23:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0489.v1

Multifractal Dynamics and Complexity Transformation in the Tanker Freight Market

Feier Chen, Xiaofeng Liang

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Tanker freight rate; Multifractal detrended fluctuation analysis; Multifractal detrending moving average; Temporal correlations; Finite size effect; Complexity Transformation

Online: 8 March 2024 (04:50:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0465.v1

Stable Variable Selection Method with Shrinkage Regression Applied to the Selection of Genetic Variants Associated with Alzheimer’s Disease

Vera Afreixo, Ana Helena Tavares, Vera Enes, Miguel Pinheiro, Leonor Rodrigues, Gabriela Moura

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: penalized regression; Akaike’ s Information Criterion; high-dimensional data; stability; overall weighted coefficients; Alzheimer’ s disease; SNP

Online: 8 March 2024 (04:29:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0437.v1

Software Effort Estimation using Ensemble Learning

Farah Oshaibi M, Mohammad AlKhanafseh, Ola Surakhi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence(AI); Bagging; Boosting; ComImp; Effort Estimation(EE); Ensemble Learning(EL); Machine Learning(ML); Merge Dataset; PCA-ComImp; Voting; Random Forest(RF); Software Project Management(SPM); Software Effort Estimation(SEE)

Online: 8 March 2024 (03:47:24 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0386.v1

Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction

Kyoung soo Kim, Taehoon Kim, Jihyun Cha

Subject: Computer Science And Mathematics, Signal Processing Keywords: Heartbeat; fNIRS; RR-interval; heart rate; HRV; Cardiac beat interval; algorithm

Online: 8 March 2024 (01:52:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0436.v1

Action Recognition of Taekwondo Unit-Actions Using Action Images Constructed by Time-Warped Motion Profile

Junghwan Lim, Chenglong Luo, Seunghun Lee, Young Eun Song, Hoeryong Jung

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Action recognition; convolution neural network; human action dataset; taekwondo

Online: 7 March 2024 (11:57:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0447.v1

Experimental Evaluation of SAT Attack on Logic Locking

Milan Vojvoda, Viliam Hromada, Filip Janikovský, Jozef Kučerák, Matúš Jókay

Subject: Computer Science And Mathematics, Security Systems Keywords: logic locking; piracy protection; SAT attack

Online: 7 March 2024 (11:17:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0423.v1

A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs

Christian B. H. Thorjussen, Kristian Hovde Liland, Lars Erik Solberg, Ingrid Måge

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Causal Inference; Modeling; Conditional Independence; Directed Acyclic Graph

Online: 7 March 2024 (11:07:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0390.v1

On Induced Topologies by Ideal, Primal, Filter and Grill

Milan Matejdes

Subject: Computer Science And Mathematics, Mathematics Keywords: ideal; prima; grill; filter; local function; finer topology; compatibility; codense system

Online: 7 March 2024 (08:09:59 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0360.v1

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

ANGYIBA ANDIGEMA, NGNOTOUOM NGNOKAM TANIA CYRIELLE, MAFO KAMGA Lethicia Danaëlle, EWANE Ekwelle

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial intelligence, climate change, public health, disaster preparedness, disease surveillance

Online: 6 March 2024 (12:06:14 CET)

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Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

Preprint REVIEW | doi:10.20944/preprints202403.0349.v1

A Systematic Literature Review of Zero Trust Architecture for UAV Security Systems in IoBT

Alanoud Abdullah Alquwayzani, Abdullah Abdulrahman Albuali

Subject: Computer Science And Mathematics, Security Systems Keywords: Security; Zero Trust Architecture (ZTA); Drone; UAV, UAV Security; Internet of Battlefield Things (IoBT); Cybersecurity; Suspicious Behavior

Online: 6 March 2024 (11:59:34 CET)

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Computer Science and Mathematics