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arXiv 2024年

作者： Dogeas, Konstantinos Erlebach, Thomas Liang, Ya-Chun Department of Computer Science Durham University United Kingdom Data Science Institute Columbia University United States

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the time required to execute the processing part becomes known only upon completion of the test. We use competitive analysis to study algorithms for minimizing the sum of completion times for n given jobs on a single machine. As our main result, we prove using a novel analysis technique that the natural 1-SORT algorithm has competitive ratio at most 1.861. For the special case of uniform test times, we show that a simple threshold-based algorithm has competitive ratio at most 1.585. We also prove a lower bound that shows that no deterministic algorithm can be better than √2-competitive even in the case of uniform test times. Copyright © 2024, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Morphogenesis of Images using Neural Cellular Automata

Morphogenesis of Images using Neural Cellular Automata

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International Conference on Computing and Networking Technology (ICCNT)

作者： Samarth Tumdi Dev Patel Daksh Dholakia Aaditya Patel Pradnya Saval Department of Computer Science & Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The examination of image morphogenesis, an intricate phenomenon characterized by the gradual metamorphosis of visual representations, stands as the central focus of this scholarly investigation. Within this context, the research endeavor delves deeply into the complex interplay between Neural Cellular Automata (NCA) and Deep Neural Networks (CRNN). An integral component of this inquiry revolves around the enhancement of the training procedure by incorporating a composite loss function, notably the Huber Loss. Moreover, the study is enriched by the extensive utilization of the Noto Color Emoji collection, which encompasses a vast array of over 2500 emojis meticulously classified into various geometrical forms such as “Circular,” “Bilateral,” and “Asymmetrical.” These diverse emojis function as stand-in for in-real-life images with a more complex color palette. At the core of this research lies the fundamental NCA system with the integrated loss modification, showcasing a remarkable ability to initiate developmental processes from a given seed pixel as compared to the original system. Through thorough experimentation and systematic analysis, the effects of both balanced and unbalanced stimuli on the regenerative capacity of the system were meticulously scrutinized. In the pursuit of broadening the horizons of image manipulation methodologies, a comprehensive evaluation framework was adopted to rigorously assess the performance of the modified model. This framework places emphasis on key metrics, particularly growth and regeneration, with the goal of surpassing the initial outcomes and establishing new standards in the realm of image morphogenesis research. By establishing interdisciplinary connections between developmental biology and computer science, this research not only advances the comprehension of image morphogenesis but also unlocks novel avenues for practical applications. These discoveries carry significant implications across various domains, spanning from

关键词： Training Visualization Systematics Satellites Image color analysis Self-organizing networks Security Modeling Standards Emojis

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EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zheng, Bowen Cheng, Ran Tan, Kay Chen Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutionary Computation (EC) paradigm with RL. However, the population-based nature of EC significantly increases computational costs, thereby restricting the exploration of algorithmic design choices and scalability in large-scale settings. To address this challenge, we introduce EvoRL1, the first end-to-end EvoRL framework optimized for GPU acceleration. The framework executes the entire training pipeline on accelerators, including environment simulations and EC processes, leveraging hierarchical parallelism through vectorization and compilation techniques to achieve superior speed and scalability. This design enables the efficient training of large populations on a single machine. In addition to its performance-oriented design, EvoRL offers a comprehensive platform for EvoRL research, encompassing implementations of traditional RL algorithms (e.g., A2C, PPO, DDPG, TD3, SAC), Evolutionary Algorithms (e.g., CMA-ES, OpenES, ARS), and hybrid EvoRL paradigms such as Evolutionary-guided RL (e.g., ERL, CEM-RL) and Population-Based AutoRL (e.g., PBT). The framework’s modular architecture and user-friendly interface allow researchers to seamlessly integrate new components, customize algorithms, and conduct fair benchmarking and ablation studies. The project is open-source and available at: https://***/EMI-Group/evorl. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Strøby Egede, Vedbæk-Bøgebakken and Relationships among Scandinavian Mesolithic Skeletal Material

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Proceedings of the Prehistoric Society 2024年 90卷 127-145页

作者： Babb, Jeff Meiklejohn, Christopher Petersen, Hans Christian Babb, Maureen Department of Mathematics and Statistics University of Winnipeg 515 Portage Avenue Winnipeg Canada Department of Anthropology University of Winnipeg 515 Portage Avenue Winnipeg Canada Institute of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense M DK-5230 Denmark Jim Peebles Science and Technology Library University of Manitoba 186 Dysart Rd. Winnipeg Canada

This paper derives from new work on Mesolithic human skeletal material from Strøby Egede, a near coastal site in eastern Sjælland, with two foci. The first confirms sex identifications from original work carried out in 1986. The second, and central focus, re-examines comments by one of us (CM) based on work in 1992, and a new statistical analysis including data from the two Strøby Egede adults. In 1998 it was suggested that the Strøby Egede sample more closely resembled Skateholm, on the coast of Skåne in southern Sweden, than Vedbæk-Bøgebakken on Sjælland, fitting lithic patterns noted earlier by Vang Petersen. We revisit the 1998 suggestion below, comparing data from Strøby Egede to those available from southern Scandinavia and Germany, and suggest that the 1998 comment was, in all probability, incorrect. The analysis below suggests overall morphological similarity between individuals in eastern Sjælland and Skåne, while noting the existence of apparent outliers. © The Author(s), 2024. Published by Cambridge University Press on behalf of The Prehistoric Society.

关键词： cluster analysis craniometric analysis human skeletal material Mesolithic principal component analysis Scandinavia Strøby Egede Vedbæk-Bøgebakken

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security 3

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hans, Arvind Vigenesh, M. Yuvaraj, S. Bhagwat, Suvarna R. Zaki, Hamza Yamang, Chiging Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India

ISBN: (纸本)9798350319125

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks. © 2023 IEEE.

关键词： destiny infrastructures networks packages wireless networks

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Swarming Computational Techniques for the Influenza Disease System

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computers, Materials & Continua 2022年第12期73卷 4851-4868页

作者： Sakda Noinang Zulqurnain Sabir Gilder Cieza Altamirano Muhammad Asif Zahoor Raja Manuel Jesús Sànchez-Chero María-Verónica Seminario-Morales Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Universidad Nacional Autonoma de Chota Cajamarca06121Peru Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Universidad Nacional de Frontera SullanaPeru Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected people,recovered individuals and cross-immune *** solutions of the IDS are provided by using the artificial neural networks(ANNs)together with the swarming computational paradigm-based particle swarmoptimization(PSO)and interior-point scheme(IPA)that are the global and local search *** ANNs-PSO-IPA has never been applied to solve the *** a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the *** correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions(reference solutions).An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the ***,the reliability and competence of the proposed computing method are enhanced through the statistical performances.

关键词： Disease influenza model reference results particle swarm optimization artificial neural networks interior-point scheme statistical investigations

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Deep Learning IoT Malware Analysis: Investigation and Understanding

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Neural Computing and Applications 2025年 1-28页

作者： Abdullah, Muhammed Amin Yu, Yongbin Cai, Jingye Addo, Daniel Bankas, Edem K. Gu, Yeong Hyeon Alqahtani, Ali Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea Center for Artificial Intelligence and Computer Science Department King Khalid University Abha 61421 Saudi Arabia

This empirical study explores the ability of lightweight convolutional neural networks (CNNs) for malware analysis in Internet of Things (IoT) environments, emphasizing the impact of input dimensionality and size on feature extraction quality. By converting malware binaries into 1D and 2D grayscale images, we systematically evaluate how varying input resolutions (e.g., 64 × 64 for 2D, 4096 for 1D) influence model performance, resource efficiency, and interpretability. Experiments on the Microsoft Malware dataset (MMD) and IoT Malware dataset (IMD) reveal that 1D CNN models outperform their 2D counterparts in accuracy, parameter efficiency, and training time, with gated recurrent unit (GRU) classifiers demonstrating superior performance across both dimensions. Post hoc visual interpretability techniques, including saliency maps and activation heatmaps, uncover critical feature patterns driving classification decisions, addressing the "black box" limitations of deep learning in cybersecurity. Our findings challenge the assumption that 2D models inherently excel in image-based tasks and provide actionable insights for designing resource-efficient IoT malware detection systems. By bridging the gap between model complexity and deployment feasibility, this work advances the application of explainable AI to protect IoT ecosystems against evolving cyber threats. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional Neural Networks (CNN) Deep learning Feature selection Internet of things Interpretability saliency maps Malware analysis Malware Images

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A Hesitant Fuzzy Multiplicative Base-criterion Multi-criteria Group Decision Making Method

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Informatica (Slovenia) 2022年第2期46卷 235-242页

作者： Narang, Monika Joshi, M.C. Pal, A.K. Department of Mathematics Kumaun University D.S.B. Campus Nainital India Department of Mathematics Statistics & Computer Science College of Basic Sciences & Humanities Govind Ballabh Pant University of Agriculture & Technology Pantnagar India

Hesitant fuzzy sets have a unique characteristic that its basic element could manifest the assessment values of different decision makers on the same option under a certain criterion. Base-criterion method is a very significant tool for calculating the weights of the criteria in multiple criteria decision-making. In this paper, we developed a novel approach hesitant fuzzy BCM based on hesitant fuzzy multiplicative preference relation for multiple criteria group decision making. The base-comparison of the preferential criterion relative to other criteria is expressed as linguistic terms, which might be converted into hesitant multiplicative elements (HMEs). HMEs are extended along the same length according to the attitude of the decision makers. Then normalized optimal hesitant fuzzy weights are calculated. The normalized optimal hesitant fuzzy weights of criteria may be transferred to crisp values by employing score function. To illustrate the applicability and suitability of hesitant fuzzy BCM, we analyse the optimal transportation mode selection problem and car selection problem under hesitant fuzzy environment. The outcomes of the proposed model indicate that the hesitant fuzzy BCM is highly consistent and can yield appreciable preference ranking of criteria and alternatives. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Decision making

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Generalising Across Domains in Video Misinformation Detection

Generalising Across Domains in Video Misinformation Detectio...

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IST-Africa

作者： David WALKER Seani RANANGA Bassey ISONG Vukosi MARIVATE Department of Computer Science Data Science for Social Impact University of Pretoria North West University South Africa

ISBN: (数字)9781905824731

ISBN: (纸本)9798350356595

This paper proposes and demonstrates the application of pre training and transfer learning in a deep neural network model to identify misinformation in YouTube videos, based on their auto-generated captions. Pre-training, in this context, is the training of smaller sub-models on domain-specific data, before those smaller models, together with all trained weights, are integrated together into one larger model. Transfer learning takes place when the larger model is able to utilise the knowledge gained by sub-models to generate new conclusions. The given model also utilises pre-trained Bidirectional Encoder Representations from Transformers (BERT) layers, further utilising the idea of pre-training and leveraging the concept of transfer learning to maximise efficacy. An appreciable improvement in overall quality is demonstrated when the proposed method is used, as compared to a naive model built to tackle this same problem. The goal of this research is to better understand the extent to which the given techniques and model architectures contribute to effective classification of misinformation across a variety of different domains.

关键词： Training Video on demand Transfer learning Transformers data models Web sites Fake news

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Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks

arXiv

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arXiv 2024年

作者： Goldfeder, Judah Roets, Quinten Guo, Gabe Wright, John Lipson, Hod Computer Science Department Columbia University United States Data Science Institute Columbia University United States

Inferring the exact parameters of a neural network with only query access is an NP-Hard problem, with few practical existing algorithms. Solutions would have major implications for security, verification, interpretability, and understanding biological networks. The key challenges are the massive parameter space, and complex non-linear relationships between neurons. We resolve these challenges using two insights. First, we observe that almost all networks used in practice are produced by random initialization and first order optimization, an inductive bias that drastically reduces the practical parameter space. Second, we present a novel query generation algorithm that produces maximally informative samples, letting us untangle the non-linear relationships efficiently. We demonstrate reconstruction of a hidden network containing over 1.5 million parameters, and of one 7 layers deep, the largest and deepest reconstructions to date, with max parameter difference less than 0.0001, and illustrate robustness and scalability across a variety of architectures, datasets, and training procedures. © 2024, CC BY.

关键词： Structured Query Language

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