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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Promoting Accurate Image Reconstruction via Synthetic Noise for Unsupervised Screw Anomaly Detection and Location

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jian Wen Lin, Wan Ju Li, Kuan Ming Mrozek, Dariusz Hung, Che Lun National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan National Kaohsiung University of Science and Technology Department of Mold and Die Kaohsiung80778 Taiwan National Taiwan University Department of Mechanical Engineering Taipei106319 Taiwan Silesian University of Technology Department of Applied Informatics Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei11221 Taiwan

With residuals-based unsupervised models, defects can be detected using only normal samples, boosting both efficiency and practicality in industrial anomaly detection (AD) applications. While these approaches offer benefits, they also present challenges in detecting anomalies in cylindrical objects, such as imprecise image reconstruction and camera blind spots. As a result, this paper introduces ScrewNet, an unsupervised anomaly detection framework designed specifically for cylindrical objects like screws. To improve the accuracy of reconstruction in the AD network, the synthetic noise module and pair training approach are employed during the training stage. The synthetic noise module, utilizing one of the CycleGAN generators, produces defects that match real-world distributions, thereby enabling the AD network to extract valuable features while preserving image structure. The pair training approach is designed to enable the AD network to achieve accurate reconstruction using similar background input-output pairs. To tackle inspection blind spots problem, an efficient, cost-effective inspection system using multiple mirrors is developed for the inference stage. This approach enables effective anomaly detection across the entire 360-degree surface of the screw. In experiments, three different types of screw datasets containing both damaged and undamaged screws are provided. Numerous experiments demonstrate the superiority of our approach over several state-of-the-art (SOTA) unsupervised AD networks. For instance, ScrewNet outperforms baselines by 4 dB in average Peak signal-to-noise ratio (PSNR) for image reconstruction quality and by 32% in average Area Under the Receiver Operating Characteristic curve (AUROC) for pixel-level anomaly segmentation. Additionally, image-level detection achieves 98.3% accuracy across the three screw datasets. The code is available at https://***/vitowen9580/ScrewNet. © 2025 IEEE.

关键词： Image reconstruction

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Multi-Parameter Analysis of Biosignals in Long-Term Control Medical Devices Management

Multi-Parameter Analysis of Biosignals in Long-Term Control ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Serhii Holdobin Natalia Brynza Olha Tiutiunyk Olena Serhiienko Ihor Sosnov Pavlo Samus Industrial and Biomedical Electronics Department Kharkiv polytechnic institute National Technical University Kharkiv Ukraine Department of Informatics and Computer Technology Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Department of Applied Mathematics Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Informatics and Computer Engineering Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Business Trade and Logistics Department Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical processes to reduce the influence of the human factor and increase the accuracy of diagnostics. An improved method of multiparametric analysis of biosignals is proposed for long-term monitoring of the state of the cardiovascular system using modern sensor devices, data processing algorithms and artificial intelligence technologies. The research is aimed at improving the methods of collecting, transmitting and analyzing biosignals, which contributes to the creation of personalized medical devices and effective prediction of cardiovascular pathologies. The issues of classification of devices and biosignals, as well as their mathematical modeling to increase the accuracy of diagnostics, are considered.

关键词： Cardiovascular system Wireless sensor networks Accuracy Medical devices Heuristic algorithms Robot sensing systems Prediction algorithms Real-time systems Classification algorithms Monitoring

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Exploiting Circular Shifts for Efficient Chaotic Image Encryption

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IEEE Access 2025年 13卷 92997-93016页

作者： Lazaros Moysis Marcin Lawnik Wassim Alexan Sotirios K. Goudos Murilo S. Baptista George F. Fragulis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Mathematical Methods in Technics and Informatics Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Communications Department Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Physics Institute for Complex Systems and Mathematical Biology SUPA The University of Aberdeen Aberdeen U.K.

This work presents a chaotic image encryption algorithm that acts on the binary level of the image for effective permutation, and on the byte level for substitution. The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. Circular shifts are also applied to shuffle the permutation and substitution rules, effectively increasing security at a very low execution cost. The encryption keys are plaintext dependent, and a key mixing step in included, so that each part of the key affects all the subsequent encryption operations. For the encryption, a new Soboleva hyperbolic tangent based map is first designed. The dynamical behavior of the map is studied and shows robust chaos, and an absence of equilibria, meaning that it can generate hidden attractors. This map is used as an entropy source, along with a chaotic pseudo random number generator (PRNG). The encryption process shows a good performance under a collection of tests, and has also a low execution time, 0.0134 sec for a $256\times 256$ image, 0.0530 sec for a $512\times 512$ image, and 0.2050 sec for a $1024\times 1024$ image. So the proposed algorithm is a viable option for fast, efficient, and secure data encryption.

关键词： Encryption Security Chaotic communication Generators Robustness Matrices Costs Standards Real-time systems Computational efficiency

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Simon’s Period Finding on a Quantum Annealer

arXiv

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arXiv 2025年

作者： Robertson, Reece Doucet, Emery Mzaouali, Zakaria Domino, Krzysztof Gardas, Bartlomiej Deffner, Sebastian Department of Computer Science & Electrical Engineering Department of Physics Quantum Science Institute UMBC Baltimore United States Department of Physics Quantum Science Institute UMBC Baltimore United States Institut für Theoretische Physik Universität Tübingen Auf der Morgenstelle 14 Tübingen72076 Germany Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland National Quantum Laboratory College Park United States

Dating to 1994, Simon’s period-finding algorithm is among the earliest and most fragile of quantum algorithms. The algorithm’s fragility arises from the requirement that, to solve an n qubit problem, one must fault-tolerantly sample O(n) linearly independent values from a solution space. In this paper, we study an adiabatic implementation of Simon’s algorithm that requires a constant number of successful samples regardless of problem size. We implement this algorithm on D-Wave hardware and solve problems with up to 298 qubits. We compare the runtime of classical algorithms to the D-Wave solution to analyze any potential advantage. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Automatic Software-Based Self Test Generation for Embedded Processors

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IFAC-PapersOnLine 2018年第6期51卷 125-130页

作者： Hudec, Ján Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava84216 Slovakia

The paper deals with automatic software-based test generation for processors. Processors are basic blocks of current complex systems on chip and embedded systems. Processors testing can be extended by functional tests or using various application programs. Such types of tests are serving as additional tests to structural testing or as tests used in verification. Functional tests (programs) are generated over an instruction set architecture and processor model description. A metric for quality evaluation of the software-based tests is obviously provided by code coverage of a processor model. A new functional test generation method is based on VHDL model of processors and genetic algorithms with using various evolutionary strategies. The contribution to the SBST methods based on GAs using the latest defined ES was identified. Functionality and effectiveness of the developed methods were evaluated in the implemented system AGenMIX over two types of RISC processor. © 2018

关键词： Software testing Application programs Embedded systems Genetic algorithms Integrated circuit testing Quality control Reduced instruction set computing System on chip Code coverage Evolutionary strategies Fitness functions Functional testing Processor Software based self test

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Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

arXiv

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arXiv 2025年

作者： Maus, Natalie Kim, Kyurae Zeng, Yimeng Jones, Haydn Thomas Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of T black-box objective functions, f1, . . . fT , simultaneously. Traditional approaches often seek a single Pareto-optimal set that balances trade-offs among all objectives. In this work, we introduce a novel problem setting that departs from this paradigm: finding a smaller set of K solutions, where K 1, . . . fT , there is at least one good solution. A motivating example for this problem setting occurs in drug design. For example, we may have T pathogens and aim to identify a set of K © 2025, CC BY-SA.

关键词： Pathogens

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Large Scale Multi-Task Bayesian Optimization with Large Language Models

arXiv

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arXiv 2025年

作者： Zeng, Yimeng Maus, Natalie Jones, Haydn Thomas Tao, Jeffrey Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Marcus, Ryan Bastani, Osbert Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel transfer exist, the performance improvement is marginal when scaling to more than a moderate number of tasks. We introduce a novel approach leveraging large language models (LLMs) to learn from, and improve upon, previous optimization trajectories, scaling to approximately 2000 distinct tasks. Specifically, we propose an iterative framework in which an LLM is fine-tuned using the high quality solutions produced by BayesOpt to generate improved initializations that accelerate convergence for future optimization tasks based on previous search trajectories. We evaluate our method on two distinct domains: database query optimization and antimicrobial peptide design. Results demonstrate that our approach creates a positive feedback loop, where the LLM’s generated initializations gradually improve, leading to better optimization performance. As this feedback loop continues, we find that the LLM is eventually able to generate solutions to new tasks in just a few shots that are better than the solutions produced by "from scratch" by Bayesian optimization while simultaneously requiring significantly fewer oracle calls. © 2025, CC BY-SA.

关键词： Structured Query Language

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Gamification of cyber ranges in cybersecurity education 20

Gamification of cyber ranges in cybersecurity education

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jelo, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

There is a lack of cybersecurity experts, and this problem is getting worse with the increasing number and importance of IT systems together with rising number and sophistication of attacks. Rapid development in cybersecurity necessitates constant training and practice to keep up with the latest threats. Studying IT, and especially cybersecurity is considered difficult and there is an urgent need to encourage interest of students in this field. One promising avenue to improving the situation is gamification. Gamification has been used in education to improve enthusiasm of students, increasing their numbers, information uptake and retention. Leveraging social connections and human competitiveness can further motivate students to take part and engage in the learning activities they would otherwise not consider. Virtualized environments offer means to scale up the practical training as well as share latest teaching methods. Cyber ranges used by educational institutions often are such open platforms that provide such practical cybersecurity training. Moreover, with some thoughtful planning they can prove to be popular serious games. In this paper, we explore opportunities for hands-on cybersecurity education by using gamified scenarios in cyber range virtualized environment. We focus on cyber testing environments, what their types are, and what it includes. We place emphasis on creating basic game scenario close to real-world situations. © 2022 IEEE.

关键词： Students

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Accelerating SDN control plane with GPGPU-based packet classification 17

Accelerating SDN control plane with GPGPU-based packet class...

引用

17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Balaz, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (纸本)9781728149677

The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms. © 2019 IEEE.

关键词： Graphics processing unit

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