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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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computer Numerical Control (CNC) Technology for Duplicing Signatures Using Microcontroller Arduino

Computer Numerical Control (CNC) Technology for Duplicing Si...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Baco, Syarifuddin Muddin, Saripuddin Aziz, Firman Martani, Ahmad Almunir, Hafid Universitas Islam Makassar Informatics Engineering Makassar Indonesia Mechanical Engineering Universitas Islam Makassar Makassar Indonesia Universitas Pancasakti Makassar Computer Science Makassar Indonesia Universitas Islam Makassar Graduate Informatics Engineering Makassar Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

The main requirement in the office sector is the administrative process. At the engineering faculty of the Islamic University of Makassar, some of the administrative processes have used technology to make it easier for staff in administrative management. However, there are still some problems in the administrative process. one of them is the ratification process that requires a wet signature when the leader concerned is out of town. This paper proposes the design of computer Numerical Control (CNC) technology to duplicate signatures using a microcontroller with the aim of solving signature problems according to their original form (wet signature) without Scan. The results show that CNC technology has succeeded in duplicating signatures with an efficient transfer speed of 2000 mm/s and a working speed of 1500 mm/s. © 2022 IEEE.

关键词： Microcontrollers

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Improved IP lookup technology for trie-based data structures

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Journal of computer and System Sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Diffusion Model for Camouflaged Object Detection 26

Diffusion Model for Camouflaged Object Detection

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Chen, Zhennan Gao, Rongrong Xiang, Tian-Zhu Lin, Fan School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Hkust Hong Kong G42 Abu Dhabi United Arab Emirates

ISBN: (纸本)9781643684369

Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://***/ZNan-Chen/diffCOD. © 2023 The Authors.

关键词： Object detection

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Image Filtering Techniques for Beam Prediction in a Real-world 6G UAV Scenario 6

Image Filtering Techniques for Beam Prediction in a Real-wor...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Rekkas, Vasileios P. Sotiroudis, Sotirios P. Sarigiannids, Panagiotis Psannis, Konstantinos E. Karagiannidis, George K. Goudos, Sotirios K. ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece University of Macedonia Department of Applied Informatics Thessaloniki54124 Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece

ISBN: (纸本)9798350339505

Millimeter-wave (mm-wave) and terahertz (THz) communication systems can satisfy the high data rate requirements in 5G, 6G, and beyond networks, but still rely on the use of extensive antenna arrays to guarantee sufficient received signal strength. Many antennas incur high beam training overhead;thus, the narrow beams require adjustment to support highly mobile applications. Deep learning (DL) vision-aided solutions can potentially forecast the optimal beams, leveraging raw RGB images captured at the base station. Image filtering techniques have been widely used in computer vision (CV) to modify and enhance the quality of an image, based on specific rules. This work applies different filters to RGB images for accurate mm-wave/THz beam prediction and feature extraction based on pre-trained convolutional neural networks (CNNs). The assessment of the developed framework is conducted on an actual dataset captured by an unmanned aerial vehicle (UAV) operating in the millimeter-wave (mm-wave) frequency range. The dataset comprises RGB images taken at the base station. Ensemble filtering techniques are also studied, enhancing the beam prediction accuracy of two state-of-the-art (SOTA) DL models. © 2023 IEEE.

关键词： Millimeter waves

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Uplink OFDMA-Based Random Access Mechanism with Bursty Arrivals for IEEE 802.11ax Systems

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 34-38页

作者： Cheng, Ray-Guang Yang, Chi-Ming Firmansyah, Bariq S. Harwahyu, Ruki Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia Department of Electrical Engineering University of Indonesia Depok16424 Indonesia

IEEE 802.11ax defines a new channel access scheme of OFDMA-based random access (UORA) for varying number of associated stations (STAs) to send their requests or data to the access point (AP) in a contention manner. This letter presents an analytical model to investigate the performance of UORA in transient condition under bursty arrivals with arbitrary distribution. The key performance metrics of access success probability;average access delay;cumulative distribution function (CDF) for the number of transmissions;and utilization of random access resource units (RA-RUs) were derived. Simulation results verified the accuracy of the proposed analytical model. © 2019 IEEE.

关键词： Analytical models

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Cross-modality perturbation synergy attack for person re-identification 24

Cross-modality perturbation synergy attack for person re-ide...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunpeng Gong Zhun Zhong Yansong Qu Zhiming Luo Rongrong Ji Min Jiang School of Informatics Xiamen University School of Computer Science and Information Engineering Hefei University of Technology

ISBN: (纸本)9798331314385

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems.

关键词：

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Rabbits Approximate, Cows Compute Exactly! 47

Rabbits Approximate, Cows Compute Exactly!

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47th International Symposium on Mathematical Foundations of computer Science, MFCS 2022

作者： Komarath, Balagopal Pandey, Anurag Saurabh, Nitin IIT Gandhinagar India Department of Computer Science Saarland University Saarland Informatics Campus Germany Department of Computer Science and Engineering IIT Hyderabad India

ISBN: (纸本)9783959772563

Valiant, in his seminal paper in 1979, showed an efficient simulation of algebraic formulas by determinants, showing that VF, the class of polynomial families computable by polynomial-sized algebraic formulas, is contained in VDet, the class of polynomial families computable by polynomialsized determinants. Whether this containment is strict has been a long-standing open problem. We show that algebraic formulas can in fact be efficiently simulated by the determinant of tetradiagonal matrices, transforming the open problem into a problem about determinant of general matrices versus determinant of tetradiagonal matrices with just three non-zero diagonals. This is also optimal in a sense that we cannot hope to get the same result for matrices with only two non-zero diagonals or even tridiagonal matrices, thanks to Allender and Wang (Computational Complexity'16) which showed that the determinant of tridiagonal matrices cannot even compute simple polynomials like x1x2 + x3x4 + + x15x16. Our proof involves a structural refinement of the simulation of algebraic formulas by width-3 algebraic branching programs by Ben-Or and Cleve (SIAM Journal of Computing'92). The tetradiagonal matrices we obtain in our proof are also structurally very similar to the tridiagonal matrices of Bringmann, Ikenmeyer and Zuiddam (JACM'18) which showed that, if we allow approximations in the sense of geometric complexity theory, algebraic formulas can be efficiently simulated by the determinant of tridiagonal matrices of a very special form, namely the continuant polynomial. The continuant polynomial family is closely related to the Fibonacci sequence, which was used to model the breeding of rabbits. The determinants of our tetradiagonal matrices, in comparison, is closely related to Narayana's cows sequences, which was originally used to model the breeding of cows. Our result shows that the need for approximation can be eliminated by using Narayana's cows polynomials instead of continuant polyno

关键词： Matrix algebra

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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

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computers, Materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep Mohan Kumar Jenifer Mahilraj D.Swathi R.Rajavarman Subhi R.M.Zeebaree Rizgar RZebari Zryan Najat Rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTM *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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