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Advancements in Intrusion Detection Systems for Internet of things Using Machine Learning

Advancements in Intrusion Detection Systems for Internet of ...

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international conference on Multimedia, Signal processing and Communication Technologies, (IMPACT)

作者： Shahid Ul Haq Ash Mohammad Abbas Department of Computer Engineering Zakir Hussain College of Engineering & Technology Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Advancement in technology leads to connecting different types of devices or things to the Internet and enables the formation of a special kind of network called the Internet of things (IoT). Intrusion detection in an IoT is a challenging task due to its unique characteristics. Machine learning schemes possess the potential to improve intrusion detection systems in case of an IoT. In this paper, we present a survey of advancements in research on the use of machine learning approaches for intrusion detection in an IoT. Our focus is on architectures, schemes, and the types of machine learning approaches used for intrusion detection. We compare different schemes based on their basis and features.

关键词： Intrusion detection Machine learning Signal processing Feature extraction Communications technology Behavioral sciences Internet of things

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Robust Deep Representation Learning for Road Crack Detection 5

Robust Deep Representation Learning for Road Crack Detection

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5th international conference on Video and Image processing (ICVIP)

作者： Mahenge, Shadrack Fred Wambura, Stephen M. Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China Dar Es Salaam Inst Technol Dept Comp Studies Dar Es Salaam Tanzania

ISBN: (纸本)9781450385893

Computer vision (CV) based inspection has recently attracted considerable attention and is progressively replacing traditional visual inspection which is subject to poor accuracy, high subjectivity and inefficiency. this paper, benefiting from hybrid structures of multichannel parallel convolutional neural networks (pCNNs), introduces a unique deep learning framework for road crack detection. Ideally, CNN-based frameworks require a relatively huge computing resources for accurate image analysis. However, the portability objective of this work necessitates the utilization of low power processing units. To that purpose, we propose robust deep representation learning for Road Crack Detection (RoCDe) which uses a multichannel pCNNs. Bayesian optimization algorithm (BOA) was used to optimize the multichannel pCNNs training with the fewest possible neural network (NN) layers to achieve the maximum accuracy, improved efficiency and minimum processing time. the CV training was done using two distinct optimizers namely Adam and RELU on a sufficiently available datasets through image preprocessing and data augmentation. Experimental results show that, the proposed algorithm can achieve high accuracy around 95% in crack detection, which is good enough to replace human inspections normally conducted on-site. this is largely due to well-calibrated predictive uncertainty estimates (WPUE). Effectiveness of the proposed model is demonstrated and validated empirically via extensive experiments and rigorous evaluation on large scale real world datasets. Furthermore, the performance of hybrid CNNs is compared with state-of-the art NN models and the results provides remarkable difference in success level, proving the strength of multichannel pCNNs.

关键词： Image processing computer vision object detection machine learning deep learning

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End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX 38th

End-to-End Differentiable Reactive Molecular Dynamics Simula...

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38th international conference on High Performance Computing, ISC High Performance 2023

作者： Kaymak, Mehmet Cagri Schoenholz, Samuel S. Cubuk, Ekin D. O’Hearn, Kurt A. Merz Jr, Kenneth M. Aktulga, Hasan Metin Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Chemistry Michigan State University East LansingMI48824 United States Google Research Mountain ViewCA United States OpenAI San FranciscoCA United States

ISBN: (纸本)9783031320408

the reactive force field (ReaxFF) interatomic potential is a powerful tool for simulating the behavior of molecules in a wide range of chemical and physical systems at the atomic level. Unlike traditional classical force fields, ReaxFF employs dynamic bonding and polarizability to enable the study of reactive systems. Over the past couple decades, highly optimized parallel implementations have been developed for ReaxFF to efficiently utilize modern hardware such as multi-core processors and graphics processing units (GPUs). However, the complexity of the ReaxFF potential poses challenges in terms of portability to new architectures (AMD and Intel GPUs, RISC-V processors, etc.), and limits the ability of computational scientists to tailor its functional form to their target systems. In this regard, the convergence of cyber-infrastructure for high performance computing (HPC) and machine learning (ML) presents new opportunities for customization, programmer productivity and performance portability. In this paper, we explore the benefits and limitations of JAX, a modern ML library in Python representing a prime example of the convergence of HPC and ML software, for implementing ReaxFF. We demonstrate that by leveraging auto-differentiation, just-in-time compilation, and vectorization capabilities of JAX, one can attain a portable, performant, and easy to maintain ReaxFF software. Beyond enabling MD simulations, end-to-end differentiability of trajectories produced by ReaxFF implemented with JAX makes it possible to perform related tasks such as force field parameter optimization and meta-analysis without requiring any significant software developments. We also discuss scalability limitations using the current version of JAX for ReaxFF simulations. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Tangram: High-Resolution Video Analytics on Serverless Platform with SLO-Aware Batching

Tangram: High-Resolution Video Analytics on Serverless Platf...

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international conference on Distributed Computing Systems

作者： Haosong Peng Yufeng Zhan Peng Li Yuanqing Xia School of Automation Beijing Institute of Technology Beijing China School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. the key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Infrastructure as a Service (IaaS) resource configuration falls short in handling highly fluctuating workloads, leading to violations of Service Level Objectives (SLOs) and inefficient resource utilization. Besides, these methods neglect the potential benefits of RoIs batching to leverage parallel processing. In this work, we introduce Tangram, an efficient serverless cloud-edge video analytics system fully optimized for both communication and computation. Tangram adaptively aligns the RoIs into patches and transmits them to the scheduler in the cloud. the system employs a unique “stitching” method to batch the patches with various sizes from the edge cameras. Additionally, we develop an online SLO-aware batching algorithm that judiciously determines the optimal invoking time of the serverless function. Experiments on our prototype reveal that Tangram can reduce bandwidth consumption and computation cost up to 74.30 % and 66.35 %, respectively, while maintaining SLO violations within 5 % and the accuracy loss negligible.

关键词： Costs Accuracy Visual analytics Prototypes Bandwidth parallel processing Inference algorithms

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parallel Lossy Compression for Large FASTQ Files 1

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Proceedings of the 15th international conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022

作者： Guerrini, Veronica Louza, Felipe A. Rosone, Giovanna Department of Computer Science University of Pisa Pisa Italy Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil

ISBN: (数字)9783031388545

ISBN: (纸本)9783031388538

In this paper we present a parallel version for the algorithm BFQzip, we introduced in [Guerrini et al., BIOSTEC – BIOINFORMATICS 2022], that modifies the bases and quality scores components taking into account both information at the same time, while preserving variant calling. the resulting FASTQ file achieves better compression than the original data. Here, we introduce a strategy that splits the FASTQ file into t blocks and processes them in parallel independently by using the BFQzip algorithm. the resulting blocks with the modified bases and smoothed qualities are merged (in order) and compressed. We show that our strategy can improve the compression ratio of large FASTQ files by taking advantage of the redundancy of reads. When splitting into blocks, the reads belonging to the same portion of the genome could end up in different blocks. therefore, we analyze how reordering reads before splitting the input FASTQ can improve the compression ratio as the number of threads increases. We also propose a paired-end mode that allows to exploit the paired-end information by processing blocks of FASTQ files in pairs. Availability: the software is freely available at https://***/veronicaguerrini/BFQzip © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bioinformatics

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Computational Linguistics Tools in Mapping Emotional Dislocation of Translated Fiction 5

Computational Linguistics Tools in Mapping Emotional Disloca...

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bekhta, Ivan Hrytsiv, Nataliia Lviv Franko Natl Univ 1 Univ Ska St UA-79000 Lvov Ukraine Lviv Polytech Natl Univ 12 Stepana Bandery St UA-79000 Lvov Ukraine

this research focuses on the interaction between human and computer in processing text of fiction. It is related to operations as applied to overall managing and creating (p)arallel (t)ranslation (c)orpus (PTC) in regard to English as well as Ukrainian language pair. Corpus linguistics comprehensive tools are used both for processing parallel translation corpus results obtained and for the afterwards analysis. Preliminary findings can be exemplary for better understanding of how corpora helps in the scrutiny of the individual style of an author dominants and in what way it can stimulate more qualitative and faithful rendition in translation. the paper provokes a number of issues on how quantitative parameterization can be useful for translation studies analysis. Also, we elucidate the possibilities of NPL manipulation in regard with tagging, hence, researching emotional dislocation as verbalized in fiction.

关键词： Translation studies parallel translation corpus text mark-up applied linguistics translation

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4b/4b/8b Precision Charge-Domain 8T-SRAM Based CiM for CNN processing

4b/4b/8b Precision Charge-Domain 8T-SRAM Based CiM for CNN P...

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IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Qibang Zang Wang Ling Goh Yi Sheng Chong Anh Tuan Do School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore Institute of Microelectronics A*STAR Singapore

Compute-in-memory (CiM) is a promising solution for solving the bottleneck of frequent data movement between the memory and processor in Von-Neumann architecture. In conventional multi-bit CiM architecture, when computing N-bit input and N-bit weight MAC operation, 2 N 1 cycles are needed for N-bit input modulation and normally 3-4−cycles with complex switch operation are needed for N-bit weight realization, which significantly degrades the final throughput and power efficiency. In this work, a C-2C DAC built in the 8T SRAM CiM array is designed for 4-bit weight and 4-bit input MAC operation, which can be completed in just one cycle. In the final power efficiency evaluation, our 4b/4b/8b CiM architecture attained up to 640 TOPS/W (normalized to 1b/1b/1b precision) which is a 6-10 times improvement as compared to the conventional multi-bit CiM architectures. the proposed architecture with 4b/4b/8b precision can provide 91.47% and 68.10% accuracy on CIFAR-10 and CIFAR-100 dataset classification, respectively.

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Research on Large Scale Data Concurrent processing Scheme of Printing Equipment Fault Diagnosis System 21

Research on Large Scale Data Concurrent Processing Scheme of...

引用

5th international conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Li, Xiang Yang, Shulin Huang, Yongliang Peng, Jiao Zhou, Meiqi Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781450384322

With the development of intelligent printing equipment, equipment fault diagnosis based on Internet technology has become a trend. the existing platform uses the traditional relational database. With the increase of data volume and types, it is difficult to meet the needs of real-time and rapid response of large-scale data processing. In view of the above problems, this paper uses the distributed database HBase, combined with the parallel processing framework MapReduce, message middleware Kafka related technology, puts forward the large-scale data storage and concurrent processing scheme of printing equipment fault diagnosis system. the scheme solves the problem of large-scale data concurrent processing in fault diagnosis system of the printing equipment and has good feasibility. © 2021 ACM.

关键词： MapReduce

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A Comprehensive Survey on Implementation of Image processing algorithms using FPGA 5

A Comprehensive Survey on Implementation of Image Processing...

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5th IEEE international conference on Recent Advances and Innovations in Engineering (IEEE - ICRAIE)

作者： Prabhu, Madhava S. Verma, Seema Middle East Coll Dept Elect & Commun Engn Muscat Oman Banasthali Univ Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9781728188676

Image processing is an unceasingly growing area with a range of applications including cryptography, medicine, video surveillance, remote sensing, and many more. Implementing sophisticated algorithms to process the large amount of data using software solutions makes the response slower, and that's where hardware implementation comes into the picture. Field Programmable Gate Arrays (FPGAs) are getting popular due to low latency, connectivity, parallel computing, and flexibility. the unique architecture of the FPGA has made it possible to use the technology for applying in many applications to have better and faster results. this paper is aiming at providing a comprehensive survey on the hardware implementations of image processing algorithms to facilitate the improvement in efficiency using FPGAs. the widely used Xilinx PYNQ is also presented in this paper as they play a major role in reducing the development time.

关键词： Image processing FPGA efficiency PYNQ Hardware implementation

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the Improved Frog Leaping Algorithm for Photovoltaic Array Parameter Identification

The Improved Frog Leaping Algorithm for Photovoltaic Array P...

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Clean Energy and Electric Power Engineering (ICCEPE), international conference on

作者： Wang Yao Zhong Chen Zhonglin Xu Yongkang Wu Changsha University of Science and Technology Changsha China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

During the process of photovoltaic array parameter identification, the use of the shuffled frog-leaping algorithm may encounter the issue of getting trapped in local optima. therefore, this paper proposes an improved hybrid shuffled frog-leaping algorithm-based method for photovoltaic array parameter identification. To tackle these issues, an improved mixed frog-leaping algorithm is proposed. the algorithm incorporates a mixed frog-leaping approach to introduce an asynchronous time-varying learning factor. this enhancement aims to improve the learning capability and search speed of the global optimal individual while maintaining algorithm convergence. By conducting comparative simulation experiments with the original hybrid shuffled frog-leaping algorithm, the results demonstrate that the improved algorithm enhances the identification speed and accuracy of the parameters in the photovoltaic cell single diode model. this improvement brings practical utility to the algorithm's application.

关键词： Photovoltaic systems Green energy Parameter estimation Accuracy Photovoltaic cells parallel processing Power systems Particle swarm optimization Optimization Convergence

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