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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Song, Ahyun Seo, Euiseong Kim, Heeyoul Sungkyunkwan University Department of Computer Science and Engineering Korea Republic of Kyonggi University Division of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9791188428120

A smart factory is an advanced manufacturing system that utilizes various cutting-edge technologies such as IIoT (Industrial Internet of Things), big data, ai(Artificial Intelligence), blockchain to automate and optimize production processes. While there is a growing demand for smart factories in recent times, the adoption and proliferation of these facilities have been delayed due to concerns about security, the reliability of collected information, and challenges in management and control. Moreover, traditional centralized smart factory systems pose a risk of operational downtime in the event of failures or attacks since a central server controls the entire system. Therefore, we propose the design of a secure, transparent, and reliable blockchain-based IIoT framework for smart factories. The framework consists of three layers: the blockchain core layer, the blockchain operation layer, and the IIoT service layer. The IIoT service layer plays a crucial role in providing various services essential for the advancement of smart factories, utilizing blockchain technology. Our proposed framework combines IIoT and blockchain technologies to leverage the advantages of decentralization, trust, security, transparency, data management, and traceability. © 2024 Global IT Research Institute - GIRI.

关键词： Blockchain

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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A Review on Resource Provisioning Technique's in Cloud Environment 15

A Review on Resource Provisioning Technique's in Cloud Envir...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ranjana, C. Anusha Bamini, A.M. Chitra, R. Division of Computer Science and Engineering Karunya Institute of Technology and Science India

ISBN: (纸本)9798331300579

Resource provisioning is the method of making sure that the cloud service can be efficiently resourced to the customers as demand increases. Resource provisioning is the procedure of selecting, deploying, and managing software like load balancers and database server management systems and hardware sources such as CPU, storage, and networks to guarantee utility performance. To do this in powerful manner, a CSP could want to take sure measures to effectively deliver on its SLAs. As an instance, the CSP may have procedures in place to feature servers or storage as call for increases. It goals to make sure that an organization can seamlessly get right of entry to the specified sources in an optimized and efficient manner. The general aim of aid provisioning is to permit the packages to utilize computational strength, garage, and offerings. Cloud aid provisioning requires the CSP to scale and manage consumer needs seamlessly the steps in resource provisioning are record Infrastructure, Configuring and booting, Provisioning API and Provisioning VMs. This paper suggests an evaluation between provisioning techniques utilized in cloud. The useful resource provisioning was formerly done with the aid of considering carrier level goals and with numerous scheduling methods Here we evaluate distinct strategies used in present day cloud computing environment. © Grenze Scientific Society, 2024.

关键词： Quality of service

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ai-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an ai-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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ChemDFM-X: towards large multimodal model for chemistry

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science China(Information sciences) 2024年第12期67卷 99-100页

作者： Zihan ZHAO Bo CHEN Jingpiao LI Lu CHEN Liyang WEN Pengyu WANG Zichen ZHU Danyang ZHANG Yansi LI Zhongyang Dai Xin CHEN Kai YU X-LANCE Lab Department of Computer Science and EngineeringMoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Suzhou Laboratory

Chemistry, as a naturally multimodal discipline, plays a crucial role in various vital fields such as pharmaceutical research and material manufacturing. Therefore, research on artificial intelligence(ai) for chemistry has garnered increasing attention. Despite the rapid development, most of the chemical ai models today mainly focus on single tasks with unimodal input [1].

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Enhanced MobileNet Architecture with Residual Blocks for Improved CT Image Classification 1

Enhanced MobileNet Architecture with Residual Blocks for Imp...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Athiramol, S. Sudheep Elavidom, M. School of Engineering Cusat Division of Computer Science and Engineering Cochin India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

This research introduces a novel modification to the MobileNet architecture, incorporating residual blocks to enhance its efficacy in the classification of computed tomography (CT) images, specifically for distinguishing malignant and benign cases. Comparative evaluations against MobileNet V1 and V2 were conducted using a diverse CT image dataset. Our proposed modified MobileNet consistently outperformed both baseline architectures across key evaluation metrics, including precision, recall, F1 score, confusion matrix, and overall accuracy. Additionally, the study investigated the impact of preprocessing techniques on classification performance. Results demonstrated a substantial improvement in accuracy when preprocessing was applied, underscoring its importance in optimizing deep learning models for medical image analysis. The incorporation of residual blocks into the MobileNet architecture proved instrumental in capturing intricate features, contributing to the observed performance boost compared to standard MobileNet architectures. This research not only highlights the significance of architectural modifications but also emphasizes the critical role of preprocessing in refining deep learning models for medical image classification. In conclusion, our findings suggest that the modified MobileNet architecture with residual blocks holds promise for enhanced CT image classification, with potential applications in early disease detection and clinical decision support systems. © 2024 IEEE.

关键词： Image classification

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GRAPHTRaiL: Translating GNN Predictions into Human-Interpretable Logical Rules 38

GRAPHTRAIL: Translating GNN Predictions into Human-Interpret...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Armgaan, Burouj Dalmia, Manthan Medya, Sourav Ranu, Sayan Department of Computer Science University of Illinois Chicago United States Department of Computer Science & Engineering Yardi School of AI IIT Delhi India

Instance-level explanation of graph neural networks (GNNs) is a well-studied area. These explainers, however, only explain an instance (e.g., a graph) and fail to uncover the combinatorial reasoning learned by a GNN from the training data towards making its predictions. In this work, we introduce GRAPHTRaiL, the first end-to-end, post-hoc, global GNN explainer that translates the functioning of a black-box GNN model to a boolean formula over the (sub)graph-level concepts without relying on local explainers. GRAPHTRaiL is unique in automatically mining the discriminative subgraph-level concepts using Shapley values. Subsequently, the GNN predictions are mapped to a human-interpretable boolean formula over these concepts through symbolic regression. Extensive experiments across diverse datasets and GNN architectures demonstrate significant improvement over existing global explainers in mapping GNN predictions to faithful logical formulae. The robust and accurate performance of GRAPHTRaiL makes it invaluable for improving GNNs and facilitates adoption in domains with strict transparency requirements. © 2024 Neural information processing systems foundation. All rights reserved.

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Predicate encryption with selective-opening security for receivers: formal definition, generic construction, and concrete instantiations for several primitives

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Designs, Codes, and Cryptography 2024年第6期92卷 1477-1500页

作者： Tseng, Yi-Fan Liu, Zi-Yuan Tso, Raylin Department of Computer Science National Chengchi University Zhinan Rd. Taipei116011 Taiwan Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

With the rise of cloud computing, multi-user scenarios have become a common setting for data sharing nowadays. The conservative security notion might not be sufficient for such a data sharing model. As a response to this challenge, there has been significant research targeting security against receiver selective-opening (RSO) attacks. However, we found that none of these studies discuss RSO security specifically for predicate encryption (PE)—an encryption mechanism naturally designed for multi-user data sharing. This manuscript first formalizes the RSO security for PE. We then present a generic PE construction that achieves RSO security based on the simulation-based definition. Our work also features several instantiations for various predicate families, including attribute-based encryption for the monotone span program, which is known as one of the most expressive PE. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Access control

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How Does Software Prefetching Work on GPU Query Processing? 20

How Does Software Prefetching Work on GPU Query Processing?

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20th International Workshop on Data Management on New Hardware, DaMoN 2024

作者： Deng, Yangshen Chen, Shiwen Hong, Zhaoyang Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology AlayaDB Ai Guangdong Shenzhen China

ISBN: (纸本)9798400706677

Improving the performance of GPU query processing is a well-studied problem in database community. However, its performance is still unsatisfactory due to the low utilization of GPU memory bandwidth. In the literature, employing software prefetching techniques to improve the bandwidth utilization is a common practice in CPU database as it overlaps computation cost and memory access latency. However, it was ignored by GPU database even though the software prefetching ability has been provided by modern GPU architecture (i.e., from NVIDIA Ampere). In order to investigate the effectiveness of software prefetching techniques on GPU query processing, we implement four software prefetching algorithms on GPU, i.e., Group Prefetch (GP), Software-Pipelined Prefetch (SPP), Asynchronous Memory Access Chaining (AMAC) and Interleaved Multi-Vectorizing (IMV) in the work. We then adapt them on hash join probe and BTree search tasks with a suite of optimizations. Last, we conduct comprehensive experiments and evaluate the performance of them. The results confirm the superiority of software prefetching techniques on GPU query processing. Specifically, they can achieve up to 1.19X speedup on hash join probe and 1.31X speedup on BTree search when compared with the implementations without software prefetching. © 2024 ACM.

关键词： Graphics processing unit

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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