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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Quantum and quantum-inspired optimization for solving the minimum bin packing problem 12

Quantum and quantum-inspired optimization for solving the mi...

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12th International conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Bozhedarov, A.A. Usmanov, S.R. Salakhov, G.v. Boev, A.S. Kiktenko, E.O. Fedorov, A.K. Russian Quantum Center Skolkovo Moscow Russia

Quantum computing devices are believed to be powerful in solving hard computational tasks, in particular, combinatorial optimization problems. In the present work, we consider a particular type of the minimum bin packing problem, which can be used for solving the problem of filling spent nuclear fuel in deep-repository canisters that is relevant for atomic energy industry. We first redefine the aforementioned problem it in terms of quadratic unconstrained binary optimization. Such a representation is natively compatible with existing quantum annealing devices as well as quantum-inspired algorithms. We then present the results of the numerical comparison of quantum and quantum-inspired methods. Results of our study indicate on the possibility to solve industry-relevant problems of atomic energy industry using quantum and quantum-inspired optimization. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Numerical methods

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The FPGA Implementation of Pseudorandom Word Generation algorithms 12

The FPGA Implementation of Pseudorandom Word Generation Algo...

引用

12th IEEE International conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Opanasenko, volodymyr Zavyalov, Stanislav V.M. Glushkov Institute of Cybernetics of Nas of Ukraine Department of Microprocessor Devices Kyiv03187 Ukraine Radionix Limited Company Kyiv03187 Ukraine

ISBN: (纸本)9798350358056

This paper introduces a novel FPGA-based hardware implementation of a pseudorandom word (PSW) generator. Using the FPGA reconfiguration property, the proposed approach allows you to change the algorithms and replace the online structure during the generator. The integration of embedded digital signal processing (DSP) blocks within the FPGA chip allows for the efficient implementation of the pseudorandom bit generator by leveraging fundamental operations like multiplication with accumulation at the gate level. The PSW generator's design and implementation are performed using the vHDL language and the CAD tool ISE 14.02 Foundation. The implementation is carried out on a Spartan-based series chip (6SLX4CSG225-3), and the paper provides a comprehensive analysis of the time and hardware costs associated with three different types of pseudorandom word generators. To validate the effectiveness of the designs, the simulation results are obtained using the ModelSim SE 10.1c, and timing diagrams are presented for these structures. © 2023 IEEE.

关键词： bit sequence generation CAD DSP FPGA generator of pseudo-random sequences random number generation reconfigurable logic simulation modeling statistical randomness

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Data scientific approach to detect the DoS attack, Probe attack, R2L attack and U2R attack 2

Data scientific approach to detect the DoS attack, Probe att...

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2nd IEEE International conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Sakthiprabha, R. Sivasundarapandian, S. Aranganathan, A. vedanarayanan, v. Rajinikanth, E. Gomathi, T. Sathyabama Institute of Science and Technology Department of ECE Chennai India Aurora's Technological and Research Institute Department of ECE Hyderabad India

ISBN: (纸本)9798350315905

Data reliability is compromised by various cyber-attacks. Computational infrastructure is completely disturbed, broken or guided by these attacks. The current status of cyberspace foretells uncertainty for the future of the Internet and its rising user base. Data collected by the sensors and other input devices can be easily stolen by the unidentified user. It is a severe threat to the programming environment and individual personals. It is necessary to take into account the advanced technologies to counterfeit these cyber-attacks. Existing algorithms are decoded over certain period of time. Because of this always important to adapt the new technology that can prevent cyber-attacks. In this paper, various cyber-attacks predictions are analyzed and combines as a group based on its features. After analyzing the various cyber-attacks and its classification, recent technologies which can prevent the cyber-attack is studied. One of the main technologies that can able to learn themselves is the machine learning. Networking environment must use advanced machine learning approaches to protect the Data. Machine learning technique is classified as supervised and unsupervised technique. Supervised machine learning technique uses features that can be extracted from the source dataset. The most effective machine learning algorithm for predicting the types of cyber-attacks has been determined through a comparison study of different algorithms. We categorize attacks into four categories: R2L attacks, DOS attacks, U2R attacks, and probe attacks. various machine learning algorithms are applied to detect and rectify the cyber-attacks. Their performances are compared and analyzed in terms of accuracy, F1 score, precision and recall. © 2023 IEEE.

关键词： Probes

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Secure federated machine learning for distributed spectrum sensing in communication networks 5

Secure federated machine learning for distributed spectrum s...

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications v

作者： Searle, Richard Gururaj, Prabhanjan Gaikwad, Shreyas Kannur, Kiran Fortanix Inc 3910 Freedom CircleSuite 104 Santa Clara CA 95054 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Federated machine learning (FML) has proved a useful technique for training of artificial intelligence and machine learning (AI/ML) models, using data that is distributed among different constituents of a network which may be geographically dispersed. Typically, the data privacy of individual constituents should be preserved, and it may also be desirable to protect the integrity and secrecy of the algorithms and trained models deployed within the network. Demonstrating the privacy-enhancing technology of Confidential computing, we present the results obtained using a novel solution for FML implementation that supports model training within a distributed network of data providers. Based upon recent research on the use of FML for distributed spectrum sensing in communication networks, we demonstrate the application of the proposed solution for distributed model training within a simulated sensor network of arbitrary topology. The presented solution provides for graph-based network configuration and model convergence within decentralized network applications. Cross-domain adaptation of the proposed solution and characteristics of confidential computing that support a zero-trust architecture are discussed, along with the integrated model integrity protection provided by attestation of trusted execution environments (TEEs). We conclude by looking ahead to the application of our solution to model training within distributed communications networks and sensor arrays, characterized by devices with limited electrical and computational power. We consider the use of physical unclonable functions (PUFs) to encrypt raw data before processing within a layered hierarchy secured with Confidential computing technology.

关键词： Federated machine learning spectrum sensing confidential computing security

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A Robust and Efficient Computational Offloading and Task Scheduling Model in Mobile Cloud computing

A Robust and Efficient Computational Offloading and Task Sch...

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2023 International conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Chandra, I. Karthikeyan, K.v. Revathi, v. Shankar, K. Tamilselvi, M. Arunkumar, J.R. Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering Easwari Engineering College Chennai India Department of Electronics and Communication Engineering Srm Institute of Technology Ramapuram Campus Chennai India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India

ISBN: (纸本)9798350334357

Mobile cloud computing (MCC) has the potential to greatly enhance the computational capabilities of smart mobile devices (SMDs) and reduce their energy consumption by offloading compute-intensive workloads from SMDs onto the resource-rich cloud. Energy-efficient compute offloading remains a problem, especially when applications are under strict time constraints. In response to this issue, this research proposes an Efficient Computational Offloading and Task Scheduling model (ECOTS) strategy to cut down on power use and minimize the time it takes to finish an application. First, we formalize the ECOTS issue as a reduction of energy consumption under the constraints of a task-dependency criterion and an absolute time limit. To solve the issue of offloading computing from mobile devices, we present it as an optimization problem. We then offer two efficient offloading strategies to address the issue. Numerical experiments are used to determine how effective the suggested algorithms are. Finally, we demonstrate using data from a working testbed that the ECOTS method can effectively reduce energy expenditures by properly adjusting the CPU clock speed of SMDs in remote computation and by improving the transmission energy for wireless range circumstances in the cloud. © 2023 IEEE.

关键词： Mobile cloud computing

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Using AI and IoT at the Edge of the network 8

Using AI and IoT at the Edge of the network

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8th International conference on Optimization and Applications, ICOA 2022

作者： Lakrouni, Sanaa Sebgui, Marouane Bah, Slimane Mohamedia School of Engineers University Mohammed v in Rabat Smart Communication Research Team Morocco

ISBN: (数字)9781665476812

ISBN: (纸本)9781665476812

In recent years, IoT devices have been widely used in a variety of sectors such as industry, smart farming, and smart homes. Its application requires performing high computational analysis in real-time. The research era of Artificial Intelligence has witnessed an intense development conducted by millions of research and applications that extend from systems recommendation to video/audio surveillance. AI algorithms have been deployed to IoT data to bring intelligent decisions for IoT applications. These numerous data increase the time of the data transition to the cloud, which becomes the bottleneck of the cloud-based architecture. The edge computing technology brings the AI algorithms to the Edge of the network to improve latency, bandwidth, and data privacy, and guarantee the high accuracy of the AI algorithms. Recently Federated learning (FL) is a machine learning technique that distributes the training among edge devices near to the data source in light of increasing privacy and leveraging from the massive data distributed among numerous edge devices. Therefore, in this paper, we introduce recent research that demonstrates the effectiveness of this approach and present the architectures, models, and methods that implement FL with IoT devices. © 2022 IEEE.

关键词： Edge computing

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Exploring State-of-the-Art Cryptography: A Systematic Exploration of Advanced Approaches for IoT Device Authentication 2

Exploring State-of-the-Art Cryptography: A Systematic Explor...

引用

2nd International conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Archana, v. Kwatra, Chetna vaid School of Computer Science and Engineering Lovely professional university Phagwara India

ISBN: (纸本)9798350344882

Security of IoT devices has become a major issue since it started expanding. Authentication is the key component for the security of IOT devices. But using the traditional cryptographic security methods can be a way more expensive in terms of storage, processing speed, and capacity and they cannot be used with Internet of Things hardware where the resources are poor. Autonomous communication between devices opens a range of application possibilities;however, it is restricted by the resource scarcity of the machines and devices in the networks. The use of advanced cryptographic techniques has facilitated secure and effective device authentication for IoT devices. This study provides an overview of numerous cryptographic techniques for IoT device authentication. Different research papers were explored in areas related to security and authentication on the Internet of Things (IoT) domain. In these papers, researchers present novel authentication and security techniques that aim at addressing the unique challenges presented by IoT devices such as constraint of resources and constrained computing power. These papers also propose various methods to enhance the device security and authentication which includes the use of encryption algorithms, digital signatures, secure communication protocols, and biometric authentication. The proposed solutions of this paper offer improved security and efficiency in comparison to other existing authentication methods. Inclusive of all, these papers contribute valuable insights into the field of IoT device authentication and provide assuring solutions to the security challenges posed by IoT devices. © 2023 IEEE.

关键词： Authentication

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Development and Evaluation of a Speech-Recognized voice Bot

Development and Evaluation of a Speech-Recognized Voice Bot

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2023 International conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Babu, K. Roobini, M.S. Gopirajan, P.v. Sadagopan, S. Anand, Rishi Rohit, B.S. SRM Institute of Science & Technology Dept of Computational Intelligence India Sathyabama Institute of Science & Technology Dept of Computer Science & Engineering India

ISBN: (纸本)9798350319200

In recent years, there have been significant advancements in speech recognition technology, which have opened up new possibilities for voice-controlled applications and devices. This report presents the design, development, and evaluation of a Speech-Recognized voice Bot (SRvB) that aims to enhance user interactions with technology through natural language processing. The SRvB utilizes cutting-edge speech recognition algorithms and machine learning techniques to convert spoken language into actionable commands. The methodology involves training a deep learning model on a large dataset of speech samples and conducting user experiments to assess its performance. The results indicate that the SRvB demonstrates promising accuracy and usability, making it a valuable tool for a wide range of applications. © 2023 IEEE.

关键词： Machine learning techniques NLP Speech-Recognized voice Bot

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Efficient Hierarchical State vector Simulation of Quantum Circuits via Acyclic Graph Partitioning

Efficient Hierarchical State Vector Simulation of Quantum Ci...

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IEEE International conference on Cluster computing (CLUSTER)

作者： Fang, Bo Ozkaya, M. Yusuf Li, Ang Catalyurek, Umit v. Krishnamoorthy, Sriram Pacific Northwest Natl Lab Richland WA 99352 USA Amazon Web Serv Seattle WA USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Early but promising results in quantum computing have been enabled by the concurrent development of quantum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and implementation strategies targeting current and anticipated quantum device architectures. In this paper, we present a graph-based approach to achieving efficient quantum circuit simulation. Our approach involves partitioning the graph representation of a given quantum circuit into acyclic subgraphs/circuits that exhibit better data locality. Simulation of each sub-circuit is organized hierarchically, with the iterative construction and simulation of smaller state vectors, improving overall performance. Also, this partitioning reduces the number of passes through data, improving the total computation time. We present three partitioning strategies and observe that acyclic graph partitioning typically results in the best time-to-solution. In contrast, other strategies reduce the partitioning time at the expense of potentially increased simulation times. Experimental evaluation demonstrates the effectiveness of our approach.

关键词： Computational modeling Graphics processing units Computer architecture Cluster computing Supercomputers Partitioning algorithms Computational efficiency

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Secure Smart Home Data by Using Blockchain Technology with Hash Algorithm 13

Secure Smart Home Data by Using Blockchain Technology with H...

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13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Lukesh, S. Jagadesh, J. Pandimurugan, v. School of Computing Department of Networking and Communications Chennai India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305463

The project 'Secure Smart Home Data by Using Blockchain Technology with Hash Algorithm' focuses on incorporating two timeless technological concepts that is, blockchain and secure hash algorithms. Smart home is a concept that is been trending these days with IoT devices taking control of the houses making people's lives much easier. But the data collected from them is not stored properly. With the traditional methodology where the data is stored on a centralized cloud network, it is prone to cyberattacks, single point of failure and also involves tampering. Because of this, the sensitive data of the users are compromised from time to time. This arises mainly due to the centralized network system of the cloud and to tackle that, we approached a decentralized network system which Blockchain. It enables us to store data across the world that too in a decentralized format resulting in a more secure and safe environment for those sensitive data. This is our proposed solution as well. To secure the smart home data with blockchain technology and to further enhance reliability, our proposed approach incorporates a modified version of SHA-512. In summary, our focus is on securing Smart Home IoT data in the cloud by integrating blockchain technology, advanced encryption methods, and robust hash algorithms like SHA-512 to ensure the utmost protection and authorized access for large-scale datasets from diverse Smart Home sources. © 2024 IEEE.

关键词： Blockchain

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