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Thin film ferroelectric photonic-electronic memory

Light(science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Blockchain Integration with Internet of Things (IoT)-Based Systems for Data Security: A Review

Blockchain Integration with Internet of Things (IoT)-Based S...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kaur, Gagandeep Shrivastava, Rajesh Gupta, Umesh Department of Computer Science and Engineering Madhav Institute of Technology and Science Gwalior India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819965434

The blockchain technology offers a secure channel for communicating between entities without the role of any third party. It is a digital ledger of transactions in a computer network that makes it hard for hackers to attack or alter the information. Banking, supply chain, precision agriculture, smart city, cyber-physical systems, industrial IoT, and health care are the various sectors in which blockchain technology has been adopted to enhance security. In recent times, these sectors are being revolutionized by digital transformation using sensor-aided physical devices forming Internet of Things (IoT) systems. Blockchain-based IoT system plays a vital role in replacing the conventional methods of storage and sharing data with a more reliable method. The integration of the two technology results in a secure, reliable, and smart system. This paper exhibits the background and working principle of blockchain technology. Also, it discusses the need of security and security challenges in IoT-based systems. Furthermore, it discusses briefly about smart contracts and motivation behind integrating blockchain technology with IoT-based systems. Finally, it proposes a secure IoT-based land registry architecture. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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A Survey on Privacy-Preserving Communication Frameworks in Machine Learning for Cybersecurity Threat Detection 4

A Survey on Privacy-Preserving Communication Frameworks in M...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Suneetha, Banothu Kesavan, R. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331529635

This survey covers the new trend of privacy-preserving communication frameworks in machine learning in the FL, DP and SMPC for cyber threat detection domains. The specific research performed between the year of 2020 and 2024 has been surveyed and included in this paper. The main contribution is monitoring cybersecurity threats using privacy-preserving frameworks in distributed systems such as IoT and industrial networks. 40 research papers have been reviewed and categorized to several related themes such as FL implementations, privacy-preserving methods, and cybersecurity threat detection. The relevance of privacy-preserving frameworks for the real-world cybersecurity scenario is illustrated with dataset CISA 2022 Vulnerability Dataset. The study of privacy-preserving communication shows that FL can be combined with DP and SMPC to achieve data privacy preservation while deduction accuracy of detecting cyber threats can be well-maintained. Moreover, the research gaps and challenges of scalability, communication overhead have been addressed and identified to facilitate the future research and development. © 2024 IEEE.

关键词： Federated learning

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Botnet Detection Through Flow-Based Deep Feature Extraction and Ensemble Classification

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Journal of The Institution of Engineers (India): Series B 2025年 1-14页

作者： Geetha, K. Brahmananda, S.H. Department of Computer Science and Engineering GITAM School of Technology Karnataka Bengaluru India Ashtaksha Labs Pvt Ltd Karnataka Bengaluru India

As Internet of Things (IoT) devices are networked and thus susceptible to many forms of attacks, cyber security risk is the primary concern in the IoT field. To tackle this issue, this study employs machine learning and deep learning techniques to identify botnet attacks. The proposed method uses a random forest classifier or other machine learning techniques to organize the data after extracting detailed characteristics using Bidirectional Long Short-Term Memory (Bi-LSTM). This methodology yields superior outcomes for challenges involving binary and multiclass classification. The IoT-23 and N-BaIoT databases were used. The IoT-23 collection consists of three normal samples and twenty malware samples from different IoT devices, whereas the N-BaIoT dataset includes scenarios like Mirai and Bashlite attacks. In the context of IoT risks, the proposed technique successfully differentiates between benign and malicious traffic data. In terms of multiclass classification, the accuracy rates for N-BaIoT and the IoT-23 dataset are 100% and 99.9%, respectively, while the acquired accuracy rates for binary classification are 99.9% for N-BaIoT and 97% for the IoT-23 dataset. © The Institution of Engineers (India) 2025.

关键词： Deep learning

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StyleGAN and DCGAN for Face Generation: A Comparative Evaluation 8th

StyleGAN and DCGAN for Face Generation: A Comparative Evalua...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Archana, M. Joseph, Ashwin Antony Ajith, Alex V. Koshy, Alan N. Saritha, S. Department of Computer Science Engineering Rajagiri School of Engineering and Technology Kochi India

ISBN: (纸本)9789819765805

Advent of GAN networks has enabled several tasks such as text to face generation easier. It helps in synthesizing several instances of data from the actual data. It gives an idea on new possibilities for existing dataset. The current scenario in literature sees a parade of GAN architectures. The performance of each GAN architecture varies in a wide range of outputs. Hence, there always exists a confusion of the GAN architecture to be chosen for the purpose of the task in question. This paper attempts to make a comparative evaluation of two famous GAN architectures, namely, StyleGAN and DCGAN. The architectures are implemented and the images generated are compared on the basis of the quality of images. It is seen that StyleGAN responds more accurately to features like smile, age, and gender. From the experiments, it is seen that the FID score of StyleGAN has a lower value as compared to that of DCGAN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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A review on the energy efficient resource allocation schemes with improved quality of services in green cloud computing

A review on the energy efficient resource allocation schemes...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Fiaz, A.S. Syed Sonai, Veeramani Computer Science and Engineering Amrita Viswa Vidhyapeetham School of Computing Chennai India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9781032669663

The vast changes in the computing utilization have created a need with the improvised resource availability and reliability. The virtualization of servers and data centers to increase their efficiency has transformed how enormous data is stored and handled. Cloud computing has created a supreme and impressive approach to virtualize servers and data centers and to make them energy efficient in order to leverage on diverse IT resources. Massive power and energy consumption by IT resources leads to an energy crisis and a change in the planet’s climate. But because these IT resources use so much energy and power, they end up being a significant source of CO2 emissions. Due to this, green cloud computing is now required in order to make IT resources cost-and energy-effective. It is necessary to do a thorough analysis of the cloud’s power efficiency in order to arrive at the best options for green cloud computing. Green Cloud Computing is well known to be a broad domain and a popular field for research because of the increasing rise of enormous data storage and computational demand. It can generate solutions that not only make IT resources energy efficient but also reduce operational expenses. This review’s objective is to highlight some of the best practices for achieving green cloud computing, including workload clustering, Auction-Based Resource Optimization techniques, Hardware and Software controlled solutions, Minimize cooling requirements scheduling, clustering, virtualization and other energy-saving techniques. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Carbon footprint

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Edge AI: A survey

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 71-92页

作者： Singh, Raghubir Gill, Sukhpal Singh Department of Computer Science University of Bath Bath United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI refers to the practice of doing AI computations near the users at the network's edge, instead of centralised location like a cloud service provider's data centre. With the latest innovations in AI efficiency, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing, the potential of edge AI has now been unlocked. This study provides a thorough analysis of AI approaches and capabilities as they pertain to edge computing, or Edge AI. Further, a detailed survey of edge computing and its paradigms including transition to Edge AI is presented to explore the background of each variant proposed for implementing Edge Computing. Furthermore, we discussed the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. We also presented the technology used in various modern IoT applications, including autonomous vehicles, smart homes, industrial automation, healthcare, and surveillance. Moreover, the discussion of leveraging machine learning algorithms optimized for resource-constrained environments is presented. Finally, important open challenges and potential research directions in the field of edge computing and edge AI have been identified and investigated. We hope that this article will serve as a common goal for a future blueprint that will unite important stakeholders and facilitates to accelerate development in the field of Edge AI. © 2023 The Authors

关键词： Machine learning

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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information Technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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