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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Devgun, Tannishtha Kumar, Gulshan Conti, Mauro School of Computer Science and Engineering Lovely Professional University Punjab India Department of Mathematics University of Padua Padua Italy International School of Advanced Studies University of Camerino Italy

ISBN: (纸本)9798350351538

Privacy problems in blockchain smart contracts arise from the inherent transparency of the technology. While blockchain ensures data integrity, it also exposes sensitive information to all participants. This lack of privacy can be a concern in industries requiring confidentiality, like finance or healthcare, prompting the need for privacy solutions in smart contracts. The existing smart contracts face the problems of over-utilization or under-utilization of privacy parameters due to the unavailability of online or offline privacy classification;smart contracts fall behind in solving privacy issues. In this paper, we introduce the first privacy classification framework for smart contracts. We call our framework oFfloaded privAcy-classified Smart contrAct for bLocKchAins (FASALKA). To be specific, FASALKA runs a novel privacy-ensured smart contract that includes a novel hybrid learning mechanism. This hybrid learning mechanism combines the potential of federated learning and reinforcement learning. We deploy Ethereum on Azure and run a set of experiments to measure the performance of our proposed FASALKA. We compare a general Ethereum framework with an Ethereum framework using our proposed FASALKA. We observe that the Ethereum framework shows 1.1% more latency than the general Ethereum;however, our proposed framework has a similar throughput of 21 TPS. Besides, FASALKA has the 100% accuracy of privacy classification, which is not present in general Ethereum. Thus, our proposed FASALKA is efficient and beneficial for the privacy-ensured blockchains. © 2024 IEEE.

关键词： Smart contract

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Low-temperature conformal vacuum deposition of OLED devices using close-space sublimation

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Journal of Semiconductors 2023年第9期44卷 62-67页

作者： Bryan Siu Ting Tam Shou-Cheng Dong Ching W.Tang State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Institute for Advanced Study The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Close-space sublimation(CSS)has been demonstrated as an alternative vacuum deposition technique for fabricating organic light-emitting diodes(OLEDs).CSS utilizes a planar donor plate pre-coated with organic thin films as an area source to rapidly transfer the donor film to a device substrate at temperatures below 200℃.CSS is also conformal and capable of depositing on odd-shaped substrates using flexible donor *** evaporation behaviors of organic donor films under CSS were fully characterized using model OLED materials and CSS-deposited films exhibited comparable device performances in an OLED stack to films deposited by conventional point *** low temperature and conformal nature of CSS,along with its high material utilization and short process time,make it a promising method for fabricating flexible OLED displays.

关键词： close-space sublimation OLED thin film low temperature vacuum deposition

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A low-fabrication-temperature, high-gain chip-scale waveguide amplifier

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science China(Information sciences) 2022年第6期65卷 247-255页

作者： Bo WANG Peiqi ZHOU Xingjun WANG Yandong HE State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University Institute of Microelectronics School of Electronics Engineering and Computer Science Peking University Frontier Science Center for Nano-optoelectronics Peking University Peking University Yangtze Delta Institute of Optoelectronics Peng Cheng Laboratory

The increasing prevalence of integrated on-chip optoelectronic devices has identified serious issues regarding inter-device transmission and coupling losses, highlighting an urgent need for on-chip waveguide amplifiers to compensate for these losses. Compared with other Er-based optical materials, erbium silicate is ideally suited to high-efficiency on-chip amplifiers and lasers because of its extremely high Er3+concentration(1022cm-3). Nevertheless, erbium silicate must be annealed above 1000℃ to crystallize and activate the Er3+, which damages other on-chip optoelectronic components and is not conducive to device ***, we report a low-fabrication-temperature, high-luminescence-efficiency gain material by adding Bi2O3to an erbium-ytterbium silicate mixed film. Our experiments demonstrate that the proposed film crystallizes at 600℃ while the activation of Er3+is also achieved, which is the lowest activation temperature of on-chip waveguide amplifier to our knowledge. This material forms the basis for a new chip-scale waveguide amplifier design, with a theoretical multi-energy-level model of Bi-Er-Yb in the mixed thin films used to analyze its signal enhancement properties. We achieve a peak on-chip gain of 23 dB in a 3.3-mm-long waveguide under the pump and signal powers of 300 mW and 1 μW, respectively. These results highlight the potential of the proposed material for realizing on-chip amplifiers and lasers for large-scale nanophotonic integrated circuits.

关键词： photonic circuit integration low fabrication temperature high gain rare earth material waveguide amplifier

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Optimization of Parameters of the OWC Wave Energy Converter Device Using a Machine Learning Approach 5

Optimization of Parameters of the OWC Wave Energy Converter ...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Karnavat, Kush Vadodaria, Nilansh Trivedi, Kshma Patel, Prashant School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Advanced Sciences VIT-AP University Department of Mathematics Amaravati India

ISBN: (纸本)9798350356236

In this study, the performance of an L-shaped front wall OWC (Oscillating Water Column) device is investigated in the presence of irregular incident waves. To efficiently explore the complex interactions between input features and the target variable, two supervised machine learning models: (i) a decision tree model and (ii) an XGBoost model are considered. The input parameters selected for this analysis are: OWC Front wall Draft, OWC Duct Length, Chamber Length, ripple Number, and ripple amplitude of the sinusoidal seabed. The output attribute is the maximum power extracted by the OWC device. The XG Boost model is used to identify critical input regions where power extraction peaks, while the decision tree model further aids in understanding the parameter influences on power extraction. A dataset of 3125 samples was generated for training, validation, and testing. By employing accumulated local effect plots with the XG Boost model, five specific regions in the input space were identified that maximize annual average power extraction. The results indicate that optimal power extraction is achieved for a specific value of each parameter, thereby validating the influence of each on OWC device performance. © 2025 IEEE.

关键词： Wave energy conversion

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Performance Juxtapose of Plant Leaf Disease Detection using Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) in MATLAB Versus Python

Performance Juxtapose of Plant Leaf Disease Detection using ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： S. Jayashree V. Sumalatha Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Leaf diseases can cause several detriments in crops' overall yield and fertility. While analyzing the various diseases that affect plants is imperative, identifying the diseases using algorithmic techniques that render optimal performance is crucial. In agriculture, detecting multiple diseases in plants is difficult. However, the provision to automate disease identification through machine learning approaches through the various phases of preprocessing and segmentation is implemented in this study. This indagation delineates the leaf disease identification using the proposed hybrid algorithmic Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) in bifurcated platforms such as MATLAB and Python. The Radial basis function is incorporated to optimize the throughput in MATLAB before observing the juxtaposed results in both platforms. The purpose of utilizing ADCRNN is to effectively combine the advantages and parallelly overcome the challenges of Deep Convolutional Neural Network (DCNN), RNN, and adaptive techniques that unsheathe the pivotal features from the provided input. The ADCRNN simulation is carried out in MATLAB and Python, and the results are successfully obtained. In addition, after optimization, the proposed ADCRNN technique’s accuracy increased to 94.5% in performance evaluation in MATLAB.

关键词： Plant diseases Accuracy Recurrent neural networks Adaptive systems Machine learning algorithms Plants (biology) Prediction algorithms MATLAB Optimization Python

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Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data

arXiv

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arXiv 2024年

作者： Tong, Tsz Pan Wang, Aoran Panagopoulos, George Pang, Jun Department of Computer Science University of Luxembourg Luxembourg Institute for Advanced Studies University of Luxembourg Luxembourg

We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generation sequencing enable detailed yet destructive gene expression assays at the single-cell level, resulting in the loss of cell evolutionary trajectories. Due to technological and cost constraints, single-cell experiments often feature cells sampled at irregular and sparse time points with a small sample size. Although trajectory-based structural inference models can accurately reveal the underlying interaction graph from observed data, their efficacy depends on the inputs of thousands of regularly sampled trajectories. The irregularly-sampled nature of single-cell data precludes the direct use of these powerful models for reconstructing GRNs. Optimal transport, a classical mathematical framework that minimize transportation costs between distributions, has shown promise in multi-omics data integration and cell fate prediction. Utilizing OT, our method constructs mappings between consecutively sampled cells to form cell-level trajectories, which are given as input to a structural inference model that recovers the GRN from single-cell data. Through case studies in two synthetic datasets, we demonstrate the feasibility of our proposed method and its promising performance over eight state-of-the-art GRN inference methods. © 2024, CC BY.

关键词： Gene expression

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TRUE: A Correlation Analysis Approach for Conducting Optimal Routing Metrics in VANETs

IEEE Networking Letters

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IEEE Networking Letters 2023年第1期5卷 55-58页

作者： Bi, Zhenguo Meng, Guiying Hawbani, Ammar Goudos, Sotirios K. Wan, Shaohua Zhao, Liang Shenyang Aerospace University School of Computer Science Shenyang110136 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Science and Technology of China School of Computer Science and Technology Hefei360022 China Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

In vehicular networks, especially in urban scenarios, road topology-based routing shows great advantages in dealing with the problems of broken links and long delays originating from sparse connections and signal attenuation. However, how road status affect data transmission performance on various routing metrics is still an intricate and vague problem. Concerned with this problem, we propose a correlation analysis approach between road network features and routing metrics (TRUE) to assist in the construction of optimal routing metrics in road-based hierarchical inter-vehicle routing. Simulation results show that TRUE has a strong ability to optimize intermediate road selection. © 2019 IEEE.

关键词： Vehicle routing

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Enhancing Non-Fungible Tokens for the Evolution of Blockchain Technology

Enhancing Non-Fungible Tokens for the Evolution of Blockchai...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Khan, Farhan Kothari, Rakshit Patel, Mayank Banoth, Niharika Geetanjali Institute Technical Studies Department of Computer Science Udaipur India

ISBN: (纸本)9781665478847

Non-fungible tokens (NFTs) are a new sort of blockchain-based token that is unique and indivisible. They were first announced in late 2017. NFTs are a type of blockchain-based virtual asset that has sparked a lot of investor interest in a very short amount of time. Since early 2021, the phenomena and its marketplaces have risen dramatically. The blockchain revolution is rolling forth a slew of changes that the art world may take advantage of. The purpose of this paper is to provide extensive information on the NFT, including its application, method of operation, buying, creating, and selling procedures, as well as its use. The NFT when paired with Metaverse, represents a significant advancement and revolution in the realm of virtual reality and blockchain, giving artists a new avenue to express their unique and valuable work. This study fills in the conceptual and practical knowledge gaps by demonstrating the usefulness of NFTs in the realms of substantial development and revolution in new private and public blockchain application. © 2022 IEEE.

关键词： Blockchain

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Low-depth quantum symmetrization

arXiv

引用

arXiv 2024年

作者： Liu, Zhenning Childs, Andrew M. Gottesman, Daniel Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

Quantum symmetrization is the task of transforming a non-strictly increasing list of n integers into an equal superposition of all permutations of the list (or more generally, performing this operation coherently on a superposition of such lists). This task plays a key role in initial state preparation for first-quantized simulations. A weaker version of symmetrization in which the input list is strictly increasing has been extensively studied due to its application to fermionic systems, but the general symmetrization problem with repetitions in the input list has not been solved previously. We present the first quantum algorithm for the general symmetrization problem. Our algorithm, based on (quantum) sorting networks, uses O(log n) depth and O(nlog m) ancilla qubits where m is the greatest possible value of the list, enabling efficient simulation of bosonic quantum systems in first quantization. Furthermore, our algorithm can prepare (superpositions of) Dicke states of any Hamming weight in O(log n) depth using O(nlog n) ancilla qubits. We also propose an O(log2 n)depth quantum algorithm to transform second-quantized states to first-quantized states. Using this algorithm, QFT-based quantum telescope arrays can image brighter photon sources, extending quantum interferometric imaging systems to a new regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Translation-Invariant Quantum Algorithms for Ordered Search are Optimal

arXiv

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arXiv 2025年

作者： Carolan, Joseph Childs, Andrew M. Kovacs-Deak, Matt Schaeffer, Luke Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses [log2 n] queries for a list of length n. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of log2 n for exact quantum algorithms is only known to lie between (ln 2)/π ≈ 0.221 and 4/log2 605 ≈ 0.433. We consider a special class of translation-invariant algorithms with no workspace, introduced by Farhi, Goldstone, Gutmann, and Sipser, that has been used to find the best known upper bounds. First, we show that any exact, k-query quantum algorithm for ordered search can be implemented by a k-query algorithm in this special class. Second, we use linear programming to show that the best exact 5-query quantum algorithm can search a list of length 7265, giving an ordered search algorithm that asymptotically uses 5 log7265 n ≈ 0.390 log2 n quantum queries. © 2025, CC BY.

关键词： Consensus algorithm

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