Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among profess...
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The distribution, traceability, and management of shipping container logistics require secure data flow and trusted transactions. Digital Twins (DTs) can realize these features by offering shipping tracking and tracea...
The distribution, traceability, and management of shipping container logistics require secure data flow and trusted transactions. Digital Twins (DTs) can realize these features by offering shipping tracking and traceability, process flow and status monitoring, and management of the physical containersall in a remote manner. However, the data of a DT itself is typically stored, controlled, and managed by a centralized entity, which is often the original creator of the physical container. Having a centralized entity can cause mistrust. The centralized entity may alter, tamper, or delete the digital twin data. To overcome this problem, this paper proposes trusted sharing and management of DTs for shipping containers by using Non-Fungible Tokens (NFTs). NFTs are digital tokens that hold unique data stored, controlled, and managed in a decentralized and immutable blockchain ledger. We extend in this paper the use of NFTs to tokenize shipping container DTs and their metadata. The proposed solution uses NFTs and Ethereum blockchain smart contracts to offer decentralization, security, transparency, traceability, and immutability to the data and processes involved in the creation, storage, and management of DTs of shipping containers. To demonstrate our solution, we create a DT of a shipping container using Microsoft Azure Digital Twins services and showed how to tokenize it using NFT. We assess the system using various test cases to evaluate its main functionalities. Furthermore, we analyze the cost of transactions and the security of the smart contracts code. We have made the code of our smart contracts publicly available on GitHub.
Stock price prediction has been a longstanding challenge in financial markets, and recent advances in deep learning techniques have spurred increased interest in developing accurate and reliable models for forecasting...
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ISBN:
(数字)9789532901351
ISBN:
(纸本)9798350390797
Stock price prediction has been a longstanding challenge in financial markets, and recent advances in deep learning techniques have spurred increased interest in developing accurate and reliable models for forecasting. The comparative study is conducted using historical stock price data from diverse financial markets and periods. The dataset is preprocessed to address issues such as non-linearity, volatility, and noise. Subsequently, the LSTM, RNN, and GRU models are implemented and fine-tuned to optimize their performance in predicting stock prices. Performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are employed to evaluate and compare the predictive accuracy of each model. Additionally, the study investigates the models' abilities to capture long-term dependencies and adapt to changing market conditions. The results of the comparative analysis provide insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction. The findings contribute to the ongoing discourse on the applicability of deep learning techniques in financial forecasting and offer practical recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions. In conclusion, the study not only contributes valuable insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction but also emphasizes the practical utility of the predictions by visually displaying the differences through charts and graphs. This dual approach advances the understanding of deep learning techniques in financial forecasting and provides actionable recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions.
The advent of 6G-enabled Internet of Everything(IoE) technologies is set to revolutionize urban infrastructures by providing fast, consistent, and low-delay capabilities for communication. 6G connectivity will integra...
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Due to the intricate of real-world road topologies and the inherent complexity of autonomous vehicles, cooperative decision-making for multiple connected autonomous vehicles (CAVs) remains a significant challenge. Cur...
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Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving del-egation of data to a service provider, who m...
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ISBN:
(数字)9798350382846
ISBN:
(纸本)9798350382853
Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving del-egation of data to a service provider, who may further delegate the computation to auxiliary worker nodes, in order to compute a polynomial over that data at a later point in time. We study techniques which are compatible with robust management of distributed computation systems, an area known as coded computing. Privacy in coded computing, however, has tradition-ally addressed the problem of colluding workers, and assumed that the server that administrates the computation is trusted. This viewpoint of privacy does not accurately reflect real-world privacy concerns, since normally, the service provider as a whole (i.e., the administrator and the worker nodes) form one cohesive entity which itself poses a privacy risk. This paper aims to shift the focus of privacy in coded computing to safeguarding the privacy of the user against the service provider as a whole, instead of merely against colluding workers inside the service provider. To this end, we leverage the recently defined notion of perfect subset privacy, which guarantees zero information leakage from all subsets of the data up to a certain size. Using known techniques from Reed-Muller decoding, we provide a scheme which enables polynomial computation with perfect subset privacy in straggler-free systems. Furthermore, by studying information super-sets in Reed-Muller codes, which may be of independent interest, we extend the previous scheme to tolerate straggling worker nodes inside the service provider.
Large particle systems are often described by high-dimensional (linear) kinetic equations that are simulated using Monte Carlo methods for which the asymptotic convergence rate is independent of the dimensionality. Ev...
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Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizonta...
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Electronic Word of Mouth (eWoM) becomes a key player for e-commerce platforms to upgrade or downgrade product or service value. Currently, reviews are one of the favorable sources for individuals to decide their opini...
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ISBN:
(数字)9798331510404
ISBN:
(纸本)9798331510411
Electronic Word of Mouth (eWoM) becomes a key player for e-commerce platforms to upgrade or downgrade product or service value. Currently, reviews are one of the favorable sources for individuals to decide their opinions about products, hotels, places, transport services, food quality, and many others. The digital era yields positives and negatives; positive includes fast access to multiple people's opinions in terms of legitimate reviews, whereas negative includes the existence of deceptive reviews inside the total review. Deceptive reviews may be used to create a biased impact on humans about the brand or services. Therefore, detecting such biased reviews from online platforms is an urgent requirement. To address the issues, a weighted ensemble model was developed in this research, which was trained and tested on the gold standard dataset. The model's outcome achieved approx 5% higher accuracy for detecting deceptive reviews than baseline models.
In recent decades, the concept of sustainability has taken on a key role in the agendas of political decision-makers, businesses, and society. Brands have incorporated attributes associated with the concept of sustain...
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