Functional dependencies (FDs) are the most common constraints in the design theory for relational databases, generalizing the concept of a key for a relation. Given an attribute subset X and an attribute A in relation...
As the device complexity keeps increasing,the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-dr...
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As the device complexity keeps increasing,the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-driven autonomous *** distributed consensus algorithm is the core component that directly dictates the performance and properties of blockchain ***,the inherent characteristics of the shared wireless medium,such as fading,interference,and openness,pose significant challenges to achieving consensus within these networks,especially in the presence of malicious jamming *** cope with the severe consensus problem,in this paper,we present a distributed jamming-resilient consensus algorithm for blockchain networks in wireless environments,where the adversary can jam the communication channel by injecting jamming *** on a non-binary slight jamming model,we propose a distributed four-stage algorithm to achieve consensus in the wireless blockchain network,including leader election,leader broadcast,leader aggregation,and leader announcement *** high probability,we prove that our jamming-resilient algorithm can ensure the validity,agreement,termination,and total order properties of consensus with the time complexity of O(n).Both theoretical analyses and empirical simulations are conducted to verify the consistency and efficiency of our algorithm.
An individual's unwanted and unpleasant reactions that arise from regular drug use are referred to as adverse drug reactions, or ADRs. Mild side effects to serious, potentially fatal diseases can be caused by thes...
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An individual's unwanted and unpleasant reactions that arise from regular drug use are referred to as adverse drug reactions, or ADRs. Mild side effects to serious, potentially fatal diseases can be caused by these reactions. In order to guarantee the security and effectiveness of pharmacological interventions, it is imperative to track and comprehend ADRs. This paper presents an approach in pharmacovigilance to classify drug reactions caused by different drugs by combining firefly algorithm with different classifiers. The firefly algorithm assigns firefly to each subsets of features and uses the objective function in order to calculate the distance and making cluster of firefly. In the end it gives us the most optimal set of features which we further use in our classification. We also have experimented on three types of classifiers which are: Random Forest, K-Nearest Neighbour, Decision Tree. In the end we have compared the accuracy, Precision, F1 Score of different classifiers and concluded that by using firefly for feature extraction we can increase accuracy, precision and F1-score. We have also compared this Firefly algorithm with Elephant Herding Optimization (EHO) used for feature selection. We dived into the advancements and challenges faced during the prediction of reactions of drugs. There is much scope in this as we can further increase our performance and efficiency by combining multiple classifiers or using different feature extraction techniques. We used a dataset consisting of 1333 entries with 24 features from Kaggle, split into training and testing sets with a 70:30 ratio. The results demonstrate that applying the Firefly Algorithm enhances model performance. Random Forest classifier achieved the highest accuracy of 97.5% with the Firefly Algorithm, compared to 97.2% without it. Similarly, KNN and Decision Tree classifiers also showed improvements in accuracy, with KNN improving from 93.5% to 95.0% and Decision Tree improving from 96.7% to 97.0%. Add
The price of passengers is closely related to the travel cost of passengers and the revenue of driverless shared bus. Therefore, the design of pricing mechanism is important to improve the market efficiency of driverl...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two ...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.
Combinatorial Optimization (CO), as a crucial domain within optimization, has been spanned across a broad spectrum of fields, imparting significant practical relevance to the study of combinatorial optimization proble...
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With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To ach...
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With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling(DRS-S)algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy(PDP) algorithm and privacy preserving approach based on clustering and noise(PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.
This paper tackles the problem of estimating the frequency distribution on the crowdsourced multidimensional categorical data under local differential privacy(LDP).Although the frequency estimation problem under LDP[1...
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This paper tackles the problem of estimating the frequency distribution on the crowdsourced multidimensional categorical data under local differential privacy(LDP).Although the frequency estimation problem under LDP[1]has attracted a lot of attention in recent years,currently,to our knowledge,the existing works are all devoted to optimizing the absolute error,rather than relative *** the work for the former,the one targeting at the latter should take the true frequency distribution into consider and design true frequency distribution-sensitive data collection protocol so that the skewed distribution,which involves smaller frequency in high probability,is with less ***,it is challenging to fulfill such a requirement,since the true frequency distribution is private information and not available.
The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are ...
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