scholarly journals MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 242
Author(s):  
Jianlong Xu ◽  
Zicong Zhuang ◽  
Zhiyu Xia ◽  
Yuhui Li
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Prediction Method ◽  
Third Party ◽  
Superior Performance ◽  
Prediction Algorithm ◽  
Service Reliability ◽  
Vast Number ◽  
Personalized Services ◽  
Service Oriented

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.

Design of Household Heat Metering Management System Based on Cloud Computing

2014 ◽  
Vol 644-650 ◽  
pp. 2235-2238
Author(s):  
Tian Jing ◽  
Xiao Guo Liu
Keyword(s):  
Cloud Computing ◽  
Management System ◽  
Large Scale ◽  
Computer Software ◽  
Heat Management ◽  
Vast Number ◽  
Personalized Services ◽  
Home Service ◽  
Heat Metering ◽  
Upper Computer

In order to meet the large-scale promotion of household heat metering in north China and respond positively the call of the national “Twelfth Five Year” plan for energy-saving and emission-reduction,the design combines the advantage of the cloud computing and cloud software and presents the overall structure of management system based on the cloud computing. Then, the paper focuses on the design of upper computer software and database[1], The upper computer software consists of two parts,one is automatic meter reading software,which is communicated with every collectors by GPRS; the other is cloud software, The cloud software improves the current heat management system by combining the smart home service concept and provides personalized services for many heating companies and vast number of families[2].

scholarly journals Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data

2021 ◽  
Author(s):  
Di Wang ◽  
Zheng Jing ◽  
Kevin He ◽  
Lana X Garmire
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
High Efficiency ◽  
Prediction Method ◽  
Population Data ◽  
Superior Performance ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Training Time ◽  
Scale Population

Abstract Summary Cox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n =10 000) and achieves better prediction accuracy than Cox-PH (P<0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data. Availability and implementation Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 928-951
Author(s):  
Ronghua Xu ◽  
Seyed Yahya Nikouei ◽  
Deeraj Nagothu ◽  
Alem Fitwi ◽  
Yu Chen
Keyword(s):  
Private Information ◽  
Public Safety ◽  
Large Scale ◽  
Service Oriented Architecture ◽  
Single Point ◽  
Third Party ◽  
Security And Privacy ◽  
Privacy Concerns ◽  
Service Oriented ◽  
Edge Networks

Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

scholarly journals Application of Deep Learning in Power load Analysis

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Prediction Method ◽  
Training Model ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Weather Information ◽  
Power Load ◽  
Prediction Time ◽  
Model Training

Aiming at the problems of slow model training speed and poor prediction effect of traditional power load prediction algorithm, a parallel load prediction method based on deep learning is proposed. The method is based on the MapReduce parallel calculating framework, and the deep belief network model, which is used to parallel training the sample data with the historical load and the weather information, and the model of the training model to predict the load value. The experimental results show that the average root-mean-square error between the predicted power load value and the actual value of the prediction method in this paper is 2.86%. The prediction accuracy is higher than the traditional method, and the training and prediction time are effectively reduced, which can adapt to the prediction requirements of large-scale power data.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

scholarly journals HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yu Li ◽  
Zeling Xu ◽  
Wenkai Han ◽  
Huiluo Cao ◽  
Ramzan Umarov ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Third Party ◽  
Superior Performance ◽  
Beta Lactamase ◽  
Learning Framework ◽  
Sequence Encoding ◽  
Global Threat

Abstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.

scholarly journals Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries

2021 ◽  
Vol 13 (3) ◽  
pp. 1251
Author(s):  
Yichi Zhang ◽  
Zhiliang Dong ◽  
Sen Liu ◽  
Peixiang Jiang ◽  
Cuizhi Zhang ◽  
...  
Keyword(s):  
International Trade ◽  
Large Scale ◽  
Prediction Method ◽  
Lithium Ion ◽  
Lithium Carbonate ◽  
Raw Material ◽  
Small Scale ◽  
Trade Restrictions ◽  
Energy Field ◽  
Structure Of Trade

As the raw material of lithium-ion batteries, lithium carbonate plays an important role in the development of new energy field. Due to the extremely uneven distribution of lithium resources in the world, the security of supply in countries with less say would be greatly threatened if trade restrictions or other accidents occurred in large-scale exporting countries. It is of great significance to help these countries find new partners based on the existing trade topology. This study uses the link prediction method, based on the perspective of the topological structure of trade networks in various countries and trade rules, and eliminates the influence of large-scale lithium carbonate exporting countries on the lithium carbonate trade of other countries, to find potential lithium carbonate trade links among importing and small-scale exporting countries, and summarizes three trade rules: (1) in potential relationships involving two net importers, a relationship involving either China or the Netherlands is more likely to occur; (2) for all potential relationships, a relationship that actually occurred for more than two years in the period in 2009–2018 is more likely to occur in the future; and (3) potential relationships pairing a net exporter with a net importer are more likely to occur than other country combinations. The results show that over the next five to six years, Denmark and Italy, Netherlands and South Africa, Turkey and USA are most likely to have a lithium carbonate trading relationship, while Slovenia and USA, and Belgium and Thailand are the least likely to trade lithium carbonate. Through this study, we can strengthen the supply security of lithium carbonate resources in international trade, and provide international trade policy recommendations for the governments of importing countries and small-scale exporting countries.

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