COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.

A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health.However,big data faces many ongoing serious challenges in the process of collection,storage,and use.Among them,data security and privacy problems have attracted extensive interest.In an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy protection.Then,we design a smart contract framework to realize distributed data access control management in a big data environment.The crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history contracts.Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior.Register contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and deletion.History contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding result.Finally,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the system.Results demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.

A New Random Forest Applied to Heavy Metal Risk Assessment

As soil heavy metal pollution is increasing year by year,the risk assess-ment of soil heavy metal pollution is gradually gaining attention.Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals.Random Forest(RF)has strong generalization ability and is not easy to overfit.In this paper,we improve the Bagging algorithm and simple voting method of RF.AW-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classifica-tion performance of RF on imbalanced datasets.Adaptive Bagging enables trees in RF to learn information from the positive samples,and weighted voting method enables trees with superior performance to have higher voting weights.Experi-ments were conducted using G-mean,recall and F1-score to set weights,and the results obtained were better than RF.Risk assessment experiments were conducted using W-RF on the heavy metal dataset from agricultural fields around Wuhan.The experimental results show that the RW-RF algorithm,which use recall to calculate the classifier weights,has the best classification performance.At the end of this paper,we optimized the hyperparameters of the RW-RF algorithm by a Bayesian optimization algorithm.We use G-mean as the objective function to obtain the opti-mal hyperparameter combination within the number of iterations.

Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments for Blockchain

Nowadays,as lightweight mobile clients become more powerful and widely used,more and more information is stored on lightweight mobile clients,user sensitive data privacy protection has become an urgent concern and problem to be solved.There has been a corresponding rise of security solutions proposed by researchers,however,the current security mechanisms on lightweight mobile clients are proven to be fragile.Due to the fact that this research field is immature and still unexplored in-depth,with this paper,we aim to provide a structured and comprehensive study on privacy protection using trusted execution environment(TEE)for lightweight mobile clients.This paper presents a highly effective and secure lightweight mobile client privacy protection system that utilizes TEE to provide a new method for privacy protection.In particular,the prototype of Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments(LMCPTEE)is built using Intel software guard extensions(SGX)because SGX can guarantee the integrity,confidentiality,and authenticity of private data.By putting lightweight mobile client critical data on SGX,the security and privacy of client data can be greatly improved.We design the authentication mechanism and privacy protection strategy based on SGX to achieve hardware-enhanced data protection and make a trusted connection with the lightweight mobile clients,thus build the distributed trusted system architecture.The experiment demonstrates that without relying on the performance of the blockchain,the LMCPTEE is practical,feasible,low-performance overhead.It can guarantee the privacy and security of lightweight mobile client private data.

A Zero-Watermark Scheme Based on Quaternion Generalized Fourier Descriptor for Multiple Images

Most of the existing zero-watermark schemes for medical images are only appropriate for a single grayscale image.When they protect a large number of medical images,repeating operations will cause a significant amount of time and storage costs.Hence,this paper proposes an efficient zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor(QGFD).Firstly,QGFD is utilized to compute the feature invariants of each color image,then the representative features of each image are selected,stacked,and reshaped to generate a feature matrix,which is then binarized to get a binary feature image.Copyright information can be converted into the copyright image by using QR code technology,which contains more information.Finally,the zero-watermark image is constructed by executing the XOR operation on the copyright image and the feature image scrambled by the Cat map.In the experiment,different parameters are selected to determine the maximum number of images that the proposed scheme can protect simultaneously while achieving good robustness.The experimental results demonstrate that the proposed scheme can effectively resist common attacks,geometric attacks and joint attacks,and effectively improve the operation efficiency of the algorithm,thus effectively decreasing the time and storage cost of copyright protection for lots of medical images.

Cooperative Detection Method for DDoS Attacks Based on Blockchain

Distributed Denial of Service(DDoS)attacks is always one of the major problems for service providers.Using blockchain to detect DDoS attacks is one of the current popular methods.However,the problems of high time overhead and cost exist in the most of the blockchain methods for detecting DDoS attacks.This paper proposes a blockchain-based collaborative detection method for DDoS attacks.First,the trained DDoS attack detection model is encrypted by the Intel Software Guard Extensions(SGX),which provides high security for uploading the DDoS attack detection model to the blockchain.Secondly,the service provider uploads the encrypted model to Inter Planetary File System(IPFS)and then a corresponding Content-ID(CID)is generated by IPFS which greatly saves the cost of uploading encrypted models to the blockchain.In addition,due to the small amount of model data,the time cost of uploading the DDoS attack detection model is greatly reduced.Finally,through the blockchain and smart contracts,the CID is distributed to other service providers,who can use the CID to download the corresponding DDoS attack detection model from IPFS.Blockchain provides a decentralized,trusted and tamper-proof environment for service providers.Besides,smart contracts and IPFS greatly improve the distribution efficiency of the model,while the distribution of CID greatly improves the efficiency of the transmission on the blockchain.In this way,the purpose of collaborative detection can be achieved,and the time cost of transmission on blockchain and IPFS can be considerably saved.We designed a blockchain-based DDoS attack collaborative detection framework to improve the data transmission efficiency on the blockchain,and use IPFS to greatly reduce the cost of the distribution model.In the experiment,compared with most blockchain-based method for DDoS attack detection,the proposed model using blockchain distribution shows the advantages of low cost and latency.The remote authentication mechanism of Intel SGX provides high security and integrity,and ensures the availability of distributed models.

Post-Cloud Computing Paradigms： A Survey and Comparison

With the rapid development of pervasive intelligent devices and ubiquitous network technologies, new network applications are emerging, such as the Internet of Things, smart cities, smart grids, virtual/augmented reality, and unmanned vehicles. Cloud computing, which is characterized by centralized computation and storage,is having difficulty meeting the needs of these developing technologies and applications. In recent years, a variety of network computing paradigms, such as fog computing, mobile edge computing, and dew computing, have been proposed by the industrial and academic communities. Although they employ different terminologies, their basic concept is to extend cloud computing and move the computing infrastructure from remote data centers to edge routers, base stations, and local servers located closer to users, thereby overcoming the bottlenecks experienced by cloud computing and providing better performance and user experience. In this paper, we systematically summarize and analyze the post-cloud computing paradigms that have been proposed in recent years. First, we summarize the main bottlenecks of technology and application that cloud computing encounters. Next, we analyze and summarize several post-cloud computing paradigms, including fog computing, mobile edge computing, and dew computing.Then, we discuss the development opportunities of post-cloud computing via several examples. Finally, we note the future development prospects of post-cloud computing.