A Graph Neural Network Recommendation Based on Long-and Short-Term Preference

The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted with.Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession.However,user preferences are dynamic.With the passage of time and some trend guidance,users may generate some short-term preferences,which are more likely to lead to user-item interactions.A GNN recommendation based on long-and short-term preference(LSGNN)is proposed to address the above problems.LSGNN consists of four modules,using a GNN combined with the attention mechanism to extract long-term preference features,using Bidirectional Encoder Representation from Transformers(BERT)and the attention mechanism combined with Bi-Directional Gated Recurrent Unit(Bi-GRU)to extract short-term preference features,using Convolutional Neural Network(CNN)combined with the attention mechanism to add title and description representations of items,finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations.In experiments conducted on five publicly available datasets from Amazon,LSGNN is superior to state-of-the-art personalized recommendation techniques.

APPOW:An Advanced Routing Protocol Based on Parameters Optimization in the Weighted Mobile Social Network

Due to the increasing number of wireless mobile devices,the possibility of mobile communications without infrastructure becomes a reality.The Decentralized Mobile Social Network(DMSN) is a paradigm where nodes can move freely and organize themselves arbitrarily.Routing in these environments is difficult for the reason of the rapid changes of the social relationship graph's topology.Meanwhile,the social ties among nodes change overtime.Therefore,an efficient data forwarding mechanism should be considered over the temporal weighted relationship graph.In this paper,an Advanced routing Protocol based on Parameters Optimization in the Weighted mobile social network(APPOW) is proposed to improve the delivery success ratio and reduce the cost of exchanging information.APPOW combines the normalized relative weights of three local social metrics,i.e.,LinkRank,similarity and contact strength,to select the next relay node.The weights of the three metrics are derived by pair-wise learning algorithm.The result shows that APPOW outperforms the state-ofthe-art SimBet Routing in delivering message and significantly reduces the average hops.Additionally,the delivery performance of APPOW is close to Epidemic Routing but without message duplications.

A Fully Nonlinear HOBEM with the Domain Decomposition Method for Simulation of Wave Propagation and Diffraction

A higher-order boundary element method(HOBEM) for simulating the fully nonlinear regular wave propagation and diffraction around a fixed vertical circular cylinder is investigated. The domain decomposition method with continuity conditions enforced on the interfaces between the adjacent sub-domains is implemented for reducing the computational cost. By adjusting the algorithm of iterative procedure on the interfaces, four types of coupling strategies are established, that is, Dirchlet/Dirchlet-Neumman/Neumman(D/D-N/N), Dirchlet-Neumman(D-N),Neumman-Dirchlet(N-D) and Mixed Dirchlet-Neumman/Neumman-Dirchlet(Mixed D-N/N-D). Numerical simulations indicate that the domain decomposition methods can provide accurate results compared with that of the single domain method. According to the comparisons of computational efficiency, the D/D-N/N coupling strategy is recommended for the wave propagation problem. As for the wave-body interaction problem, the Mixed D-N/N-D coupling strategy can obtain the highest computational efficiency.

Safety Protection Design of Virtual Machine Drift Flow in Cloud Data Center Based on VXLAN Technology

Based on the analysis of the security problems existing in the cloud platform of the data center, this paper proposes a set of cloud platform security protection scheme being with virtualization technology. This paper focuses on the overall architecture of cloud platform as well as the design of virtualization security architecture. Meantime, it introduces the key technologies of VXLAN in detail. The scheme realizes flexible scheduling of security resources through virtual pooling of independent security gateway and virtual machine isolation through VXLAN technology. Moreover, it guides all horizontal traffic to independent security gateway for processing, unified management of security gateway through cloud platform by using Huawei NSH business chain technology. This scheme effectively solves the horizontal transmission of security threat among virtual machines, and realizes the fine security control and protection for the campus data center.

Optimization of Route Repair in Mobile Ad-hoc Network

A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a random mobility model, this paper derives the probability equation of the relative distance (RDIS) between any two mobile hosts in an ad-hoc network. Consequently, combining with average equivalent hop distance (AEHD), a host can estimate the routing hops between itself and any destination host each time the RD/RR procedure is triggered, and reduce the flooding area of RD/RR messages. Simulation results show that this optimized route repair (ORR) algorithm can significantly decrease the communication overhead of RR process by about 35%.

Ground roll separation method via threshold fi ltering and constraint of seismic wavelet support in curvelet domain

Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold fi ltering(CTF)is performed by using the diff erence of the curvelet coeffi cient of the refl ection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coeffi cient.Under the constraints of seismic wavelet and local weighting coeffi cient,the L1 norm of the refl ection coeffi cient is minimized in the curvelet domain to recover the damaged refl ection wave and attenuate the residual noise.The local weighting coeffi cient in this paper is obtained by calculating the local correlation coeffi cient between the high-pass fi ltering result and the CFT result.We applied the TFWS method to simulate and fi eld data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the refl ection events.

Construction and Application of Cloud Computing Model for Reciprocal and Collaborative Knowledge Management

Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.

Identifier Management of Industrial Internet Based on Multi-Identifier Network Architecture

The industrial Internet realizes intelligent control and optimized operation of the industrial system through network interconnection.The industrial Internet identifier is the core element to accomplish this task.The traditional industrial Internet identifier resolution technologies depend excessively on IP networks,and cannot meet the requirements of ubiquitous resource-restraint Internet of Things(IoT)devices.An industrial Internet identifier resolution management strategy based on multi-identifier network architecture is proposed in this paper,which supports content names,identities,locations,apart from the traditional IP address.The application of multiple types of identifiers not only solves the problem of IP addresses exhaustion,but also enhances the security,credibility,and availability of the industrial Internet identification resolution system.An inter-translation scheme between multiple identifiers is designed to support multiple identifiers and the standard ones.We present an addressing and routing algorithm for identifier resolution to make it convenient to put our strategy into practice.

Social Network Analysis of COVID-19 Research and the Changing International Collaboration Structure

Research in Information Science and interdisciplinary areas suggested the formation of a growing network of international research collaboration.The massive transmission of COVID-19 worldwide especially after the identification of the Omicron variant could fundamentally alter the factors shaping the network's development.This study employs network analysis methods to analyze the structure of the COVID-19 research collaboration from 2020 to 2022,using two major academic publication databases and the VOSviewer software.A novel temporal view is added by examining the dynamic changes of the network,and a fractional counting method is adopted as methodological improvements to previous research.Analysis reveals that the COVID-19 research network structure has undergone substantial changes over time,as collaborating countries and regions form and re-form new clusters.Transformations in the network can be partly explained by key developments in the pandemic and other social-political events.China as one of the largest pivots in the network formed a relatively distinct cluster,with potential to develop a larger Asia-Pacific collaboration cluster based on its research impact.

A predictive deconvolution method for non-white-noise refl ectivity

Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.

On the new member’s subsecret distribution protocol in dynamic group secret-sharing

Secret-sharing is a common method to protect important data, such as the private key of a public-key system. Dynamic Group Secret-sharing (DGS) is a system where all of the members in a group hold a subsecret of the key information and where the number of members in the group is variable. This kind of secret-sharing is broadly used in many special distribution systems, such as Self-secure Ad-hoc Network. Distributing this subsecret to a new member when he enters the group is the common method that ensures all the members participate in the same secret-sharing. However, no’atisfactory subsecret distribution scheme exists at present. This paper proposes a new protocol that tries to satisfy both security and efficiency.

Variable-length sequential dynamic features-based malware detection

In order to solve the problem that traditional signature-based malware detection systems are inefficacious in detecting new malware,a practical malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of malware detection.

Prototype of Multi-Identifier System Based on Voting Consensus

With the rapid development of the Internet,the expansion of identifiers and data brings a huge challenge to the network system.However,the network resources such as Domain Name System(DNS)are monopolized by a single agency which brings a potential threat to cyberspace.The existing network architecture cannot fundamentally solve the problems of resource monopoly and low performance.Based on the blockchain,this paper designs and implements a new Multi-Identifier System(MIS),providing the analysis and management for different identifiers in the multi-identifier network.Our preliminary emulation results prove the correctness and efficiency of the algorithm.Besides,the prototype system of MIS has been tested on the real operators’network,realizing the function of co-governing,security supervision and data protection.

ERP Virtual Simulation Experiment Teaching Platform based on UFIDA Cloud Computing

Economic Management Professional Academic Education are increasingly becoming personalization,intelligence and application.Colleges and universities should actively use cloud computing and big data.Also Internet of Things and other advanced information technologies to build an economics and management ERP virtual simulation experiment teaching platform.Cloud computing and big data,virtual simulation experiment teaching resources with"resource library+project library+enterprise management simulation sandbox training"as the core can build an online and offline collaborative and practical experiment teaching platform.It is expected to achieve the ideal effect of integration of three spaces.Such as physics and resources and social digital teaching.Moreover,it can also benefit human-computer collaboration and interactive teaching and inquiry learning.

The Influence of Short Video on City Image Dissemination in the New Media Era——Taking Tik Tok as an Example

With the popularity and rise of Chongqing,Chengdu,Xi’an and other cities on the short video platform,short video is increasingly becoming a new weapon for the construction and dissemination of the city image.Researching on the content,methods and effects of the hottest video APP Tik Tok,this paper adopts research methods of case analysis and text analysis to investigate how short videos promote the spread of urban image,and explore how they can enhance urban image transmission in the new media era in a better way.

Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service（Iaa S） cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S（DHCI） architecture, which includes four key modules： monitoring,scheduling, Virtual Machine（VM） management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host（s） or rack（s） where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open Stack.Substantial experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

Photoluminescence from Er^(3+) ion and SnO_2 nanocrystal co-doped silica thin films

Er3＋ ions embedded in silica thin films co-doped by SnO2 nanocrystals are fabricated by sol-gel and spin coating methods. Uniformly distributed 4-am SnO2 nanocrystals are fabricated, and the nanocrystals showed tetragonal rutile crystalline structures confirmed by transmission electron microscope and X-ray diffraction measurements. A strong characteristic emission located at 1.54 〉m from the Era＋ ions is iden- tified, and the influences of Sn doping concentrations on photoluminescence properties are systematically evaluated. The emission at 1.54 #m from Era＋ ions is enhanced by more than three orders of magnitude, which can be attributed to the effective energy transfer from the defect states of SnO2 nanocrystals to nearby Er3＋ ions, as revealed by the selective excitation experiments.