Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

基于Feature Service的工业用地管理系统研究与应用——以江阴市为例

开展工业用地调查是做好低效产业用地再开发、工业企业绩效评价等工作的先决条件。利用Feature Service、数据库等技术,可以实现工业用地数据在线编辑。文章以江阴市为例,在需求分析基础上,进行了系统设计开发。江阴市自然资源和规划局通过工业用地管理系统运行,掌握了真实可靠的工业用地数据,实现了工业用地数据动态更新,为招商引资、产业转型、节约集约用地提供了数据支撑和决策依据。江苏省各县(市、区)每年定期开展工业用地更新调查工作,该系统在全省具有一定推广价值。

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

Exploring the Commuters’ Willingness-to-Pay and Its Influencing Factors for an Improved Public Bus Service in Dhaka City

Improved transportation services are a crucial component of urban growth, particularly in emerging cities like Dhaka. Ensuring an improved public bus service quality is a challenge for the city’s transport planners and policy makers. Nevertheless, this challenge can’t be met without the support of the residents of this city. This study intends to evaluate the commuters’ willingness to pay (WTP) for an improved and better public bus service quality in Dhaka city. It also attempts to explore the factors affecting the commuters’ WTP amounts. In order to accomplish the study’s goals, a stated preference survey was designed to enquire into the whys and wherefores of female passengers’ harassment on public buses and also to prefer some influential service quality features. WTP values of respondents were calibrated using binary and ordinal logistic models, and these models were developed using SPSS version 26. The results indicate that the majority of respondents were willing to pay more for better service facilities, and they point to security as the most important factor in determining how much extra fare commuters are willing to pay. The results also demonstrate that commuters’ WTP amounts are highly influenced by the respondents’ monthly income. Results from this study have important policy implications, such as protecting women’s safety on public transportation and taking commuters’ socio-demographic characteristics into account before enacting any legislation or increasing fares.

Research and implementation on multi-dimension trust evaluation method with web service

Integrating with practical e-commerce application, this paper introduces a novel multi-dimension evaluation method to depict and calculate the trust values. The multi-dimension evaluation metrics include functional and nonfunctional properties and corresponding weights. The continuous measurement values and the Markov chain mechanism are adopted to compute the trust value and detect the malicious behaviors. The current evaluation has larger influence factor on the next transaction behavior. A trust model is implemented with web service which consists of publication, filtrating, calculating and storage center. It is easily extended and the user only defines each property and its weights according to specific requirements, then the trust values are got. In order to conveniently manage and avoid the dead-lock, some constraint rules are proposed. The results show that the method based on multi-dimension can reflect objectively the dynamic change of trust values.

Multi-Feature Fusion Based Relative Pose Adaptive Estimation for On-Orbit Servicing of Non-Cooperative Spacecraft

On-orbit servicing, such as spacecraft maintenance, on-orbit assembly, refueling, and de-orbiting, can reduce the cost of space missions, improve the performance of spacecraft, and extend its life span. The relative state between the servicing and target spacecraft is vital for on-orbit servicing missions, especially the final approaching stage. The major challenge of this stage is that the observed features of the target are incomplete or are constantly changing due to the short distance and limited Field of View (FOV) of camera. Different from cooperative spacecraft, non-cooperative target does not have artificial feature markers. Therefore, contour features, including triangle supports of solar array, docking ring, and corner points of the spacecraft body, are used as the measuring features. To overcome the drawback of FOV limitation and imaging ambiguity of the camera, a "selfie stick" structure and a self-calibration strategy were implemented, ensuring that part of the contour features could be observed precisely when the two spacecraft approached each other. The observed features were constantly changing as the relative distance shortened. It was difficult to build a unified measurement model for different types of features, including points, line segments, and circle. Therefore, dual quaternion was implemented to model the relative dynamics and measuring features. With the consideration of state uncertainty of the target, a fuzzy adaptive strong tracking filter( FASTF) combining fuzzy logic adaptive controller (FLAC) with strong tracking filter(STF) was designed to robustly estimate the relative states between the servicing spacecraft and the target. Finally, the effectiveness of the strategy was verified by mathematical simulation. The achievement of this research provides a theoretical and technical foundation for future on-orbit servicing missions.

基于Web Service的OSA接口标准性能扩展研究

首先介绍了Parlay/OSA标准规范,比较技术规范的两类实现技术,并就规范技术的不足扩展了适合多媒体个人移动业务的基于Web服务技术的计费业务性能特征实现体系。

基于XML的Web Services GUI的实现

基于XML的W eb Service GUI具有便于编写和创建的优点,因此提出了一种基于XML技术(如X form,XS-LT)创建的W eb Service GUI体系结构,该模型很好地支持了在W eb浏览器中直接调用W eb Service,对其拥有的UI特征和相关的XML文件作了详细的分析,并对W SGUI作出了展望。

Distributed collaborative CAD system based on Web Service

This paper presents a novel Web Service based distributed collaborative CAD system employing feature as its collaborative design element and uses XML to define feature operations and communication protocol between the server and the client. To reduce network load and increase response ability of the system, the feature information is updated incrementally on the client. The system supports collaborative designing on heterogeneous platforms. Its framework and communication protocols are analyzed in detail. The experimental results from the developed prototype system showed that it can effectively support collaborative design under the distributed environment.

FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.

The detection method of low-rate DoS attack based on multi-feature fusion

As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics of a low average rate and concealment.With features extracted from the network traffic,a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper.An attack feature set containing the Acknowledge character(ACK)sequence number,the packet size,and the queue length is used to classify normal and LDoS attack traffics.Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor(KNN)classifier separately,and to obtain the decision contour matrix.Then a posteriori probability in the matrix is fused,and the fusion decision index D is used as the basis of detecting the LDoS attacks.Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.

特征模型驱动的Web Services组装方案及其工具支持

Web Services正逐渐成为主流的平台独立的软件构件,并广泛地存在于INTERNET分布式环境中,通过组装Web Services生成系统的开发方法正在逐步引起人们的关注并受到重视,但目前还没有提出一套相对成熟的、系统化的组装方案用于指导Web Services组装系统的整个开发过程.将特征模型作为贯穿整个Web Services组装过程的模型工具,利用它驱动Web Services组装系统的需求分析、流程建模、组装、部署和执行,提出了“特征模型驱动的Web Services组装”解决方案,并结合一个具体的实例对FWSC(feature model driven Web Services composition)方案的基本思想、原理以及关键步骤进行了介绍.该工作可以有效地提高Web Services组装系统的建模、开发速度和质量,并增强Web Services组装系统在需求发生变化时的动态调整和演化能力.

Language Features and Culture Features on Short Message

Mobile phone is regarded as"the fifth media"after newspaper,radio,TV and the Internet.The mobile phone short message further highlights the importance of written signs in communication".The thumb revolution"is eagerly anticipating one kind of trend by the hand replace of mouth,sound substitute for the quiet around us.My paper will analyze the language features and the culture features of mobile phone short messages which are written in Chinese and English.

Event-based Two-stage Non-intrusive Load Monitoring Method Involving Multi-dimensional Features

This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.

Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.

Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

Enhanced Cloud Service Provisioning for Social Networks

In the era of cloud computing and social networks prosperity, new requirements rise up for cloud services that meet social network’s needs. Several cloud service providers deliver social network services for cloud services users. The aim of this paper is to develop a framework to pick the best cloud service providers from group of available providers based on many Quality-of-Service (“QoS”) criteria attributes in order to enhance efficiency, accuracy and service provisioning. This paper also aims to provide a classification for QoS criteria attributes, which is divided into five main attributes and sub-attributes, helping in enhancing ranking process in the provided framework.

Planning Strategy of Tourism Service Towns under the Land and Space Planning System:A Case Study of Taiping Town in Nanchang City

In May 2019,the construction of the land and space planning system was officially launched.The Ministry of Natural Resources of the State Council issued the Notice on Comprehensively Implementing Land and Space Planning,which clearly requires all regions to comprehensively launch the land and space planning and puts forward specific work requirements[1].Later,in order to guide the preparation and implementation of land and space planning,various technical standards were successively issued.The dual-assessment technical guidelines,land use classification standards,and the compilation guidelines for provincial,city and county planning were gradually submitted for review and trial implementation.In this context,there have been more and more planning theories and practical studies based on the background of land and space planning.For tourist service towns,how to protect the style and features of the towns under the land and space planning system and preserve the local characteristics is a problem that needs to be solved urgently[2].

Web Service在配电网企业的实现与应用

Web Service作为最流行的远程调用技术,对配电网企业信息集成系统意义重大。本文针对配电网企业典型网络环境,提出了一种基于SOA的Web Service架构设计、特征模型和特征规范,应用于配电网企业信息集成系统建设。