Table of contents

P2P net loan platform is the emerging force of economic development. The value evaluation on data assets of P2P net loan platform plays an important role in distinguishing the value of the data assets, providing a reference to improve the value of the data assets, and promoting the future development of the P2P platform. By analyzing the characteristics of the data assets of the P2P net loan platform, this paper proposes to evaluate the value of the data assets by using the B-S model. In the process of evaluation, the analytic hierarchy process is used to confirm the value of the underlying data assets and improve the B-S model. Finally, we conduct an empirical study on five P2P net loan platforms by using the improved model, and make suggestions on how to improve the value of data assets of P2P net loan platform.

In the big data era, the intelligent transportation field also has begun to use big data technology to mine and analyze the massive traffic data, from which to extract valuable data information, for people to travel to provide convenience and for the relevant traffic decisions to provide an important basis. This paper mainly describes the intelligent transportation and big data mining, and analyzes the application of big data and data mining technology in intelligent transportation decision analysis system, which provides a new way to improve traffic congestion and relieve traffic pressure in big data era.

Is it accidental or fate that people meet each other in space and time? If it is fate, what kind of scientific model does it contain? Mining the scientific model implied in 'meet is fate' is to analyse the correlation between 'meet' and 'fate', and to reveal its dynamics mechanism. 'Meet' is a kind of space-time interaction, and 'fate' is the prediction of future social relation, so the correlation analysis between 'meet' and 'fate' can be modelled as dynamic correlation analysis between space-time interaction and social relation of human group behaviour. How to achieve that from the original space-time behaviour data is a challenge. To address it, this article proposes a theoretical framework based on the theories and methods about geographic information science and complexity science. This article presents this framework for the first time to provide a feasible solution to verify and study this philosophical view of 'meet is fate' scientifically, and explores a meaningful research direction for the inter-cross application between geographic information science and human dynamics at the age of mobile big data.

In order to solve the problem of learning resource description in big data environment, this paper proposes a learning resource description system based on metamodel, that is, to establish a set of metadata model that is not only in line with the standards of learning resource description, but also has extensibility. By referring or mapping to the element set of the common metamodel, the elements of the metadata model of various resources are obtained to achieve the compatibility between different metadata models. This paper combines the learning resource model in the context of big data, and analyses the transmission functions of foreign language and Chinese medicine culture, including communication of language, communication of Chinese medicine culture, communication of Chinese medicine trade and communication of Chinese medicine financing.

With the application of the Internet of Things in oil production, a large number of equipment parameters and environment parameters in production process can be collected. The data can be applied to production equipment troubleshooting and product quality prediction. Prior gasoline quality testing has strict requirements for equipment and operation, but with a low efficiency. The paper applies RNN in a new situation and proposes a gasoline production quality prediction model fused with dimensionality reduction which is based on improved RNN, to achieve the real-time monitoring for gasoline production quality. Since the collected data is time-series data, the data features are complex, high-dimensional, and contain a large amount of noise and outliers. Therefore, the data needs to be cleaned, and feature engineering is used to achieve feature selection and dimensionality reduction. The model is trained and validated by an actual data set of gasoline production in an offshore petroleum field. The results show that the established prediction model fused with dimensionality reduction technique and based on improved RNN can predict the quality of gasoline products in real time and ensure a high accuracy.

In order to capture the characteristics of carsharing users, we applied a data-driven method to classify the users into typical clusters according to the contributions to the carsharing companies. The data used in this study are collected in the order information from the carsharing company in Beijing, China. The RFM model is employed to determine the influencing factors for clustering analysis. Accordingly, the most recent consumption, consumption frequency and spending are adopted to represent the user value. The users can thus be classified into three groups, named as potential users, high value users and the loss of users. It is expected that the results can improve the user management of the carsharing company.

The increasingly massive amount and open access of literature provide a data foundation for technology insight based on big data analysis. This paper proposed a new technology insight framework -Technology Dependency Graph (TDG). Firstly, an adversarial multitask learning and distantly-supervised learning were applied to extract the technology entity and dependency relation with limited labeled sample. Then, a TDG was constructed with the entities as vertices and the dependency relations as edges. A TDG contains rich and valuable semantic information which represents the support, contribution or relying on relationship between technologies. At the same time, the social network properties of TDG allow researchers to analyze and mine hot topics, key technologies, and technology architecture by using network theories, methods and tools. In the case study, the TDG of DSSC (dye-sensitized solar cell) was constructed. Furthermore, the technology dependency architecture for DSSC was constructed according to a spanning tree out of the TDG, which provides a global perspective for the research of DSSC.

Automatic text summarization is one of the research hotspots in natural language processing (NLP). Recently, there has been a considerable research interest in summarization. While most of the current researches about automatic text summarization are only based on word embedding features, which has the problem of too few text features and can't effectively utilize some other features of the text. In this paper, we propose a novel deep learning model called Document Summarization using Word and Part-of-speech based on Attention Mechanism (WPABS). We employ the word embedding and part-of-speech embedding to make full use of the features of the text and evaluate our model on English datasets Gigaword and DUC-2004. The experimental results show that our model is better than most methods. Also, compared with WABS(Word Attention-Based Summarization), our proposed model performs better. It is proved that it is useful to combine the word and part-of-speech.

The popularity of using information technology in class has consistently drawn a great deal of attention from researchers; however, few have focused on learners and explored this phenomenon from learners' points of view. This paper reports research which aimed to investigate university students' perceptions of using technology in foreign language lessons. 289 Chinese tertiary students' views on using technology were collected through Padlet promptly after they had experienced the use of a series of technology tools in class, which ranged from multimedia to tools for communication with students' mobile devices. The data were mainly qualitative and analyzed with thematic coding analysis. The results indicate technology's numerous pedagogical benefits to these learners and the findings from this study could be of reference to teachers who wish to attempt technology-implemented classes.

The residents electronic health records of 8 municipalities of Guangdong province as of December 2017 were extracted. Through data analysis, the author finds out the characteristics and problems of the collected data, which are mainly manifested in the following aspects: high data missing rate, low accuracy and low authenticity of data, erroneous and illegal data caused by incorrect data input, different representation methods caused by data from different sources, inconsistencies and non-compliance with the integrity of references. And the corresponding solutions to these problems are proposed. Approaches to improve standardization, integrity, accuracy and authenticity of data, were also discussed in this paper to promote the sharing and utilization of medical information resources. It would help to build an interconnection and interoperability platform for health big data platform.

Accurately grasping electricity consumption behaviour and trend in different period can provide decision support for electric resource scheduling. It is necessary to analyze the electricity consumption time sequence. To serve the purpose, a clustering method called VDSI (Variance of Difference between two Sequence Items) is presented by analyzing the typical daily electricity consumption. In the method, time sequence of electricity consumption is split into sub-sequences based on a predefined time window. Firstly, any two time sub-sequences are converted to a new one, in which any item is the difference between items of the two subsequences. Then, the variance is used to measure the distance of the two sub-sequences. Finally, hierarchical clustering algorithm is used to cluster sequences in different time window. By comparing with traditional sequence clustering, the VDSI can obtain more accurate clustering results.

With globalization, the international standards of the companies have varied a lot. When a company wants to open new international branch companies, a suitable model has to be formulated so that the specific number and the location of each company can be optimally determined. In this paper, we try to contribute to such effort. First, a multi-objective programming is established to be based on main influencing factors such as each city's economic level. Then an adaptive particle swarm optimization model is utilized to locate branch companies. Due to constant renovation of communication mode, a set covering model is built by new influence factors such as submarine cable communication. Finally, using the penalty function ant colony optimization algorithm obtains an approximate optimal solution. The experimental results effectively demonstrate the proposed models' feasibility, power, and usefulness.

The advent of the big data age provides an effective tool for citizens who can better participate in the implementation of public policies. Citizens' participation in the process of public policy implementation is conducive to the better realization of the objectives of the subjects of public policy implementation, to the implementation of the "bottom-up" public policy model, to the maximization of public interest and to the citizens' needs to adapt to public policies. However, the enthusiasm and degree of citizen's participation in the implementation of public policy are relatively low, because of some subjective and objective constraints in the process. We should cultivate citizens' ability and consciousness to participate in public policies, establish a two-way interactive mechanism between the government and citizens, and strengthen the information disclosure in the process of public policy implementation.

This study explores the influence of consumers in the tea ceremony field through the space atmosphere environment, which affects the operation of the tea house operators. Through literature discussion, it is found that the factors affecting the business style of teahouses key factors are: space atmosphere, experience marketing, place attachment, happiness. After empirical investigation, it was found that the spatial atmosphere has a positive correlation with experience marketing; experience marketing has a positive correlation with place attachment and happiness. Place attachment has a positive correlation with happiness. The results of this study specifically identify the factors affecting the operation and influence of modern teahouses.

Discriminant analysis is an important analytical method in cryptanalysis. When only known ciphertext, the encryption algorithm for identifying and classifying is an important part of distinguishing analysis. In this paper, a random forest classifier is used to classify eight block ciphers of ECB mode and eight block ciphers of CBC mode. By designing a feature based on ciphertext recombination and location-specific, the results show that the designed features can effectively extract the information of ciphertext data. In ECB mode, 8 algorithms can be successfully classified with an accuracy of more than 87%. In CBC mode, it can also be classified with higher accuracy than random.

Age classification and regression are two main approaches to age prediction in social media, and these two approaches have their own characteristics and strength. For instance, the classification model can flexibly utilize distinguished models in machine learning, while the regression model can capture the connections between different ages. In order to exploit the advantages of both age classification and regression models, a novel approach to age prediction is proposed, namely joint learning for age prediction. Specifically, an auxiliary Long-Short Term Memory (LSTM) layer is employed to learn the auxiliary representation from the classification setting, and simultaneously join the auxiliary representation into the main LSTM layer for the age regression setting. In the learning process, the auxiliary classification LSTM model and the main regression LSTM model are jointly learned. Empirical studies demonstrate that our joint learning approach significantly improves the performance of age prediction using either individual classification or regression model.

Topological data analysis is a method of extracting shape information of big data by means of algebraic topology in mathematics. Persistent homology is a very important method in topological data analysis. It constructs multi-scale simplicial complexes (also called filtration) to approximate the underlying space of the data set. By studying these simplicial complexes, the topological features of each dimension of big data are summarized. However, it does not give us the uncertainty of each simplicial complex to approximate the underlying space of the data set. This paper defines an entropy called persistent density entropy, which gives the uncertainty of each simplicial complex approximating the underlying space. The examples demonstrate that it is able to find the best simplicial complex that approximates the underlying space and can be used to detect outliers to a certain extent.

Real-time strategy (RTS) games simulate battles between large numbers of units and pose significant challenges to artificial intelligence which are complex areas of confrontation. In the field of RTS games, confrontation planning under uncertainty remains an unsolved challenge. There are two main types of uncertainty in RTS games. The first is the partial observability of the game, causing uncertainty. Second, there is uncertainty as the game is against the game and the player cannot predict what the opponent will do. This paper uses the Bayesian model as a logical alternative to solve the uncertainty caused by the confrontation game. The experimental results show that the proposed model can effectively solve the tactical recommendation problem in real-time strategy games.

In realistic scenarios, an age regression model learned from one social media (named the source media) generally performs rather poorly when it is tested on another social media (named the target media). In this paper, a textual adaptation approach is proposed to cross-media age regression which aims to improve the age regression performance by exploiting textual features in the labeled data from the source media and unlabeled data from the target media. The basic idea to achieve this lies in the fact that many textual features are shared by the data from both social media. Specifically, two views generated by random subspace generation (RSG) are leveraged to train two separate regressors in a co-training algorithm for adding automatically-labeled samples in the target media. Moreover, we tackle the confidence evaluation challenge in co-training by the query by committee (QBC) approach. Empirical studies demonstrate the effectiveness of the proposed approach to cross-media age regression.

In order to ensure the safety of railway station interlocking system, the subsystem for route creating is used as a case study, and a formal modeling method based on multiple composite scenarios analysis is provided. In the proposed method, multiple UML sequence diagrams are adopted to specify requirements of the system, and consistent requirements specification are acquired by combining pre-condition and post-condition of object constraint language with domain knowledge to analyze conflicts in multiple UML sequence diagrams. Besides, a model conversion method for transforming behaviour sequences to finite state process model is proposed, despite these behaviour sequences are usually synchronous, asynchronous, concurrency and alternate, which interacted with each other in UML sequence diagram. Finally, an FSP model for system's formal model is generated by combinatorial operation, it is conformed to system's functional requirements. The correctness and feasibility of the proposed method have been confirmed by formal model generation for railway station interlocking system.

This paper presents a study of the efficiency of the logistics industry in Wuhan with the use of data envelopment analysis. Such a study bases on the careful consideration of specific input variables and the output variables. The former includes the number of trucks, the transportation and warehousing and postal fixed investment, the urban road area, and the urban road length, and the latter consists of the freight volume and the freight turnover. The data are in the period from 2004 to 2016. The results demonstrate that Wuhan realizes efficient inputs and outputs in 2004, 2005, 2009-2011, 2014, 2016. In addition, there are redundant inputs in other years. Therefore, this paper puts forward some suggestion on improving efficiency of logistics industry in Wuhan.

The definition and characteristics of big data are summarized. From the era of big data, the production of command and control system data leads to 3 issues of data storage and processing, situation generation and decision-making, and data visualization. It takes the construction of a big data center and discusses the basics. The accusation framework and the coping strategies adopted by cloud computing under big data conditions provide reference for the application of big data technology in command and control systems, and point out the development trend of the next instruction system.

With the development of network technology and resource virtualization platform such as e-Science, workflow technology has expanded its coverage from business management to scientific computing. It cooperates with scientists from different fields to achieve complex data processing. The workflow model stands as the core of the workflow management system, and its quality directly affects the execution ability of the whole system. Workflow modeling technology has been widely studied, and many kinds of modeling methods have emerged. This paper proposes a modeling method based on spatio-temporal topology which fuses data flow and control flow together, and presents a workflow management architecture.

The tone captures how the leaders of listed company confidence to the performance and tone changes correlate with revisions of future outlook. Current predicting stock market behaviour is using numerous quantitative financial factors. Recent publications have demonstrated that some implied sentiment information such as tone changes in annual reports can be successfully used to predict the stock price in the U.S market. However, the investors' reflection to the tone changes in annual reports in Asia market, especially in Asian financial center Hong Kong, is still unknown. In this paper, the chairman's statement tone changes in annual reports from the Hong Kong market have been studied in the first time. This study evaluates three different tone changes methods and combing with financial indicators to predict the stock price. The experimental results prove that the tone changes of annual reports can predict the stock price in the long trend, which implies the low market efficiency in Hong Kong. Moreover, some experiments have been investigated whether the financial crisis can be predicted from the chairman's tone changes.

This paper built a scale of Chinese traditional cultural value (CTCVS) that based on the previous research and interview results provided by the elderly interviewers. Then a questionnaire was conducted to question the elderly citizens living in China about their perception of CTCVS and scales of their rural vacation destination decision-making, which contained two parts: rural vacation destination choice scale (RVDCS) and perceptions of barriers to rural vacations scale (PBRVS). With principal components analysis and analysis of variance (ANOVA), results showed that Chinese traditional cultural values have an impact on both the choice of destinations of the elderly rural vacation, and the perception of barriers to the elderly rural vacations. In this study, some recommendations for improving marketing techniques targeting the elderly who might have a vacation in rural areas were put forward for rural resorts and related enterprises.

Why the United States took the lead in adopting trade protection policies is the most critical issues in studying Sino-US trade relations. This paper answers this question by constructing an economic game model and integrating political game ideas. That is to say, in the economic perspective, if the United States predicts that the protection strategy may bring great benefits to the country, adopting a free strategy will bring more benefits to the country, and it is expected that the protection measures taken by the country will cause the damage caused by the retaliatory actions of the other party to be small. The United States has a strong incentive to adopt trade protection. The political point of view is mainly the reasons for the United States' persistence of the "terrorism" mentality of the second country, the "first US" belief, and the intention to hold the Chinese technology throat.

In recent years, the rapid development of the knowledge payment industry has been widely concerned. Based on the current situation of college students' perceived value to customers of knowledge-paying products, the paper collected 395 valid questionnaires by questionnaire survey method, exploring the effect of perceived value on satisfaction and purchase intention. The results show that the main factor that affects consumers' intention to buy online knowledge-paying products is perceived value. Among them, functional value, emotional value, and social value all have positive and significant effects on the purchase intention, and the higher the perceived value and satisfaction are, the stronger the purchase intention is. In the perceived value, functional value has the most significant effect on the purchase intention.

As the total output value of Chinese animation industry rises year by year, Chinese animation companies need to seek their own suitable development path to ensure consumers' satisfaction with animation brands, thus improving the economy benefit of the company. In view of this, the research collected 387 valid samples through the questionnaire survey to explore the antecedents of animation brand satisfaction. The research results show that the animation content, market performance, and brand personality of animation brands have significant positive impacts on the perceived value and satisfaction of animation fans. Among them, the animation content has the most significant impact on perceived value and satisfaction, while perceived value has some mediating effect.

Firstly, the risk assessment system of terrorist attacks is established, and the relevant indexes of terrorist events are screened, combined and quantified. This paper gets the index weights by AHP method, and the degree of damage is calculated. Then it uses DBSCAN method to cluster the sample data and optimize the parameters Eps and MinPts. The results show that potential terrorist organizations or individuals are predicted, and this method can be applied to other similar data analysis problems.

The network resources in webs of informal relationships between employees have an important influence on the working lives of people. This study examines the role of social networks in the employee intentions to quit from a quantitative approach. It investigates action network and friendship network of employees from two high-tech companies. The results indicate that employees who are less central are less committed employees who will be more likely to "fall off" the organization. From the network analysis, the likelihood of turnover could be predicted.

According to statistics, crimes are not random in spatial and temporal distribution. The key to predicting policing is predicting in advance when and where a crime may occur, and providing a reference for preventive measures of police officers. In this paper, a hybrid model of LSTM and STARMA is established. Crime data is complicated. It can be decomposed into trend components, seasonal components, and random components. The LSTM model is established for the trend components and the seasonal components . The STARMA model is established about random components. It solves the problem that the STARMA cannot be modeled on nonlinear or non-stationary data. Verification results show that the model is effective in crime prediction.

There have been many researches on stock trend forecasting in existing literatures, however, there is little research on the actions which managers will take basing on stock price change. This article is based on this point to study the stock market and the actions that manager will take. Based on the proposed automatic selection model, we put it on the stock market to study whether the managers will sell shares in the company with the change of stock price. The results is that the selection of model has good generalization ability, and when the predicted value is higher than the actual value, the manager will sell off the company's shares, and vice versa. However, the accuracy of prediction will be further improved by combining factors such as company size and policy environment.

With the progress of modern society, the meeting plays a very important role in communicating information, exploring the latest development in a certain field and discussing the solution of the problem, and it is a popular hope to arrange meetings by computer instead of person. This paper introduces the research on the establishment of decision algorithm for automatic meeting arrangement system. The meeting organizer should not only consider meeting time and venue, but also consider representatives' satisfaction, which includes two aspects: the first is the room cost, and the second is whether or not to meet the reservation requirements. Considering these factors, a linear programming model is given in this paper. With this model, the reasonable allocation of rooms and the optimal allocation of vehicles can be realized. On the other hand, the appropriate meeting time and venue can also be determined.

In our daily life, there are still some funds that are slow to circulate and the amount is relatively large, that is, the balance of stored-value cards, such as public transport cards, subway cards, etc. We can see that the stored value card plays an increasingly important role in people's lives, so the research on that card is forward-looking. In this article, the author intends to design a new type of financial product for the funds that flows relatively slow. It will not only integrate social resources and create considerable returns, but will also generate a certain percentage of income. In return, let each stored value card holders obtain profit. The specific mechanism of this financial product will be designed in this article. In order to find the experimental object, the author decided to investigate the campus card, collect and analyze the data, use professional statistical software in research, and plan to design a financial product suitable for students.

At present, mode selection of communications countermeasure reconnaissance is mainly determined by the method of "attempt to switch, optimize the effect". In view of the method's limitation, this dissertation puts forward four mode selections of communications countermeasure reconnaissance, which are abstracted as uncertainty decision-making, based on the mathematical method of decision theory. In various tactical situations, four optimal decision-making schemes are proposed in accordance to four criteria, including equal possibility criterion, max-min criterion, max-max criterion and regret value criterion. The results of calculation examples show that the approach is valid, and provides the optimal decisions under different tactical backgrounds.

The exchange rate is the currency ratio between countries, which determines what standard is equitable for the distribution of the benefits of economic transactions between countries. There are many factors affecting the change of RMB exchange rate, such as inflation rate differential, commodity price, economic growth rate differential, foreign exchange reserve and international balance of payments. In this paper, the least square method is used to analyze the exchange rate's influencing factors. The results show that the balance of payments, commodity prices and the dollar against the RMB exchange rate has a negative correlation. Inflation, gross domestic product, foreign exchange reserves and the dollar against the RMB Exchange rate has a positive correlation.

We present numerical simulation results of thermally induced gas flow in the gap between a ratchet surface and a moving wall using the recently proposed ellipsoidal statistical Fokker-Planck (ESFP) algorithm. In this algorithm, the evolution of individual molecular velocity is modelled as a continuous stochastic process, and the corresponding Langevin equation is implemented in a particle Monto Carlo manner. The simulation aims at the calculation of the thermally induced shear stress acting on the top wall. In the transition regime, the mechanical power output and thermodynamic efficiency are calculated for various moving speeds of the upper wall and inclination angles of the specular surface. The proposed method facilitates the evaluation of the efficiency of converting transverse heat flux across the channel into the tangential propelling force for this device. The dependence of efficiency of the inclination angle is identified, and the optimal efficiency is found to be not much different from that in the free molecular regime.

since 2015, service sector has being the largest part of Jiangsu province's economy. Under the New Normal economic situation in China since 2010, Jiangsu province is in urgent need of upgrading its industrial structure. Firstly this paper analyzes the opening level in Jiangsu province's service sector from the aspects of the foreign direct investment and the service trade. Then we analyze empirically the impacts of the opening level in the service sector on upgrading it's industrial structure based on the VAR model by on the samples from 1995-2015. In conclusion, (1)We find that the opening level in Jiangsu province's service sector in the aspects of foreign direct investment and service trade is not very high. (2)The openness in Jiangsu's service trade has some positive impacts on the industrial structure upgrading to some extent but not very remarkable. (3)The positive impacts on Jiangsu's industrial structure upgrading by the shock of openness to foreign direct investment is stronger and last longer than that by the shock of openness to service trade. In the end, it is suggested that Jiangsu province should continue its opening-up in the service sector, especially to promote the efficiency of utilizing foreign direct investment and developing its service trade.

In this paper, an effective feature representation named color pairs is proposed, which is robust to illumination and viewpoint changes. It was combined with Scale-invariant local ternary pattern (SILTP) texture features used for person re-identification. And we improved the method of Cross-view Quadratic Discriminant Analysis (XQDA) by adding a close function. It improves the performance of discriminant matric. This method were compared with other hand-crafted methods of feature extraction and measurement matching, then was proved to be advanced.

In order to measure the distance between our vehicle and the target vehicle by monocular vision and eliminate the estimation error bring by changing of vehicle pose, we propose the distance estimation method based on the vehicle pose information, which can be used to eliminate the error of distance estimation effectively cause by the change of the pitch angle and roll angle of unmanned vehicle. In addition, the pose information could also help us estimate whether the vehicle is in a slope, thus engage in distance estimation for the vehicles. Several groups of data was collected for the experiments. And the result prove the validity of the algorithm in distance estimation.

With the development of information technology and sensor technology, we can get and use lots of multi-sensors data more frequently, which acquired from military action. It has become difficult for users to find information that satisfies their needs, so it has become a great challenge for information science and technology that how to organize and process the massive documents quickly, exactly, and fully. This paper propose a method of combat data management using ArcSDE spatial data engine with ESRI and relation database, based on analyzing all kinds of method of combat data management. The authors developed combat data analysis system based on ArcEngine, implementing data visualization, put data into SDE database and Dynamic display. The paper explains some interfaces in AE briefly, and the system has achieved the anticipated effect under the test.

This paper mainly contains the following three aspects of research. Firstly, this paper starts from the definition of advertisement click prediction, analyzes the distribution and characteristics of the dataset and preprocesses the dataset. On this basis, based on the understanding of advertising and the characteristics in practical application, ten different types of features were extracted. Secondly, the paper first uses SVM, naive Bayesian model, decision tree model and neural network model to predict the click-through rate and analyze their shortcomings. The experimental results show that the SVM model method used in this paper can obtain better prediction results than other methods under the same characteristics.

This research aims to find the determinant of user research and persona building :mobile games to design questionnaire. The method presented in this paper is comparing tlsimilarities and differences of mobile games and PC games to conclude the characteristics of mobile games. Besides, we formulate the principles in cognitive psychology that might guiethe design of questionnaire of mobile games user persona. In this study, we conclude thmobile games are easier to operate, interrupted and have limited development of hardwadevices and storage comparing with the PC games. In addition, mobile games are easier 1begin and need more SNS support. Moreover, this research analyzes the cognition fields for thmobile games in the five sense of people, human ecological perception and human aesthetperception.

In the field of forensic science, the wear area of planar shoeprint is a useful information for analysis of criminal suspects. However, the identification of shoeprint wear areas is often subjective to the expert's personal experience. In this paper, considering the characteristics of planar shoeprint images, a segmentation algorithm based on mean-shift is proposed to process planar shoeprint image to get the wear area. Firstly, the multiplicative intrinsic component optimization method(MICO) is employed to pre-segment the image. This method combines the level set with the offset field correction method to segment the outline of the unevenly printed shoeprint image. Secondly, we use mean shift to segment the image. Finally, we compare the actual wear area with the segmented wear area. It is found that the segmentation effect is good. By pattern recognition method, the wear area of the shoeprint is segmented to provide more objective technical support for detecting cases.

Augmented Reality (AR) has developed rapidly in recent years and is widely used in education teaching due to its strong sense of immersion and interaction. In this context, this paper analyzes the key technologies of AR and designs a complete display system of human body tissues and structures with interactive functions based on Unity3D and Vuforia. Finally, this system is packaged into an android app. The running results show that the app has a strong sense of immersion and interaction, and it also has strong expansibility so that we can change the human structure model into any other models.

We present an unsupervised method for co-segmentation of a set of 3D shapes from the same class with the aim of segmenting the input shapes into consistent semantic parts and establishing their correspondence across the set. Starting from meaningful pre-segmentation of all given shapes individually, we construct the correspondence between same candidate parts and obtain the labels via functional maps. And then, we use these labels to mark the input shapes and obtain results of co-segmentation. The core of our algorithm is to seek for an optimal correspondence between semantically similar parts through functional maps and mark such shape parts. Experimental results on the benchmark datasets show the efficiency of this method and comparable accuracy to the state-of-the-art algorithms.

The representation relationship between the user perceptual knowledge and the design knowledge is discussed. The model of transformation of the user perceptual knowledge and the design knowledge is built. And the key technologies of it are analyzed. The four design dimensions are proposed, namely, the design process dimension, the design goal dimension, the design organization dimension and the design starting point. Taking the shape design of vacuum cleaner for example, this paper utilizes the oral analysis method and the semantic differential method to analyze the transformation from the user perceptual knowledge to the design knowledge. The model of the transformation is validated. It is helpful for improving the computer aided industrial design and the network collaborative design of the electric appliance products

To solve the problem of operational capability evaluation of weapon equipment system-of-systems (WESoS), a capability-based method is proposed based on an improved generalized grey incidence analysis (GGIA) model. A hierarchy description framework is proposed, which establishes a mapping relationship between weapons and operational capabilities. Four types of capability requirement functions are proposed to assess the performance of weapons on tactical indicators. The deficiencies of the traditional GGIA model are analyzed, and the improvement is proposed to calculate the satisfaction degree of weapon equipment to a certain operational capability. Then, regardless of other subjective factors, the contribution degree of a certain operational capability to WESoS is determined combined with the maximum entropy model and genetic algorithm. Finally, the feasibility and effectiveness of the proposed algorithm are verified by experimental comparison.

At present, most hospitals are facing the situation of overcrowding, and how to speed up the patient's medical service speed is an urgent problem. In order to solve the problem of long waiting time and low utilization rate of medical resources in outpatient, a priority scheduling algorithm based on bottleneck analysis in complex network is proposed. According to the patients' tasks, the algorithm sets up two graphs of medical service and medical resources, and projects it into a complex network model of medical resources, considering the network characteristics such as betweenness, network efficiency, medical service and medical resource characteristics, the bottleneck of medical resources is calculated.Then, bottleneck medical resources and medical services related to bottleneck resources are determined based on bottleneck analysis. Finally, the patient scheduling is carried out according to the bottleneck priority rules. The rationality and superiority of the model and algorithm presented in this paper are verified by the example of an outpatient clinic in a certain hospital.

Under the background of the rapid spread of the Internet, a variety of English vocabularies learning models have emerged in mobile Internet terminals. On the one hand, its development inherits the traditional English learning theory. On the other hand, it also reveals its own uniqueness. The acquisition of human information is time-sensitive and the generalization of information is full of the horizons of language learners. The problem is how to effectively reduce negative impacts and improve learning efficiency. The EM algorithm can effectively filter English vocabularies and generate a mean value from a mixture of k different normal distributions, which makes English vocabulary online learning more efficient.

In order to explore the application of blockchain technology in business information system, the category feature and core technology of blockchain technology has been analyzed in this paper. Especially, the research has contained the data sharing interaction, tamper resistance, access rights and timeliness via combining the characteristics of business information system. The deployment application mode and miner distribution method were carried out after a lot of explored research, and some difficult problems that need to be solved were put forward too. Gratefully, the multiple advantages of blockchain technology have brought significant opportunities for business information systems to achieve the goal of acceleration and increasing efficiency.

In order to study the intelligent early warning methods for vegetables in facilities agriculture, taking radish root disease as an example, early warning model of the decision tree was constructed through agricultural vegetable planting data. The model intelligently analyzes the temperature, humidity and soil conditions in the greenhouse and gives an early warning result. Finally, the model was tested and has good prediction results. This method has important practical significance for early warning of vegetable disease, reducing the use of pesticides, improving food safety and reducing soil pollution.

Information technology such as Internet, Internet of Things and mobile communication is the infrastructure of information communication under network digital space. With increasingly completion of information technology, people can instantly access and get information in digital network space. The cognition and learning of the human brain can achieve real-time synchronization with cyberspace. Digital cyberspace is like an external brain and a public external brain, and they are part of human brain's memory and thinking. This paper defines the human brain, the external brain and the public external brain, and summarizes attributes and relationships of the three, moreover proposes human cognition and action models in the digital age to understand human behavior. This model provides an engineering cognitive perspective and thinking method for understanding human behavior and social activities in the digital age.

Human beings live in the physical space, think and judge in the information space, and act back in the physical space. Their thinking and judgment greatly depend on information accumulation and the environmental stimulation in their physical spaces. Therefore, their cognition and actions are formed under the guidance of both the physical space and information space. Based on the three-element engineering model, this paper takes the present characteristics of time to reinterpret the properties and relationships among the physical space, information space and out-structure space of information. The findings of this analysis are intended to provide a precise guide for people on their individual or collective life in society, especially as a result of the increasing nature of information processing ability in the present era. As well, the findings of this study are anticipated to provide some guidance for people's thoughts concerning the developing society, which they can use to understand the anticipated nature of society in the future.

With the development of smart phones and mobile terminals, mobile health would play an important role in the treatment of heart disease. This paper presents an ECG monitoring medical System based on mobile platform. This system Consists of Data Collection Terminal, ECG Monitoring Terminal, Health Records Management Platform,Mobile Terminal. ECG data is collected by Data Collection Terminal. Eventually, ECG data will be sent to the platform via Bluetooth and other communication technology. The doctor could feedback the diagnose to the user's mobile Terminal. Our main target is to provide timely and reliability services to patients and reduce cost and increase efficiency in home health-care, community health-care.

In recent years, China's science and technology have been rapidly developed, making Internet of Things technology widely used in various fields. As an important guarantee for the normal operation of the Internet of Things, electronic information technology is also very important for the stable operation and subsequent development of the Internet of Things. This paper mainly analyzes the application status of electronic information technology in the field of Internet of Things.

With the coming of the big data age, data mining attracts more and more attention from all trades and professions. Due to the vast computation cost of data mining, the public service platform for big data mining has become the urgent needs, especially for the model training tasks. In this way, how to perform this kind of task scheduling becomes critical. This paper focuses on the assignment of tasks on multiple computing resources to optimize the total operation time. Firstly, a task scheduling algorithm based on the greedy and genetic algorithm is proposed to set the computation resource requirement for each task. Moreover, a greedy strategy is used to decide the task operation order and the assignment mapping between the tasks and the computation resources. Finally, the proposed algorithm proves to be efficient by several experiments.

In recent years, the Chinese government has paid more and more attention to the construction of ecological civilization, and the governance of air pollution is one of the necessary links. Aiming at the problem of the time series prediction of air pollutants, this paper develops a time series prediction model based on deep learning method. In this paper, Beijing's hourly PM2.5 concentration information and weather information are used as input. Through GRU model, four models are trained according to the four seasons of spring, summer, autumn, and winter, and the effects of the four models on predicting the corresponding seasonal PM2.5 are evaluated by using corresponding test sets. After repeated experiments and constant adjustment of model parameters, the prediction error and prediction accuracy of the model are analyzed and compared, then the feasibility and advantages of this method are verified. The results delineate that the prediction accuracy of the model based on the GRU model is high, and the method is valid for the time series prediction of air pollutants.

A dynamic QR code quickpass terminal application system is designed in this paper, the system is a convenient receiving application system for QR code scanning and being scanned, the dynamic QR code (each bill a single code) can be generated and displayed based on the network and the real-time order information, replacing the commonly used forged static QR code patches, so as to improve the previous payment security environment and greatly reduce unnecessary losses for merchants and operators.

This article mainly focuses on how to improve the existing Human-Computer Interaction (HCI) design by considering the methodology of self-efficacy to let the users be more willing to interact with a novel system. The discussion is based on the fact that the proliferation of internet enterprises brings higher demand for better user experience because pleasant user experience could contribute to the promotion of their products. The question is that the existing principles of HCI design (especially display design) are based on the designers' understanding of how to improve user experience instead of the users' point of view. So this article has reviewed the theory of self-efficacy and the existing display design principles. Then the paper makes suggestions to improve the design of the user interface by increasing users' level of self-efficacy and evaluates the change of users' feeling during the interaction. At last, this article has concluded that considering users' level of self-efficacy could make the users more willing to try new products and increase their user experience.

A holistic solution for Traffic Burst Oriented Adaptive Resource Provisioning in Cloud (TRIERS for short) has been proposed to tackle the problem of maintaining performance scalability and service agility in the presence of sudden bursts of workload traffic. The TRIERS framework consists of three core components: workload classification by characterization, traffic prediction in class, and dynamic resource configuration and scheduling. Extensive experiments have been conducted to validate its practicality in terms of guarantee ratio (25.7% improved), resource utilization (5.6% improved) and total energy consumption (17.3% saved), indicating the effectiveness of TRIERS for adaptive resource provisioning in workload traffic burst.

With the development of social networks, services and applications based on social networks have become more and more abundant. While providing convenient services and applications for users, the providers have also accumulated many user information. How to use information to provide users with accurate and high-quality services is an issue that needs to be solved. The division of user groups is the basis for solving this problem. User preference is the important information for division, and clustering is the means of division. In order to solve the problem of user group partition efficiency in big data environment, this paper presents a new user preference similarity calculation method. On this basis, initial clustering based on Fast Unfolding algorithm and how to determine the attribution classes of update nodes based on the initial clustering results, the clustering method of incrementally preferring similar users is proposed. Experimental results show that the proposed algorithm greatly improves the division efficiency of user groups.

In this paper, a security patrol method using UAV (Unmanned Aerial Vehicle) [1] as a carrier was proposed to solve the problems caused by traditional patrol methods such as low patrol frequency, low patrol efficiency and high security patrol cost. The ZigBee[2] module (the chip using CC2530 of ti) was adopted to locate the UAV. Also, the Kalman filter algorithm was used to reduce the influence of noise on the airborne patrol system to improve the positioning accuracy. This system was equipped with a ground station. and users could select nodes at the ground station for planned flight. The data exchanged between the ground station and the UAV through the XBee S3B module[3]. The UAV transmitted the images captured by the camera to the ground station in real time through the Athreros AR9271 WIFI launcher for security personnel to check in time. The ground station also handled the situation on the spot. As the UAV reached the area with poor GPS signal, we used ZigBee networking to locate and enhance the accuracy of positioning. Security patrols with UAVs(Unmanned Aerial Vehicles) as carriers improved traditional patrol methods to some extent. After a certain amount of data analysis, the results show that the ZigBee positioning data processed by Kalman filter are reliable.

Spatial high utility co-location patterns mining takes pattern utility participation index as interest measure, but it still lacks standard method for calculating pattern utility participation index. This paper presents some reasonable definitions of spatial high utility colocation patterns based on the datasets of instance with utility value, such as Feature Actual Participation Weight, Feature Utility Participation Index and Pattern Utility Participation Index and many more. A basic algorithm BWHMA(high utility co-location patterns mining based on feature actual participation weight) is proposed. For improving the efficiency of algorithm BWHMA, one pruning algorithm FUPRA(based on feature utility participation rate) and one optimization algorithm FRPWA(based on feature actual participation weight) are put forward. Finally, a large number of comparative experiments have been done on both synthetic and real datasets. The experiments show that the proposed algorithms are feasible, correct and valid.

To standardize the authoring of user requirement specifications (URS) for manufacturing execution system (MES) proposed by manufacturing enterprises from users' perspectives, and facilitate a quick and effective communication between them and MES vendor or integrator, an approach for URS definition was introduced based on the ISA-95 standard, in which a method for requirement elicitation based on goal and scenario was combined. In this approach, a framework for MES problem space was constructed first from three dimensions: generic activity model for manufacturing operations management (MOM), manufacturing operations categories and supporting activities, and then a series of guidelines for standard application, scenario authoring and requirement arrangement were established to guide the users in seeking the needs and writing them in a normalized sentence form step by step. Additionally, a structure of the table of content for URS documentation was recommended to help the users organizing the textual requirement description. Finally, a case study for applying this approach was performed in a pharmaceutical company's example, which illustrates the application prospect in MES product selection.

As the popular area of educational data mining, course performance prediction is the prediction of the final performance of students in a course, assisting educators' personalized teaching and reducing the pressure of students. With proper features and algorithms specified, prediction models with high accuracy can be built. Course performance prediction for basic courses of universities requires feature selection from both subjective and objective aspects. GPA, grades of prerequisite courses, assignment scores and the inquiry count are selected as the objective features and the individual interest is specified as the subjective feature. The output of the prediction is the students' final performance divided into 5 levels. The Gaussian support vector machine, the polynomial support vector machine, BP neural network, random forest and logistic regression were employed as the classifier, with the accuracy and AP of the five algorithms compared. It is found that the Gaussian support vector machine combined with selected features can reach the optimal accuracy and AP, reaching 99%. With the Gaussian support vector machine applied, a course performance prediction model for basic courses of universities is proposed, which provides a novel method for the study on course performance prediction for basic courses of universities.

As the rapid development of the Internet and the e-commerce technology, more and more online shop based on e-commerce technology need a personalized recommendation technology to help them achieve their information filtering and automatic recommendation of goods. Therefore, how to analyze and mining these data, and understand the customer's preferences and preferred mode, then designed personalized websites to meet the needs of different customer groups, thereby increasing competitiveness, it has become imperative. On account of the deficiencies of traditional collaborative filtering, this paper uses a collaborative filtering based on preferences of user recommendation algorithm by comparison. Using Server log analyzes users how long on the page and how many times visiting the page, to find users are prefer to some product, so that we can make the relevant recommendation.

The precise segmentation of brain tumors is the most important and critical step in the diagnosis and radiotherapy of brain tumors. An automatic segmentation algorithm of brain tumor MRI image based on Tamura texture feature and SVM model is proposed in this paper. Firstly, the local grayscale features of four modal magnetic resonance images are combined with the Tamura texture metrics, which are roughness, contrast, directionality, and regularity. In this way, the information in the image is extracted as much as possible. Then, the known samples are put into the SVM model and classifier training is performed. Finally, other brain tumor images are processed with the trained SVM model. The performance of this method is evaluated using the 2013 BRATS test data-set. The encouraging evaluation results is obtained.

Vector Autoregression Model (VAR) is applied to study the relationship between China's fixed asset investment in the whole society and its influence factors in this paper. The main influencing factors of fixed asset investment studied in this paper are the ratio of money supply to GDP, total import and export and total population. The conclusion is that the ratio of money supply to GDP and the total population at the end of the year have significant negative impacts on fixed asset investment in the whole society. The fixed asset investment in the last period has a significant positive effect on the fixed asset investment in the current period, which indicates that the investment of fixed assets in the whole society has certain inertia and stickiness. In terms of granger causality, the ratio of money supply to GDP, the total population at the end of the year and total import and export are granger reasons for the fixed asset investment in the whole society.

The choice of the optimal clustering number (K_opt) is very important for forming the clustering results. As clustering is a kind of unsupervised learning method, it is still difficult to determine the K_opt. The clustering validity index (CVI) is an effective method for determining the K_opt and evaluating the clustering results generated by clustering algorithms. However, many of the existing CVIs cannot efficiently with overlapping datasets. In this paper, we propose WCH, a new cluster validity index for overlapping datasets. The WCH index is constructed based on features of inner-cluster tightness, inter-cluster separation and inter-cluster overlapping of datasets. The WCH index can effectively process datasets with high overlapping and can accurately find the corresponding K_opt. The comparison experiments are carried between our new WCH index and four existing CVIs on five simulation datasets. The experimental results have shown that the new WCH index is stable in forming the K_opt for many kinds of overlapping datasets.

In this paper, a streaming media live broadcast system is designed and initially implemented based on MSE (Media Source Extensions API). This paper selects HTTP-FLV as the server transport protocol and implements an HTML native FLV player. The streaming live broadcast solution achieved has better performance, lower power consumption and excellent user experience than the traditional Flash Player live broadcast solution.

In order to solve the vulnerability of the network structure in the real world, this paper constructs a P2P network model based on neighbor-neighbor lists. We propose two algorithms, network repairing and pruning, to improve the effectiveness and robustness of the network. Simulation experiments show that the proposed model and algorithms can effectively enhance the self-healing of P2P network.

In this study, a consensus-based robust clustering and leader election algorithm is presented for homogeneous Unmanned Aerial Vehicle(UAV) clusters. It can adaptively cluster and elect leader of each cluster of UAVs if any UAV can fail anytime and communication network changes dynamically. The algorithm is based on a distributed consensus algorithm, combining the sensing capabilities with the autonomous decision-making capabilities of the UAV to realize adaptive, scalable and robust clustering, leader election as well as group member management for UAV clusters.

In order to improve the efficiency of image information retrieval in multimedia information management system, a retrieval technology of multimedia information management system based on fuzzy directivity control is proposed. The retrieval multimedia information management system is decomposed with LGB to realize the image multimedia information feature compression, and the control constraint parameter model of the multimedia information management system is constructed. The fuzzy directivity control method is used to adjust the retrieval category adaptively, and the edge contour feature quantity of the image in the multimedia information management system is extracted. The fuzzy PID control algorithm is used to cluster and fuse the extracted edge contour features of multimedia images, which can realize the accurate retrieval and scheduling of the images in the multimedia information management system. The simulation results show that the proposed algorithm has high precision and zero frame difference in image retrieval in multimedia information management system, which shows good multimedia information management and system control ability.

Aiming at the problems of complex data sources, poor management level and blocked market information in the orchard, this paper constructs the index system of apple big data and designs the functional framework and technical framework. Through collecting heterogeneous data sources such as fruit tree cultivation, meteorology, soil, farming work, diseases and pests, market circulation and fruit quality for mining analysis, the big data application system of apple industry is established, which provides apple industry development planning, precise irrigation and fertilization of orchards, apple quality demand analysis, domestic and international marketing risk early warning and other functions for fruit farmers, agricultural organizations and governments. At present, the system is reliable and practical, and plays an important role in big data of fruit industry in China.

Named entity recognition is a fundamental task of Natural Language Processing and belongs to the category of sequence labeling problems. In the text, the named entity is the main carrier of information, which is used to express the main content of the text. Accurately identifying these contents is essential for implementing various natural language processing techniques such as information extraction, information retrieval, machine translation, and question and answer system. Named entities in business documents contain a lot of important information that can bring significant business value to the business. In this paper, we propose the method of combining Bi-directional long-short-term memory network and conditional random field, combining n-gram features and character features, and introducing attention mechanism to identify the tenderee, bidding agent and project number three entities in the bidding documents. Compared with the LSTM, BiLSTM can obtain the context information better and extract more features. The CRF uses the features obtained by BiLSTM to decode and obtain the final labeling result. In the comparative experiment of the collected data sets of 20,000 bidding documents, the BiLSTM-CRF model proposed in this paper can produce better labeling effect than other models and meet our expectations.

Blockchain is an innovative application model that integrates distributed data storage, peer-to-peer transmission, consensus mechanisms, digital encryption technology and other computer technologies. It is decentralized, secure, and Information disclosure. In the blockchain, digital encryption technology has a core position. The security of user information and transaction data is a necessary condition for the promotion of blockchain. The development of cryptography technology promotes and restricts the further development of blockchain. This paper outlines the infrastructure of blockchain, including the data layer, network layer, consensus layer, contract layer and application layer. The principles of encryption technology is introduced briefly, such as hash function, asymmetric cryptosystem, digital signature. The application of cryptography in all levels of blockchain is analyzed, including data layer, network layer, consensus layer, etc. It shows that cryptography runs through the whole blockchain system. The existing security problems of blockchain is analyzed, and the future research direction is expected.

Multi-service provider network traffic scheduling is an important research topic. Previous scheduling methods based on DNS resolves the issues of high latency and package loss due to cross-ISP access. However, there are still some problems like large bandwidth fluctuations and low bandwidth utilization in existing methods due to low scheduling accuracy. In this article, we propose a scheduling method based on application perception to solve these issues. Our scheduling method consists three components: application perception, traffic prediction of uplinks and network traffic scheduling based on DNS. It performs scheduling for each elephant flow application to improve scheduling accuracy. The experiment result shows that the application perception based method improves scheduling performance from both scheduling stability and bandwidth utilization.

With the development of cyberspace security "asymmetry in attack and defense", heterogeneous redundancy design is gradually being widely used. Heterogeneous redundancy design can enhance the robustness and security of the system through the differentiation of heterogeneous components. But the complex structural design of the system makes it difficult to apply the existing attack surface metric to measure the security of heterogeneous redundant system. In this paper, based on the attack surface metric proposed by Manadhata, we provide a new attack surface metric suitable for heterogeneous redundant system with voting mechanism. In this new metric, we define a new notion named attack surface arbitration, and also propose a new method for quantifying result of arbitration. The experiment result shows that the new attack surface metric can properly describe the attack surface of a heterogeneous redundant system with voting mechanism, which changes with the adjustment of voting algorithm.

Aiming at the problem that a large number of students' electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student's electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.

With the exploding of information on Internet, relationship graphs have widely used in research areas, such as subgraph search, subgraph matching and subgraph mining. However, graph in real lives is always large-scale with millions of nodes and edges, leading to the analysis on the graph time-consuming and space-consuming. This paper analyze on the structure feature of graphs and discover the claster in the graph. Based on the claster feature of graphs, the author proposed a new algorithm which focuses on the similar vertexes query on the graph. At first, apply k-mean method to decompose the graph into small part, and then the similar degree on the part can be computed.

The rapid development of computer vision has had a significant impact on all areas of life. Real-time image capture using computer images for small movements has strong utility in some areas of life. Based on the EgoHands dataset, this paper uses the SSD detection method and the MobileNet neural network to establish a recognition model for students' gestures. In the end, the model adopted in this paper achieved a score of 96.86% for mAP.

The rapid development of computer hardware and Internet makes the information transmission speed and storage capacity of the network explode. The development of basic technology has led to the advance of many multimedia and rich text technologies that serve the public. In order to solve the problem that users and companies need to process and retrieve massive amounts of information, web crawler technology extracts the specified information from web pages and then analyzes it, providing a directional function of crawling information from large amounts of information. This article explains the web crawler that uses Python language to extract specific movie information from the website in detail and analyzes the detailed process of web crawler written in Python language.

With the advent of the era of big data, human society is facing unprecedented opportunities and challenges. In many fields where big data is widely used, e-commerce is undoubtedly a crucial one. While the advanced data analysis technology penetrates into every link of e-commerce transaction, it not also brings about convenience, but also causes many adverse effects. On the one hand, this paper analyzed both the current situation of consumer privacy security on e-commerce platform and the increasingly mature application of differential privacy in various IT fields. It is found to be promising that with its good feature of privacy ambiguity, consumer privacy protection in differential privacy mechanism can meet the requirements of a more robust development of e-commerce in big data era. On the other hand, this paper briefly discussed about consumer protection in legal level, inspired by relevant researches done on GDPR.

Safe operation of water conservancy facilities is the important condition to safeguard safe operation of hydraulic engineering, thus health management of water conservancy facilities can be ignored. In this thesis, the author applies PHM to health management of water conservancy facilities, constructs health diagnosis and safety precaution system of water conservancy facilities based on PHM, analyzes the system framework and functions, and provides reference for safe operation of hydraulic engineering in China.

This paper aims to reduce the airline's vacancy rate and make full use of aviation resources to increase the profit by constructing the air passenger image model and forecasting the passenger loss rate based on mining data technology. Firstly, this paper groups the passengers to establish the LRFMC model and obtain the weights of the five indicators by AHP analytic hierarchy process. After weighting the five indicators, the K-means rapid clustering method is used to cluster the passengers. Secondly, construct a logistic regression model to predict the probability of loss. Lastly, based on the conclusions of the established passenger segmentation and loss model, different services and marketing strategies are proposed for different travel groups to attract passengers to take flights and to improve attendance and benefits.

In order to improve cities' non-engineering measure capacity to cope with rain-flood disasters and provide emphasis for government's flood protection, the urban rain-flood risk division was studied. The cloud model can be used to deal with qualitative and quantitative transformation and adapt to randomness and fuzziness of urban rain-flood risk evaluation. By taking Changzhou as a research object, 3 criterion layers, including hazard inducting environment, hazard-affected body and disaster-inducing factors and 11 indexes were selected. The cloud model and entropy evaluation method were chosen to evaluate the urban rain-flood risks. Besides, ArcGIS software was used to draw the urban rain-flood risk division map of Changzhou. The findings showed that regions with the higher rain-flood risk grade included most of areas in Jintan District and Xinbei District, some areas in Wujin District and Zhonglou District. The corresponding flood protection measures should be taken.

With the popularization of computer technology, the transformation of business models has gradually become one of the main influencing products of computer technology development. The emergence of the "new retail" model means that the traditional retail model has become less and less suitable for the current commercial market. This paper combines the current cutting-edge methods of deep learning in the field of computer vision to optimize the traditional retail model and propose a new retail model. This model can effectively solve the problem of poor human resources in the traditional retail model. Moreover, the new retail model can apply relevant technologies such as MCNN to count relevant important business indexes such as population density, and can also be used for security inspection and intelligent queuing.

Since the marine environment is more complex than the terrestrial environment, a Multi-parameters model of Reflection Coefficient (ORPM) is established to study the propagation of radio waves in the marine environment.By comparing reflection coefficients off a turbulent ocean and a calm ocean, it is found that the turbulent and reflection coefficient are inversely related and the finding is correspond with actual situation.Then do research on the propagation of high frequencies (HF) radio in the atmosphere. By combining it with ORPM, the Model for the Maximum Number of Hops (NOHF) is established to figure out the maximum number of hops the signal can take before its strength falls below a usable signal-to noise ratio (SNR). Finally the sensitivity analysis of ORPM and NOHF is performed, and the results are clear.

In the stock market crash in China's capital market in 2015, the bubble quickly spread from the Internet industry to the entire stock market. It can be seen that the financial system has inherent instability and endogenous vulnerabilities. A small shock may be amplified by the financial system and have a contagious effect, which ultimately has a huge impact on the entire financial system and the entire economy. Therefore, it is possible to accurately predict the trend of China's stock market volatility, and it is of great significance and reference value for China's economic development to prevent risks and stabilize the economy in a timely manner. Based on the characteristics of industry index volatility, this paper studies the risk transmission mechanism between industries by examining the main risk source industry, risk transmission intermediary industry, risk conduction characteristics between industries, and the dynamic changes of risk conduction, and finally through R language processing. The data is used to predict the volatility of China's stock market in the first half of 2018.

The current Internet penetration in African is significantly below the world average level, while African has the second largest population in the world. This article aims at analysing the current situation of the African Internet to see the insights behind the massive related datasets, containing the design and implementation processes of a resulted platform. The platform consists of three web pages for showing the results, databases, and webpage controller implemented by Java. After collecting available datasets online, we now need a visualized tool to analyse the reasons behind those raw data, to explore the attributes and environment of the complex network, hence we derived a web-page platform for the task, where researchers can query detailed information according to certain requirements. The first page of our platform shows the structure of the African network topology, the second web page allows users to interact with the data, generating corresponding graphic results, and the third web page handles queries, request with certain manner defined by users.

With the rapid development of transportation industry, highway construction in China is advancing by leaps and bounds, and the demand for pavement materials is also increasing. With the depletion of petroleum resources, the price of petroleum asphalt, which is widely used in pavement, is rising, and it is not easy to degrade petroleum asphalt, so the utilization ratio of waste asphalt is low and the environment is polluted. Therefore, it is necessary to seek some substitute or a kind of renewable environmental protection asphalt, and the large-scale production of vegetable asphalt provides a new way for it. In this paper, plant asphalt is mixed with 90 base asphalt in different proportion (0%, 10% and 15%), and the mixture asphalt is determined. To explore the effect of plant asphalt on the three indexes, temperature sensitivity and ageing performance of base asphalt.

In order to analyze the traffic congestion of urban road network, the data preprocessing method is improved, and the three-dimensional spatio-temporal state data is transformed into one-dimensional data, and the spatio-temporal constraint is added on the basis of FP-Growth algorithm to achieve more efficient mining congestion propagation law. The purpose of this paper is to study the traffic congestion propagation law in the morning and evening peaks of the working day and all day and rest days through two experiments, and obtain the traffic congestion propagation law in working days and rest days. The research results show that using the urban road network traffic state data, the analysis method of this paper can more effectively mine the propagation law of recurrent traffic congestion.

Using the floating car data obtained by simulation to carry out theoretical research on link travel time evaluation and prediction, it has the advantage of conveniently setting different situations and providing reference for practical applications. In this paper, we have acquired the floating car data based on microscopic simulation, and then the complex trapezoidal quadrature formula is used to obtain link travel time of a single vehicle. When setting different ratios of floating cars, the average link travel time is obtained by the mean-median method. The results show that the effect is better than the arithmetic average method, and the higher the ratio of floating cars, the higher the accuracy of evaluation. The markov model has been used to correct the gray prediction model, with that we establish travel time prediction model based on the grey markov chain. The experimental results show that the prediction accuracy is high.

Direct shear test is an effective method for studying the contact problem between soil and structure. Based on the numerical simulation method of finite element, three-dimensional numerical simulation of this test is carried out. By analyzing the shear stress and shear deformation under different normal stress states, the shear strength of contact surface and the deformation characteristics are obtained. The results show that the numerical simulation method of finite element can simulate the soil-structure interaction well.

An oil spill accident at sea contributes to an economic loss for oil tanker companies, and the uncertainty of shipping price has a serious impact on the oil tanker investment decision. In view of this, this study establishes an optimal investment decision model, considering the cost of oil spill under uncertain freight conditions with the expectation of investment income as the main optimization target. First, on the basis of the principle of demand and supply function curve, the relation equation of the fluctuating freight rate is established according to the traffic volume, and the size of the freight rate is obtained by considering any alterations of the transportation scale. Second, based on the tanker freight demand uncertainty, a model considering the additional maintenance costs of the oil spill as well as the optimal investment timing and scale is established. Finally, an empirical analysis is undertaken to know the optimal investment timing and scale of the tanker. The results show that on one hand, with the increase in freight uncertainty, the waiting value and scale to reduce the risk from uncertainty increase, and the additional maintenance costs for preventing oil spill will reduce the probability of oil spills and the requirement for investment timing and scale.

Evaluating the level of urban rail transit services objectively is of great significance to improving its operational efficiency.This paper analyzes and constructs the urban rail transit service level index system from the perspective of passengers, and proposes a comprehensive evaluation model of service level based on Set pair analysis.Through the calculation of weights and connections, analysis of each impact indicator is carried out from four levels.Taking Wuxi Metro Line 1 as an example, this model was used to evaluate its service level comprehensively.The example verification shows that the service level of Wuxi Metro Line 1 is relatively high. The application of the model can comprehensively evaluate the service level of urban rail transit, and can reflect the limiting factors affecting the service level.It provides theoretical support for the development of urban rail transit operations.

This paper reports a work to investigate the employers' beliefs to the usage of safety footwear in the Chinese construction industry. Questionnaire survey based on the Theory of Planned Behaviour (TPB) is applied as the research technique and six items corresponding to participants' behavioural beliefs, normative beliefs, and control beliefs were included in the survey form. Investigation was carried out among 15 construction project managers who were in charge of 15 construction projects. The means of the TPB components revealed that participants have positive behavioural beliefs and control beliefs toward the behaviour, while negative normative beliefs to the behaviour. It was suggested that normative beliefs is needed to be improved and there is plenty of room for this potential changing.

This paper studies a discrete time-cost tradeoff problem in repetitive projects, in which each activity has several alternative modes and must be scheduled in sequence from the first unit to the last unit. The objective is to minimize the total project cost consisting of direct costs and idle costs of all activities while meeting a pre-specified deadline. A heuristic procedure based on the linear programing relaxation is presented to solve this problem with the goal of finding high-quality solutions for medium-or large-size problems within a short period of time. Computational experiments are conducted to verify the effectiveness of the proposed procedure.

In order to analyze the commuting behavior of residents more accurately, the quantitative family heterogeneity is integrated into commuting travel choice behavior analysis. Based on the data of residents' travel survey in Xi'an, MNL models with neglecting household heterogeneity and Multi-Level MNL models with household heterogeneity are established respectively, and the parameters are calibrated. The model calibration results show that 19% of the residents' commuting behavior selection is caused by household heterogeneity. Household heterogeneity cannot be ignored. Compared with the multi-layer MNL model, the MNL model overestimates the extent to which some explanatory variables affect the explanatory variables. At the same time, the analysis proves that having a car in the family is one of the key factors in inducing car travel. The transfer of bus and bicycle modes of transportation is highly accepted by commuters. We can increase the share of buses and reduce the use of cars by encouraging passengers to use bus and bicycle transfers.

Self-driving cars as a new technology need time to test their safety. How to supervise the burgeoning autonomous vehicles is a new challenge for the countries that support this technology. How to effectively manage the development of the automatic driving and ensure traffic safety will certainly be one of the important tasks on traffic management in the future. At present, there is no complete assessment system for the evaluation of the standard and safety performance of autonomous vehicles. Under these circumstances, what kind of requirements does the self-driving vehicles on road test need to achieve can be authorized to formal use for the public is a serious question now. This paper uses the method of mathematical statistics to estimate the lower limit of how many distances the Chinese autonomous vehicles need to drive on the road test to provide relevant reference for the traffic management department.

There has always been a connection between architecture and furniture in the construction of Chinese and Western architectural space throughout history. It reflects the inherent relationship between architecture and furniture in Farnsworth House which is designed by Mies Van Der Rohe who is one of the masters of modernism architecture, and it also conveys the design philosophy of "truth is the meaning of fact". This article tries to analyse the correlation between architecture and furniture from the angles of design idea, space modulus, structure system, material and the texture and node processing in Farnsworth House, and research the philosophy of design of Mies and his related works. The method which is based on integrated design has a positive meaning to deepen the space expression, strengthen the spatial perception, improve the whole space quality, and it also has the important inspiration and the reference value for the contemporary cooperative design between furniture and architectural space.

In view of the current technical status assessment of concrete slab beam bridge, the problem of the disease severity can not be objectively and accurately reflected. This paper takes the typical disease of the concrete slab bridge as the research object to carry out the risk matrix evaluation. In order to eliminate the risk matrix in the risk matrix, we introduce the Borda order value method to establish a qualitative and quantitative comprehensive risk assessment model.On this basis, based on the analytic hierarchy process (AHP), the weight of risk factors is calculated to form a typical disease risk assessment system of plate girder bridge, which can reduce the influence of subjective factors in the bridge technical evaluation process.

In recent years, as one of the components of urban public transportation, water bus has developed rapidly in cities whose water systems is rich.Based on the background of the project of"Future Technology City"in the"13th Five-Year Plan"of Hangzhou, combined with its regional characteristics, this paper proposes a new model of bus—water BRT(water bus),which analyzes and summarizes the conceptual characteristics of water BRT and framework of system. According to the status of regional traffic and characteristics of module in the Science and Technology City, the paper forecasts the demand of passenger flow, analyzes the setting of the waterway and terminal, comprehensively evaluates the selection and operation of the ship, and finally proposes a reasonable programme of planning.

It is necessary to evaluate the recognition efficiency of traffic signs at intersections under different fog intensities, provides a reference for the addition of traffic signs to the domestic fog and haze weather, and to ensure the safety of drivers and maintain the smooth flow of traffic. In this paper, first, the driver's recognition model of urban road intersection signs is established through his psychological reaction on the road under the normal weather condition, and calculated the recognition time under different speed through the recognition model. Second, using this data can fit the relationship between visibility of fog and haze and recognition time, and we can get the relation equation. Finally, the driver was tested by wearing an Eye Movement Apparatus on the head under the condition of normal weather and different haze levels, then the driving trajectory was recorded. Through the analysis, the driver's blink time, glance time and visual time of the intersection signs were marked, the driver's recognition time was finally obtained. Through the analysis of the recognition time of the 12 signs, it was verified that the models for fog and recognition time were reasonable and accurate.

The purpose of this study was to scientifically and quantitatively evaluate highway construction foreman's competency. This article divided the foreman's competency into four dimensions: knowledge dimension, personality dimension, ability dimension and development dimension, including 22 competency elements. The rationality of the competency model was tested by confirmatory factor analysis method and structural equation model. A support vector machine model was trained to evaluate the overall competency of highway construction foreman. The case study shows that the SVM evaluation model has high accuracy and can reliably identify the foreman with excellent competency.

The paper analyses the features of risks in marine dangerous cases and accidents that happened in Weihai, defines the levels of regional risks and draws the risk-region spatial distribution map with respect to the accident level, the risk-area spatial distribution, the type of vessel in danger, the period of time of accident happening and dangerous situation etc. through acquired remote-sensing images of Tian-gong-2 (a Chinese satellite) and the statistical data on marine dangerous cases and accidents (MDCAs) in Weihai by using RS and GIS technology. On the basis of the features of risks in MDCAs in Weihai, the paper recommends some measures to prevent and control risks, for instance, design a three-level search & rescue network and improve the safeguard mechanism. The work will further enhance the Weihai marine search and rescue capability and foster development of the regional marine economy quickly and stably.

For one thing, maritime transport has expanded the scope and scale of human activities unprecedentedly. For another thing, it has promoted foreign exchanges between countries, it has played a huge role in the formation of the world pattern and the development of globalization, and has promoted the realization of a powerful transportation country. By constructing a maritime soft power evaluation model, we analyze the gap of maritime soft power between China and powerful transportation countries. It is mainly reflected in that marine insurance (including P&I insurance), shipping finance, shipping brokerage and other maritime service industries are still underdeveloped, the right to speak in the formulation of international shipping laws and regulations is still weak and China has not yet become an international maritime dispute resolution center. To enhance the soft power of maritime transport and assist the construction of powerful transportation country, we should further grasp more international discourse rights of shipping rules, actively participate in international maritime transport, improve the maritime legal system, cultivate high-quality maritime talents, and promote Chinese maritime culture.

To enhance the operational safety of civil aviation, based on the data of civil aviation unsafe incidents on 2006-2015, the assessment index was constructed with typical accident and incident events, and the operational safety risk assessment model based on grey whitening clustering was proposed in this paper. Then the weight of each index is determined by the principle of maximizing deviation. Finally, the safety risk in these 10 years was evaluated by actual operation data. According to the result, it was consistent with the actual operation of civil aviation, which verified the rationality of the safety risk assessment indexes taking into account of the typical accidents and incidents. It had changed the traditional safety assessment index and methods of civil aviation based on people, machines, environment and administration, which had positive significance to improve the safety production capacity of civil aviation.

According to the definition of daily transport production quality, the evaluation index system of daily transport production quality was constructed. Firstly decision matrix was built with the attribute value of each sample and the decision matrix was standardized. This paper analysed the sample data to obtain each principal component's values and weight through the principal component analysis and thus to get the optimal value and worst value of each principal component. Then this paper defined four parameters according to the grey relational analysis theory and gave the corresponding calculation method. Finally this paper defined the grey relative closeness coefficient parameter which was used to evaluate transport production quality based on TOPSIS. The method was applied to the Beijing railway bureau. This paper compared the results to those of the principal component analysis and grey correlation analysis, which proved that the method had certain feasibility.

For the sake of exploring influences of perceived organizational support (POS) on safety management performance (SMP), and improving the level of SMP, the connotation indicators of POS and SMP in Airlines were set up, and the hypothesis model of POS and SMP was proposed. The 280 workers in 12 Airlines in China were enquired by means of questionnaires and the structural equation model (SEM) was used to verify the hypothesis. Results show that there is a significant positive correlation of the organizational identification of employee's value with safety policy & target, safety risk management, safety supervision & measurement, safety culture, safety education & training, and downtrend of safety accidents. The organizational care of employee's benefit is positively related to safety education & training, and the organizational support of employee's work is also positively related to safety policy & objectives, and change of unsafe incidents. In safety management, the Airlines can improve their POS, especially organizational identification of staff value, to achieve high SMP.

In order to study the similarities and differences between the route selection strategies of full-service and low-cost airlines, Spring Airlines and Eastern Airlines are the representatives of low-cost and full-service airlines respectively, and based on the actual operation data of Spring Airlines and Eastern Airlines routes in the past seven years, the panel-probit model is used to investigate the impact of route characteristics, airport characteristics, characteristics of competing airlines, and the characteristics of airlines on the route selection behaviors of full-service and low-cost airlines. The results show that the route HHI, time slot control airport, airport size and number of competitors have a significant impact on the route decision of the Eastern Airlines, while the time slot control airport, airport HHI and serviced airports have a significant impact on the route decision of the Spring Airlines. Finally, based on the empirical results, the similarities and differences between the decision of Eastern Airlines and Spring Airlines routes are summarized, which provides a theoretical basis for the decision-making of full-service and low-cost airlines.

The geographical environment of Hainan Province is so superior that a lot of migrant population immigrates in winter. The urban development of Hainan Province is relatively lagging, and the level of public transportation development is low, which is difficult to adapt to new needs. In this paper, using the qualitative research method of grounded theory, through the open coding, spindle coding and selective coding of interview data, the five core categories are obtained: the seasonal imbalance between supply and demand, government policy and management, the anomie behaviors of migrant population, the provision ways of public transport services and the social integration of migrant population. And then the main lines of the five core categories are given, the relationship among them is also deeply analyzed. In addition, this paper constructs a theoretical model and proposes from four aspects to deal with the challenges.

With the rapid development and full use of modern information and communication technologies such as "Big Data, Cloud Computing, Internet of Things, Mobile Internet", comprehensive energy service has become a commanding point of strategy of energy companies. Based on the analysis of domestic and foreign comprehensive energy services, this study takes Grid company as an example and discusses the strategic choice and strategic focus of energy enterprises with game thinking in the process of business transformation.

This paper was based on the panel data of 13 provinces in China's main grain producing areas from 2005 to 2015, this paper analyzed the rural financial service efficiency of China's main grain producing areas by using super-efficiency DEA model, window Malmquist index and exploratory spatial data analysis. The study shows that the rural financial service efficiency of major grain producing areas in China is in a stable and effective state as a whole. The growth of total factor productivity is mainly due to the improvement of technical efficiency. There is a positive spatial correlation between the rural financial service efficiency of major grain producing areas.

In order to improve the accuracy, practicability and prediction accuracy of the single prediction model, a logistics demand combination forecasting model based on cargo throughput is established. Based on the original statistical data of throughput, the two models of gray and exponential smoothing are established respectively. On this basis, the weighted assignment method of variance reciprocal is used to construct the combined forecasting model. According to the prediction results of different prediction models analyzed by the three evaluation indexes of average relative error, maximum fitting error and minimum fitting error, the three evaluation indexes of the combined prediction model are respectively 6.344%, 16.345% and 0.343%, which are smaller than the single item model. It indicates that the established combined forecasting model can effectively improve the accuracy of the throughput prediction model based on overcoming the shortcomings of the single-term throughput prediction model.

Modern clean energy electric intelligent driverless cars are still on the road to commercial operation. Urban rail transit subway modern green energy electric intelligent unmanned driving technology has been maturely applied in many subway lines at home and abroad. People who have seen various sci-fi movies and have repeatedly broken through the expectations of real-world artificial intelligence technology can't help but wonder: Is there a subway that can be opened without a driver, can it be smarter and more "smart"? Is it like "Transformers", really become "robots", have brains, will make their own decisions? The imaginary dream has gradually become a reality! When people are still amazed by the modern clean energy, intelligent and unmanned subway, and many routes around the world, the line of the unmanned green energy electrical intelligent system independently developed by the trains of the domestic rail transit subway line has arrived.!

IKEA is a company that mainly sells home and furniture products. Its products have very strong Nordic style characteristics, so it has also received people's attention and love. It also occupies a very large proportion in China's home market. Therefore, China's home furnishing enterprises also need to study the design success factors of IKEA household products, and on this basis, continuously optimize and perfect their own product design work. In this paper, from market positioning, design concept and user experience these three aspects the in-depth analysis of the elements of IKEA product design success are carried out.

In this paper, a beamforming algorithm based on oblique projection technique is proposed for a hybrid radar of polarization sensitive array (PSA), frequency diversity array (FDA) and multiple-input multiple-out (MIMO) radar, termed as PSFDA-MIMO radar. Firstly, the signal model of the proposed PSFDA-MIMO radar is established. Secondly, oblique projection technique is proposed to solve the beamforming problem of the novel radar. Finally, the simulation results show that the overall performance of beamforming based on the proposed radar is better than that of other combined radar systems.

Alzheimer Disease (AD) is a neurodegenerative disease and the most common form of dementia, which is characterized by progressive memory loss and cognitive decline. The pathologic symptoms of AD are amyloid beta (Aβ) accumulation and neurofibrillary tangles (NFTs) caused by protein tau. Age is the greatest risk factor for AD, and the opportunities of developing AD increase two-fold every 5 years after age 65. AD also has a raising tendency on healthcare costs as the population have longer life span than before. In 2017, about 5.3 million people over the age of 65 in the United States were diagnosed with AD and under the influence of AD caused dementia. Researchers has been predicted that over 100 million patients will suffer from AD by 2050. Consequently, there is an urgent need to improve the investigation and treatment of AD. This paper summaries risk factors of two types of AD, molecular networks and symptoms of AD, and also current and future treatments of AD.

In clinic, temporal lobe epilepsy (TLE) patients with visually normal MRI findings are more difficult to definitively diagnose than MRI-positive TLE patients. Spectral power can reflect the frequency characteristics of brain activity. In order to find biomarkers for discriminating MRI-negative TLE patients from healthy controls, magnetoencephalograms were recorded for 20 left and 23 right TLE patients with normal MRI results and 22 healthy controls in a no-task, eyes-closed condition. The regional and global powers of the patients and controls in the main frequency bands were analysed. Compared with the healthy controls, the left TLE group presented significantly increased powers in the left temporal region, whereas the right TLE group exhibited significantly increased powers in the right temporal region in the delta and theta bands. In the global power, the right TLE group showed significant increases in the delta and theta bands when compared with the healthy controls. Our results suggest that in the delta and theta bands, regional average powers in the left and right temporal regions can be taken as biomarkers for distinguishing MRI-negative TLE patients from healthy controls.

DNA double-stranded breaks stimulate the digital pulse response of p53, which has the characteristic of excitability, and the mechanism of p53 pulse response has been extensively studied. However, the mechanism of the excitability of p53 pulse response remains to be elucidated. Here, we establish mathematical models of the p53 pulse response with different regulatory structures. Through simulation analysis and parameter space search, we find that the self-positive feedback of ATM can guarantee the excitability of p53 pulse response; We also analyze the effect of ATM-related parameters on the excitability of p53 pulse response. Our results are necessary for further exploring the mechanism of p53 pulse response and the cell fate decisions.

This article mainly introduces the application program of blockchain technology in the evaluation of students' professional ability. Through K-means clustering algorithm, students' academic performance and academic achievements in school, training, competitions, practice and other activities outside school are analyzed, in order to objectively and effectively evaluate the students' professional ability, which provides reasonable advice for student employment. In the system design, the blockchain technology is applied to the student ability evaluation system. The blockchain technology provides distributed storage and is nondestructive and traceability, so as to achieve the overall monitoring of students' professional ability evaluation process, student assessment data can be traced and student learning credit can be increased anytime anywhere. This paper presents a design scheme of the student ability evaluation system based on blockchain technology, which includes a student ability analysis method based on clustering algorithm and an overall system architecture diagram based on blockchain technology, which provides possibilities for creating an ecosystem of student ability evaluation for the future.

Biometrics refers to the technique of identifying people based on their physical characteristics. Among these many technologies of biometrics, iris recognition is currently the frontier research field and one of the most promising technologies. This paper focuses on the application of information technology in biometrics and outlines how to use the multi-spectral characteristics of living human eyes to identify forged irises, and improve the effectiveness and safety of identifications. Finally, the paper introduces the problem to be solved of iris recognition and its prospects.

Bipolar disorder (BD), as a type of mood disorder, refers to the disease that has both manic episodes and depressive episodes. At present, the clinical diagnosis and treatment of bipolar disorder is often unsatisfactory, the disease always has a high rate of misdiagnosis and recurrence. The specific pathogenesis of bipolar disorder is still unclear, but related studies have shown that bipolar disorder is highly heritable, so a large number of studies have been carried out around the genomics of the disease, including some research combined with machine learning. Motivated by these research, we use the Single Nucleotide Polymorphism (SNP) which through the Genome-wide association analysis (GWAS) obtained as molecular genetic markers, combined with Convolutional Neural Network (CNN) constructing a recognition model for bipolar disorder. The experimental results show that the overall recognition rate can up to 79%, compared to similar studies, our model has achieved excellent performance. In addition, unlike traditional GWAS for finding pathogenic sites, our study first attempted to use it as a feature selection method of data preprocessing in machine learning, the experimental results prove the feasibility of this method. We believe that our research will provide some new ideas for the precision diagnosis and treatment of bipolar disorder in the future.

In China, when people assembled appropriate drugs together in a group, they called the group prescription. Lots of prescriptions in TCM are prepared by compatible theory of TCM. Compatibility of TCM means to two or more different herbs are used together. Principal, assistant, complement and mediating guide is an important component of prescriptions compatible theory of TCM, but there exist some problems in its application: firstly, there have been some different views as to whether or not certain drug is principal drug; secondly, the concept of principal, assistant, complement and mediating guide is not accurate. In this paper, we proposed a new methodology based on molecular docking to classify drugs in the prescription into different kinds according to compatible theory of principal, assistant, complement and mediating guide.

With the rapid development of virtualization technology, security issues have also become increasingly prominent. All kinds of virtualization platforms use access control mechanisms to prevent illegal or legal entities from illegally accessing unauthorized resources.This paper discusses the access control mechanism adopted in Docker container, analyzes its shortcomings, and proposes a DRBAC based Docker role access control mechanism. This method is based on the role allocation method, dynamically expands the existing model, and adds a role access control mechanism to the Docker container. Through the distributed trust management and access control mechanism of DRBAC, we can solve the hidden danger brought by the main body because of delegated authority to other entities autonomously. When the system accesses Docker resources, this method can effectively control the access management and delegate problem of teh main body. In this way, we improve the access security of Docker container to host resources.

The microservices architecture has been favored by system designers and has gradually become the mainstream solution in enterprise-level application architecture design since its introduction. This article aims at the urgent need for information transformation and upgrading faced by the garment industry, and designs a system architecture based on microservices to solve the two problems it faces: the first is how to design an enterprise resource planning (ERP) system that improves the efficiency of enterprise management which is ability to develop and apply quickly; the second is how to upgrade module-level functions on the original information management system smoothly. This paper decomposes the business process into microservices and designs an asynchronous communication mechanism between microservices, enhances system scalability and robustness. This paper uses Docker so that the enterprise's original ERP system can be compatible with the new system and gradually upgrade its function modules.

Focusing on the financial IC chip technology that is urgently needed in China and the information security of IC card chips which is related to the security of the national financial system, this paper summarizes and researches into the development of China's financial IC card industry, the mainstream module, design standards and specifications, and major technical routes of card chips. In addition, it also centers round the security protection of financial IC card chips. Based on the analysis and introduction of mainstream international and national commercial cipher algorithm it studies the attacking modes and principles of power and electromagnetic analysis attacks and side-channel attack technologies faced by China's IC card chips, and finally summarizes main protection technologies against various attacks.

The AISAS model originally refers to a reconstructed consumer behavior model based on the Network Times Market Characteristics. However, following the Chinese Dream thought in new media becomes a lifestyle in the publicity and education among college students, it is also necessary to follow the rules of media communication in the Internet era. Through analysis of 235 questionnaires, with using the indicators of the AISAS model, the research group summarized the characteristics of college students' information contact behaviors of the Chinese Dream, and on this basis, we proposed the improvement strategies aiming at propagating the Chinese Dream.

With the rapid development of e-commerce, security issues have attracted more and more attention. In this paper, PKI-based electronic commerce security key escrow technology and protocol are studied, the role of PKI in e-commerce security and the application characteristics of PKI are explained, and the design scheme of a digital authentication system based on PKI is described.

In recent years, the domestic society has made great progress, and various advanced technologies and concepts have been applied and developed continuously. Machine learning algorithm is a new algorithm which plays a great role in all walks of life. Machine learning is a process of self-improvement using the system itself, and computer programs can automatically improve performance with the accumulation of experience. It is proposed for many specific learning tasks, so that computers can extract features from many data and discover hidden rules, so machine learning has been widely used in data mining. In this paper, the application of machine learning algorithm in data mining is studied in detail. With the help of mobile terminal data, the outdoor terminal of GSM network is positioned effectively, and a three-stage positioning algorithm is proposed, which greatly improves the positioning speed and accuracy.

In the era of big data, data is growing explosively. Data quality control has become a key factor in maximizing data value. It is important and urgent to establish a scientific data quality detection method. Benford's law has become an effective tool for detection of data quality and identification of anomaly data in various fields. On the basis of expounding the basic principles of Benford's law, this paper summarizes its application in different levels of natural and social sciences, explores the data conditions of the law and the factors affecting the accuracy of the detection, and proposes the model improvement idea in three aspects of extending detection scope, combination of several methods and strengthening the explanation of detection results. The development of Benford's Law in the future requires scholars from all fields to study more about its essence, strengthen its integration with other data processing technologies, and then expand its application.

In order to realize rapid grabbing of stacked objects, we propose a quick and accurate method to realize target segmentation and pose estimation using only depth information. We use filtering to remove the noises in detection. Then we detect the contours and use the averages and gradients of depth to find the contour of the top plane. We calculate the centroid and normal vector of the top plane to get the pose estimation. Thus we can realize the rapid grabbing of the top object, then achieve rapid grabbing of stacked objects one by one.

K-means algorithm is introduced. An improved algorithm is proposed for the disadvantages of randomly selecting the initial center of clustering and the vulnerability to the effects of outliers. The Distance Mean method (DM) was used to remove the outliers, then high-density and max-distance (HDMD) was used to improve the selection of the initial center of clustering. The comparison experiment before and after the improvement was carried out. Experimental results show that the improved algorithm is stable and accurate. The improved algorithm was applied to computer language teaching and achieved good classification effects. The improved algorithm is used to research and analyse mobile customer records, the effect is in conformity with the actual situation.

To solve the problem of inadequate target recognition ability of traditional infrared measurement system in the test mission of multi-stage rocket/warhead, shrapnel, disruptor ammo and other targets, an infrared radiation characteristic measuring system is established based on the infrared radiation characteristic measurement principle, direct expansion source calibration and atmospheric parameters measurement. It has been proved that the method can make effective and reliable identification of typical infrared targets, which is of great significance for auxiliary decision-making of the infrared measurement systems.

According to the requirements of the mobile digital TV system, the present paper creatively combined cross fractal antenna, induction array antenna, gradual dielectric constant film, graphene conductive ink and nano silver conductive ink, designed a gradual dielectric constant induction array fractal antenna used for mobile digital TV system. The result of test indicates that this antenna completely covered the working frequency band of the mobile digital TV system. This antenna has the advantages of small size, wide bandwidth and more redundant performance, and it has stable radiation performance and excellent physical and mechanical performance. This antenna can ensure the good transmission quality of wireless signal in mobile digital TV system in various harsh electromagnetic environments.

In this study, the invulnerability performance of power communication network is elaborated in detail firstly, which is considered to be measured by two parameters of robustness and network efficiency. Then, the actual power communication data network model and power communication transmission network model are constructed. On this basis, different attack modes are selected, and the invulnerability performance of power communication network is analyzed through simulation experiments. Finally, experiments show that the impact on network invulnerability caused by selective attacks on important nodes is far greater than that of random attacks, and parameters of robustness and network efficiency can be used to measure the invulnerability of power communication network. In addition, the research results show that the node's invulnerability can be measured by the degree of network efficiency decline, and the greater the network efficiency decline, the better the node's invulnerability, corresponding to more important nodes in the whole network.

In order to create a kind of wide-ranged and nanoscale accurate displacement measurement which requires low manufacturing costs and has strong anti-interference capabilities and stable performance, the nanometer time-grating sensor based on AC electric field coupling has been researched. Functions like the settings of the frequency, amplitude and phase of standard waveform can be achieved by combining the LabVIEW software of virtual instrument development platform with the hardware of the PXI-5422 arbitrary waveform generator from NI company. The experiment concludes that phase adjustment can improve the accuracy through adjusting the amplitude of excitation signal which can avoid the effect that the different installation sites have on nanometer time-grating. The virtual instrument technology applying in the experiment of nanometer time-grating gives technological support to the improvement and progress of the function of excitation signal, which provide convenience to the research on the sensor.

This paper investigates the use of time reversal processing techniques to compensate for multimodal and dispersive effects in a low-power structural health monitoring system for pipelines. We demonstrate the use of time reversal as a pitch-catch operation between two transducer arrays to illuminate changes caused by damage on a pipe. The response is then propagated backwards through the medium from the same array. We demonstrate how this method provides a metric for classifying the magnitude of damage in a pipe while relaxing the restrictions on excitation frequency and bandwidth that are imposed on many other nondestructive techniques.

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers.

The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction.

Retraction published: 23 September 2022

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers.

The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction.

Retraction published: 23 September 2022