A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance(SCF)is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain.In recent years,with the deep integration of supply chain and Internet,Big Data,Artificial Intelligence,Internet of Things,Blockchain,etc.,the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes.However,with the rapid development of new technologies,the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones.The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains.In this article,a distributed approach of big data mining is proposed for financial fraud detection in a supply chain,which implements the distributed deep learning model of Convolutional Neural Network(CNN)on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly.By training and testing on the continually updated SCF dataset,the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors,so as to enhance the financial fraud detection with high precision and recall rates,and reduce the losses of frauds in a supply chain.

Performance index based learning controls for the partial non-regular systems using lifting technique

Deficiencies of the performance-based iterative learning control(ILC) for the non-regular systems are investigated in detail,then a faster control input updating and lifting technique is introduced in the design of performance index based ILCs for the partial non-regular systems.Two kinds of optimal ILCs based on different performance indices are considered.Finally,simulation examples are given to illustrate the feasibility of the proposed learning controls.

A Crowdsourcing Recommendation that Considers the Influence of Workers

In the context of the continuous development of the Internet,crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises,the public and society.Among them,a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion quality.Therefore,this paper proposes a crowdsourcing recommendation framework based on workers’influence(CRBI).This crowdsourcing framework completes the entire process design from task distribution,worker recommendation,and result return through processes such as worker behavior analysis,task characteristics construction,and cost optimization.In this paper,a calculation model of workers’influence characteristics based on the ablation method is designed to evaluate the comprehensive performance of workers.At the same time,the CRBI framework combines the traditional open-call task selection mode,builds a new task characteristics model by sensing the influence of the requesting worker and its task performance.In the end,accurate worker recommendation and task cost optimization are carried out by calculating model familiarity.In addition,for recommending workers to submit task answers,this paper also proposes an aggregation algorithm based on weighted influence to ensure the accuracy of task results.This paper conducts simulation experiments on some public datasets of AMT,and the experimental results show that the CRBI framework proposed in this paper has a high comprehensive performance.Moreover,CRBI has better usability,more in line with commercial needs,and can well reflect the wisdom of group intelligence.

Visualization Analysis for Business Performance of Chinese Listed Companies Based on Gephi

When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool for data analysis and visualization in the era of big data.It can convert the evaluation results of all listed companies into nodes and edges,and directly display them in the form of graphs,thus making up for the defects of traditional methods.This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object.First uses tushare and web crawlers to collect the financial statement data of these companies.And then,uses the Economic Value Added model to calculate the EVA of each listed company and build graph data.Next,import the graph data into gephi to generate the distribution graph of all listed companies’performance,and summarize the distribution characteristics of business performance.Finally,select a listed company that you want to analyze in detail,using the traditional DuPont analysis method to conduct micro level visualization analysis of the business performance to find the main factors affecting the company’s operating performance.Incorporating gephi into traditional performance analysis methods will make the results of traditional analytical methods more effective and complete.

Expanding Hot Code Path for Data Cleaning on Software Graph

Graph analysis can be done at scale by using Spark GraphX which loading data into memory and running graph analysis in parallel.In this way,we should take data out of graph databases and put it into memory.Considering the limitation of memory size,the premise of accelerating graph analytical process reduces the graph data to a suitable size without too much loss of similarity to the original graph.This paper presents our method of data cleaning on the software graph.We use SEQUITUR data compression algorithm to find out hot code path and store it as a whole paths directed acyclic graph.Hot code path is inherent regularity of a program.About 10 to 200 hot code path account for 40%-99%of a program’s execution cost.These hot paths are acyclic contribute more than 0.1%-1.0%of some execution metric.We expand hot code path to a suitable size which is good for runtime and keeps similarity to the original graph.

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.

An Ui Design Optimization Strategy for General App in Big Data Environment

Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applications attach greatimportance to users’ experiences. The rationalized UI design should allow a user not onlyenjoy the visual design experience of the new product but also operating it morepleasingly. This process is to enhance the attractiveness and performance of the newproduct and thus to promote the active usage and consuming conduct of users. In thispaper, an UI design optimization strategy for general APP in the big data environment isproposed to get better user experience while effectively obtaining information. Anexperimental example of a library APP is designed to optimize the user experience. Theexperimental results show that the user-centered UI design is the core of optimization,and user portrait based on big data platforms is the key to UI design.

On Visualization Analysis of Stock Data

Big data technology is changing with each passing day,generating massive amounts of data every day.These data have large capacity,many types,fast growth,and valuable features.The same is true for the stock investment market.The growth of the amount of stock data generated every day is difficult to predict.The price trend in the stock market is uncertain,and the valuable information hidden in the stock data is difficult to detect.For example,the price trend of stocks,profit trends,how to make a reasonable speculation on the price trend of stocks and profit trends is a major problem that needs to be solved at this stage.This article uses the Python language to visually analyze,calculate,and predict each stock.Realize the integration and calculation of stock data to help people find out the valuable information hidden in stocks.The method proposed in this paper has been tested and proved to be feasible.It can reasonably extract,analyze and calculate the stock data,and predict the stock price trend to a certain extent.

An Integrated Model for Sport Consumer Repurchase Intention:A Case in the Commercial Martial Arts Schools

The retention of customers is fundamental to the success of sport organizations for a variety of reasons,not the least of which is it is less expensive for an organization to keep a current customer than to gain a new one.Since customer repurchase intention is an important indicator to predict repurchase behavior,exploring the factors that influence this behavior has important theoretical and practical implications in the commercial martial arts school market.Although previous research provides a foundation for the factors that influence a customer's repurchase intention,additional empirical work is warranted.This study was designed to integrate the findings of previous studies and examine how utilitarian factors(perceived value and switching cost),a hedonic factor(perceived enjoyment),and social/psychological factors(confirmation and satisfaction)directly or indirectly influenced consumers'repurchase intentions in the context of commercial martial arts schools.The results indicated that customer satisfaction had the strongest impact on customer repurchase intention,followed by perceived enjoyment,switching costs,confirmation,and perceived value.

Visual abstraction of dynamic network via improved multi-class blue noise sampling

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

PGAM5-Mediated PHB2 Dephosphorylation Contributes to Diabetic Cardiomyopathy by Disrupting Mitochondrial Quality Surveillance

Disruption of the mitochondrial quality surveillance(MQS)system contributes to mitochondrial dysfunction in diabetic cardiomyopathy(DCM).In this study,we observed that cardiac expression of phosphoglycerate mutase 5(PGAM5),a mitochondrial Ser/Thr protein phosphatase,is upregulated in mice with streptozotocin-induced DCM.Notably,DCM-related cardiac structural and functional deficits were negated in cardiomyocyte-specific Pgam5 knockout(Pgam5^(CKO))mice.Hyperglycemic stress impaired adenosine triphosphate production,reduced respiratory activity,and prolonged mitochondrial permeability transition pore opening in acutely isolated neonatal cardiomyocytes from control Pgam5^(f/f) mice,and these effects were markedly prevented in cardiomyocytes from Pgam5^(CKO) mice.Likewise,three main MQS-governed processes—namely,mitochondrial fission/fusion cycling,mitophagy,and biogenesis—were disrupted by hyperglycemia in Pgam5^(f/f),but not in Pgam5^(CKO),cardiomyocytes.On the basis of bioinformatics prediction of interaction between PGAM5 and prohibitin 2(PHB2),an inner mitochondrial membrane-associated scaffolding protein,co-immunoprecipitation,and immunoblot assays demonstrated that PGAM5 dephosphorylates PHB2 on Ser91.Transfection of cardiomyocytes with phosphodefective or phosphomimetic Ser91 mutants of PHB2 confirmed a critical role for PGAM5-mediated dephosphorylation of PHB2 in mitochondrial dysfunction associated with hyperglycemic stress.Furthermore,knockin mice expressing phosphomimetic PHB2^(S91D) were resistant to diabetes-induced cardiac dysfunction.Our findings highlight the PGAM-PHB2 axis as a novel and critical regulator of mitochondrial dysfunction in DCM.

Inhibition of ammonia-oxidizing bacteria promotes the growth of ammonia-oxidizing archaea in ammonium-rich alkaline soils

Disparities in the substrate affinity and tolerance threshold for ammonia have been believed to play a key role in driving niche differentiation between ammonia-oxidizing archaea (AOA) and bacteria (AOB);however, recent surveys argue that direct competition between AOA and AOB is also important in this phenomenon. Accordingly, it is reasonable to predict that diverse AOA lineages would grow in ammonium (NH_(4)^(+))-rich alkaline arable soils if AOB growth is suppressed. To test this hypothesis, a microcosm study was established using three different types of alkaline arable soils, in which a high NH_(4)^(+) concentration (200 μg N g^(-1) dry soil) was maintained by routinely replenishing urea and the activities of AOB were selectively inhibited by 1-octyne or 3,4-dimethylpyrazole phosphate (DMPP). Compared with amendment with urea alone, 1-octyne partially retarded AOB growth, while DMPP completely inhibited AOB. Both inhibitors accelerated the growth of AOA, with significantly higher ratios of abundance of AOA to AOB observed with DMPP amendment across soils. Nonmetric multidimensional scaling analysis (NMDS) indicated that different treatments significantly altered the community structures of both AOA and AOB and AOA OTUs enriched by high-NH_(4)^(+) amendment were taxonomically constrained across the soils tested and closely related to Nitrososphaera viennensis EN76 and N. garnensis. Given that these representative strains have been demonstrated to be sensitive to high ammonia concentrations, our results suggest that it is the competitiveness for ammonia, rather than disparities in substrate affinity and tolerance threshold for ammonia, that drives niche differentiation between these phylotypes and AOB in NH_(4)^(+)-rich alkaline soils.

Nonlinear control and synchronization of a class of nonlinear coupled dynamical systems

In this paper,a control problem for a class of nonlinear coupled dynamical systems is proposed and a continuous nonlinear feedback control law is designed using direct Lyapunov method to solve the proposed control problem.Moreover,synchronization problem for a special case of this class nonlinear coupled dynamical systems is concerned.Numerical examples show the effectiveness and advantage of the designed continuous nonlinear control law and derived synchronization result.