A uniform linear array (ULA) represents a fundamental sparse array configuration characterized by constant inter-element spacing. In this letter, for any positive integer$r$, we propose an$r$th-order generalized five-ULA sparse array ($r$-G5USA), which includes super augmented nested array (SANA) and a class of enhanced multi-ULA sparse array (EMUSA) structure as special cases. Closed-form expressions are derived for both the array configuration and the weight function of$r$-G5USA. Specifically, the proposed$r$-G5USA not only maintains a high number of uniform degrees of freedom (uDOFs) on the order of$\mathcal {O}(\frac{2N^{2}}{3} + \frac{2N}{3})$, but also enables flexible control of the first$2r-1$or$2r-2$weight functions, reducing their values to unity. Simulation results demonstrate that$r$-G5USA exhibits superior performance over existing sparse arrays in terms of uDOFs, coupling leakage (CL), and direction-of-arrival (DOA) estimation accuracy.

We present a decade of evolution and production experience with Jupiter datacenter network fabrics. In this period Jupiter has delivered 5x higher speed and capacity, 30% reduction in capex, 41% reduction in power, incremental deployment and technology refresh all while serving live production traffic. A key enabler for these improvements is evolving Jupiter from a Clos to a direct-connect topology among the machine aggregation blocks. Critical architectural changes for this include: A datacenter interconnection layer employing Micro-Electro-Mechanical Systems (MEMS) based Optical Circuit Switches (OCSes) to enable dynamic topology reconfiguration, centralized Software-Defined Networking (SDN) control for traffic engineering, and automated network operations for incremental capacity delivery and topology engineering. We show that the combination of traffic and topology engineering on direct-connect fabrics achieves similar throughput as Clos fabrics for our production traffic patterns. We also optimize for path lengths: 60% of the traffic takes direct path from source to destination aggregation blocks, while the remaining transits one additional block, achieving an average block-level path length of 1.4 in our fleet today. OCS also achieves 3x faster fabric reconfiguration compared to pre-evolution Clos fabrics that used a patch panel based interconnect.

With the advancement and popularization of healthcare informatization, healthcare fraud has happened frequently. Doctors, patients and pharmacies are involved, and these various subjects cooperate with each other to form a complicated fraud relationship. Healthcare fraud has caused great damage to the safety of the health insurance fund. However, fraudster detection problem has not been solved properly at present. Therefore, Fraudster Detection Based on Modularity Optimization Algorithm (FDMOA) approach is developed to find doctor fraudsters in a good accuracy, which models the medical insurance records as a heterogeneous weighted network of doctors and medicines, and uses a modularity optimization algorithm to divide doctors and drugs into corresponding communities on a heterogeneous network. Finally, anomaly detection strategy was used in the community to find an abnormal doctor. Experiments show that the proposed method outperforms distance-based, clustering-based, and LOF algorithms.

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.

With the development of modern society, business processes are widely used, and they play an important role in peo-ple's daily life. In government agencies, there are many business processes employed to deal with different kinds of transactions. Such as one of the medical insurance refund processes is used to claim for maternity insurance. In order to analyze, evaluate and optimize the business process in different situations, this paper develops to use the queueing network theory to establish the corresponding business process model. Then apply this model to analyze the performance of the business process by using four performance metrics i.e., average queue length, average job sojourn time, job arrival intensity and throughput. Finally, this paper optimizes the business process based on three situations. It has been proven that the optimization methods are scientific and the business process after the optimization is more efficient.

Process frameworks contain the main logic of business. Frameworks extracting and filling can formulate a new process efficiently and accurately. Aiming at the construction guidance of new business processes with framework, a direct-viewing solution based on the similarity analysis of process diagrams is proposed. In this solution, the process diagrams are abstracted into process trees to get process sequences and then the similarity is calculated out of process skeletons and process entities, thus the processes which are similar both in structure and function can be found. The process trees and process sequences refer to simple data structures that are easy to analyse and enhance process comprehensibility. In addition, similarity measurement from both the skeletons and the entities can control process analysis from the overall situation and avoid information omission and deviation. Meanwhile, general process frameworks are extracted out for reference by clustering the processes through improving similarity cluster algorithm, and the instructive recommendation to the processes under construction can be given according to the existing processes. Based on this solution, economic expenditure can be reduced to a large extent and existing resources can be reused.

In PaaS, the issue of resource provisioning becomes more challenging because a great number of applications share and compete for resources simultaneously. PaaS platform should be able to maximize the resources utilization, while satisfying the performance requirements of all applications. However on-line request workload can be fluctuated during the run time of applications and static resource allocation may cause either under provisioning or over provisioning problem. In this paper, we propose an adaptive resource provisioning approach to dynamically allocate resources for applications according to the workload variation. In case of performance violation, our approach can make an efficient plan to keep application performance within a valid range.

As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.

In order to implement the complex business process involved in multi-computer applications, it is necessary for correlative computer applications to cooperate with each other and connect to each other. Under the traditional client/server architecture, there are many difficulties in implementing connections to each application. It can offer a better foundation of architecture that constructs an application server and forms the multi-tier architecture of the client/application server/database server using a component based software technique. However, this architecture needs the correlative applications to connect to each other continuously. Apparently, it is confined under the environment of noncontinuous connection. In this paper we propose the concept of offline component agent and the multi-tiered architecture based on an offline component agent, which can effectually implement interconnection of multi-applications under the environment of non-continuous connection. Offline component agents provided by the server application and configured at the client application process business logic and data logic in correlative server applications. The client application and offline component agent maintain continuous connection, but the offline component agent and server application may not maintain continuous connection, these two parts cooperating with each other according to special arithmetic. The merit of the architecture of software multi-tier components includes clarity of the interface between different applications, consistency between software structure and problem structure, better encapsulation of software logic, and the advantages of safe management and simplicity of maintenance and version management. We explicate the architecture based on the offline component agent.