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Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage...
On the basis of depth study of commercial bank credit risk control model literature, this paper introduced the concepts of credit risk and credit risk control. We research the main influencing factors of commercial bank credit risk control scientifically by artificial neural network theory, and then set a commercial bank credit risk control index system which contains 3 levels of 27 indexes. Improved...
In this paper, a tool for automatically generating test programs for ARM VMSAv8-64 memory management units is described. The solution is based on the MicroTESK framework being developed at ISP RAS. The tool consists of two parts: an architecture-independent test program generation core and VMSAv8-64 specifications. Such separation is not a new principle in the area -- it is applied in a number of...
Social networking sites these days are great source of communication for internet users. So these are important source for understanding the emotions of people. In this paper, we use data mining techniques for the purpose of classification to perform sentiment analysis on the views people have shared in Twitter. We collect dataset, i.e. The tweets from twitter that are in natual language and apply...
It is very important and practical to make data analysis for intrusion detection based on large scale data. For the current system problem in simulation and off-line analysis, a set of system is proposed as intrusion detection and analysis for truly website. The system is integrated with two subsystems of intrusion detection and large data analysis. Through network construction and software design,...
Software testing is an essential activity in software development process that has been widely used as a means of achieving software reliability and quality. Software practitioners rely on test coverage to decide whether software under test has achieved an acceptable level of reliability and can be released. The researchers in the field of software testing focus on defining meaningful test coverage...
Programs often contain branches to break off from their main execution. These branches contribute to the total cost of testing, because they also need to be tested. The paper presents a new approach to improve the testing of such branches by annotating them. Our tool automatically generates test-sequences. Then, invariants in the form of pre-and post-condition over the executions passing each annotated...
This paper proposes a novel contribution factor (CF) approach to predict diversified daily peak load of low voltage (LV) substations. The CF for each LV template developed in part I of the paper is determined by a novel method—clusterwise weighted constrained regression (CWCR). It takes into account the contribution from different customer classes to substation peaks, respecting the natural difference...
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification,...
We propose Guided Random Testing (GRT), which uses static and dynamic analysis to include information on program types, data, and dependencies in various stages of automated test generation. Static analysis extracts knowledge from the system under test. Test coverage is further improved through state fuzzing and continuous coverage analysis. We evaluated GRT on 32 real-world projects and found that...
While being highly automated and easy to use, existing techniques of random testing suffer from low code coverage and defect detection ability for practical software applications. Most tools use a pure black-box approach, which does not use knowledge specific to the software under test. Mining and leveraging the information of the software under test can be promising to guide random testing to overcome...
This paper highlights the importance of using student data to drive improvement in education planning. It then presents techniques of how to obtain knowledge from databases such as large arrays of student data from academic Institution databases. Further, it describes the development of a tool that will enable faculty members to identify, predict and classify students based on academic performance...
In this paper, based on Low-rank Representation (LRR) we present a new method, Transposed Discriminative Low-Rank Representation (TDLRR), for face recognition in which both training and testing images are corrupted. By adding a discriminative term into LRR function, we obtained a low-rank matrix recovery with the increase the discriminative ability between different classes. LRR of transposed data...
Graphs are a fundamental model to describe complex statistical relationships over many scientific domains. In this context, graphs are commonly used to investigate relational phenomena which are not directly observable. Our work formulates a Latent Network Inference problem and develops inference methods in a common context for scientific applications where there is an absence of ground truth.
The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world...
Business critical web applications are the most popular services provided to the client by the financial sector. These applications are bringing handsome revenue for the financial industry every year. These services are also a frequent target of attackers. Poor coding practice leads applications to vulnerability that are exploited by attackers. Information and privileges such as access to databases,...
The speedily growing field of big data analytics has started to play a pivot role in the advancement of healthcare practices and research. It has provided tools to mount up, manage, analyze, and incorporate large volumes of unrelated, structured, and unstructured data produced by current healthcare systems. Big data analytics is a useful technique that is useful to provide better analysis of disease...
Linkage of routine and administrative databases from multiple sources provides an advantageous form of understanding chronic diseases, such as arthropathy conditions. Data mining classification algorithms can be a cost-effective approach to identify patients' cohorts with certain disorders within these complex databases. However, selecting good potential predictors, given a certain condition from...
In this paper, we report on an empirical study we have conducted at Ericsson to understand the handling of crash reports (CRs). The study was performed on a dataset of CRs spanning over two years of activities on one of Ericsson's largest systems (+4 Million LOC). CRs at Ericsson are divided into two types: Internal and External. Internal CRs are reported within the organization after the integration...
Testing aims at detecting (regression) bugs in production code. However, testing code is just as likely to contain bugs as the code it tests. Buggy test cases can silently miss bugs in the production code or loudly ring false alarms when the production code is correct. We present the first empirical study of bugs in test code to characterize their prevalence and root cause categories. We mine the...
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