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We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
Automatically generating unit tests is a powerful approach to exercise complex software. Unfortunately, current techniques often fail to provide relevant input values, such as strings that bypass domain-specific sanity checks. As a result, state-of-the-art techniques are effective for generic classes, such as collections, but less successful for domain-specific software. This paper presents TestMiner,...
This paper investigates an efficient modal method for reducing weak transmission stability boundaries and identifying voltage control areas. This method divides the power system network into regions to eventually reduce the control candidates for each controller and minimize the interaction between each voltage control area. To determine the optimized number of areas, proper selection of threshold...
Differential evolution (DE) algorithm mainly uses the distance and direction information from the current population to guide search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. It can be used to extract the global information about...
Recent developments in the storage of system data in the Navy's data repository, LEAPS, using the FOCUS product meta-model have opened the doors to graph-theory applications in the design of Navy ship systems in the early stages of design. In this paper, we demonstrate the ability to extract graphs from ship data and present pertinent applications of such graphs including a vulnerability metric for...
Process mining techniques have been applied to the visualization, interpretation, and analysis of medical processes. However, only a very limited amount of process data necessary for these analyses is publicly available, especially in the medical field because of patients' privacy. This limits novel medical process research to using insufficiently large or randomly-generated synthetic datasets. Our...
Venn and Euler diagrams are well-defined mathematical diagram types, which are the major representation methods of Set Theory. Venn and Euler diagrams are part of major Mathematics examinations in secondary education such as London Ordinary Level and SAT. Although computer assessment of different diagram types has been addressed, no such research has been done for Venn and Euler diagrams. In this...
Many comic books are now published as digital books, which easily provide color contents compared to the physical books. The motivation of automatic colorization of comic books now arises. Previous studies colorize sketches without other clues or with spatial color annotations. They are expected to reduce workloads of comic artists but still require spatial color annotations for desirable colorizations...
Fuzzy association rules are one of the most important data mining techniques. They allow to discover useful and meaningful information that help in decision-making. Many algorithms have been proposed to extract fuzzy association rules. A major drawback of these proposed algorithms is their high run-time for extracting fuzzy association rules. To overcome this problem, we introduce in this paper a...
Operation services are reusable and shareable units of configuration code executed by configuration management tools (CMTs), achieving continuous deployment and continuous delivery. With the prevalence of DevOps (Development and Operations), thousands of operation services have been developed for various software systems, and they are publicly available through the online repositories of popular CMTs...
The creation of Learning Spaces on the Web, like the exhibition rooms of virtual museums, supported by an ontology that enables a conceptual navigation over the learning objects exposed, is an hard and complex task but of uttermost importance for the success of the knowledge acquisition process. In our opinion, the creation must be systematic and reusable from case to case, based on the query of the...
On the way to full production control, wire bonding equipment requires data-driven condition-based maintenance of the mechanical setup. In this paper, we identify aspects of regular mechanical equipment setups that severely affect bonding quality and equipment health in mass production. We show that mechanical equipment setups lower process stability in aluminum wire bonding. Typical faults in mechanical...
Modern databases contain an enormous amount of information stored in a structured format. This information is processed to acquire knowledge. However, the process of information extraction from a Database System is cumbersome for non-expert users as it requires an extensive knowledge of DBMS languages. Therefore, an inevitable need arises to bridge the gap between user requirements and the provision...
Understanding the relationship between coverage and test-templates (a generic term we use to describe the inputs for the random stimuli generator) is an important layer in understanding the state and progress of the verification process. Today, this is extremely hard to achieve and is based on expert knowledge. Template Aware Coverage (TAC) is a novel approach to meeting this challenge. Based on collecting...
Parallelizing compilers are a promising solution to tackle key challenges ofMPSoC programming. One fundamental aspect for a profitable parallelization is to estimate the performance of the applications on the target platforms. There is a wide range of state-of-theart performance estimation techniques, such as, simulation-based, measurement-based, among others. They provide performance estimates typically...
The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial proportion are not much significant for many data analysis endeavors. Discovery of a small number of personalized interesting patterns from the large output set according to a particular user's interest is an important as well as challenging task. Existing works on pattern summarization...
A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which...
For purpose of solve the problems of failure of multi-source and complex diagnostic decision in the process of Aircraft Integrated Drive Generator fault diagnosing, from the perspective of data mining, reference the variable precision rough sets to dispose the diagnostic information system, deduce judgment theorems for judging β lower approximation consistent sets by the binary relation and dependence...
Traditional multi-step attack correlation approaches based on intrusion alerts face the challenge of recognizing attack scenarios because these approaches require complex pre-defined association rules as well as a high dependency on expert knowledge. Meanwhile, they barely consider the privacy issues. Under such circumstance, a novel algorithm is proposed to construct multi-step attack scenarios based...
The paper introduces concepts of a discrete neural function and a neural element over a Galois field. A criterion of the possibility of discrete function spectral analysis over a finite field is established. Discrete neural function invariant operations over a Galois field are described.
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