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Today's high-performance computing (HPC) systems are heavily instrumented, generating logs containing information about abnormal events, such as critical conditions, faults, errors and failures, system resource utilization, and about the resource usage of user applications. These logs, once fully analyzed and correlated, can produce detailed information about the system health, root causes of failures,...
Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
Modern, densely instrumented, smart buildings generate large amounts of raw data. This poses significant challenges from both the data management perspective as well as leveraging the associated information for enabling advanced energy management, fault detection and control strategies. Networks of intelligent sensors, controllers and actuators currently allow fine grained monitoring of the building...
A large number of aviation equipment maintenance data exhibit seasonal behavior, such as aircraft failure rate. Consequently, seasonal forecasting problems are of considerable importance in aviation maintenance support. Aircraft failure rate is an important parameter of aviation equipment RMS (Reliability-Maintainability-Supportability). It is indispensable to scientifically predict the aircraft failure...
A novel procedure to determine foot shape is propose. The relationship between shape features is used to discriminate shape. Five foot shape features that are Left Foot Length (LFL), Left Foot Breadth (LFB), Left Ball Girth Circumference (LBGC), Left Instep Length (LIL) and Left Fibulare Instep Length (LFIL) from a random sample of 161 Malaysians ladies were studied. Two groups of foot shape feature...
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
The system that models biological objects (e.g. colloids, ensembles of protein molecules) with competing interactions is considered. Such objects attract each other on short distances and repel at larger separations. The model is the system of particles on a triangular lattice interacting via a pair potential which is attractive for the nearest neighbors and repulsive for the third ones. Monte Carlo...
The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically...
Public safety has been discussed for many years, but how to use and understand crime data is still difficult. Limitations of previous research were mainly restricted by technology. The significant technological advance since last century provided a tool for researchers to compute large amount of data and complicate models while shrinking process time too. This research tried to combine results from...
Data validation and reconciliation (DVR) plays key role in industries because it uses process information and statistical methods to estimate correct measurements from the observed data. DR is very effective when measurement is free from gross error. Data observed from chemical processes can be serially correlated. Serial correlation in data can arise when a process, takes time to adjust, is exposed...
In the era of the Internet, people are active in multiple online services, and they usually have accounts on more than one online service. Each account is a virtual identity of the user. In order to trace individual's online behavior at any time and any places, linking virtual identities belonging to the same natural person across different online service domains is very important. Existing methods...
Existing clustering algorithms need to specify the number of clusters and to select initial points using human input, which lead to inferior clustering and optimisation outputs. Here, an improved grey decision-making model based on the thought of affinity propagation algorithm and grey correlation analysis is proposed to solve these problems. According to the panel data class and the inter-class candidate...
The aims of this study were to forecast change of Chinese social donation in recent years by grey Verhulst model to find out characteristics of social donation and grey correlation analysis which was proposed by Professor Deng Julong is used to detect of an interpersonal relationship between the number of social donations from oversea and the number of foundations. This paper provides a quantitative...
This paper studied the spatial distribution of Wuhan land use degree in 2015. In addition, the spatial autocorrelation of land use degree and its impact factors were analyzed, including population density, primary industry, secondary industry, tertiary industry, road density and terrain relief degree. A spatial autoregressive model and Bayesian Geographically Weighted Regression (BGWR) was established...
5G communication will bring a surge traffic in cellular network. The traffic in cellular network not only has strong variability by time, but also has strong spatio-temporal correlation, which brings large difficulty to predict. In order to make reasonable use of communication network resources, it is important to describe and predict the spatio-temporal information of traffic in cellular network...
Wireless sensor networks (WSNs) are installed in the terrain for observing the physical and environmental parameters. The nodes in the network are resource constrained in nature and faces several challenges for producing the data from the unfriendly environment. Large amount of data is generated from WSN and suffers from data fault, inaccuracy and inconsistency. To increase the reliability of application,...
Modeling low voltage consumption and generation individually is becoming an essential task for DSOs to plan infrastructure investments more efficiently and manage the network more actively in the effort of making grids smarter. In this paper, three different approaches for modeling such individuals is exposed. A quasi-sequential approach which holds the exact distributions of consumption and generation...
To capture the trends of concerned topics in specific field, people often use topic discovery methods to get this goal. The traditional topic discovery algorithms are generally divided into two types, text clustering algorithm and text topic model. The former lacks of attention on semantic information, and the latter always ignores relativity of the topic. These affect the topic discovery and topic...
In semiconductor manufacturing for automotive as for many other industries, reliability tests are designed and implemented in order to predict failure rate in real life and applications. Physics-of-failure is used on the rejects observed in field and during the reliability tests to check them to stress the components as in life applications. Besides this qualitative study between field and reliability...
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces...
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