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Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose...
In data mining, link prediction for the networks is one of the areas of greatest interest today. Research achievements of link prediction problem can be applied in many fields such as study genetically transferred diseases, online marketing, e-commerce services, discover the structure of criminal networks, friend request in social networks … However, most of researchers focused on predicting the existence...
Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts...
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
Recommender systems have attracted much attention in last decades, which can help the users explore new items in many applications. As a popular technique in recommender systems, item recommendation works by recommending items to users based on their historical interactions. Conventional item recommendation methods usually assume that users and items are stationary, which is not always the case in...
Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
The mass monitoring data collected by the on-line monitoring of the substation is stored in the Hadoop Distributed File System (HDFS), and the index table structure of the online monitoring data is optimized and stored in the distributed structured database (HBase) Quick access to monitoring data. Based on Hadoop 's online monitoring data processing experiment platform, a fast fault identification...
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,...
Heap overflow is one of the most widely exploited vulnerabilities, with a large number of heap overflow instances reported every year. It is important to decide whether a crash caused by heap overflow can be turned into an exploit. Efficient and effective assessment of exploitability of crashes facilitates to identify severe vulnerabilities and thus prioritize resources. In this paper, we propose...
This article presents a survey of the factors that indicate evasion in distance education, as well as the data mining techniques that are being used in the detection of evasion. As a methodology, we have used the systematic review, analyzing the works published in the last five years. The result indicated that there are multiple factors that influence evasion, which were systematized in four dimensions,...
The correcting process for strokes extracted from Chinese characters is the necessary step to extract the errors of writing errors automatically. Visualization of extracted strokes is the prerequisite for manual correction. Therefore, visualization and adaptive correction methods are proposed. To reduce the cognitive burden of correcting, color, brightness, saturation and order number is comprehensively...
This work is focused on the exploration and application of entities extraction techniques for the codification and identification of geographical locations present in the geographic distribution section within botanic documents, such as the plant species manual of Costa Rica. Additional to the identification and codification, an algorithm to bind the geo-coding to a gazetteer is presented. This algorithm...
Good nutrition is an essential component of life. Undernutrition is the root cause of death of over 3.5 million children under the age of five in India. To address this issue of malnutrition, though overarching national policy is desirable, it may not be effective if the root cause of malnutrition varies across regions of the country. In this context, the attempt made in this paper is two-fold. First,...
Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation...
Two GF-1 WFV images on August 3, 2015 and October 2, 2015 were selected to extract the cultivated area of paddy rice in Jianhu county of Jiangsu Province. Vegetation indexes were extracted from the original spectrum data in order to extract paddy rice area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy...
The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation...
Through the continuous collection and in-depth analysis of the quality monitoring data of colleges and universities, we combine the efficiency processing of big data and data evaluation, monitor the status of higher education normally, and construct a higher education quality monitoring and evaluation platform based on Spark. This platform is teaching centered with schools as its basis, including...
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