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Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examining...
In terms of the retail commodity sale forecast, people did more in particular aspect with commodity's single sale attribute such as the sale volume, the sale money, the season factor, but all has not considered the most important factor-profit, the profit is the key factor of retail enterprises winning the survival and development. However, such a one-sided analysis is not conducive to assist the...
We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model mining techniques: We target subsets of sequential...
The field of learning analytics has the potential to enable education institutions to increase their understanding of their students' learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. In this work we investigate the database of learners'...
In this paper, we propose a method that extracts information from sequences of financial ratios and investigate the usefulness of this information for bankruptcy prediction, which constitutes an important class of financial services. We use the annual financial reports available from an external financial information services provider to extract predictors based on the Markov for Discrimination (MFD)...
In data warehousing Gibbs Sampling is applied in missing value processing due to the many defects in the traditional methods. As long as Full Conditional Distribution Condition (FCDC) is met, Gibbs sampling can solve issues such as high workload and biased data. The method's high operability - it even can be completed in common used tool like Excel - makes it a practical method for real data preprocessing.
Sliding-window multi-stream join (SWMJ) is a fundamental operation for correlating information from different streams. We provide a solution to the problem of assessing significance of the SWMJ result by focusing on the relative frequency of windows satisfying a given equijoin predicate as the most important parameter of the SWMJ result. In particular, we derive an analytic formula for computing the...
In this paper we are interested in discovering collaborative writing patterns in student data collected from a system we designed to support student collaborative writing, and which has been used by over 1,000 students in the past year. A particular functionality that we are investigating is the extraction and display to learners and teachers of the process followed during the course of the writing...
World Wide Web is growing rapidly. So it is necessary to study the user web navigation behavior to improve the quality of web services, offered to the web user. Analysis of user web navigation behavior is achieved through modeling web navigation history. Markov model is widely used to model the user web navigation sessions. Lower-order Markov model provides high coverage, but with low accuracy. Higher-order...
This paper discusses methods and parameter settings that help to estimate texture in SAR images. In general, this is a difficult task for SAR images that are characterized by speckle noise and which span a wide range of pixel magnitudes. We applied Gauss Markov Random Field (GMRF) models and Enhanced Model Based Despeckling (EMBD) to 1 meter resolution amplitude images of the German TerraSAR-X mission...
Based on the dynamic association rules, this paper puts forward the formal definition of meta-rules which makes use of the support vector and confidence vector as evaluation of rules, and introduces the usual mining process of the Meta-association Rules for dynamic association rule by the model of AR-Markov, the examples show that this method is effective in the analysing and predicting the change...
Latent Dirichlet allocation (LDA) is a commonly used topic modeling method for text analysis and mining. Standard LDA treats documents as bags of words, ignoring the syntactic structures of sentences. In this paper, we propose a hybrid model that embeds hidden Markov models (HMMs) within LDA topics to jointly model both the topics and the syntactic structures within each topic. Our model is general...
Consider a group of peers, an ideal random peer sampling service should return a peer, which is an unbiased independent random sample of the group. This paper focuses on peer sampling service based on view shuffling (aka gossip-based peer sampling), where each peer is equipped with a local view of size c. This view should correspond to a uniform random sample of size c of the whole system in order...
With the application and popularization of autonomic computing in the field of aerospace exploration, large-scale database management and critical network control, existing self-reflection models based on natural language or diagram can not meet the requirements of analysis and verification. In this article, two kinds of self-reflection models, one is the static and the other is dynamic, capable of...
Data streaming (DS) over peer-to-peer (P2P) networks has been intensively studied in recent years and there have been various schemes proposed already. To evaluate these schemes, either measurement in experimental implementations, or simulation and theoretical analysis have been used. The former is inadequate as data are collected from different experiments, while the latter lacks a proper theoretical...
In this paper we study the overhead introduced by secure functions in considering two models of non-repudiation protocols. The models are specified using the Markovian process algebra PEPA. The basic model suffers from the well known state space explosion problem when tackled using Markov chain analysis. Following previous study of performance modelling on security protocols, mean value analysis and...
This paper proposes a strategy to associate Statecharts with a Markov Decision Process for performance evaluation. Statecharts are adapted to represent the possible decision choices and the costs incurred from decisions. Markov Decision Process is used to evaluate the long term effects of decisions. This strategy can aid users without a good knowledge of the performance evaluation process, and may...
In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. These data are often used to build models that explain relationships between the measured variables, and are eventually used for planning and control purposes. However, these measurements cannot always be exact, systems can...
Traffic burstiness has a significant impact on network performance. Burstiness can cause buffer overflows and packet drops and is particularly problematic in the context of small-buffer networks, which have been considered as a building block of the optical core infrastructure in the future Internet. To permit efficient operation of such networks, network traffic has to be ??paced?? by transmitting...
How to control energy consumption is one of the most important issues for the battery-powered mobile stations in mobile networks. IEEE 802.16e standard proposes an energy saving mechanism that named "sleep mode" for conserving the power of the mobile stations. According to the operating mechanism of the sleep mode for downlink traffic in the type I power saving class, modeling the data frames...
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