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Discovering and modeling lead-lag relations is a critical task in a variety of domains, including energy management, financial markets and environment monitoring. This task becomes more challenging when processing massive and highly dynamic data sources, often produced by sensors and live feeds that collect data about evolving entities in the real world. To cope with this data volume and velocity,...
Due to the sheer volume of opinion rich web resources such as discussion forum, review sites, blogs, and news corpora available in digital form, much of the current research is focusing on the area of sentiment analysis. People are intended to develop a system that can identify and classify opinion or sentiment as represented in an electronic text. An accurate method for predicting sentiments could...
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create socalled local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible...
Understanding urban human mobility is crucial for epidemic control, urban planning, traffic forecasting systems and, more recently, various mobile and network applications. Nowadays, a variety of urban human mobility data have been gathered and published. Pervasive GPS data can be collected by mobile phones. A mobile operator can track people's movement in cities based on their cellular network location...
A Rule Set model consists of a small number of short rules for interpretable classification, where an instance is classified as positive if it satisfies at least one of the rules. The rule set provides reasons for predictions, and also descriptions of a particular class. We present a Bayesian framework for learning Rule Set models, with prior parameters that the user can set to encourage the model...
Although it is possible to design and manufacture MPSoCs with hundreds of processors, there is still a gap in the ability to debug hardware, software, and applications for such chips. Current state-of-the-art works related to MPSoC debugging suffer from poor integration, scalability in data storage, and simple graphical data representation. This work proposes a modular debugging framework to aid the...
Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given the sheer size and restricted access of online social network data, efficient computation of subgraph counts is highly challenging. Although a number of algorithms have been proposed to estimate the relative counts of subgraphs in OSNs with restricted access, there are only few works which try to solve a more...
Reversible data hiding (RDH) is a specific information hiding technique in which both the embedded data and the original cover medium can be exactly extracted from the marked data. In this paper, we present a general expansion-shifting model for RDH by introducing the so-called reversible embedding function (REF) which maps each point of Zn to a nonempty subset of Zn. Moreover, to guarantee the reversibility,...
This paper proposes an inaudible and robust audio-information-hiding scheme based on the singular-spectrum analysis (SSA) and a psychoacoustic model. SSA is used to decompose the host signals into several additive oscillatory components. The hidden information is embedded into the host signals by modifying amplitudes of some oscillatory components. To satisfy the inaudibility, we propose a novel method...
We deal with the problem of initial analysis of data coming from evaluation sheets of subjects with Autism Spectrum Disorders (ASDs). In our research, we use an original evaluation sheet including questions about competencies grouped into 17 spheres. In the paper, we are focused on a feature selection problem. The main goal is to use appropriate data to build simpler and more accurate classifiers...
One of the most important tools for studying fluid flow behavior in oil and gas reservoirs is reservoir simulation. It is constructed based on a comprehensive geological information. A comprehensive numerical reservoir model has tens of millions of grid blocks. Therefore, it becomes computationally expensive and time consuming to run the model for different reservoir simulation scenarios. There are...
Process mining refers to the discovery, conformance, and enhancement of process models from event logs currently produced by several information systems (e.g. workflow management systems). By tightly coupling event logs and process models, process mining makes it possible to detect deviations, predict delays, support decision making, and recommend process redesigns.Event logs are data sets containing...
Clinical skills education is an essential component of the teaching plan in medical science courses, such as nursing education. Simulation-based learning is an effective teaching method in any practical or vocational-based training. The development of simulation-based teaching has been impacted by the integration of emerging technologies, such as Intelligent Tutoring Systems (ITSs), which results...
A method for processing plenoptic images and reconstructing 3D models of real objects is presented in this paper. The reconstruction of the object is made by using the capability of the plenoptic camera to perceive the depth. Through six shots, which shoot the object in a central perspective, it will be shown how it is possible to recreate the object in a digital 3D space, in its entirety, at low...
With the significant advances in online social networks which provide precious knowledge for personalized recommendation, it is necessary to design effective and efficient method to deal with such data. In this paper, we focus on the recommendation system to integrate the preference information and trust relations among users, following the trust-based recommendation model (TbRM) [1] which considers...
We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold. We then provide a propositional satisfiability based encoding. We extend the...
A challenge task of data mining is to process massive data in the big data era. MapReduce is an attractive model to overcome this challenge. This paper presents a new method to accelerate the process of learning Markov blanket Bayesian network(MBBN). Markov blanket is a better model type of Bayesian network in some complex datasets. The time and space cost of learning Markov blanket is large, and...
L-system is a prevailing modeling method for generating fractals, especially self-similar patterns such as plants. However it's too hard to design an appropriate L-system to get the desired visual models of plants. In order to generate a favorable plant model, usually we need to deduce backwards or guess the production rules of the L-system and then try to modify some control parameters over and over...
We describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data, and the tracking of their lifetime and popularity over time. Unlike the social media or news data where the underlying topics evolve over time, the topic nuances in science result in new scientific directions to emerge. Therefore, we model the longitudinal literature data with a new...
Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims...
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