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We propose a novel neural-network based model for predicting rainfall in any given city in India. Our initial implementation primarily focuses on a rural city in Bihar, a state which experiences erratic rainfall behavior. We start with simpler models based on time-series analysis and linear regression, ultimately combining them using a novel look back-window method. The initial results are promising...
Frequent sequence mining methods often make use of constraints to control which subsequences should be mined, e.g., length, gap, span, regular-expression, and hierarchy constraints. We show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. In more detail, we propose a set of simple and intuitive "pattern expressions"...
Lifelong learning models are popularly used with big data analysis as it learns better with the volume and variety of data. The model learns independently through an augmented learning mechanism that does not require manual support. Learning wrong and irrelevant rules are expected as it follows an unsupervised approach and therefore, the model is supported with a filtering mechanism. The rules that...
A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management...
Prioritizing a database of items in response to a given query object is a fundamental task in information retrieval and machine learning. We examine a specific realization of this problem in the context of a collection of biomedical articles. Given a query PubMed article, we investigate the problem of identifying and ranking recommended papers that are topically related to the query article. The two...
Morphological analysis is an essential step for processing the Korean language, due to highly agglutinative properties of the language. In this paper, we propose a novel approach for constructing a Korean morphological analyzer that can capture linguistic properties using graphemes as basic processing units. Since our model does not utilize prior linguistic knowledge, the model can be applied to other...
In this paper, we aimed to guide about latest development and studies about students' performance analysis and Learning Analytics in Massively Open Online Courses (MOOCs) for researchers related with the topics. For this purpose short review for usage of performance prediction and Learning Analytics in MOOCs is investigated In our study, to help readers get familiar with our topic, firstly literature...
In the recent years, sentiment analysis has emerged as a major research problem in the field of Natural Language Processing. Here, the problem is to identify the sentiment/emotion in given sentence/paragraph. Usually it is positive, negative and neutral. Here, we consider only binary classification task (positive and negative). We have considered the best performing sentiment analysis model which...
“Virtual statistics” are performance measures that are conditional on the occurrence of an event; virtual waiting time of a customer arriving to a queue at time t is one example. In this paper, we describe a k-nearest-neighbor method for estimating virtual statistics post-simulation from the retained sample paths, examining both its small-sample and asymptotic properties. We implement leave-one-replication-out...
Simulation experiments are not conducted in a vacuum. They are performed to address specific research questions that require evaluation of testable hypotheses. However, the connections among goals, hypotheses, and experiments are often characterized in an ad hoc manner. In this paper, we examine symbiotic dependencies among goals, hypotheses, and experiments within the context of computational discovery...
A new approach is introduced in this paper for dynamic modeling and dimensionality reduction from time series of curves. For this purpose, a dynamic mixture of experts model whose regression coefficients evolve from curve to curve according to a Gaussian random walk over low dimensional factors, is proposed. The resulting model is neither else than a particular state-space model involving discrete...
Human mobility analysis and modeling is a very interdisciplinary field of research. Mobility models play an important role particularly in assessing the simulative performance of mobile and opportunistic networks. The mobility traces, which most of these models are based on, however, mostly suffer from several shortcomings. In this paper, we use the extensive Lausanne Data Collection Campaign (LDCC)...
User Experience Design (UXD) addresses the increasing importance of emotional aspects in user product interaction and aims at creating holistic experiences. While UXD is a rather young field within product development, other disciplines outside engineering design (e.g. gaming, sports) traditionally focus on fascinating their users. Based on the approach of transferring insights from experience focused...
Agent-based modelling is becoming widely used for studies in Social Sciences. However, its application faces limitations coming from its bias to software development, which precludes a more active involvement of social researchers. In order to deal with this problem, this work proposes using domain-specific modelling languages based on the socio-psychological Activity Theory. These languages apply...
The task of generating network-based evidence to support network forensic investigation is becoming increasingly prominent. Undoubtedly, such evidence is significantly imperative as it not only can be used to diagnose and respond to various network-related issues (i.e., performance bottlenecks, routing issues, etc.) but more importantly, can be leveraged to infer and further investigate network security...
Travel demand models use speed-flow equations to accurately predict the speed of traffic on urban roads. However, in urban networks, the correlations can be quite complex because they are affected by rather short road segments and signalized intersections resulting in interrupted traffic flows. In this context, the green timeshare of a traffic signal influences the capacity of a corresponding road...
The security of Cyber-Physical Systems (CPS) has been recently receiving significant attention from the research community. To this end, this paper sheds the light on a number of security approaches for CPS from two perspectives, namely, control-theoretic and cyber security. Further, threat detectors in various CPS environments are highlighted and discussed. The aim is to demonstrate the lack of coherent...
The emergence of social networks and the communication facilities they offer have generated an enormous informational mass. This social content is used in several research and industrial works and has had a great impact in different processes. In this paper, we present an overview of social information use in Information Retrieval (IR) and Recommendation systems. We first describe several user profile...
Securing critical systems such as Cyber-Physical Systems (CPS) is an important feature especially when it comes to critical transmitted data in a real-time environment. At the same time, the implementation of security counter-measures in such systems may impact transmission delays of critical tasks. For this reason selecting proper security mechanisms in such critical systems is an important issue...
The development of database systems and applications requires the use of metrics to evaluate the quality and the efficiency of each phase, especially as regards the physical phase, where logical, physical and hardware optimizations are mainly used. Since the 1980s, a large range of cost models has been proposed. Each cost model is dedicated to the calculation of specific metrics and mainly pertains...
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