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The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation...
Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with Fuzzy Analogy. More specifically, this study analyze the predictive performance of this analogy-based technique when using toleration, deletion or k-nearest neighbors (KNN) imputation...
With foresight into the state of the wireless channel, a robot can make various optimization decisions with regards to routing packets, planning mobility paths, or switching between diverse radios. However, the process of predicting link quality (LQ) is nontrivial due to the streaming and dynamic nature of radio wave propagation, which is complicated by robot mobility. Due to robot movement, the wireless...
For the single-user multi-terminal scenario, a novel user service behavior prediction and optimization algorithm is proposed in this paper, which can realize the accurate prediction of service requirements and optimal selection of service terminals, so as to improve user QoE effectively. Firstly, by using an adaptive feedback based weight correction method, a modified entropy weighted Markov model...
Defect prediction on unlabeled datasets is one of the most active research areas in software engineering. Generally, cross-project defect prediction (CPDP) and unsupervised learning defect prediction are utilized to address this problem. The fundamental idea of CPDP is the transfer learning that reuses the prediction model built by labeled source projects. However, because of the difference of data...
Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could...
The data science system at Udemy1, a global online education marketplace, is described. This data science system currently supports recommendation and search, but will be extended to support e-learning as well. The motivations behind and the approach to the system are explained, which allows multiple individual data scientists to all become ‘full stack’, taking control of their own destinies from...
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of...
Elasticity is the most important feature that differentiatescloud computing from traditional IT infrastructure. Itdefines the capacity of cloud infrastructure provider to acceleratethe provision or the deprovision of the resources needed to deployclient's services. Auto-scaling resource is typically done usingtwo models: reactive and proactive. Most previous researchesmanage elasticity with a single...
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions...
Automatic Vehicle Location (AVL) is becoming an important tool in Intelligent Transportation Systems (ITS) in the past few years, as it is an effective way of collecting and transmitting data regarding the vehicle's trip for real-time or future use. A methodology for analyzing the state of the art regarding the application of these systems is proposed in a form of a systematic literature review, by...
The interest for wireless sensor networks (WSNs) is continuously growing especially with the emergence of applications such as smart grids, smart cities, e-health, etc. where billions of objets (sensors) will be permanently connected to the Internet. In this context, the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for the next generation multi-hop...
Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming...
The main goal of supervised data analytics is to model a target phenomenon given a limited amount of samples, each represented by an arbitrarily large number of variables. Especially when the number of variables is much larger than the number of available samples, variable selection is a key step as it allows to identify a possibly reduced subset of relevant variables describing the observed phenomenon...
In recent years, there has been an explosion of social recommender systems (SRS) research. However, the dominant trend of these studies has been towards designing new prediction models. The typical approach is to use social information to build those models for each new user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most...
The healthcare industry is a complex system with many moving parts. One issue in this field is the misuse of medical insurance systems, such as Medicare. In this paper, we build a machine learning model to detect when physicians exhibit anomalous behavior in their medical insurance claims. This new research has the potential to give some insight in determining if, and when, physicians are acting outside...
Region of interest ROI image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Foveated imaging exploits the fact that the spatial resolution of the human visual system (HVS) is highest around the point of fixation (foveation point) and decreases dramatically with increasing eccentricity...
User influence can be described as power — the ability of a person to influence the thoughts or actions of others. In this paper, we investigate the attributes of 244,175 users and the content of their messages, and they are from Sina Weibo which is one of the most notable micro-blogging services. Our first insight is to quantify the influence of individuals within a period of time by using a novel...
The task of predicting future relationships in a social network, known as link prediction, has been studied extensively in the literature. Many link prediction methods have been proposed, ranging from common neighbors to probabilistic models. Recent work by Yang et al. has highlighted several challenges in evaluating link prediction accuracy. In dynamic networks where edges are both added and removed...
The selection of a model for academic risk prediction systems is usually based on the global performance of the model. However, this global performance is not an important factor for the end-user of the system. For the end-user, the performance of the model for his or her specific case is the most important aspect of that model. Given that the model is usually selected at design time, the end-user...
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