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Predicting the future popularity of online content is highly important in many applications. Under preferential attachment influence popular items get more popular thereby resulting in long tailed distribution problem. Consequently, new items which can be popular (potential ones), are suppressed by the already popular items. This paper proposes a novel model which is able to identify potential items...
1Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels...
The process of clustering similar words is crucial for a broad range of applications such as text classification and word sense disambiguation. Several approaches for deriving word similarity have been proposed. Some, like latent semantic analysis, are derived from the distributional hypothesis. Others extract relationships between terms by drawing upon predefined linguistic patterns. In this work,...
Trust game is a money exchange game that has been widely used in behavioral economics for studying trust and collaboration between humans. In this game, exchange of money is entirely attributable to the existence of trust between users. The trust game could be one-shot, i.e. the game ends after one round of money exchange, or repeated, i.e. it lasts several rounds. Predicting user behavior in the...
In this article, we apply different machine learning (ML) techniques for building objective models, that permit to automatically assess the image quality in agreement with human visual perception. The six ML methods proposed are discriminant analysis, k-nearest neighbors, artificial neural network, non-linear regression, decision tree and fuzzy logic. Both the stability and the robustness of designed...
A time series is the most commonly used representation for the evolution of a given variable over time. In a time series forecasting problem, a model aims at predicting the series' future values, assuming that all information needed to do so is contained in the series' past behavior. Since the phenomena described by the time series does not always exist in isolation, it is possible to enhance the...
Due to its economical and environmental benefits to society and industry, integrating solar power is continuously growing in many utilities and Independent System Operators (ISOs). However, the intermittent nature of the renewable energy brings new challenges to the system operators. One key to resolve this problem is to have a ubiquitously efficient solar power output forecasting system, so as to...
A considerable amount of energy efficient routing algorithms have been proposed to save energy and prolong network lifetime. Those algorithms mainly focus on forwarding packets along the minimum energy path to the sink to merely minimize energy consumption, which causes an unbalanced distribution of residual energy among sensor nodes, and eventually results in a network partition. In this paper, we...
Fast and efficient design space exploration is a critical requirement for designing computer systems, however, the growing complexity of hardware/software systems and significantly long run-times of detailed simulators often makes it challenging. Machine learning (ML) models have been proposed as popular alternatives that enable fast exploratory studies. The accuracy of any ML model depends heavily...
Accurate estimation of forest biomass is critical in the study of global carbon balance and climate change. This research was undertaken in the Yushan forest, in southeast china. We used metrics extracted from hyperspectral data as predictor variables to establish three types of biomass prediction models, and then the models are verified by cross-validation method. Overall, all of the three types...
Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers,...
Component based enterprise systems are becoming extremely complex in which the availability and usability are influenced intensively by the system's anomalies. Anomaly prediction is highly important for ensuring a system's stability, which aims at preventing anomaly from occurring through pre-failure warning. However, due to the system's complex nature and the noise from monitoring, capturing pre-failure...
Deep Convolutional Neural Networks(DCNNs) have recently shown great performance in many high-level vision tasks, such as image classification, object detection and more recently outdoor semantic segmentation. However, the convolutional layer only process the local regions in the image, ignoring the global context information. To overcome this poor localization property of Convolutional Neural Networks(CNNs),...
This paper presents a method to compute the combined effects of process variations and various noise models in predicting performance metrics of analog and mixed-signal integrated circuits and subsystems. Without resorting to the traditional Monte Carlo simulation and specialized noise simulators, the method incorporates both behavioral simulation and statistical simulation to gain speed while ensuring...
Many predictive resource scaling approaches have been proposed to overcome the limitations of the conventional reactive approaches most often used in clouds today. In general, due to the complexity of clouds, these reactive approaches were often forced to make significant limiting assumptions in either the operating conditions/requirements or expected workload patterns. As such, it is extremely difficult...
Usually, the most critical modules of the system receive extra attention. But even these modules might be too large to be thoroughly inspected so it is useful to know where to apply the majority of the efforts. Thus, knowing which code changes are more prone to contain vulnerabilities may allow security experts to concentrate on a smaller subset of submitted code changes. In this paper we discuss...
A quality model for assessing the changeability level of java code is important for software development. It permits developer to know which classes to be improved for having a better software maintainability. Moreover, a good quality model must be created based on a set of well-selected attributes and metrics. Currently, no research work proposes a changeability assessment model that takes into consideration...
Software changes are inevitable during maintenance, Object-oriented software (OOS) in particular. For change not to be performed in the “dark”, software change impact analysis (SCIA) is used. However, due to the exponential growth in the size and complexity of OOS, classes are not without faults and the existing SCIA techniques only predict change impact set. This means that a change implemented on...
As a part of the automatic study of visual attention of affected populations with neurodegenerative diseases and to predict whether new gaze records a complaint of these diseases, we should design an automatic model that predicts salient areas in video. Past research showed, that people suffering form dementia are not reactive with regard to degradations on still images. In this paper we study the...
Nowadays, psychoacoustic analysis of a sound is often applied for quick quantification of its auditory perception. Such hearing-related quantities also referred to as psychoacoustic parameters are meant to be a good representation of the human auditory perception. Predictive models are available to estimate the perception of these quantities. However, calculated psychoacoustic parameters show a lack...
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