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Background: An increasing research effort has devoted to just-in-time (JIT) defect prediction. A recent study by Yang et al. at FSE'16 leveraged individual change metrics to build unsupervised JIT defect prediction model. They found that many unsupervised models performed similarly to or better than the state-of-the-art supervised models in effort-aware JIT defect prediction. Goal: In Yang et al.'s...
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a...
The paper provides the mathematical model describing the movement of different kinds of avalanche snow mass and its interaction with obstacles based on the modified method of particle dynamics. Further, the authors introduce their algorithm to calculate avalanche impact on buildings and structures; this algorithm underlies a computer program that allows you to set the basic parameters of a building,...
In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption...
This paper proposes an estimation method for the latent variable Rasch model based on the method of least squares which allows a continuous data set using. The research suggests the application of original approaches within the method for the solution of some applied problems. The authors explain how to use it for task assignment and work organization, decision-making under certainty and the securities...
The cloud computing ecosystem comprises hundreds of providers, offering diverse computing services, incompatible APIs, and significantly different pricing models. Cloud application management platforms hide the heterogeneity of the services and APIs, allowing, to varying degrees, portability between providers. These tools remove technical barriers to switching providers, but they do not provide a...
Recently, wireless technology experiences a fast growth to meet user demand and push toward the boundary limit of system performance. The simulation and verification framework play important role for accelerating investigation of technology proof of concept, field-trial, and large-scale commercial prototyping. In this paper, we present system-level simulation of heterogeneous model and unified HW/SW...
Several applications in numerical scientific computing involve very large sparse matrices with a regular or irregular sparse structure. These matrices can be stored using special compression formats (storing only non-zero elements) to reduce memory space and processing time. The choice of the optimal format is a critical process that involves several criteria. The general context of this work is to...
In industrial power generation plants, subsystem monitoring and analytics play a vital role in quantifying the knowledge about different factors that impact their overall performance. Multi-dimensional performance metrics, e.g. thermal efficiency, in-service time, mean-time-to-failure etc., are calculated that may have different data constraints, modelling techniques, and execution frameworks. Automating...
Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging. While non-parametric methods have been relatively well studied in the literature, efficient formulations for parametric models (which may offer benefits in small sample size regimes) have only emerged...
In current interval-valued linear regression models, meaningless predictions may be generated because the lower bounds of the predicted intervals may be greater than their upper bounds. To avoid this problem, we propose a constrained interval-valued linear regression model based on random set theory. However, due to the introduction of constraints in this model, the expectation of the errors is no...
Today's advanced driver assistance systems (ADAS) are increasingly becoming more complex. The next step in this direction is the development of automated driving systems. However, along with the complexity, the effort required for development and validation of these systems is increasing as well. In order to be able to master this complexity in terms of cost and time, simulations are being used more...
In active learning for Automatic Speech Recognition (ASR), a portion of data is automatically selected for manual transcription. The objective is to improve ASR performance with retrained acoustic models. The standard approaches are based on confidence of individual sentences. In this study, we look into an alternative view on transcript label quality, in which Gaussian Supervector Distance (GSD)...
Energy efficiency in high performance computing (HPC) systems is a relevant issue nowadays, which is approached from multiple edges and components (network, I/O, resource management, etc). HPC industry turned its focus towards embedded and low-power computational infrastructures (of RISC architecture processors) to improve energy efficiency, therefore, we use an ARM-based cluster, known as millicluster,...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
Nowadays, public bike-sharing systems are broadly adopted and deployed in many major cities, however, as public facilities, bicycles will be prone to damage and need to be replaced frequently, which results in high system maintenance costs. One of the root causes of bicycle damages is the serious load-unbalance of bicycle usage. In this paper, we propose a hybrid bicycle allocation strategy for bicycle...
A major challenge in Cloud computing is resource provisioning for computational tasks. Not surprisingly, previous work has established a number of solutions to provide Cloud resources in an efficient manner. However, in order to realize a holistic resource provisioning model, a prediction of the future resource consumption of upcoming computational tasks is necessary. Nevertheless, the topic of prediction...
With the continuous drive towards integrated circuits scaling, efficient performance modeling is becoming more crucial yet, more challenging. In this paper, we propose a novel method of hierarchical performance modeling based on Bayesian co-learning. We exploit the hierarchical structure of a circuit to establish a Bayesian framework where unlabeled data samples are generated to improve modeling accuracy...
The problem of sample size determination (SSD) for any black box model is addressed in this work. Four novel SSD algorithms namely HC, SOOP, HC+SOOP and V-SOOP, based on hypercube sampling, space filling and optimization study are proposed to tackle the issues of over-fitting, accuracy and computational speed of surrogate models. In this version, the novel algorithms are shown to run simultaneously...
This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between...
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