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Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a...
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,...
Cloud computing enables end users to execute high-performance computing applications by renting the required computing power. This pay-for-use approach enables small enterprises and startups to run HPC-related businesses with a significant saving in capital investment and a short time to market. When deploying an application in the cloud, the users may a) fail to understand the interactions of the...
Studies the issue of forecasting the parameter values that describe production-and-economic systems and the projects that are realized in such systems; it examines the preparation of statistical data with gaps and suggests the advantage of using different time slots of the collected data for their further joint use in prognostic models. The article reviews the applicability of the existing forecasting...
Background: Due to tight scheduling and limitedbudget, it may not be possible to resolve all the existing bugsin a current release of a software product. The accumulation ofthe deferred bugs in the issue tracking system are obligations (liabilities) of the software team similar to financial analogyof 'debt'. Defect debt is known as latent defects which arenot resolved in the current release. Aim:...
Build systems play a crucial role in modern software engineering. Recent studies have shown that many builds fail, mostly due to neglected maintenance. This blocks teams from continuing the development and costs time and resources to fix. The target of the thesis is to reduce build breakage by investigating changes that lead to failing builds, identifying bad and best practices for build configuration,...
Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on...
Researchers often focus on the development process and the final product (source code) to investigate and predict software defects. Unfortunately, these models may not be applicable to software projects in which there is no access to the data sources regarding development process. For example, in cases when a company conducts tests on behalf of its business contractors, it is only possible to evaluate...
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz properties in order to compute inferences over unobserved function values. Unfortunately,...
We would like to present the idea of our Continuous Defect Prediction (CDP) research and a related dataset that we created and share. Our dataset is currently a set of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data we mine from software repositories. Our dataset embraces 1265 software projects, 30,022...
The Internet of things (IoT) has emerged in numerous domains for collecting and exchanging large datasets in order to ensure a continuous monitoring and realtime decision-making. IoT incorporates sensors for carrying out raw data acquisition, while data processing and analysis tasks are addressed by high performance computational facilities, such as cloud-based infrastructures (remote processing approach)...
dynamic cloud workloads necessitate forecasting methodologies for accurate resource provisioning affecting both cloud providers and clients. This paper focuses on forecasting in the cloud in order to understand its underlying workload dynamics. It analyzes recent workload traces and discovers characteristics that are not adequately captured by traditional linear & nonlinear models employed for...
Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are writing the code of the application, might help...
The amount of software in modern vehicles is constantly growing. However, the risk for functional and quality deficiencies increases simultaneously with size. This results in industry for example in inevitable and unexpected refactorings of software models, which is slowing down development processes in turn. In this industrial case study, we evaluate model growth predictors applied to foresee critical...
Background Software systems are relying more and more on multi-core hardware requiring a parallel approach to address the problems and improve performances. Unfortunately, parallel development is error prone and many developers are not very experienced with this paradigm also because identifying, reproducing, and fixing bugs is often difficult. Objective The main goal of this paper is the identification...
In this paper, we study the problem of predicting the visual quality of a specific test sample (e.g. a video clip) experienced by a specific user, based on the ratings by other users for the same sample and the same user for other samples. A simple linear model and algorithm is presented, where the characteristics of each test sample are represented by a set of parameters, and the individual preferences...
We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations...
There are two major challenges to the personalized recommendation method, one is the sparseness of characteristic attribute, the other is the excessive reliance on scoring data. To solve above problems, a personalized recommendation algorithm (PRM-Grey) based on grey theory is presented. Firstly, the nearest neighbor matrix formed through the similarity between the characteristic matrix rows. Then,...
Recently, with the surge of students pursuing graduate studies after completing their bachelors, there is a lack of open source resources which could point out universities and programs, based on an individual's profile. In this paper, we present our novel approach of predicting universities for graduate studies based on one's whole profile. A model is built which is able to predict the list of top-‘n’...
To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network...
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