Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
Not a member yet
1506 research outputs found
Sort by
Unified Abstract Mechanism to Model Language Learning Activities
Get PDFLanguage learning applications define exercises that are pedagogical tools to introduce new language concepts. The development of this type of applications is complex due to the diversity of language learning methodologies, the variety of execution environments and the number of different technologies that can be used. This article proposes a conceptual model to develop the activities of language learning applications. It defines a new abstraction mechanism to model these activities as part of a model-driven approach to develop applications supporting different language learning processes running on different hardware and software platforms. We define a metamodel that describes the entities and relationships representing language learning activities as well as a series of examples that use the proposed abstraction mechanism to represent different language learning activities. The modelling process is simplified using a common representation that does not affect neither the visual presentation, nor the interaction of each activity. The article includes an evaluation that analyses the product correctness, robustness, extensibility, and reusability of the obtained code. These results conclude that the code generated using the proposed approach overcomes the code generated following a traditional approach
Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources
Get PDFTask Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation
Performance Evaluation of Parallel Haemodynamic Computations on Heterogeneous Clouds
Get PDFThe article presents performance evaluation of parallel haemodynamic flow computations on heterogeneous resources of the OpenStack cloud infrastructure. The main focus is on the parallel performance analysis, energy consumption and virtualization overhead of the developed software service based on ANSYS Fluent platform which runs on Docker containers of the private university cloud. The haemodynamic aortic valve flow described by incompressible Navier-Stokes equations is considered as a target application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring the parallel speedup of computations carried out on virtualized heterogeneous resources. The performance measured on Docker containers is compared with that obtained by using the native hardware. The alternative solution algorithms are explored in terms of the parallel performance and power consumption. The investigation of a trade-off between the computing speed and the consumed energy is performed by using Pareto front analysis and a linear scalarization method
Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification
Get PDFIn this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data
Assessment of the Viability of a Biometric Characteristic in the Context of Biometric Authentication on Mobile Devices
Get PDFThe issue of safe utilization of mobile devices is becoming an increasingly important problem, among others due to the widespread use of such devices to access sensitive data (such as electronic documents or banking data). In our work we analyze the use of biometric techniques in order to secure a mobile device, with particular emphasis on the viability of selected biometric characteristics. For this purpose, we investigate the possibility of applying machine learning models to assess the authenticity of a biometric characteristic. Results of our tests have shown that the most effective method of assessing the viability of a biometric characteristic involves blink and smile detection
Human Pose Estimation Using Per-Point Body Region Assignment
Get PDFIn recent years, the task of human pose estimation has become increasingly important, due to the large scale of usage, including VR applications, as well as higher-level tasks, such as human behavior understanding. In this paper, we introduce a novel two-stage deep learning approach named Segmentation-Guided Pose Estimation (SGPE). The pipeline is based on two neural networks working in a sequential fashion, while both models effectively process unorganized point clouds on the input. First, the segmentation network performs a pointwise classification into the corresponding body regions. In the next step, the point cloud with the per-point region assignment, forming the fourth input channel, is passed to the regression network. This way, both local and global features of the point cloud are preserved, helping the model fully maintain the body pose structure. Our strategy achieves competitive results on all of the examined benchmark datasets, and outperforms state-of-the-art methods
Cloud Solutions for Private Permissionless Blockchain Deployment
Get PDFThis paper aims to survey the security and scalability problems occurring in private permissionless blockchain systems and solutions to them. The emphasis is put on the blockchain systems hosted by cloud vendors in the form of Blockchain-as-a-Service (BaaS). The currently available solutions offered by the most appreciated cloud providers are reviewed. The most promising services are tested for the real deployment of the consent management system (CMS). Implementing the CMS atop BaaS leads to creating Consent-as-a-Service (CaaS). Through experiments, the proposed system's replication ability and its scalability are examined, along with assessing the feasibility of the consent management system development in the provided cloud environment
Non-Intrusive Data Inspection for Message-Based Systems
Get PDFOver the years, research into debugging distributed systems with message passing communication has focused on verifying the implementation of functionality, such as race condition detection, and not on the exchanged data. In this paper we explore this previously undervalued approach. We present a new component to gather exchanged messages. We create a simplified model of message passing and the component's design based on it. Then, we discuss how to utilise the component to create tools which provide currently missing debugging information. In the end, we implement the component as part of the O2 framework and conduct benchmarks. We obtain promising results -- the component does not decrease the throughput
Trigger Performance Monitoring and Rate Predictions Preparation for Run 3 at ATLAS Experiment
Get PDFBespoke Cost Monitoring software collates data on the performance of all aspects of the ATLAS experiment's High Level Trigger software. These data are exported for subsequent analysis offline, and are used to understand the resource usage of the individual trigger selections in terms of the amount of CPU time and the amount of raw detector data which was required to perform the selection. For the LHC's Run 3, the ATLAS High Level Trigger is re-implemented in a multi-threaded framework with both intra-event and inter-event algorithm parallelism. We will describe some of the complications and considerations which arise from monitoring event metrics in a highly parallel environment
Improvement of Information Retrieval Systems by Using Hidden Vertical Search
Get PDFThe exponential growth of the number of documents in digital libraries and on the Web calls for very intensive development of retrieval systems. One possible architectural approach to IRS, an architecture with hidden verticals, is proposed in this paper. In IRS with hidden verticals, documents from the searched corpus are stored into a predefined set of classes. The user's query is classified before the search, and searching is done only within the corresponding class. The performance of the proposed system is compared to the performance of standard IRS (that contains a unique inverted index) and IRS with cluster pruning (in which searching corpus is clustered and query is compared to the clusters' centroids first, then search is done only in the most similar cluster). Search time in the proposed system is 7.9 times shorter than in the standard IRS and 1.7 times shorter than in the system with cluster pruning. The precision of the proposed system is 2.59 times higher than the precision of the standard IRS, and 1.68 times better compared to the IRS with cluster pruning. The recall of the proposed system is 1.09 times smaller than the recall of the standard IRS, but it is 1.28 times better than the recall of the IRS with cluster pruning. Based on the above results, we can say that proposed approach reduces search time and increases search precision with a minimal reduction in recall