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This article describes approaches to building a set of nodes and blocks that are sufficient to create the matching processor for the parallel dataflow computing system. Investigations of various sets of nodes and blocks of the matching processor have been carried out. As a result, some regularities were revealed characteristic for hardware-software solutions that implement the dataflow computing model...
Model-based simulation and monitoring are becoming part of advanced learning environments. In this paper, we propose a model-based simulation and monitoring framework for management of learning assessment and we describe its architecture and main functionalities. The proposed framework allows user-friendly learning simulation with a strong support for collaboration and social interactions. Moreover,...
The Model-Driven Architecture (MDA) is based on an understanding of a hierarchy of levels that are placed on top of each other and that are connected with instantiation. For practical MDA use, it is important to be clear about the kinds of objects that reside on the different levels and the relations between them as well as relations to objects outside of the MDA domain. This article aims at enhancing...
Agent-based Modeling (ABM) has become quite popular to the simulation community for its usability and wide area of applicability. However, speed is not usually a trait that ABM tools are characterized of attaining. This paper presents HLogo, a parallel variant of the NetLogo ABM framework, that seeks to increase the performance of ABM by utilizing Software Transactional Memory and multi-core CPUs,...
The Internet of Things (IoT) has penetrated various domains, from smart grids to precision agriculture, facilitating remote sensing and control. However, IoT devices are target to a spectrum of reliability and security issues. Therefore, capturing the normal behavior of these devices and detecting abnormalities in program execution is key for reliable deployment. However, existing program anomaly...
Fog computing is mainly proposed for IoT applications that are geospatially distributed, large-scale, and latency sensitive. This poses new research challenges in real-time and scalable provisioning of IoT services distributed across Fog-Cloud computing platforms. Data-centric IoT services, as a dominant type of IoT services in large-scale deployments, require design solutions to speed up data processing...
In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy...
The paper is devoted to development of intelligent context-aware energy management system of power semiconductor converters inside a SmartGrid using the principles of cognitive, object-oriented analysis. Mathematical software for decision-making system of power semiconductor converters was developed using combination of different converter control strategies. The proposed solution includes processing...
Recent advances in sample efficient reinforcement learning algorithms in quasi-deterministic environments highlight the requirement for computationally inexpensive visual representations. Here we investigate non-parametric dimensionality reduction techniques based on random linear transformations and we provide empirical evidence on the importance of high-variance projections using sparse random matrices...
Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model,...
Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing. In this paper, two kinds of schemes for improving the Continuous Bag-of-Words...
Temporal Pooling (TP) is a recent technique for processing temporal events by forming declarative representations of the complete sequences. In this paper, we examine and extend the functionality of the existing TP algorithm from the Hierarchical Temporal Memory (HTM) framework and introduce the Self-Organising Temporal Pooling (SOTP) architecture. The SOTP draws together the Merge Self-Organising...
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,...
Attention mechanism advances the neural machine translation (NMT) by reducing the confusion introduced by irrelevant words in long sentences. However, the confusion caused by ambiguous words hasn't been handled yet and it may be a bottleneck for the NMT model. This paper validates the hypothesis and proposes a simple and flexible framework, which enables the NMT model to only focus on the relevant...
A key open question in the area of software modeling is which costs and benefits it brings to software development and maintenance. For answering this question, better empirical studies into software modeling are needed. In this paper I focus on what I believe are the pitfalls in- and prospects for such types of studies. This paper is an abstract for an invited keynote at the Modeling in Software...
Robotic agents, when not equipped with traditional means to capture information about their surroundings, must autonomously learn to extract this information from a very complex environment. In the context of developmental robotics, we use unsupervised representation learning, and more specifically deep autoencoders, in order to capture visual representations. These generic visual representations...
5G, the fifth generation of mobile communication networks, is considered as one of the main IoT enablers. Connecting billions of things, 5G/IoT will be dealing with trillions of GBytes of data. Securing such large amounts of data is a very challenging task. Collected data varies from simple temperature measurements to more critical transaction data. Thus, applying uniform security measures is a waste...
Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, modeling anindividual's phone call response behavior may assist them intheir daily activities for managing call interruptions. A key stepof such modeling is to discovering call response behavioral rulesbased on multi-dimensional contexts related to individual'sbehavior. Currently, researchers use classification...
It is not uncommon today that sensor devices connected to the Internet solely send their data to Cloud-based servers for storage and processing. This does not only mean clients requesting data have to contact the Cloud-based service, even if the data is available in the local network, but also that data is sent to external services with unknown or ambiguous privacy policies. The great potential in...
Due to the number of cloud providers, as well as the extensive collection of services, cloud computing provides very flexible environments, where resources and services can be provisioned and released on demand. However, reconfiguration and adaptation mechanisms in cloud environments are very heterogeneous and often exhibit complex constraints. For example, when reconfiguring a cloud system, a set...
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