Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature
Get PDFNatural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on non-textual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology
Securing Distributed Computer Systems Using an Advanced Sophisticated Hybrid Honeypot Technology
Get PDFComputer system security is the fastest developing segment in information technology. The conventional approach to system security is mostly aimed at protecting the system, while current trends are focusing on more aggressive forms of protection against potential attackers and intruders. One of the forms of protection is also the application of advanced technology based on the principle of baits - honeypots. Honeypots are specialized devices aimed at slowing down or diverting the attention of attackers from the critical system resources to allow future examination of the methods and tools used by the attackers. Currently, most honeypots are being configured and managed statically. This paper deals with the design of a sophisticated hybrid honeypot and its properties having in mind enhancing computer system security. The architecture of a sophisticated hybrid honeypot is represented by a single device capable of adapting to a constantly changing environment by using active and passive scanning techniques, which mitigate the disadvantages of low-interaction and high-interaction honeypots. The low-interaction honeypot serves as a proxy for multiple IP addresses and filters out traffic beyond concern, while the high-interaction honeypot provides an optimum level of interaction. The proposed architecture employing the prototype of a hybrid honeypot featuring autonomous operation should represent a security mechanism minimizing the disadvantages of intrusion detection systems and can be used as a solution to increase the security of a distributed computer system rapidly, both autonomously and in real-time
Modeling of Object-Oriented Programs with Petri Net Structured Objects
Get PDFThe article presents a method for constructing a model of an object-oriented program in terms of multilabeled Petri nets. Only encapsulation - one of the three concepts of object-oriented paradigm - is considered. To model a different aspects of encapsulation a Petri net structured object is proposed. It consists of a Petri net defining its behavior and a set of organized access points specifying its structural properties. Formal composition operations to construct a program model from the models of its methods, classes, objects, functions, and modules are introduced and a source code translation algorithm to Petri net representation is proposed. A special section of the article considers in detail a process of model construction of a real object-oriented program (OOP). Source code of the program, figures with Petri net objects modeling different elements of the program and the resulting model of the program are presented
Semantic Ontology Design for a Multi-Cooperative First Responder Interoperable Platform
Get PDFNatural and man-made catastrophic events appear be steadily increasing in intensity and frequency. Proper preparation, response and recovery are essential if mankind and its vital systems are to cope with and survive large-scale disasters. The organisations responsible for delivering emergency response services often under-perform due to a lack of proper interoperation and collaboration. This paper discuss the interoperability issues for data interchange among first responders agency participating in emergency situation and provides exhaustive overview of the recent studies and attempts to solve the problem. The approach taken by the EU funded REDIRNET project for development of a core ontology enabling exchange of data among first responder agencies is presented. The novelty included in the ontology tree is described and the implementation in the meta-data gateway is introduced. The paper provides insight in the implementation of the REDIRNET platform and the designed semantic interoperable services. The paper ends with the discussion of the presented work and the concluding remarks
CAT: Context-Aware Trust-Oriented Worker Selection in Social Crowd
Get PDFFinding trustworthy worker is a longstanding issue in crowdsourcing systems. On traditional crowdsouring platforms, like Amazon Mechanical Turk\reffn:Amazon Mechanical Turk, the trustworthiness of a worker is usually based on the contextual information, like different types of tasks and different reward amounts of tasks. However, with the combination of OSNs and the crowdsourcing applications in social crowd, in addition to the above mentioned task based contexts, the social contexts like the social relationships and the social positions of participants can greatly assist requestors to select trustworthy workers. In order to select the trustworthy workers in social crowd, in this paper, we first present a contextual social network structure which contains complex social contexts. Then we propose a trust evaluation model taking both contexts information and the requirements of requestors into consideration, which leads to the trust worker selection in social crowd as a classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. For solving this challenging problem, we propose a new efficient and effective approximation Context-Aware Trust-Oriented Worker Selection algorithm CAT. The results of our experiments conducted on four real OSN datasets illustrate the superiority of our method in trustworthy worker selection
Precise Non-Intrusive Real-Time Gaze Tracking System for Embedded Setups
Get PDFThis paper describes a non-intrusive real-time gaze detection system, characterized by a precise determination of a subject's pupil centre. A narrow field-of-view camera (NFV), focused on one of the subject's eyes follows the head movements in order to keep the pupil centred in the image. When a tracking error is observed, feedback provided by a second camera, in this case a wide field-of-view (WFV) camera, allows quick recovery of the tracking process. Illumination is provided by four infrared LED blocks synchronised with the electronic shutter of the eye camera. The characteristic shape of corneal glints produced by these illuminators allows optimizing the image processing algorithms for gaze detection developed for this system. The illumination power used in this system has been limited to well below maximum recommended levels. After an initial calibration procedure, the line of gaze is determined starting from the vector defined by the pupil centre and a valid glint. The glints are validated using the iris outline to avoid glint distortion produced by changes in the curvature on the ocular globe. In order to minimize measurement error in the pupil-glint vector, algorithms are proposed to determine the pupil centre at sub-pixel resolution. Although the paper describes a desk-mounted prototype, the final implementation is to be installed on board of a conventional car as an embedded system to determine the line of gaze of the driver
SMoT+NCS: Algorithm for Detecting Non-Continuous Stops
Get PDFSeveral algorithms have been proposed in the last years for discovering stops in trajectories of moving objects. Some methods consider as stops the subtrajectories that i) have speed lower than the average trajectory speed, ii) present significant direction changes, iii) have gaps, or iv) intersect a given spatial region. In these approaches a time constraint should be met for the subtrajectory to be considered as a stop, and this constraint is absolute (it is met or not). Indeed, these approaches consider stops as a continuous subtrajectory. In this paper, we show that for several application domains the stops do not need to be continuous, and the time constraint should be relaxed. In summary, we present the definitions of non-continuous stops and present an algorithm to discover a new kind of stops. We evaluate the proposed algorithm with a running example and real trajectory data, comparing it to the most similar approach in the literature, the SMoT algorithm
Task Scheduling Using Hamming Particle Swarm Optimization in Distributed Systems
Get PDFAn efficient allocation of tasks to the processors is a crucial problem in heterogeneous computing systems. Finding an optimal schedule for such an environment is an NP-complete problem. Near optimal solutions are obtained within a finite duration using heuristics/meta-heuristics are used instead of exact optimization methods. Heuristics and meta-heuristics are the efficient technologies for scheduling tasks in distributed environment because of their ability to deliver high quality solutions in a reasonable time. Discrete Particle Swarm Optimization (DPSO) is a newly developed meta-heuristic computation technique. To enhance the final accuracy and improve the convergence speed of DPSO, this paper presents a modified DPSO algorithm by adjusting its inertia weight based on Hamming distance and also makes a dependency between the two random parameters r_1 and r_2 to control the balance of individual's and collective information in the velocity updating equation. Three criteria such as make span, mean flow time and reliability cost are used to assess the efficiency of the proposed DPSO algorithm for scheduling independent tasks on heterogeneous computing systems. Computational simulations are performed based on a set of benchmark instances to evaluate the performance of the proposed DPSO algorithm compared to existing methods
Kernel Feature Extraction for Hyperspectral Image Classification Using Chunklet Constraints
Get PDFA novel semi-supervised kernel feature extraction algorithm to combine an efficient metric learning method, i.e. relevant component analysis (RCA), and kernel trick is presented for hyperspectral imagery land-cover classification. This method obtains projection of the input data by learning an optimal nonlinear transformation via a chunklet constraints-based FDA criterion, and called chunklet-based kernel relevant component analysis (CKRCA). The proposed method is appealing as it constructs the kernel very intuitively for the RCA method and does not require any labeled information. The effectiveness of the proposed CKRCA is successfully illustrated in hyperspectral remote sensing image classification. Experimental results demonstrate that the proposed method can greatly improve the classification accuracy compared with traditional linear and conventional kernel-based methods
Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition
Get PDFThis paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al