Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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A Novel Cooperation and Competition Strategy Among Multi-Agent Crawlers
Get PDFMulti-Agent theory which is used for communication and collaboration among focused crawlers has been proved that it can improve the precision of returned result significantly. In this paper, we proposed a new organizational structure of multi-agent for focused crawlers, in which the agents were divided into three categories, namely F-Agent (Facilitator-Agent), As-Agent (Assistance-Agent) and C-Agent (Crawler-Agent). They worked on their own responsibilities and cooperated mutually to complete a common task of web crawling. In our proposed architecture of focused crawlers based on multi-agent system, we emphasized discussing the collaborative process among multiple agents. To control the cooperation among agents, we proposed a negotiation protocol based on the contract net protocol and achieved the collaboration model of focused crawlers based on multi-agent by JADE. At last, the comparative experiment results showed that our focused crawlers had higher precision and efficiency than other crawlers using the algorithms with breadth-first, best-first, etc
Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities
Get PDFCollaborative tagging which is the keystone of the social practices of web 2.0 has been highly developed in the last few years. In this paper, we propose a new method to analyze user profiles according to their tagging activity in order to improve resource recommendation. We base upon association rules which is a powerful method to discover interesting relationships among large datasets on the web. Focusing on association rules we can find correlations between tags in a social network. Our aim is to recommend resources annotated with tags suggested by association rules, in order to enrich user profiles. The effectiveness of the recommendation depends on the resolution of social tagging drawbacks. In our recommender process, we demonstrate how we can reduce tag ambiguity and spelling variations problems by taking into account social similarities calculated on folksonomies, in order to personalize resource recommendation. We surmount also the lack of semantic links between tags during the recommendation process. Experiments are carried out with two different scenarios: the first one is a proof of concept over two baseline datasets and the second one is a real world application for diabetes disease
A New Sparse Representation Algorithm for 3D Human Pose Estimation
Get PDFThis paper addresses the problem of recovering 3D human pose from single 2D images using Sparse Representation. While recent Sparse Representation (SR) based 3D human pose estimation methods have attained promising results estimating human poses from single images, their performance depends on the availability of large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but it is relatively easy to acquire a large amount of unlabeled data. Moreover, all SR based 3D pose estimation methods only consider the information of the input feature space and they cannot utilize the information of the pose space. In this paper, we propose a new framework based on sparse representation for 3D human pose estimation which uses both the labeled and unlabeled data. Furthermore, the proposed method can exploit the information of the pose space to improve the pose estimation accuracy. Experimental results show that the performance of the proposed method is significantly better than the state of the art 3D human pose estimation methods
Classification of Broadcast News Audio Data Employing Binary Decision Architecture
Get PDFA novel binary decision architecture (BDA) for broadcast news audio classification task is presented in this paper. The idea of developing such architecture came from the fact that the appropriate combination of multiple binary classifiers for two-class discrimination problem can reduce a miss-classification error without rapid increase in computational complexity. The core element of classification architecture is represented by a binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes, utilizing two types of decision functions. The first one is represented by a simple rule-based approach in which the final decision is made according to the value of selected discrimination parameter. The main advantage of this solution is relatively low processing time needed for classification of all acoustic classes. The cost for that is low classification accuracy. The second one employs support vector machine (SVM) classifier. In this case, the overall classification accuracy is conditioned by finding the optimal parameters for decision function resulting in higher computational complexity and better classification performance. The final form of proposed BDA is created by combining four BD discriminators supplemented by decision table. The effectiveness of proposed BDA, utilizing rule-based approach and the SVM classifier, is compared with two most popular strategies for multiclass classification, namely the binary decision trees (BDT) and the One-Against-One SVM (OAOSVM). Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with the BDT architecture. On the contrary, an optimization technique for selecting the optimal set of training data is needed in order to overcome the OAOSVM
Multimodal Subscriber Interfaces for Infocommunication Systems
Get PDFThe quality enhancement of infocommunication systems based on integration of semantically different information transmitted over different communication channels is discussed. The increase of the informational efficiency of the multimodal communication systems is provided by using multimodal subscriber interfaces, compared to traditional telecommunications. The proposed approach is new in the subject area and requires a variety of research studies to develop innovative elements of the theory of multimodal information transmission. A notion of information value is introduced in order to estimate and chose functional structures and parameters that possess the most favorable informational characteristics during development of multimodal communication systems
Application Performance Optimization in Multicloud Environment
Get PDFThrough the development and accessibility of the Internet, nowadays the cloud computing has become a very popular. Through the development and accessibility of the Internet, nowadays the cloud computing has become a very popular. This concept has the potential to change the use of information technologies. Cloud computing is the technology that provides infrastructure, platform or software as a service via the network to a huge number of remote users. The main benefit of cloud computing is the utilization of elastic resources and virtualization. Two main properties are required from clouds by users: interoperability and privacy. This article focuses on interoperability. Nowadays it is difficult to migrate an application between clouds offered by different providers. The article deals with that problem in multicloud environment. Specifically, it focuses on the application performance optimization in a multicloud environment. A new method is suggested based on the state of the art. The method is divided into three parts: multicloud architecture, method of a horizontal scalability, and taxonomy for multicriteria optimization. The principles of the method were applied in a design of multicriteria optimization architecture, which we verified experimentally. The aim of our experiment is carried on a portal offering a platform according to the users' requirements
Making Property-Based Testing Easier to Read for Humans
Get PDFSoftware stakeholders who do not have a technical profile (i.e. users, clients) but do want to take part in the development and/or quality assurance process of software, have an unmet need for communication on what is being tested during the development life-cycle. The transformation of test properties and models into semi-natural language representations is one way of responding to such need. Our research has demonstrated that these transformations are challenging but feasible, and they have been implemented into a prototype tool called readSpec. The readSpec tool transforms universally-quantified test properties and stateful test models - the two kinds of test artifacts used in property-based testing - into plain text interpretations. The tool has been successfully evaluated on the PBT artifacts produced and used within the FP7 PROWESS project by industrial partners
Infrastructure-Agnostic Programming and Interoperable Execution in Heterogeneous Grids
Get PDFIn distributed environments, no matter the type of infrastructure (cluster, grid, cloud), portability of applications and interoperability are always a major concern. Such infrastructures have a high variety of characteristics, which brings a need for systems that abstract the application from the particular details of each infrastructure. In addition, managing parallelisation and distribution also complicates the work of the programmer. In that sense, this paper demonstrates how an e-Science application can be easily developed with the COMPSs programming model and then parallelised in heterogeneous grids with the COMPSs runtime. With COMPSs, programs are developed in a totally-sequential way, while the user is only responsible for specifying their tasks, i.e. computations to be spawned asynchronously to the available resources. The COMPSs runtime deals with parallelisation and infrastructure management, so that the application is portable and agnostic of the underlying infrastructure
Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition
Get PDFNonparametric Discriminant Analysis (NDA) possesses inherent advantages over Linear Discriminant Analysis (LDA) such as capturing the boundary structure of samples and avoiding matrix inversion. In this paper, we present a novel method for constructing an updated Nonparametric Discriminant Analysis (NDA) model for face recognition. The proposed method is applicable to scenarios where bursts of data samples are added to the existing model in random chunks. Also, the samples which degrade the performance of the model need to be removed. For both of these problems, we propose incremental NDA (INDA) and decremental NDA (DNDA) respectively. Experimental results on four publicly available datasets viz. AR, PIE, ORL and Yale show the efficacy of the proposed method. Also, the proposed method requires less computation time in comparison to batch NDA which makes it suitable for real time applications
Evaluation of MoG Video Segmentation on GPU-based HPC System
Get PDFAutomated and intelligent video surveillance systems play an important role in the modern world. Since the number of various video streams that must be analyzed concurrently grows, such systems can assist humans in performing tiresome tasks. In order to be effective, video surveillance systems have to meet several requirements: they must be accurate and able to process the received video stream in real-time. A robust system should not depend on lighting conditions, illumination changes and other sources of scene variation. A common component of surveillance systems is a module that performs background estimation and foreground segmentation. The MoG (Mixture of Gaussians) algorithm is a widely used statistical technique of video segmentation. The estimation process is time-consuming, especially for complex mixture models containing many components. The work presented here focuses on the performance evaluation of MoG algorithm aiming to assess feasibility of OpenCL-based processing of high resolution video on GPU accelerated platforms