The challenge of the Semantic Web Mining technologies in the e-Learning domain can relate to the provision of personalized experiences for the users. Particularly, these applications can take into consideration the in...
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ISBN:
(纸本)0889864802
The challenge of the Semantic Web Mining technologies in the e-Learning domain can relate to the provision of personalized experiences for the users. Particularly, these applications can take into consideration the individual needs and requirements of learners. In this paper, we propose a framework for personalised e-Learning based on aggregate usage profiles and a domain ontology. We have distinguished two stages in the whole process, one of offline tasks that includes data preparation, ontology creation and usage mining and one of online tasks that concerns the production of recommendations.
Let 5 be a string of length N compressed into a context-free grammar S of size n. We present two representations of S achieving O(logN) random access time, and either O(n · αk(n)) construction time and space on ...
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ISBN:
(纸本)9780898719932
Let 5 be a string of length N compressed into a context-free grammar S of size n. We present two representations of S achieving O(logN) random access time, and either O(n · αk(n)) construction time and space on the pointer machine model, or 0(n) construction time and space on the RAM. Here, αk(n) is the inverse of the kth row of Ackermann's function. Our representations also efficiently support decompression of any substring in S: we can decompress any substring of length m in the same complexity as a single random access query and additional O(m) time. Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar-compressed strings without decompression. For instance, we can find all approximate occurrences of a pattern P with at most k errors in time O(n(min(|P|k,k4 + |P|) + logN) + occ), where occ is the number of occurrences of P in S. Finally, we are able to generalize our results to navigation and other operations on grammar-compressed trees. All of the above bounds significantly improve the currently best known results. To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy-paths in grammars.
The paper deals with automatic software-based test generation for processors as basic blocks of current complex systems on chip and embedded systems. Testing processors needs continually new test generation methods, a...
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The paper deals with automatic software-based test generation for processors as basic blocks of current complex systems on chip and embedded systems. Testing processors needs continually new test generation methods, algorithms and test application techniques for their verification, manufacture and infield testing and reliable life-time run. The functional tests of processors are mainly generated over an instruction set architecture and processor model description. Such types of tests are serving as additional tests to structural testing or as tests used in verification. They run in similar form and frequency as real programmes. Such tests are categorized as the software-based self tests. A metric for quality evaluation of the software-based tests is obviously provided by code coverage of a processor model. A functional test generation method is based on VHDL model of processors and genetic algorithms with using various evolutionary strategies. The contribution to the SBST methods based on GAs using the latest defined ES was identified. Functionality and effectiveness of the developed methods were evaluated in the implemented system AGenMIX with enhanced function of genetic algorithms computation over three types of RISC processor.
This paper presents an image-based application using Graph Based Visual Saliency (GB VS) and Scale-Invariant Feature Transform (SIFT), aiming at simple image classification of well-known touristic monuments in the geo...
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ISBN:
(纸本)9780889869219
This paper presents an image-based application using Graph Based Visual Saliency (GB VS) and Scale-Invariant Feature Transform (SIFT), aiming at simple image classification of well-known touristic monuments in the geographic area of Heraklion, Crete, Greece. For this purpose, photographs taken at various sites of interest are being compared to an existing database containing photos of these sites at different angles and zoom. The time required in such application is an important element. To this goal, the proposed application employs SIFT algorithm to compare the user-taken photographs with the database photographs, that have been previously processed according to the Graph Based Visual Saliency technique, in order to minimize the "noise" of the monument's background and keep only the SIFT features that will help faster and more accurate classification. The application is then able to classify these photographs fast, helping the user to better understand what he sees and in which area he had this photograph.
Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the poss...
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Network Processors are used at the core of the Internet, providing routers, switches and other essential network devices with the necessary processing power to deliver proper data forwarding and other network related ...
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In recent years, industrial control systems (ICS) are widely used in many occasions, which makes information security of ICS an important issue. This study discusses the communication and management on ICS to make ICS...
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Due to the rapid spread of tablet computers, smartphones, and other mobile information devices, wireless communication technology is fully developed and deployed widely. Mobile Internet access has been a main and impo...
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This paper presents an efficient and fast system for object detection in a 3D scene using the capabilities of Microsoft Kinect sensor in depth map generation. Besides, the proposed method introduces a real size estima...
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ISBN:
(纸本)9780889869219
This paper presents an efficient and fast system for object detection in a 3D scene using the capabilities of Microsoft Kinect sensor in depth map generation. Besides, the proposed method introduces a real size estimation of the detected objects. Successful 3D scene's object detection and real size calculation are crucial features in computer vision to the goal of making machines that see objects like humans do. In our system we employ effective depth map processing techniques, along with edge detection, connected components detection and filtering approaches, in order to design a complete algorithm for efficient object detection and real size calculation, even in complex scenes with many objects. Experimental results on three different 3D scenes are presented, showing the efficiency of the proposed design.
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