As world is moving towards Ultra High Definition (UHD) content and display technology, high quality visual content is becoming a necessity. Deblocking filter is one of the tools used in today's video coding standards to enhance the quality of compressed video. Deblocking consumes significant percentage (~20%-33%) of total decoding cycles. Therefore, many decoder implementations tend to offload deblocking operation to additional hardware IP block or GPU to achieve real-time performance. However, hardware IP increases die area and lacks flexibility; on other hand GPUs are power hungry. In this paper, we present a reconfigurable processor based software solution along with a deblocking specific intrinsic catering to wide range of video coding standards for handling this performance bottleneck. Our experimental results show, proposed approach improves deblocking performance by a factor greater than 10 and results in processing time in the order of 140 ms for 4K UHD HEVC (60 fps, 30 mbps) stream.

Readability is used to provide users with highquality service in text recommendation or text visualization.With the increasing use of handheld devices, reading device is regarded as an important factor for readability.Therefore, this paper investigates the relationship between readability and reading devices such as a smart phone, a tablet, and paper.We suggest readability factors that are strongly related with the readability of a specific device by showing the correlations between various factors in each device and human-rated readability.Our experimental results show that each device has its own readability characteristics, and thus different weights should be imposed on readability factors according to the device type.In order to prove the usefulness of the results, we apply the device-dependent readability to news article recommendation.

One of the critical problems in natural language interfaces is the discordance between the expressions covered by the interface and those by the knowledge base. In the graph-based knowledge base such as an ontology, all possible queries can be prepared in advance. As a solution of the discordance problem in natural language interfaces, this paper proposes a method that translates a natural language query into a formal language query such as SPARQL. In this paper, a user query is translated into a formal language by choosing the most appropriate query from the prepared queries. The experimental results show a high accuracy and coverage for the given knowledge base.

This paper proposes a novel method to translate tags attached to multimedia contents for cross-language retrieval. The main issue in this problem is the sense disambiguation of tags given with few textual contexts. In order to solve this problem, the proposed method represents both tags and its translation candidates as networks of co-occurring tags since a network allows richer expression of contexts than other expressions such as co-occurrence vectors. The method translates a tag by selecting the optimal one from possible candidates based on a network similarity even when neither the textual contexts nor sophisticated language resources are available. The experiments on the MIR Flickr-2008 test set show that the proposed method achieves 90.44% accuracy in translating tags from English into German, which is significantly higher than the baseline methods of a frequency based translation and a co-occurrence-based translation.

Information extraction from world wide web has been paid great attention to. Since a table is a well-organized and summarized knowledge expression for a domain, it is of great importance to extract information from the tables. However, many tables in web pages are used not to transfer information but to decorate the pages. Therefore, it is one of the most critical tasks in web table mining to discriminate the meaningful tables from the decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for the discrimination. This paper proposes a novel method to discriminate them using a composite kernel which combines a parse tree kernel and a linear kernel. Since a web table is represented as a parse tree by a HTML parser, the parse tree kernel can be naturally used in determining the similarity between trees, and the linear kernel with content features is used to make up for the weak points of the parse tree kernel. The support vector machines with the composite kernel distinguish with high accuracy the meaningful tables from the decorative ones. A series of experiments show that the proposed method achieves the state-of-the-art performance.

The use of ontology in order to provide a mechanism to enable machine reasoning has continuously increased during the last few years. This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontology-based document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two predefined categories per given document features. In this paper, Web pages are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web pages and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined.

We suggest a semiautomatic ontology construction method based on hub words. To do this, we define the words that are related to many other words as hub words. We determine the hub words by the statistical analysis of documents. Additionally, we discuss the base ontology construction process based on hub words and automatic ontology extension method. The proposed ontology can be used like an index file for traditional document retrieval and can offer more semantic information than simple index files.

Presents a new approach for word sense disambiguation, specially for Korean transitive verbs, based on a supervised method. This approach integrates a set of clues based on syntactic relations for word sense disambiguation. Instead of using an unordered set of the surrounding words of a given ambiguous verb, we use co-occurrences as a clue for disambiguation based on some syntactic relations such as predicate-argument, modified-modified, adjacency, or a mixture of these. The experimental results show that the accuracy of our disambiguation method on highly ambiguous verbs with fine-grained sense distinctions is always better than the default strategy of assigning all occurrences to the most frequent sense.