Soonyoung Jung

TRIM72 negatively regulates myogenesis via targeting insulin receptor substrate-1.

Lipid rafts have been known to be platforms to initiate cellular signal transduction of insulin-like growth factor (IGF) inducing skeletal muscle differentiation and hypertrophy. Here, tripartite motif 72 (TRIM72), with a really interesting new gene (RING)-finger domain, a B-box, two coiled-coil domains, and a SPRY (SPla and RYanodine receptor) domain, was revealed to be predominantly expressed in the sarcolemma lipid rafts of skeletal and cardiac muscles. Adenoviral TRIM72 overexpression prevented but RNAi-mediated TRIM72 silencing enhanced C2C12 myogenesis by modulating the IGF-induced insulin receptor substrate-1 (IRS-1) activation through the molecular association of TRIM72 with IRS-1. Furthermore, myogenic activity was highly enhanced with increased IGF-induced Akt activation in the satellite cells of TRIM72−/− mice, compared to those of TRIM72+/+ mice. Because TRIM72 promoter analysis shows that two proximal E-boxes in TRIM72 promoter were essential for MyoD- and Akt-dependent TRIM72 transcription, we can conclude that TRIM72 is a novel antagonist of IRS-1, and is essential as a negative regulator of IGF-induced muscle differentiation.

Automatic extraction of user's search intention from web search logs

Web search users complain of the inaccurate results produced by current search engines. Most of these inaccurate results are due to a failure to understand the user’s search goal. This paper proposes a method to extract users’ intentions and to build an intention map representing these extracted intentions. The proposed method makes intention vectors from clicked pages from previous search logs obtained on a given query. The components of the intention vector are weights of the keywords in a document. It extracts user’s intentions by using clustering the intention vectors and extracting intention keywords from each cluster. The extracted the intentions on a query are represented in an intention map. For the efficiency analysis of intention map, we extracted user’s intentions using 2,600 search log data a current domestic commercial search engine. The experimental results with a search engine using the intention maps show statistically significant improvements in user satisfaction scores.

Extracting Search Intentions from Web Search Logs

Web search users complain of inaccurate results of the current search engines. Most of inaccurate results are from failing to understand user???s search goal. This paper proposes a method to mine user???s intentions and to build an intention map representing their information needs. It selects intention features from search logs obtained from previous search sessions on a given query and extracts user???s intentions by using clustering and labeling algorithms. The mined user???s intentions on the query are represented in an intention map. For the efficiency analysis of intention maps, we extracted user intentions using 2,600 search log data of a current domestic commercial web search engine. The experimental results using a web search engine with the intention maps show statistically significant improvements in user satisfaction scores.

The Biometric Based Convertible Undeniable Multi-Signature Scheme to Ensure Multi-Author Copyrights and Profits

Digital content is easy to reproduce and manipulate. It is difficult to distinguish the original content from pirated copies. A cryptographic method is needed to protect content author’s ownership and secure content distribution. The method should be extended to the case of multiple authors since the content is completed with the assistance of many authors. In this paper, the biometric-based, convertible, and undeniable multi-signature scheme is proposed. The private and public keys are generated using a signer’s biometric data and a random secret value. Thus, lending the private key to the proxy signer is not possible. All the signers should participate in multi-signature generation and verification stages. The proposed scheme also provides a signature conversion process in which undeniable multi-signature is converted to the ordinary one. We demonstrate how our scheme is useful to ensure multi-author copyrights and profits.

Nearest-neighbour query processing with non-spatial predicates for service allocation in smart space environment

The extensive capability of sensors let sensors autonomously collect various information on smart objects and store them to the spatial database through the wireless sensor networks. Based on spatial database, location-dependent information services (LDISs) can supply the resources according to the user locations. In LDISs, nearest-neighbour queries which return the closest object around the query location is recognised as the key component for searching the easily accessible services in smart spaces. However, existing works only consider the Euclidean distance. Thus, they have limitations to provide user-centric services that require the consideration for not only the distance but also the status of smart objects. Motivated by the issues of nearest-neighbour queries, this study proposes the new type of query called specified nearest-neighbour (SNN). SNN query considers the status and the locations of smart objects. For the SNN, the authors suggest a novel signature-based R -tree (SR-tree) index structure that handles non-spatial information of objects efficiently. Further, the authors propose an SNN query processing technique. Finally, they evaluate the performance of the proposed algorithm in various circumstances. Performance results indicate that SNN algorithm with SR-tree outperforms the existing works in terms of computational cost and disk input/output (I/O).

Parallel Itinerary-based RNN Query Processing in Location-aware WSNs

The Reverse Nearest Neighbor (RNN) query is to find the objects in objects dataset D that have Q closer to them than any other object in D. Formally RNN(Q) = {Oi ∈ D| NN(Oi) = Q}. Owing to technical advances of sensor and wireless techniques, sensor nodes are deployed over a wide range and applied to various applications with the RNN query. To date, centralized and in-network scheme based RNN query processing approaches have been researched. However, these approaches collect data from sensors regardless of query issuing and inevitably deplete energy and CPU capacity. Therefore, in this paper, we propose the parallel itinerary-based RNN (PIRNN) query processing algorithm. The PIRNN algorithm does not rely on any centralized or in-network data collection scheme. Moreover, PIRNN disseminates multiple itineraries concurrently and restricts the search range to decrease query latency. In order to support the performance of PIRNN algorithm, we revise two representative RNN processing methods, SAA and HP, used in mobile networks. The extensive simulation results prove that the PIRNN method yields better performance and less energy consumption over the conventional one.

User-Qualified Group Search using Bidirectional Sweep Planes

In this paper, we propose a nearest user-qualified group (NUG) query that searches a group of objects to obtain a result. In detail, given a dataset P, query q, distance δ, and cardinality k, the NUG query returns the nearest group of objects from q, such that more than k objects within δ distance from the point, called a representative, are in the group. Although the NUG query has large spectrum of applications, an efficient processing algorithm for NUG queries has not been studied so far. Therefore, we propose the plane sweep-based incremental search algorithm and heuristic that stops the plane sweep early to reduce the search space. A performance study is conducted on both synthetic and real datasets and our experimental results show that the proposed algorithm can improve the query performance in a variety of conditions.

Deep Representation of Raw Traffic Data: An Embed-and-Aggregate Framework for High-Level Traffic Analysis

In Intelligent Transportation Systems (ITS), it is widely used to extract a fixed-size feature vector from raw traffic data for high-level traffic analysis. In several existing works, the statistical approach has been used for extracting feature vectors, which directly extracts features by averaging speed or travel time of each vehicle. However, we can achieve a better representation by taking advantage of state-of-the-art machine learning algorithms instead of the statistical approach. In this paper, we propose a two-phase framework named embed-and-aggregate framework for extracting features from raw traffic data, and a feature extraction algorithm (Traffic2Vec) based on our framework exploiting state-of-the-art machine learning algorithms such as deep learning. We also implement a traffic flow prediction system based on Traffic2Vec as a proof-of-concept. We conducted experiments to evaluate the applicability of the proposed algorithm, and show its superior performance in comparison with the prediction system based on the statistical feature extraction method.

A corpus-based approach to classifying emotions using Korean linguistic features

Recently, social network services have become the popular communication tools among internet and mobile users. And it has been shared various opinions, which could be included various emotions. Emotion analysis aims to extract various emotion information, such as joy, happy, funny, fear, sad, and lonely, and so on, from texts expressed in natural language. Previous studies about emotion analysis on texts written in Korean have focused generally on the basic sentiments such as positive/neutral/negative preferences or 4–10 emotion classes. In this paper, we propose an emotion analysis method based on supervised learning to classify various emotions from messages written in Korean. We had found the feature set optimized to each emotion class through evaluating the combinations of various linguistic features and built a model to classify the emotion using the optimized feature set. To do this, it was constructed the corpus that is manually annotated with 25 emotion classes. We performed a 10-fold cross variation experiment for evaluating the performance of the proposed method. Our method obtained F-value ranged from 73.1 to 98.0% for each of 25 emotion classes. The optimized feature sets for most of emotion classes include commonly word 2-gram, POS 1-gram, and character 1-gram feature.

Acquiring Korean Lexical Entry from a Raw Corpus

This paper proposes a computational lexical entry acquisition model based on a representation model of the mental lexicon. The proposed model acquires lexical entries from a raw corpus by unsupervised learning like human. The model is composed of full-form and morpheme acquisition modules. In the full-from acquisition module, core full-forms are automatically acquired according to the frequency and recency thresholds. In the morpheme acquisition module, a repeatedly occurring substring in different full-forms is chosen as a candidate morpheme. Then, the candidate is corroborated as a morpheme by using the entropy measure of syllables in the string. The experimental results with a Korean corpus of which size is about 16 million full-forms show that the model successively acquires major full-forms and morphemes with the precision of 100% and 99.04%, respectively.