Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

Abstract: Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.

The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing

Abstract: Task scheduling is an important and challenging issue of Cloud computing. Existing solutions to task scheduling problems are unsuitable for Cloud computing because they only focus on a specific purpose like the minimization of execution time or workload and do not use characteristics of Cloud computing for task scheduling. A task scheduler in Cloud computing has to satisfy cloud users with the agreed QoS and improve profits of cloud providers. In order to solve task scheduling problems in Cloud computing, this paper proposes a task scheduling model based on the genetic algorithm. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine availability. The function iterates genetic operations to make an optimal task schedule. Experimental results show effectiveness and efficiency of the genetic algorithm-based task scheduling model in comparison with existing task scheduling models, which are the round-robin task scheduling model, the load index-based task scheduling model, and the ABC based task scheduling model.

Learning 3D Object Categories by Looking Around Them

Abstract: Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.

Anti-inflammatory effect of myristicin on RAW 264.7 macrophages stimulated with polyinosinic-polycytidylic acid.

Abstract: Myristicin (1-allyl-5-methoxy-3,4-methylenedioxybenzene) is an active aromatic compound found in nutmeg (the seed of Myristica fragrans), carrot, basil,cinnamon, and parsley. Myristicin has been known to have anti-cholinergic, antibacterial,and hepatoprotective effects, however, the effects of myristicin on virus-stimulated macrophages are not fully reported. In this study, the anti-inflammatory effect of myristicin on double-stranded RNA (dsRNA)-stimulated macrophages was examined. Myristicin did not reduce the cell viability of RAW 264.7 mouse macrophages at concentrations of up to 50 μM. Myristicin significantly inhibited the production of calcium, nitric oxide (NO),interleukin (IL)-6, IL-10, interferon inducible protein-10, monocyte chemotactic protein(MCP)-1, MCP-3, granulocyte-macrophage colony-stimulating factor, macrophage inflammatory protein (MIP)-1α, MIP-1β, and leukemia inhibitory factor in dsRNA[polyinosinic-polycytidylic acid]-induced RAW 264.7 cells (P < 0.05). In conclusion,myristicin has anti-inflammatory properties related with its inhibition of NO, cytokines,chemokines, and growth factors in dsRNA-stimulated macrophages via the calcium pathway.

Anti-Inflammatory activity of chrysophanol through the suppression of NF-kappaB/caspase-1 activation in vitro and in vivo.

Abstract: Chrysophanol is a member of the anthraquinone family and has multiple pharmacological effects, but the exact mechanism of the anti-inflammatory effects of chrysophanol has yet to be thoroughly elucidated. In this study, we attempted to determine the effects of chrysophanol on dextran sulfate sodium (DSS)-induced colitis and lipopolysaccharide (LPS)-induced inflammatory responses in mouse peritoneal macrophages. The findings of this study demonstrated that chrysophanol effectively attenuated overall clinical scores as well as various pathological markers of colitis. Additionally, chrysophanol inhibited the production of tumor necrosis factor (TNF)-α, interleukin (IL)-6 and the expression of cyclooxygenase (COX)-2 levels induced by LPS. We showed that this anti-inflammatory effect of chrysophanol is through suppression of the activation of NF-κB and caspase-1 in LPS-stimulated macrophages. These results provide novel insights into the pharmacological actions of chrysophanol as a potential molecule for use in the treatment of inflammatory diseases.