Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

In this study, we experiment the Cubic Spline Interpolation annotation method in order to reduce annotation size for stable or uncertainty cause by the object movement. In the experiment result, we found that Cubic Spline Interpolation method is able to save up to 80 percent of annotation size with 15 frames annotation and below. Moreover, it produces error rate at 31 pixels and averagely less than 4 pixels. Our proposed method to employ dynamic sampling by dynamically change the sampling rate according to object speed shows significant decreases in error rates from 69 percent to 65 percent.

Representation Method of the Moving Object Trajectories by Interpolation with Dynamic Sampling

There is a huge amount of indexed web resources are used by millions of users in every country the Earth. It is this huge amount of the content and the integration of information across space and time that leads to the Web containing itself a social network. In this paper, we proposed a frame- work for construction and visualization of a social network by collaborative web approach, in which a consensus method is proposed to identify personal information from different web search spaces (include personal/organizational sites, blogs, publications) of user. A personal ontology is automatically generated from the personal information. Construction of social network is carried out by analyzing relationship among people, contents and organizations found from the different search spaces of each target user.

Collaborative Web for Personal Ontology Generation and Visualization for a Social Network

Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLensdatasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.

A User-Item Predictive Model for Collaborative Filtering Recommendation

In this paper, the notion of the Anticipatory Pruning Network (APN) is introduced and developed for the propositional part of the 2LP system; 2LP (Linear Programming and Logic Programming) is a constraint logic programming system which has been developed and implemented at the Logic Based System Lab at Brooklyn College/CUNY. Using the compilation of rules in the style of the Rete algorithm, the APN maps program clauses into a network. The APN prunes a search space by consistency checking and inconsistency propagation through the network and resets itself upon backtracking. The APN extends forward checking to continuous constraint domains. Overall, the benchmarks show the APN to be an effective forward checking mechanism for both discrete and continuous problem domains for Simplex based constraint solvers. In particular, the APN is an effective pruning method for constrained mixed integer, linear optimization problems.

Anticipatory pruning networks and forward checking in CLP over continuous domains

Recently, as various image-to-image translation studies have been progressed, it is possible to generate high-quality images. In particular, generation models using unpaired data produce meaningful results even building data at a low cost. However, these studies, which are based on Generative Adversarial Networks (GANs), is composed a very heavy architecture. Unlike the commonly used other deep learning models, generally the GANs model consists of two or more in a particular case deep architecture, which has a large computational cost. To solve this limitation, this paper proposes an efficient generator module called DCBlock (Depthwise separable Channel Attention Block). DCBlock consists of a depthwise separable convolution with a relatively low computational cost to replace the standard convolution commonly used in the image to image translation, and channel attention to compensate for information loss caused by depthwise separable convolution. DCBlock showed similar performance to the existing original model while reducing the number of parameters that represents the amount of computation by up to 91.6%. Besides, we experiment with the proposed method for various novel researches and prove that the problem is solved. Keywords-component Generative Adversarial Networks , Unpaired Image-to-Image translation, Efficient model architecture, deep learning

Geun-Sik Jo

Papers

Representation Method of the Moving Object Trajectories by Interpolation with Dynamic Sampling

Collaborative Web for Personal Ontology Generation and Visualization for a Social Network

A User-Item Predictive Model for Collaborative Filtering Recommendation

Anticipatory pruning networks and forward checking in CLP over continuous domains

DCBlock : Efficient Module for Unpaired Image to Image Translation Using GANs.