박노성 교수

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not

We consider the problem of protecting a set of animals such as rhinos and elephants in a game park using D drones and R ranger patrols (on the ground) with R ≥ D. Using two years of data about animal movements in a game park, we propose the probabilistic spatio-temporal graph (pSTG) model of animal movement behaviors and show how we can learn it from the movement data. Using 17 months of data about poacher behavior, we also learn the probability that a region in the game park will be targeted by

There are many trials to provide an optimal route for minimum energy consumption in a wireless sensor network. Currently, however, the mechanisms to find minimum energy property graph (MEPG) do not properly take into account the efficiency in time and storage, the optimality in results, and the feasibility in real systems. In this paper, we propose an efficient and first optimal algorithm to find the MEPG, in which all minimum energy paths are included, not only significantly reducing its total

It is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel training method called manifold-matching, and a new GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds representing the vector representations of real and fake images. If these two manifolds match, it means that real and fake images are statistically identical. To assist the manifold-matching task, we

This article, the first entry in the new Predictive Analytics column, looks at the problem of animal poaching. The authors describe their Anti-Poaching Engine system, which builds on behavior models of both rhinos and poachers to protect as many animals as possible.

It is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel training method called manifold-matching, and a new GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds representing the vector representations of real and fake images. If these two manifolds match, it means that real and fake images are statistically identical. To assist the manifold-matching task, we

Generative adversarial networks (GANs) are considered as a totally different type of generative models. However, it is well known that GANs are very hard to train. There have been proposed many different techniques in order to stabilize their training procedures. In this paper, we propose a novel training method called manifold matching and a new GAN model called manifold matching GAN (MMGAN). In MMGAN, vector representations extracted from the last layer of the discriminator are used to train t

Asynchronous low-power MACs based on preamble sampling are considered some of the most promising low-power protocols for wireless sensor networks. Although many such protocols have been suggested, they suffer from the inefficiency of essential features such as a means for preamble collision avoidance. We suggest a multiple preamble transmission scheme rather than the previous exclusive method in which only one sender can transmit a series of preambles to prevent preamble collisions and other nei

Abstract Most of the community detection algorithms assume that the complete network structure $$\mathcal {G}=(\mathcal {V},\mathcal {E})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>G</mml:mi><mml:mo>=</mml:mo><mml:mo>(</mml:mo><mml:mi>V</mml:mi><mml:mo>,</mml:mo><mml:mi>E</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricte

Abstract Influence estimation (IE) and influence maximization (IM) are among the most extensively studied problems in social network analysis. Assuming diffusion (i.e., the spread of diseases) within a social network, IE aims to estimate the influence (i.e., the number of infected nodes) for a given set of seeds; and IM aims to identify a given number of seed nodes that maximize the influence. For both IE and IM, widely-adopted strategies involve repeating Monte Carlo (MC) simulations of diffusi