Gyomin Jeong

Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate t

In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model

In a wireless network, a sophisticated algorithm is required to schedule simultaneous wireless transmissions while satisfying interference constraint that two neighboring nodes can not transmit simultaneously. The scheduling algorithm need to be excellent in performance while being simple and distributed so as to be implementable. The result of Tassiulas and Ephremides (1992) imply that the algorithm, scheduling transmissions of nodes in the 'maximum weight independent set' (MWIS) of network gra

Motivated by applications of distributed linear estimation, distributed control, and distributed optimization, we consider the question of designing linear iterative algorithms for computing the average of numbers in a network. Specifically, our interest is in designing such an algorithm with the fastest rate of convergence given the topological constraints of the network. As the main result of this paper, we design an algorithm with the fastest possible rate of convergence using a nonreversible

Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we

This paper considers structural and algorithmic problems in stochastic loss networks. The very popular Erlang approximation can be shown to provide relatively poor performance estimates, especially for loss networks in the critically loaded regime. This paper proposes a novel algorithm for estimating the stationary loss probabilities in stochastic loss networks based on structural properties of the exact stationary distribution, which is shown to always converge, exponentially fast, to the asymp

In this paper, we propose a novel speech emotion recognition model called Cross Attention Network (CAN) that uses aligned audio and text signals as inputs. It is inspired by the fact that humans recognize speech as a combination of simultaneously produced acoustic and textual signals. First, our method segments the audio and the underlying text signals into equal number of steps in an aligned way so that the same time steps of the sequential signals cover the same time span in the signals. Toget

We study a basic information ranking problem in networks where each node holds an individual preference over a set of items and the goal for each node is to identify a sorted list of items with the largest aggregate preference. We would like to achieve this with a fully decentralized algorithm that uses a limited per-node memory and limited pair-wise communications. We show how this problem can be reduced to a plurality selection problem where the goal for each node is to identify an item with t

Fully convolutional structures provide feature maps acquiring local contexts of an image by only stacking numerous convolutional layers. These structures are known to be effective in modern state-of-the-art object detectors such as Faster R-CNN and SSD to find objects from local contexts. However, the quality of object detectors can be further improved by incorporating global contexts when some ambiguous objects should be identified by surrounding objects or background. In this paper, we introdu

In a recent result, Weitz [31] established equivalence between the marginal distribution of a node, say v, in any binary pair-wise Markov Random Field (MRF), say G, with the marginal distribution of the root node in the selfavoid walk tree of the G starting at v. In this paper, we exploit this remarkable connection to obtain insights in the performance of the widely popular Belief Propagation heuristic for computing marginal distribution (sum-product) and max-marginal distribution (max-product).

Distributed computation of average is essential for many tasks such as estimation, eigenvalue computation, scheduling in the context of wireless sensor and ad-hoc networks. The wireless communication imposes the gossip constraint: each node can communicate with at most one other node at a given time. Recent interest in emerging wireless sensor network has led to exciting developments in the context of gossip algorithms for averaging. Most of the known algorithms are iterative and based on certai

We consider the question of computing Maximum A Posteriori (MAP) assignment in an arbitrary pair-wise Markov Random Field (MRF). We present a randomized iterative algorithm based on simple local updates. The algorithm, starting with an arbitrary initial assignment, updates it in each iteration by first, picking a random node, then selecting an (appropriately chosen) random local neighborhood and optimizing over this local neighborhood. Somewhat surprisingly, we show that this algorithm finds a n

Abstract Analysis of customers' sensibilities and preferences is an important strategy in a market that is becoming increasingly more customer oriented. The authors propose the Textile Design Recommendation System (TDRS) as an effective tool for focusing on customers' needs in the textile industry. The authors have developed a user interface tool that can suggest textile designs according to the users' needs. Collaborative filtering was adopted to search a textile design, and the performance of