• Title, Summary, Keyword: multi-task learning

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A Research Review on Effective Use of IS drawn on Multi-level Dynamic Capability (정보시스템 분석수준 별 역동적 역량에 기반한 효율적 사용에 관한 연구 리뷰)

  • Kang, Hyunjeong
    • The Journal of Information Systems
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    • v.29 no.2
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    • pp.27-50
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    • 2020
  • Purpose The research on the effective use of IS needs to embrace the alignment to organization learning process, which expands the limited focus on dynamic capability of IS use. In addition, it should be done in multi-level analysis with system, user, task, and organization. The current study suggests the inclusion of multi-level analysis of effective use of IS in the perspective of exploration and exploitation. Design/methodology/approach This review selected the representative studies in IS discipline which have investigated the effective use of IS, dynamic capability, operational capability, exploration, exploitation, or organizational learning. In the search of academic archives with those keywords, seventeen papers which have been most cited were chosen and validated whether the focus constructs are directly theorized or validated the suggested keywords. In addition, the level of analysis was verified whether it includes one or more levels of system, individual, task, or organization. Based on the initial analysis of dynamic capability, the further review of research on explorational and exploitational capabilities was implemented. Findings The present review study on previous literature on effective use of IS presented that it is largely implemented in the level of individual but few of them has included organization level. Similarly, the direct investigation of explorational and exploitational use of IS has not been done so much. The needs of study on effective use of IS in depth have been inquired for a decade. However, the review presented that it still lacks profound theories and empirical validations compared to those of adoption stage of IS. Based on the review, future research on the transition between explorational and exploitational use of IS is suggested.

Weakly-supervised Semantic Segmentation using Exclusive Multi-Classifier Deep Learning Model (독점 멀티 분류기의 심층 학습 모델을 사용한 약지도 시맨틱 분할)

  • Choi, Hyeon-Joon;Kang, Dong-Joong
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.19 no.6
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    • pp.227-233
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    • 2019
  • Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

The Improved Joint Bayesian Method for Person Re-identification Across Different Camera

  • Hou, Ligang;Guo, Yingqiang;Cao, Jiangtao
    • Journal of Information Processing Systems
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    • v.15 no.4
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    • pp.785-796
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    • 2019
  • Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.

Neural Learning-Based Inverse Kinematics of a Robotic Finger (뉴럴 러닝 기반 로봇 손가락의 역기구학)

  • Kim, Byoung-Ho
    • Journal of Korean Institute of Intelligent Systems
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    • v.17 no.7
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    • pp.862-868
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    • 2007
  • The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

Combining multi-task autoencoder with Wasserstein generative adversarial networks for improving speech recognition performance (음성인식 성능 개선을 위한 다중작업 오토인코더와 와설스타인식 생성적 적대 신경망의 결합)

  • Kao, Chao Yuan;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.670-677
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    • 2019
  • As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.

Rainfall Recognition from Road Surveillance Videos Using TSN (TSN을 이용한 도로 감시 카메라 영상의 강우량 인식 방법)

  • Li, Zhun;Hyeon, Jonghwan;Choi, Ho-Jin
    • Journal of Korean Society for Atmospheric Environment
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    • v.34 no.5
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    • pp.735-747
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    • 2018
  • Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

Wiki Usage of LIS Undergraduates for Collaborative Learning (문헌정보학과 학생들의 위키를 활용한 협력학습에 대한 연구)

  • Park, Sung Jae
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.23 no.4
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    • pp.93-108
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    • 2012
  • The purpose of this study is to find any contradictions which arise with the use of Wiki in the classroom, and to address such contradictions in order to promote learning among LIS students. This study employed a multi-methodology, including Wiki usage analysis, and interviews with 12 students who participated in an LIS class. Observations revealed that group projects are common in academic classes. Interviewees agreed that their performance through collaborative efforts was higher than that through individually performed activities. However, there were no pre-experiences with Wiki in learning and task-oriented cooperation which gave rise to a controversy. In addition, even though a new technology, as a more advanced form, was suggested, students cooperated with their peers according to their tradition without using the recommended new technology. Therefore, students should be taught about Wiki usage and experience the effective learning which is available to them through collaboration with their peers. Additionally, LIS curriculum should incorporate relationship-oriented activities using Web 2.0 applications with the expectation of enhanced learning among students.

Multi-Class SVM+MTL for the Prediction of Corporate Credit Rating with Structured Data

  • Ren, Gang;Hong, Taeho;Park, YoungKi
    • Asia pacific journal of information systems
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    • v.25 no.3
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    • pp.579-596
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    • 2015
  • Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

Privacy Disclosure and Preservation in Learning with Multi-Relational Databases

  • Guo, Hongyu;Viktor, Herna L.;Paquet, Eric
    • Journal of Computing Science and Engineering
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    • v.5 no.3
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    • pp.183-196
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    • 2011
  • There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

Self-Learning Control of Cooperative Motion for Humanoid Robots

  • Hwang, Yoon-Kwon;Choi, Kook-Jin;Hong, Dae-Sun
    • International Journal of Control, Automation, and Systems
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    • v.4 no.6
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    • pp.725-735
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    • 2006
  • This paper deals with the problem of self-learning cooperative motion control for the pushing task of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm(SGA) is used to find the cooperative motion, which is to minimize the total energy consumption for the entire humanoid robot body. And the multi-layer neural network based on backpropagation(BP) is also constructed and applied to generalize parameters, which are obtained from the optimization procedure by SGA, in order to control the system.