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한국정보처리학회> JIPS(Journal of Information Processing Systems)

JIPS(Journal of Information Processing Systems) update

  • : 한국정보처리학회
  • : 공학분야  >  전자공학
  • : KCI등재
  • : SCOPUS
  • : 연속간행물
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : International journal of information processing systems(~2007)→Journal of information processing system(2008~)

수록정보
수록범위 : 1권1호(2005)~17권3호(2021) |수록논문 수 : 930
JIPS(Journal of Information Processing Systems)
17권3호(2021년 06월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1A Federated Multi-Task Learning Model Based on Adaptive Distributed Data Latent Correlation Analysis

저자 : Shengbin Wu , Yibai Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 441-452 (12 pages)

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Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

KCI등재 SCOPUS

2Attentive Transfer Learning via Self-supervised Learning for Cervical Dysplasia Diagnosis

저자 : Jinyeong Chae , Roger Zimmermann , Dongho Kim , Jihie Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 453-461 (9 pages)

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Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch selfsupervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

KCI등재 SCOPUS

3Effective Pre-rating Method Based on Users' Dichotomous Preferences and Average Ratings Fusion for Recommender Systems

저자 : Shulin Cheng , Wanyan Wang , Shan Yang , Xiufang Cheng

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 462-472 (11 pages)

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With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

KCI등재 SCOPUS

4Analysis of Impact on ERP Customization Module Using CSR Data

저자 : Byung-keun Yoo , Seung-hee Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 473-488 (16 pages)

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The enterprise resource planning (ERP) system is a standardized and advanced business process that many companies are implementing now-a-days through customization. However, it affects the efficiency of operations as these customizations are based on uniqueness. In this study, we analyzed the impact of customized modules and processing time on customer service request (CSR), by utilizing the stacked CSR data during the construction and operation of ERP, focusing on small and medium-sized enterprises (SMEs). As a result, a positive correlation was found between unit companies and the length of ERP implementation; ERP modules and the length of ERP implementation; ERP modules and unit companies; and the type of ERP implementation and ERP module. In terms of CSR, a comparison of CSR processing time of CBO (customized business object) module and STD (standard) module revealed that while the five modules did not display statistically significant differences, one module demonstrated a statistically very significant difference. In sum, the analysis indicates that the CBO-type CSR and its processing cost are higher than those of STD-type CSR. These results indicate that companies planning to implement an ERP system should consider the ERP module and their customization ratio and level. It not only gives the theoretical validity that should be considered as an indicator for decision making when ERP is constructed, but also its implications on the impact of processing time suggesting that the maintenance costs and project scheduling of ERP software must also be considered. This study is the first to present the degree of impact on the operation and maintenance of customized modules based on actual data and can provide a theoretical basis for applying SW change ratio in the cost estimation of ERP system maintenance.

KCI등재 SCOPUS

5An Offloading Scheduling Strategy with Minimized Power Overhead for Internet of Vehicles Based on Mobile Edge Computing

저자 : Bo He , Tianzhang Li

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 489-504 (16 pages)

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By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

KCI등재 SCOPUS

6Implementation of an Autostereoscopic Virtual 3D Button in Non-contact Manner Using Simple Deep Learning Network

저자 : Sang-hee You , Min Hwang , Ki-hoon Kim , Chang-suk Cho

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 505-517 (13 pages)

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This research presented an implementation of autostereoscopic virtual three-dimensional (3D) button device as non-contact style. The proposed device has several characteristics about visible feature, non-contact use and artificial intelligence (AI) engine. The device was designed to be contactless to prevent virus contamination and consists of 3D buttons in a virtual stereoscopic view. To specify the button pressed virtually by fingertip pointing, a simple deep learning network having two stages without convolution filters was designed. As confirmed in the experiment, if the input data composition is clearly designed, the deep learning network does not need to be configured so complexly. As the results of testing and evaluation by the certification institute, the proposed button device shows high reliability and stability.

KCI등재 SCOPUS

7Unidirectional Flow: A Survey on Networks, Applications, and Characteristic Attributes

저자 : Laxmisha Rai

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 518-536 (19 pages)

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Studies and applications related to unidirectional flow are gaining attention from researchers across disciplines in the recent years. Flow can be viewed as a concept, where the material, fluid, people, air, and electricity are moving from one node to another over a transportation network, water network, or through electricity distribution systems. Unlike other networks such as computer networks, most of the flow networks are visible and have strong material existence and are responsible for the flow of materials with definite shape and volume. The flow of electricity is also unidirectional, and also share similar features as of flow of materials such as liquids and air. Generally, in a flow network, every node in the network participates and contributes to the efficiency of the network. In this survey paper, we would like to evaluate and analyze the depth and application of the acyclic nature of unidirectional flow in several domains such as industry, biology, medicine, and electricity. This survey also provides, how the unidirectional flow and flow networks play an important role in multiple disciplines. The study includes all the major developments in the past years describing the key attributes of unidirectional flow networks, including their applications, scope, and routing methods.

KCI등재 SCOPUS

8A Survey of Automatic Code Generation from Natural Language

저자 : Jiho Shin , Jaechang Nam

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 537-555 (19 pages)

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Many researchers have carried out studies related to programming languages since the beginning of computer science. Besides programming with traditional programming languages (i.e., procedural, object-oriented, functional programming language, etc.), a new paradigm of programming is being carried out. It is programming with natural language. By programming with natural language, we expect that it will free our expressiveness in contrast to programming languages which have strong constraints in syntax. This paper surveys the approaches that generate source code automatically from a natural language description. We also categorize the approaches by their forms of input and output. Finally, we analyze the current trend of approaches and suggest the future direction of this research domain to improve automatic code generation with natural language. From the analysis, we state that researchers should work on customizing language models in the domain of source code and explore better representations of source code such as embedding techniques and pre-trained models which have been proved to work well on natural language processing tasks.

KCI등재 SCOPUS

9A Video Expression Recognition Method Based on Multi-mode Convolution Neural Network and Multiplicative Feature Fusion

저자 : Qun Ren

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 556-570 (15 pages)

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The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multimode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

KCI등재 SCOPUS

10Secure Ob ject Detection Based on Deep Learning

저자 : Keonhyeong Kim , Im Young Jung

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 3호 발행 연도 : 2021 페이지 : pp. 571-585 (15 pages)

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Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

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