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한국정보처리학회> JIPS(Journal of Information Processing Systems)> Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

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Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

Dounia Arezki , Hadria Fizazi
  • : 한국정보처리학회
  • : JIPS(Journal of Information Processing Systems) 17권4호
  • : 연속간행물
  • : 2021년 08월
  • : 690-706(17pages)
JIPS(Journal of Information Processing Systems)

DOI


목차

1. Introduction
2. Related Work
3. Materials
4. Method
5. Experimental Results
6. Conclusion
Acknowledgement
References

키워드 보기


초록 보기

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

UCI(KEPA)

간행물정보

  • : 공학분야  > 전자공학
  • : KCI등재
  • : SCOPUS
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : 학술지
  • : 연속간행물
  • : 2005-2021
  • : 966


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1Study on Data Processing of the IOT Sensor Network Based on a Hadoop Cloud Platform and a TWLGA Scheduling Algorithm

저자 : Guoyu Li , Kang Yang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1035-1043 (9 pages)

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An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

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2The Nexus among Globalization, ICT and Economic Growth: An Empirical Analysis

저자 : Ximei Liu , Zahid Latif , Daoqi Xiong , Mengke Yang , Shahid Latif , Kaif Ul Wara

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1044-1056 (13 pages)

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Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.

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3Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning

저자 : Guanwen Wang , Zhengtao Yu , Yantuan Xian , Yu Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1057-1070 (14 pages)

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Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

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4Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes

저자 : Guik Jung , Hyunsoo Ha , Sangjun Lee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1071-1082 (12 pages)

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Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

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5Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

저자 : Zhiguo Lv , Weijing Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1083-1096 (14 pages)

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Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

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6Wireless Mobile Sensor Networks with Cognitive Radio Based FPGA for Disaster Management

저자 : G. A. Preethi Ananthachari

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1097-1114 (18 pages)

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The primary objective of this work was to discover a solution for the survival of people in an emergency flood. The geographical information was obtained from remote sensing techniques. Through helpline numbers, people who are in need request support. Although, it cannot be ensured that all the people will acquire the facility. A proper link is required to communicate with people who are at risk in affected areas. Mobile sensor networks with field-programmable gate array (FPGA) self-configurable radios were deployed in damaged areas for communication. Ad-hoc networks do not have a centralized structure. All the mobile nodes deploy a temporary structure and they act as a base station. The mobile nodes are involved in searching the spectrum for channel utilization for better communication. FPGA-based techniques ensure seamless communication for the survivors. Timely help will increase the survival rate. The received signal strength is a vital factor for communication. Cognitive radio ensures channel utilization in an effective manner which results in better signal strength reception. Frequency band selection was carried out with the help of the GRA-MADM method. In this study, an analysis of signal strength for different mobile sensor nodes was performed. FPGA-based implementation showed enhanced outcomes compared to software-based algorithms.

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7Improved Deep Residual Network for Apple Leaf Disease Identification

저자 : Changjian Zhou , Jinge Xing

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1115-1126 (12 pages)

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Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

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8Research on the Impact of Agricultural Mechanization Service on Wheat Planting Cost: A Case Study of Henan Province

저자 : Zhang Cheng

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1127-1137 (11 pages)

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Given the different effects of agricultural mechanization on various stages of wheat planting in Henan, this article selects 78 observation samples from Henan, a major wheat-growing province. It uses different research methods (multiple linear regression, social network analysis model, multi-layer sensory nerves network) to conduct a comparative study, and the calculation results of the model show that the experimental results have a strong convergence and consistency. Agricultural mechanization services have significant effects on the three stages of wheat planting: harvesting, plowing and sowing. A higher degree of mechanized service in several stages can reduce the cost of growing wheat on family farms.

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9A Survey on Image Emotion Recognition

저자 : Guangzhe Zhao , Hanting Yang , Bing Tu , Lei Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1138-1156 (19 pages)

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Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

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10KAWS: Coordinate Kernel-Aware Warp Scheduling and Warp Sharing Mechanism for Advanced GPUs

저자 : Viet Tan Vo , Cheol Hong Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1157-1169 (13 pages)

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Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

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1High Rate Denial-of-Service Attack Detection System for Cloud Environment Using Flume and Spark

저자 : Janitza Punto Gutierrez , Kilhung Lee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 675-689 (15 pages)

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Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.

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2Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

저자 : Dounia Arezki , Hadria Fizazi

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 690-706 (17 pages)

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Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

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3The Method for Generating Recommended Candidates through Prediction of Multi-Criteria Ratings Using CNN-BiLSTM

저자 : Jinah Kim , Junhee Park , Minchan Shin , Jihoon Lee , Nammee Moon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 707-720 (14 pages)

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To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual “TripAdvisor” dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

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4A Joint Allocation Algorithm of Computing and Communication Resources Based on Reinforcement Learning in MEC System

저자 : Qinghua Liu , Qingping Li

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 721-736 (16 pages)

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For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

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5Evaluation of Artificial Intelligence-Based Denoising Methods for Global Illumination

저자 : Soroor Malekmohammadi Faradounbeh , Seongki Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 737-753 (17 pages)

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As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.

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6Audio and Video Bimodal Emotion Recognition in Social Networks Based on Improved AlexNet Network and Attention Mechanism

저자 : Min Liu , Jun Tang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 754-771 (18 pages)

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In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

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7A Model for Illegal File Access Tracking Using Windows Logs and Elastic Stack

저자 : Jisun Kim , Eulhan Jo , Sungwon Lee , Taenam Cho

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 772-786 (15 pages)

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The process of tracking suspicious behavior manually on a system and gathering evidence are labor-intensive, variable, and experience-dependent. The system logs are the most important sources for evidences in this process. However, in the Microsoft Windows operating system, the action events are irregular and the log structure is difficult to audit. In this paper, we propose a model that overcomes these problems and efficiently analyzes Microsoft Windows logs. The proposed model extracts lists of both common and key events from the Microsoft Windows logs to determine detailed actions. In addition, we show an approach based on the proposed model applied to track illegal file access. The proposed approach employs three-step tracking templates using Elastic Stack as well as key-event, common-event lists and identify event lists, which enables visualization of the data for analysis. Using the three-step model, analysts can adjust the depth of their analysis.

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8Pointwise CNN for 3D Object Classification on Point Cloud

저자 : Wei Song , Zishu Liu , Yifei Tian , Simon Fong

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 787-800 (14 pages)

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Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

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9Security in Network Virtualization: A Survey

저자 : Seung Hun Jee , Ji Su Park , Jin Gon Shon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 801-817 (17 pages)

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Network virtualization technologies have played efficient roles in deploying cloud, Internet of Things (IoT), big data, and 5G network. We have conducted a survey on network virtualization technologies, such as software-defined networking (SDN), network functions virtualization (NFV), and network virtualization overlay (NVO). For each of technologies, we have explained the comprehensive architectures, applied technologies, and the advantages and disadvantages. Furthermore, this paper has provided a summarized view of the latest research works on challenges and solutions of security issues mainly focused on DDoS attack and encryption.

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10Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model

저자 : Yuyang Zeng , Ruirui Zhang , Liang Yang , Sujuan Song

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 4호 발행 연도 : 2021 페이지 : pp. 818-833 (16 pages)

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To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

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