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한국정보처리학회> JIPS(Journal of Information Processing Systems)> Security in Network Virtualization: A Survey

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Security in Network Virtualization: A Survey

Seung Hun Jee , Ji Su Park , Jin Gon Shon
  • : 한국정보처리학회
  • : JIPS(Journal of Information Processing Systems) 17권4호
  • : 연속간행물
  • : 2021년 08월
  • : 801-817(17pages)
JIPS(Journal of Information Processing Systems)

DOI


목차

1. Introduction
2. Network Virtualization
3. Software-Defined Networking
4. Network Functions Virtualization
5. Network Virtualization Overlay
6. Open Issues and Challenges
7. Conclusion
References

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초록 보기

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.

UCI(KEPA)

I410-ECN-0102-2022-500-000779496

간행물정보

  • : 공학분야  > 전자공학
  • : KCI등재
  • : SCOPUS
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : 학술지
  • : 연속간행물
  • : 2005-2022
  • : 978


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1Delivering Augmented Information in a Session Initiation Protocol-Based Video Telephony Using Real-Time AR

저자 : Sung-bong Jang , Young-woong Ko

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 1-11 (11 pages)

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Online video telephony systems have been increasingly used in several industrial areas because of coronavirus disease 2019 (COVID-19) spread. The existing session initiation protocol (SIP)-based video call system is being usefully utilized, however, there is a limitation that it is very inconvenient for users to transmit additional information during conversation to the other party in real time. To overcome this problem, an enhanced scheme is presented based on augmented real-time reality (AR). In this scheme, augmented information is automatically searched from the Internet and displayed on the user's device during video telephony. The proposed approach was qualitatively evaluated by comparing it with other conferencing systems. Furthermore, to evaluate the feasibility of the approach, we implemented a simple network application that can generate SIP call requests and answer with AR object pre-fetching. Using this application, the call setup time was measured and compared between the original SIP and pre-fetching schemes. The advantage of this approach is that it can increase the convenience of a user's mobile phone by providing a way to automatically deliver the required text or images to the receiving side.

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2Lightweight Single Image Super-Resolution by Channel Split Residual Convolution

저자 : Buzhong Liu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 12-25 (14 pages)

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In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

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3Systematic Review on Chatbot Techniques and Applications

저자 : Dong-min Park , Seong-soo Jeong , Yeong-seok Seo

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 26-47 (22 pages)

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Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

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4A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

저자 : Ganghua Liu , Wei Tian , Yushun Luo , Juncheng Zou , Shu Tang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 48-58 (11 pages)

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Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

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5A Hierarchical Bilateral-Diffusion Architecture for Color Image Encryption

저자 : Menglong Wu , Yan Li , Wenkai Liu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 59-74 (16 pages)

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During the last decade, the security of digital images has received considerable attention in various multimedia transmission schemes. However, many current cryptosystems tend to adopt a single-layer permutation or diffusion algorithm, resulting in inadequate security. A hierarchical bilateral diffusion architecture for color image encryption is proposed in response to this issue, based on a hyperchaotic system and DNA sequence operation. Primarily, two hyperchaotic systems are adopted and combined with cipher matrixes generation algorithm to overcome exhaustive attacks. Further, the proposed architecture involves designing pixelpermutation, pixel-diffusion, and DNA (deoxyribonucleic acid) based block-diffusion algorithm, considering system security and transmission efficiency. The pixel-permutation aims to reduce the correlation of adjacent pixels and provide excellent initial conditions for subsequent diffusion procedures, while the diffusion architecture confuses the image matrix in a bilateral direction with ultra-low power consumption. The proposed system achieves preferable number of pixel change rate (NPCR) and unified average changing intensity (UACI) of 99.61% and 33.46%, and a lower encryption time of 3.30 seconds, which performs better than some current image encryption algorithms. The simulated results and security analysis demonstrate that the proposed mechanism can resist various potential attacks with comparatively low computational time consumption.

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6User-to-User Matching Services through Prediction of Mutual Satisfaction Based on Deep Neural Network

저자 : Jinah Kim , Nammee Moon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 75-88 (14 pages)

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With the development of the sharing economy, existing recommender services are changing from user-item recommendations to user-user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)-deep neural network (DNN)-based model that can predict each supplier's satisfaction from the consumer perspective and each consumer's satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.

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7Multistage Pulse Jamming Suppression Algorithm for Satellite Navigation Receiver

저자 : Xiaobo Yang , Jining Feng , Ying Xu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 89-96 (8 pages)

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A novel multistage pulse jamming suppression algorithm was proposed to solve the anti-pulse jamming problem encountered in navigation receivers. Based on the characteristics of the short duration of pulse jamming and distribution characteristics of satellite signals, the pulse jamming detection threshold was derived. From the experiments, it was found that the randomness of pulse jamming affects jamming suppression. On this basis, the principle of the multistage anti-pulse jamming algorithm was established. The effectiveness of the anti-jamming algorithm was verified through experiments. The characteristics of the algorithm include simple determination of jamming detection threshold, easy programming, and complete suppression of pulse jamming.

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8Improved Dynamic Programming in Local Linear Approximation Based on a Template in a Lightweight ECG Signal-Processing Edge Device

저자 : Seungmin Lee , Daejin Park

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 97-114 (18 pages)

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Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

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9A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

저자 : Syed Nazir Hussain , Azlan Abd Aziz , Jakir Hossen , Nor Azlina Ab Aziz , G. Ramana Murthy , Fajaruddin Bin Mustakim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 115-129 (15 pages)

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Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

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10Identifying Critical Factors for Successful Games by Applying Topic Modeling

저자 : Mookyung Kwak , Ji Su Park , Jin Gon Shon , Abstract

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 130-145 (16 pages)

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Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

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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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