Digital Library

Vol. 18, No. 1, Feb. 2022  Cover

1. Delivering Augmented Information in a Session Initiation Protocol-Based Video Telephony Using Real-Time AR
   Sung-Bong Jang, Young-Woong Ko
   Vol. 18, No. 1, pp. 1-11, Feb. 2022  
   https://doi.org/10.3745/JIPS.04.0231
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   Keywords: Augmented Information Delivery, Augmented Reality, session initiation protocol, Video Telephony
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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.


2. Lightweight Single Image Super-Resolution by Channel Split Residual Convolution
   Buzhong Liu
   Vol. 18, No. 1, pp. 12-25, Feb. 2022  
   https://doi.org/10.3745/JIPS.02.0168
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   Keywords: Channel Split Residual, Double-Upsampling, Lightweight, super-resolution
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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 high- resolution 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.


3. Systematic Review on Chatbot Techniques and Applications
   Dong-Min Park, Seong-Soo Jeong, Yeong-Seok Seo
   Vol. 18, No. 1, pp. 26-47, Feb. 2022  
   https://doi.org/10.3745/JIPS.04.0232
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   Keywords: Chatbot, Natural Language Processing, Natural Language Understanding, Natural Language Generation, pattern recognition, Semantic web, date mining, Text-Aware Computing
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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.


4. A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration
   Ganghua Liu, Wei Tian, Yushun Luo, Juncheng Zou, Shu Tang
   Vol. 18, No. 1, pp. 48-58, Feb. 2022  
   https://doi.org/10.3745/JIPS.04.0233
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   Keywords: Edge Amplitude, Image restoration, Kernel, Spatial Scale, Windowed-Total-Variation
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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.


5. A Hierarchical Bilateral-Diffusion Architecture for Color Image Encryption
   Menglong Wu, Yan Li, Wenkai Liu
   Vol. 18, No. 1, pp. 59-74, Feb. 2022  
   https://doi.org/10.3745/JIPS.03.0171
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   Keywords: Bilateral-Diffusion, Color Image Encryption, DNA Sequence Operation, Hyperchaotic System
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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 pixel- permutation, 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.


6. User-to-User Matching Services through Prediction of Mutual Satisfaction Based on Deep Neural Network
   Jinah Kim, Nammee Moon
   Vol. 18, No. 1, pp. 75-88, Feb. 2022  
   https://doi.org/10.3745/JIPS.04.0234
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   Keywords: Convolutional Neural Network (CNN), Deep Learning, Deep neural network (DNN), Matching Service, Recommender Service
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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.


7. Multistage Pulse Jamming Suppression Algorithm for Satellite Navigation Receiver
   Xiaobo Yang, Jining Feng, Ying Xu
   Vol. 18, No. 1, pp. 89-96, Feb. 2022  
   https://doi.org/10.3745/JIPS.03.0172
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   Keywords: anti-jamming, Multistage, Pulse Jamming, Satellite Navigation Receiver
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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.


8. Improved Dynamic Programming in Local Linear Approximation Based on a Template in a Lightweight ECG Signal-Processing Edge Device
   Seungmin Lee, Daejin Park
   Vol. 18, No. 1, pp. 97-114, Feb. 2022  
   https://doi.org/10.3745/JIPS.03.0173
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   Keywords: Device Discovery, Partition-Based, RDM
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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.


9. A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets
   Syed Nazir Hussain, Azlan Abd Aziz, Md. Jakir Hossen, Nor Azlina Ab Aziz, G. Ramana Murthy, Fajaruddin Bin Mustakim
   Vol. 18, No. 1, pp. 115-129, Feb. 2022  
   https://doi.org/10.3745/JIPS.04.0235
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   Keywords: CNN-LSTM Neural Network, Electricity Consumption Prediction, Large Gaps of Missing Values, Prediction of Missing Values in Time-Series Data, smart home system
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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 perfor- mance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.


10. Identifying Critical Factors for Successful Games by Applying Topic Modeling
    Mookyung Kwak, Ji Su Park, Jin Gon Shon
    Vol. 18, No. 1, pp. 130-145, Feb. 2022  
    https://doi.org/10.3745/JIPS.04.0236
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    Keywords: Critical Factors, SRDG Model, Successful Game, topic modeling
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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-desire- goal (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.


11. A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection
    Yanping Shen, Kangfeng Zheng, Chunhua Wu
    Vol. 18, No. 1, pp. 146-158, Feb. 2022  
    https://doi.org/10.3745/JIPS.03.0174
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    Keywords: Feature selection, Intrusion Detection, Kernel extreme learning machine, Parameter optimization, Particle Swarm Optimization
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    With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.


12. Shared Memory Model over a Switchless PCIe NTB Interconnect Network
    Seung-Ho Lim, Kwangho Cha
    Vol. 18, No. 1, pp. 159-172, Feb. 2022  
    https://doi.org/10.3745/JIPS.01.0085
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    Keywords: Interconnect Network, HPC, Multi-Thread, NTB, OpenSHMEM, PCIe
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    The role of the interconnect network, which connects computing nodes to each other, is important in high- performance computing (HPC) systems. In recent years, the peripheral component interconnect express (PCIe) has become a promising interface as an interconnection network for high-performance and cost-effective HPC systems having the features of non-transparent bridge (NTB) technologies. OpenSHMEM is a programming model for distributed shared memory that supports a partitioned global address space (PGAS). Currently, little work has been done to develop the OpenSHMEM library for PCIe-interconnected HPC systems. This paper introduces a prototype implementation of the OpenSHMEM library through a switchless interconnect network using PCIe NTB to provide a PGAS programming model. In particular, multi-interrupt, multi-thread-based data transfer over the OpenSHMEM shared memory model is applied at the implementation level to reduce the latency and increase the throughput of the switchless ring network system. The implemented OpenSHMEM programming model over the PCIe NTB switchless interconnection network provides a feasible, cost-effective HPC system with a PGAS programming model.