Digital Library

Vol. 19, No. 1, Feb. 2023  Cover

1. Enhanced Regular Expression as a DGL for Generation of Synthetic Big Data
   Kai Cheng, Keisuke Abe
   Vol. 19, No. 1, pp. 1-16, Feb. 2023  
   https://doi.org/10.3745/JIPS.04.0262
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   Keywords: Big data analytics, Data Generation Language (DGL), Performance Analysis, regular expression, synthetic data generation, Type/format Inference
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   Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally.


2. Centralized Clustering Routing Based on Improved Sine Cosine Algorithm and Energy Balance in WSNs
   Xiaoling Guo, Xinghua Sun, Ling Li, Renjie Wu, Meng Liu
   Vol. 19, No. 1, pp. 17-32, Feb. 2023  
   https://doi.org/10.3745/JIPS.03.0181
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   Keywords: Centralized Clustering Routing Algorithm, Energy balance, LEACH, Sine cosine algorithm, WSNs
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   Centralized hierarchical routing protocols are often used to solve the problems of uneven energy consumption and short network life in wireless sensor networks (WSNs). Clustering and cluster head election have become the focuses of WSNs. In this paper, an energy balanced clustering routing algorithm optimized by sine cosine algorithm (SCA) is proposed. Firstly, optimal cluster head number per round is determined according to surviving node, and the candidate cluster head set is formed by selecting high-energy node. Secondly, a random population with a certain scale is constructed to represent a group of cluster head selection scheme, and fitness function is designed according to inter-cluster distance. Thirdly, the SCA algorithm is improved by using monotone decreasing convex function, and then a certain number of iterations are carried out to select a group of individuals with the minimum fitness function value. From simulation experiments, the process from the first death node to 80% only needs about 30 rounds. This improved algorithm balances the energy consumption among nodes and avoids premature death of some nodes. And it greatly improves the energy utilization and extends the effective life of the whole network.


3. Development of Automatic Conversion System for Pipo Painting Image Based on Artificial Intelligence
   Minku Koo, Jiyong Park, Hyunmoo Lee, Giseop Noh
   Vol. 19, No. 1, pp. 33-45, Feb. 2023  
   https://doi.org/10.3745/JIPS.02.0189
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   Keywords: computer vision, image processing, OpenCV, Pipo Painting
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   This paper proposes an algorithm that automatically converts images into Pipo, painting images using OpenCVbased image processing technology. The existing “purity,” “palm,” “puzzling,” and “painting,” or Pipo, painting image production method relies on manual work, so customized production has the disadvantage of coming with a high price and a long production period. To resolve this problem, using the OpenCV library, we developed a technique that automatically converts an image into a Pipo painting image by designing a module that changes an image, like a picture; draws a line based on a sector boundary; and writes sector numbers inside the line. Through this, it is expected that the production cost of customized Pipo painting images will be lowered and that the production period will be shortened.


4. Layout Optimization Method of Railway Transportation Route Based on Deep Convolution Neural Network
   Cong Qiao, Qifeng Gao, Huayan Xing
   Vol. 19, No. 1, pp. 46-54, Feb. 2023  
   https://doi.org/10.3745/JIPS.04.0263
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   Keywords: Ant Colony, Convolutional Neural Network, Layout Optimization, Railway, Transportation, Transportation Route
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   To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.


5. Research on Community Knowledge Modeling of Readers Based on Interest Labels
   Kai Wang, Wei Pan, Xingzhi Chen
   Vol. 19, No. 1, pp. 55-66, Feb. 2023  
   https://doi.org/10.3745/JIPS.04.0264
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   Keywords: Interest Stability, Knowledge Modeling, Reader Community, Social Label, Topic Feature
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   Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including “reader community-opinion leader-text content” was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-ofthe-art approaches, which indicates that the UIT has better performance in the smooth time dimension.


6. Dog-Species Classification through CycleGAN and Standard Data Augmentation
   Chan Park, Nammee Moon
   Vol. 19, No. 1, pp. 67-79, Feb. 2023  
   https://doi.org/10.3745/JIPS.02.0190
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   Keywords: cycleGAN, data augmentation, DNN, GAN, Image Classification
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   In the image field, data augmentation refers to increasing the amount of data through an editing method such as rotating or cropping a photo. In this study, a generative adversarial network (GAN) image was created using CycleGAN, and various colors of dogs were reflected through data augmentation. In particular, dog data from the Stanford Dogs Dataset and Oxford-IIIT Pet Dataset were used, and 10 breeds of dog, corresponding to 300 images each, were selected. Subsequently, a GAN image was generated using CycleGAN, and four learning groups were established: 2,000 original photos (group I); 2,000 original photos + 1,000 GAN images (group II); 3,000 original photos (group III); and 3,000 original photos + 1,000 GAN images (group IV). The amount of data in each learning group was augmented using existing data augmentation methods such as rotating, cropping, erasing, and distorting. The augmented photo data were used to train the MobileNet_v3_Large, ResNet-152, InceptionResNet_v2, and NASNet_Large frameworks to evaluate the classification accuracy and loss. The top-3 accuracy for each deep neural network model was as follows: MobileNet_v3_Large of 86.4% (group I), 85.4% (group II), 90.4% (group III), and 89.2% (group IV); ResNet-152 of 82.4% (group I), 83.7% (group II), 84.7% (group III), and 84.9% (group IV); InceptionResNet_v2 of 90.7% (group I), 88.4% (group II), 93.3% (group III), and 93.1% (group IV); and NASNet_Large of 85% (group I), 88.1% (group II), 91.8% (group III), and 92% (group IV). The InceptionResNet_v2 model exhibited the highest image classification accuracy, and the NASNet_Large model exhibited the highest increase in the accuracy owing to data augmentation.


7. Dynamic Tracking Aggregation with Transformers for RGB-T Tracking
   Xiaohu Liu, Zhiyong Lei
   Vol. 19, No. 1, pp. 80-88, Feb. 2023  
   https://doi.org/10.3745/JIPS.01.0092
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   Keywords: Cross-modal Fusion, Dynamic Tracking Aggregation, RGB-T Tracking, Transformers
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   RGB-thermal (RGB-T) tracking using unmanned aerial vehicles (UAVs) involves challenges with regards to the similarity of objects, occlusion, fast motion, and motion blur, among other issues. In this study, we propose dynamic tracking aggregation (DTA) as a unified framework to perform object detection and data association. The proposed approach obtains fused features based a transformer model and an L1-norm strategy. To link the current frame with recent information, a dynamically updated embedding called dynamic tracking identification (DTID) is used to model the iterative tracking process. For object association, we designed a long short-term tracking aggregation module for dynamic feature propagation to match spatial and temporal embeddings. DTA achieved a highly competitive performance in an experimental evaluation on public benchmark datasets.


8. Design and Implementation of a Blockchain Based Interworking of oneM2M and LWM2M IoT Systems
   Donggyu Kim, Uk Jo, Yohan Kim, Yustus Eko Oktian, Howon Kim
   Vol. 19, No. 1, pp. 89-97, Feb. 2023  
   https://doi.org/10.3745/JIPS.01.0093
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   Keywords: Blockchain, IPE (Interworking Proxy application Entity), IoT Platforms
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   With the growth of Internet-of-Things (IoT) technologies, the number of IoT devices developers need to manage has increased exponentially. Many IoT standards have been proposed to allow those devices to communicate efficiently in day-to-day tasks. However, we lack trusted interworking entities for devices from different standards to collaborate securely. This paper proposes a blockchain platform that bridges multiple heterogeneous IoT platforms to co-exist and interwork. Specifically, we designed an interworking proxy application entity (IPE) implemented as a chaincode in Hyperledger Fabric to collect and process data coming from/to oneM2M and LWM2M architecture. The use of blockchain will guarantee network reliability and data integrity so that cross-standard communications can be audited and processed securely. Based on our evaluation, we show that the interworking between oneM2M and LWM2M through our blockchain platform is feasible. Furthermore, the proposed system can process up to 206 transactions per second with 1,000 running applications, which is about an 87% increase from the previously referenced study.


9. A Deep Learning-Based Image Semantic Segmentation Algorithm
   Chaoqun Shen, Zhongliang Sun
   Vol. 19, No. 1, pp. 98-108, Feb. 2023  
   https://doi.org/10.3745/JIPS.02.0191
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   Keywords: Attention Mechanism, FCN, Image Semantic Segmentation, Skip Structure, VGG16
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   This paper is an attempt to design segmentation method based on fully convolutional networks (FCN) and attention mechanism. The first five layers of the Visual Geometry Group (VGG) 16 network serve as the coding part in the semantic segmentation network structure with the convolutional layer used to replace pooling to reduce loss of image feature extraction information. The up-sampling and deconvolution unit of the FCN is then used as the decoding part in the semantic segmentation network. In the deconvolution process, the skip structure is used to fuse different levels of information and the attention mechanism is incorporated to reduce accuracy loss. Finally, the segmentation results are obtained through pixel layer classification. The results show that our method outperforms the comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU).


10. A Defect Detection Algorithm of Denim Fabric Based on Cascading Feature Extraction Architecture
    Shuangbao Ma, Renchao Zhang, Yujie Dong, Yuhui Feng, Guoqin Zhang
    Vol. 19, No. 1, pp. 109-117, Feb. 2023  
    https://doi.org/10.3745/JIPS.04.0265
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    Keywords: Cascading Feature Extraction Architecture, Denim Defect Detection, ImageNet, robustness, Transfer Learning
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    Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1–3 percentage points.


11. Recent Trends on Smart City Security: A Comprehensive Overview
    Hyuk-Jun Kwon, Mikail Mohammed Salim, Jong Hyuk Park
    Vol. 19, No. 1, pp. 118-129, Feb. 2023  
    https://doi.org/10.3745/JIPS.03.0182
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    Keywords: Blockchain, Security, Post Quantum, Privacy, Smart City
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    The expansion of smart cities drives the growth of data generated from sensor devices, benefitting citizens with enhanced governance, intelligent decision-making, optimized and sustainable management of available resources. The exposure of user data during its collection from sensors, storage in databases, and processing by artificial intelligence-based solutions presents significant security and privacy challenges. In this paper, we investigate the various threats and attacks affecting the growth of future smart cities and discuss the available countermeasures using artificial intelligence and blockchain-based solutions. Open challenges in existing literature due to the lack of countermeasures against quantum-inspired attacks are discussed, focusing on postquantum security solutions for resource-constrained sensor devices. Additionally, we discuss future research and challenges for the growing smart city environment and suggest possible solutions.


12. Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction
    Pengcheng Li, Changjiu Ke, Hongyu Tu, Houbing Zhang, Xu Zhang
    Vol. 19, No. 1, pp. 130-138, Feb. 2023  
    https://doi.org/10.3745/JIPS.04.0266
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    Keywords: Optimization Graph, Shared Attention, Spatio-temporal Attention, traffic flow forecasting
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    The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.