• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1330-1350
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• 2022

As a preprocessing operation of transmitter antennas, the hybrid precoding is restricted by the limited computing resources of the transmitter. Therefore, this paper proposes a novel hybrid precoding that guarantees the communication efficiency with low complexity and a fast computational speed. First, the analog and digital precoding matrix is derived from the maximum eigenvectors of the channel matrix in the sub-connected architecture to maximize the communication rate. Second, the extended power iteration (EPI) is utilized to obtain the maximum eigenvalues and their eigenvectors of the channel matrix, which reduces the computational complexity caused by the singular value decomposition (SVD). Third, the Aitken acceleration method is utilized to further improve the convergence rate of the EPI algorithm. Finally, the hybrid precoding based on the EPI method and the Aitken acceleration algorithm is evaluated in millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. The experimental results show that the proposed method can reduce the computational complexity with the high performance in mmWave massive MIMO systems. The method has the wide application prospect in future wireless communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2425-2458
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• 2022

The continuous evolution and proliferation of computer technology and our increasing dependence on computer technology have created a new class of threats: "cyber threats." These threats can be defined as activities that can undermine a society's ability to maintain internal or external order while using information technology. Cyber threats can be mainly divided into two categories, namely cyber-terrorism and cyber-warfare. A variety of malware programs are often used as a primary weapon in these cyber threats. A significant amount of research work has been published covering different aspects of cyber threats, their countermeasures, and the policy-making for cyber laws. This article aims to review the research conducted in various important aspects of cyber threats and provides synthesized information regarding the fundamentals of cyber threats; discusses the countermeasures for such threats; provides relevant details of high-profile cyber-attacks; discusses the developments in global policy-making for cyber laws, and lastly presents promising future directions in this area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1063-1075
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• 2022

In recent years, container techniques have been broadly applied to cloud computing systems to maximize their efficiency, flexibility, and economic feasibility. Concurrently, studies have also been conducted to ensure the security of cloud computing. Among these studies, moving-target defense techniques using the high agility and flexibility of cloud-computing systems are gaining attention. Moving-target defense (MTD) is a technique that prevents various security threats in advance by proactively changing the main attributes of the protected target to confuse the attacker. However, an analysis of existing MTD techniques revealed that, although they are capable of deceiving attackers, MTD techniques have practical limitations when applied to an actual cloud-computing system. These limitations include resource wastage, management complexity caused by additional function implementation and system introduction, and a potential increase in attack complexity. Accordingly, this paper proposes a software-defined MTD system that can flexibly apply and manage existing and future MTD techniques. The proposed software-defined MTD system is designed to correctly define a valid mutation range and cycle for each moving-target technique and monitor system-resource status in a software-defined manner. Consequently, the proposed method can flexibly reflect the requirements of each MTD technique without any additional hardware by using a software-defined approach. Moreover, the increased attack complexity can be resolved by applying multiple MTD techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3390-3405
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• 2022

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.45-49
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• 2022

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.64-79
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• 2022

The concept of Virtual Character has been developed for a long time with people's demand for cultural and entertainment products such as games, animations, and movies. In recent years, with the rapid development of concepts and industries such as social media, self-media, web3.0, artificial intelligence, virtual reality, and Metaverse, Virtual Character has also expanded new derivative concepts such as Virtual Idol, Virtual YouTuber, and Virtual Digital Human. With the development of technology, people's life is gradually moving towards digitalization and virtualization. At the same time, under the global environment of the new crown epidemic, human social activities are rapidly developing in the direction of network society and online society. From the perspective of digital media content, this paper studies the production technology of Virtual Character related products in the Chinese market, and analyzes the future development direction and possibility of the Virtual Character industry in combination with new media development directions and technical production methods. Consider and provide reference for the development of combined applications of digital media content industry, Virtual Character and Metaverse industry.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.82-92
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• 2021

Purpose: It has been reported that the diversity and abundance of microbes in the vagina decrease due to the use of antimicrobial agents, and the high recurrence rate of female vaginitis due to this suggests that a new treatment is needed. Methods: In the experiment, we detected that 10% potassium sorbate solution, 1% eucalyptus oil solution, 1% tea tree oil solution, 400 µL/10 mL grapefruit seed extract solution, 100% lactic acid, 10% acetic acid solution, and 10% lactic acid solution were prepared and used. After adjusting the pH to 4, 5, and 6 with lactic acid and acetic acid in the mixed culture medium, each bacterium was inoculated into the medium and incubated for 72 h at 35℃. Incubate and 0 h each. 24 h. 48 h. The number of bacteria was measured after 72 h. Results: In the mixed culture test between lactic acid bacteria and pathogenic microorganisms, lactic acid bacteria showed good results at pH 5-5.5. Potassium sorbate, which has varying antibacterial activity based on the pH, killed pathogenic bacteria and allowed lactic acid bacteria to survive at pH 5.5. Conclusion: The formulation ratio obtained through this study could be used for the development of a feminine cleanser that can be used as a substitute for antibacterial agents. Further, the findings of this study may be able to solve the problem of antimicrobial resistance in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4224-4243
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• 2021

Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.