• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2816-2830
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• 2022

Number Theoretic Transform (NTT) is a method to design efficient multiplier for large integer multiplication, which is widely used in cryptography and scientific computation. On top of that, it has also received wide attention from the research community to design efficient hardware architecture for large size RSA, fully homomorphic encryption, and lattice-based cryptography. Existing NTT hardware architecture reported in the literature are mainly designed based on radix-2 NTT, due to its small area consumption. However, NTT with larger radix (e.g., radix-4) may achieve faster speed performance in the expense of larger hardware resources. In this paper, we present the performance evaluation on NTT architecture in terms of hardware resource consumption and the latency, based on the proposed radix-2 and radix-4 technique. Our experimental results show that the 16-point radix-4 architecture is 2× faster than radix-2 architecture in expense of approximately 4× additional hardware. The proposed architecture can be extended to support the large integer multiplication in cryptography applications (e.g., RSA). The experimental results show that the proposed 3072-bit multiplier outperformed the best 3k-multiplier from Chen et al. [16] by 3.06%, but it also costs about 40% more LUTs and 77.8% more DSPs resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.237-242
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• 2022

Acquiring high-quality images in control and measurement systems is one of the important factors. Among image acquisition technologies, SFF (Shape from Focus) is a technology for recovering a 3D shape by acquiring 2D images with different focus levels by moving an object at a predetermined step size along the optical axis. For SFF, when an object is moved at a constant step size, mechanical vibration, referred as jitter noise, occurs in each step along the optical axis. In this paper, a new state estimation filter is designed and applied for reducing the jitter noise. For the application of the proposed method, the jitter noise and focus curves are modeled as Gaussian function. Experimental results demonstrate the effectiveness of proposed method.

• ETRI Journal
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• v.44 no.4
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• pp.641-653
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• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.85-90
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• 2022

The article is devoted to the consideration of the features of the application of criminal and legal countermeasures against cybercrime in the conditions of martial law. While conducting this research, we found an opportunity to formulate the author's recommendations for solving the most complex law enforcement problems, as well as to propose changes to the Criminal Code of Ukraine aimed at eliminating the flaws of the analyzed Law, the adoption of which will contribute to the achievement of higher efficiency of the relevant criminal law prescriptions. It is argued that the removal of the previously existing in the footnote of Art. 361 of the Criminal Code of a fundamentally important caveat regarding the fact that when assessing "significant damage", the mentioned property equivalent was to be taken into account only when such damage consisted in causing material damage, which led to a significant and unjustified narrowing of the scope of potential application of Part 4 of Article 361 of the Criminal Code.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2209-2221
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• 2023

As the supply of electric vehicles increases, research on wireless charging methods for convenience has been increasing. Because the electric vehicle wireless transmission device is installed on the ground and the electric vehicle battery is installed on the floor of the vehicle, the transmission and reception antennas are approximately 15-30 cm away, and thus strong magnetic fields are exposed during wireless charging. When a metallic foreign object is placed in the magnetic field area, an eddy current is induced to the metallic foreign object, and heat is generated, creating danger of fire and burns. Therefore, this study proposes a method to detect metallic foreign objects in the magnetic field area of a wireless electric vehicle charging system. An active detection-only coil array was used, and an LC resonance circuit was constructed for the frequency of the supply power signal. When a metallic foreign object is inserted into the charging zone, the characteristics of the resonance circuit are broken, and the magnitude and phase of the voltage signal at both ends of the capacitor are changed. It was confirmed that the proposed method has about 1.5 times more change than the method of comparing the voltage magnitude at one node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2241-2258
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• 2023

This study developed digital literacy instruments and tested their effectiveness on college students' perceptions of AI technologies. In creating a new digital literacy test tool, we reviewed the concept and scale of digital literacy based on previous studies that identified the characteristics and measurement of AI literacy. We developed 23 preliminary questions for our research instrument and used a quantitative approach to survey 318 undergraduates. After conducting exploratory and confirmatory factor analysis, we found that digital literacy in the age of AI had four ability sub-factors: critical understanding, artificial intelligence social impact recognition, artificial intelligence technology utilization, and ethical behavior. Then we tested the sub-factors' predictive powers on the perception of AI's usefulness and ease of use. The regression result shows that the most common powerful predictor of the usefulness and ease of use of AI technology was the ability to use AI technology. This finding implies that for college students, the ability to use various tools based on AI technology is an essential competency in the AI era.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.