Munkhtsetseg¹, Jeongwook Seo^2,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2989-3003, 2023, DOI:10.32604/csse.2023.033342

Abstract In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client… More >

Gang Xu¹, Yibo Cao¹, Shiyuan Xu¹, Xin Liu^2,*, Xiu-Bo Chen³, Yiying Yu¹, Xiaojun Wang⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5429-5441, 2022, DOI:10.32604/cmc.2022.028562

Abstract With the increasing popularity of cloud storage, data security on the cloud has become increasingly visible. Searchable encryption has the ability to realize the privacy protection and security of data in the cloud. However, with the continuous development of quantum computing, the standard Public-key Encryption with Keyword Search (PEKS) scheme cannot resist quantum-based keyword guessing attacks. Further, the credibility of the server also poses a significant threat to the security of the retrieval process. This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems. Firstly, we design a lattice-based encryption primitive to… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3937-3952, 2022, DOI:10.32604/cmc.2022.023127

Abstract Artificial intelligent based dialog systems are getting attention from both business and academic communities. The key parts for such intelligent chatbot systems are domain classification, intent detection, and named entity recognition. Various supervised, unsupervised, and hybrid approaches are used to detect each field. Such intelligent systems, also called natural language understanding systems analyze user requests in sequential order: domain classification, intent, and entity recognition based on the semantic rules of the classified domain. This sequential approach propagates the downstream error; i.e., if the domain classification model fails to classify the domain, intent and entity recognition fail. Furthermore, training such intelligent… More >

Gang Xu^1,2, Yibo Cao¹, Shiyuan Xu¹, Ke Xiao¹, Xin Liu³, Xiubo Chen^4,*, Mianxiong Dong⁵

Computer Systems Science and Engineering, Vol.41, No.3, pp. 945-958, 2022, DOI:10.32604/csse.2022.022100

Abstract In recent decades, log system management has been widely studied for data security management. System abnormalities or illegal operations can be found in time by analyzing the log and provide evidence for intrusions. In order to ensure the integrity of the log in the current system, many researchers have designed it based on blockchain. However, the emerging blockchain is facing significant security challenges with the increment of quantum computers. An attacker equipped with a quantum computer can extract the user's private key from the public key to generate a forged signature, destroy the structure of the blockchain, and threaten the… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 1-11, 2022, DOI:10.32604/iasc.2022.018757

Abstract Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent… More >