マクロ-フェムトセルシステムの最適化モデルに関する研究

早大学位記番号:新8266早稲田大

Conditional anonymous remote healthcare data sharing over blockchain

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an “on-chain & off-chain” model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance

A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications

Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources

Exploiting Contextual Word Embedding of Authorship and Title of Articles for Discovering Citation Intent Classification

The number of scientific publications is growing exponentially. Research articles cite other work for various reasons and, therefore, have been studied extensively to associate documents. It is argued that not all references carry the same level of importance. It is essential to understand the reason for citation, called citation intent or function. Text information can contribute well if new natural language processing techniques are applied to capture the context of text data. In this paper, we have used contextualized word embedding to find the numerical representation of text features. We further investigated the performance of various machine-learning techniques on the numerical representation of text. The performance of each of the classifiers was evaluated on two state-of-the-art datasets containing the text features. In the case of the unbalanced dataset, we observed that the linear Support Vector Machine (SVM) achieved 86% accuracy for the “background” class, where the training was extensive. For the rest of the classes, including “motivation,” “extension,” and “future,” the machine was trained on less than 100 records; therefore, the accuracy was only 57 to 64%. In the case of a balanced dataset, each of the classes has the same accuracy as trained on the same size of training data. Overall, SVM performed best on both of the datasets, followed by the stochastic gradient descent classifier; therefore, SVM can produce good results as text classification on top of contextual word embedding

Integrated Deep Neural Networks-Based Complex System for Urban Water Management

Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis (PCA) and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system

Polymorphisms in PI3K/AKT genes and gene‑smoking interaction are associated with susceptibility to tuberculosis

AbstractBackground Phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) are involved in the clearance of Mycobacterium tuberculosis (MTB) by macrophages.Aim This study aimed to investigate the effects of polymorphisms in the PI3K/AKT genes and the gene-smoking interaction on susceptibility to TB.Methods This case-control study used stratified sampling to randomly select 503 TB patients and 494 control subjects. Logistic regression analysis was used to determine the association between the polymorphisms and TB. Simultaneously, the marginal structure linear dominance model was used to estimate the gene-smoking interaction.Results Genotypes GA (OR 1.562), AA (OR 2.282), and GA + AA (OR 1.650) at rs3730089 of the PI3KR1 gene were significantly associated with the risk to develop TB. Genotypes AG (OR 1.460), GG (OR 2.785), and AG + GG (OR 1.622) at rs1130233 of the AKT1 gene were significantly associated with the risk to develop TB. In addition, the relative excess risk of interaction (RERI) between rs3730089 and smoking was 0.9608 (95% CI: 0.5959, 1.3256, p < 0.05), which suggests a positive interaction.Conclusion We conclude that rs3730089 and rs1130233 are associated with susceptibility to TB, and there was positive interaction between rs3730089 and smoking on susceptibility to TB