An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure

Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance

LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications

The Controller Area Network (CAN) is considered as the de-facto standard for the in-vehicle communications due to its real-time performance and high reliability. Unfortunately, the lack of security protection on the CAN bus gives attackers the opportunity to remotely compromise a vehicle. In this paper, we propose a Lightweight Encryption and Authentication Protocol (LEAP) with low cost and high efficiency to address the security issue of the CAN bus. LEAP exploits the security-enhanced stream cipher primitive to provide encryption and authentication for the CAN messages. Compared with the state-of-the-art Message Authentication Code (MAC) based approaches, LEAP requires less memory, is 8X faster, and thwarts the most recently proposed attacks.Comment: 7 pages, 9 figures, 3 table

CDA: A clustering degree based influential spreader identification algorithm in weighted complex network

Identifying the most influential spreaders in a weighted complex network is vital for optimizing utilization of the network structure and promoting the information propagation. Most existing algorithms focus on node centrality, which consider more connectivity than clustering. In this paper, a novel algorithm based on clustering degree algorithm (CDA) is proposed to identify the most influential spreaders in a weighted network. First, the weighted degree of a node is defined according to the node degree and strength. Then, based on the node weighted degree, the clustering degree of a node is calculated in respect to the network topological structure. Finally, the propagation capability of a node is achieved by accounting the clustering degree of the node and the contribution from its neighbors. In order to evaluate the performance of the proposed CDA algorithm, the susceptible-infected-recovered model is adopted to simulate the propagation process in real-world networks. The experiment results have showed that CDA is the most effective algorithm in terms of Kendall's tau coefficient and with the highest accuracy in influential spreader identification compared with other algorithms such as weighted degree centrality, weighted closeness centrality, evidential centrality, and evidential semilocal centrality

DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values

© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. As a widely known chronic disease, diabetes mellitus is called a silent killer. It makes the body produce less insulin and causes increased blood sugar, which leads to many complications and affects the normal functioning of various organs, such as eyes, kidneys, and nerves. Although diabetes has attracted high attention in research, due to the existence of missing values and class imbalance in the data, the overall performance of diabetes classification using machine learning is relatively low. In this paper, we propose an effective Prediction algorithm for Diabetes Mellitus classification on Imbalanced data with Missing values (DMP_MI). First, the missing values are compensated by the Naïve Bayes (NB) method for data normalization. Then, an adaptive synthetic sampling method (ADASYN) is adopted to reduce the influence of class imbalance on the prediction performance. Finally, a random forest (RF) classifier is used to generate predictions and evaluated using comprehensive set of evaluation indicators. Experiments performed on Pima Indians diabetes dataset from the University of California at Irvine, Irvine (UCI) Repository, have demonstrated the effectiveness and superiority of our proposed DMP_MI

Security feature measurement for frequent dynamic execution paths in software system

© 2018 Qian Wang et al. The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing

Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm

Effect of Phase Change Material Storage on the Dynamic Performance of a Direct Vapor Generation Solar Organic Rankine Cycle System

Solar energy is a potential source for a thermal power generation system. A direct vapor generation solar organic Rankine cycle system using phase change material storage was analyzed in the present study. The overall system consisted of an arrangement of evacuated flat plate collectors, a phase-change-material-based thermal storage tank, a turbine, a water-cooled condenser, and an organic fluid pump. The MATLAB programming environment was used to develop the thermodynamic model of the whole system. The thermal storage tank was modeled using the finite difference method and the results were validated against experimental work carried out in the past. The hourly weather data of Karachi, Pakistan, was used to carry out the dynamic simulation of the system on a weekly, monthly, and annual basis. The impact of phase change material storage on the enhancement of the overall system performance during the charging and discharging modes was also evaluated. The annual organic Rankine cycle efficiency, system efficiency, and net power output were observed to be 12.16%, 9.38%, and 26.8 kW, respectively. The spring and autumn seasons showed better performance of the phase change material storage system compared to the summer and winter seasons. The rise in working fluid temperature, the fall in phase change material temperature, and the amount of heat stored by the thermal storage were found to be at a maximum in September, while their values became a minimum in February

Modelling, simulation and comparison of phase change material storage based direct and indirect solar organic Rankine cycle systems

The thermodynamic performance of a novel direct solar organic Rankine cycle system and conventional indirect solar organic Rankine cycle system is compared in this study. The working fluid is vaporized directly in the solar collectors in direct solar organic Rankine cycle system while heat transfer fluid is used to vaporize the working in indirect solar organic Rankine cycle system. The evacuated flat plate collectors array covering a total aperture area of 150 m2 is employed as a heat source and a phase change material tank having a surface area of 25.82 m2 is used as thermal storage for both configurations. R245fa and water are chosen as heat transfer fluids for direct and indirect solar organic Rankine cycle systems, respectively. However, R245fa is used as a working fluid for both configurations. The performance of both configurations is compared by carrying out weekly, monthly and annual dynamic simulations in MATLAB by using hourly weather data of Islamabad, Pakistan. The direct solar organic Rankine cycle system outperforms the indirect solar organic Rankine cycle system in terms of thermal efficiency and net power. The annual system efficiency and an annual average net power of the direct solar organic Rankine cycle system are 71.96% and 64.38% higher than indirect solar organic Rankine cycle system respectively. However, average annual heat stored by phase change material during charging mode of indirect solar organic Rankine cycle system is 4.24 MJ more than direct solar organic Rankine cycle system. Conversely, direct solar organic Rankine cycle system has provided annual daily average power of 33.80 kW extra to heat transfer fluid during the discharging mode of phase change material storage. Furthermore, with phase change material storage, the capacity factor is increased by 17 % and 21.71 % on annual basis for direct and indirect solar organic Rankine cycle systems, respectively