A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

Position Unmanned Aerial Vehicles in the Mobile Ad Hoc Network

This paper considers the problem of employing multiple unmanned aerial vehicles (UAVs) to the mobile ad hoc network (MANET) as relay backbone nodes to construct the backbone network, to improve the network connectivity, and to address many issues in the MANET such as linkage, capacity, load balance, and reliability. With considering the dynamic nature of the problem, this study provides several linear location problem models and their extensions to accommodate these issues. Due to the size of linear location models associated with a large number of constraints, the problem becomes computational challenging even with modest size of nodes. To overcome the computational barrier, we recast these location problem models using a quadratic unconstrained binary optimization (QUBO) framework and solve these QUBO models with a Tabu search heuristic with preprocessing. The analysis of the solutions that are produced by QUBO together with the comparisons made with the linear model highlight both the attractiveness and robustness of the proposed approach. The results of this study provide support to future advanced routing protocol development.     

基于直接连接的分布式数据库查询优化实现方法研究

首先对分布式数据库查询执行代价模型进行分析,然后对直接连接中的连接运算的方法、连接关系的传输方法和执行场地等问题进行研究,并计算所有评估方法的执行代价,从中选择出最小执行代价的执行方法,最终确定了执行的场地、连接的方法和传输方法.     

Do social media literacy skills help in combating fake news spread? Modelling the moderating role of social media literacy skills in the relationship between rational choice factors and fake news sharing behaviour

This study modelled the rational factors that predict fake news sharing behaviour. It also tested the moderating role of social media literacy skills. The focus was on social media users in Nigeria. An online survey was conducted to gather the responses from participants across Nigerian geopolitical zones. Structural equation modelling (SEM) Smart PLS 3.6 was used to analyse the data. We found that information sharing, the news finds me perception, trust in social media and status-seeking lead to fake news sharing among social media users in Nigeria. Specifically, trust in social media and status-seeking had a greater effect on fake news sharing behaviour. We also found that social media literacy skills significantly moderate the relationship between information sharing, status-seeking, the news finds me perception, trust in social media and fake news sharing in such a way that the effects/relationships are stronger among those with low social media literacy skills. This outcome contributes to theory and practice which was highlighted in the concluding aspect of this study.     

Compact yet efficient hardware implementation of artificial neural networks with customized topology

There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-the-fly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetic is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. This resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.     

基于多维相似度和情感词扩充的相同产品特征识别

针对现有相同产品特征识别方法受限于词典覆盖率或语料规模的不足,提出一种基于多维相似度和情感词扩充的识别方法。通过双向长短时记忆条件随机场(bi-directional long short-term memory and conditional random field, Bi-LSTM-CRF)模型抽取产品特征的扩充情感词,综合特征词的语素相似度、同义词林相似度和TF-IDF(term frequency-inverse document frequency)余弦相似度,采用K-medoids聚类算法,识别相同的产品特征。试验结果表明,在手机和笔记本数据集上,该方法的最大调整兰德指数分别达到0.579和0.595 9,而最小熵值分别达到0.782 6和0.745 7,均优于结合语素的调整Jaccard相似度、Word2Vec相似度和基于二分K-means的Word2Vec相似度三种基线试验方法。     

纳米TiO2在抗菌塑料中的应用

介绍了纳米材料在抗菌塑料中应用的优点。综述了纳米TiO2抗菌塑料及其制品的发展状况。报道了近年来单一树脂基体和多种树脂基体的纳米TiO2抗菌塑料的研究和开发成果;纳米TiO2抗菌塑料制品的有报道的成果集中在塑料薄膜。     

How do the internet technological developments shift the consumption pattern of paper products? Evidence from China

Using a monthly data between 2008 and 2019, this study investigated how the development of the Internet technologies has shaped the consumer consumption pattern of different types of paper products in China during this period. In detail, this study covers four major types of paper products in the Chinese market: newsprint, printing paper, household paper, and packaging paper. The developments of Internet technologies were decomposed into two aspects: the communication technology (CT) and the software platform technology (PT). Granger causality analysis was employed to determine whether the consumption pattern of paper products was affected by the development of the Internet. Furthermore, impulse-response analyses were utilized to measure how the consumption pattern of each paper product was shaped by the development of each aspect of Internet technologies, i.e., CT or PT. The empirical results suggested that the Internet developments are causing the consumption patterns shifts of all four paper products. Specifically, the consumptions of newsprint and printing paper were driven by both CT and PT. However, the consumptions of packaging paper and household paper were driven by the PT only. Compared to the development of CT, we found that the quick evolution of PT plays a more significant role in shifting people's consumption of paper products in China. The empirical results revealed by this study could provide valuable economics and policy implications to both academia and relevant industries.     

Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework

Cryptocurrencies have brought many innovations and discussions to economic life. Digital assets, which are very popular by investors, are frequently used for many purposes such as store of value, exchange, and speculation. It creates a research area that intentions cryptocurrency experts prioritize in crypto investments. In this paper, therefore, the fuzzy Full Consistency Method-Bonferroni (FUCOM-F’B) model is conducted to determine the priorities of drivers for investing in cryptocurrencies. The selected twenty-three drivers are classified based on five aspects, including functionality, financial, legal infrastructure, technology, and security. Based on the findings, “strong electronic encryption” and “use of digital signature” are the most significant drivers for preferring a cryptocurrency. A validation check is performed to verify the reliability, usefulness, and stability of the proposed approach. Further, the introduced approach allows taking the ambiguities and subjectivity into account which exist in the decision-making procedure. The suggested framework can be a helpful decision support tool for regulators, policymakers, practitioners, and cryptocurrency investors.