Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.     

A Theoretical Perspective to Study the Optical Properties of Tetrafluoroborates

In this study, a series of tetrafluoroborates with non-π-conjugated [BF₄] tetrahedra are investigated systematically by first-principles calculations. Theoretical studies demonstrate that tetrafluoroborates with alkali and/or alkaline-earth metals are more favorable for deep-ultraviolet transmission and are comparable to the classical deep-ultraviolet (deep-UV) material, MgF₂. Furthermore, bandgap decrease with the increasing of ionic radii in alkali and/or alkaline-earth metals. Introducing highly polarizable cations with d₁₀-configuration or cations with lone pair electrons into the structure will decrease the bandgaps. The birefringence and second harmonic generation effects are not large enough in tetrafluoroborates because polarizability anisotropy and hyperpolarizability in non-π-conjugated [BF₄] tetrahedra are much smaller than those in π-conjugated groups. However, the second harmonic generation effect for [BF₄] tetrahedra has a higher contribution in comparison with that due to birefringence. To effectively synthesize the borate fluorides or fluorooxoborates in the deep-UV region, raw materials with B−F bonds are preferred.     

A Confidential QR Code Approach with Higher Information Privacy

In present times, barcode decoders on mobile phones can extract the data content of QR codes. However, this convenience raises concerns about security issues when using QR codes to transmit confidential information, such as e-tickets, coupons, and other private data. Moreover, current secret hiding techniques are unsuitable for QR code applications since QR codes are module-oriented, which is different from the pixel-oriented hiding manner. In this article, we propose an algorithm to conceal confidential information by changing the modules of the QR Code. This new scheme designs the triple module groups based on the concept of the error correction capability. Additionally, this manner can conceal two secret bits by changing only one module, and the amount of hidden confidential information can be twice the original amount. As a result, the ordinary data content (such as URL) can be extracted correctly from the generated QR code by any barcode decoders, which does not affect the readability of scanning. Furthermore, only authorized users with the secret key can further extract the concealed confidential information. This designed scheme can provide secure and reliable applications for the QR system.     

Panel‐based stratified cluster sampling and analysis for photovoltaic outdoor measurements

We study a stratified multisite cluster‐sampling panel time series approach in order to analyse and evaluate the quality and reliability of produced items, motivated by the problem to sample and analyse multisite outdoor measurements from photovoltaic systems. The specific stratified sampling in spatial clusters reduces sampling costs and allows for heterogeneity as well as for the analysis of spatial correlations due to defects and damages that tend to occur in clusters. The analysis is based on weighted least squares using data‐dependent weights. We show that this does not affect consistency and asymptotic normality of the least squares estimator under the proposed sampling design under general conditions. The estimation of the relevant variance–covariance matrices is discussed in detail for various models including nested designs and random effects. The strata corresponding to damages or manufacturers are modelled via a quality feature by means of a threshold approach. The analysis of outdoor electroluminescence images shows that spatial correlations and local clusters may arise in such photovoltaic data. Further, relevant statistics such as the mean pixel intensity cannot be assumed to follow a Gaussian law. We investigate the proposed inferential tools in detail by simulations in order to assess the influence of spatial cluster correlations and serial correlations on the test's size and power. ©2016 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.     

Estimation of a smooth quantile function under the proportional hazards model

The problem of estimating a smooth quantile function, Q(·), at a fixed point p, 0 < p < 1, is treated under a nonparametric smoothness condition on Q. The asymptotic relative deficiency of the sample quantile based on the maximum likelihood estimate of the survival function under the proportional hazards model with respect to kernel type estimators of the quantile is evaluated. The comparison is based on the mean square errors of the estimators. It is shown that the relative deficiency tends to infinity as the sample size, n, tends to infinity.     

高精度光电数据采集系统的实验研究

针对光电弱信号检测,采用Σ-ΔAD转换器AD7706和AVR单片机ATmega8515设计了适用于低频量测量的高精度数据采集器,采用Σ-ΔAD转换器ADC16471、AVR单片机ATmega8515、C51单片机C8051F020和以太网控制器RTL8019AS设计了适用于高精度线阵CCD的数据采集器,并分别对两套数据采集器进行了实验研究与性能分析。     

自启动的非归零/归零码和光电时钟信号发生器及码型转换

采用双波长注入一包含伪随机码发生器与相位调制器的光电振荡器可以同时得到非归零(NRZ)码,归零(RZ)码以及光,电时钟信号输出。该方案使用了光域耦合的双环路结构,在不增加有源器件的条件下实现边模抑制。相位调制器用于反馈调制并同时实现占空比可调的非归零码到归零码的转换。双波长的注入排除了编码信号在振荡器中引入的非时钟频率成分。实验给出了10 Gb/s工作速率下的结果,得到了抖动为637 fs的光信号输出。转换得到的归零码信号占空比约为33%。输出电时钟信号的相位噪声在频偏10 kHz处为-109 dBc/Hz,边模抑制比为58 dB。     

A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.