Consideration of Data Security and Privacy Using Machine Learning Techniques

As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data

RELATIONSHIPS OF TEMPERATURE AND HUMIDITY TO THE BIODEGRADATION OF PETROLEUM HYDROCARBONS IN SOILS

This work focused on monitoring CO2production, microbial growth and residual hydrocarbon concentration during bioremediation experiments performed on laboratory soil microcosms. A natural soil was artificially contaminated with hexadecane and adjusted with inorganic nutrients to stimulate biodegradation. Microbial growth, CO2production and residual hexadecane were periodically monitored at different soil water contents ranging from 0.15 to 0.25 g water g_1 of dry soil and at different temperatures ranging from 20 to 25oC. Results showed that the humidity has a greater effect on microbial activity and contaminant degradation than the temperature. The study established the experimental regression equation of temperature and humidity to the hexadecane mineralization rate, an important parameter in assessing the ability to convert organic carbon into inorganic carbon. The difference between the results of the hexadecane mineralization rate obtained from the experiment and calculated from the regression equation is not too high, from 2% to 20%

Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation

We consider a stochastic individual-based model in continuous time to describe a size-structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We address here the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier technics we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the non-standard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling

A Generalization of Ćirić Quasicontractions

We proved a fixed point theorem for a class of maps that satisfy Ćirić's contractive condition dependent on another function. We presented an example to show that our result is a real generalization

Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions

Sequence Analysis and Potentials of the Native RbcS Promoter in the Development of an Alternative Eukaryotic Expression System Using Green Microalga Ankistrodesmus convolutus

The availability of highly active homologous promoters is critical in the development of a transformation system and improvement of the transformation efficiency. To facilitate transformation of green microalga Ankistrodesmus convolutus which is considered as a potential candidate for many biotechnological applications, a highly-expressed native promoter sequence of ribulose-1,5-bisphosphate carboxylase/oxygenase small subunit (AcRbcS) has been used to drive the expression of β-glucuronidase (gusA) gene in this microalga. Besides the determination of the transcription start site by 5′-RACE, sequence analysis revealed that AcRbcS promoter contained consensus TATA-box and several putative cis-acting elements, including some representative light-regulatory elements (e.g., G-box, Sp1 motif and SORLIP2), which confer light responsiveness in plants, and several potential conserved motifs (e.g., CAGAC-motif, YCCYTGG-motifs and CACCACA-motif), which may be involved in light responsiveness of RbcS gene in green microalgae. Using AcRbcS promoter::gusA translational fusion, it was demonstrated that this promoter could function as a light-regulated promoter in transgenic A. convolutus, which suggested that the isolated AcRbcS promoter was a full and active promoter sequence that contained all cis-elements required for developmental and light-mediated control of gene expression, and this promoter can be used to drive the expression of heterologous genes in A. convolutus. This achievement therefore advances the development of A. convolutus as an alternative expression system for the production of recombinant proteins. This is the first report on development of gene manipulation system for unicellular green alga A. convolutus