Point modules of quantum projective spaces

In this note we give an explicit description of the irreducible components of the reduced point varieties of quantum polynomial algebras.Comment: 4 pages, a short not

The point variety of quantum polynomial rings

We show that the reduced point variety of a quantum polynomial algebra is the union of specific linear subspaces in $\mathbb{P}^n$, we describe its irreducible components and give a combinatorial description of the possible configurations in small dimensions.Comment: 10 pages, an extended version of arxiv.org/abs/1506.0651

The Truth Behind Fast Fashion: A Solution to the Issue

This paper acknowledges the dominance of large fast fashion retailers in the clothing market. Due to globalization, there have been significant changes over the years as a result of their implementation of the clothing industry. This paper reveals that retailers have held enormous amounts of wealth and power without any oversight. Permitting them to profit from the absence of rules and laws everywhere in the world. A market with perfect competition has been drastically changed to one where maximizing opportunities for profit is the only goal. The industry has suffered greatly as a result of their willingness to pursue such gains, and this shift has created enormous problems. We come to the conclusion that more policies must be put into place in order to balance a competitive market populated by retailers like Zara and H&M

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-

Conditional advancement of machine learning algorithm via fuzzy neural network

Improving overall performance is the ultimate goal of any machine learning (ML) algorithm. While it is a trivial task to explore multiple individual validation measurements, evaluating and monitoring overall performance can be complicated due to the highly nonlinear nature of the functions describing the relationships among different validation metrics, such as the Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Therefore, it is naturally desirable to have a reliable validation algorithm or model that can integrate all existing validation metrics into a single value. This consolidated metric would enable straightforward assessment of an ML algorithm’s performance and identify areas for improvement. To deal with such a complex nonlinear problem, this study suggests a novel parameterized model named Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which takes any set of input–output precise-imprecise data and uses a neuro-adaptive learning strategy to tune the parameters of the pre-defined membership functions. Our method can be accepted as an elegant and the state-of-the-art method for the nonlinear function approximation, which could be added directly to any convolutional neural networks (CNN) loss functions as the regularization term to generate a constrained-CNN-FUZZY model optimization. To demonstrate the ability of the purposed method and provide a practical explanation of the capability of ANFIS, we use deep CNN as a testing platform to consider the fact that one of the biggest challenges CNN-developers faced today is to reduce the mismatching between the provided input data and the predicted results monitored by different validation metrics. We first create a toy dataset using MNIST and investigate the properties of the proposed model. We then use a medical dataset to demonstrate our method’s efficacy on brain lesion segmentation. In both datasets, our method shows reliable validation results to guide researchers towards choosing performance metrics in a problem-aware manner, especially when the results of different validation metrics are too similar among models to determine the best one

Conditional advancement of machine learning algorithm via fuzzy neural network

Improving overall performance is the ultimate goal of any machine learning (ML) algorithm. While it is a trivial task to explore multiple individual validation measurements, evaluating and monitoring overall performance can be complicated due to the highly nonlinear nature of the functions describing the relationships among different validation metrics, such as the Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Therefore, it is naturally desirable to have a reliable validation algorithm or model that can integrate all existing validation metrics into a single value. This consolidated metric would enable straightforward assessment of an ML algorithm’s performance and identify areas for improvement. To deal with such a complex nonlinear problem, this study suggests a novel parameterized model named Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which takes any set of input–output precise-imprecise data and uses a neuro-adaptive learning strategy to tune the parameters of the pre-defined membership functions. Our method can be accepted as an elegant and the state-of-the-art method for the nonlinear function approximation, which could be added directly to any convolutional neural networks (CNN) loss functions as the regularization term to generate a constrained-CNN-FUZZY model optimization. To demonstrate the ability of the purposed method and provide a practical explanation of the capability of ANFIS, we use deep CNN as a testing platform to consider the fact that one of the biggest challenges CNN-developers faced today is to reduce the mismatching between the provided input data and the predicted results monitored by different validation metrics. We first create a toy dataset using MNIST and investigate the properties of the proposed model. We then use a medical dataset to demonstrate our method’s efficacy on brain lesion segmentation. In both datasets, our method shows reliable validation results to guide researchers towards choosing performance metrics in a problem-aware manner, especially when the results of different validation metrics are too similar among models to determine the best one