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General General

How to Utilize My App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes.

In Entropy (Basel, Switzerland)

Acquiring knowledge about users' opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

Triantafyllou Ioannis, Drivas Ioannis C, Giannakopoulos Georgios


app business strategy, app reviews, feature extraction methods, machine learning methods, reviews classification, text analysis, text classification, topics extraction

General General

Learning in Feedforward Neural Networks Accelerated by Transfer Entropy.

In Entropy (Basel, Switzerland)

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.

Moldovan Adrian, Caţaron Angel, Andonie Răzvan


backpropagation, causality, deep learning, gradient descent, neural network, transfer entropy

General General

Linear and Fisher Separability of Random Points in the d-Dimensional Spherical Layer and Inside the d-Dimensional Cube.

In Entropy (Basel, Switzerland)

Stochastic separation theorems play important roles in high-dimensional data analysis and machine learning. It turns out that in high dimensional space, any point of a random set of points can be separated from other points by a hyperplane with high probability, even if the number of points is exponential in terms of dimensions. This and similar facts can be used for constructing correctors for artificial intelligent systems, for determining the intrinsic dimensionality of data and for explaining various natural intelligence phenomena. In this paper, we refine the estimations for the number of points and for the probability in stochastic separation theorems, thereby strengthening some results obtained earlier. We propose the boundaries for linear and Fisher separability, when the points are drawn randomly, independently and uniformly from a d-dimensional spherical layer and from the cube. These results allow us to better outline the applicability limits of the stochastic separation theorems in applications.

Sidorov Sergey, Zolotykh Nikolai


1-convex set, Fisher linear discriminant, Fisher separability, linear separability, random points, stochastic separation theorems

General General

Approximate Bayesian Inference.

In Entropy (Basel, Switzerland)

This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference.

Alquier Pierre


Bayesian statistics, Gibbs posterior, Langevin Monte Carlo, Laplace approximations, Markov chain Monte Carlo, PAC-Bayes, approximate Bayesian computation, expectation-propagation, machine learning, sequential Monte Carlo, variational approximations

General General

Time-Delay Characteristics of Complex Lü System and Its Application in Speech Communication.

In Entropy (Basel, Switzerland)

Although complex Lü systems have been considered in many studies, application of the self-time-delay synchronization (STDS) of complex Lü systems in secure speech communications does not appear to have been covered in much of the literature. Therefore, it is meaningful to study the STDS of complex Lü systems and its application in secure speech communication. First, a complex Lü system with double time-delay is introduced and its chaotic characteristics are analyzed. Second, a synchronization controller is designed to achieve STDS. Third, the improved STDS controller is used to design a speech communication scheme based on a complex Lü system. Finally, the effectiveness of the controller and communication scheme are verified by simulation.

Guo Junmei, Ma Chunrui, Wang Zuoxun, Zhang Fangfang


complex Lü system, controller, self-time-delay synchronization, speech communication

General General

Protein Conformational States-A First Principles Bayesian Method.

In Entropy (Basel, Switzerland)

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a 'distribution' over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method's derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

Rogers David M


Bayesian clustering, Bernoulli mixture, unsupervised classification