Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

Abstract--The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithmcapable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm. I. INTRODUCTION Regression analysis has the objective of describing the relationship between measurable variables [1]. This analysis can be used to make predictions of not yet observed samples, to study a system's behavior or to calculate the statistical properties of such system. F. O. de Franca is with Federal University of ABC, Center for Mathematics, Computationand Cognition, Heuristics, Analysis and Learning Laboratory, São Paulo, Brazil, email: folivetti@ufabc.edu.br,

What's New: Non-profit RESOLVE's* new TrailGuard AI* camera uses Intel-powered artificial intelligence (AI) technology to detect poachers entering Africa's wildlife reserves and alert park rangers in near real-time so poachers can be stopped before killing endangered animals. TrailGuard AI builds on anti-poaching prototypes funded by Leonardo DiCaprio Foundation and National Geographic Society. "By pairing AI technology with human decision-makers, we can solve some of our greatest challenges, including illegal poaching of endangered animals. With TrailGuard AI, Intel's Movidius technology enables the camera to capture suspected poacher images and alerts park rangers, who will ultimately decide the most appropriate response." How It Works: TrailGuard AI uses Intel Movidius Vision Processing Units (VPUs) for image processing, running deep neural network algorithms for object detection and image classification inside the camera.

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.

This is because the concentration of energy at a specific range of frequencies might be indicative of mechanical faults [1], cardiac anomalies [2], astronomical discoveries [3, 4], and whale calls from submarine audio recordings [5] to name a few. The standard practice to isolate components within a specific frequency range from a time-series observation, referred to as filtering, isto convolve the observations with an object called linear filter. This convolution removes all frequencies that do not correspond to the desired frequency range, thus, filtering out unimportant frequencies. Thetheoretical rationale behind this approach is supported by the application of the Convolution Theorem [6] to power spectral densities (PSD): the PSD of a filtered time series corresponds to the PSD of the linear filter (user-designed) multiplied by the PSD of the observed time series (not controllable). This result allows for designing thelinear filter so as to remove unwanted frequency components to then perform the numerical convolution.

Together with twelve renowned art and cultural institutions, Ars Electronica recently initiated the European ARTifical Intelligence Lab initiiert. The Europe-wide initiative, scheduled to run for three years, is co-financed by the Creative Europe Program of the European Union and offers artists the opportunity to take part in a residency with scientific institutions: These include the Muntref Centro de Arte y Ciencia, the Laboratorio de Neurociencia de la Universidad Torquato Ditella in Buenos Aires or the University of Edinburgh. Interested artists who wish to develop new artistic approaches at the interface of neuroscience and artificial intelligence can apply for this first residency until February 17, 2019. The results of the residency will then be presented at the Ars Electronica Festival in Linz and at twelve network partners throughout Europe. Mariano Sardón: In a conversation with Gerfried Stocker, the artistic director of Ars Electronica, during one of his visits to Buenos Aires a few years ago, we thought that some areas of science, such as Neuroscience and Artificial Intelligence, were generating a lot of results and processes which would inevitably impact on our society, and such an impact would need a space for reflection and development in a wide perspective, introducing artists in the context.

Meteorological forecasting provides reliable prediction about the future weather within a given interval of time. Meteorological forecasting can be viewed as a form of hybrid diagnostic reasoning and can be mapped onto an integrated conceptual framework. The automation of the forecasting process would be helpful in a number of contexts, in particular: when the amount of data is too wide to be dealt with manually; to support forecasters education; when forecasting about underpopulated geographic areas is not interesting for everyday life (and then is out from human forecasters' tasks) but is central for tourism sponsorship. We present logic MeteoLOG, a framework that models the main steps of the reasoner the forecaster adopts to provide a bulletin. MeteoLOG rests on several traditions, mainly on fuzzy, temporal and probabilistic logics. On this basis, we also introduce the algorithm Tournament, that transforms a set of MeteoLOG rules into a defeasible theory, that can be implemented into an automatic reasoner. We finally propose an example that models a real world forecasting scenario.

Deep image clustering is a recent research area, but with exciting published works [15]. The approaches use the most diverse architectures varying the structure of the deep networks, theclustering algorithms and the combination of both parts. Approachessuch as the Deep Clustering Network (DCN) [9] use a pretrained autoencoder combined with the k-means algorithm. Methods such as Joint Unsupervised Learning (JULE) [10] combines deep convolutional networks with hierarchical clustering. Deep Embbed Cluster (DEC) [11], also uses a pretrained autoencoder, then removes the decoder part and uses the encoder as a feature extractor to feed the clustering method. After that, the network is fine-tuned using the cluster assignment hardening loss. Meanwhile, the clusters are iteratively tuned by minimizing the KL-divergence between the distribution of soft labels and the auxiliary target distribution.

Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousands of popular Brazilian songs. Here, 'popular' does not only refer to the genre named MPB (Brazilian Popular Music) but to nine different genres that were considered particular to the Brazilian case. The main goals of the present work are to extract and engineer harmonically related features from chords data and to use it to classify popular Brazilian music genres towards establishing a connection between harmonic relationships and Brazilian genres. We also emphasize the generalisation of the method for obtaining the data, allowing for the replication and direct extension of this work. Our final model is a combination of multiple classification trees, also known as the random forest model. We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API. The variables considered in this work also give an intuition about how they relate to the genres.

Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space. In order to scale the method and keep its benefits, we propose an algorithm (LineBO) that restricts the problem to a sequence of iteratively chosen one-dimensional sub-problems. We show that our algorithm converges globally and obtains a fast local rate when the function is strongly convex. Further, if the objective has an invariant subspace, our method automatically adapts to the effective dimension without changing the algorithm. Our method scales well to high dimensions and makes use of a global Gaussian process model. When combined with the SafeOpt algorithm to solve the sub-problems, we obtain the first safe Bayesian optimization algorithm with theoretical guarantees applicable in high-dimensional settings. We evaluate our method on multiple synthetic benchmarks, where we obtain competitive performance. Further, we deploy our algorithm to optimize the beam intensity of the Swiss Free Electron Laser with up to 40 parameters while satisfying safe operation constraints.