Anomaly or fault detection on machines is relevant in many industries, like wind power generation. Bearing failures, a common cause of breakdowns, pose significant challenges, leading to increased downtime and maintenance costs. Recent machine learning techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM), have been exploited to extract spatial and temporal relationships from data to detect failures. Many literature works use the Paderborn bearing vibration data in their studies, which mainly rely on either CNNs or LSTM models to accomplish the detection task. In this paper, we use a hybrid CNN-LSTM network architecture to achieve high accuracy for bearing fault detection using the Paderborn dataset. In addition, a two-sliding window technique is used to prepare the data to pass through both types of layers. Our study emphasizes the importance of selecting appropriate architectures and considering bearing characteristics and operation conditions for effective fault detection. Our results achieved up to 99.58% accuracy. However, when tested on different machine operating conditions, the model’s performance degraded, indicating the necessity of other methods to address this issue. In addition, we verified that an improper model tunning can degrade its performance, even if a complex hybrid method is considered, highlighting the importance of hyperparameters tunning in fault detection problems.

Machine-learning (ML) algorithms have demonstrated the potential to tackle several material science challenges, ranging from predicting quantum molecular properties to screening novel molecules with tailored properties via inverse design. In this realm, molecular descriptors and graph neural networks (GNNs) based on molecular geometry have yielded promising results for a variety of ML tasks. Nevertheless, the majority of these studies trained and validated their model with geometries optimized through density functional theory (DFT), implying that geometries at the same level of theory will be available for unseen molecular data. Unfortunately, generating these 3D geometries is computationally expensive, limiting their application to explore a myriad of molecular candidates. In contrast, universal function approximators such as neural nets (NNs) can learn any desired function, meaning that GNNs can map non-equilibrium geometries to predict ground-state properties. In this sense, this work investigates the impact of a computationally fast (but less accurate) method to predict molecular properties calculated at the DFT B3LYP/6-31G(2df, p) level. Precisely, we assess the predictive performance of the enn-s2s model for twelve molecular properties from the QM9 dataset with geometries optimized via the Merck Molecular Force Field (MMFF94) and DFT B3LYP/6-31G(2df, p) framework. As a result, 9 out of 12 properties demonstrated a low error gap between feeding the enn-s2s with the MMFF94- and DFT-optimized geometry, thus confirming NNs as a feasible strategy to overcome the before-mentioned limitation for practical application.

Chat groups are well-known for their capacity to promote viral political and marketing campaigns, spread fake news, and create rallies by hundreds of thousands on the streets. Also, with the increasing public awareness regarding privacy and surveillance, many platforms have started to deploy end-to-end encrypted protocols. In this context, the group's conversations are not accessible in plain text or readable format by third-party organizations or even the platform owner. Then, the main challenge that emerges is related to getting insights from users' activity of those groups, but without accessing the messages. Previous approaches evaluated the user engagement by assessing user's activity, however, on limited conditions where the data is encrypted, they cannot be applied. In this work, we present a framework for measuring the level of engagement of group conversations and users, without reading the messages. Our framework creates an ensemble of interaction networks that represent the temporal evolution of the conversation, then, we apply the proposed Engagement Index (EI) for each interval of conversations to asses users' participation. Our results in five datasets from real-world WhatsApp Groups indicate that, based on the EI, it is possible to identify the most engaged users within a time interval, create rankings and group users according to their engagement and monitor their performance over time.

Several complex dynamical systems are embedded in geographical space. Geographical data have proven its importance in several domains. For instance, the formation and scrutiny of climate networks have emerged as a new research topic in environmental literature. However, there is still a lack of investigations of scenarios with very high spatial resolution, such as those considering meteorological data. Recently, a new concept, named (geo)graphs, was proposed. (Geo)graphs are graphs, or networks, in which the nodes have an assigned geographical location. Besides embedding nodes into space, these graphs are readily manipulated with a geographical information system, and, thus, represent a suitable tool for dealing with very high-resolution scenarios, such as meteorological data. In this context, here, we apply a (geo)graph approach to model a radar-derived rainfall data set. We represent the nodes as a point-type shapefile and the edges as a line-type shapefile, which are standard file types in geoinformatics. After, we analyze the topological properties of a family of (geo)graphs considering distinct thresholds. The analysis of these networks reveals a spatially well-defined community structure, which, interestingly, is consistent with topographical/altimetric and land use/land cover data. These results show the relation between geographical properties and the topological structure of the network might be applied to different ecological studies, from sustainable development to urban planning and disaster risk reduction.

Qualitative data clustering is a fundamental data analysis task, with applications in many areas, like medicine, sociology, and economics. An appealing way to deal with this task is via Integer Linear Programming, as it avoids inappropriate inferences by the final user. This approach has two main advantages: the data are directly used, without the need of being converted to quantitative values, and the optimal number of clusters is automatically obtained by solving the optimization problem. However, it might create large and redundant models, which can limit the size of the problems it can be applied. Recently, models that are more compact and able to avoid some redundancy have been proposed in the literature. These models consume less memory and are faster to obtain the optimal solution set. In this study, a new model is introduced and compared with the state-of-the-art alternatives using datasets from different application domains. Empirical results show that the new model outperforms its predecessors, achieving the optimal solution set with lower computational time and memory consumption.

One of the most used neural network model for clustering data is the Self-Organizing Map (SOM). Over the years, it has been applied in many areas, from computing to biology, and therefore a wide range of data types have been considered. Originally, the SOM was developed to take real-valued data into account. Thus, learning other data types, such as binary and category data, remains a challenge. This work proposes an alternative and effective modified SOM, to better cluster binary and categorical data.

Real scenes are composed of multiple points possessing distinct characteristics. Selectively, only part of the scene undergoes scrutiny at a time, and the mechanism responsible for this task is named selective visual attention. Spatial location with the highest contrast might highlight from scene reaching level of awareness (bottom-up attention). On the other hand, attention may also be voluntarily directed to a particular object in the scene (object-based attention), which requires the recognition of a specific target (top-down modulation). In this paper, a new visual selection model is proposed, which combines both early visual features and object-based visual selection modulations. The possibility of the modulation regarding specific features enables the model to be applied to different domains. The proposed model integrates three main mechanisms. The first handles the segmentation of the scene allowing the identification of objects. In the second one, the average of saliency of each object is computed, which provides the modulation of the visual attention for one or more features. Finally, the third builds the object saliency map, which highlights the salient objects in the scene. We show that top-down modulation has a stronger effect than bottom-up saliency when a memorized object is selected, and this evidence is clearer in the absence of any bottom-up clue. Experiments with synthetic and real images are conducted, and the obtained results demonstrate the effectiveness of the proposed approach for visual selection. (C) 2015 Elsevier B.V. All rights reserved.

Many interactive image processing approaches are based on semi-supervised learning, which employ both labeled and unlabeled data in its training process. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. In this paper, the particle competition and cooperation model is extended to perform interactive image segmentation. Each image pixel is converted into a graph node, which is connected to its nearest neighbors according to their visual features and location in the original image. Labeled pixel generates particles that propagate their label to the unlabeled pixels. The particle model also takes the contributions from the adjacent pixels to classify less confident labeled pixels. Computer simulations are performed on real-world images, including images from the Microsoft GrabCut dataset, which allows a straightly comparison with other techniques. The segmentation results show the effectiveness of the proposed approach.

In this paper, we propose a new semi-supervised growing neural gas (GNG) model, named Consensus-Based Semi-Supervised GNG, or CSSGNG, in which both labeled and unlabeled data are used to train the network. In contrast to former adaptations of the GNG to semi-supervised classification, such as the SSGNG and OSSGNG models, the CSSGNG does not assign a single scalar label value to each neuron. Instead of the scalar, a vector containing the representativeness level of every class is associated with each neuron. Moreover, to propagate the labels among the neurons the CSSGNG employs a consensus approach. Computer experiments show that our model on average can deliver better classification results in comparison to the SSGNG and OSSGNG models.

Augmented Reality combined with adaptive hypermedia plays an important role on providing effective information visualization systems. In this paper, we propose a comprehensive architecture model in order to provide adaptive information visualization systems with augmented reality. We also provide a novel visual metaphor for real-valued, low-dimensional data with optimal values for each feature inspired on the pseudo-flower metaphor.

One important feature observed in several complex networks is the structure of communities, or modular structure. Detecting communities is still a big challenge for researchers, specially the development of models to deal with dynamic networks. Here, we propose a new method for detecting communities by using a dynamical model. The first step consists of generating a spatial representation, named particle, for each vertex in the network. With these two representation, network structure and the spatial particles, we define the model's dynamics by means of two interactions types: the first is related to the network structure, or relational, and it is responsible for approaching particles representing neighbor vertices; the second, repulsive, is generated according to the spatial position of each particle and is responsible to make each unrelated particle, according to the network structure, to repel each other. Thus, after a couple of iteration, we observe the formation of groups of particles representing communities. On the other hand, distinct communities are separated according to the spatial positions of their particles. Simulation results show that our model achieves good results on the two benchmark models taken into account and that it can also deal with dynamic networks owing to its intrinsic dynamics.

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a “divide-and-conquer” effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Decision tree induction algorithms are well known techniques for assigning objects to predefined categories in a transparent fashion. Most decision tree induction algorithms rely on a greedy top-down recursive strategy for growing the tree, and pruning techniques to avoid overfitting. Even though such a strategy has been quite successful in many problems, it falls short in several others. For instance, there are cases in which the hyper-rectangular surfaces generated by these algorithms can only map the problem description after several sub-sequential partitions, which results in a large and incomprehensible tree. Hence, we propose a new decision tree induction algorithm based on clustering which seeks to provide more accurate models and/or shorter descriptions more comprehensible for the end-user. We do not base our performance analysis solely on the straightforward comparison of our proposed algorithm to baseline methods. Instead, we propose a data-dependent analysis in order to look for evidences which may explain in which situations our algorithm outperforms a well-known decision tree induction algorithm.

In machine learning study, semi-supervised learning has received increasing interests in the last years. It is applied to classification problems where only a small portion of the data points is labeled. In these situations, the reliability of these labels is extremely important because it is common to have mislabeled samples in a data set and these may propagate their wrong labels to a large portion of the data set, resulting in major classification errors. In spite of its importance, wrong label propagation in semi-supervised learning has received little attention from researchers. In this paper we propose a particle walk semi-supervised learning method with both competitive and cooperative mechanisms. Then we study error propagation by applying the proposed model in modular networks. We show that the model is robust against mislabeled samples and it can produce good classification results even in the presence of considerable proportion of mislabeled data. Moreover, our numerical analysis uncover a critical point of mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. These studies have practical importance to design secure and robust machine learning techniques.

Semi-Supervised Learning (SSL) is a machine learning research area aiming the development of techniques which are able to take advantage from both labeled and unlabeled samples. Additionally, most of the times where SSL techniques can be deployed, only a small portion of samples in the data set is labeled. To deal with such situations in a straightforward fashion, in this paper we introduce a semi-supervised learning approach based on neuronal synchrony in a network of coupled integrate-and-fire neurons. For that, we represent the input data set as a graph and model each of its nodes by an integrate-and-fire neuron. Thereafter, we propagate the class labels from the seed samples to unlabeled samples through the graph by means of the emerging synchronization dynamics. Experimentations on synthetic and real data show that the introduced technique achieves good classification results regardless the feature space distribution or geometrical shape.

Chaotic phase synchronization among coupled oscillators is a phenomenon of interest in many physical and engineering systems. It has also been observed in biological systems, where groups of different functional units interact with each other in order to produce coherent behaviors in higher levels. While biological systems have facility to capture salient object(s) in a given scene, visual selection is still a challenging task to artificial visual systems. In this paper, a visual selection mechanism based on chaotic phase synchronization is proposed. Oscillators representing the salient object in a given scene are phase synchronized, while no synchronization is observed for background objects. In this way, the salient object is highlighted. Due to the modeling by phase synchronization instead of complete synchronization, the proposed model is robust, biologically inspired and good simulation results were achieved.

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real images and the simulation results show its effectiveness.

In this paper a visual selection mechanism based on the pulse-coupled neural network is proposed for discriminating one object among others in a given visual scene by delivering the focus of attention to the salient object. In comparison to other visual selection approaches, this model presents at least two new features. First, it is able to highlight not only objects or components of objects in simple forms, but also objects in complex forms, including those that are non-linear separable, and second it provides a shifting mechanism from one previously selected object to another. Computer simulations are performed and the results show that the proposed model is promising as a selection mechanism embedded in a visual attention system.

Self-organization is a highly observed process of brain to solve several cognitive tasks and many artificial systems based on this mechanism have been developed. In this paper, a self-organized dynamical model is proposed for pixel clustering. This approach employs a network consisting of interacting elements with each representing a pixel value and receiving attractions from other elements within a certain neighborhood. Those attractions, determined by a predefined similarity measure, drive the elements to converge to their corresponding cluster center. With this model, neither the number of pixel clusters nor the initial guessing of cluster centers is required. Computer simulations for clustering of real images are performed to show the efficiency of the proposed approach.

In this paper, a Visual Selection Mechanism based on a lattice of coupled chaotic Wilson-Cowan oscillators is proposed. The oscillators representing each object in a given visual scene are synchronized to produce a chaotic trajectory. Cooperation and competition mechanisms are also introduced to accelerate oscillating frequency of the salient object as well as to slow down other objects in the same scene. The model can not only discriminate each object among others in a given visual scene, but also deliver the focus of attention to the salient object. In comparison to other visual selection approaches, this model presents at least two new features. First, it is able to highlight objects in complex forms, including those that are non-linear separable. Second, oscillators representing the salient object will jump from chaotic phase to periodic phase. This behavior matches well to biological experiments on pattern recognition of rabbit. Computer simulations are performed and the results show that the proposed model is promising as a Selection Mechanism embedded in a Visual Attention System.