The rapid growth of electronic waste (e-waste) has led to an urgent need for efficient recycling processes to recover valuable materials and reduce environmental impact.Waste Printed Circuit Boards (WPCBs) constitute significant e-waste and contain valuable components and precious metals.Computer vision systems can automate the classification, disassembly, and recycling of WPCBs.However, obtaining large annotated datasets for machine learning in this domain is costly and often unavailable.This paper investigates using few-shot and supervised contrastive learning in electronic component detection.We propose a model incorporating contrastive learning components for detecting electronic components in scenarios with limited training data or annotated labels.Our experimental results show that, in limited-data scenarios, contrastive learning outperforms the original versions of Faster R-CNN object detector.This study contributes to developing efficient recycling solutions for e-waste management and resource recovery.

Abstract Human interaction starts with a person approaching another one, respecting their personal space to prevent uncomfortable feelings. Spatial behavior, called proxemics, allows defining an acceptable distance so that the interaction process begins appropriately. In recent decades, human-agent interaction has been an area of interest for researchers, where it is proposed that artificial agents naturally interact with people. Thus, new alternatives are needed to allow optimal communication, avoiding humans feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, it is assumed that the personal space is fixed and known in advance, and the agent is only expected to make an optimal trajectory toward the person. In this work, we focus on studying the behavior of a reinforcement learning agent in a proxemic-based environment. Experiments were carried out implementing a grid-world problem and a continuous simulated robotic approaching environment. These environments assume that there is an issuer agent that provides non-conformity information. Our results suggest that the agent can identify regions where the issuer feels uncomfortable and find the best path to approach the issuer. The results obtained highlight the usefulness of reinforcement learning in order to identify proxemic regions.

The growth in the numbers of phishing attacks, along with the volume of successful frauds, demonstrates vul-nerabilities of the protection tools and exposes the advance in the refinement of the attacks. In more than 70% of cases, the improvements rely on the presence of homographic terms as a mechanism to embed reliability in malicious pages. In this scenario, the present study proposes an intelligent approach denominated CatchPhish, which, through the attack target brand identification, can infer the veracity of the page evaluated. CatchPhish uses a Siamese neural network capable of identifying the presence of typosquatting mentions in phishing pages. In the experiments, the proposed approach achieved 99.30% of assertiveness. In addition, the proposed approach stands out for its ability to produce terms for training, so, instead of providing the tool with a high amount of distorted terms, it provides the mark preceded by the correct spelling, which circumvents a strong obstacle in the construction of protection mechanisms.

One method for encouraging the public interest in the use of historical maps as a source of reliable knowledge is to represent them in a more familiar aspect, such as the style of the current-day popular application Google Maps’ satellite view. We present a method for synthesizing satellite-images from historical maps, translating their visuals using conditional generative adversarial networks (conditional GANs). We discuss a typical representation of these dated documents to allow such translations. We observe how the semantics involved in the process influence the outcomes. Finally, we discuss the effective result of bringing the past to a familiar look for the viewer.

Interactive reinforcement learning is an approach in which an external trainer helps an agent to learn through advice. A trainer is useful in large or continuous scenarios; however, when the characteristics of the environment change over time, it can affect the learning. Robust reinforcement learning is a reliable approach that allows an agent to learn a task, regardless of disturbances in the environment. In this work, we present an approach that addresses interactive reinforcement learning problems in a dynamic environment with continuous states and actions. Our results show that the proposed approach allows an agent to complete the cart-pole balancing task satisfactorily in a dynamic, continuous action-state domain.

Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to perform the detection. The image approach is promising since it does not need specific sensors and can be easily embedded in different devices. However, besides the high performance, the computational cost of the used deep learning methods is a challenge to their deployment in portable devices. In this work, we propose a new deep learning architecture that requires fewer floating-point operations (flops) for fire recognition. Additionally, we propose a portable approach for fire recognition and the use of modern techniques such as inverted residual block, convolutions like depth-wise, and octave, to reduce the model's computational cost. The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops. One of our models presents 71% fewer parameters than FireNet, while still presenting competitive accuracy and AUROC performance. The proposed methods are evaluated on FireNet and FiSmo datasets. The obtained results are promising for the implementation of the model in a mobile device, considering the reduced number of flops and parameters acquired.

Although secular, handwritten signature is one of the most reliable biometric method used by most countries. In the last ten years, the application of technology for verification of handwritten signatures has evolved strongly, including forensic aspects. Some factors, such as the complexity of the background and the small size of the region of interest - signature pixels - increase the difficulty of the targeting task. Other factors that make it challenging are the various variations present in handwritten signatures such as location, type of ink, color and type of pen, and the type of stroke. In this work, we propose an approach to locate and extract the pixels of handwritten signatures on identification documents, without any prior information on the location of the signatures. The technique used is based on a fully convolutional encoder-decoder network combined with a block of refinement layers for the alpha channel of the predicted image. The experimental results demonstrate that the technique outputs a clean signature with higher fidelity in the lines than the traditional approaches and preservation of the pertinent characteristics to the signer's spelling. To evaluate the quality of our proposal, we use the following image similarity metrics: SSIM, SIFT, and Dice Coefficient. The qualitative and quantitative results show a significant improvement in comparison with the baseline system.

Deep learning is a machine learning paradigm that has been widely exploited in the last years due to its high performance in many different problems, most of them related to computer vision. The Structured Pyramidal Neural Network (SPNN) is an artificial neural network which implements some of the deep learning concepts, such as multiple processing layers and receptive fields, with no need to pre-define the features of the problem as inputs. This network architecture has presented equivalent or even better results than other deep network approaches applied to solve specific tasks, but with a much lower computational cost. However, one of the SPNN limitations is the difficulty in contributing to human interpretations, since it has opaque learning, like most of the neural networks. Thus, we propose a non-negative model of the SPNN, to obtain better interpretability of the network learning. We restrict the values of the weights and biases of the network to be non-negative. The proposed model is evaluated in a gender recognition problem using the Face Recognition Technology (FERET) database. The results show that SPNN including non-negative constraint returns comparable recognition rates, but providing gains in the interpretability and stability of the model.

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

Lateral Inhibition Pyramidal Neural Network (LIPNet) is a type of pyramidal neural network efficiently used in computer vision for pattern recognition. The first set of layers of the network is responsible for the implicit feature extraction; the second and final set of layers, then, classifies the input patterns. This network incorporates in its architecture some biological inspirations such as the deep-learning held in the brain and the existence of receptive and inhibitory fields in the human visual system. Additionally, there is another theory based on the functioning of biological neural networks not considered in the LIPNet: parts-based learning. This type of learning aims to understand a pattern starting with its simplest concepts. Studies have shown that one can achieve this learning by introducing a non-negative constraint on the LIPNet weights. This way, the non-negative network could get an interpretable and not opaque learning distinguishing it from traditional neural networks. Thus, we use Particle Swarm Optimization (PSO) in this paper to apply this constraint on the LIPNet. We also propose a display model of the network learning. Through this model, it is possible to compare the internal representation of the non-negative model learning and the traditional LIPNet model. The results show that the LIPNet with non-negative constraint presented a better interpretability of the patterns.

Theories inspired by the working and the structure of the human brain have been applied in various problems in computer vision, such as Artificial Neural Networks (ANNs). The concept of receptive and inhibitory fields have been adopted with success to improve the capability of the ANNs and are present in the deep learning models, such as ConvNet and LIPNet, that have been successfully used in many computer vision tasks. However, both shallow and deep ANN models need some expertise to define their architecture, as well as the definition of some of their parameters. A recently introduced ANN, called CANet, embeds the concepts of receptive fields, lateral inhibition, and autoassociative memory using a constructive algorithm that requires few parameters to perform its learning process. This paper presents a new constructive-pruning algorithm for CANet that contains even fewer parameters and can self-choose the quantity of neurons in the constructive layer of the model, called CANet-2. Also, we analyze the behavior of the model with the use of activation functions from the ReLU family in its constructive layer. Experiments in facial expression recognition showed that the proposed constructive algorithm along with the SoftPlus activation function improved CANet-2 in relation to the original version.

Deep neural networks have been used successfully for several different computer vision-related tasks, including facial expression recognition. In spite of the good results, it is still not clear why these networks achieve such good recognition rates. One way to learn more about deep neural networks is to visualise and understand what they are learning, and to do so techniques such as deconvolution could play a significant role. In this paper, we train a Convolutional Neural Network (CNN) and Lateral Inhibition Pyramidal Neural Network (LIPNet) to learn facial expressions. Then, we use the deconvolution process to visualise the learned features of the CNN and we introduce a novel mechanism for visualising the internal representation of the LIPNet. We perform a series of experiments, training our networks with the Cohn-Kanade data set and show what kind of facial structures compose the learned emotion expression representation. Then, we use the trained networks to recognise images from the Jaffe data set and demonstrate that the learned representations are present in different face images, emphasizing the generalization aspects of these networks. We discuss the different representations that each network learns and how they differ from each other. We also discuss how each learned representation contributes to the recognition process and how they can be compared to the emotional notation Facial Action Coding System - Facs. Finally, we explain how the principles of invariance, redundancy and filtering, common for deep networks, contribute to the learned features and to the facial expression recognition task in general.

There are many researches on forecasting time series for building trading systems for financial markets. Some of these studies have shown that it is possible to obtain satisfactory results, thereby contradicting the theory of Efficient Markets Hypothesis (EMH) that suggests that prices are randomly generated over time. This paper proposes an intelligent system based on historical closing prices that uses technical analysis, the Artificial Bee Colony Algorithm (ABC), a selection of past values (lags), nearest neighbor classification (k-NN) and its variation, the Adaptative Classification and Nearest Neighbor (A-k-NN). A very important step for time series prediction is the correct selection of the past observations (lags). Our method uses this strategy since it uses the k-NN and A-k-NN to decide on the buy and seIl points, combined with the ABC algorithm which is used to search for the best parameter settings of system and a good set of lags. This paper compares the results obtained by the proposed method with the buy and hold strategy and with other work that performed similar experiments with the same trading model and the same stocks. The key measure for performance comparison is the profitability in the analyzed period. The proposed method generates much larger profits compared to the other method and to the buy and hold strategy. Our method outperforms the other methods in thirteen out of the fifteen stocks tested, minimizing the risk of market ex pos ure.

The human visual system is one of the most fascinating and complex mechanisms of the central nervous system that enables our capacity to see. It is through the visual system that we are able to accomplish from the most simple task such as object recognition to the most complex visual interpretation, understanding and perception. Inspired by this sophisticated system, two models based on the properties of the human visual system are proposed. These models are designed based on the concepts of receptive and inhibitory fields. The first model is a pyramidal neural network with lateral inhibition, called lateral inhibition pyramidal neural network. The second proposed model is a supervised image segmentation system, called segmentation and classification based on receptive fields. This work shows that the combination of these two models is beneficial, and the results obtained are better than that of other state-of-the-art methods.

In this paper, the face verification problem is addressed. A neural network with autoassociation memory and receptive fields based architecture is proposed. It is called AAPNet (AutoAssociative Pyramidal Neural Network). The proposed neural network integrates feature extraction and image reconstruction in the same structure. For a given recognition task, at least one instance of the AAPNet must be trained for each known class. Thus, the AAPNet outputs how similar is a given probe image to its class. The AAPNet is applied in a face verification task using thumbnail-sized faces and achieves better results when compared to state-of-the-art models.

This paper presents a new neural network to perform the visual pattern classification task. The neural network is calledI-PyraNetwhich is a hybrid implementation of the PyraNet and the concepts of the inhibitory fields. In order to improve the results obtained by this neural network, it is also presented the 2-D Gabor filter. Furthermore, both, the neural network and the filter, are applied over a face detection task and are compared to the results obtained by a SVM.

This paper presents a new model to realize a supervised image segmentation task. It is based on the concept of receptive fields that intends to analyze pieces of an image considering not only the pixels or group of them, but also the relationship between them and their neighbors, called segmentation and classification with receptive fields (SCRF). Also, in order to work with the SCRF model, is proposed here a new artificial neural network, called IPyraNet, which is a hybrid implementation of the recently described PyraNet and the nonclassical receptive fields inhibition. Furthermore, the model and the network are applied together in order to realize a satellite image segmentation task.

This paper presents a new artificial neural network, called I-PyraNet. This new architecture is based on the combination between concepts of the recently described PyraNet and the nonclassical receptive fields inhibition, integrating the feature extraction and the classification stages into the same structure which is formed by 2-D and 1-D layers. The main difference between the PyraNet and the I-PyraNet is that while in the first a 2-D neuron always provide the same output, in the I-PyraNet the signal of the output of a 2-D neuron will invert when it appears inside a inhibitory field. Furthermore, the I-PyraNet is applied over a face detection task where different configurations of the network are tested.