"Principles and Standards for School Mathematics" in the Classroom.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Detection of Malicious Code Variants Based on Deep Learning

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書評 Computational Homology

Reconstructing the shape of an object from images is an important problem in computer vision that has led to a variety of solution strategies. This survey covers photometric stereo, i.e., techniques that exploit the observed intensity variations caused by illumination changes to recover the orientation of the surface. In the most basic setting, a diffuse surface is illuminated from at least three directions and captured with a static camera. Under some conditions, this allows to recover per-pixel surface normals. Modern approaches generalize photometric stereo in various ways, e.g., relaxing constraints on lighting, surface reflectance and camera placement or creating different types of local surface estimates. Starting with an introduction for readers unfamiliar with the subject, we discuss the foundations of this field of research. We then summarize important trends and developments that emerged in the last three decades. We put a focus on approaches with the potential to be applied in a broad range of scenarios. This implies, e.g., simple capture setups, relaxed model assumptions, and increased robustness requirements. The goal of this review is to provide an overview of the diverse concepts and ideas on the way towards more general techniques than traditional photometric stereo.

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A Survey of Photometric Stereo Techniques

Inspired by Eckhorn’s neuron model that emulates a mammal’s visual cortex, this paper proposes a new kind of neural-like P system, called a coupled neural P (CNP) system. The CNP system consists of some coupled neurons, each with three components: receptive field, modulation, and output module. CNP systems are a kind of distributed parallel-computing model with a directed graph structure like spiking neural P systems. Moreover, CNP systems have a nonlinear coupled-modulation characteristic and a dynamic threshold mechanism. The computational power of CNP systems is discussed. Specifically, it is proved that CNP systems as number-generating devices are Turing universal. Moreover, we provide a small universal CNP system for function-computing devices.

Coupled Neural P Systems

Segmenting images with gradient-based edge detection using Membrane Computing

In this paper we present a parallel algorithm to solve the thresholding problem by using Membrane Computing techniques This bio-inspired algorithm has been implemented in a novel device architecture called CUDA™, (Compute Unified Device Architecture) We present some examples, compare the obtained time and present some research lines for the future

A parallel implementation of the thresholding problem by using tissue-like P systems

In this paper, we propose a bio-inspired membrane computational framework for constructing discrete Morse complexes for binary digital images. Our approach is based on the discrete Morse theory and we work with cubical complexes. As example, a parallel algorithm for computing homology groups of binary 3D digital images is designed.

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Membrane parallelism for discrete Morse theory applied to digital images

In this paper, we present a direct computational application of Homological Perturbation Theory (HPT, for short) to computer imagery. More precisely, the formulas of the A ∞–coalgebra maps Δ 2 and Δ 3 using the notion of AT-model of a digital image, and the HPT technique are implemented. The method has been tested on some specific examples, showing the usefulness of this computational tool for distinguishing 3D digital images.

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Searching high order invariants in computer imagery

La construccion de un cerebro conectivo comienza en las edades mas tempranas del desarrollo humano. Sin embargo, el conocimiento que ya se tiene sobre los cerebros individual y colectivo apenas se ha incorporado en el desarrollo del pensamiento matematico en Educacion Infantil, donde comienzan a gestarse elementos clave para tomar decisiones, resolver problemas de la vida cotidiana, tratar con datos y comprender el entorno. Desde esta perspectiva la presente investigacion marca como objetivo general analizar el proceso de ensenanza-aprendizaje de las matematicas en Educacion Infantil a partir del conexionismo, considerando como objetivos especificos, por un lado, determinar las caracteristicas de una practica matematica que promueva las conexiones y, por otro lado, identificar los distintos tipos de conexiones matematicas para fomentar la inteligencia conectiva. La investigacion se lleva a cabo a lo largo de dos anos consecutivos bajo un paradigma interpretativo con un enfoque metodologico basado en el uso combinado de Investigacion-Accion y Teoria Fundamentada. Los resultados han permitido concretar un prototipo de actividad o conjunto de actividades que, en forma de secuencia didactica, promueve tres tipos de conexiones matematicas para desarrollar la inteligencia conectiva en Educacion Infantil: conceptuales, que producen nexos entre contenidos matematicos diversos; docentes, que vinculan diversos conceptos matematicos a traves de una metodologia activa y de vivenciar las experiencias matematicas con otras materias; y practicas, que relacionan las matematicas con el entorno.

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Ainhoa Berciano

Papers

Segmenting images with gradient-based edge detection using Membrane Computing

A parallel implementation of the thresholding problem by using tissue-like P systems

Membrane parallelism for discrete Morse theory applied to digital images

Searching high order invariants in computer imagery

Inteligencia conectiva para la educación matemática infantil