"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

Corner Detection is a well known problem in Digital Imagery. In this paper we present a novel approach to detect corners in digital images. In particular, in the framework of Membrane Computing, given a 2D-image, we have designed a theoretical algorithm to solve a type of the corner detection problem, using tissue-like P systems with promoters. Finally, we show some examples of how this algorithm works theoretically.

First steps for a corner detection using membrane computing

En este articulo se analizan las conexiones matematicas de tipo conceptual que realizan 24 estudiantes de 4 anos y su evolucion a lo largo de un curso academico. Para tal fin, se han recopilado de forma sistematica las actividades realizadas por los y las estudiantes en las que se han producido conexiones de conceptos matematicos para, posteriormente, analizarlas tanto cualitativa como cuantitativamente. Los resultados indican que: 1)en todas las actividades se han observado distintos tipos de conexiones entre conceptos que han permitido establecer el siguiente itinerario de aprendizaje: identificaciones y discriminaciones, relaciones, operadores, iniciacion a la representacion grafica y, finalmente, acercamiento al lenguaje matematico; 2) se produce una evolucion positiva de las conexiones conceptuales a lo largo del curso, tanto en relacion al numero de dias dedicados a trabajar conexiones entre conceptos matematicos como en relacion al numero de ninos y ninas que resuelven correctamente las actividades propuestas. A partir de estos resultados, se concluye que es necesario trabajar progresivamente las conexiones conceptuales en el aula de Educacion Infantil, facilitando la comprension profunda de los conceptos matematicos trabajados.

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Conexiones matemáticas de tipo conceptual en niños de 4 años

Effective homology of k-D digital objects (partially) calculated in parallel

The homology of binary 3-dimensional digital images (digital volumes) provides concise algebraic description of their topology in terms of connected components, tunnels and cavities. Homology generators corresponding to these features are represented by nontrivial 0- cycles, 1-cycles and 2-cycles, respectively. In the framework of cubical representation of digital volumes with the topology that corresponds to the 26-connectivity between voxels, we introduce a method for algorithmic computation of a coproduct operation that can be used to decompose 2-cycles into products of 1-cycles (possibly trivial). This coproduct provides means of classifying different kinds of cavities; in particular, it allows to distinguish certain homotopically non-equivalent spaces that have isomorphic homology. We define this coproduct at the level of a cubical complex built directly upon voxels of the digital image, and we construct it by means of the classical Alexander-Whitney map on a simplicial subdivision of faces of the voxels.

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Decomposing cavities in digital volumes into products of cycles

En este trabajo se evalua una de las competencias especificas propuestas en el nuevo curriculo de Grado para la docencia de estadistica elemental en una muestra de 144 futuros profesores de educacion primaria. En particular, la competencia evaluada ha sido “Conocer, analizar y aplicar conocimientos basicos para la practica de aula en EP, tanto en sus aspectos conceptuales como didacticos”. Para ello, hemos disenado y pasado un cuestionario con el fin de detectar las carencias de dicha competencia en la muestra mencionada, evaluando los conocimientos basicos de estadistica para la practica de aula en EP, en sus aspectos conceptuales. Los resultados obtenidos muestran un escaso nivel competencial, debido a que hay parcelas de la materia en las que se aprecian importantes errores en un alto porcentaje.

Ainhoa Berciano

Papers

First steps for a corner detection using membrane computing

Conexiones matemáticas de tipo conceptual en niños de 4 años

Effective homology of k-D digital objects (partially) calculated in parallel

Decomposing cavities in digital volumes into products of cycles

Prueba exploratoria sobre competencias de futuros maestros de primaria: conocimiento de conceptos básicos de estadística