Land use and sexual harassment: A geospatial analysis based on the volunteer HarassMap-Egypt

Sexual harassment and gang rape in Egypt have garnered attention from both traditional and digital media. This study employed a volunteer HarassMap to analyse sexual harassment crimes (SHCs) across Egypt from a spatial perspective. The specific aims were to apply the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to locate clusters of reported SHCs, and to assess their spatial dependence on land use types. To accomplish this task, ring buffers of 100, 200, 300, 400, and 500 metres were established around each crime scene to determine which land use was mostly associated with the incidence of these SHCs. Local bivariate relationships were used to explore the associations between SHC and each land-use category. Results from the HDBSCAN algorithm revealed four crime clusters within the study domain, mainly located in Greater Cairo, Alexandria, and Behaira. Notably, commercial establishments and transit stations showed a significantly positive correlation with SHC. The study shows how land uses shape SHC and showed that it is possible to identify environmental risk factors for harassment. These risk factors can help policymakers, urban planners, and community stakeholders prevent and reduce sexual harassment and gender inequality, and promote just and inclusive societies.Peer reviewe

Planificación de las pruebas del software

La complejidad actual en la construcción de los sistemas de software, ha impuesto la necesidad de mejorar los procesos de construcción con el menor costo y esfuerzo posible. Las pruebas del software, a diferencia de otras etapas del proceso de construcción, no tienen la misma visibilidad para el usuario, lo que determina que ante presiones de tiempo o costos, sea esta etapa la que se redefina según lo planeado, a pesar de que esta decisión atenta directamente sobre la calidad del software. Para asegurar la correcta construcción del software, en relación a las pruebas, se hace indispensable pensarlas integradas y lo más automatizadas posible. En el proyecto de investigación, se ha trabajado sobre la derivación semiautomática de Casos de Prueba a partir de escenarios futuros y en el presente trabajo se ha avanzado sobre la planificación de esos Casos de Prueba obtenidos, para asegurar que la funcionalidad propuesta en los escenarios contemple las dependencias de recursos y funcionales existentes entre las tareas. De esta manera se utiliza la integración de los escenarios para identificar las dependencias y ordenar la ejecución de los Casos de Prueba en las denominadas suites de prueba, que permiten analizar la funcionalidad parcial y total del futuro sistema de software.Eje: Ingeniería del Software.Red de Universidades con Carreras en Informátic

Planificación de las pruebas del software

La complejidad actual en la construcción de los sistemas de software, ha impuesto la necesidad de mejorar los procesos de construcción con el menor costo y esfuerzo posible. Las pruebas del software, a diferencia de otras etapas del proceso de construcción, no tienen la misma visibilidad para el usuario, lo que determina que ante presiones de tiempo o costos, sea esta etapa la que se redefina según lo planeado, a pesar de que esta decisión atenta directamente sobre la calidad del software. Para asegurar la correcta construcción del software, en relación a las pruebas, se hace indispensable pensarlas integradas y lo más automatizadas posible. En el proyecto de investigación, se ha trabajado sobre la derivación semiautomática de Casos de Prueba a partir de escenarios futuros y en el presente trabajo se ha avanzado sobre la planificación de esos Casos de Prueba obtenidos, para asegurar que la funcionalidad propuesta en los escenarios contemple las dependencias de recursos y funcionales existentes entre las tareas. De esta manera se utiliza la integración de los escenarios para identificar las dependencias y ordenar la ejecución de los Casos de Prueba en las denominadas suites de prueba, que permiten analizar la funcionalidad parcial y total del futuro sistema de software.Eje: Ingeniería del Software.Red de Universidades con Carreras en Informátic

A simplified machine learning approach for recognizing human activity

With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system.  Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performance. Therefore, the proposed manuscript presents a model for activity recognition system where the accuracy of recognition operation and system performance are retained with good balance. The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performanc

Planificación de las pruebas del software

La complejidad actual en la construcción de los sistemas de software, ha impuesto la necesidad de mejorar los procesos de construcción con el menor costo y esfuerzo posible. Las pruebas del software, a diferencia de otras etapas del proceso de construcción, no tienen la misma visibilidad para el usuario, lo que determina que ante presiones de tiempo o costos, sea esta etapa la que se redefina según lo planeado, a pesar de que esta decisión atenta directamente sobre la calidad del software. Para asegurar la correcta construcción del software, en relación a las pruebas, se hace indispensable pensarlas integradas y lo más automatizadas posible. En el proyecto de investigación, se ha trabajado sobre la derivación semiautomática de Casos de Prueba a partir de escenarios futuros y en el presente trabajo se ha avanzado sobre la planificación de esos Casos de Prueba obtenidos, para asegurar que la funcionalidad propuesta en los escenarios contemple las dependencias de recursos y funcionales existentes entre las tareas. De esta manera se utiliza la integración de los escenarios para identificar las dependencias y ordenar la ejecución de los Casos de Prueba en las denominadas suites de prueba, que permiten analizar la funcionalidad parcial y total del futuro sistema de software.Eje: Ingeniería del Software.Red de Universidades con Carreras en Informátic

User acceptance of smart watch for medical purposes : an empirical study

This study aims to investigate the most effective and interesting variables that urge use of the smartwatch (SW) in a medical environment. To achieve this aim, the study was framed using an innovative and integrated research model, which is based on combining constructs from a well-established theoretical model’s TAM and other features that are critical to the effectiveness of SW which are content richness and personal innovativeness. The Technology Acceptance Model (TAM) is used to detect the determinants affecting the adoption of SW. The current study depends on an online questionnaire that is composed of (20) items. The questionnaire is distributed among a group of doctors, nurses, and administration staff in medical centers within the UAE. The total number of respondents is (325). The collected data were implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. The results of the current study show that the main constructs in the model contribute differently to the acceptance of SW. Based on the previous assumption, content richness and innovativeness are critical factors that enrich the user’s perceived usefulness. In addition, perceived ease of use was significantly predictive of either perceived usefulness or behavioral intention. Overall findings suggest that SW is in high demand in the medical field and is used as a common channel among doctors and their patients and it facilitates the role of transmitting information among its users. The outcomes of the current study indicate the importance of certain external factors for the acceptance of the technology. The genuine value of this study lies in the fact that it is based on a conceptual framework that emphasizes the close relationship between the TAM constructs of perceived usefulness and perceived ease of use to the construct of content richness, and innovativeness. Finally, this study helps us recognize the embedded motives for using SW in a medical environment, where the main motive is to enhance and facilitate the effective roles of doctors and patients

Measuring Re-identification Risk

Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from their representation. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome's Topics API for interest-based advertising. We complement our theoretical bounds by showing provably good attack algorithms for re-identification that we use to estimate the re-identification risk in the Topics API. We believe this work provides a rigorous and interpretable notion of re-identification risk and a framework to measure it that can be used to inform real-world applications

Discretization-Based Feature Selection as a Bilevel Optimization Problem

Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-points. From this cut-points vector, the decision of deleting or selecting any feature is induced. Indeed, the number of generated cut-points varies from one feature to another. Thus, the higher the number of cut-points, the higher the probability of selecting the considered feature; and vice versa. This fact leads to the deletion of possibly important features having a single or a low number of cut-points, such as the infection rate, the glycemia level, and the blood pressure. In order to solve the issue of the dependency relation between the feature selection (or removal) event and the number of its generated potential cut-points, we propose to model the DBFS task as a bilevel optimization problem and then solve it using an improved version of an existing co-evolutionary algorithm, named I-CEMBA. The latter ensures the variation of the number of features during the migration process in order to deal with the multimodality aspect. The resulting algorithm, termed bilevel discretization-based feature selection (Bi-DFS), performs selection at the upper level while discretization is done at the lower level. The experimental results on several high-dimensional datasets show that Bi-DFS outperforms relevant state-of-the-art methods in terms of classification accuracy, generalization ability, and feature selection bias