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

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

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

Simulation for digital manufacturing

Digitalisation has been among the most-often discussed developments of our modern society for decades and it increasingly stretches to manufacturing. Industrial processes merge with information technologies, accelerated by rapidly increasing amount of data and newly developed smart algorithms. This thesis focuses on demands of digital manufacturing and a neutral evaluation of smart algorithms. Digitalisation is a vast field. Various solutions have been suggested lately and establish further continuously. Companies feel increasingly pressured to amend their structures to smart and agile factories. These wide-spanning refurbishments often lack concrete objectives and clear target figures for successful implementation. This limits the clarity for comparing different solutions. Deriving from a discussion on purposes of digitalisation, simulation and calculation models have been established to evaluate and rate the most valuable approaches. A test and development system is established, which is suitable to compare different smart production IT solutions. Based on this practical case, a concrete evaluation is described. An exemplary production line is evaluated to find requirements for improved flexibility. After a critical discussion about the suitability of the suggested solution, assistance systems and mathematical models are introduced with which development and optimisation of smart production structures can be implemented in a given manufacturing system.Digitalisierung gehört seit Jahrzehnten zu den am häufigsten diskutierten Entwicklungen unserer heutigen Gesellschaft und erstreckt sich zunehmend auch auf die Produktion. Industrielle Prozesse verbinden sich mit Informationstechnik, beschleunigt durch rasant steigende Datenmengen und neu entwickelte, smarte Algorithmen. Diese Arbeit fokussiert sich auf die Anforderungen digitaler Fertigung und eine neutrale Bewertung smarter Algorithmen. Digitalisierung ist ein breites Feld. Verschiedene Lösungen wurden zuletzt vorgeschlagen und entwickeln sich kontinuierlich weiter. Unternehmen stehen zunehmend unter Druck, ihre Strukturen zu smarten und agilen Fabriken zu entwickeln. Diese weitreichenden Erneuerungen lassen oft konkrete Ziele und klare Zielvorgaben für eine erfolgreiche Implementierung vermissen. Dies reduziert die Klarheit im direkten Vergleich verschiedener Lösungen. Ausgehend von einer Diskussion über den Zweck der Digitalisierung, wurden Simulations- und Berechnungsmodelle entwickelt um vielversprechende Anwendungen zu bewerten und zu klassifizieren. Ein Test- und Entwicklungssystem wurde eingerichtet, um verschiedene smarte IT-Lösungen im Produktionsumfeld vergleichen zu können. Nach einer kritischen Diskussion, in wie fern die vorgeschlagene Lösung geeignet ist, werden Assistenzsysteme und mathematische Modelle vorgestellt, die die Entwicklung und Optimierung smarter Produktionsstrukturen für ein gegebenes Fertigungssystem unterstützt

Sentiment analysis and resources for informal Arabic text on social media

Online content posted by Arab users on social networks does not generally abide by the grammatical and spelling rules. These posts, or comments, are valuable because they contain users’ opinions towards different objects such as products, policies, institutions, and people. These opinions constitute important material for commercial and governmental institutions. Commercial institutions can use these opinions to steer marketing campaigns, optimize their products and know the weaknesses and/ or strengths of their products. Governmental institutions can benefit from the social networks posts to detect public opinion before or after legislating a new policy or law and to learn about the main issues that concern citizens. However, the huge size of online data and its noisy nature can hinder manual extraction and classification of opinions present in online comments. Given the irregularity of dialectal Arabic (or informal Arabic), tools developed for formally correct Arabic are of limited use. This is specifically the case when employed in sentiment analysis (SA) where the target of the analysis is social media content. This research implemented a system that addresses this challenge. This work can be roughly divided into three blocks: building a corpus for SA and manually tagging it to check the performance of the constructed lexicon-based (LB) classifier; building a sentiment lexicon that consists of three different sets of patterns (negative, positive, and spam); and finally implementing a classifier that employs the lexicon to classify Facebook comments. In addition to providing resources for dialectal Arabic SA and classifying Facebook comments, this work categorises reasons behind incorrect classification, provides preliminary solutions for some of them with focus on negation, and uses regular expressions to detect the presence of lexemes. This work also illustrates how the constructed classifier works along with its different levels of reporting. Moreover, it compares the performance of the LB classifier against Naïve Bayes classifier and addresses how NLP tools such as POS tagging and Named Entity Recognition can be employed in SA. In addition, the work studies the performance of the implemented LB classifier and the developed sentiment lexicon when used to classify other corpora used in the literature, and the performance of lexicons used in the literature to classify the corpora constructed in this research. With minor changes, the classifier can be used in domain classification of documents (sports, science, news, etc.). The work ends with a discussion of research questions arising from the research reported

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

WICC 2017 : XIX Workshop de Investigadores en Ciencias de la Computación

Actas del XIX Workshop de Investigadores en Ciencias de la Computación (WICC 2017), realizado en el Instituto Tecnológico de Buenos Aires (ITBA), el 27 y 28 de abril de 2017.Red de Universidades con Carreras en Informática (RedUNCI