An app based on cooperative learning for the detection of danger points and the prevention of risk areas in a city

This article gives a general review of the presence of crime in today’s society, its impact in the daily life of the citizens and proposes the use of Safe Paths, a mobile application focused on risk prevention based on social collaboration and cooperative learning to identify dangerous areas and give alerts based on their users’ location and the risks around to them. It also describes some technical aspects of Safe Paths such as its architecture from the MVC approach; the use cases and actors involved in said application and finally shows its graphical user interface

Aplicaciones inteligentes sobre internet de las cosas y grandes volúmenes de datos: un enfoque riguroso

Día tras días se generan millones y millones de nuevos datos y la cantidad de información a procesar es un desafío creciente. Entre los más destacados podemos mencionar el crecimiento exponencial de la “Internet de las cosas” o Internet of Things (IoT) en inglés en los sistemas modernos. En este sentido, no sólo el almacenamiento constante de información es un problema a resolver, sino también la extracción inteligente de información y su posterior análisis para introducir mejoras en los sistemas. Este problema ha sido atacado desde la Inteligencia Artificial y la Optimización Combinatoria, pero se han encontrado algunas debilidades como la falta de modelado y diseño y de la aplicación rigurosa de técnicas de Ingeniería de Software. Esto hace que el problema se ataque de una manera “ad-hoc”, por lo cual es necesario consolidar un enfoque de mayor formalidad en todas las etapas del proceso. La presente investigación busca aplicar técnicas formales de Ingeniería de Software como Modelado y Model Checking al manejo y análisis de grandes volúmenes de datos. Como caso de estudio concreto se trabajará con sensores para el Mantenimiento de parámetros del ambiente (como humedad o temperatura) del Laboratorio de Redes y Sistemas de Computación mediante protocolos de IoT.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021.

Polarization, which refers to the formation of two opposing groups based on the users' beliefs and opinions, has a growing body of literature. However, social media polarization differs from offline polarization in that beliefs change almost instantaneously on social media as a result of events unfolding. We investigate the uses of social media communication that has resulted in polarized opinions among individuals prior to, during, and after the January 6th Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observed that polarization is a dynamic phenomenon: as an event unfolds, polarization changes, and knowing how it changes is important for timely crisis resolution. We propose three measures of polarization that could be used to examine polarization accurately during a crisis

Sentiment Analysis of Name Entity for Text

Abstract-Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of art technologies, we build up one flowchart to get sentiment for aspects of event. During the process, name entities with the same meaning are clustered and sentiment carrier are filtered. In this way sentiment can be got even user express feeling for the same object with different words

Exploring metro vibrancy and its relationship with built environment: a cross-city comparison using multi-source urban data

Recent urban transformations have led to critical reflections on the blighted urban infrastructures and called for re-stimulating vital urban places. Especially, the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration. To date, limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically. This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment. Massive smart card data is processed to extract metro ridership, which denotes the vibrancy around the metro station in physical space. Social media check-ins are crawled to measure the vitality of metros in virtual spaces. Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method. Certain built environment characteristics, including land use, transportation and buildings are modeled as independent variables. The significant influences of built environmental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model. With experiments conducted in Shenzhen, Singapore and London, this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated. The regression analysis suggests that in all the three cities, more affluent urban areas tend to have higher metro virbrancy, while the road density, land use and buildings tend to impact metro vibrancy in only one or two cities. These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts. These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future

The Adoption of social media analytics for crisis management – Challenges and Opportunities

We live in a time when anyone can change from a passive bystander to an active communicator during a crisis. This makes user-generated content a potentially valuable source of information for emergency management agencies. However, at present, agencies still hesitate to use social media during crises. This research seeks to identify the challenges emergency management agencies face in using social media analytics within their organisations. We conducted a systematic literature review and interviewed ten emergency management professionals across six expert interview sessions. Afterwards, we used the Technology-Organization-Environment Framework to conceptualise our findings. Our study reveals fruitful opportunities for the continuous collaboration of both information systems research and emergency management agencies. Accordingly, information systems research can support emergency management agencies in using social media data for efficient crisis management by enhancing awareness of the benefits of social media analytics and helping to overcome organisational and technological challenges

Social Media Communication by Local Governments and Its Implications for Urban Planning

Social media has altered traditional communication and enriched traditional social networks. In addition to its use for personal communication and business marketing, social media has also been proved to be a valuable tool for urban planners and managers. However, there are relatively few studies about how social media communication may inform the design of urban master plans. The objectives of the thesis are to understand how the city governments have used social media to engage with the general public on urban planning issues, and assess if social media contents can be used to inform urban planning. The 10 top digital cities with mid-range population size rated by the Center for Digital Government (CDG) were selected as study sites. A combination of statistical analyses and manual topic classification were used to reveal the patterns of the social media discussion. The outputs were then compared with the comprehensive plans of these cities. The results showed that (1) social media contents encompass a broad range of planning issues, and have been used as supplemental information to improve the comprehensive plans; (2) there is no statistical difference between Facebook and Twitter discussion on planning issues percentage-wise; and (3) Overall, the comprehensive plan provides more detailed and structured visions and strategies to address planning issues compared with fragmented social media discussion. Adviser: Zhenghong Tan