NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Embedded Based Smart ICU-For Intelligent Patient Monitoring

Smart ICUs are networks of audio-visual communication and computer systems that link critical care doctors and nurses (intensivists) to intensive care units (ICUs) in other, remote hospitals. The intensivists in the “command center” can communicate by voice with the remote ICU personnel and can receive video communication and clinical data about the patients. Direct patient care is provided by the doctors and nurses in the remote ICU who do not have to be intensivists themselves. In recent years there has been an increase in the number of patients needing ICU care without a corresponding increase in the supply of intensivists. Smart ICUs can be a valuable resource for hospitals faced with the need to expand capacity and improve care for a growing elderly population. Evidence from some early-adopter hospitals indicates that it can leverage management of patient care by intensivists, reduce mortality rates, and reduce LOS. However, positive outcomes appear to depend on the organizational environment into which the Smart ICU is introduced. The dramatic improvements in mortality and LOS reported by some early-adopter hospitals have not been matched in most. The limited research available suggests that the best outcomes may occur in ICUs that: Can make organizational arrangements to support the management of patient care by intensivists using Smart ICU; Have little or no intensivist staff available to them in the absence of Smart ICU; Have relatively high severity-adjusted mortality and LOS rates; Are located in remote or rural areas where safe and efficient transfer of patients to regional centers for advanced critical care presents difficulties. Smart ICU connects a central command center staffed by intensivists with patients in distant ICUs. Continuous, real-time audio, video, and electronic reports of vital signs connect the command center to the patients’ bedsides. Computer-managed decision support systems track each patient’s status and give alerts when negative trends are detected and when changes in treatment patterns are scheduled. The patient data include physiological status (e.g., ECG and blood oxygenation), treatment (e.g., the infusion rate for a specific medicine or the settings on a respirator), and medical records.

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures

From Attention to Engagement: The Transformation of the Content Industry

This presentation was given by Lear Center Director Martin Kaplan at a public forum in Barcelona. The event was sponsored by the Barcelona Media Center