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Purpose: We evaluated the responses to chromatic perception (wavelength) stimuli in the visible spectrum among children from an elementary school in Nezahualcoyotl Country, Mexico. Materials and Methods: A software developed ad hoc was used to measure how children perceived the colors blue, green, yellow, and red. The tests were in monocular form, and responses were pooled based on their similarity according to the Pearson correlation index and cluster analysis by unweighted averages. A chi-square test was applied to the groups for significant differences. Results: Data from each wavelength were analyzed in relation to age, sex, and ocular laterality. At a wavelength of 450 nm, children aged 6-7 years old (group 1) of both sexes perceived the wavelength as skewed to the left -9.39 nm, while children aged 8- 11 years old (group 2) visualized a leftward skew of -4.40 nm (P < 0.0001). Then, at 530 nm, shifts of -5.39 and -6.72 nm were reported in the groups 1 and 2 (P < 0.0001), while at 580 nm, the shifts were -2.59 and -2.62 nm for both groups (P < 0.0001). Finally, at 710 nm, the shifts were +2.49 and +2.74 nm (P < 0.0001). Conclusion: At 450 nm, younger children perceived wavelengths far from normal, but, as they aged, their perceptions normalized. At 530 nm, childre's perceptions shifted to the left in all cases, getting closer to normality. Finally, at 580 nm, the shift was still leftward but moved to the right at 710 nm in all cases. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things paradigm promises a multi-disciplinary revolution covering different spheres of our daily lives. However, the ubiquitous nature of IoT requires inclusive approaches in order to agree on a common understanding about its implications. Particularly, in order to unlock its huge potential and maximize its benefits, it is necessary to minimize the risks that are associated with security and privacy concerns. In this work, we propose a comprehensive architectural design to capture the main security and privacy requirements during the lifecycle of a smart object. The resulting architecture has been designed, instantiated, and implemented within the scope of different European IoT initiatives, in order to promote the design and development of secure and privacy-aware IoT-enabled services. [ABSTRACT FROM AUTHOR]

Recent advances on ubiquitous computing and communication technologies are enabling a seamless integration of smart devices in the Internet infrastructure, promoting a new generation of innovative and valuable services for people. Nevertheless, the potential of this resulting ecosystem may be threatened if security and privacy concerns are not properly addressed. In this work, we propose an ARM-compliant IoT security framework and its application on smart buildings scenarios, integrating contextual data as fundamental component in order to drive the building management and security behavior of indoor services accordingly. This framework is instantiated on a holistic platform called City explorer, which is extended with discovery and security mechanisms. Such platform has been validated in a reference smart building, where reasonable results of energy savings, services discovery and authorization are achieved. [ABSTRACT FROM AUTHOR]

The growing availability of mobile devices has lead to an arising development of smart cities services that share a huge amount of (personal) information and data. Without accurate and verified management, they could become severe back-doors for security and privacy. In this paper, we propose a smart city infrastructure able to integrate a distributed privacy-preserving identity management solution based on attribute-based credentials (p-ABC), a user-centric Consent Manager, and a GDPR-based Access Control mechanism so as to guarantee the enforcement of the GDPR's provisions. Thus, the infrastructure supports the definition of specific purpose, collection of data, regulation of access to personal data, and users' consents, while ensuring selective and minimal disclosure of personal information as well as user's unlinkability across service and identity providers. The proposal has been implemented, integrated, and evaluated in a fully-fledged environment consisting of MiMurcia, the Smart City project for the city of Murcia, CaPe, an industrial consent management system, and GENERAL_D, an academic GDPR-based access control system, showing the feasibility. [ABSTRACT FROM AUTHOR]

Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future. [ABSTRACT FROM AUTHOR]