A Private Quantum Bit String Commitment

We propose an entanglement-based quantum bit string commitment protocol whose composability is proven in the random oracle model. This protocol has the additional property of preserving the privacy of the committed message. Even though this property is not resilient against man-in-the-middle attacks, this threat can be circumvented by considering that the parties communicate through an authenticated channel. The protocol remains secure (but not private) if we realize the random oracles as physical unclonable functions in the so-called bad PUF model with access before the opening phase.Comment: 8 pages, 4 figure

Modeling of a plasmonic biosensor based on a graphene nanoribbon superlattice

We present a semi-analytical theoretical model, which describes the operation of a selective molecular sensor [1] employing a double resonance between a dipole-active molecular vibration mode, tunable surface plasmons in a periodic structure of graphene nanoribbons (NRs), and the incident light, in the THz-to-IR range, used for testing. The model is based on the solution of Maxwell’s equa tions for the NR structure deposited on a dielectric substrate, using the electromagnetic Green’s function, and is extended to the case of an additional (buffer) layer present between the NRs and the substrate. Both the graphene NRs and the layer of adsorbed molecules are considered as two-dimensional, since their thicknesses are very small in comparison with the wavelength of the incident light. The model is applied to different molecular systems, the protein studied in Ref. [1], for which an excellent agreement with experimental data is obtained, and an organometallic molecule Cd(CH3)2. Two different assumptions concerning the way of sticking of the analyte molecules to the sensor’s surface are considered and the limitations of this sensing principles are discussed.Funding from the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Financing UID/FIS/04650/2019. Authors also acknowledge FEDER and the Portuguese Foundation for Science and Technology (FCT) for support through projects POCI-01-0145-FEDER-028114 and PTDC/FIS-MAC/28887/2017. MIV also acknowledges support from the European Commission through the project “Graphene-Driven Revolutions in ICT and Beyond”- Core 3 (Ref. No. 881603)

the case of Brazil

The authors are grateful for the participation of the people who sent photos of the localities affected by the oil spill disaster and are also grateful for the support of the Laboratory of Cartography of the Federal University of Rio de Janeiro (GeoCart-UFRJ). Specifically, Dra. Raquel Souto is grateful for the assistance granted by the Coordination for the Improvement of Higher Education Personnel, through the Brazilian National Post-doctoral Program, which made it possible to carry out this and other research on participatory mapping in the last three years. Publisher Copyright: © 2022, Academia Brasileira de Ciencias. All rights reserved.Many maritime disasters lead to oil pollution, which undermines ecosystem balance, human health, the prosperity of countries and coastal areas across borders, and people’s livelihoods. This is a problem that affects the whole world. Governments must strive to ensure that operations in the marine environment are safe and avoid oil pollution by adopting methods that anticipate future scenarios to mitigate the effects of this pollution when it occurs. This study investigates a method of managing contaminated coastal areas, aiming to contribute to the management of the environmental crisis caused by disasters through the use of online collaborative mapping by volunteer collaborators. Volunteer collaborators have been sending georeferenced data and photographs of locations affected by pollution.publishersversionpublishe

Smart human mobility in smart cities

Nowadays, society has challenged the scienti c community to nd solutions able to use technology to solve the gentri cation3 of city centers. Within this context, smart cities have had an important role because they view each citizen as a data source. In the same way, the Internet of Things network increases the number of physical devices generating peta-bytes of information into a Smart city architecture. Thus an appropriate Machine Learning approach is required to process and analyze collected data. In this paper, we apply three di erent Machine Learning techniques such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and a combined architecture, which we call CNN-LSTM, to the data generated by LinkNYC Kiosks devices | based on the city of New York |, and come to the conclusion the combined model gets better results in predicting human mobility

Representing human mobility patterns in urban spaces

Human mobility is important in understanding urban spaces. Citizens interact with urban spaces using the available infrastructures, not just in the mobility sector but in public services, and in Information and Communications Technology (ICT) services, that simultaneously record their footprints. Besides, the number of mobile users is increasing very rapidly in the Internet of Things (IoT) era. These additional devices will produce a great amount of data and create new big challenges for network infrastructure. Because of this new connectivity platform, and the fast growth of wireless communication, it’s important to discuss the arrival of 5G systems. They will have a large impact on coverage, spectral efficiency, data rate of global mobile traffic, and IoT devices, and in turn it will be possible to analyze the lifestyle and understand the mobility of people, such as the most frequently visited urban spaces. Therefore, this paper is relevant in the context of smart cities and will allow for an easy connection between citizens and technology innovation hub, acquiring detailed data on human movements. Based on the analysis of generated data we try to widen this view and present an integrated approach to the analysis of human mobility using LinkNYC kiosks and 311 Service Requests in New York city

WalkingStreet: understanding human mobility phenomena through a mobile application

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020