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2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

Virtualization plays a key role in the area of Mobile Cloud Computing (MCC). In MCC, the protection of distributed VMs and mobile users’ sensitive data, in terms of security and privacy, is highly required. This paper presents a novel cloud proxy known as Three Policies Secure Cloud Proxy (Proxy-3S) that combines three security policies: VM users’ access control, VMs’ secure allocation and VMs’ secure communication. The proposed approach aims to keep the distributed VMs safe in different servers on the cloud. It enhances the access authorization to permit intensive distributed application tasks on the cloud or mobile devices while processing and communicating private information between VMs. Furthermore, an algorithm that enables secure communication among distributed VMs and protection of sensitive data in VMs on the cloud is proposed. Several experiments were conducted using a real-world healthcare distributed application. The experiments achieved promising results for high-level data protection and good efficiency rating compared to existing works.

2022 ◽  
Vol 3 (1) ◽  
pp. 1-30
Ajay Krishna ◽  
Michel Le Pallec ◽  
Radu Mateescu ◽  
Gwen Salaün

Consumer Internet of Things (IoT) applications are largely built through end-user programming in the form of event-action rules. Although end-user tools help simplify the building of IoT applications to a large extent, there are still challenges in developing expressive applications in a simple yet correct fashion. In this context, we propose a formal development framework based on the Web of Things specification. An application is defined using a composition language that allows users to compose the basic event-action rules to express complex scenarios. It is transformed into a formal specification that serves as the input for formal analysis, where the application is checked for functional and quantitative properties at design time using model checking techniques. Once the application is validated, it can be deployed and the rules are executed following the composition language semantics. We have implemented these proposals in a tool built on top of the Mozilla WebThings platform. The steps from design to deployment were validated on real-world applications.

2021 ◽  
Vol 111 (11) ◽  
pp. 3540-3574
Abhijit Banerjee ◽  
Emily Breza ◽  
Arun G. Chandrasekhar ◽  
Markus Mobius

The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naïve learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent’s social influence in this generalized DeGroot model is essentially proportional to the degree-weighted share of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We show information aggregation preserves “wisdom” in the sense that initial signals are weighed approximately equally in a model of network formation that captures the sparsity, clustering, and small-world properties of real-world networks. We also identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. Simulating the modeled learning process on a set of real-world networks, we find that there is on average 22.4 percent information loss in these networks. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real-world network data show that with clustered seeding, information loss climbs to 34.4 percent. (JEL D83, D85, Z13)

2022 ◽  
Vol 8 (1) ◽  
pp. 1-30
Xinyu Ren ◽  
Seyyed Mohammadreza Rahimi ◽  
Xin Wang

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

2021 ◽  
Robert A. Cortes ◽  
Emily Grossnickle Peterson ◽  
David J. M. Kraemer ◽  
Robert A. Kolvoord ◽  
David Uttal ◽  

Assessing whether learning in one domain is transferable to abilities in other domains often eludes traditional testing. Thus, a question with bearing on the promise of neuroscience for education is whether neural changes that accompany in-school curriculum learning can improve prediction of learning transfer. Separately, debate in philosophy and psychology has long concerned whether spatial processes underlie seemingly nonspatial/verbal human reasoning (e.g., mental model theory; MMT). If so, education that fosters spatial cognition might yield transfer to improved verbal reasoning. Here, in real-world classrooms studied in a quasi-experimental design, a STEM curriculum devised to foster spatial cognition yielded improved spatial abilities and-consistent with MMT-transferred beyond the spatial domain to improved verbal reasoning. Further supporting MMT, the more students’ spatial ability improved, the more their verbal reasoning improved, and spatial ability improvement mediated curriculum transfer. At the neural level, longitudinal fMRI detected curriculum-driven changes in activity, connectivity, and representational similarity of brain regions implicated in spatial cognition. Critically, changes in spatial cognition-linked neural activity robustly predicted curriculum transfer-more accurately than testing and grades-and mediated this transfer. Reports by the National Research Council and others note that spatial abilities reliably predict STEM achievement, but that broad adoption of spatial cognition-focused curricula depends on classroom-based evidence of efficacy and mechanisms-of-change. The present findings support the real-world application of MMT to classrooms via “spatial education.” Further, demonstrating that in-school neural change can predict transfer over-and-above performance-based assessment suggests the long-term achievability of neurally-informed curriculum development that leverages neural change to identify and design transferable curricula.

Alex Simpson ◽  
Sreeram V Ramagopalan

In this month’s round up, we discuss a number of recent publications and guidelines addressing the use of real-world evidence to evaluate the clinical benefit of health technology assessments and what the publications mean practically for manufacturers.

Sheng Liu ◽  
Zuo-Jun Max Shen ◽  
Xiang Ji

Problem definition: We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which are made available by smart city infrastructure, such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. Academic/practical relevance: As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. Methodology: We formalize the bike lane planning problem in view of the cyclists’ utility functions and derive an integer optimization model to maximize the utility. To capture cyclists’ route choices, we develop a bilevel program based on the Multinomial Logit model. Results: We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route-choice-based planning model as a mixed-integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Managerial implications: Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists’ route choices. The proposed framework drives the data-driven urban-planning scheme in smart city operations management.

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