Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

Why not empower knowledge workers and lifelong learners to develop their own environments?

In industrial and educational practice, learning environments are designed and implemented by experts from many different fields, reaching from traditional software development and product management to pedagogy and didactics. Workplace and lifelong learning, however, implicate that learners are more self-motivated, capable, and self-confident in achieving their goals and, consequently, tempt to consider that certain development tasks can be shifted to end-users in order to facilitate a more flexible, open, and responsive learning environment. With respect to streams like end-user development and opportunistic design, this paper elaborates a methodology for user-driven environment design for action-based activities. Based on a former research approach named 'Mash-Up Personal Learning Environments'(MUPPLE) we demonstrate how workplace and lifelong learners can be empowered to develop their own environment for collaborating in learner networks and which prerequisites and support facilities are necessary for this methodology

Disparity between the Programmatic Views and the User Perceptions of Mobile Apps

User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty - B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives

At Ease with Your Warnings: The Principles of the Salutogenesis Model Applied to Automatic Static Analysis

The results of an automatic static analysis run can be overwhelming, especially for beginners. The overflow of information and the resulting need for many decisions is mentally tiring and can cause stress symptoms. There are several models in health care which are designed to fight stress. One of these is the salutogenesis model created by Aaron Antonovsky. In this paper, we will present an idea on how to transfer this model into a triage and recommendation model for static analysis tools and give an example of how this can be implemented in FindBugs, a static analysis tool for Java.Comment: 5 pages, 4 figure

Space assets and technology for bushfire management

The financial, emotional, and ecological impacts of bushfires can be devastating. This report was prepared by the participants of the Southern Hemisphere Space Studies Program 2021 in response to the topic: “How space assets and technologies can be applied to better predict and mitigate bushfires and their impacts.” To effectively reach the diverse set of stakeholders impacted by bushfires, Communication was identified as a key enabler central to any examination of the topic. The three pillars “predict”, “mitigate” and “communicate” were identified to frame the task at hand. Combining the diverse skills and experience of the class participants with the interdisciplinary knowledge gained from the seminars, distinguished lectures, and workshops during the SHSSP21 program, conducted a literature review With specific reference to the 2019-20 Australian fire season, we looked at the current state of the art, key challenges, and how bushfires can be better predicted and mitigated in the future. Comparing this to the future desired state, we identified gaps for each of the three domains, and worked across teams to reach consensus on a list of recommendations. Several of these recommendations were derived independently by two or more of the three groups, highlighting the importance of a holistic and collaborative approach. The report details a number of recommendations arising from this Where applicable, we also aligned our discussion with the experience and lessons from other countries and agencies to consider,learn from and respond to the international context, as others develop systems using space technology to tackle similar wildfire issues

All the wiser: Fake news intervention using user reading preferences

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ