Multimodal Generic Framework for Multimedia Documents Adaptation

Today, people are increasingly capable of creating and sharing documents (which generally are multimedia oriented) via the internet. These multimedia documents can be accessed at anytime and anywhere (city, home, etc.) on a wide variety of devices, such as laptops, tablets and smartphones. The heterogeneity of devices and user preferences has raised a serious issue for multimedia contents adaptation. Our research focuses on multimedia documents adaptation with a strong focus on interaction with users and exploration of multimodality. We propose a multimodal framework for adapting multimedia documents based on a distributed implementation of W3C’s Multimodal Architecture and Interfaces applied to ubiquitous computing. The core of our proposed architecture is the presence of a smart interaction manager that accepts context related information from sensors in the environment as well as from other sources, including information available on the web and multimodal user inputs. The interaction manager integrates and reasons over this information to predict the user’s situation and service use. A key to realizing this framework is the use of an ontology that undergirds the communication and representation, and the use of the cloud to insure the service continuity on heterogeneous mobile devices. Smart city is assumed as the reference scenario

Leverage a Trust Service Platform for Data Usage Control in Smart City

In the Internet of Thing, data is almost collected, aggregated and analyzed without human intervention by machine-to-machine communications resulting in raising serious challenges on access control. Particularly in Smart City ecosystems in which multi-modal data comes from heterogeneous sources, data owners cannot imagine how their data is used to extract sensitive information. Thus, there is a critical need for novel access control methods that minimize privacy risks while increase ability of personalized access control. Our solution is to build a trust-based usage control mechanism called TUCON that enables stakeholders to set access control policies based on their trust relationships with data consumers. In this study, we introduce two novel paradigms integrated in the Smart City shared platform: a Trust Service Platform and a Data Usage Control, then bring them together to establish the new mechanism. The conceptual model, the architecture, the formalization, and the practical development of TUCON is described in detail. We also show the roles and the interactions of TUCON components in the Smart City platform. Our contributions lie in a new trust model with a trust computation procedure based on semantic web technologies, a novel trust-based usage control conceptual model including a formalization, a practical expression and an architecture for Smart City systems. We believe this study provides better understanding on both trust and usage control in the Internet of Things and opens several important research directions in the future

Influencing Factors on Consumers’ Willingness to Share Energy Data on Online Energy Platforms

Climate change requires an adaptation of the energy system towards an efficient use of renewable energies. For efficient control and optimization of the energy system, energy consumption and production data at household level play an essential role. Sharing platforms can enable the bundling and controlling of energy data from individual households. However, there is often a lack of acceptance among potential users to share their own data on such platforms. Therefore, this paper investigates the willingness of consumers to share their personal energy data. In particular, several factors that influence this willingness are examined. Decisive for the willingness are incentives for consumers in return for sharing their energy data. These can be offered in personal added value or collective added value. This paper shows that the factors perceived behavioral control, personal attitude and subjective norm have an influence on the willingness of private users to share energy data if a personal benefit or a collective benefit is provided. The age of users and their privacy concerns affect the willingness to share only in case personal value is added. These findings are valuable for the development and operation of online energy platforms

Using Artificial Intelligence to Improve Traffic flows, with Consideration of Data Privacy

USDOT Grant 69A3551747109This project develops an artificial neural network (ANN), a class of Artificial Intelligence (AI) systems, to accurately model and predict future delays at an intersection. Developing such modeling and prediction systems raises considerable data privacy concerns and it is incumbent upon municipal, state, and federal branches of government to prioritize citizens and their concerns before the implementation of new smart community technologies that are fueled by unprecedented levels of data collection. The technique proposed in this study identifies nonlinear, time-varying mapping between the inputs to the ANN and its output, the predicted delay. The traffic data measured at a Long Beach intersection with heavy truck traffic are used to build a realistic simulation in Vissim, a microscopic traffic flow simulator. The authors designed and performed experiments on the developed Vissim model to train the ANN delay predictor and validate the generalization ability of the predictor. The simulation results agree with the on-site delay measurements. This suggests the ANN predictor can accurately predict the delay at the intersection with heavy-truck penetration. Because smart technologies raise data privacy concerns, the research team led 32 study participants on \u201cdatawalks\u201d designed to gauge comfort levels and attitudes toward devices that collect personally identifiable information. Study participants encountered public WiFi routers, surveillance cameras, automated license plate readers and other surveillance technologies. They used a custom app to respond to prompts related to data collection, sharing and analysis. Study participants\u2019 responses, along qualitative data collected during a \u201cdebriefing\u201d conversation following each walk, provided insights into residents\u2019 attitudes toward smart communities technologies and identified privacy concerns. The quantitative and qualitative findings in this study inform a series of recommendations that research teams can follow to implement real-world test labs at busy truck intersections while fostering public trust, installing these modelling and prediction systems, and ensuring the overall safety and efficiency of the intersection\u2019s traffic flow

Managerial and Entrepreneurial Decision Making

Since the conceptualization of bounded rationality, management scholars started investigating how people—managers and entrepreneurs—really make decisions within (and for) organizations. The aim of this eBook is to deeply investigate trends that have flourished within this pivotal research area in conceptual and/or empirical terms, trying to provide new insights on how managers and entrepreneurs make decisions within and for organizations. In this vein, readers that approach this eBook will be taken by hand and accompanied to the discovery of how the mind of decision makers is at the basis of organizational developments or failures. In this regard, published contributions in this eBook underline how executives and entrepreneurs must be ecologically rational, thus be aware of the negative and positive effects that biases can have depending on the context and use them at their advantage. Managerial and entrepreneurial decision-making are phenomena that cannot be detached from the environment in which executives and entrepreneurs are embedded, claiming to establish new approaches to research that looks at decision-making as an individual/group/organization-environment dialectical and multi-level phenomenon

Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives

Background: Healthcare services in Canada are slowly shifting from in-hospital care to more patient-centred, home-care services. Collecting and sharing personal data from individuals via Internet of Things (IoT) devices is a critical part of this change that potentially leads to better decision-making and better support for patients from healthcare providers. However, there are challenges that come from using technology, including concerns around trust in organizations holding individuals’ data, as well as privacy and security related to data sharing that needs to be considered as part of this new model of care. Objective: This study seeks to investigate users' trust in sharing their data collected using healthcare IoT devices via different types of organizations. Methods: This research project leveraged a literature review and online questionnaires to understand how general users of IoT for Health trust different types of organizations (large companies, government, healthcare providers, and insurance companies). A total of 400 participants were recruited using Mechanical Turk for the online questionnaire, using a between-subjects design. Each participant answered questions about one type of organization, where a scenario related to the use of different IoT technologies, information about data sharing and a list of privacy concerns were presented. Based on this scenario, participants were asked to answer 16 trust-related questions. Results were analyzed using Analysis of Variance (ANOVA), followed by post-hoc comparisons using the pairwise t-test with the Bonferroni correction. Results: The study showed no significant differences in regards to privacy concerns (LConcern) in Canada, United States (USA), and Europe (F (2, 389) = 0.736, P = .480). Overall levels of trust (LTrust) in the USA varied significantly between large companies, government, healthcare providers, and insurance companies (F (3, 388) = 10.107, P < .05). The same results were observed in Canada with a significant difference between the four types of organizations (F (3, 125) = 6.882, P < .05), USA (F (3, 128) = 4.488, P =.05), and in Europe, as well (F (3, 127) = 4.451, P < 0.05). Conclusion: Initial evidence supports differences in users' perception of trust in healthcare IoT data sharing among the aforementioned types of organizations and levels of concern amongst users regarding privacy and data ownership. Differences in the perception of trust were also identified between the different regions of the participants. Future research using more specific types of organization and larger samples for each age group are needed to fill knowledge gaps. In addition, further research is also needed to understand how external factors can affect user’s levels of trust and acceptance of healthcare IoT with potential consequences for the implementation of new healthcare delivery models

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical