Background: Alcohol use is associated with an automatic tendency to approach alcohol, and the retraining of this tendency (cognitive bias modification [CBM]) shows therapeutic promise in clinical settings. To improve access to training and to enhance participant engagement, a mobile version of alcohol avoidance training was developed.Objective: The aims of this pilot study were to assess (1) adherence to a mobile health (mHealth) app; (2) changes in weekly alcohol use from before to after training; and (3) user experience with regard to the mHealth app.Methods: A self-selected nonclinical sample of 1082 participants, who were experiencing problems associated with alcohol, signed up to use the alcohol avoidance training app Breindebaas for 3 weeks with at least two training sessions per week. In each training session, 100 pictures (50 of alcoholic beverages and 50 of nonalcoholic beverages) were presented consecutively in a random order at the center of a touchscreen. Alcoholic beverages were swiped upward (away from the body), whereas nonalcoholic beverages were swiped downward (toward the body). During approach responses, the picture size increased to mimic an approach movement, and conversely, during avoidance responses, the picture size decreased to mimic avoidance. At baseline, we assessed sociodemographic characteristics, alcohol consumption, alcohol-related problems, use of other substances, self-efficacy, and craving. After 3 weeks, 37.89% (410/1082) of the participants (posttest responders) completed an online questionnaire evaluating adherence, alcohol consumption, and user satisfaction. Three months later, 19.03% (206/1082) of the participants (follow-up responders) filled in a follow-up questionnaire examining adherence and alcohol consumption.Results: The 410 posttest responders were older, were more commonly female, and had a higher education as compared with posttest dropouts. Among those who completed the study, 79.0% (324/410) were considered adherent as they completed four or more sessions, whereas 58.0% (238/410) performed the advised six or more training sessions. The study identified a significant reduction in alcohol consumption of 7.8 units per week after 3 weeks (95% CI 6.2-9.4, P<.001; n=410) and another reduction of 6.2 units at 3 months for follow-up responders (95% CI 3.7-8.7, P<.001; n=206). Posttest responders provided positive feedback regarding the fast-working, simple, and user-friendly design of the app. Almost half of the posttest responders reported gaining more control over their alcohol use. The repetitious and nonpersonalized nature of the intervention was suggested as a point for improvement.Conclusions: This is one of the first studies to employ alcohol avoidance training in a mobile app for problem drinkers. Preliminary findings suggest that a mobile CBM app fulfils a need for problem drinkers and may contribute to a reduction in alcohol use. Replicating these findings in a controlled study is warranted.
Augmented reality (AR) has moved into the spotlight of technological developments to enhance tourist experiences, presenting a need to develop meaningful AR applications. However, few studies so far have focused on requirements for a user-centric AR application design. The study aims to propose a method on translating psychological and behavioral indicators of users into relevant technical design elements for the development of mobile AR tourism applications in the context of urban heritage tourism. The research was conducted in three phases to generate a quality function deployment (QFD) model based on interviews, focus groups and questionnaires of international tourists and industry professionals. Key categories, content requirements, function requirements, and user resistance were defined for the identification of requirements. The outcomes of the study outline tourist requirements based on behavioral and psychological indicators and propose a method for translating them into technical design elements for tourist mobile AR applications.
Cozmo is a real-life robot designed to interact with people playing games, making sounds, expressing emotions on a LCD screen and many other pre-programmable functions. We present the development and implementation of an educational platform for Cozmo mobile robot, with several features, including web server for user interface, computer vision, voice recognition, robot trajectory tracking control, among others. Functions for educational purposes were implemented, including mathematical operations, spelling, directions, and questions functions that gives more flexibility for the teachers to create their own scripts. In this system, a cloud voice recognition tool was implemented to improve the interactive system between Cozmo and the users. Also, a cloud computing vision system was used to perform object recognition using Cozmo's camera, to be applied on educational games. Other functions were created with the purpose of controlling the emotions and the motors of Cozmo to create more sophisticated scripts. To apply the functions on Cozmo robot, an interpreter algorithm was developed to translate the functions into Cozmo's programming language. To validate this work, the proposed framework was presented to several elementary school teachers (classes with students between 4 and 12). Students and teacher's impressions are reported in this text, and indicate that the proposed system can be a useful educational tool.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
This project addresses the fundamental societal problem that encryption as a technique is available since decades, but has never been widely adopted, mostly because it is too difficult or cumbersome to use for the public at large. PGP illustrates this point well: it is difficult to set-up and use, mainly because of challenges in cryptographic key management. At the same time, the need for encryption has only been growing over the years, and has become an urgent problem with stringent requirements – for instance for electronic communication between doctors and patients – in the General Data Protection Regulation (GDPR) and with systematic mass surveillance activities of internationally operating intelligence agencies. The interdisciplinary project "Encryption for all" addresses this fundamental problem via a combination of cryptographic design and user experience design. On the cryptographic side it develops identity-based and attribute-based encryption on top of the attribute-based infrastructure provided by the existing IRMA-identity platform. Identity-based encryption (IBE) is a scientifically well-established technique, which addresses the key management problem in an elegant manner, but IBE has found limited application so far. In this project it will be developed to a practically usable level, exploiting the existing IRMA platform for identification and retrieval of private keys. Attribute-based encryption (ABE) has not reached the same level of maturity yet as IBE, and will be a topic of further research in this project, since it opens up attractive new applications: like a teacher encrypting for her students only, or a company encrypting for all employees with a certain role in the company. On the user experience design side, efforts will be focused on making these encryption techniques really usable (i.e., easy to use, effective, efficient, error resistant) for everyone (e.g., also for people with disabilities or limited digital skills). To do so, an iterative, human-centred and inclusive design approach will be adopted. On a fundamental level, scientific questions will be addressed, such as how to promote the use of security and privacy-enhancing technologies through design, and whether and how usability and accessibility affect the acceptance and use of encryption tools. Here, theories of nudging and boosting and the unified theory of technology acceptance and use (known as UTAUT) will serve as a theoretical basis. On a more applied level, standards like ISO 9241-11 on usability and ISO 9241-220 on the human-centred design process will serve as a guideline. Amongst others, interface designs will be developed and focus groups, participatory design sessions, expert reviews and usability evaluations with potential users of various ages and backgrounds will be conducted, in a user experience and observation laboratory available at HAN University of Applied Sciences. In addition to meeting usability goals, ensuring that the developed encryption techniques also meet national and international accessibility standards will be a particular point of focus. With respect to usability and accessibility, the project will build on the (limited) usability design experiences with the mobile IRMA application.
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
