The privacy properties of Tor Browser and the privacy sensitivity of its user base preclude the collection of traditional telemetry and analytics to understand the problems users face. To address the lack of telemetry and analytics, we developed ReporTor, a plugin to facilitate anonymous, voluntary reporting of problems during naturalistic browsing via Tor Browser. We confirmed the utility and effectiveness of ReporTor by reporting the problems we encountered during a month of naturalistic web browsing via Tor Browser. Reports submitted via ReporTor enabled nuanced, in-depth analysis of the causes underlying the reported problems. Integrating ReporTor into Tor Browser can leverage its anonymous user-driven issue reporting to surface the challenges users encounter when visiting websites with Tor Browser. Analyzing and addressing the reports can enhance the user experience of Tor Browser for everyday web browsing.

3D representations of large-scale and urban scenes are crucial across various industries, including autonomous driving, urban planning, natural resource supervision and many more. Large-scale industrial reconstructions are inherently complex and multifaceted. Many existing surveys primarily focus on academic progressions and often neglect the intricate and diverse needs of industry. This survey aims to bridge this gap by providing a comprehensive analysis of 3D reconstruction methods, with a focus on industrial requirements such as scalability and integration of human interaction. Our approach involves utilizing Affinity Diagramming to systematically analyze qualitative data gathered from industrial partners. This methodology enables us to gain deep insights into how recent literature addresses these specific industrial needs. The survey encompasses various aspects, including input and reconstruction modalities, architectural models, datasets, evaluation metrics, and the incorporation of prior knowledge. We further discuss practical implications derived from our analysis, highlighting key considerations for future advancements in 3D reconstruction methods tailored for large-scale applications.

We present a methodology for co-designing a human-agent interface and a case study in online grooming detection for incorporating multiple-stakeholders perspectives into the design of human-agent platforms, with the goal of establishing a common dialogue between the stakeholders. We propose that iterative co-design techniques staged in a provotypical fashion could successfully capture and overcome tensions between stakeholders perspectives on how human-agent interactions ought to be manifested. Our initial work found that our iterative co-design framework could be successful in resolving these tensions. However, multiple design iterations that focus on finding compromises between stakeholders can lead to acceptance of novel interaction designs.

Misinformation has developed into a critical societal threat that can lead to disastrous societal consequences. Although fact-checking plays a key role in combating misinformation, relatively little research has empirically investigated work practices of professional fact-checkers. To address this gap, we conducted semi-structured interviews with 21 fact-checkers from 19 countries. The participants reported being inundated with information that needs filtering and prioritizing prior to fact-checking. The interviews surfaced a pipeline of practices fragmented across disparate tools that lack integration. Importantly, fact-checkers lack effective mechanisms for disseminating the outcomes of their efforts which prevents their work from fully achieving its potential impact. We found that the largely manual and labor intensive nature of current fact-checking practices is a barrier to scale. We apply these findings to propose a number of suggestions that can improve the effectiveness, efficiency, scale, and reach of fact-checking work and its outcomes.

We designed and developed Fakey, a game to improve news literacy and reduce misinformation spread by emulating a social media feed. We analyzed player interactions with articles in the feed collected over 19 months within a real-world deployment of the game. We found that Fakey is effective in priming players to be suspicious of articles from questionable sources. Players who interact with more articles in the game enhance their skills in spotting mainstream content, thus confirming the utility of Fakey for improving news literacy. Semi-structured interviews with those who played the game revealed that players find it simple, fun, and educational. The principles and mechanisms used by Fakey can inform the design of social media functionality to help people distinguish between credible and questionable content in their news feeds.

Fact checking by professionals is viewed as a vital defense in the fight against misinformation. While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation. To explore if this is the case, we conduct a data-driven study of misinformation on the Twitter platform, focusing on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowds response to popular misleading claims about COVID-19.In this work, we curate a dataset of false claims and statements that seek to challenge or refute them. We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments. Our findings show that professional fact-checking tweets have limited volume and reach. In contrast, we observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation. More importantly, we find contrasting differences in the way the crowd refutes tweets, some tweets appear to be opinions, while others contain concrete evidence, such as a link to a reputed source. Our work provides insights into how misinformation is organically countered in social platforms by some of their users and the role they play in amplifying professional fact checks. These insights could lead to development of tools and mechanisms that can empower concerned citizens in combating misinformation. The code and data can be found in this link.1

Purpose Security questions are one of the techniques used to recover forgotten passwords. However, security questions have both security and memorability limitations. To limit their security vulnerabilities, stronger answers need to be used. As serious games can motivate users to change their security behaviour, the purpose of this paper is to explore the features and functionalities that users would require in a serious game that educates them to provide stronger answers to security questions. Design/methodology/approach A lab study was conducted to collect users’ feedback on the desired game features and functionalities. In Stage 1, participants selected security questions/answers. In Stage 2, participants played a game and evaluated the usability and the provided features. Findings The main findings reveal that most participants found the current features and functionalities to be desirable; socially oriented functionalities (e.g. getting help from other players) did not seem desirable because users feared that their acquaintances could gain access to their security questions. Originality/value This research recommends that designers of serious games for security education should: use intrinsic rewards to motivate users to have a better learning experience; provide easier challenges during the training period and provide harder challenges only when the game determines that the users learned to play the game; and design their games for mobile devices because even users who usually do not play games would play a security education game on a mobile device.

A majority of Stroke survivors have an arm impairment (up to 80%), which persists over the long term (>12 months). Physiotherapy experts believe that a rehabilitation Aide-Memoire could help these patients [25]. Hence, we designed, with the input of physiotherapists, Stroke experts and former Stroke patients, the Aide-Memoire Stroke (AIMS) App to help them remember to exercise more frequently. We evaluated its use in a controlled field evaluation on a smartphone, tablet and smartwatch. Since one of the main features of the app is to remind Stroke survivors to exercise we also investigated reminder modalities (i.e., visual, vibrate, audio, speech). One key finding is that Stroke survivors opted for a combination of modalities to remind them to conduct their exercises. Also, Stroke survivors seem to prefer smartphones compared to other mobile devices due to their ease of use, usability, familiarity and being easier to handle with one arm.

One of the main reasons why smartphone users do not adopt screen locking mechanisms is due to the inefficiency of entering a PIN/pattern each time they use their phone. To address this problem we designed a context-sensitive screen locking application which asked participants to enter a PIN/pattern only when necessary, and evaluated its impact on efficiency and satisfaction. Both groups of participants, who prior to the study either locked or did not lock their phone, adopted our application and felt that unlocking their phone only when necessary was more efficient, did not annoy them and offered a reasonable level of security. Participants responded positively to the option of choosing when a PIN/pattern is required in different contexts. Therefore, we recommend that designers of smartphone locking mechanisms should consider ceding a reasonable level of control over security settings to users to increase adoption and convenience, while keeping smartphones reasonably secure.

Modern mobile devices come with an array of sensors that support many interesting applications. However, sensors have different sampling costs (e.g., battery drain) and benefits (e.g., accuracy) under different circumstances. In this work we investigate the trade-off between the cost of using a sensor and the benefit gained from its use, with application to data-driven authentication on mobile devices. Current authentication practice, where user behaviour is first learned from the sensor data and then used to detect anomalies, typically assumes a fixed sampling rate and does not consider the battery consumption and usefulness of sensors. In this work we study how battery consumption and sensor effectiveness (e.g., for detecting attacks) vary when using different sensors and different sensor sampling rates. We use data from both controlled lab studies, as well as field trials, for our experiments. We also propose an adaptive sampling technique that adjusts the sampling rate based on an expected device vigilance level. Our results show that it is possible to reduce the battery consumption tenfold without significantly impacting the detection of attacks.

Current methods of behavioral data collection from mobile devices either require significant involvement from participants to verify the 'ground truth' of the data, or approximations that involve post-experiment comparisons to seed data. In this paper we argue that user involvement can be gracefully reduced by performing more intelligent seed comparisons. We aim to reduce the participant involvement to the 'most interesting' temporal slots, both during the experiment and in post-experiment verification. We carried out a 2 week study with 4 users, consisting of an initial opportunistic gathering of mobile sensor data. Our findings suggest that by using such a method we can significantly reduce user involvement.