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Background: Vascular access-related infection (VARI) is a serious issue in pediatric end-stage kidney disease (ESKD) patients on regular hemodialysis. Objective: To estimate the frequency of vascular access-related infections in hemodialysis (HD) children and to assess the effect of the implementation of CDC guidelines and tools on the prevention of VARI. Subjects and Methods: This study was conducted on 110 patients with ESKD on regular hemodialysis at the Pediatric Dialysis Unit, Ain Shams University, from August 2019 to September 2021, retrospectively for one year and prospectively for another year after implementing the CDC infection control procedures. Besides full history taking, vascular access history, and thorough clinical examination, lab investigations for VARI diagnosis and echocardiography were performed. CDC infection control procedures were implemented for one year, and VARI was re-evaluated. Results: The total number of patients enrolled in the study was 110, they were 58 males (52.7%) and 52 females (47.3%). The incidence of infection per 1000 vascular access days in both years was highest in patients with NTVCs (10.447 /1000 NTVC days, 6.861 /1000 NTVCs days), followed by patients with TVCs (1.051/1000 TVC days, 0.604 /1000 TVC days) then patients with AVF, (0.141/1000 AVF days, 0.046 /1000 AVF days). The mortality from vascular access-related infections in the first year was higher than in the second year, with 5 patients (10.2%) and 1 patient (3.8%), respectively. The average duration of hospital stays (PPY) due to VARI (total admission days 8.09 and ward admission days 6.41) in the first year were significantly higher than in the second year, 4.04, 3.20 days/PPY, respectively. Conclusion: The incidence of vascular access infection (VAI) is higher with non-tunneled catheters and lowest with Arteriovenous fistulae (AVF). To reduce the risk of VARI and hospitalization, proper infection control procedures must be implemented.

Although eye tracking brings many benefits to users of mobile devices and developers of mobile applications, it poses significant privacy risks to both: the users of mobile devices, and the bystanders that surround users, are within the front-facing camera's field of view. Recent research demonstrates that tracking an individual's gaze reveals personal and sensitive information. This paper presents an investigation of the privacy perceptions and the subjective acceptance of users towards eye tracking on handheld mobile devices. In a four-phase user study (N=17), participants used a smartphone eye tracking app, were interviewed before and after viewing a video showing the amount of sensitive and personal data that could be derived from eye movements, and had their privacy concerns measured. Our findings 1) show factors that influence users' and bystanders' attitudes toward eye tracking on mobile devices such as the algorithms' transparency and the developers' credibility and 2) support designing mechanisms to allow for privacy-aware eye tracking solutions on mobile-devices.

Thermal attacks are an emerging threat that enables the reconstruction of user input after interaction with a device by analysing heat traces. There are several ways to protect users from thermal attacks that require different degrees of user involvement. In this paper, we first present a structured literature review to identify 15 protection strategies. Then, we investigate user perceptions of these strategies in an online study (N = 306). Our results show that users intuitively use protection strategies that also work against other side-channel attacks. Further, users are willing to sacrifice convenience for the sake of verifying a strategy's efficacy. Yet, an ideal holistic defence from thermal attacks is one that is readily integrated into user interfaces by manufacturers in a way that the user can verify it. Further, users like resourceless strategies that fit their habits. We use the literature review and study results to identify a user-centred design space for thermal attack protection. We conclude the paper with specific recommendations for users, device manufacturers and interface providers to better protect individuals from thermal attacks.

Gaze is promising for hands-free interaction on mobile devices. However, it is not clear how gaze interaction methods compare to each other in mobile settings. This paper presents the first experiment in a mobile setting that compares three of the most commonly used gaze interaction methods: Dwell time, Pursuits, and Gaze gestures. In our study, 24 participants selected one of 2, 4, 9, 12 and 32 targets via gaze while sitting and while walking. Results show that input using Pursuits is faster than Dwell time and Gaze gestures especially when there are many targets. Users prefer Pursuits when stationary, but prefer Dwell time when walking. While selection using Gaze gestures is more demanding and slower when there are many targets, it is suitable for contexts where accuracy is more important than speed. We conclude with guidelines for the design of gaze interaction on handheld mobile devices.

Gaze is promising for natural and spontaneous interaction with public displays, but current gaze-enabled displays require movement-hindering stationary eye trackers or cumbersome head-mounted eye trackers. We propose and evaluate GazeCast - a novel system that leverages users' handheld mobile devices to allow gaze-based interaction with surrounding displays. In a user study (N = 20), we compared GazeCast to a standard webcam for gaze-based interaction using Pursuits. We found that while selection using GazeCast requires more time and physical demand, participants value GazeCast's high accuracy and flexible positioning. We conclude by discussing how mobile computing can facilitate the adoption of gaze interaction with pervasive displays.

Recently, quadruped robots have been well developed with potential applications in different areas, such as care homes, hospitals, and other social areas. To ensure their integration in such social contexts, it is essential to understand people's proxemic preferences around such robots. In this paper, we designed a human-quadruped-robot interaction study (N = 32) to investigate the effect of 1) different facing orientations and 2) the gaze of a moving robot on human proxemic distance. Our work covers both static and dynamic interaction scenarios. We found a statistically significant effect of both the robot's facing direction and its gaze on preferred personal distances. The distances humans established towards certain robot behavioral modes reflect their attitudes, thereby guiding the design of future autonomous robots.

Enabling gaze interaction in real-time on handheld mobile devices has attracted significant attention in recent years. An increasing number of research projects have focused on sophisticated appearance-based deep learning models to enhance the precision of gaze estimation on smartphones. This inspires important research questions, including how the gaze can be used in a real-time application, and what type of gaze interaction methods are preferable under dynamic conditions in terms of both user acceptance and delivering reliable performance. To address these questions, we design four types of gaze scrolling techniques: three explicit technique based on Gaze Gesture, Dwell time, and Pursuit; and one implicit technique based on reading speed to support touch-free, page-scrolling on a reading application. We conduct a 20-participant user study under both sitting and walking settings and our results reveal that Gaze Gesture and Dwell time-based interfaces are more robust while walking and Gaze Gesture has achieved consistently good scores on usability while not causing high cognitive workload., Comment: Accepted by ETRA 2023 as Full paper, and as journal paper in Proceedings of the ACM on Human-Computer Interaction

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications., Comment: 37 Pages, Paper accepted by ACM Computing Surveys

Thermal attacks refer to the possibility of capturing heat traces that result from interacting with user interfaces to reveal sensitive input, such as passwords. The technical feasibility and effectiveness of thermal attacks have already been demonstrated. Yet, several preconditions have to be met for successful thermal attacks. In this paper, we investigate user awareness of thermal attacks and to which extent the attack's preconditions are met in the users' daily lives. We present results from an online study with 101 participants showing that users are frequently at risk of thermal attacks based on their behavior, e.g., due to leaving devices unattended, or their choice of authentication method. Further, only 7 of our 101 participants had heard of thermal attacks. Based on our results, we discuss the implications on user security, operators of public spaces, and the development of thermal attack-resistant input methods.

We explore how attackers behave during shoulder surfing. Unfortunately, such behavior is challenging to study as it is often opportunistic and can occur wherever potential attackers can observe other people's private screens. Therefore, we investigate shoulder surfing using virtual reality (VR). We recruited 24 participants and observed their behavior in two virtual waiting scenarios: at a bus stop and in an open office space. In both scenarios, avatars interacted with private screens displaying different content, thus providing opportunities for shoulder surfing. From the results, we derive an understanding of factors influencing shoulder surfing behavior.

A significant drawback of text passwords for end-user authentication is password reuse. We propose a novel approach to detect password reuse by leveraging gaze as well as typing behavior and study its accuracy. We collected gaze and typing behavior from 49 users while creating accounts for 1) a webmail client and 2) a news website. While most participants came up with a new password, 32% reported having reused an old password when setting up their accounts. We then compared different ML models to detect password reuse from the collected data. Our models achieve an accuracy of up to 87.7% in detecting password reuse from gaze, 75.8% accuracy from typing, and 88.75% when considering both types of behavior. We demonstrate that using gaze, password reuse can already be detected during the registration process, before users entered their password. Our work paves the road for developing novel interventions to prevent password reuse.

In this work, we explore attacker behavior during shoulder surfing. As such behavior is often opportunistic and difficult to observe in real world settings, we leverage the capabilities of virtual reality (VR). We recruited 24 participants and observed their behavior in two virtual waiting scenarios: at a bus stop and in an open office space. In both scenarios, participants shoulder surfed private screens displaying different types of content. From the results we derive an understanding of factors influencing shoulder surfing behavior, reveal common attack patterns, and sketch a behavioral shoulder surfing model. Our work suggests directions for future research on shoulder surfing and can serve as a basis for creating novel approaches to mitigate shoulder surfing.

"Virtual-Physical Perceptual Manipulations" (VPPMs) such as redirected walking and haptics expand the user's capacity to interact with Virtual Reality (VR) beyond what would ordinarily physically be possible. VPPMs leverage knowledge of the limits of human perception to effect changes in the user's physical movements, becoming able to (perceptibly and imperceptibly) nudge their physical actions to enhance interactivity in VR. We explore the risks posed by the malicious use of VPPMs. First, we define, conceptualize and demonstrate the existence of VPPMs. Next, using speculative design workshops, we explore and characterize the threats/risks posed, proposing mitigations and preventative recommendations against the malicious use of VPPMs. Finally, we implement two sample applications to demonstrate how existing VPPMs could be trivially subverted to create the potential for physical harm. This paper aims to raise awareness that the current way we apply and publish VPPMs can lead to malicious exploits of our perceptual vulnerabilities., Comment: 15 pages, 7 figures

We investigate the effectiveness of thermal attacks against input of text with different characteristics; we study text entry on a smartphone touchscreen and a laptop keyboard. First, we ran a study (N = 25) to collect a dataset of thermal images of short words, websites, complex strings (special characters, numbers, letters), passphrases and words with duplicate characters. Afterwards, 20 different participants visually inspected the thermal images to attempt to identify the text input. We found that long and complex strings are less vulnerable to thermal attacks, that visual inspection of thermal images reveals different parts of the entered text (36% on average and up to 82%) even if the attack is not fully successful, and that entering text on laptops is more vulnerable to thermal attacks than on smartphones. We conclude with three learned lessons and recommendations to resist thermal attacks.

We investigate the use of gaze behaviour as a means to assess password strength as perceived by users. We contribute to the effort of making users choose passwords that are robust against guessing-attacks. Our particular idea is to consider also the users' understanding of password strength in security mechanisms. We demonstrate how eye tracking can enable this: by analysing people's gaze behaviour during password creation, its strength can be determined. To demonstrate the feasibility of this approach, we present a proof of concept study (N = 15) in which we asked participants to create weak and strong passwords. Our findings reveal that it is possible to estimate password strength from gaze behaviour with an accuracy of 86% using Machine Learning. Thus, we enable research on novel interfaces that consider users' understanding with the ultimate goal of making users choose stronger passwords.

Strict password policies can frustrate users, reduce their productivity, and lead them to write their passwords down. This paper investigates the relation between password creation and cognitive load inferred from eye pupil diameter. We use a wearable eye tracker to monitor the user's pupil size while creating passwords with different strengths. To assess how creating passwords of different strength (namely weak and strong) influences users' cognitive load, we conducted a lab study (N = 15). We asked the participants to create and enter 6 weak and 6 strong passwords. The results showed that passwords with different strengths affect the pupil diameter, thereby giving an indication of the user's cognitive state. Our initial investigation shows the potential for new applications in the field of cognition-aware user interfaces. For example, future systems can use our results to determine whether the user created a strong password based on their gaze behavior, without the need to reveal the characteristics of the password.

Contact lenses have traditionally been used for vision correction applications. Recent advances in microelectronics and nanofabrication on flexible substrates have now enabled sensors, circuits and other essential components to be integrated on a small contact lens platform. This has opened up the possibility of using contact lenses for a range of human-machine interaction applications including vision assistance, eye tracking, displays and health care. In this article, we systematically review the range of smart contact lens materials, device architectures and components that facilitate this interaction for different applications. In fact, evidence from our systematic review demonstrates that these lenses can be used to display information, detect eye movements, restore vision and detect certain biomarkers in tear fluid. Consequently, whereas previous state-of the-art reviews in contact lenses focused exclusively on biosensing, our systematic review covers a wider range of smart contact lens applications in HMI. Moreover, we present a new method of classifying the literature on smart contact lenses according to their six constituent building blocks, which are the sensing, energy management, driver electronics, communications, substrate and the interfacing modules. Based on recent developments in each of these categories, we speculate the challenges and opportunities of smart contact lenses for human-machine interaction. Moreover, we propose a novel self-powered smart contact lens concept with integrated energy harvesters, sensors and communication modules to enable autonomous operation. Our review is therefore a critical evaluation of current data and is presented with the aim of guiding researchers to new research directions in smart contact lenses., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.

Nowadays, most mobile devices support biometric authentication schemes like fingerprint or face unlock. However, these probabilistic mechanisms can only be activated in combination with a second alternative factor, usually knowledge-based authentication. In this paper, we show that this aspect can be exploited in a bypass attack. In this bypass attack, the attacker forces the user to "bypass" the biometric authentication by, for example, resetting the phone. This forces the user to enter an easy-to-observe passcode instead. We present the threat model and provide preliminary results of an online survey. Based on our results, we discuss potential countermeasures. We conclude that better feedback design and security-optimized fallback mechanisms can help further improve the overall security of mobile unlock mechanisms while preserving usability., Comment: Poster Session SOUPS 2019

Users often need to authenticate at situated displays in or- der to, for example, make purchases, access sensitive in- formation, or confirm an identity. However, the exposure of interactions in public spaces introduces a large attack surface (e.g., observation, smudge or thermal attacks). A plethora of authentication models and input modalities that aim at disguising users’ input has been presented in the past. However, a comprehensive analysis on the re- quirements for secure and usable authentication on public displays is still missing. This work presents 13 design con- siderations suitable to inform practitioners and researchers during the development process of authentication systems for situated displays in public spaces. It draws on a com- prehensive analysis of prior literature and subsequent dis- cussion with five experts in the fields of pervasive displays, human-computer-interaction and usable security.

Secure authentication on situated displays (e.g., to access sensitive information or to make purchases) is becoming increasingly important. A promising approach to resist shoulder surfing attacks is to employ cues that users respond to while authenticating; this overwhelms observers by requiring them to observe both the cue itself as well as users’ response to the cue. Although previous work proposed a variety of modalities, such as gaze and mid-air gestures, to further improve security, an understanding of how they compare with regard to usability and security is still missing as of today. In this paper, we rigorously compare modalities for cue-based authentication on situated displays. In particular, we provide the first comparison between touch, mid-air gestures, and calibration-free gaze using a state-of-the-art authentication concept. In two in-depth user studies (N=37) we found that the choice of touch or gaze presents a clear trade-off between usability and security. For example, while gaze input is more secure, it is also more demanding and requires longer authentication times. Mid-air gestures are slightly slower and more secure than touch but users hesitate to use them in public. We conclude with three significant design implications for authentication using touch, mid-air gestures, and gaze and discuss how the choice of modality creates opportunities and challenges for improved authentication in public.

Secure authentication on situated displays (e.g., to access sensitive information or to make purchases) is becoming increasingly important. A promising approach to resist shoulder surfing attacks is to employ cues that users respond to while authenticating; this overwhelms observers by requiring them to observe both the cue itself as well as users’ response to the cue. Although previous work proposed a variety of modalities, such as gaze and mid-air gestures, to further improve security, an understanding of how they compare with regard to usability and security is still missing as of today. In this paper, we rigorously compare modalities for cue-based authentication on situated displays. In particular, we provide the first comparison between touch, mid-air gestures, and calibration-free gaze using a state-of-the-art authentication concept. In two in-depth user studies (N=37) we found that the choice of touch or gaze presents a clear trade-off between usability and security. For example, while gaze input is more secure, it is also more demanding and requires longer authentication times. Mid-air gestures are slightly slower and more secure than touch but users hesitate to use them in public. We conclude with three significant design implications for authentication using touch, mid-air gestures, and gaze and discuss how the choice of modality creates opportunities and challenges for improved authentication in public.

We introduce and evaluate a novel approach for detecting smooth pursuit eye movements that increases the number of distinguishable targets and is more robust against false positives. Being natural and calibration-free, Pursuits has been gaining popularity in the past years. At the same time, current implementations show poor performance when more than eight on-screen targets are being used, thus limiting its applicability. Our approach (1) leverages the slope of a regression line, and (2) introduces a minimum signal duration that improves both the new and the traditional detection method. After introducing the approach as well as the implementation, we compare it to the traditional correlation-based Pursuits detection method. We tested the approach up to 24 targets and show that, if accepting a similar error rate, nearly twice as many targets can be distinguished compared to state of the art. For fewer targets, accuracy increases significantly. We believe our approach will enable more robust pursuit-based user interfaces, thus making it valuable for both researchers and practitioners.

The objective of this article is to optimize the overall traffic flow on freeways using multiple ramp metering controls plus its complementary Dynamic Speed Limits (DSLs). An optimal freeway operation can be reached when minimizing the difference between the freeway density and the critical ratio for maximum traffic flow. In this article, a Multi-Agent Reinforcement Learning for Freeways Control (MARL-FWC) system for ramps metering and DSLs is proposed. MARL-FWC introduces a new microscopic framework at the network level based on collaborative Markov Decision Process modeling (Markov game) and an associated cooperative Q-learning algorithm. The technique incorporates payoff propagation (Max-Plus algorithm) under the coordination graphs framework, particularly suited for optimal control purposes. MARL-FWC provides three control designs: fully independent, fully distributed, and centralized; suited for different network architectures. MARL-FWC was extensively tested in order to assess the proposed model of the joint payoff, as well as the global payoff. Experiments are conducted with heavy traffic flow under the renowned VISSIM traffic simulator to evaluate MARL-FWC. The experimental results show a significant decrease in the total travel time and an increase in the average speed (when compared with the base case) while maintaining an optimal traffic flow.

Previous work on eye tracking and eye-based human-computer interfaces mainly concentrated on making use of the eyes in traditional desktop settings. With the recent growth of interest in wearable computers, such as smartwatches, smart eyewears and low-cost mobile eye trackers, eye-based interaction techniques for mobile computing are becoming increasingly important. PETMEI 2016 focuses on the pervasive eye tracking paradigm as a trailblazer for mobile eye-based interaction to take eye tracking out into the wild, to mobile and pervasive settings. We want to stimulate and explore the creativity of these communities with respect to the implications, key research challenges, and new applications for pervasive eye tracking in ubiquitous computing. The long-term goal is to create a strong interdisciplinary research community linking these fields together and to establish the workshop as the premier forum for research on pervasive eye tracking.

Computational docking is the core process of computer-aided drug design; it aims at predicting the best orientation and conformation of a small drug molecule when bound to a target large protein receptor. The docking quality is typically measured by a scoring function: a mathematical predictive model that produces a score representing the binding free energy and hence the stability of the resulting complex molecule. We analyze the performance of both learning techniques on the scoring power, the ranking power, docking power, and screening power using the PDBbind 2013 database. For the scoring and ranking powers, the proposed learning scoring functions depend on a wide range of features (energy terms, pharmacophore, intermolecular) that entirely characterize the protein-ligand complexes. For the docking and screening powers, the proposed learning scoring functions depend on the intermolecular features of the RF-Score to utilize a larger number of training complexes. For the scoring power, the DL\_RF scoring function achieves Pearson's correlation coefficient between the predicted and experimentally measured binding affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking power, the DL scoring function ranks the ligands bound to fixed target protein with accuracy 54% for the high-level ranking and with accuracy 78% for the low-level ranking while the RF scoring function achieves (46% and 62%) respectively. For the docking power, the DL\_RF scoring function has a success rate when the three best-scored ligand binding poses are considered within 2 \AA\ root-mean-square-deviation from the native pose of 36.0% versus 30.2% of the RF scoring function. For the screening power, the DL scoring function has an average enrichment factor and success rate at the top 1% level of (2.69 and 6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoring function., Comment: 29 pages, 7 figures

Recent research underscored the resurgence of webcam-based eye tracking as a cost-effective, ecologically valid, and scalable choice, attributed to advancements in machine learning-assisted calibration methods. Online gambling poses a public health concern, necessitating innovative research methods and evidence-based strategies for harm reduction. This study investigates users’ fixation patterns on responsible gambling (RG) information in different formats on gambling websites using webcam-based eye tracking. Participants browsed six gambling websites to search for RG information. 19 participants’ data were analyzed on Areas of Interest (AOIs) categorized based on RG formats. The study has found that usability varied across RG formats and also highlights both feasibility and challenges of webcam-based eye tracking for online behavior research, with practical implications to gambling stakeholders.