The Experts below are selected from a list of 339 Experts worldwide ranked by ideXlab platform
David M Rubin - One of the best experts on this subject based on the ideXlab platform.
-
addressing the eye fixation problem in gaze tracking for human computer interface using the vestibulo ocular reflex
arXiv: Human-Computer Interaction, 2020Co-Authors: Adam Pantanowitz, Kimoon Kim, Chelsey Chewins, Isabel N K Tollman, David M RubinAbstract:A custom head-mounted system to track smooth eye movements for control of a Mouse Cursor is implemented and evaluated. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the Cursor, and the Cursor moves towards the target. Two users participated in three tests to quantify the differences between incremental tracking of smooth eye movement resulting from the Vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was 402 % more accurate than tracking saccadic eye movements, with an average position resolution of 0.77 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.87 cm. Using the incremental tracking of smooth eye movements, the user was able to place the Cursor within a target as small as a 9 x 9 pixel square. However, when using the step change tracking of saccadic eye movements, the user was unable to position the Cursor within the 9 x 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.68 s, whereas for the step change tracking of saccadic eye movements, it was 2.83 s.
-
addressing the eye fixation problem in gaze tracking for human computer interface using the vestibulo ocular reflex
Informatics in Medicine Unlocked, 2020Co-Authors: Adam Pantanowitz, Kimoon Kim, Chelsey Chewins, Isabel N K Tollman, David M RubinAbstract:Abstract The characteristics of smooth versus saccadic eye movement tracking were compared for the purpose of human-computer interface. The study was performed by implementing a custom head-mounted system to track smooth eye movements for control of a Mouse Cursor. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the Cursor, and the Cursor moves towards the target. Two users participated in tests to quantify the differences between incremental tracking of smooth eye movement resulting from the vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was greater than four times more accurate than tracking saccadic eye movements, with an average position resolution of 0.80 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.21 cm. Using the incremental tracking of smooth eye movements, the users were able to place the Cursor within a 9 × 9 pixel square 90% of the time. However, when using the step change tracking of saccadic eye movements, the users were unable to position the Cursor within the 9 × 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.45 s, whereas for the step change tracking of saccadic eye movements, it was 2.61 s. The smooth eye tracking system, while substantially slower than saccadic eye movement tracking, enabled more predictable, reliable, precise, and accurate control of the Mouse Cursor.
Leiva, Luis A. - One of the best experts on this subject based on the ideXlab platform.
-
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
'Association for Computing Machinery (ACM)', 2021Co-Authors: Brückner Lukas, Arapakis Ioannis, Leiva, Luis A.Abstract:Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that Mouse Cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model Mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance
-
My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking
'Association for Computing Machinery (ACM)', 2021Co-Authors: Leiva, Luis A., Arapakis Ioannis, Iordanou CostasAbstract:This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their Mouse Cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of Mouse Cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.Comment: In Proceedings of the 2021 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR '21), March 14-19, 2021, Canberra, Australi
-
A Price-Per-Attention Auction Scheme Using Mouse Cursor Information
'Association for Computing Machinery (ACM)', 2020Co-Authors: Arapakis Ioannis, Penta Antonio, Joho Hideo, Leiva, Luis A.Abstract:Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw Mouse Cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes
-
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
'Association for Computing Machinery (ACM)', 2020Co-Authors: Brückner Lukas, Arapakis Ioannis, Leiva, Luis A.Abstract:Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that Mouse Cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model Mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance.Peer reviewe
Malek Adjouadi - One of the best experts on this subject based on the ideXlab platform.
-
integrated electromyogram and eye gaze tracking Cursor control system for computer users with motor disabilities
Journal of Rehabilitation Research and Development, 2008Co-Authors: Craig A Chin, Armando Barreto, Gualberto J Cremades, Malek AdjouadiAbstract:This research pursued the conceptualization, imple- mentation, and testing of a system that allows for computer cur- sor control without requiring hand movement. The target user group for this system are individuals who are unable to use their hands because of spinal dysfunction or other afflictions. The sys- tem inputs consisted of electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an eye-gaze tracking (EGT) system. Each input was processed by an algorithm that produced its own Cursor update informa- tion. These algorithm outputs were fused to produce an effective and efficient Cursor control. Experiments were conducted to compare the performance of EMG/EGT, EGT-only, and Mouse Cursor controls. The experiments revealed that, although EMG/ EGT control was slower than EGT-only and Mouse control, it effectively controlled the Cursor without a spatial accuracy limi- tation and also facilitated a reliable click operation.
Adam Pantanowitz - One of the best experts on this subject based on the ideXlab platform.
-
addressing the eye fixation problem in gaze tracking for human computer interface using the vestibulo ocular reflex
arXiv: Human-Computer Interaction, 2020Co-Authors: Adam Pantanowitz, Kimoon Kim, Chelsey Chewins, Isabel N K Tollman, David M RubinAbstract:A custom head-mounted system to track smooth eye movements for control of a Mouse Cursor is implemented and evaluated. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the Cursor, and the Cursor moves towards the target. Two users participated in three tests to quantify the differences between incremental tracking of smooth eye movement resulting from the Vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was 402 % more accurate than tracking saccadic eye movements, with an average position resolution of 0.77 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.87 cm. Using the incremental tracking of smooth eye movements, the user was able to place the Cursor within a target as small as a 9 x 9 pixel square. However, when using the step change tracking of saccadic eye movements, the user was unable to position the Cursor within the 9 x 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.68 s, whereas for the step change tracking of saccadic eye movements, it was 2.83 s.
-
addressing the eye fixation problem in gaze tracking for human computer interface using the vestibulo ocular reflex
Informatics in Medicine Unlocked, 2020Co-Authors: Adam Pantanowitz, Kimoon Kim, Chelsey Chewins, Isabel N K Tollman, David M RubinAbstract:Abstract The characteristics of smooth versus saccadic eye movement tracking were compared for the purpose of human-computer interface. The study was performed by implementing a custom head-mounted system to track smooth eye movements for control of a Mouse Cursor. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the Cursor, and the Cursor moves towards the target. Two users participated in tests to quantify the differences between incremental tracking of smooth eye movement resulting from the vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was greater than four times more accurate than tracking saccadic eye movements, with an average position resolution of 0.80 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.21 cm. Using the incremental tracking of smooth eye movements, the users were able to place the Cursor within a 9 × 9 pixel square 90% of the time. However, when using the step change tracking of saccadic eye movements, the users were unable to position the Cursor within the 9 × 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.45 s, whereas for the step change tracking of saccadic eye movements, it was 2.61 s. The smooth eye tracking system, while substantially slower than saccadic eye movement tracking, enabled more predictable, reliable, precise, and accurate control of the Mouse Cursor.
Luis A Leiva - One of the best experts on this subject based on the ideXlab platform.
-
predicting user engagement with direct displays using Mouse Cursor information
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016Co-Authors: Ioannis Arapakis, Luis A LeivaAbstract:Predicting user engagement with direct displays (DD) is of paramount importance to commercial search engines, as well as to search performance evaluation. However, understanding within-content engagement on a web page is not a trivial task mainly because of two reasons: (1) engagement is subjective and different users may exhibit different behavioural patterns; (2) existing proxies of user engagement (e.g., clicks, dwell time) suffer from certain caveats, such as the well-known position bias, and are not as effective in discriminating between useful and non-useful components. In this paper, we conduct a crowdsourcing study and examine how users engage with a prominent web search engine component such as the knowledge module (KM) display. To this end, we collect and analyse more than 115k Mouse Cursor positions from 300 users, who perform a series of search tasks. Furthermore, we engineer a large number of meta-features which we use to predict different proxies of user engagement, including attention and usefulness. In our experiments, we demonstrate that our approach is able to predict more accurately different levels of user engagement and outperform existing baselines.
-
building a better Mousetrap compressing Mouse Cursor activity for web analytics
Information Processing and Management, 2015Co-Authors: Luis A Leiva, Jeff HuangAbstract:Abstract Websites can learn what their users do on their pages to provide better content and services to those users. A website can easily find out where a user has been, but in order to find out what content is consumed and how it was consumed at a sub-page level, prior work has proposed client-side tracking to record Cursor activity, which is useful for computing the relevance for search results or determining user attention on a page. While recording Cursor interactions can be done without disturbing the user, the overhead of recording the Cursor trail and transmitting this data over the network can be substantial. In our work, we investigate methods to compress Cursor data, taking advantage of the fact that not every Cursor coordinate has equal value to the website developer. We evaluate 5 lossless and 5 lossy compression algorithms over two datasets, reporting results about client-side performance, space savings, and how well a lossy algorithm can replicate the original Cursor trail. The results show that different compression techniques may be suitable for different goals: LZW offers reasonable lossless compression, but lossy algorithms such as piecewise linear interpolation and distance-thresholding offer better client-side performance and bandwidth reduction.