Large-format touchscreens have become commonplace in classrooms and meeting rooms, yet little is known about how their scale affects input behavior. Operating these displays engages more upper-limb joints—especially the shoulder—thereby altering movement dynamics. In a 65-inch touchscreen study, we found that conventional Fitts' law explains ≤ 80% of the variance in movement time, as performance is primarily driven by amplitude. To address the modeling bias resulting from this amplitude dominance, we refine the index of difficulty as IDx = log_2(A/(W + c) + 1). Rather than treating c as a mere fitting parameter, we interpret it as an intrinsic property of pointing dynamics: it quantifies the systematic deviation from the canonical speed-accuracy tradeoff implied by the "as quickly and accurately as possible" instruction. By calibrating c with the proposed anti-overfitting criteria, we improve model fits for both finger and pen input, raising R² to above 0.97. We present the calibration rules, interpret c across contexts, validate IDx robustness via leave-one-out cross-validation, and demonstrate its generalizability on data from prior studies—including a large tabletop experiment. Finally, we translate the findings into practical guidelines for UI and experimental design.

Abstract The idea of target expansion was proposed two decades ago for manual target acquisition, but it is not feasible to implement this idea in traditional user interfaces as the interactive system cannot know exactly which target is the desired one and should be expanded among several candidates. With the increasing maturity of eye tracking technology, gaze input has moved from an academically promising technique to an input method with built-in support in Windows 10; and target expansion has already become very feasible in the context of gaze input, as the user’s eye gaze is inherently an indicator of the desired target due to the natural eye-hand coordination in everyday tasks. However, a comprehensive evaluation is still lacking. In this study, two experiments were conducted, each with a different group of subjects, to investigate the effects of target expansion under different expansion feedback styles (visible vs. invisible), expansion factors, as well as different target appearances (i.e., circular vs. rectangular). The experimental results indicated that (1) the index of difficulty in eye pointing tasks (IDeye) does not depend on the initial size of the target, but on its final size, and that the corresponding human performance can be accurately predicted using the IDeye model instead of Fitts’ law; and that (2) the visible expansion style could disrupt the user’s fixations, making the measured human performance less efficient to some extent, but overall the theoretical predictions using the IDeye model were almost the same as the baselines. Following the experimental results, this study also provided some practical suggestions for UI design.

Anomaly detection is one of the most significant tasks in multivariate time series analysis, while it remains challenging to model complex patterns for improving detection accuracy and to interpret the root causes of anomalies. However, existing studies either consider only the temporal dependencies, or simply reconstruct the original input for detection, both neglecting the hidden relationships among multivariate. We propose an adversarial graph neural network based anomaly detection model, called SGAT-AE, which consists of aSelf-learningGraphATtention network (SGAT), anAuto-Encoder (AE), and an adversarial training component. Specifically, SGAT is a prediction model that discovers the graph dependency relationships among multivariate and acts as a sample generator to confuse AE, while AE reconstructs the samples and acts as a discriminator that distinguishes a real sample from a generated one. A novel adversarial training between SGAT and AE is applied to amplify the errors of anomalies such that the prediction performance of SGAT is improved and the overfitting of AE is avoided. In addition, we aggregate the prediction error, the reconstruction error, and the adversarial error for anomaly detection, and develop a graph based anomaly interpretation method that locates the root causes from both local and global perspectives. Extensive experiments with five real-world data offer evidence that the proposed solution SGAT-AE is capable of achieving better performance when compared with the state-of-the-art proposals.

While being able to read with screen magnifiers, low vision people have slow and unpleasant reading experiences. Eye tracking has the potential to improve their experience by recognizing fine-grained gaze behaviors and providing more targeted enhancements. To inspire gaze-based low vision technology, we investigate the suitable method to collect low vision users’ gaze data via commercial eye trackers and thoroughly explore their challenges in reading based on their gaze behaviors. With an improved calibration interface, we collected the gaze data of 20 low vision participants and 20 sighted controls who performed reading tasks on a computer screen; low vision participants were also asked to read with different screen magnifiers. We found that, with an accessible calibration interface and data collection method, commercial eye trackers can collect gaze data of comparable quality from low vision and sighted people. Our study identified low vision people’s unique gaze patterns during reading, building upon which, we propose design implications for gaze-based low vision technology.

HCI researchers have long recognized the significant effects of movement direction on human performance, and this factor has been carefully addressed to benefit user interface design. According to our previous study (2012), the weights of the two target dimensions, width W and height H, in the extended index of difficulty (ID) for 2D pointing tasks are asymmetric and appear to vary periodically based on movement direction (θ), following a cosine function. However, this periodic effect of movement direction is uncertain for direct 2D touch pointing tasks, and a thorough understanding of the effects of movement direction on direct pointing tasks, such as on touch input surfaces, is still lacking. In this paper, we conducted two experiments on a 24-inch touch screen, with tilted and horizontal orientations respectively, to confirm the periodic effect in the context of direct pointing and illustrate its variations across different pointing tasks. At the same time, we propose a quantification formula to measure the real differences in task difficulty caused by the direction factor. To the best of our knowledge, this is the first study to do so. Using this formula, the ID values in different directions can be unified to the same scale and compared, providing a new perspective for understanding and evaluating human performance in different interaction environments.

Recently, several works have studied the problem of view selection in graph databases. However, existing methods cannot fully exploit the graph properties of views, e.g., supergraph views and common subgraph views, which leads to a low view utility and duplicate view content. To address the problem, we propose an extended graph view that persists all the edge-induced subgraphs to answer the subgraph and supergraph queries simultaneously. Furthermore, we present the graph gene algorithm (GGA), which relies on a set of view transformations to reduce the view space and optimize the view benefit. Extensive experiments on real-life and synthetic datasets demonstrated GGA outperformed other selection methods in both effectiveness and efficiency.

With the portable and affordable gaze input devices being marketed for end users, gaze-based interactions were getting increasingly popular. Unfortunately, the understanding about the dominant task of gaze input, i.e. eye pointing task, was still not sufficient although a performance model had been specifically proposed in previous study because of that 1) the original model was based on a specific circular target condition, without the ability to predict the performance of acquiring conventional rectangular targets and that 2) there was a lack of explanation from the perspective of the anatomical structure of the eyes. In this paper, we proposed a 2D extension to take account of more general target conditions. Carrying out two experiments, we evaluated the effectiveness of the new model and furthermore we found that the index of difficulty that we redefined for 2D eye pointing (IDeye) was able to properly reflect the asymmetrical impacts of target width and height, and consequently the IDeye model could more accurately and properly predict the performance when acquiring 2D targets than Fitts’ law, no matter what kind of saccades or eye orientations (i.e. saccadic eye movement directions) was employed to acquire the desired targets. According to the results, we provided more useful implications and recommendations for gaze-based applications.

Fitts' law is the most widely applied model in the field of HCI. However, this model and its existing extensions are still limited for 2D pointing task especially when the effects of movement direction (Θ) remain in the task. In this paper, we employ the concept of projection to account for the effects of target width (W) and height (H) on movement time so that we seamlessly integrate the four factors, i.e. Θ, amplitude (A), W and H, into the new extension of Fitts' law, which can uncover not only the periodicity of the asymmetrical impacts of W and H with the variation of Θ but also their interrelation. Carrying out two experiments, we verify that the vertical projection of W and the horizontal projection of H in the line of movement direction can be viewed as the determinants of movement time. Finally, we offer recommendations for 2D pointing experiments and discuss the implications for interface designs.

We propose a quantitative model for dwell-based eye pointing tasks. Using the concepts of information theory to analogize eye pointing, we define an index of difficulty (IDeye) for the corresponding tasks in a similar manner to the definition that Fitts made for hand pointing. According to our validations in different situations, IDeye, which takes account of the distinct characteristics of rapid saccades and involuntary eye jitters, can accurately and meaningfully describe eye pointing tasks. To the best of our knowledge, this work is the first successful attempt to model eye gaze interactions.

In order to improve the stability of eye cursor, we introduce three methods, force field (FF), speed reduction (SR), and warping to target center (TC) to modulate eye cursor trajectories by counteracting eye jitter, which is the main cause of destabilizing the eye cursor. We evaluate these methods using two controlled experiments. One is an attention task experiment, which indicates that both FF and SR significantly alleviate the instability of eye cursor, but TC is not as we anticipated. The other is a 2D pointing task experiment, which shows that FF and SR as well as the improved implementation of SR (iSR) indeed improve human performance in dominant dwell-based eye pointing tasks of eye-based interactions. The method iSR is especially effective to accelerate eye pointing (10.5% and 8.5%) and reduce error rate (6.1% and 2.7%) when target diameter D = 45 and 60 pixels.