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We conducted two studies exploring how an area cursor technique can improve the eye-gaze interface. We first examined the bubble cursor technique. We developed an eye-gaze-based cursor called the bubble gaze cursor and compared it to a standard eye-gaze interface and a bubble cursor with a mouse. The results revealed that the bubble gaze cursor interface was faster than the standard point cursor-based eye-gaze interface. In addition, the usability and mental workload were significantly better than those of the standard interface. Next, we extended the bubble gaze cursor technique and developed a bubble gaze lens. The results indicated that the bubble gaze lens technique was faster than the bubble gaze cursor method and the error rate was reduced by 54.0%. The usability and mental workload were also considerably better than those of the bubble gaze cursor.

ABSTRACT Eye movement modelling examples (EMME) are instructional videos that display a teacher’s eye movements as “gaze cursor” (e.g. a moving dot) superimposed on the learning task. This study investigated if previous findings on the beneficial effects of EMME would extend to online lecture videos and compared the effects of displaying the teacher’s gaze cursor with displaying the more traditional mouse cursor as a tool to guide learners’ attention. Novices (N = 124) studied a pre-recorded video lecture on how to model business processes in a 2 (mouse cursor absent/present) × 2 (gaze cursor absent/present) between-subjects design. Unexpectedly, we did not find significant effects of the presence of gaze or mouse cursors on mental effort and learning. However, participants who watched videos with the gaze cursor found it easier to follow the teacher. Overall, participants responded positively to the gaze cursor, especially when the mouse cursor was not displayed in the video.

Cursor tracking data contains information about website visitors which may provide new ways to understand visitors and their needs. This paper presents an Amazon Mechanical Turk study where participants were tracked as they used modified variants of the Wikipedia and BBC News websites. Participants were asked to complete reading and information-finding tasks. The results showed that it was possible to differentiate between users reading content and users looking for information based on cursor data. The effects of website aesthetics, user interest and cursor hardware were also analysed which showed it was possible to identify hardware from cursor data, but no relationship between cursor data and engagement was found. The implications of these results, from the impact on web analytics to the design of experiments to assess user engagement, are discussed.

Large language models (LLMs) have demonstrated the promise to revolutionize the field of software engineering. Among other things, LLM agents are rapidly gaining momentum in software development, with practitioners reporting a multifold increase in productivity after adoption. Yet, empirical evidence is lacking around these claims. In this paper, we estimate the causal effect of adopting a widely popular LLM agent assistant, namely Cursor, on development velocity and software quality. The estimation is enabled by a state-of-the-art difference-in-differences design comparing Cursor-adopting GitHub projects with a matched control group of similar GitHub projects that do not use Cursor. We find that the adoption of Cursor leads to a statistically significant, large, but transient increase in project-level development velocity, along with a substantial and persistent increase in static analysis warnings and code complexity. Further panel generalized-method-of-moments estimation reveals that increases in static analysis warnings and code complexity are major factors driving long-term velocity slowdown. Our study identifies quality assurance as a major bottleneck for early Cursor adopter

This paper describes a wearable wireless mouse-cursor controller that optically tracks the degree of tilt of the user's head to move the mouse relative distances and therefore the degrees of tilt. The raw data can be processed locally on the wearable device before wirelessly transmitting the mouse-movement reports over Bluetooth Low Energy (BLE) protocol to the host computer; but for exploration of algorithms, the raw data can also be processed on the host. The use of standard Human-Interface Device (HID) profile enables plug-and-play of the proposed mouse device on modern computers without requiring separate driver installation. It can be used in two different modes to move the cursor, the joystick mode and the direct mapped mode. Experimental results show that this head-controlled mouse to be intuitive and effective in operating the mouse cursor with fine-grained control of the cursor even by untrained users.

To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods, despite the sparsity of their compatibility tensor, still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match, and then the core of CURSOR, a fiber-CUR-based tensor generation method, directly calculates entries of the compatibility tensor by leveraging the initial second-order match result. This significantly decreases the time complexity and tensor density. A probability relaxation labeling (PRL)-based matching algorithm, especially suitable for sparse tensors, is developed. Experiment results on large-scale synthetic datasets and widely-adopted benchmark sets demonstrate the superiority of CURSOR over existing methods. The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods to improv

We assume that internal shocks of Gamma Ray Bursts (GRBs) consist of multiple sub-jets with a collimation half-angle $\sim {\rm several }\timesγ_i^{-1}$, where $γ_i$ is the Lorenz factor of each sub-jet. If by chance a sub-jet is first emitted off-axis from the line of sight, the observed peak energy can be in the X-ray region. Next if by chance a subsequent sub-jet is emitted along the line of sight, then the peak energy will be in the gamma ray region and the gamma ray may arrive after the X-ray precursor from the former sub-jet depending on parameters. This model predicts a new class of GRBs with extremely weak and short gamma ray emission but X-ray precursor and/or post-cursor as well as an afterglow.

The human visual system processes complex scenes to focus attention on relevant items. However, blind people cannot visually skim for an area of interest. Instead, they use a combination of contextual information, knowledge of the spatial layout of their environment, and interactive scanning to find and attend to specific items. In this paper, we define and compare three cursor-based interactions to help blind people attend to items in a complex visual scene: window cursor (move their phone to scan), finger cursor (point their finger to read), and touch cursor (drag their finger on the touchscreen to explore). We conducted a user study with 12 participants to evaluate the three techniques on four tasks, and found that: window cursor worked well for locating objects on large surfaces, finger cursor worked well for accessing control panels, and touch cursor worked well for helping users understand spatial layouts. A combination of multiple techniques will likely be best for supporting a variety of everyday tasks for blind users.

In a cursor-control task in which the motion of the cursor is rotated randomly relative to the movement of the hand, the sensed directions of hand and cursor are mutually biased. In our previous study, we used implicit and explicit measures of the bias of sensed hand direction toward the direction of the cursor and found different characteristics. The present study serves to explore further differences and commonalities of these measures. In Experiment 1, we examined the effects of different relative reliabilities of visual and proprioceptive information on the explicitly and implicitly assessed bias of sensed hand direction. In two conditions, participants made an aiming movement and returned to the start position immediately or after a delay of 6 s during which the cursor was no longer visible. The unimodal proprioceptive information on final hand position in the delayed condition served to increase its relative reliability. As a result, the bias of sensed hand direction toward the direction of the cursor was reduced for the explicit measure, with a complementary increase of the bias of sensed cursor direction, but unchanged for the implicit measure. In Experiment 2, we examined

As Extended Reality (XR) systems increasingly map and understand the physical world, interacting with these blended representations remains challenging. The current push for "natural" inputs has its trade-offs: touch is limited by human reach and fatigue, while gaze often lacks the precision for fine interaction. To bridge this gap, we introduce World Mouse, a cross-reality cursor that reinterprets the familiar 2D desktop mouse for complex 3D scenes. The system is driven by two core mechanisms: within-object interaction, which uses surface normals for precise cursor placement, and between-object navigation, which leverages interpolation to traverse empty space. Unlike previous virtual-only approaches, World Mouse leverages semantic segmentation and mesh reconstruction to treat physical objects as interactive surfaces. Through a series of prototypes, including object manipulation and screen-to-world transitions, we illustrate how cross-reality cursors may enable seamless interactions across real and virtual environments.

Do patterns of eye-hand coordination observed during real-world object interactions apply to digital, screen-based object interactions? We adapted a real-world object interaction task (physically transferring cups in sequence about a tabletop) into a two-dimensional screen-based task (dragging-and-dropping circles in sequence with a cursor). We collected gaze (with webcam eye-tracking) and cursor position data from 51 fully-remote, crowd-sourced participants who performed the task on their own computer. We applied real-world time-series data segmentation strategies to resolve the self-paced movement sequence into phases of object interaction and rigorously cleaned the webcam eye-tracking data. In this preliminary investigation, we found that: 1) real-world eye-hand coordination patterns persist and adapt in this digital context, and 2) remote, online, cursor-tracking and webcam eye-tracking are useful tools for capturing visuomotor behaviours during this ecologically-valid human-computer interaction task. We discuss how these findings might inform design principles and further investigations into natural behaviours that persist in digital environments.

Operating a GUI through eyeball is a complex mechanism and not used as often as mouse or trackball. But there are situations where eye-mouse devices can play a tremendous role especially where the hands of the user are not available or busy to perform other activities. The difficulties of implementing an eye-cursor control system are many. The article illustrates some inventions on eye-cursor control system, which attempt to eliminate the difficulties of the prior art mechanisms.

Loops that iterate over SQL query results are quite common, both in application programs that run outside the DBMS, as well as User Defined Functions (UDFs) and stored procedures that run within the DBMS. It can be argued that set-oriented operations are more efficient and should be preferred over iteration; but from real-world use cases, it is clear that loops over query results are inevitable in many situations, and are preferred by many users. Such loops, known as cursor loops, come with huge trade-offs and overheads w.r.t. performance, resource consumption and concurrency. We present Aggify, a technique for optimizing loops over query results that overcomes all these overheads. It achieves this by automatically generating custom aggregates that are equivalent in semantics to the loop. Thereby, Aggify completely eliminates the loop by rewriting the query to use this generated aggregate. This technique has several advantages such as: (i) pipelining of entire cursor loop operations instead of materialization, (ii) pushing down loop computation from the application layer into the DBMS, closer to the data, (iii) leveraging existing work on optimization of aggregate functions, result

Organizational members who conceal information about adverse behaviors present a substantial risk to that organization. Yet the task of identifying who is concealing information is extremely difficult, expensive, error-prone, and time-consuming. We propose a unique methodology for identifying concealed information: measuring people’s mouse-cursor movements in online screening questionnaires. We theoretically explain how mouse-cursor movements captured during a screening questionnaire differ between people concealing information and truth tellers. We empirically evaluate our hypotheses using an experiment during which people conceal information about a questionable act. While people completed the screening questionnaire, we simultaneously collected mouse-cursor movements and electrodermal activity—the primary sensor used for polygraph examinations—as an additional validation of our methodology. We found that mouse-cursor movements can significantly differentiate between people concealing information and people telling the truth. Mouse-cursor movements can also differentiate between people concealing information and truth tellers on a broader set of comparisons relative to electroder

While Large Language Models (LLMs) have demonstrated remarkable capabilities, research shows that their effectiveness depends not only on explicit prompts but also on the broader context provided. This requirement is especially pronounced in software engineering, where the goals, architecture, and collaborative conventions of an existing project play critical roles in response quality. To support this, many AI coding assistants have introduced ways for developers to author persistent, machine-readable directives that encode a project's unique constraints. Although this practice is growing, the content of these directives remains unstudied. This paper presents a large-scale empirical study to characterize this emerging form of developer-provided context. Through a qualitative analysis of 401 open-source repositories containing cursor rules, we developed a comprehensive taxonomy of project context that developers consider essential, organized into five high-level themes: Conventions, Guidelines, Project Information, LLM Directives, and Examples. Our study also explores how this context varies across different project types and programming languages, offering implications for the next

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.

Software developers frequently refactor code. Often, a single logical refactoring change involves changing multiple related components in a source base such as renaming each occurrence of a variable or function. While many code editors can perform such common and generic refactorings, they do not support more complex refactorings or those that are specific to a given code base. For those, as a flexible - albeit less interactive - alternative, developers can write refactoring scripts that can implement arbitrarily complex logic by manipulating the program's tree representation. In this work, we present Forest, a structural code editor that aims to bridge the gap between the interactiveness of code editors and the expressiveness of refactoring scripts. While structural editors have occupied a niche as general code editors, the key insight of this work is that they enable a novel structural multi-cursor design that allows Forest to reach a similar expressiveness as refactoring scripts; Forest allows to perform a single action simultaneously in multiple program locations and thus support complex refactorings. To support interactivity, Forest provides features typical for text code edit

We introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user's environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback.

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.

We present a new non-blocking doubly-linked list implementation for an asynchronous shared-memory system. It is the first such implementation for which an upper bound on amortized time complexity has been proved. In our implementation, operations access the list via cursors. Each cursor is associated with an item in the list and is local to a process. The implementation supports two update operations, insertBefore and delete, and two move operations, moveRight and moveLeft. An insertBefore(c, x) operation inserts an item x into the list immediately before the cursor c's location. A delete(c) operation removes the item at the cursor c's location and sets the cursor to the next item in the list. The move operations move the cursor one position to the right or left. The update operations use single-word Compare&Swap instructions. The move operations only read shared memory and never change the state of the data structure. If all update operations modify different parts of the list, they run completely concurrently. Let cp(op) be the maximum number of active cursors at any one time during the operation op. The amortized complexity of each update operation op is O(cp(op)) and each m