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Writing is one of the most common activities undertaken on a computer, and the activity of writing has been widely studied. Given that writing is an intensively cognitive process, it makes sense that the type of writing that is being produced would have an effect on the writer's gaze and typing behaviors. However, only a few studies have explored this relationship. In this paper, we study the gaze-typing behaviors, specifically, the coordination between eye gaze and typing dynamics, of writers who are producing original articles in different genres: reminiscent, logical and creative. Our study focuses on Chinese typing, particularly via the Pinyin input method, which generates text via a two step method, and requires additional cognitive processes compared to typing in phonographic languages such as English. Our study involves 46 native Chinese speakers of varying ages from children to elderly. Our method deploys statistics- and sequence-based features to infer the mental state of the author during the writing process. The statistics-based features focus on modeling the overall gaze-typing behaviors during the process and the sequence-based features focus on the transition of the gaze-typing behaviors as the piece of writing progresses. Using a linear support-vector machine, we achieve an overall accuracy over 88% for the article-genre detection by using a leave-one-subject-out cross-validation evaluation. [ABSTRACT FROM AUTHOR]

Understanding human visual attention is essential for understanding human cognition, which in turn benefits human—computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously “learns” its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild. [ABSTRACT FROM AUTHOR]

Increasing number of people work long hours with computers under high cognitive load. This could potentially cause mental stress in workplaces. Prolonged exposure to mental stress contributes to poor working experience and even severe health problems. Despite the growing demand, the existing intelligent stress detection methods are limited when applied to actual workplaces. They often measure physiological and physical signals, via intrusive devices, to detect stress. The intrusiveness hampers their accessibility and applicability in daily life and workplaces. To overcome that, behavior-based methods were proposed. Models that explore mouse and gaze behaviors during computer usages were demonstrated to be particularly effective. However, the current methods rely on using prior knowledge of the user interface (UI) layout to construct models. Their applicability thus is limited, especially in real workplaces where task UI is often dynamic. This paper presents a novel stress detection method to address the challenges. It attains non-intrusiveness and UI-agnostic by modeling the relative movement and coordination of mouse and gaze. The method is evaluated on a dynamic-UI task, namely, web searching. An accuracy of 78.8% is achieved using a commercial eye-tracker for gaze estimation, beating the state-of-the-art approaches by around 20%. We further use webcam to estimate gaze locations substituting for the eye-tracker, to enhance the model accessibility. The method yields 68.6% accuracy of stress detection without using any special devices. Experimental results demonstrate the effectiveness and applicability of our method. It opens up a new avenue for cognitive-aware adaptive user interface, intelligent working environment, and related applications. [ABSTRACT FROM AUTHOR]

This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data for more than 110 000 fire cases with a wide range of fire and vent opening conditions are collected. Temperature limit to heat detectors is applied to mimic the loss of temperature data in real fire scenarios. P-Flashv2 is shown to be able to make predictions with a maximum lead time of 60 s and its performance is benchmarked against eight different model architectures. Results show that P-Flashv2 has an overall accuracy of ∼ 87.7 % and ∼ 89.5% for flashover predictions with a lead time setting of 30 s and 60 s, respectively. Additional model testing is conducted to assess P-Flashv2 prediction capability in real fire scenarios. Evaluating the model again with full-scale experimental data, P-Flashv2 has an overall prediction accuracy of ∼ 82.7 % and ∼ 85.6 % for cases with the lead time of setting 30 s and 60 s, respectively. Results from this study show that the proposed machine learning based model, P-Flashv2, can be used to facilitate data-driven fire fighting and reduce fire fighter deaths and injuries. [ABSTRACT FROM AUTHOR]

In this paper, we present a new type of spatial queries called Nearest Surrounder (NS) queries. An NS query determines the nearest polygon-shaped spatial objects (referred to as nearest surrounder objects) and their orientations with respect to a query point from an object set. Besides, we derive two NS query variants, namely, multitier NS (m-NS) queries and angle-constrained NS (ANS) queries. An m-NS query searches multiple layers of NS objects for the same range of angles from a query point. An ANS query searches for NS objects within a specified range of angles. To evaluate NS queries and their variants, we explore angle-based and distance-based bound properties of polygons, and devise two efficient algorithms, namely, Sweep and Ripple, based on R-tree. The algorithms access objects in an order according to their orientations and distances with respect to a given query point, respectively. They are efficient as they can finish a search with one index lookup. Besides, they can progressively deliver a query result. Through empirical studies, we evaluate the proposed algorithms and report their performance for both synthetic and real object sets. [ABSTRACT FROM AUTHOR]

This paper 1 1 Certain commercial products are identified in this paper in order to specify adequately the equipment used. Such identification does not imply recommendation by the National Institute of Standards and Technology, nor does it imply that this equipment is the best available for the purpose. presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time-dependent sensor signals were obtained from 60 normal/ignition cooking experiments. A total of 200,000 data instances are documented and analyzed. The raw data are preprocessed. Selected features are generated for time series data focusing on real-time detection applications. Utilizing the leave-one-out cross validation method, three machine learning models are built and tested. Parametric studies are carried out to understand the diversity, volume, and tendency of the data. Given the current dataset, the detection algorithm based on Support Vector Machine (SVM) provides the most reliable prediction (with an overall accuracy of 96.9%) on pre-ignition conditions. Analyses indicate that using a multi-step approach can further improve overall prediction accuracy. The development of an accurate detection algorithm can provide reliable feedback to intercept ignition of unattended cooking and help reduce fire losses. • Time series sensor data for cooking with electric-coil and gas cooktops are presented. • Data preprocessing and feature selection for machine learning on real-time fire prevention application are discussed. • Workflow for constructing and testing machine learning models is demonstrated. • Support Vector Machine (SVM) detection algorithms can predict 96.9% of the data points with hazardous conditions. • Multi-step models can enhance data classification and improve detection accuracy. [ABSTRACT FROM AUTHOR]