Reduce User Effort Research Articles

GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification.

Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and we use incremental learning technology for allowing the system to memorize the gesture incrementally over time. An experiment has been performed on different gestures in different user situations that demonstrates the accuracy of one-class classification (OCC), and proves the effectiveness and reliability of the approach. Gesture authentication based on 3D depth cameras could be achieved with reduced user effort.

View suggestion for interactive segmentation of indoor scenes

Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming. In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods.

Performing Complex Tasks by Users With Upper-Extremity Disabilities Using a 6-DOF Robotic Arm: A Study

In this paper, we report on the results of a study that was conducted to examine how users suffering from severe upper-extremity disabilities can control a 6 degrees-of-freedom (DOF) robotics arm to complete complex activities of daily living. The focus of the study is not on assessing the robot arm but on examining the human-robot interaction patterns. Three participants were recruited. Each participant was asked to perform three tasks: eating three pieces of pre-cut bread from a plate, drinking three sips of soup from a bowl, and opening a right-handed door with lever handle. Each of these tasks was repeated three times. The arm was mounted on the participant's wheelchair, and the participants were free to move the arm as they wish to complete these tasks. Each task consisted of a sequence of modes where a mode is defined as arm movement in one DOF. Results show that participants used a total of 938 mode movements with an average of 75.5 (std 10.2) modes for the eating task, 70 (std 8.8) modes for the soup task, and 18.7 (std 4.5) modes for the door opening task. Tasks were then segmented into smaller subtasks. It was found that there are patterns of usage per participant and per subtask. These patterns can potentially allow a robot to learn from user's demonstration what is the task being executed and by whom and respond accordingly to reduce user effort.

Remote Display Access Using Remote Frame Buffer and IO Streaming

This paper propose a virtual network computing (VNC) based architecture for accessing the desktops of remote computers from a cellular phone. A viewer is provided on the cellular phone that enables the user to see and manipulate the desktop of various remote systems such as MS Windows, Macintosh, and UNIX. The system to be accessed must be running a VNC server and it must be attached to a network. A proxy is used to send the image of the desktop to the cellular phone, to convert different devices, to suppress network traffics, and to support recovery from an unscheduled disconnection. To reduce user effort and solve problems inherent to the cellular phone‟s small screen, several functions are provided on the cellular viewer. Frequently used screen areas can be assigned and restored quickly by using the Shortcut function. The Guidance function can be used to show the current key assignments. Two areas can be viewed at the same time by the Twin view function. In this paper a prototype of the proposed architecture has been implemented using Java and has been tested on a Java-enabled cellular phone emulator.

Improving Teacher Effectiveness: Designing Better Assessment Tools in Learning Management Systems

Current-generation assessment tools used in K-12 and post-secondary education are limited in the type of questions they support; this limitation makes it difficult for instructors to navigate their assessment engines. Furthermore, the question types tend to score low on Bloom’s Taxonomy. Dedicated learning management systems (LMS) such as Blackboard, Moodle and Canvas are somewhat better than informal tools as they offer more question types and some randomization. Still, question types in all the major LMS assessment engines are limited. Additionally, LMSs place a heavy burden on teachers to generate online assessments. In this study we analyzed the top three LMS providers to identify inefficiencies. These inefficiencies in LMS design, point us to ways to ask better questions. Our findings show that teachers have not adopted current tools because they do not offer definitive improvements in productivity. Therefore, we developed LiquiZ, a design for a next-generation assessment engine that reduces user effort and provides more advanced question types that allow teachers to ask questions that can currently only be asked in one-on-one demonstration. The initial LiquiZ project is targeted toward STEM subjects, so the question types are particularly advantageous in math or science subjects.

A Decision-Theoretic Model of Assistance

There is a growing interest in intelligent assistants for a variety of applications from sorting email to helping people with disabilities to do their daily chores. In this paper, we formulate the problem of intelligent assistance in a decision-theoretic framework, and present both theoretical and empirical results. We first introduce a class of POMDPs called hidden-goal MDPs (HGMDPs), which formalizes the problem of interactively assisting an agent whose goal is hidden and whose actions are observable. In spite of its restricted nature, we show that optimal action selection for HGMDPs is PSPACE-complete even for deterministic dynamics. We then introduce a more restricted model called helper action MDPs (HAMDPs), which are sufficient for modeling many real-world problems. We show classes of HAMDPs for which efficient algorithms are possible. More interestingly, for general HAMDPs we show that a simple myopic policy achieves a near optimal regret, compared to an oracle assistant that knows the agent's goal. We then introduce more sophisticated versions of this policy for the general case of HGMDPs that we combine with a novel approach for quickly learning about the agent being assisted. We evaluate our approach in two game-like computer environments where human subjects perform tasks, and in a real-world domain of providing assistance during folder navigation in a computer desktop environment. The results show that in all three domains the framework results in an assistant that substantially reduces user effort with only modest computation.

AutoProof meets some verification challenges

AutoProof is an automatic verifier for functional properties of programs written in Eiffel. This paper illustrates some of AutoProof’s capabilities when tackling the three challenges of the VerifyThis verification competition held at FM 2012, as well as on three other problems proposed in related events. AutoProof ’s design focuses on making it practically applicable with reduced user effort. Tackling the challenges demonstrates to what extent this design goal is met in the current implementation: while some of AutoProof’s current limitations prevent us from verifying the complete specification of the prefix sum and binary search tree algorithms, we can still prove some partial properties on interesting special cases, but with the advantage of requiring little or no specification.

Automatic generation of trimap for image matting

Image matting is a process of extracting a foreground object from an image. Providing trimap is an important challenge of image matting. An attentively created trimap is required to get accurate alpha-matte. Accuracy of trimap-based approach reduces drastically as the trimap becomes thicker and coarser. Providing trimap manually is an exhausting and time consuming task for user. Scribble-based approach solves the problem by reducing user effort but with reduced accuracy. In this paper, we propose an approach to automatically generate trimap. It uses learning-based matting to generate a high quality alpha-matte in less time. It helps in obtaining accurate and fast result without user effort. The experimental results show the automatically generated trimap and alpha-matte are qualitatively and quantitatively better.

Interactive handwriting recognition with limited user effort

Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly better than simply ignoring it and transcribing the document from scratch. A more effective approach is to follow an interactive approach in which both the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Nevertheless, in some applications, the user effort available to transcribe documents is limited and fully supervision of the system output is not realistic. To circumvent these problems, we propose a novel interactive approach which efficiently employs user effort to transcribe a document by improving three different aspects. Firstly, the system employs a limited amount of effort to solely supervise recognised words that are likely to be incorrect. Thus, user effort is efficiently focused on the supervision of words for which the system is not confident enough. Secondly, it refines the initial transcription provided to the user by recomputing it constrained to user supervisions. In this way, incorrect words in unsupervised parts can be automatically amended without user supervision. Finally, it improves the underlying system models by retraining the system from partially supervised transcriptions. In order to prove these statements, empirical results are presented on two real databases showing that the proposed approach can notably reduce user effort in the transcription of handwritten text in (old) documents.

Interface Design and Human Factors Considerations for Model-Based Tight Glycemic Control in Critical Care

Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input. The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload. The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user's immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users. The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.

Content Learning on Websites: The Effects of Information Personalization

Information personalization is a popular and effective way used especially by online content providers to reduce user effort for the assessment of abundant information load. Based on learning and goal setting theories, in this paper we argue that the successful role of information personalization can be attributed to the effect of increased content learning, which is a key driver of consumer website evaluation and loyalty. In addition, we distinguish between two dimensions of information personalization and suggest that effort reduction due to information personalization is not necessarily beneficial. Based on two experimental studies using a health information website, we demonstrate that personalization content, which decreases the effort through offering dense information tailored to the users’ needs, leads to beneficial behavioral outcomes (website evaluation and revisit intention) due to the mediating role of increased content learning. Personalization interaction, which decreases the effort through the process by which the personalized content is generated, has the opposite effects due to the decreased ability of information retention.

Advanced query formulation in deductive databases

A novel operation-oriented framework for deductive databases is introduced. Several types of end-user queries to deductive databases are classified, and sample queries are used to demonstrate how they can be expressed in our framework. The sample queries and their sample evaluations show essentially reduced user effort and essentially increased user capability in the expression of advanced queries in deductive databases. These are also necessary features in modern information retrieval from fact databases. We also establish criteria for advanced query formulation in deductive databases from the end-user viewpoint and analyze failures of conventional and present deductive DBMSs in meeting them.