Touching annotations: A visual metaphor for navigation of annotation in digital documents

Direct touch manipulation interactions with technology are now commonplace and significant interest is building around their use in the culture and heritage domain. Such interactions can give people the opportunity to explore materials and artefacts in ways that would otherwise be unavailable. These are often heavily annotated and can be linked to a large array of related digital content, thus enriching the experience for the user. Research has addressed issues of how to present digital documents and their related annotations but at present it is unclear what the optimal interaction approach to navigating these annotations in a touch display context might be.In this paper we investigate the role of two alternative approaches to support the navigation of annotations in digitised documents in the context of a touch interface. Through a control study we demonstrate that, whilst the navigation paradigm displays a significant interaction with the type of annotations task performed, there is no discernible advantage of using a natural visual metaphor for annotation in this context. This suggests that design of digital document annotation navigation tools should account for the context and navigation tasks being considered.     

The Role of 3-D Sound in Human Reaction and Performance in Augmented Reality Environments

Three-dimensional sound's effectiveness in virtual reality (VR) environments has been widely studied. However, due to the big differences between VR and augmented reality (AR) systems in registration, calibration, perceptual difference of immersiveness, navigation, and localization, it is important to develop new approaches to seamlessly register virtual 3-D sound in AR environments and conduct studies on 3-D sound's effectiveness in AR context. In this paper, we design two experimental AR environments to study the effectiveness of 3-D sound both quantitatively and qualitatively. Two different tracking methods are applied to retrieve the 3-D position of virtual sound sources in each experiment. We examine the impacts of 3-D sound on improving depth perception and shortening task completion time. We also investigate its impacts on immersive and realistic perception, different spatial objects identification, and subjective feeling of "human presence and collaboration". Our studies show that applying 3-D sound is an effective way to complement visual AR environments. It helps depth perception and task performance, and facilitates collaborations between users. Moreover, it enables a more realistic environment and more immersive feeling of being inside the AR environment by both visual and auditory means. In order to make full use of the intensity cues provided by 3-D sound, a process to scale the intensity difference of 3-D sound at different depths is designed to cater small AR environments. The user study results show that the scaled 3-D sound significantly increases the accuracy of depth judgments and shortens the searching task completion time. This method provides a necessary foundation for implementing 3-D sound in small AR environments. Our user study results also show that this process does not degrade the intuitiveness and realism of an augmented audio reality environment     

Comparing the task effectiveness of instant messaging and electronic mail for geographically dispersed teams in Taiwan

To improve task effectiveness of geographically dispersed teams, media selection approaches a complex issue involving media features and user characteristics. This study compares user perceptions about task effectiveness between instant messengers (IM) and e-mail (EM). Using a field experiment, the design contrasted teams using EM with teams using IM. Thirty-four teams of two graduate students were enrolled as participants from five universities in Taiwan. Two people were separated geographically and had to communicate accordingly. Results indicate that IM-supported teams generated more ideas and task success. However, no significant difference was found in task difficulty between the IM and EM users.     

IMMIView: a multi-user solution for design review in real-time

IMMIView is an interactive system that relies on multiple modalities and multi-user interaction to support collaborative design review. It was designed to offer natural interaction in visualization setups such as large-scale displays, head mounted displays or TabletPC computers. To support architectural design, our system provides content creation and manipulation, 3D scene navigation and annotations. Users can interact with the system using laser pointers, speech commands, body gestures and mobile devices. In this paper, we describe how we design a system to answer architectural user requirements. In particular, our system takes advantage of multiple modalities to provide a natural interaction for design review. We also propose a new graphical user interface adapted to architectural user tasks, such as navigation or annotations. The interface relies on a novel stroke-based interaction supported by simple laser pointers as input devices for large-scale displays. Furthermore, input devices such as speech and body tracking allow IMMIView to support multiple users. Moreover, they allow each user to select different modalities according to their preference and modality adequacy for the user task. We present a multi-modal fusion system developed to support multi-modal commands on a collaborative, co-located, environment, i.e. with two or more users interacting at the same time, on the same system. The multi-modal fusion system listens to inputs from all the IMMIView modules in order to model user actions and issue commands. The multiple modalities are fused based on a simple rule-based sub-module developed in IMMIView and presented in this paper. User evaluation performed over IMMIView is presented. The results show that users feel comfortable with the system and suggest that users prefer the multi-modal approach to more conventional interactions, such as mouse and menus, for the architectural tasks presented.     

基于视觉引导多AGV系统的改进A*路径规划算法

研究基于视觉引导自动引导车(AGV)的改进A*路径规划算法.首先,设计一种包含导航、定位和任务信息的图形编码标志方法,AGV通过识别位于车身前方网格型路径中有序排布的编码标志进行快速定位和下一位置预判,为多AGV规划奠定基础;其次,根据网格型路径构成的动态随机网络,提出一种改进A*算法,将AGV在运动时产生的动态时间耗费作为参考指标,以实现多AGV在路径网络中的路径规划和冲突避让策略,提高固定路网资源的利用效率;最后,对多AGV在网格型路径中协同工作的场景进行仿真,实验结果表明,所提出的改进算法可以有效应用于多AGV系统,并且提升整体系统的工作效率.     

An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection

This paper introduces the computer security domain of anomaly detection and formulates it as a machine learning task on temporal sequence data. In this domain, the goal is to develop a model or profile of the normal working state of a system user and to detect anomalous conditions as long-term deviations from the expected behavior patterns. We introduce two approaches to this problem: one employing instance-based learning (IBL) and the other using hidden Markov models (HMMs). Though not suitable for a comprehensive security solution, both approaches achieve anomaly identification performance sufficient for a low-level focus of attention detector in a multitier security system. Further, we evaluate model scaling techniques for the two approaches: two clustering techniques for the IBL approach and variation of the number of hidden states for the HMM approach. We find that over both model classes and a wide range of model scales, there is no significant difference in performance at recognizing the profiled user. We take this invariance as evidence that, in this security domain, limited memory models (e.g., fixed-length instances or low-order Markov models) can learn only part of the user identity information in which we're interested and that substantially different models will be necessary if dramatic improvements in user-based anomaly detection are to be achieved.     

Individual differences and behavioral metrics involved in modeling web navigation

This paper presents an empirical study aiming at investigating individual differences and behavioral metrics involved in modeling web navigation. Factors that have an influence on web navigation behavior were identified with the aid of task analysis, and their relevance in predicting task outcomes (performance, satisfaction, perceived disorientation) was tested with the aid of multiple regression analysis. Several types of navigation metrics were calculated based on web logging data and used as indicators of user characteristics and task outcomes. Results show that spatial-semantic cognitive mechanisms seem to be crucial in adequately performing web navigation tasks. The fact that user characteristics and task outcomes can be estimated with reasonable accuracy based on navigation metrics suggests the possibility of building adaptive navigation support in web applications.     

Assessing the impact of geometric design intent annotations on parametric model alteration activities

The effective representation and communication of design intent plays a crucial role in CAD model alteration activities. In history-based parametric modeling systems, design intent information is usually expressed implicitly within the model. However, there is evidence that suggests that an explicit representation can increase productivity and quality and facilitate the transferring of design knowledge throughout the different stages of the product lifecycle. In this paper, we assess the effectiveness of 3D annotations as mechanisms for explicit design intent representation and examine their impact in model alteration processes that require a direct interaction with the model's geometry. We present the results of a series of studies aimed at measuring user performance and model quality in two scenarios. First, we hypothesized that annotations are valuable tools to provide design information when inadequate modeling assumptions can be made by designers. Second, we evaluated annotations as tools to communicate design decisions when multiple options are available. In both cases, results show statistically significant benefits of annotated models, suggesting the use of this technique as a valuable approach to improve design intent communication.     

An assisting,constrained 3D navigation technique for multiscale virtual 3D city models

Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In order to explore these information spaces, navigation techniques for controlling the virtual camera are required to facilitate wayfinding and movement. However, navigation is not a trivial task and many available navigation techniques do not support users effectively and efficiently with their respective skills and tasks. In this article, we present an assisting, constrained navigation technique for multiscale virtual 3D city models that is based on three basic principles: users point to navigate, users are lead by suggestions, and the exploitation of semantic, multiscale, hierarchical structurings of city models. The technique particularly supports users with low navigation and virtual camera control skills but is also valuable for experienced users. It supports exploration, search, inspection, and presentation tasks, is easy to learn and use, supports orientation, is efficient, and yields effective view properties. In particular, the technique is suitable for interactive kiosks and mobile devices with a touch display and low computing resources and for use in mobile situations where users only have restricted resources for operating the application. We demonstrate the validity of the proposed navigation technique by presenting an implementation and evaluation results. The implementation is based on service-oriented architectures, standards, and image-based representations and allows exploring massive virtual 3D city models particularly on mobile devices with limited computing resources. Results of a user study comparing the proposed navigation technique with standard techniques suggest that the proposed technique provides the targeted properties, and that it is more advantageous to novice than to expert users.     

Designing for flexible interaction between humans and automation: delegation interfaces for supervisory control

OBJECTIVE: To develop a method enabling human-like, flexible supervisory control via delegation to automation. BACKGROUND: Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance. METHOD: We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described. RESULTS: On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles. CONCLUSION: Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level. APPLICATION: Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning.     

Model-based cluster and discriminant analysis with the MIXMOD software

The Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM), and it is possible to combine these to yield different strategies for obtaining a sensible maximum for the likelihood (or complete-data likelihood) function. MIXMOD is currently intended to be used for multivariate Gaussian mixtures, and fourteen different Gaussian models can be distinguished according to different assumptions regarding the component variance matrix eigenvalue decomposition. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance) are included, their suitability depending on the particular perspective (cluster analysis or discriminant analysis). Written in C++, MIXMOD is interfaced with SCILAB and MATLAB. The program, the statistical documentation and the user guide are available on the internet at the following address: http://www-math.univ-fcomte.fr/mixmod/index.php.     

Nonstationary color tracking for vision-based human-computer interaction

Skin color offers a strong cue for efficient localization and tracking of human body parts in video sequences for vision-based human-computer interaction. Color-based target localization could be achieved by analyzing segmented skin color regions. However, one of the challenges of color-based target tracking is that color distributions would change in different lighting conditions such that fixed color models would be inadequate to capture nonstationary color distributions over time. Meanwhile, using a fixed skin color model trained by the data of a specific person would probably not work well for other people. Although some work has been done on adaptive color models, this problem still needs further studies. We present our investigation of color-based image segmentation and nonstationary color-based target tracking, by studying two different representations for color distributions. We propose the structure adaptive self-organizing map (SASOM) neural network that serves as a new color model. Our experiments show that such a representation is powerful for efficient image segmentation. Then, we formulate the nonstationary color tracking problem as a model transduction problem, the solution of which offers a way to adapt and transduce color classifiers in nonstationary color distributions. To fulfill model transduction, we propose two algorithms, the SASOM transduction and the discriminant expectation-maximization (EM), based on the SASOM color model and the Gaussian mixture color model, respectively. Our extensive experiments on the task of real-time face/hand localization show that these two algorithms can successfully handle some difficulties in nonstationary color tracking. We also implemented a real-time face/hand localization system based on such algorithms for vision-based human-computer interaction.     

A Statistical Framework for Image Category Search from a Mental Picture

Starting from a member of an image database designated the "query image," traditional image retrieval techniques, for example, search by visual similarity, allow one to locate additional instances of a target category residing in the database. However, in many cases, the query image or, more generally, the target category, resides only in the mind of the user as a set of subjective visual patterns, psychological impressions, or "mental pictures." Consequently, since image databases available today are often unstructured and lack reliable semantic annotations, it is often not obvious how to initiate a search session; this is the "page zero problem." We propose a new statistical framework based on relevance feedback to locate an instance of a semantic category in an unstructured image database with no semantic annotation. A search session is initiated from a random sample of images. At each retrieval round, the user is asked to select one image from among a set of displayed images-the one that is closest in his opinion to the target class. The matching is then "mental." Performance is measured by the number of iterations necessary to display an image which satisfies the user, at which point standard techniques can be employed to display other instances. Our core contribution is a Bayesian formulation which scales to large databases. The two key components are a response model which accounts for the user's subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and two databases of 20,000 and 60,000 images demonstrate the efficiency of the search process.     

Effectiveness of automatic and expert generated narrative and guided instructions for task-oriented web browsing

This study investigated how instructions may be created to facilitate web browsing tasks. Two types of instructions were considered. Narrative instructions are text-based while guided instructions use graphic annotations. One way to create both types of instructions is to utilize the specialty of human experts. A method is also provided for automatic generation of both types of instructions based on the learning of user experience in web browsing. An experiment was conducted to test the effect of types of instruction, sources of instruction, and task complexity setting on performance variables in web browsing. The results of the experiment indicated that (1) by using web annotations, guided instructions resulted in better performance and satisfactions than narrative instructions in web browsing, (2) based on learning of web browsing activities, automatically generated guided instructions are comparable to expert-created guided instructions in terms of their effect on the performance of web browsing.     

Neural ranking for automatic image annotation

Automatic image annotation aims to predict labels for images according to their semantic contents and has become a research focus in computer vision, as it helps people to edit, retrieve and understand large image collections. In the last decades, researchers have proposed many approaches to solve this task and achieved remarkable performance on several standard image datasets. In this paper, we propose a novel learning to rank approach to address image auto-annotation problem. Unlike typical learning to rank algorithms for image auto-annotation which directly rank annotations for image, our approach consists of two phases. In the first phase, neural ranking models are trained to rank image’s semantic neighbors. Then nearest-neighbor based models propagate annotations from these semantic neighbors to the image. Thus our approach integrates learning to rank algorithms and nearest-neighbor based models, including TagProp and 2PKNN, and inherits their advantages. Experimental results show that our method achieves better or comparable performance compared with the state-of-the-art methods on four challenging benchmarks including Corel5K, ESP Games, IAPR TC-12 and NUS-WIDE.

     

Evaluation of a Geospatial Annotation Tool for Unmanned Vehicle Specialist Interface

Map-based interfaces have been developed to support collaborative control of unmanned vehicles (i.e., robots). Annotation on the map (or geospatial annotation) has been proposed as an effective way to support team collaboration; however, there is a lack of research focused on the design of geospatial annotation tools to promote usability and task performance. The utility of location reference in geospatial annotations for communication and information sharing is the focus of this article. Two annotation tool designs were developed. The annotation contents were directly anchored on the map in the first design, whereas in the second design annotations were summarized in a separate panel on the interface. Evaluation participants followed instructions from a simulated team leader and assigned unmanned vehicles to different tasks for two simulated scenarios that include searching for victims and collecting hazardous materials samples. The results demonstrate the potential of using geospatial annotations to enrich communication and support map-based unmanned vehicle control. Participants appreciated the direct location reference feature of the first design and had generally shorter response time, but felt that the second design provided better usability and lower task workload. These results suggest that the user experience depends on the manner of obtaining information from the annotation tools, and the integration of the tool with user's task flow and other interface components, such as the map display. The presented results can be used as a basis for designing geospatial annotation tools for team collaboration that better fit user needs and preferences.     

VizAssist: an interactive user assistant for visual data mining

We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js.     

Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people

A challenging area of research is the development of a navigation system for visually impaired people in an indoor environment such as a railway station, commercial complex, educational institution, and airport. Identifying the current location of the users can be a difficult task for those with visual impairments. The entire selection of the navigation path depends upon the current location of the user. This work presents a detailed analysis of the recent user positioning techniques and methodologies on the indoor navigation system based on the parameters, such as techniques, cost, the feasibility of implementation, and limitations. This paper presents a denoising auto encoder based on the convolutional neural network (DAECNN) to identify the present location of the users. The proposed approach uses the de-noising autoencoder to reconstruct the noisy image and the convolution neural network (CNN) to classify the users' current position. The proposed method is compared with the existing deep learning approaches such as deep autoencoder, sparse autoencoder, CNN, multilayer perceptron, radial basis function neural network, and the performances are analyzed. The experimental findings indicate that the DAECNN methodology works better than the existing classification approaches.