Indoor localization is challenging in terms of both the accuracy and possible using scenarios. In this paper, we introduce the design and implementation of a toy car localization and navigation system, which demonstrates that a projected light based localization technique allows multiple devices to know and exchange their fine-grained location information in an indoor environment. The projected light consists of a sequence of gray code images which assigns each pixel in the projection area a unique gray code so as to distinguish their coordination. The light sensors installed on the toy car and the potential “passenger” receive the light stream from the projected light stream, based on which their locations are computed. The toy car then utilizes A* algorithm to plan the route based on its own location, orientation, the target’s location and the map of available “roads”. The fast speed of localization enables the toy car to adjust its own orientation while “driving” and keep itself on “roads”. The toy car system demonstrates that the localization technique can power other applications that require fine-grained location information of multiple objects simultaneously.

In this paper, we analyze the association between a social media user's photo content and their interests. Visual content of photos is analyzed using state-of-the-art deep learning based automatic concept recognition. An aggregate visual concept signature is thereby computed for each user. User tags manually applied to their photos are also used to construct a tf-idf based signature per user. We also obtain social groups that users join to represent their social interests. In an effort to compare the visual-based versus tag-based user profiles with social interests, we compare corresponding similarity matrices with a reference similarity matrix based on users' group memberships. A random baseline is also included that groups users by random sampling while preserving the actual group sizes. A difference metric is proposed and it is shown that the combination of visual and text features better approximates the group-based similarity matrix than either modality individually. We also validate the visual analysis against the reference inter-user similarity using the Spearman rank correlation coefficient. Finally we cluster users by their visual signatures and rank clusters using a cluster uniqueness criteria.

Knowing the geo-located venue of a tweet can facilitate better understanding of a user's geographic context, allowing apps to more precisely present information, recommend services, and target advertisements. However, due to privacy concerns, few users choose to enable geotagging of their tweets resulting in a small percentage of tweets being geotagged; furthermore, even if the geo-coordinates are available, the closest venue to the geo-location may be incorrect. In this paper, we present a method for providing a ranked list of geo-located venues for a non-geotagged tweet, which simultaneously indicates the venue name and the geo-location at a very fine-grained granularity. In our proposed method for Venue Inference for Tweets ({\VIT}), we construct a heterogeneous social network in order to analyze the embedded social relations, and leverage available but limited geographic data to estimate the geo-located venue of tweets. A single classifier is trained to predict the probability of a tweet and a geo-located venue being linked, rather than training a separate model for each venue. We examine the performance of four types of social relation features and three types of geographic features embedded in a social network when predicting whether a tweet and a venue are linked, with a best accuracy of over 88%. We use the classifier probability estimates to rank the predicted geo-located venues of a non-geotagged tweet from over 19k possibilities, and observed an average top-5 accuracy of 29%.

Establishing common ground is one of the key problems for any form of communication. The problem is particularly pronounced in remote meetings, in which participants can easily lose track of the details of dialogue for any number of reasons. In this demo we present a web-based tool, MixMeet, that allows teleconferencing participants to search the contents of live meetings so they can rapidly retrieve previously shared content to get on the same page, correct a misunderstanding, or discuss a new idea.

New technology comes about in a number of different ways. It may come from advances in scientific research, through new combinations of existing technology, or by simply from imagining what might be possible in the future. This video describes the evolution of Tabletop Telepresence, a system for remote collaboration through desktop videoconferencing combined with a digital desk. Tabletop Telepresence provides a means to share paper documents between remote desktops, interact with documents and request services (such as translation), and communicate with a remote person through a teleconference. It was made possible by combining advances in camera/projector technology that enable a fully functional digital desk, embodied telepresence in video conferencing and concept art that imagines future workstyles.

While synchronous meetings are an important part of collaboration, it is not always possible for all stakeholders to meet at the same time. We created the concept of hypermeetings to support meetings with asynchronous attendance. Hypermeetings consist of a chain of video-recorded meetings with hyperlinks for navigating through the video content. HyperMeeting supports the synchronized viewing of prior meetings during a videoconference. Natural viewing behavior such as pausing generates hyperlinks between the previously recorded meetings and the current video recording. During playback, automatic link-following guided by playback plans present the relevant content to users. Playback plans take into account the user's meeting attendance and viewing history and match them with features such as speaker segmentation. A user study showed that participants found hyperlinks useful but did not always understand where they would take them. The study results provide a good basis for future system improvements.

A localization system is a coordinate system for describing the world, organizing the world, and controlling the world. Without a coordinate system, we cannot specify the world in mathematical forms; we cannot regulate processes that may involve spatial collisions; we cannot even automate a robot for physical actions. This paper provides an overview of indoor localization technologies, popular models for extracting semantics from location data, approaches for associating semantic information and location data, and applications that may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain pros and cons of different technologies and models. More specifically, we will first explore users' needs in a museum scenario. Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point out promising localization research directions. Similarly, we will also discuss context information required by different applications and explore models and ontologies for connecting users, objects, and environment factors with semantics. By identifying gaps between various models and real application requirements, we can draw a road map for future location semantics research.

To facilitate distributed communication in mobile settings, we developed a system for creating and sharing gaze anno-tations using head mounted displays, such as Google Glass. Gaze annotations make it possible to point out objects of interest within an image and add a verbal description to it. To create an annotation, the user simply looks at an object of interest in the image and speaks out the information connected to the object. The gaze location is recorded and inserted as a gaze marker and the voice is transcribed using speech recognition. After an annotation has been created, it can be shared with another person. We performed a user study that showed that users experienced that gaze annota-tions add precision and expressiveness compared to an annotation to the whole image.

We present a novel system for detecting and capturing paper documents on a tabletop using a 4K video camera mounted overhead on pan-tilt servos. Our automated system first finds paper documents on a cluttered tabletop based on a text probability map, and then takes a sequence of high-resolution frames of the located document to reconstruct a high quality and fronto-parallel document page image. The quality of the resulting images enables OCR processing on the whole page. We performed a preliminary evaluation on a small set of 10 document pages and our proposed system achieved 98% accuracy with the open source Tesseract OCR engine.

Web-based tools for remote collaboration are quickly becoming an established element of the modern workplace. During live meetings, people share web sites, edit presentation slides, and share code editors. It is common for participants to refer to previously spoken or shared content in the course of synchronous distributed collaboration. A simple approach is to index with Optical Character Recognition (OCR) the video frames, or key-frames, being shared and let user retrieve them with text queries. Here we show that a complementary approach is to look at the actions users take inside the live document streams. Based on observations of real meetings, we focus on two important signals: text editing and mouse cursor motion. We describe the detection of text and cursor motion, their implementation in our WebRTC-based system, and how users are better able to search live documents during a meeting based on these detected and indexed actions.

Location-enabled applications now permeate the mobile computing landscape. As technologies like Bluetooth Low Energy (BLE) and Apple's iBeacon protocols begin to see widespread adoption, we will no doubt see a proliferation of indoor location enabled application experiences. While not essential to each of these applications, many will require that the location of the device be true and verifiable. In this paper, we present LocAssure, a new framework for trusted indoor location estimation. The system leverages existing technologies like BLE and iBeacons, making the solution practical and compatible with technologies that are already in use today. In this work, we describe our system, situate it within a broad location assurance taxonomy, describe the protocols that enable trusted localization in our system, and provide an analysis of early deployment and use characteristics. Through developer APIs, LocAssure can provide critical security support for a broad range of indoor location applications.

We present a general framework and working system for predicting likely affective responses of the viewers in the social media environment after an image is posted online. Our approach emphasizes a mid-level concept representation, in which intended affects of the image publisher is characterized by a large pool of visual concepts (termed PACs) detected from image content directly instead of textual metadata, evoked viewer affects are represented by concepts (termed VACs) mined from online comments, and statistical methods are used to model the correlations among these two types of concepts. We demonstrate the utilities of such approaches by developing an end-to-end Assistive Comment Robot application, which further includes components for multi-sentence comment generation, interactive interfaces, and relevance feedback functions. Through user studies, we showed machine suggested comments were accepted by users for online posting in 90% of completed user sessions, while very favorable results were also observed in various dimensions (plausibility, preference, and realism) when assessing the quality of the generated image comments.

In this paper we report findings from two user studies that explore the problem of establishing common viewpoint in the context of a wearable telepresence system. In our first study, we assessed the ability of a local person (the guide) to identify the view orientation of the remote person by looking at the physical pose of the telepresence device. In the follow-up study, we explored visual feedback methods for communicating the relative viewpoints of the remote user and the guide via a head-mounted display. Our results show that actively observing the pose of the device is useful for viewpoint estimation. However, in the case of telepresence devices without physical directional affordances, a live video feed may yield comparable results. Lastly, more abstract visualizations lead to significantly longer recognition times, but may be necessary in more complex environments.

Tutorials are one of the most fundamental means of conveying knowledge. In this paper, we present a suite of applications that allow users to combine different types of media captured from handheld, standalone, or wearable devices to create multimedia tutorials. We conducted a study comparing standalone (camera on tripod) versus wearable capture (Google Glass). The results show that tutorial authors have a slight preference for wearable capture devices, especially when recording activities involving larger objects.

Connecting digital information to physical objects can enrich their content and make them more vivid. Traditional augmented reality techniques reach this goal by augmenting physical objects or their surroundings with various markers and typically require end users to wear additional devices to explore the augmented content. In this paper, we propose POLI, which allows a system administrator to author digital content with his/her mobile device while allows end-users to explore the authored content with their mobile devices. POLI provides three novel interactive approaches for authoring digital content. It does not change the nature appearances of physical objects and does not require users to wear any additional hardware on their bodies.

As video-mediated communication reaches broad adoption, improving immersion and social interaction are important areas of focus in the design of tools for exploration and work-based communication. Here we present three threads of research focused on developing new ways of enabling exploration of a remote environment and interacting with the people and artifacts therein.

Edge targets, such as buttons or menus along the edge of a screen, are known to afford fast acquisition performance in desktop mousing environments. As the popularity of touch based devices continues to grow, understanding the affordances of edge targets on touchscreen is needed. This paper describes results from two controlled experiments that examine in detail the effect of edge targets on performance in touch devices. Our results shows that on touch devices, a target's proximity to the edge has a significant negative effect on reaction time. We examine the effect in detail and explore mitigating factors. We discuss potential explanations for the effect and propose implications for the design of efficient interfaces for touch devices.

We present our ongoing research on automatic segmentation of motion gestures tracked by IMUs. We postulate that by recognizing gesture execution phases from motion data that we may be able to auto-delimit user gesture entries. We demonstrate that machine learning classifiers can be trained to recognize three distinct phases of gesture entry: the start, middle and end of a gesture motion. We further demonstrate that this type of classification can be done at the level of individual gestures. Furthermore, we describe how we captured a new data set for data exploration and discuss a tool we developed to allow manual annotations of gesture phase information. Initial results we obtained using the new data set annotated with our tool show a precision of 0.95 for recognition of the gesture phase and a precision of 0.93 for simultaneous recognition of the gesture phase and the gesture type.

Websites can record individual users' activities and display them in a variety of ways. There is a tradeoff between detail and abstraction in visualization, especially when the amount of content increases and becomes more difficult to process. We conducted an experiment on Mechanical Turk varying the quality, detail, and visual presentation of information about an individual's past work to see how these design features affected perceptions of the worker. We found that providing detail in the display through text increased processing time and led to less positive evaluations. Visually abstract displays required less processing time but decreased confidence in evaluation. This suggests that different design parameters may engender differing psychological processes that influence reactions towards an unknown person.

Collaboration Map (CoMap) is an interactive visualization tool showing temporal changes of small group collaborations. As dynamic entities, collaboration groups have flexible features such as people involved, areas of work, and timings. CoMap shows a graph of collaborations during user-adjustable periods, providing overviews of collaborations' dynamic features. We demonstrate CoMap with a co-authorship dataset extracted from DBLP to visualize 587 publications by 29 researchers at a research organization.

In a variety of peer production settings, from Wikipedia to open source software development to crowdsourcing, individuals may encounter, edit, or review the work of unknown others. Typically this is done without much context to the person's past behavior or performance. To understand how exposure to an unknown individual's activity history influences attitudes and behaviors, we conducted an online experiment on Mechanical Turk varying the content, quality, and presentation of information about another Turker's work history. Surprisingly, negative work history did not lead to negative outcomes, but in contrast, a positive work history led to positive initial impressions that persisted in the face of contrary information. This work provides insight into the impact of activity history design factors on psychological and behavioral outcomes that can be of use in other related settings.

In this paper, we report findings from a study that compared basic video-conferencing, emergent kinetic video-conferencing techniques, and face-to-face meetings. In our study, remote and co-located participants worked together in groups of three. We show, in agreement with prior literature, the strong adverse impact of being remote on participation-levels. We also show that local and remote participants perceived differently their own contributions and others. Extending prior work, we also show that local participants exhibited significantly more overlapping speech with remote participants who used an embodied proxy, than with remote participants in basic-video conferencing (and at a rate similar to overlapping speech for co-located groups). We also describe differences in how the technologies were used to follow conversation. We discuss how these findings extend our understanding of the promise and potential limitations of embodied video-conferencing solutions.

Our research focuses on improving the effectiveness and usability of driving mobile telepresence robots by increasing the user's sense of immersion during the navigation task. To this end we developed a robot platform that allows immersive navigation using head-tracked stereoscopic video and a HMD. We present the result of an initial user study that compares System Usability Scale (SUS) ratings of a robot teleoperation task using head-tracked stereo vision with a baseline fixed video feed and the effect of a low or high placement of the camera(s). Our results show significantly higher ratings for the fixed video condition and no effect of the camera placement. Future work will focus on examining the reasons for the lower ratings of stereo video and and also exploring further visual navigation interfaces.

Name of an identity is strongly influenced by his/her cultural background such as gender and ethnicity, both vital attributes for user profiling, attribute-based retrieval, etc. Typically, the associations between names and attributes (e.g., people named "Amy" are mostly females) are annotated manually or provided by the census data of governments. We propose to associate a name and its likely demographic attributes by exploiting click-throughs between name queries and images with automatically detected facial attributes. This is the first work attempting to translate an abstract name to demographic attributes in visual-data-driven manner, and it is adaptive to incremental data, more countries and even unseen names (the names out of click-through data) without additional manual labels. In the experiments, the automatic name-attribute associations can help gender inference with competitive accuracy by using manual labeling. It also benefits profiling social media users and keyword-based face image retrieval, especially for contributing 12% relative improvement of accuracy in adapting to unseen names.