Publications

FXPAL publishes in top scientific conferences and journals.

2016
Publication Details
  • Document Engineering DocEng 2016
  • Sep 13, 2016

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In this paper we describe DocuGram, a novel tool to capture and share documents from any application. As users scroll through pages of their document inside the native application (Word, Google Docs, web browser), the system captures and analyses in real-time the video frames and reconstitutes the original document pages into an easy to view HTML-based representation. In addition to regenerating the document pages, a DocuGram also includes the interactions users had over them, e.g. mouse motions and voice comments. A DocuGram acts as a modern copy machine, allowing users to copy and share any document from any application.
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  • Mobile HCI 2016
  • Sep 6, 2016

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Most teleconferencing tools treat users in distributed meetings monolithically: all participants are meant to be connected to one another in the more-or-less the same manner. In reality, though, people connect to meetings in all manner of different contexts, sometimes sitting in front of a laptop or tablet giving their full attention, but at other times mobile and involved in other tasks or as a liminal participant in a larger group meeting. In this paper we present the design and evaluation of two applications, Penny and MeetingMate, designed to help users in non-standard contexts participate in meetings.
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  • CBRecSys: Workshop on New Trends in Content-Based Recommender Systems at ACM Recommender Systems Conference
  • Sep 2, 2016

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The abundance of data posted to Twitter enables companies to extract useful information, such as Twitter users who are dissatisfied with a product. We endeavor to determine which Twitter users are potential customers for companies and would be receptive to product recommendations through the language they use in tweets after mentioning a product of interest. With Twitter's API, we collected tweets from users who tweeted about mobile devices or cameras. An expert annotator determined whether each tweet was relevant to customer purchase behavior and whether a user, based on their tweets, eventually bought the product. For the relevance task, among four models, a feed-forward neural network yielded the best cross-validation accuracy of over 80% per product. For customer purchase prediction of a product, we observed improved performance with the use of sequential input of tweets to recurrent models, with an LSTM model being best; we also observed the use of relevance predictions in our model to be more effective with less powerful RNNs and on more difficult tasks.
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  • Ro-Man 2016
  • Aug 26, 2016

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Two related challenges with current teleoperated robotic systems are lack of peripheral vision and awareness, and difficulty or tedium of navigating through remote spaces. We address these challenges by providing an interface with a focus plus context (F+C) view of the robot location, and where the user can navigate simply by looking where they want to go, and clicking or drawing a path on the view to indicate the desired trajectory or destination. The F+C view provides an undistorted, perspectively correct central region surrounded by a wide field of view peripheral portion, and avoids the need for separate views. The navigation method is direct and intuitive in comparison to keyboard or joystick based navigation, which require the user to be in a control loop as the robot moves. Both the F+C views and the direct click navigation were evaluated in a preliminary user study.

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Mobile Telepresence Robots (MTR) are an emerging technology that extend the functionality of telepresence systems by adding mobility. MTRs nowadays, however, rely on stationary imaging systems such as a single narrow-view camera for vision, which can lead to reduced operator performance due to view-related deficiencies in situational awareness. We therefore developed an improved imaging and viewing platform that allows immersive telepresence using a Head Mounted Device (HMD) with head-tracked mono and stereoscopic video. Using a remote collaboration task to ground our research, we examine the effectiveness head-tracked HMD systems in comparison to a baseline monitor-based system. We performed a user study where participants were divided into three groups: fixed camera monitor-based baseline condition (without HMD), HMD with head-tracked 2D camera and HMD with head-tracked stereo camera. Results showed the use of HMD reduces task error rates and improves perceived collaborative success and quality of view, compared to the baseline condition. No major difference was found, however, between stereo and 2D camera conditions for participants wearing an HMD.
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  • SIGIR 2016
  • Jul 18, 2016

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Social media offers potential opportunities for businesses to extract business intelligence. This paper presents Tweetviz, an interactive tool to help businesses extract actionable information from a large set of noisy Twitter messages. Tweetviz visualizes tweet sentiment of business locations, identifies other business venues that Twitter users visit, and estimates some simple demographics of the Twitter users frequenting a business. A user study to evaluate the system's ability indicates that Tweetviz can provide an overview of a business's issues and sentiment as well as information aiding users in creating customer profiles.
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  • Hypertext 2016
  • Jul 12, 2016

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Web videos are becoming more and more popular. Current web technologies make it simpler than ever to both stream videos and create complex constructs of interlinked videos with additional information (video, audio, images, and text); so called hypervideos. When viewers interact with hypervideos by clicking on links, new content has to be loaded. This may lead to excessive waiting times, interrupting the presentation -- especially when videos are loaded into the hypervideo player. In this work, we propose hypervideo pre-fetching strategies, which can be implemented in players to minimize waiting times. We examine the possibilities offered by the HTML5
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  • 3rd IEEE International Workshop on Mobile Multimedia Computing (MMC)
  • Jul 11, 2016

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Mobile Audio Commander (MAC) is a mobile phone-based multimedia sensing system that facilitates the introduction of extra sensors to existing mobile robots for advanced capabilities. In this paper, we use MAC to introduce an accurate indoor positioning sensor to a robot to facilitate its indoor navigation. More specifically, we use a projector to send out position ID through light signal, use a light sensor and the audio channel on a mobile phone to decode the position ID, and send navigation commands to a target robot through audio output. With this setup, our system can simplify user’s robot navigation. Users can define a robot navigation path on a phone, and our system will compare the navigation path with its accurate location sensor inputs and generate analog line-following signal, collision avoidance signal, and analog angular signal to adjust the robot’s straight movements and turns. This paper describes two examples of using MAC and a positioning system to enable complicated robot navigation with proper user interface design, external circuit design and real sensor installations on existing robots.
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  • ICME 2016
  • Jul 11, 2016

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Captions are a central component in image posts that communicate the background story behind photos. Captions can enhance the engagement with audiences and are therefore critical to campaigns or advertisement. Previous studies in image captioning either rely solely on image content or summarize multiple web documents related to image's location; both neglect users' activities. We propose business-aware latent topics as a new contextual cue for image captioning that represent user activities. The idea is to learn the typical activities of people who posted images from business venues with similar categories (e.g., fast food restaurants) to provide appropriate context for similar topics (e.g., burger) in new posts. User activities are modeled via a latent topic representation. In turn, the image captioning model can generate sentences that better reflect user activities at business venues. In our experiments, the business-aware latent topics are effective for adapting to captions to images captured in various businesses than the existing baselines. Moreover, they complement other contextual cues (image, time) in a multi-modal framework.

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We previously created the HyperMeeting system to support a chain of geographically and temporally distributed meetings in the form of a hypervideo. This paper focuses on playback plans that guide users through the recorded meeting content by automatically following available hyperlinks. Our system generates playback plans based on users' interests or prior meeting attendance and presents a dialog that lets users select the most appropriate plan. Prior experience with playback plans revealed users' confusion with automatic link following within a sequence of meetings. To address this issue, we designed three timeline visualizations of playback plans. A user study comparing the timeline designs indicated that different visualizations are preferred for different tasks, making switching among them important. The study also provided insights that will guide research of personalized hypervideo, both inside and outside a meeting context.

4th International Workshop on Interactive Content Consumption (WSICC'16)

Publication Details
  • ACM TVX 2016
  • Jun 22, 2016

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WSICC has established itself as a truly interactive workshop at EuroITV'13, TVX'14, and TVX'15 with three successful editions. The fourth edition of the WSICC workshop aims to bring together researchers and practitioners working on novel approaches for interactive multimedia content consumption. New technologies, devices, media formats, and consumption paradigms are emerging that allow for new types of interactivity. Examples include multi-panoramic video and object-based audio, increasingly available in live scenarios with content feeds from a multitude of sources. All these recent advances have an impact on different aspects related to interactive content consumption, which the workshop categorizes into Enabling Technologies, Content, User Experience, and User Interaction. The resources from past editions of the workshop are available on the http://wsicc.net website.
Publication Details
  • WSICC Workshop at TVX
  • Jun 22, 2016

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Hypervideo usage scenarios like physiotherapy trainings or instructions for manual tasks make it hard for users to use an input device like a mouse or touch screen on a hand-held device while they are performing an exercise or use both hands to perform a manual task. In this work, we are trying to overcome this issue by providing an alternative input method for hypervideo navigation using speech commands. In a user test, we evaluated two different speech recognition libraries, annyang (in combination with the Web Speech API) and PocketSphinx.js (in combination with the Web Audio API), for their usability to control hypervideo players. Test users spoke 18 words, either in German or English, which were recorded and then processed by both libraries. We found out that annyang shows better recognition results. However, depending on other factors of influence, like the occurrence of background noise (reliability), the availability of an internet connection, or the used browser, PocketSphinx.js may be a better fit.
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  • WSICC Workshop at TVX
  • Jun 22, 2016

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Hypervideo based physiotherapy trainings bear an opportunity to support patients in continuing their training after being released from a rehabilitation clinic. Many exercises require the patient to sit on the floor or a gymnastic ball, lie on a gymnastics mat, or do the exercises in other postures. Using a laptop or tablet with a stand to show the exercises is more helpful than for example just having some drawings on a leaflet. However, it may lead to incorrect execution of the exercises while maintaining eye contact with the screen or require the user to get up and select the next exercise if the devices is positioned for a better view. A dual screen application, where contents are shown on a TV screen and the flow of the video can be controlled from a mobile second device, allows patients to keep their correct posture and the same time view and select contents. In this paper we propose first studies for user interface designs for such apps. Initial paper prototypes are discussed and refined in two focus groups. The results are then presented to a broader range of users in a survey. Three prototypes for the mobile app and one prototype for the TV are identified for future user tests.
Publication Details
  • WSICC Workshop at TVX
  • Jun 22, 2016

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The creation of hypervideos usually requires a lot of planning and is time consuming with respect to media content creation. However, when structure and media are put together to author a hypervideo, it may only require minor changes to make the hypervideo available in other languages or for another user group (like beginners versus experts). However, to make the translation of media and all navigation elements of a hypervideo efficient and manageable, the authoring tool needs a GUI that provides a good overview of elements that can be translated and of missing translations. In this work, we propose screen concepts that help authors to provide different versions (for example language and/or experience level) of a hypervideo. We analyzed different variants of GUI elements and evaluated them in a survey. We draw guidelines from the results that can help with the creation of similar systems in the future.
Publication Details
  • International Workshop on Interactive Content Consumption
  • Jun 22, 2016

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The confluence of technologies such as telepresence, immersive imaging, model based virtual mirror worlds, mobile live streaming, etc. give rise to a capability for people anywhere to view and connect with present or past events nearly anywhere on earth. This capability properly belongs to a public commons, available as a birthright of all humans, and can been seen as part of an evolutionary transition supporting a global collective mind. We describe examples and elements of this capability, and suggest how they can be better integrated through a tool we call TeleViewer and a framework called WorldViews, which supports easy sharing of views as well as connecting of providers and consumers of views all around the world.

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Most current mobile and wearable devices are equipped with inertial measurement units (IMU) that allow the detection of motion gestures, which can be used for interactive applications. A difficult problem to solve, however, is how to separate ambient motion from an actual motion gesture input. In this work, we explore the use of motion gesture data labeled with gesture execution phases for training supervised learning classifiers for gesture segmentation. We believe that using gesture execution phase data can significantly improve the accuracy of gesture segmentation algorithms. We define gesture execution phases as the start, middle and end of each gesture. Since labeling motion gesture data with gesture execution phase information is work intensive, we used crowd workers to perform the labeling. Using this labeled data set, we trained SVM-based classifiers to segment motion gestures from ambient movement of the device t. We describe initial results that indicate that gesture execution phase can be accurately recognized by SVM classifiers. Our main results show that training gesture segmentation classifiers with phase-labeled data substantially increases the accuracy of gesture segmentation: we achieved a gesture segmentation accuracy of 0.89 for simulated online segmentation using a sliding window approach.
Publication Details
  • Information Processing & Management
  • Jun 11, 2016

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Search log analysis has become a common practice to gain insights into user search behaviour, it helps gain an understanding of user needs and preferences, as well as how well a system supports such needs. Currently log analysis is typically focused on the low-level user actions, i.e. logged events such as issued queries and clicked results; and often only a selection of such events are logged and analysed. However, the types of logged events may differ widely from interface to interface, making comparison between systems difficult. Further, analysing a selection of events may lead to conclusions out of context— e.g. the statistics of observed query reformulations may be influenced by the existence of a relevance feedback component. Alternatively, in lab studies user activities can be analysed at a higher level, such as search tactics and strategies, abstracted away from detailed interface implementation. However, the required manual codings that map logged events to higher level interpretations prevent this type of analysis from going large scale. In this paper, we propose a new method for analysing search logs by (semi-)automatically identifying user search tactics from logged events, allowing large scale analysis that is comparable across search systems. We validate the efficiency and effectiveness of the proposed tactic identification method using logs of two reference search systems of different natures: a product search system and a video search system. With the identified tactics, we perform a series of novel log analyses in terms of entropy rate of user search tactic sequences, demonstrating how this type of analysis allows comparisons of user search behaviours across systems of different nature and design. This analysis provides insights not achievable with traditional log analysis.
Publication Details
  • ACM International Conference on Multimedia Retrieval (ICMR)
  • Jun 6, 2016

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We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments of all documents in the collection, using word vectors from the transcripts, and then select the most central segments for the summaries. We evaluate the method quantitatively on the AMI Meeting Corpus using gold standard reference summaries and the Rouge metric, and qualitatively on lecture recordings using a novel two-tiered approach with human judges. The results show that our method compares favorably with others in terms of Rouge, and outperforms the baselines for human scores, thus also validating our evaluation protocol.
Publication Details
  • LREC 2016
  • May 23, 2016

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Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. "want a Xperia" as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58% of want user tweets and 35% of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products.

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The negative effect of lapses during a behavior-change program has been shown to increase the risk of repeated lapses and, ultimately, program abandonment. In this paper, we examine the potential of system-driven lapse management -- supporting users through lapses as part of a behavior-change tool. We first review lessons from domains such as dieting and addiction research and discuss the design space of lapse management. We then explore the value of one approach to lapse management -- the use of "cheat points" as a way to encourage sustained participation. In an online study, we first examine interpretations of progress that was reached through using cheat points. We then present findings from a deployment of lapse management in a two-week field study with 30 participants. Our results demonstrate the potential of this approach to motivate and change users' behavior. We discuss important open questions for the design of future technology-mediated behavior change programs.

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Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace.
Publication Details
  • CHI 2016 (Late Breaking Work)
  • May 7, 2016

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We describe a novel thermal haptic output device, ThermoTouch, that provides a grid of thermal pixels. Unlike previous devices which mainly use Peltier elements for thermal output, ThermoTouch uses liquid cooling and electro-resistive heating to output thermal feedback at arbitrary grid locations. We describe the design of the prototype, highlight advantages and disadvantages of the technique and briefly discuss future improvements and research applications.
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  • IEEE Multimedia Magzine
  • May 2, 2016

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Silicon Valley is home to many of the world’s largest technology corporations, as well as thousands of small startups. Despite the development of other high-tech economic centers throughout the US and around the world, Silicon Valley continues to be a leading hub for high-tech innovation and development, in part because most of its companies and universities are within 20 miles of each other. Given the high concentration of multimedia researchers in Silicon Valley, and the high demand for information exchange, I was able to work with a team of researchers from various companies and organizations to start the Bay Area Multimedia Forum (BAMMF) series back in November 2013.

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With modern technologies, it is possible to create annotated interactive non-linear videos (a form of hypervideo) for the Web. These videos have a non-linear structure of linked scenes to which additional information (other media like images, text, audio, or additional videos) can be added. A variety of user interactions - like in- and between-scene navigation or zooming into additional information - are possible in players for this type of video. Like linear video, quality of experience (QoE) with annotated hypervideo experiences is tied to the temporal consistency of the video stream at the client end - its flow. Despite its interactive complexity, users expect this type of video experience to flow as seamlessly as simple linear video. However, the added hypermedia elements bog playback engines down. Download and cache management systems address the flow issue, but their effectiveness is tied to numerous questions respecting user requirements, computational strategy, and evaluative metrics. In this work, we a) define QoE metrics, b) examine structural and behavioral patterns of interactive annotated non-linear video, c) propose download and cache management algorithms and strategies, d) describe the implementation of an evaluative simulation framework, and e) present the algorithm test results.

Social Media-Based Profiling of Business Locations

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  • Fuji Xerox Technical Report
  • Mar 17, 2016

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We present a method for profiling businesses at specific locations that is based on mining information from social media. The method matches geo-tagged tweets from Twitter against venues from Foursquare to identify the specific business mentioned in a tweet. By linking geo-coordinates to places, the tweets associated with a business, such as a store, can then be used to profile that business. From these venue-located tweets, we create sentiment profiles for each of the stores in a chain. We present the results as heat maps showing how sentiment differs across stores in the same chain and how some chains have more positive sentiment than other chains. We also estimate social group size from photos and create profiles of social group size for businesses. Sample heat maps of these results illustrate how the average social group size can vary across businesses.