This paper introduces iRoCo (intuitive Robot Control) – a framework for ubiquitous human-robot collaboration using a single smartwatch and smartphone. By integrating probabilistic differentiable filters, iRoCo optimizes a combination of precise robot control and unrestricted user movement from ubiquitous devices. We demonstrate and evaluate the effectiveness of iRoCo in practical teleoperation and drone piloting applications. Comparative analysis shows no significant difference between task performance with iRoCo and gold-standard control systems in teleoperation tasks. Additionally, iRoCo users complete drone piloting tasks 32% faster than with a traditional remote control and report less frustration in a subjective load index questionnaire. Our findings strongly suggest that iRoCo is a promising new approach for intuitive robot control through smartwatches and smart-phones from anywhere, at any time. The code is available at www.github.com/wearable-motion-capture

Though mopping the floor is a mundane and tedious daily task, enabling robots to perform it comparably to humans remains a challenge. Hand-coding desired mopping behaviors for variable surfaces and situations is particularly difficult. In this paper, we develop a robotic system for mopping the floor by mimicking the human behavior demonstrated in videos. Our baseline robotic system uses traditional computer vision techniques for tracking and inverse kinematics. Our proposed robot mop learning system comprises advanced computer vision techniques, Time Contrastive Network (TCN), and reinforcement learning. Using these, we devise a reward function for the mopping task. We use a Universal 10e robotic arm attached to a mop to perform the mopping task and a first-person camera attached on top of the robotic arm to provide feedback for robotic learning. We evaluate our proposed robot mop learning system's imitative similarity using optical flow, distance in mop location, and force applied to the floor, as well as cleaning efficiency using a white glove method.

Some errors were introduced while preparing the final source files for the original article published in August 2013 in the 9,4 issue of TOMM. The errata are summarized here below together with attached revised pages showing the corrected elements indicated in red. The full CVoR (Corrected Version of Record) can be accessed in the ACM Digital Library, DOI=http://dx.doi.org/10.1145/2501643.2501652 —Page 8: New Figure 6(a) —Page 16: New Figures 8(a), 8(b), and 9(a) —Page 17: New Figure 10(b) —Page 18: New Figures 11 and 12; corrected text reference —Page 19: Final sentence deleted

Video on Demand (VoD) services allow users to select and locally consume remotely stored content. We investigate the use of caching to solve the scalability issues of several existing VoD providers. We propose metrics and goals that define the requirements of a caching framework for CDNs of VoD systems. Using data logs collected from Motorola equipment from Comcast VoD deployments we show that several classic caching solutions do not satisfy the proposed goals. We address this issue by developing novel techniques for predicting future values of several metrics of interest. We rely on computed predictions to define the penalty imposed on the system, both network and caching sites, when not storing individual items. We use item penalties to devise novel caching and static content placement strategies. We use the previously mentioned data logs to validate our solutions and show that they satisfy all the defined goals.

Video on Demand (VoD) services allow users to select and locally consume remotely stored content. We investigate the use of caching to solve the scalability issues of several existing VoD providers. We propose metrics and goals that define the requirements of a caching framework for CDNs of VoD systems. Using data logs collected from Motorola equipment from Comcast VoD deployments we show that several classic caching solutions do not satisfy the proposed goals. We address this issue by developing a novel technique for predicting future values of several metrics of interest. We use these predictions to evaluate the penalty imposed on the system (network and caches) when not caching individual items. We use item penalties to propose novel caching and static placement strategies. We use the mentioned data logs to validate our solutions and show that they satisfy all the defined goals.

Video on Demand (VoD) services provide a wide range of content options and enable subscribers to select, retrieve and locally consume desired content. In this work we propose caching solutions to improve the scalability of the content distribution networks (CDNs) of existing VoD architectures. We first investigate metrics relevant to this caching framework and subsequently define goals that should be satisfied by an efficient solution. We propose novel techniques for predicting future values of metrics of interest. We use our prediction mechanisms to define the cost imposed on the system (network and caches) by items that are not cached. We use this cost to develop novel caching and static placement strategies. We validate our solutions using log data collected from Motorola equipment from several Comcast VoD deployments.

In this work, we offer a novel system, MicroStation (μStation) that allows ubiquitous data broadcasting applications using the FM radio on mobile devices such as smartphones. μStation includes two key modules to enable data broadcasting based on existing mobile FM radio hardware. Channel Selector assigns different FM channels to neighboring μStation broadcasters to avoid collision and guides μStation listeners to find their broadcasting of interest. Data Codec realizes bit-level communication between mobile devices through existing FM radio hardware. We describe an implementation of μStation on the Nokia N900 smartphone, and provide low-level APIs and services to support application development. We also demonstrate two representative applications: Facebook-FM and Sync-Flash. These applications demonstrate the capability of μStation to readily enable a new class of ubiquitous data broadcasting applications on mobile devices.

Television has always been a popular entertainment medium with considerable impact on consumer purchase behaviors. However, unlike mobiles and PCs, it has yet to support an explicit search capability that extends beyond simple content navigation. A core reason for this is lack of usable input interfaces for TV coupled with the difficulty of creating and executing queries based on rich media context. In this paper, we present TV Answers a system that combines a novel context capture capability (Freeze-Frame) with an intelligent Web services mediator (Edge Proxy) to "crowd-source" user-generated queries. We describe the design and implementation of an early prototype of TV Answers and highlight the challenges and opportunities presented by such systems. Our goal is to demonstrate the viability of using "social search" as a complement to existing algorithm-driven search solutions for rich media consumption.

Television and video consumption are growing rapidly worldwide, driving usage of second and third screens for related interactions and information. Since television has a proven impact on consumer purchase behaviors, there is commercial interest in technologies that model viewers' intent, interests

We present the design and implementation of a new indexing technique, Mondrian tree. The Mondrian tree indexing method partitions the entire universe of discourse into spatial alarm monitoring regions and alarm-free regions. This enables us to reduce the number of on-demand alarm-free region computations, significant savings of both server load and client-to-server communication cost. We evaluate the efficiency of the Mondrian tree indexing approach and show that the Mondrian tree offers significant performance enhancements on spatial alarm processing at both the server side and the client side.

Searching for small objects (e.g., logos) in images is a critical yet challenging problem. It becomes more difficult when target objects differ significantly from the query object due to changes in scale, viewpoint or style, not to mention partial occlusion or cluttered backgrounds. With the goal to retrieve and accurately locate the small object in the images, we formulate the object search as the problem of finding subimages with the largest mutual information toward the query object. Each image is characterized by a collection of local features. Instead of only using the query object for matching, we propose a discriminative matching using both positive and negative queries to obtain the mutual information score. The user can verify the retrieved subimages and improve the search results incrementally. Our experiments on a challenging logo database of 10,000 images highlight the effectiveness of this approach.

Existing mechanisms for querying wireless sensor networks leak client interests to the servers performing the queries. The leaks are not only in terms of specific regions of interest but also of client access patterns. In this article we introduce the problem of preserving the privacy of clients querying a wireless sensor network owned by untrusted organizations. We first propose an efficient protocol, SPYC, that ensures full client privacy in settings where the servers providing access to the network are honest-but-curious and whose collaboration does not extend beyond well-defined administrative purposes. Furthermore, we study the same query privacy problem in a setting where servers exhibit malicious behavior or where powerful external attackers have access to sensor network traffic information. In this setting we propose two metrics for quantifying the privacy achieved by a client's query sequence. We then extend SPYC with a suite of practical algorithms, then analyze the privacy and efficiency levels they provide. Our TOSSIM simulations show that the proposed extensions are communication efficient while significantly improving client privacy levels.

We present techniques for synchronizing nodes that periodically broadcast content and presence updates to colocated nodes over an ad hoc network, where nodes may exhibit Byzantine malicious behavior. Instead of aligning duty cycles, our algorithms synchronize the periodic transmissions of nodes. This allows nodes to save battery power by switching off their network cards without missing updates from their neighbors. We propose several novel attack classes and show that they are able to disrupt synchronization even when launched by a single attacker. Finally, we devise a rating based algorithm (RBA) that rates neighbors based on the consistency of their behavior. By favoring well-behaved nodes in the synchronization process, we show that RBA quickly stabilizes the synchronization process and reduces the number of lost updates by 85 percent. Our evaluation also shows that all our algorithms are computationally efficient and, for the setup considered, extend the device lifetime by 30 percent over an always-on Wi-Fi scenario.

We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.

The explosion in content availability, coupled with the diversity in consumption devices, has fragmented the market. Users need new mechanisms for content discovery and navigation, while providers and advertisers need new ways to analyze and monetize usage. In this paper, we advocate 'social bookmarking' as a solution, given its proven utility in enabling affinity-based discovery online. But, adapting such web-centric ideas to rich media consumption raises technical and business challenges. With TV Clips, we introduce a system that tackles these concerns for broadcast television with extensibility to 3-screen usage. We describe the initial architecture and prototype, and discuss its evolution to support product-centric requirements like scalability and rights management. A secondary goal is to share our experiences in developing this prototype for a commercial environment (i.e., TV) and motivate further research in adapting 'social search' strategies to such media-rich domains.

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

As online Collaborative Question Answering (CQA) servicessuch as Yahoo! Answers and Baidu Knows are attracting users, questions, and answers at an explosive rate, the truly urgent and important questions are increasingly getting lost in the crowd. That is, questions that require immediate responses are pushed out of the way by the trivial but more recently arriving questions. Unlike other questions in collaborative question answering (CQA) for which users might be willing to wait until good answers appear, urgent questions are likely to be of interest to the asker only if answered in the next few minutes or hours. For such questions, late responses are either not useful or are simply not applicable. Unfortunately, current collaborative question-answering systems do not distinguish urgent questions from the rest, and could thus be ineffective for urgent information needs. We explore text- and data- mining methods for automatically identifying urgent questions in the CQA setting. Our results indicate that modeling the question context (i.e., the particular forum/category where the question was posted) can increase classification accuracy compared to the text of the question alone.

In this paper, we present techniques for synchronizing nodes that periodically broadcast content and presence updates to co-located nodes over an ad-hoc network, where nodes may exhibit Byzantine malicious behavior. We first propose an algorithm for synchronizing the periodic transmissions of all the nodes in an attacker-free multi-hop network. This allows nodes to save battery power by switching off their network cards without missing updates from their neighbors. We then introduce a suite of spoofing attacks and show that they are able to disrupt synchronization and destabilize the network even when launched by a single attacker in large, multi-hop networks. Finally, we devise a rating based algorithm that rates neighbors based on the consistency of their behaviors. By favoring well-behaved nodes in the synchronization process, we show that we can address the issue of Byzantine malicious behavior very effectively. Our evaluation shows that the algorithms are computationally efficient and, for the setup considered, extend the device lifetime by 30% over an always-on Wi-Fi scenario. Moreover, in the presence of attacks, our rating based algorithm quickly stabilizes the synchronization process and reduces the number of lost updates by 85%.

Even as mobile devices as well as wireless networks become increasingly capable, newer content-intensive application place increasing demands on the resources of these devices. Some of these applications make conflicting resource demands making it difficult for device platforms to guarantee acceptable user experience. Thus, a resource aware middleware is needed that mobile applications can leverage to manage their content. In this paper we discuss Cache & Carry, a framework for mobile devices that intelligently manages scarce resources on the mobile device across multiple applications. The framework takes into consideration changing mobile device characteristics such as available battery and storage as well as changing network characteristics such as availability of multiple networks, their perceived cost, their available bandwidths etc., to provide an optimal end user experience.

Encouraging cooperation between users of mobile devices operating in ad hoc mode is a difficult task mostly because the scarce battery and bandwidth resources of devices suggest that selfish behavior may be most beneficial. The insecure usage of credits to reward cooperation can easily provide an incentive for cheating, thus, on the long term only leading to selfishness. In this paper we propose several secure credit based mechanisms enforcing fairness in a hybrid wireless content retrieval system operating both in cellular and ad hoc connectivity modes. Our solution consists of mechanisms for securely and privately discovering desired content on neighboring devices, simultaneously exchanging credit and content shares in a verifiable manner and for generating and expiring non-forgeable credits. We present experimental results of a partial prototype of our system implemented on MPx and E680i cellular phones and HP iPaq hx4700 PDAs, along with extensive simulation results showing that our solution significantly reduces the effectiveness of selfish behavior, making it an unattractive strategy.

With the globalization of markets and the creation of software to address these markets, educators face new curriculum challenges. They must expose students not only to core science skills, but also to a number of other skills that will enable them to innovate effectively on a global stage. This position paper discusses the evolution of business models and software requirements over the last decade, and the associated changes to CS curricula that will enable today's CS students to become tomorrow's innovators

Seamless management of resource constrained wireless devices along with enterprise hosts is complex because of the multitude of management protocols and architectures. An atomic and coordinated management action that spans the scale of the enterprise is needed. In this paper we present the architecture and our experiences in building a Universal Manager that manages all mobile and non-mobile devices in the enterprise. We came up with a multi-protocol gateway that blends intermittently connected mobile devices seamlessly with enterprise hosts. Our implementation of SyncML based mobile devices integrated with SNMP based enterprise manager is deployed in the enterprise and performance results are presented.

This paper describes the design and implementation of MOBY, a network for mobile peer-to-peer exchange of services and data. Constraints on computing power of mobile devices, limited hardware, networking, and software resources, and ad-hoc nature of mobile clients pose considerable challenges from the points of view of supporting performance goals, ease of service integration, and adaptation. These challenges are addressed in MOBY by dynamic service location and client mapping, surrogates for mobile clients, and standardized interfaces built upon off-the-shelf software components.

We present a framework for supporting multi-device stream orchestration for mobile users. As computing power becomes cheaper and larger numbers of devices become networked, we believe that users will have access to multiple devices in their neighborhood. Such a multi-device environment presents a significantly powerful environment for mobile users compared to thin wireless clients. Our architecture enables a mobile user to treat these disparate devices as a virtually integrated entity, enabling the user to experience rich communication sessions. The framework automatically detects devices in a user's neighborhood, understands their capabilities, transparently transforms content and routes a session to multiple devices based on their capabilities. We describe the problem space, our architecture and implementation experience.

Deploying distributed services over a complex network topology presents a challenge: one of mapping the objects to locations in an optimal manner. This mapping needs to be dynamic, taking current network conditions into consideration. Remapping services is manual-intensive, requires operator effort and may result in service downtime. The Mojave project described here investigates an architecture for implementing malleable (auto-configuring) services using reactive and mobile agents. In contrast to past efforts, Mojave views agents as a wrapper technology implemented over a Jini/tuplespace-based architecture. The paper describes the Mojave architecture and implementation, experiences in building an adaptive systems manager application, and benefits of the Mojave architecture for thin-client computing.

Magritte is an attempt to construct a high-bandwidth front-end to an object-base containing meta-data about SCAD designs. SCAD is a small part of a family of visualization applications where the end-user concurrently manipulates large collections of active data. Such end-user interfaces require a different paradigm of interaction than the object-at-a-time interfaces of current databases. Proposals here can be divided into mechanisms for scene creation and those for scene integration. The former allow a user to create a single scene with ease. The latter help in desktop management by allowing scenes to be combined and correlated. The implementation experience points out a number of shortcomings in current database offerings that need to be solved so as to ease the design of high-bandwidth front-ends.>

The first phase of DAMOCLES, which involves developing a data management system subset of an entire design tracking system using the observer approach, is described. The designer is provided with a cockpit data management view of the project, without changing the way in which design groups do data management. The observer approach creates a cockpit view for the designer without changing the current point-task view. The cockpit view presents a designer with a design state perspective consisting of an object-oriented view of design data, tool flow and methodology information, history and audit trail information, etc. The designer can use the cockpit view to get the larger picture about project progress as and when the designer chooses. This view is extracted from the traditional designer environment by DAMOCLES while the designer is working through the point-task view.>