Deep Neural Network (DNN) inference on edge devices is now a common practice. However, tailoring a model for multiple devices involves a lot of time and effort. While elastic models, also known as weight-sharing models, have been proposed as an efficient solution to create high-accuracy, low-latency modules, the design of an elastic model's candidate space (search space) has been underexplored. We identified a new characteristic in candidate spaces, which we named sensitivity, made a design rationale for candidate spaces based on it, and then built a preliminary algorithm to generate candidate spaces. Results show that we can get a range of models (with a 2.75× FLOPs range) on the Pareto frontier of the space by training only once.

Rural children in South Korea exhibit higher foreign language anxiety and lower English competency. For such marginalized contexts where local communities cannot support children's learning, we propose and explore a new pedagogical approach, CRAYON (Community-based RelAY Online educatioN). In CRAYON, a pool of non-professional tutors take turns to meet and teach rural children in short relay sessions through mobile technologies. It uncovers and promotes volunteers' internal willingness to participate in community-based teaching, which could otherwise be fragmented and dormant in their tightly-woven daily lives. It greatly lessens the barriers to participation from multiple dimensions, i.e., time, space, and expertise, and encourages interested volunteers to easily join without taking much burden. As such, the approach can create new learning opportunities and help rural children overcome their motivational and environmental hurdles. Tutors could approach each child and share short but precious time with her; helping her experience repetitive and sufficient exposure to the language, each time with a newly met tutor. We conducted a short relay session-based English learning program for 5 rural children for 4 weeks in South Korea with 15 tutors. From the field deployment, we find that the rural children and the undergraduate tutors engaged in effective interactions and scaffolding, despite the constraints of partitioned short sessions. A particular pattern of interaction, i.e., continuous learner engagement support, emerged as they drew out the interactions over a short period of time. It was highly encouraging to observe that all children, including those who were disengaged in their classroom environments, actively participated in the CRAYON sessions. The findings elicited from the study have important implications in multiple dimensions. They suggest the possibility of extending the scope of learning environments to include first-met tutors and learners beyond re-established relationship. In a larger perspective, the findings imply a new direction to overcome the challenges of low childhood literacy in under-resourced areas. With adequate and sufficient support from educational institutions and CRAYON, this study argues that volunteer tutors with less experience can deliver effective instruction by sharing just a short period of time, and help a child who has been lagging behind the pace of the school catch up and re-engage.

Improving the communication performance of parallel programs is an important but difficult problem in a large-scale distributed memory-based cluster. Efforts to improve parallel scalability often face severe huddles in managing communication overheads. This paper proposes a framework of a space-filing curve(SFC)-based task-remapping for communication intensive parallel applications. An SFC-based mapping, when applied for task-mapping of parallel applications preserves locality in terms of communications and produce a less fragmented task-mapping, reducing communication overheads. The framework also provides tools for performance analysis to see if the proposed task-mapping is appropriate for a given application running on a target system. It further develops a binary classifier as a predictor to decide whether or not to apply the proposed mapping before run-time. We evaluate the framework with three communication intensive applications in Cartesian coordinates: P3DFFT solver and Channel code using 2D domain decomposition model, and Poisson solver using 3D domain decomposition. The evaluation is conducted on a large-scale cluster system of fat-tree topology with up to 1,024 compute nodes. The proposed task-mapping achieves the overall performance improvement ranging from ~30% to ~66% over the baseline approach depending on the workloads. Also, when used in combination with the binary classifier-based predictor, it achieves the expected performance gains from 4% to 8%.

Public spaces1 are a basis of urban lives. The mode and culture of sharing could be an indicator of the quality of life in the cities. For example, the buses and restaurants should comfort and bring satisfaction to individual visitors, including the vulnerable with special needs. However, their operation mostly occurs in rather a closed and exclusive manner [2]. An inherent limitation to such an inclusive sharing lies in the exclusive modes of traditional device interfaces; a variety of devices, called public devices hereafter, are installed in the public space and determine operational details of the space. As such, the space itself is shared, however, the public devices are controlled in exclusive ways. Could the sharing of the public space be operated in a democratic way?

Public spaces are equipped with 'public actuators', e.g., HVAC, lighting fixtures, speakers, or streaming TV channels to ensure their visitors' comfort. However, many public actuators rarely allow the visitors to adjust their operation, limiting their utility and fairness across the visitors. Also, the social bar is often too high to speak up one's preference and attempt to change an actuator's operation. Social control and use of IoT devices is an underexplored new direction of research even with its huge potential and implication, but comes with high complexity and scale. This paper proposes a novel architecture, namely, Social Control-and-Use Architecture for IoT Devices, which provides a systematic view and an effective tool to handle the complication and intricacy in system design. It also proposes Hivemind, a first-of-a-kind system developed, upon the architecture, for sharing IoT-enabled actuators in a public space. It transforms an exclusively-controlled actuator in a public space into a true public actuator, supporting visitors to instantly participate in the democratic collective control. Also, a myriad of off-the-shelf actuators are easily incorporated without modification to their implementation. The field deployment of Hivemind shows its comprehensive service coverage as well as the users' approval on the democratic collective control of public actuators.

Public spaces, where we gather, commune, and take a rest, are the essential parts of a modern urban landscape, enriching citizen's everyday life [3]. How we share these spaces are considered an indicator of the quality of life. Public spaces thus have a responsibility to provide comfort and satisfaction to any visitors. However, in most times, the operations of the spaces are managed in rather an exclusive manner.

Continuous mobile sensing applications (CMSAs) run 24 hours in the background, consuming a significant amount of energy. Due to CMSA's dynamic behavior according to the users' context, it is very difficult to predict the energy consumption of CMSA. User trace-based solutions have been proposed to effectively estimate the energy consumption of CMSA, but they suffer burdensome user trace collection. We propose Scenethesizer, a scenario-based energy estimation system, which generates and augments the user traces for unseen application usage scenarios and estimate the energy consumption of CMSA based on the generated user traces.

Energy-efficiency is a key performance metric of mobile sensing applications. However, assessment of energy-efficiency is greatly limited in practice. The main difficulty is that it requires assessment of power consumption in various user's real-life situation in the long term. This poster presents DeepPower, a system for assessing energy-efficiency of mobile sensing applications in fast and scalable manner. DeepPower introduces a sensor trace-based power use prediction technique, which significantly reduces the cost of assessing power consumption compared to existing power emulation techniques. Our experiments with three mobile sensing applications and five 1-hour-long sensor traces show that DeepPower can predict hardware usage of 1-hour-long sensor traces in less than a second, achieving average error rate of 4.6%.

Life cycle management of battery powered-IoT devices in large scale deployments is difficult due to the non-existence of a compatible approach to estimate their battery health. Most existing approaches require either battery parameters, determination of which is beyond IoT devices' capability due to hardware limitation, or special applicable conditions that do not always hold due to devices' dynamic operating environments. In this paper, we propose a novel approach for facilitating the life cycle management of large-scale deployments through online estimation of battery health. Our approach is based on V-edge dynamics which capture and characterize instantaneous voltage drops. Our evaluation carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating the battery health up to 80% accuracy.

The automotive navigation often embarrasses drivers by providing guidance that is awkward, incomprehensible, or almost impossible to follow. We point out the lack of on-the-spot awareness as the key reason behind this situation. The current navigation does not consider a driver's characteristics such as driving ability, as well as the detailed conditions of the current driving environment. In this paper, we explore the cases of embarrassment related to navigation usage. We collected a total of 56 cases of embarrassing moments from three drivers' experiences and derived 8 categories of embarrassing moments.

Today's smartphone application (hereinafter `app') markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users' awareness. We propose PowerForecaster to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps' power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel power emulator as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users' behaviors and emulating the target app's power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.

Energy efficiency is critical in many IoT applications with sensors that deliver data over wireless communications. Duty-cycling has been a major method for reducing energy consumption. One popular duty-cycling is MAC duty-cycling where an MCU commands the periodic or adaptive turning on and off of an RF chip. Recently, there has been an increase in the number of RF chips for IoT that are equipped with PHY duty-cycling, a new capability of autonomously switching on and off an RF chip without the use of an MCU. These two schemes working at different layers have different pros and cons in terms of operating time scale, the amount of energy saved when the RF chip is switched off, all of which depend on the characteristics of the MCU and the RF chip. In this paper, we propose a novel protocol named HD-MAC (Hierarchical Duty-cycling MAC) that hierarchically integrates duty-cycling in the MAC and physical layers. By smartly applying the new function of chip-level duty-cycling, HD-MAC is able to further reduce the amount of on-time in MAC duty-cycling; hence, energy efficiency can be improved. To optimize HD-MAC’s energy efficiency while achieving a given delay requirement, we formulate an optimization problem and solve it to obtain the optimal parameters in the cross-layer context. We implement HD-MAC on Contiki OS and perform extensive experiments using a real sensor mote Firefly with a CC1200 RF chip. We demonstrate that the energy efficiency of HD-MAC is up to 72% higher than that of existing protocols while still satisfying the delay requirement and sustaining similar reliability.

Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.

We introduce Zaturi, a system enabling parents to create an audio book for their babies by utilizing micro spare time at work. We define micro spare time at work as tiny fragments of time with low cognitive loads that frequently occur at work, such as waiting for an elevator. We show that putting together micro spare time at work helps a working parent (1) build a tangible symbol conveying his/her thoughts to the beloved baby and (2) develop his/her own feelings of parental achievement without compromising regular working hours. Zaturi lets the parent immediately be aware of micro spare time and provides a crafted interface to seamlessly record the book piece by piece, so that the baby can enjoy listening to the book recorded in the parent's own voice. Through an extensive design process, we characterize the notion of micro spare time and build a working prototype of Zaturi. We also report parents' perceptions and family reactions after a two-week deployment.

High-end mobile GPUs are now becoming an integral part of mobile devices. However, a mobile GPU constitutes a major portion of power consumption on the devices, and mobile games top as the most popular class of graphics applications. This paper presents the design and implementation of RAVEN, a novel, on-the-fly frame rate scaling system for mobile gaming applications. RAVEN utilizes human visual perception of graphics change to opportunistically achieve power saving without degrading user experiences. The system develops a light-weight frame comparison technique to measure and predict perception-aware frame similarity. It also builds a low resolution virtual display which clones the device screen for performing similarity measurement at a low-power cost. It is able to work on an existing commercial smartphone and support applications from app stores without any modifications. It has been implemented on Nexus 5X, and its performance has been measured with 13 games. The system effectively reduces the overall power consumption of mobile devices while maintaining satisfactory user experiences. The power consumption is reduced by 21.78% on aver-age and up to 34.74%.

A mobile GPU constitutes the majority of power consumption on a mobile device and mobile games top as the most popular class of graphics applications. In this demo, we present FROG, a novel frame rate optimization system for mobile gaming applications. FROG makes use of human visual perception to graphics and regulates application's frame rendering process on-the-fly for maximizing power saving without degrading the user experience. The system works on an existing commercial smartphone and support the legacy gaming applications from app stores without requiring any changes from applications

Imagine a densely packed crowd that gathers to convey a common message, such as people in a candlelight vigil or a protest. We envision an innovation through mobile computing technologies to empower such a crowd by enabling them simply to hold their phones up and create a massive collective visualization on top of them. We propose Card-stunt as a Service (CaaS). CaaS is a service enabling a densely packed crowd to instantly visualize symbols using their mobile devices and a server-side service. The key challenge toward realizing an instant collective visualization is how to achieve instant, infrastructure-free, decimeter-level localization of individuals in a massively packed crowd, while maintaining low latency. CaaS addresses the challenges by mobile visible-light angle-of-arrival (AoA) sensing and scalable constrained optimization. It reconstructs relative locations of all individuals and dispatches individualized timed pixels to each one so that they can do their part in the overall visualization. We evaluate CaaS with extensive experiments under diverse reality settings as well as under synthetic workloads scaling up to tens of thousands of people. We deploy CaaS to 49 participants so that they successfully perform a collective visualization cheering up MobiSys.

Consider a massive crowd who gathered together to convey their common voice to public, e.g., supporters of a team sitting together in a stadium, people doing a candlelight vigil in a public square, and so on. Imagine that they hold up their smartphone displays which collectively compose a huge public screen; the crowd's messages are now shown big on the top of them. We present CaaS [3], a mobile service to realize such an instant, massive, collective visualization with commodity smartphones and cloud services. In this demo, we demonstrate the collective localization feature of CaaS so that the audience can watch a given pattern or symbol collectively displayed on top of arbitrarily positioned phones (See the video demo, https://goo.gl/GfsORc).

Social events such as networking parties are excellent opportunities to expand one's social network and make new social ties, as well as simply have fun and enjoy oneself. With the emergence of pervasive technology, we now have the opportunity to discover face-to-face socializing behaviors of individual guests and support them in their socializing goals. Quantitative measures of socializing behaviors will be useful in diverse applications from social therapy to social event management. We propose a system that detects and analyzes group-level socializing behaviors to support real-time and ex post facto social applications deployed in real social event situations. In our approach, we exploit attendees' characteristics of socializing such as proximity and body orientation to determine social proximity among guests and subsequently detect socializing groups. We performed preliminary studies and multiple deployments of our system and example applications in real social networking parties. Evaluation shows the system can detect socializing groups accurately and support applications that satisfy the interests of guests and party hosts.

Research shows the critical role of social relationships in behavior change, and the advancement of mobile technologies brings new opportunities of using online social support for persuasive applications. In this paper, we propose Relational Norm Intervention (RNI) model for behavior change, which involves two individuals as a target user and a helper respectively. RNI model uses Negative Reinforcement and Other-Regarding Preferences as motivating factors for behavior change. The model features the passive participation of a helper who will undergo artificially generated discomforts (e.g., limited access to a mobile device) when a target user performs against a target behavior. Based on in-depth discussions from a two-phase design workshop, we designed and implemented BeUpright, a mobile application employing RNI model to correct sitting posture of a target user. Also, we conducted a two-week study to evaluate the effectiveness and user experience of BeUpright. The study showed that RNI model has a potential to increase efficacy, in terms of behavior change, compared to conventional notification approaches. The most influential factor of RNI model in the changing the behavior of target users was the intention to avoid discomforting their helpers. RNI model also showed a potential to help unmotivated individuals in behavior change. We discuss the mechanism of RNI model in relation to prior literature on behavior change and implications of exploiting discomfort in mobile behavior change services.

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

In this poster, we propose an emotion analysis method using context information gathered from sensor data. We observe that there are certain situations in which a specific emotion is strongly felt, i.e. dominant. We develop a "scenario-based" approach that focuses on creating such situations with dominant emotion, using a set of context information from sensor data. These "scenarios" undergo validation process through a preliminary survey. Finally, an application prototype detects these scenarios to return the analyzed emotion to the user.

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Especially, CoMon+ improves the negotiation method proposed in our earlier work, CoMon [30], to exploit multiple processing plans enabled by various personal sensing devices; each plan can be alternatively used for cooperation, which in turn will maximize overall power saving. We implement a CoMon+ prototype and show that it provides significant benefit for mobile sensing applications, e.g., saving 27-71 percent of smartphone power consumption depending on cooperation cases. Also, our deployment study shows that CoMon+ saves an average 19.7 percent of battery under daily use of a prototype application compared to the case without CoMon+ running.

The unique aquatic nature of swimming makes it difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose the use of a smartphone-based multiplayer exergame named MobyDick . MobyDick is designed to be played while swimming, where a team of swimmers collaborate to hunt down a virtual monster. To this end, we take into account both human factors and technical challenges under swimming contexts. First, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Second, we develop a swimming activity recognition system to enable precise and real-time game inputs. Third, we devise a multiplayer game design by employing the unique interaction mode viable in an underwater environment, where the abilities of human communication are highly limited. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and we deploy it in real swimming pool environments ( n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multiplayer swimming games.

Knowing individuals' relational orientation is imperative for effective offline, as well as online, interactions and collaborations. We use attachment theory to examine the link between Facebook users' relational orientation (in terms of attachment styles: anxiety and avoidance) and their relational activities. Our research examines whether and how the two key relational processes identified in offline social relationships (self-expression and responsiveness) are manifested on online social networks and related to attachment styles. We describe our dataset of 640 Facebook users, their attachment scale survey results, and their 525,334 posts. We define four features that map onto relational activities on Facebook: status updates and status updates with emotional words (self-expression); comments and likes (responsiveness). We find significant relationships between the users' attachment styles and their self-expression and responsiveness activities on Facebook. A key takeaway of our research is that without relying on self-reported surveys, a computational analysis of a Facebook user's self-expressing and responding activities alone can reveal the user's underlying relational orientation (i.e., attachment style).

Our preliminary study reveals that individuals use various management strategies for limiting smartphone use, ranging from keeping smartphones out of reach to removing apps. However, we also found that users often had difficulties in maintaining their chosen management strategies due to lack of self-regulation. In this paper, we present NUGU, a group-based intervention app for improving self-regulation of limiting smartphone use through leveraging social support: groups of people limit their use together by sharing their limiting information. NUGU is designed based on social cognitive theory, and it has been developed iteratively through two pilot tests. Our three-week user study (n = 62) demonstrated that compared with its non-social counterpart, the NUGU users' usage amount significantly decreased and their perceived level of managing disturbances improved. Furthermore, our exit interview confirmed that NUGU's design elements are effective for achieving limiting goals.

Emerging continuous sensing apps introduce new major factors governing phones' overall battery consumption behaviors: (1) added nontrivial persistent battery drain, and more importantly (2) different battery drain rate depending on the user's different mobility condition. In this paper, we address the new battery impacting factors significant enough to outdate users' existing battery model in real life. We explore an initial approach to help users understand the cause and effect between their physical activity and phones' battery life. To this end, we present Sandra, a novel mobility-aware smartphone battery information advisor, and study its potential to help users redevelop their battery model. We perform an extensive explorative study and deployment for 30 days with 24 users. Our findings reveal what they essentially learned, and in which situations they found Sandra very helpful. We share the lessons learned to help in the design of future mobility-aware battery advisors.

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.

Recently, a number of continuous sensing applications have been actively proposed in research communities and commercially released in the market. However, due to their unique power characteristics, user behavior-dependent battery drain, they bring new challenges for users' power management on these applications. In this demonstration, we present a comprehensive approach to support users' power management for continuous sensing applications. First, at pre-installation time, we provide an instant, personalized power estimation of a continuous sensing application. Without exhaustive trial and error, users can decide judiciously to install a certain application or not. Second, at runtime, we provide mobility-aware battery information. With this information, users can better estimate the phone's remaining battery life based on their imminent mobility conditions and take necessary actions in advance such as carrying an additional battery or minimizing the use of applications.

Many theories and empirical research show the critical role of social relationships in shaping human behavior. Mobile and persuasive technologies present increased opportunities for affecting people's behavior. We propose the relational intervention model, which provokes a paired helper when the user performs a target behavior. In this demo, we present BeUpright, a posture-correction mobile application based on the relational intervention model.

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.

Exergame controllers are intended to add fun to monotonous exercise. However, studies on exergame controllers mostly focus on designing new controllers and exploring specific application domains without analyzing human factors, such as performance, comfort, and effort. In this paper, we examine the characteristics of a speed-based exergame controller that bear on human factors related to body movement and exercise. Users performed tasks such as changing and maintaining exercise speed for avatar control while their performance was measured. The exergame controller follows Fitts' law, but requires longer movement time than a gamepad and Wiimote. As well, resistance force and target speed affect performance. User experience data confirm that the comfort and mental effort are adequate as practical game controllers. The paper concludes with discussion on applying our findings to practical exergame design.

Language delay is a developmental problem of children who do not acquire language as expected for their chronological ages. Without timely intervention, language delay can act as a lifelong risk factor. Speech-language pathologists highlight that effective parent participation in everyday parent-child conversation is important to treat children's language delay. For effective roles, however, parents need to alter their own lifelong-established conversation habits, requiring extensive period of conscious effort and staying alert. In this paper, we present new opportunities for mobile and social computing to reinforce everyday parent-child conversation with therapeutic implications for children with language delays. Specifically, we propose TalkBetter, a mobile in-situ intervention service to help parents in daily parent-child conversation through real-time meta-linguistic analysis of ongoing conversations. Through extensive field studies with speech-language pathologists and parents, we report the multilateral motivations and implications of TalkBetter. We present our development of TalkBetter prototype and report its performance evaluation.

The goal of this work is to provide an abstraction of ideal sound environments to a new emerging class of Mobile Multi-speaker Audio (MMA) applications. Typically, it is challenging for MMA applications to implement advanced sound features (e.g., surround sound) accurately in mobile environments, especially due to unknown, irregular loudspeaker configurations. Towards an illusion that MMA applications run over specific loudspeaker configurations (i.e., speaker type, layout), this work proposes AMAC, a new Adaptive Mobile Audio Coordination system that senses the acoustic characteristics of mobile environments and controls individual loud-speakers adaptively and accurately. The prototype of AMAC implemented on commodity smartphones shows that it provides the coordination accuracy in sound arrival time in several tens of microseconds and reduces the variance in sound level substantially.

Interpersonal touch is our most primitive social language strongly governing our emotional well-being. Despite the positive implications of touch in many facets of our daily social interactions, we find wide-spread caution and taboo limiting touch-based interactions in workplace relationships that constitute a significant part of our daily social life. In this paper, we explore new opportunities for ubicomp technology to promote a new meme of casual and cheerful interpersonal touch such as high-fives towards facilitating vibrant workplace culture. Specifically, we propose High5, a mobile service with a smartwatch-style system to promote high-fives in everyday workplace interactions. We first present initial user motivation from semi-structured interviews regarding the potentially controversial idea of High5. We then present our smartwatch-style prototype to detect high-fives based on sensing electric skin potential levels. We demonstrate its key technical observation and performance evaluation.

Finding a missing child is an important problem concerning not only parents but also our society. It is essential and natural to use serendipitous clues from neighbors for finding a missing child. In this paper, we explore a new architecture of crowd collaboration to expedite this mission-critical process and propose a crowd-sourced cooperative mobile application, CoSMiC. It helps parents find their missing child quickly on the spot before he or she completely disappears. A key idea lies in constructing the location history of a child via crowd participation, thereby leading parents to their child easily and quickly. We implement a prototype application and conduct extensive user studies to assess the design of the application and investigate its potential for practical use.

The unique aquatic nature of swimming makes it very difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose MobyDick, a smartphone-based multi-player exergame designed to be used while swimming, in which a team of swimmers collaborate to hunt down a virtual monster. In this paper, we present a novel, holistic game design that takes into account both human factors and technical challenges. Firstly, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Secondly, we develop a single phone-based inertial and barometric stroke activity recognition system to enable precise, real-time game inputs. Thirdly, we carefully devise a multi-player interaction mode viable in the underwater environment highly limiting the abilities of human communication. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and deploy it to real swimming pool environments (n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multi-player swimming games.

In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans for application requests and selectively applies them according to resource availability and demands at runtime. Through the selection, it resolves resource contention among applications and helps them efficiently share resources. With such system-level supports, applications become capable of providing long-running services under dynamic circumstances with scarce resources. Also, the platform can host a number of applications stably, exploiting its full resource capacity. We build an Orchestrator prototype on off-the-shelf mobile devices and sensor motes and show its effectiveness in terms of application supportability and resource use efficiency.

Our goal is to show that it is possible to automatically infer social relationship types among people who stay together in an organization by analyzing communication patterns. We collected indoor co-location data and instant messenger data from 22 participants for one month. Based on the data, we designed and explored several indicators which are considered to be useful for mining social relationship types. We applied machine learning techniques using the indicators and found that it is possible to develop an intelligent method to infer social relationship types.

Social exergames provide immersive experiences of social interaction via online multiplayer games, ranging from simple group exercises (e.g., virtual cycling/rowing) to more structured multiplayer games (e.g., cooperative boat racing). In exergame design, interpersonal synchrony plays an important role as it enhances social rapport and pro-social behavior. In this paper, we build ExerSync platform that supports various assistive mechanisms for facilitating interpersonal synchrony even with heterogeneous exercise modalities. We consider a rhythm of body movements in repetitive aerobic exercises and explore the design space of incorporating rhythm into exergames. We build a prototype system and comparatively evaluate the effectiveness of various assistive mechanisms. The experiment results show that rhythm significantly lowers the perceived workloads and provides better competence and engagement, but the accuracy of interpersonal synchrony is not dependent on the use of rhythm.

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

No abstract available.

Contentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. Providing a classified view of the opposing views of the issues can help readers easily understand the issue from multiple perspectives. We present a disputant relation-based method for classifying news articles on contentious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame and attain balanced understanding, free from a specific biased viewpoint. The method is performed in three stages: disputant extraction, disputant partitioning, and article classification. We apply a modified version of HITS algorithm and an SVM classifier trained with pseudorelevant data for article analysis. We conduct an accuracy analysis and an upper-bound analysis for the evaluation of the method.

This paper discusses an approach for transforming solitary exercises into social exergames. We frame our discussion by highlighting the relation between the original exercises and game interactions, and by analyzing an example exergame which is successfully transformed from its original solitary exercise. We present a user study of the exergame that evaluates the need for holistic transformation strategies from solitary exercises into social exergames.

On school field trips, chaperoning teachers' foremost concern is the safety of the children, particularly ensuring that none of them go missing. However, they have limited attention resources and face many challenges in keeping track of their charges. We present RubberBand, an assistive application that helps alleviate the teacher's burden. Our approach adapts to diverse field trip environmental and child behavioral dynamicity, utilizing observations of the relative dispersion of children and their tendency to form sub-groups.

In this paper, we propose a new class of message routing scheme for a distributed network which can scale up to the needs of a very large number of mobile users. We leverage the work in the GeoKad paper which uses the Distributed Geographic Table, allowing node retrieval from resources that are close to any given region. The paper addresses the problem regarding efficient message passing and peer misses. We propose a new routing scheme that addresses this kind of limitation. In fact, the problem can be solved in two ways, either by preserving the state of peer and further use a prediction algorithm or by better message routing. The proposed scheme is based on swarm intelligence (SI) and its attribute of exploit and explore. We leverage the statistical properties of time-varying network connectivity for opportunistic message forwarding. Considering the present 3G or Wi-Fi environment this approach can also help in optimizing energy as it can prevent query over flooding which is mostly encountered in such network scenarios.

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications.

No abstract available.

No abstract available.

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

We demonstrate a pervasive social exergame platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. Also, we show the potential of using multiple exercise devices as game controllers and incorporating multiple heterogeneous controllers into a game. Specifically, we consider a class of exercise equipment used for repetitive, individual, and aerobic (RIA) exercises such as treadmill running, stationary cycling, hula hooping, and jump roping.

In this paper, we propose a novel efficient dataflow execution method for mobile context monitoring applications. As a key approach to minimize the execution overhead, we propose a new dataflow execution model, producer-oriented model. Compared to the conventional consumer-oriented model adopted in stream processing engines, our model significantly reduces execution overhead to process context monitoring dataflow reflecting unique characteristics of context monitoring. To realize the model, we develop DataBank, an execution container that takes charge of the management and delivery of the output data for the associated operator. We demonstrate the effectiveness of DataBank by implementing three useful applications and their dataflow graphs, i.e., MusicMap, FindMyPhone, and CalorieMonitor. Using the applications, we show that DataBank reduces the CPU utilization by more than 50%, compared to the methods based on the consumer-oriented model; DataBank enables more context monitoring applications to run concurrently.

In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive computing, a number of useful mobile context-aware applications have been proposed, but their actual deployment is slow due to complexity of context processing and heavy resource and battery usage. To address such challenges, we have been building MobiCon for many years, upon which diverse context-aware applications are developed and deployed without concerns about complexity of context processing and resource optimization.

Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent sensing workloads raise a new challenge incurring severe resource contention among themselves and with other foreground applications. To address the challenge, we develop SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications. As its key approach, we develop a novel sensing-flow-aware coordination. We first introduce the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams. Leveraging the identified frame structures, SymPhoney develops frame-based coordination and scheduling mechanisms, which effectively coordinates the resource use of concurrent contending applications and maximize their utilities even under severe resource contention. We implemented several sensing applications on top of the SymPhoney engine and performed extensive experiments, showing effective coordination capability of SymPhoney.

We demonstrate PosFree, a platform supporting posture-free motion-based mobile games. It allows the developers to support diverse postural gameplay contexts in the motion-based mobile games, without much knowledge on the specific characteristics of motion sensors with respect to posture changes. It detects orientation of mobile devices at runtime and automatically converts motion sensor data as if the devices are manipulated in the standard posture, as well as provides an intuitive user interface to help players understand their postural gameplay contexts. The platform prototype is currently implemented inside the Android Framework, and thereby off-the-shelf motion-based mobile games can utilize the feature of PosFree without modifying their original source codes.

In this paper, we propose an in-situ Crowd-aware Sensing Platform, called "CrowdMon", which envisions the cooperation among mobile users in highly crowded urban areas such as metro and square. CrowdMon establishes a spontaneous connection from co-located users in a semantic proximity and enables them to share contextual information such as location, ambient music, and mood of places. To the best of our knowledge, CrowdMon is the first attempt to support crowd-aware services at a platform level. We show interesting use cases of CrowdMon and an initial system design to realize the crowd-based context sharing.

Bias in the news media is an inherent flaw of the news production process. The bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as the Iraq war. This article presents NewsCube, a novel Internet news service which aims to mitigate the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified views on a news event. As such, it helps readers understand the event from a plurality of views and to formulate their own, more balanced, viewpoints. The media bias problem has been studied extensively in mass communications and social science. This article reviews related mass communication and journalism studies and provides a structured view of the media bias problem and its solution. We propose media bias mitigation as a practical solution and demonstrate it through NewsCube. We evaluate and discuss the effectiveness of NewsCube through various performance studies.

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performanceATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared staging architecture and highly efficient processing techniques, which address the scalability challenges caused by massive location updates, a number of ATP monitoring queries and processing complexity of ATP monitoring. Finally, we demonstrate the extensive performance study of ActraMon using realistic city-wide ATP workloads.

Collaborative filtering relies on numerical ratings for recommendations. While users consider various aspects of content as a basis of their evaluation, a numeric rating provides only an aggregated report of final assessment. The performance of a collaborative recommender system could be enhanced if

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.

Field trips in kindergarten imply excellent chances to attain a wide spectrum of educational clues for the children. However, in-depth observation on their exploratory behaviors is uniquely challenging. Teachers mostly take all possible precautions against any incidents, sparing little time and attention for observation. We collaborated with kindergarten teachers to develop a system for delegated observation of the children's exploratory behaviors by using smartphones and sensor technologies.

In this article, we present a serendipitous social network that makes use of individuals' contextual information to connect people in shared immediate situations. We build a prototype microblogging service that uses a serendipitous social network as a substrate. We performed an initial user study and found that users engaged in shared immediate situations are likely to exchange posts relevant to their situation that provide useful information or reflect emotional understanding.

No abstract available.

We demonstrate E-Gesture, a collaborative architecture for energy-efficient gesture recognition on a hand-worn sensor device and an off-the-shelf smartphone that greatly reduces energy consumption while achieving high accuracy recognition under dynamic mobile situations. E-gesture employs a novel gesture segmentation and classification architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing energy consumption characteristics in both sensor and smartphone.

Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.

In upcoming ubiquitous environment, many real-time sensing applications will emerge. These applications show different scale and characteristics on delivery demands. The applications commonly depend on real-time understanding on data from widely distributed data sources. Also, they have highly diverse and complex delivery demands in terms of data and delay, e.g., data value ranges of interest, spatial and temporal resolution and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery schemes cannot support the applications effectively. We present a novel data delivery scheme to support real-time sensing applications. Our scheme provides efficient delivery paths over Internet-based delivery infrastructure, which is comprised of a collection of dispersed nodes forming an overlay network. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving high level of service satisfaction and efficiency at the same time. Also, it distributes path management overhead to multiple nodes by adopting a hierarchical path management approach. We evaluate our scheme through a large-scale simulation. The results show that it achieves a high level of scalability and bandwidth efficiency.

This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The stream-based services utilize voluminous and continuously updated data streams as their input. The characteristics of data streams bring new requirements on the development and provision of the services. To satisfy the requirements, a unique service model and a runtime system are designed in MISSA. The key concept of our service model is to separate service logic from handling data streams. This significantly mitigates the burden on service developers by allowing them to only concentrate on the service logic. Job of handling data streams is completely delegated to the runtime. In this paper, we first present the importance of stream-based services and their requirements. Also, we describe a best route finding service as an example to motivate the need for MISSA. Then, we envision overall architecture for provisioning of stream-based services and detail our design of service model and runtime. Lastly, we demonstrate the efficiency of service model and runtime through experiments.

A kindergarten is an interesting community of young children. The children continuously share their interactions and experiences, and grow along similar developmental stages. In this setting, studying relative differences among them can be an interesting approach to investigating how to help their individual and social development. In this study, we present our intuition on inter-child behavioral relativity and apply it to a real kindergarten environment. We conduct a close user study necessitating the monitoring of the children's behavior. Then, utilizing wearable sensor technologies, we perform a field study to explore various interesting aspects of behavioral relativity in an automatic and quantitative fashion. We consulted the kindergarten teachers with our results obtained from our field study in order to validate the practical benefits in the kindergarten environment. We further discuss the potential, limitations, and opportunities of our approach.

Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.

The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users' context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.

The bias in the news media is an inherent flaw of the news production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. This paper presents NewsCube, a novel Internet news service aiming at mitigating the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified viewpoints on a news event of interest. As such, it effectively helps readers understand a fact from a plural of viewpoints and formulate their own, more balanced viewpoints. While media bias problem has been studied extensively in communications and social sciences, our work is the first to develop a news service as a solution and study its effect. We discuss the effect of the service through various user studies.

Social Web Application (SWA) design and development is a complex process that requires the understanding and coordination of several domains of knowledge. Yet there remain few if any holistic frameworks to manage this process. In this paper we present such a framework, R4. It coordinates and relates

We designed and implemented a pervasive game called Swan Boat that targets the bland and tedious nature of running on a treadmill, making it fun through social interaction and immersive game play. We developed Swan Boat on top of PSD, a platform for pervasive games, and using the Interactive Treadmill hardware. We conducted a user study to evaluate our game.

In this paper, we present BMQ-Processor, a high-performance border-crossing event (BCE) detection framework for large-scale monitoring applications. We first characterize a new query semantics, namely, border monitoring query (BMQ), which is useful for BCE detection in many monitoring applications. It monitors the values of data streams and reports them only when data streams cross the borders of its range. We then propose BMQ-Processor to efficiently handle a large number of BMQs over a high volume of data streams. BMQ-Processor efficiently processes BMQs in a shared and incremental manner. It develops and operates over a novel stateful query index, achieving a high level of scalability over continuous data updates. Also, it utilizes the locality embedded in data streams and greatly accelerates successive BMQ evaluations. We present data structures and algorithms to support 1D as well as multidimensional BMQs. We show that the semantics of border monitoring can be extended toward more advanced ones and build region transition monitoring as a sample case. Lastly, we demonstrate excellent processing performance and low storage cost of BMQ-Processor through extensive analysis and experiments.

Proactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.

This article proposes a novel video abstraction framework for online review services of story-oriented videos such as dramas. Among the many genres of TV programs, a drama is one of the most popularly watched on the Web. The abstracts generated by the proposed framework not only give a summary of a video but also effectively help viewers understand the overall story. In addition, our method is duration-flexible. We get clues about human understanding of a story from scenario writing rules and editorial techniques that are popularly used in the process of video production to explicitly express a narrative, and propose a new video abstraction model, called a Narrative Abstraction Model . The model effectively captures the narrative structure embedded in a story-oriented video and articulates the progress of the story in a weighted directed graph, called a Narrative Structure Graph (NSG) . The model provides a basis for a flexible framework for abstract generation using the NSG as the intermediary representation of a video. Different abstracts can be appropriately generated based upon different user requirements. To show the effectiveness of the proposed model and method, we developed a video abstraction system realizing the framework, and successfully applied it to large volumes of TV dramas. The evaluation results show that the proposed framework is a feasible solution for online review services.

Border Monitoring Query (BMQ) has different query semantic from conventional continuous range query. It monitors the values of data streams and reports them only when data streams cross the borders of its range. In this paper, we first emphasize the importance and usefulness of BMQ through attractive service scenarios. Then, we propose BMQ-Index, which is specialized to BMQ evaluation. It efficiently processes a large number of BMQs in a shared and incremental manner. For shared processing, BMQ-Index adopts a query indexing approach, thereby achieving a high level of scalability. For incremental processing, BMQ-Index employs an incremental access method. Thus, successive BMQ evaluations are significantly accelerated. We present an index structure and a search algorithm to support onedimensional as well as multi-dimensional BMQ. Lastly, we demonstrate the performance benefits of BMQ-Index through analysis and experiments.

TV program review services, especially drama review services, are one of the most popular video on demand services on the Web. In this paper, we propose a novel video abstraction model for a review service of story-oriented video such as dramas. In a drama review service, viewers want to understand the story in a short time and service providers want to provide video abstracts at minimum cost. The proposed model enables the automatic creation of a video abstract that still allows viewers to understand the overall story of the source video. Also, the model has a flexible structure so that the duration of an abstract can be adjusted depending on the requirements given by viewers. We get clues for human understanding of a story from scenario writing rules and editorial techniques which are popularly used in the process of video producing. We have implemented the proposed model and successfully applied it to several TV dramas.

Guaranteed service will provide high quality services to real-time applications, e.g., audio or video, over packet networks such as the Internet. To support guaranteed service, a service discipline must guarantee a delay bound to each session. In addition, a preferred service discipline should achieve high network utilization and good scalability. The service disciplines studied so far have problems in achieving these two objectives at the same time. Generalized processor sharing (GPS) service disciplines can have low network utilization. Rate-controlled (RC) service disciplines have difficulty in scalability because of regulators. For service curve (SC) service disciplines, both the network utilization and the scalability depend on the adopted SC. To date, there have been no studies on an SC which can make an SC discipline achieve these two objectives. We propose a new service discipline based on SC service disciplines. The proposed discipline achieves these two goals in a variable-sized packet environment. We show that the discipline can achieve the network utilization achievable by the RC service disciplines. We further show that our SC requires O(1) complexity for deadline calculation. Different from the RC service disciplines, the SC service discipline with our SC does not need regulators at all. Thus, it has better scalability than the RC service disciplines and is work-conserving. We also show that the proposed SC makes SC service disciplines have strictly higher network utilization than the GPS service disciplines including the multi-rate service discipline.

We study design alternatives for, and describe implementations and performance of, a scalable and highly available Web server accelerator. The accelerator runs under an embedded operating system and improves Web server performance by caching data. The basic design alternatives include a content router or a TCP router (without content routing) in front of a set of Web cache accelerator nodes, with the cache memory distributed across the accelerator nodes. Content based routing reduces cache node CPU cycles but can make the front-end router a bottleneck. With the TCP router, a request for a cached object may initially be sent to the wrong cache node; this results in larger cache node CPU cycles, but can provide a higher aggregate throughput, because the TCP router becomes a bottleneck at a higher throughput than the content router. Based on measurement of implementations, we quantify the throughput ranges in which different designs are preferable. We also examine a combination of content based and TCP routing techniques. We examine optimizations, such as different communication and data delivery methods, replication of hot objects, and cache replacement policies that take into account the fact that there might be different bottlenecks in the system at different times; depending upon which resource is likely to become a bottleneck, a different cache replacement algorithm is applied.

The ability to construct intracoded frame from motion-compensated intercoded frames directly in the compressed domain is important for efficient video manipulation and composition. In the context of motion-compensated discrete cosine transform (DCT)-based coding of video as in MPEG video, this problem of DCT-domain inverse motion compensation has been studied and, subsequently, improved faster algorithms were proposed. These schemes, however, treat each 8/spl times/8 block as a fundamental unit, and do not take into account the fact that in MPEG, a macroblock consists of several such blocks. We show how shared information within a macroblock, such as a motion vector and common blocks, can be exploited to yield substantial speedup in computation. Compared to previous brute-force approaches, our algorithms yield about 44% improvement. Our technique is independent of the underlying computational or processor model, and thus can be implemented on top of any optimized solution. We demonstrate an improvement by about 19%, and 13.5% in the worst case, on top of the optimized solutions presented in existing literature.

We describe the design, implementation and performance of a Web server accelerator which runs on an embedded operating system and improves Web server performance by caching data. The accelerator resides in front of one or more Web servers. Our accelerator can serve up to 5000 pages/second from its cache on a 200 MHz PowerPC 604. This throughput is an order of magnitude higher than that which would be achieved by a high-performance Web server running on similar hardware under a conventional operating system such as Unix or NT. The superior performance of our system results in part from its highly optimized communications stack. In order to maximize hit rates and maintain updated caches, our accelerator provides an API which allows application programs to explicitly add, delete, and update cached data. The API allows our accelerator to cache dynamic as well as static data, analyze the SPECweb96 benchmark, and show that the accelerator can provide high hit ratios and excellent performance for workloads similar to this benchmark.

MPEG-2 video standards are targeted for high-quality video broadcast and distribution and are optimized for efficient storage and transmission. However, it is difficult to process MPEG-2 for video browsing and database applications without first decompressing the video. Yeo and Liu (1995) have proposed fast algorithms for the direct extraction of spatially reduced images from MPEG-1 video. Reduced images have been demonstrated to be effective for shot detection, shot browsing and editing, and temporal processing of video for video presentation and content annotation. In this paper, we develop new tools to handle the extra complexity in MPEG-2 video for extracting spatially reduced images. In particular, we propose new classes of discrete cosine transform (DCT) domain and DCT inverse motion compensation operations for handling the interlaced modes in the different frame types of MPEG-2, and we design new and efficient algorithms for generating spatially reduced images of an MPEG-2 video. The algorithms proposed in this paper are fundamental for efficient and effective processing of MPEG-2 video.

In a distributed environment, presentation of structured, composite multimedia information poses new challenges in dealing with variable bandwidth (BW) requirement and synchronization of media data objects. The detailed knowledge of BW requirement obtained by analyzing the document structure can be used to create a prefetch schedule that results in efficient utilization of system resources. A distributed environment consists of various system components that are either dedicated to a client or shared across multiple clients. Shared system components could benefit from Fine Granularity Advanced Reservation (FGAR) of resources based on true BW requirement. Prefetching by utilizing advance knowledge of BW requirement can further improve resource utilization. In this paper, we describe the JINSIL retrieval system that takes into account the available bandwidth and buffer resources and the nature of sharing in each component on the delivery path. It reshapes BW requirement, creates prefetch schedule for efficient resource utilization in each component, and reserves necessary BW and buffer. We also consider good choices for placement of prefetch buffers across various system components.

Time is an essential component i n i n teractive m ultimedia documents (or systems).We present the elastic time model for multimedia documents.Using the metaphor of a spring system, it allows authors to associate with each multimedia object a minimum and a maximum length and a length at rest.Authors can connect the (elastic) objects by de ning temporal relationships among them.If the given specication is consistent, a document is produced which is also elastic, with a minimum, a maximum, and an optimal length.As such, our elastic model associates with a document a range of feasible solutions in addition to an optimal one.The author can then select from the acceptable range an alternative length for the document, and the system will compute a revised solution that takes the additional global constraint i n to eect.The system can answer questions, such as: \Can I show this multimedia presentation in 10 minutes?If so, how should all the objects be scheduled?"Furthermore, the system also takes fairness into consideration and distributes any necessary stretching or shrinking across multimedia objects contained in a document.As such, the elastic time model provides expressive p o w er and exibility in document au-The author's current address is: