Search

History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience.

In this workshop, we intend to bring scholars and practitioners of MobileHCI conference who are interested in designing technology use for children and their parents. Parents are concerned with technology addiction for their children but generally feel loss of control in the ubiquity of mobile devices. We believe mobile design community can contribute meaningful ideas in this space. The goal of this workshop is for participants to co-develop a shared vocabulary and framework for "designing bounded and nuanced technology" as a strategy to be deployed in conceiving and implementing mobile technologies for family use.

This paper proposes an intuitive tool for users to create 3D architectural models through 2D sketch input. A user only needs to draw the outline of a frontal or oblique view of a building. Our system recognizes the parts drawn in a sketch and estimates their types. The estimated information is then used to compose the corresponding 3D model. Besides, our system provides additional assistant tools for rapid editing. The modeling process can be iterative and incremental. To accomplish a building complex, a user can gradually create their models from one view to another. Our experiment shows that the proposed interface with sketch analysis tools eases the process of 3D building modeling.

This study examines the characteristics of mobile instant-messaging users' relationships with their social contacts and the effects of both relationship and interruption context on four measures of receptivity: Attentiveness, Responsiveness, Interruptibility, and Opportuneness. Overall, interruption context overshadows relationship characteristics as predictors of all four of these facets of receptivity; this overshadowing was most acute for Interruptibility and Opportuneness, but existed for all factors. In addition, while Mobile Maintenance Expectation and Activity Engagement were negatively correlated with all receptivity measures, each such measure had its own set of predictors, highlighting the conceptual differences among the measures. Finally, delving more deeply into potential relationship effects, we found that a single, simple closeness question was as effective at predicting receptivity as the 12-item Unidimensional Relationship Closeness Scale.

The combination of growth in compute capabilities and availability of large datasets has led to a re-birth of deep learning. Deep Neural Networks (DNNs) have become state-of-the-art in a variety of machine learning tasks spanning domains across vision, speech, and machine translation. Deep Learning (DL) achieves high accuracy in these tasks at the expense of 100s of ExaOps of computation; posing significant challenges to efficient large-scale deployment in both resource-constrained environments and data centers. One of the key enablers to improve operational efficiency of DNNs is the observation that when extracting deep insight from vast quantities of structured and unstructured data the exactness imposed by traditional computing is not required. Relaxing the "exactness" constraint enables exploiting opportunities for approximate computing across all layers of the system stack. In this talk we present a multi-TOPS AI core [3] for acceleration of deep learning training and inference in systems from edge devices to data centers. We demonstrate that to derive high sustained utilization and energy efficiency from the AI core requires ground-up re-thinking to exploit approximate computing across the stack including algorithms, architecture, programmability, and hardware. Model accuracy is the fundamental measure of deep learning quality. The compute engine precision in our AI core is carefully calibrated to realize significant reduction in area and power while not compromising numerical accuracy. Our research at the DL algorithms/applications-level [2] shows that it is possible to carefully tune the precision of both weights and activations to as low as 2-bits for inference and was used to guide the choices of compute precision supported in the architecture and hardware for both training and inference. Similarly, distributed DL training's scalability is impacted by the communication overhead to exchange gradients and weights after each mini-batch. Our research on gradient compression [1] shows by selectively sending gradients larger than a threshold, and by further choosing the threshold based on the importance of the gradient we achieve achieve compression ratio of 40X for convolutional layers, and up to 200X for fully-connected layers of the network without losing model accuracy. These results guide the choice of interconnection network topology exploration for a system of accelerators built using the AI core. Overall, our work shows how the benefits from exploiting approximation using algorithm/application's robustness to tolerate reduced precision, and compressed data communication can be combined effectively with the architecture and hardware of the accelerator designed to support these reduced-precision computation and compressed data communication. Our results demonstate improved end-to-end efficiency of the DL accelerator across different metrics such as high sustained TOPs, high TOPs/watt and TOPs/mm2 catering to different operating environments for both training and inference.

Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques showing successes on challenging artificial intelligence (AI) problems. Applications in domains such as image/video processing, autonomous cars, natural language processing, speech synthesis and recognition, genomics and many others have embraced deep learning as the foundation. DNNs achieve superior accuracy for these applications with high computational complexity using very large models which require 100s of MBs of data storage, exaops of computation and high bandwidth for data movement. In spite of these impressive advances, it still takes days to weeks to train state of the art Deep Networks on large datasets - which directly limits the pace of innovation and adoption. In this paper, we present a multi-pronged approach to address the challenges in meeting both the throughput and the energy efficiency goals for DNN training.

Children with developmental delayed can make progress through early intervention and training. However, from the interviews with occupational therapists and parents, we found that current training techniques lack variety and bore children in a short while. To improve the training effect, we proposed PinchFun, a cooperative game aiming to provide fine motor training for preschool children (under 6 years old) with developmental delay, in which the parent can cooperate with the child to achieve the game goals and adjust the game difficulty to meet different developmental milestones. The game employs the Leap motion controller and force-sensitive resistor (FSR) to detect the hand gesture and level of pressure of fingers. PinchFun integrates the physical assistive devices, i.e. clips and rubber band, and the virtual interactive game to optimize learning impact and gaming experience.

Online fitness videos make it possible and popular to do exercise at home. However, it is not easy to notice the details of motions by merely watching training videos. We propose a new type of motion guidance system that simulates the way that the human body moves as driven by muscle contractions. We have designed external artificial muscles on a sleeve to create a pulling sensation that can guide the forearm's pronation (internal rotation) and the forearm's supination (external rotation). The sleeve consists of stepper motors to provide pulling force, fishing lines and elastic bands to imitate muscle contraction to drive the forearm to rotate instinctively. We present two preliminary experiments. The first one shows that this system can effectively guide the forearm to rotate in the correct direction. The second one shows that users can be guided to the targeted angle by utilizing a tactile cue. We also report users' feedback through the experiments and provide design recommendations and directions for future research.