bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the utility of cardiorespiratory fitness as a population health surveillance indicator for children and youth: An international analysis of results from the 20-m shuttle run test
Authors: Lang Justin J.
Published: January 2018

Emerging evidence has demonstrated the strong link between cardiorespiratory fitness and multiple aspects of health (i.e., physiological, physical, psychosocial, cognitive), independent of physical activity, among school-aged children and youth. Cardiorespiratory fitness is a trait that does not vary substantially from day-to-day, and provides an indication of recent physical activity levels, making it an important possible indicator of population health. Thus, the objective of this dissertation was to investigate the utility of cardiorespiratory fitness, measured using the 20-m shuttle run test, as a broad, holistic health indicator for population health surveillance among children and youth. To achieve this objective we completed 7 manuscripts, all prepared for submission to peer-reviewed, scientific journals: (1) Systematic review of the relationship between 20-m shuttle run performance and health indicators among children and youth. (2) Review of criterion-referenced standards for cardiorespiratory fitness: what percentage of 1 142 026 international children and youth are apparently healthy? (3) Cardiorespiratory fitness is associated with physical literacy in a large sample of Canadian children aged 8 to 12 years. (4) International variability in 20-m shuttle run performance in children and youth: Who are the fittest from a 50-country comparison? A systematic review with pooling of aggregate results. (5) Making a case for cardiorespiratory fitness surveillance among children and youth. (6) International normative 20-m shuttle run values from 1 142 026 children and youth representing 50 countries. (7) Temporal trends in the cardiorespiratory fitness of children and adolescents representing 19 high-income and upper middle-income countries between 1981 and 2014. Combined, this dissertation provides support for the importance of cardiorespiratory fitness for health surveillance among school-aged children and youth. Results from the international analysis highlighted the variability across countries, with countries in north-central Europe and Africa having the highest cardiorespiratory fitness, and countries in South America having the lowest cardiorespiratory fitness. The results indicated that declines in cardiorespiratory fitness may have stabilized in recent years in some high- and middle-income countries. This dissertation also identified 2 methods (criterion- and normative-referenced standards) to interpret cardiorespiratory fitness levels among children and youth, methods that could be used to inform future consensus, surveillance, and cardiorespiratory fitness guidelines.
Spontaneous breaking of (2 + 1)-dimensional Lorentz symmetry by an antisymmetric tensor
Authors: Huang Jia-Hui , Guo Guang-Zhou , Xie Hao-Yu , Liu Qi-Shan , Deng Fang-Qing
Published: January 2018

One kind of spontaneous (2 + 1)-dimensional Lorentz symmetry breaking is discussed. The symmetry breaking pattern is SO(2, 1) [Formula: see text] SO(1, 1). Using the coset construction formalism, we derive the Goldstone covariant derivative and the associated covariant gauge field. Finally, the two-derivative low-energy effective action of the Nambu–Goldstone bosons is obtained.
A Low Power CMOS-Based VCO Design with I-MOS Varactor Tuning Control
Authors: Kumar Manoj , Dwivedi Dileep
Published: January 2018

This paper presents a new design of low power voltage controlled oscillator (VCO) circuit using three transistors NOR-gate and I-MOS (inversion mode) varactor tuning method. Variation in the oscillation frequency has been obtained by varying the output load capacitance with the use of I-MOS varactor tuning consisting of two PMOS transistors connected in parallel. Variable capacitance across the I-MOS varactor has been achieved by varying the source/drain voltage ([Formula: see text] and back-gate voltage ([Formula: see text]. Variation of [Formula: see text] from 1[Formula: see text]V to 2[Formula: see text]V provides the frequency deviation from 1.970[Formula: see text]GHz to 1.379[Formula: see text]GHz with I-MOS width of 8 [Formula: see text]m at power supply voltage ([Formula: see text] of 1.8[Formula: see text]V. Power consumption of the circuit is 1.296[Formula: see text]mW with [Formula: see text] of 1.8[Formula: see text]V. The results have been obtained for different I-MOS varactor widths like 5[Formula: see text][Formula: see text]m, 8[Formula: see text][Formula: see text]m and 10[Formula: see text][Formula: see text]m. Further, variations in the frequency have been obtained from 0.650 GHz to 2.584 GHz with the Vdd variation from 1[Formula: see text]V to 3[Formula: see text]V. In addition, by variations of [Formula: see text] from 0[Formula: see text]V to 1.8[Formula: see text]V and [Formula: see text] from 1[Formula: see text]V to 3[Formula: see text]V, the proposed oscillators operate in the frequency range from 0.556[Formula: see text]GHz to 2.584[Formula: see text]GHz for 8[Formula: see text][Formula: see text]m width of I-MOS varactor. Proposed VCO circuit show a phase noise of [Formula: see text][Formula: see text]dBc/Hz at 1[Formula: see text]MHz offset from the carrier frequency and the figure of merit (FoM) for the VCO is 154.51[Formula: see text]dB/Hz. Proposed VCO shows an improved performance in terms of power consumption, output frequency and FoM.
Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach
Authors: Zhang Yongping , Shang Pengjian , Xiong Hui , Xia Jianan
Published: January 2018

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.
Fictitious spin-12 operators and correlations in quadrupole nuclear spin system
Authors: Furman G. B. , Goren S. D. , Meerovich V. M. , Sokolovsky V. L.
Published: January 2018

The Hamiltonian and the spin operators for a spin 3/2 are represented in the basis formed by the Kronecker productions of the [Formula: see text] Pauli matrices. This reformulation allows us to represent a spin 3/2 as a system of two coupled fictitious spins 1/2. Correlations between these fictitious spins are studied using well-developed methods. We investigate the temperature and field dependences of correlations, such as mutual information, classical correlations, entanglement, and geometric and quantum discords in the fictitious spin-1/2 system describing a nuclear spin 3/2 which is placed in magnetic and inhomogeneous electric fields. It is shown that the correlations between the fictitious spins demonstrate properties which differ from those of real two-spin systems. In contrast to real systems all the correlations between the fictitious spins do not vanish with increasing external magnetic field; at a high magnetic field the correlations tend to their limiting values. Classical correlations, quantum and geometric discords reveal a pronounced asymmetry relative to the measurements on subsystems (fictitious spins) even in a uniform magnetic field and at symmetrical EFG, [Formula: see text]. The correlations depend also on the distribution of external charges, on the parameter of symmetry [Formula: see text]. At [Formula: see text] quantum and geometric discords have finite values in a zero magnetic field. The proposed approach may be useful in analysis of properties of particles with larger angular momentum, can provide the way to discover new physical phenomenon of quantum correlations, and can be a useful tool for similar definitions of other physical quantities of complex systems.
RADAR
Authors: Chen Longbiao , Fan Xiaoliang , Wang Leye , Zhang Daqing , Yu Zhiyong , Li Jonathan , Nguyen Thi-Mai-Trang , Pan Gang , Wang Cheng
Published: January 2018

Typhoons and hurricanes cause extensive damage to coast cities annually, demanding urban authorities to take effective actions in disaster response to reduce losses. One of the first priority in disaster response is to identify and clear road obstacles, such as fallen trees and ponding water, and restore road transportation in a timely manner for supply and rescue. Traditionally, identifying road obstacles is done by manual investigation and reporting, which is labor intensive and time consuming, hindering the timely restoration of transportation. In this work, we propose RADAR, a low-cost and real-time approach to identify road obstacles leveraging large-scale vehicle trajectory data and heterogeneous road environment sensing data. First, based on the observation that road obstacles may cause abnormal slow motion behaviors of vehicles in the surrounding road segments, we propose a cluster direct robust matrix factorization (CDRMF) approach to detect road obstacles by identifying the collective anomalies of slow motion behaviors from vehicle trajectory data. Then, we classify the detected road obstacles leveraging the correlated spatial and temporal features extracted from various road environment data, including satellite images and meteorological records. To address the challenges of heterogeneous features and sparse labels, we propose a semi-supervised approach combining co-training and active learning (CORAL). Real experiments on Xiamen City show that our approach accurately detects and classifies the road obstacles during the 2016 typhoon season with precision and recall both above 90%, and outperforms the state-of-the-art baselines.
A Comparative Evaluation of Spatial Targeting Behaviour Patterns for Finger and Stylus Tapping on Mobile Touchscreen Devices
Authors: Buschek Daniel , Kinshofer Julia , Alt Florian
Published: January 2018

Models of 2D targeting error patterns have been applied as a valuable computational tool for analysing finger touch behaviour on mobile devices, improving touch accuracy and inferring context. However, their use in stylus input is yet unexplored. This paper presents the first empirical study and analyses of such models for tapping with a stylus. In a user study (N = 28), we collected targeting data on a smartphone, both for stationary use (sitting) and walking. We compare targeting patterns between index finger input and three stylus variations -- two stylus widths and nib types as well as the addition of a hover cursor. Our analyses reveal that stylus targeting patterns are user-specific, and that offset models improve stylus tapping accuracy, but less so than for finger touch. Input method has a stronger influence on targeting patterns than mobility, and stylus width is more influential than the hover cursor. Stylus models improve finger accuracy as well, but not vice versa. The extent of the stylus accuracy advantage compared to the finger depends on screen location and mobility. We also discuss patterns related to mobility and gliding of the stylus on the screen. We conclude with implications for target sizes and offset model applications.
PupilNet, Measuring Task Evoked Pupillary Response using Commodity RGB Tablet Cameras
Authors: Wangwiwattana Chatchai , Ding Xinyi , Larson Eric C.
Published: January 2018

Pupillary diameter monitoring has been proven successful at objectively measuring cognitive load that might otherwise be unobservable. This paper compares three different algorithms for measuring cognitive load using commodity cameras. We compare the performance of modified starburst algorithm (from previous work) and propose two new algorithms: 2 Level Snakuscules and a convolutional neural network which we call PupilNet. In a user study with eleven participants, our comparisons show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. We show that the difference between PupilNet and a gold standard head-mounted gaze tracker varies only from -2.6% to 2.8%. Finally, we also show that PupilNet gives similar conclusions about cognitive load during a longer duration typing task.
hEYEbrid
Authors: Lander Christian , löchtefeld Markus , Krüger Antonio
Published: January 2018

We introduce hEYEbrid, a calibration-free method for spontaneous and long-term eye gaze tracking, with competitive gaze estimation. It is based on a hybrid concept that combines infrared eye images with corneal imaging. For this, two eye cameras are mounted on a glasses frame. In this way, the pupil can be tracked quickly with high precision. This information is translated into the corneal image, which is used to create a connection to the environment, acting like a scene camera. In a user study with 20 participants, we evaluated our approach against an extended version of the system, called 3C-hEYEbrid, and a state-of-the-art head-mounted Pupil Labs eye tracker. We show that hEYEbrid provides accurate gaze estimation in unconstrained environments and is robust against calibration drift (e.g. caused by taking off and putting on the device). In addition, we present a mobile and wearable implementation of hEYEbrid and 3C-hEYEbrid, that is also usable with a monocular Pupil Labs eye tracker. It connects the head-mounted device to a mobile phone, enabling gaze estimation in real time. hEYEbrid represents a significant step towards pervasive gaze-based interfaces.
iTour
Authors: Hsu Chih-Hsiang , Ku Chia-Lun , Chang Yung-Ju , Wang Yu-Shuen , Trân Uyn-Dinh , Cheng Wen-Hao , Yang Chu-Yuan , Hsieh Ching-Yu , Lin Chun-Cheng
Published: January 2018

Although tourist maps are useful resources for people to visit scenic areas, they are also commonly distorted and omit details according to the purposes and functions of a map. In this paper, we present iTour, a semi-automatic system that turns tourist maps into digital maps. By involving users in matching the road network of a tourist map and the paired standard map, our system computes road network correspondence between the two maps. By doing so, users can navigate on such GPS-enabled tourist maps using mobile devices. This transformation creates the possibility of augmenting a large number of tourist maps with digital map features. To evaluate the performance of matching road networks, we compared the presented semi-automatic interface to a manual interface. The results showed that the semi-automatic interface saved participants significant effort in generating correspondence and was perceived to require significantly less time by the participants. In addition, we conducted a field study of the iTour in comparison to using a tourist map and Google Maps together. Our results showed that iTour helped participants find their way during travel. The participants provided positive feedback on the combination of tourist maps and GPS location because of its highlights of important landmarks, showing users' locations relative to those landmarks, and saving the effort of switching tourist maps and Google Maps.
EyePACT
Authors: Khamis Mohamed , Buschek Daniel , Thieron Tobias , Alt Florian , Bulling Andreas
Published: January 2018

The parallax effect describes the displacement between the perceived and detected touch locations on a touch-enabled surface. Parallax is a key usability challenge for interactive displays, particularly for those that require thick layers of glass between the screen and the touch surface to protect them from vandalism. To address this challenge, we present EyePACT, a method that compensates for input error caused by parallax on public displays. Our method uses a display-mounted depth camera to detect the user's 3D eye position in front of the display and the detected touch location to predict the perceived touch location on the surface. We evaluate our method in two user studies in terms of parallax correction performance as well as multi-user support. Our evaluations demonstrate that EyePACT (1) significantly improves accuracy even with varying gap distances between the touch surface and the display, (2) adapts to different levels of parallax by resulting in significantly larger corrections with larger gap distances, and (3) maintains a significantly large distance between two users' fingers when interacting with the same object. These findings are promising for the development of future parallax-free interactive displays.
Multimodal Deep Learning for Activity and Context Recognition
Authors: Radu Valentin , Tong Catherine , Bhattacharya Sourav , Lane Nicholas D. , Mascolo Cecilia , Marina Mahesh K. , Kawsar Fahim
Published: January 2018

Wearables and mobile devices see the world through the lens of half a dozen low-power sensors, such as, barometers, accelerometers, microphones and proximity detectors. But differences between sensors ranging from sampling rates, discrete and continuous data or even the data type itself make principled approaches to integrating these streams challenging. How, for example, is barometric pressure best combined with an audio sample to infer if a user is in a car, plane or bike? Critically for applications, how successfully sensor devices are able to maximize the information contained across these multi-modal sensor streams often dictates the fidelity at which they can track user behaviors and context changes. This paper studies the benefits of adopting deep learning algorithms for interpreting user activity and context as captured by multi-sensor systems. Specifically, we focus on four variations of deep neural networks that are based either on fully-connected Deep Neural Networks (DNNs) or Convolutional Neural Networks (CNNs). Two of these architectures follow conventional deep models by performing feature representation learning from a concatenation of sensor types. This classic approach is contrasted with a promising deep model variant characterized by modality-specific partitions of the architecture to maximize intra-modality learning. Our exploration represents the first time these architectures have been evaluated for multimodal deep learning under wearable data -- and for convolutional layers within this architecture, it represents a novel architecture entirely. Experiments show these generic multimodal neural network models compete well with a rich variety of conventional hand-designed shallow methods (including feature extraction and classifier construction) and task-specific modeling pipelines, across a wide-range of sensor types and inference tasks (four different datasets). Although the training and inference overhead of these multimodal deep approaches is in some cases appreciable, we also demonstrate the feasibility of on-device mobile and wearable execution is not a barrier to adoption. This study is carefully constructed to focus on multimodal aspects of wearable data modeling for deep learning by providing a wide range of empirical observations, which we expect to have considerable value in the community. We summarize our observations into a series of practitioner rules-of-thumb and lessons learned that can guide the usage of multimodal deep learning for activity and context detection.
Effects of Lateral Eye Displacement on Comfort While Reading from a Video Display Terminal
Authors: Haynes Malcolm , Starner Thad
Published: January 2018

Some small field-of-view (FOV) head worn displays (HWD), like Epson's Moverio BT-300, are mounted directly in the user's line of sight. In contrast, Google Glass is mounted “out of the way” and above the line of sight. Other displays like the Vuzix M100 or Optinvent ORA-1 allow the user to adjust the display position, and some users have expressed a desire for the display to be laterally displaced toward the ear, out of the main line of sight. How far toward the ear can a small FOV display be mounted and still be used comfortably? Using a 30-minute reading task and an emulated display with the FOV of a typical smart phone (9.2°x 16.3°), we study a user's perceived comfort level while reading at four horizontally displaced positions. We ask participants to rate their comfort every five minutes using a 5-point Likert scale knob (5 being most comfortable), for a total of seven measurements. Scores are summed over the seven measurements to form a summed comfort score. We find that 0° (Md = 34.0; p«0.001), 10° (Md = 33.5; p«c0.001), and 20° (Md = 33.5; p«c0.001) are more comfortable than 30° (Md = 29.5) and that 0° (p<0.01) and 10° (p<0.01) are more comfortable than 20°. Reading performance and workload measures were numerically similar across all conditions. Given the main results of the experiment, post-hoc analysis on other measurements such as preference and asthenopia, and participant comments, we suggest that small FOV displays should be mounted at lateral displacement angles of 20° and less for sustained use.
RDeepSense
Authors: Yao Shuochao , Zhao Yiran , Shao Huajie , Zhang Aston , Zhang Chao , Li Shen , Abdelzaher Tarek
Published: January 2018

Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications. Although existing studies have demonstrated the effectiveness and feasibility of running deep neural network inference operations on mobile and embedded devices, they overlooked the reliability of mobile computing models. Reliability measurements such as predictive uncertainty estimations are key factors for improving the decision accuracy and user experience. In this work, we propose RDeepSense, the first deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices. RDeepSense enables the predictive uncertainty by adopting a tunable proper scoring rule as the training criterion and dropout as the implicit Bayesian approximation, which theoretically proves its correctness. To reduce the computational complexity, RDeepSense employs efficient dropout and predictive distribution estimation instead of the model ensemble or sampling-based method for inference operations. We evaluate RDeepSense with four mobile sensing applications using Intel Edison devices. Results show that RDeepSense can reduce around 90% of the energy consumption while producing superior uncertainty estimations and preserving at least the same model accuracy compared with other state-of-the-art methods.
Predicting the Suitability of Service Animals Using Instrumented Dog Toys
Authors: Byrne Ceara , Zuerndorfer Jay , Freil Larry , Han Xiaochuang , Sirolly Andrew , Cilliland Scott , Starner Thad , Jackson Melody
Published: January 2018

Working dogs1 are significantly beneficial to society; however, a substantial number of dogs are released from time consuming and expensive training programs because of unsuitability in behavior. Early prediction of successful service dog placement could save time, resources, and funding. Our research focus is to explore whether aspects of canine temperament can be detected from interactions with sensors, and to develop classifiers that correlate sensor data to predict the success (or failure) of assistance dogs in advanced training. In a 2-year longitudinal study, our team tested a cohort of dogs entering advanced training in the Canine Companions for Independence (CCI) Program with 2 instrumented dog toys: a silicone ball and a silicone tug sensor. We then create a logistic model tree classifier to predict service dog success using only 5 features derived from dog-toy interactions. During randomized 10-fold cross validation where 4 of the 40 dogs were kept in an independent test set for each fold, our classifier predicts the dogs' outcomes with 87.5% average accuracy. We assess the reliability of our model by performing the testing routine 10 times over 1.5 years for a single suitable working dog, which predicts that the dog would pass each time. We calculate the resource benefit of identifying dogs who will fail early in their training, and the value for a cohort of 40 dogs using our toys and our methods for prediction is over $70,000. With CCI's 6 training centers, annual savings could be upwards of $5 million per year.
Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones
Authors: Ruan Sherry , Wobbrock Jacob O. , Liou Kenny , Ng Andrew , Landay James A.
Published: January 2018

With the ubiquity of mobile touchscreen devices like smartphones, two widely used text entry methods have emerged: small touch-based keyboards and speech recognition. Although speech recognition has been available on desktop computers for years, it has continued to improve at a rapid pace, and it is currently unknown how today's modern speech recognizers compare to state-of-the-art mobile touch keyboards, which also have improved considerably since their inception. To discover both methods' “upper-bound performance,” we evaluated them in English and Mandarin Chinese on an Apple iPhone 6 Plus in a laboratory setting. Our experiment was carried out using Baidu's Deep Speech 2, a deep learning-based speech recognition system, and the built-in Qwerty (English) or Pinyin (Mandarin) Apple iOS keyboards. We found that with speech recognition, the English input rate was 2.93 times faster (153 vs. 52 WPM), and the Mandarin Chinese input rate was 2.87 times faster (123 vs. 43 WPM) than the keyboard for short message transcription under laboratory conditions for both methods. Furthermore, although speech made fewer errors during entry (5.30% vs. 11.22% corrected error rate), it left slightly more errors in the final transcribed text (1.30% vs. 0.79% uncorrected error rate). Our results show that comparatively, under ideal conditions for both methods, upper-bound speech recognition performance has greatly improved compared to prior systems, and might see greater uptake in the future, although further study is required to quantify performance in non-laboratory settings for both methods.
Urban Impulses
Authors: Krumm John
Published: January 2018

In modeling human behavior, we expect people to make noticeable reactions to the events they witness. For people at a scheduled event like a concert or sports game, we can measure reactions by looking at geotagged social media posts. We work from a database of known events from a commercial ticket broker and a database of geotagged tweets to show how we can derive impulse response functions of tweet counts as event responses. Tweet counts typically rise in anticipation of the event and gradually fall after the event starts. We draw an analogy between evoked responses in functional magnetic resonance imaging (fMRI) from mental stimuli and social media responses from local event stimuli. Our analysis of event and tweet data shows that our derived impulse responses are statistically significant and that we can use the functions to accurately predict reactions to some event types. We give examples of impulse response functions derived from repeated events at different venues.
Modelling Passengers' Reaction to Dynamic Prices in Ride-on-demand Services
Authors: Guo Suiming , Chen Chao , Liu Yaxiao , Xu Ke , Chiu Dah Ming
Published: January 2018

In emerging ride-on-demand (RoD) services such as Uber and Didi (in China), dynamic prices play an important role in regulating supply and demand, trying to improve the service quality for both drivers and passengers. In this paper, we take a new perspective to study RoD services besides the supply or demand, and focus on passengers' reaction to dynamic prices. Passengers' reaction can be regarded as a process of searching for the best price before getting on a car, and the searching process reflects passengers' demand elasticity -- “how eager they are requesting a ride”. We collect data of passengers' reaction from a real RoD service provider in China, and analyze the patterns of passengers' reaction. The analysis results show that both the dynamic prices and passengers' demand elasticity influence their reaction. We then adopt and extend a previous model for sequential search from a known distribution to understand passengers' reaction, and use our data to obtain the search costs under various circumstances, which could be interpreted as passengers' demand elasticity. Insights on the search cost and other relevant quantities are discussed. Our expectation is that the result of the study should be helpful not only for service providers in designing dynamic pricing algorithms, but also for passengers and policy makers in understanding the effects and implications of dynamic pricing.
Snoopy
Authors: Lu Chris Xiaoxuan , Du Bowen , Wen Hongkai , Wang Sen , Markham Andrew , Martinovic Ivan , Shen Yiran , Trigoni Niki
Published: January 2018

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms. One can access online banking or even make payments on a smartwatch without a paired phone. This makes smartwatches more attractive and vulnerable to malicious attacks, which to date have been largely overlooked. In this paper, we demonstrate Snoopy, a password extraction and inference system which is able to accurately infer passwords entered on Android/Apple watches within 20 attempts, just by eavesdropping on motion sensors. Snoopy uses a uniform framework to extract the segments of motion data when passwords are entered, and uses novel deep neural networks to infer the actual passwords. We evaluate the proposed Snoopy system in the real-world with data from 362 participants and show that our system offers a ~ 3-fold improvement in the accuracy of inferring passwords compared to the state-of-the-art, without consuming excessive energy or computational resources. We also show that Snoopy is very resilient to user and device heterogeneity: it can be trained on crowd-sourced motion data (e.g. via Amazon Mechanical Turk), and then used to attack passwords from a new user, even if they are wearing a different model.

This paper shows that, in the wrong hands, Snoopy can potentially cause serious leaks of sensitive information. By raising awareness, we invite the community and manufacturers to revisit the risks of continuous motion sensing on smart wearable devices.
Early Destination Prediction with Spatio-temporal User Behavior Patterns
Authors: Imai Ryo , Tsubouchi Kota , Konishi Tatsuya , Shimosaka Masamichi
Published: January 2018

Predicting user behavior makes it possible to provide personalized services. Destination prediction (e.g. predicting a future location) can be applied to various practical applications. An example of destination prediction is personalized GIS services, which are expected to provide alternate routes to enable users to avoid congested roads. However, the destination prediction problem requires critical trade-offs between timing and accuracy. In this paper, we focus on early destination prediction as the central issue, as early recognition in destination prediction has not been fully explored. As an alternative to the traditional two basic approaches with trajectory tracking that narrow down the candidates with respect to the trip progress, and Next Place Prediction (NPP) that infers the future location of a user from user habits, we propose a new probabilistic model based on both conventional models. The advantage of our model is that it drastically narrows down the destination candidates efficiently at the early stage of a trip, owing to the staying information derived from the NPP approach. In other words, our approach achieves high prediction accuracy by considering both approaches at the same time. To implement our model, we employ SubSynE for state-of-the-art prediction based on trajectory tracking as well as a multi-class logistic regression based on user contexts. Despite the simplicity of our model, the proposed method provides improved performance compared to conventional approaches based on the experimental results using the GPS logs of 1,646 actual users from the commercial services.
Towards Wearable Everyday Body-Frame Tracking using Passive RFIDs
Authors: Jin Haojian , Yang Zhijian , Kumar Swarun , Hong Jason I.
Published: January 2018

We introduce RF-Wear, an accurate and wearable solution to track movements of a user's body using passive RFIDs embedded in their clothing. RF-Wear processes wireless signals reflected off these tags to a compact single-antenna RFID reader in the user's pocket. In doing so, RF-Wear enables a first-of-its-kind body-frame tracking mechanism that is lightweight and convenient for day-to-day use, without relying on external infrastructure. At the heart of RF-Wear is a novel primitive that computes angles between different parts of the user's body using the RFID tags attached to them. RF-Wear achieves this by treating groups of RFID tags as an array of antennas whose orientation can be computed accurately relative to the handheld reader. By computing the orientation of individual body parts, we demonstrate how RF-Wear reconstructs the real-time posture of the user's entire body frame. Our solution overcomes multiple challenges owing to the interactions of wireless signals with the body, the 3-D nature of human joints and the flexibility of fabric on which RFIDs are placed. We implement and evaluate a prototype of RF-Wear on commercial RFID readers and tags and demonstrate its performance in body-frame tracking. Our results reveal a mean error of 8--12° in tracking angles at joints that rotate along one degree-of-freedom, and 21°- azimuth, 8°- elevation for joints supporting two degrees-of-freedom.
MORP
Authors: Sarker Ankur , Shen Haiying , Stankovic John A.
Published: January 2018

The Wireless Power Transfer (WPT) system that enables in-motion charging (or wireless charging) for Electric Vehicles (EVs) has been introduced to resolve battery-related issues (such as long charging time, high cost, and short driving range) and increase the wide-acceptance of EVs. In this paper, we study the WPT system with the objectives of minimizing energy consumption, travel time, charging monetary cost on the way, and range anxiety for online EVs. Specifically, we propose the Multi-Objective Route Planner system (MORP) to guide EVs for the multi-objective routing. MORP incorporates two components: traffic state prediction and optimal route determination. For the traffic state prediction, we conducted analysis on a traffic dataset and observed spatial-temporal features of traffic patterns. Accordingly, we introduce the horizontal space-time Autoregressive Integrated Moving Average (ARIMA) model to predict vehicle counts (i.e., traffic volume) for locations with available historical traffic data. And, we use the spatial-temporal ordinary kriging method to predict vehicle counts for locations without historical traffic data. Based on vehicle counts, we use the non-parametric kernel regression method to predict velocity of road sections, which is used to predict travel time and then, energy consumption of a route of an EV with the help of the proposed energy consumption model. We also estimate charging monetary cost and EV related range anxiety based on unit energy cost, predicted travel time and energy consumption, and current onboard energy. We design four different cost functions (travel time, energy consumption, charging monetary cost, and range anxiety) of routing and formulate a multi-objective routing optimization problem. We use the predicted parameters as inputs of the optimization problem and find the optimal route using the adaptive epsilon constraint method. We evaluate our proposed MORP system in four different aspects (including traffic prediction, velocity prediction, energy consumption prediction, and EV routing). From the experimental studies, we find the effectiveness of the proposed MORP system in different aspects of the online EV routing system.
CityTransfer
Authors: Guo Bin , Li Jing , Zheng Vincent W. , Wang Zhu , Yu Zhiwen
Published: January 2018

Chain businesses have been dominating the market in many parts of the world. It is important to identify the optimal locations for a new chain store. Recently, numerous studies have been done on chain store location recommendation. These studies typically learn a model based on the features of existing chain stores in the city and then predict what other sites are suitable for running a new one. However, these models do not work when a chain enterprise wants to open business in a new city where there is not enough data about this chain store. To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e.g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city. In particular, CityTransfer is a two-fold knowledge transfer framework based on collaborative filtering, which consists of the transfer rating prediction model, the inter-city knowledge association method and the intra-city semantic extraction method. Experiments using data of chain hotels from four different cities crawled from Ctrip (a popular travel reservation website in China) and the urban characters extracted from several other data sources validate the effectiveness of our approach on store site recommendation.
SHOW
Authors: Lin Xinye , Chen Yixin , Chang Xiao-Wen , Liu Xue , Wang Xiaodong
Published: January 2018

Smart watch is becoming a new gateway through which people stay connected and track everyday activities, and text-entry on it is becoming a frequent need. With the two de facto solutions: tap-on-screen and voice input, text-entry on the watch remains a tedious task because 1. Tap-on-screen is error prone due to the small screen; 2. Voice input is strongly constrained by the surroundings and suffers from privacy leak. In this paper, we propose SHOW, which enables the user to input as they handwrite on horizontal surfaces, and the only requirement is to use the elbow as the support point. SHOW captures the gyroscope and accelerometer traces and deduces the user's handwriting thereafter. SHOW differs from previous work of gesture recognition in that: 1. it employs a novel rotation injection technique to substantially reduce the effort of data collection; 2. it does not require whole-arm posture, hence is better suited to space-limited places (e.g. vehicles). Our experiments show that SHOW can effectively generate 60 traces from one real handwriting trace and achieve high accuracy at 99.9% when recognizing the 62 different characters written by 10 volunteers. Furthermore, having more screen space after removing the virtual keyboard, SHOW can display 4x candidate words for autocompletion. Aided by the tolerance of character ambiguity and accurate character recognition, SHOW achieves over 70% lower mis-recognition-rate, 43% lower no-response-rate in both daily and general purposed text-entry scenarios, and 33.3% higher word suggestion coverage than the tap-on-screen method using a virtual QWERTY keyboard.
WiStep
Authors: Xu Yang , Yang Wei , Wang Jianxin , Zhou Xing , Li Hong , Huang Liusheng
Published: January 2018

Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.
TouchCam
Authors: Stearns Lee , Oh Uran , Findlater Leah , Froehlich Jon E.
Published: January 2018

On-body interaction, which employs the user's own body as an interactive surface, offers several advantages over existing touchscreen devices: always-available control, an expanded input space, and additional proprioceptive and tactile cues that support non-visual use. While past work has explored a variety of approaches such as wearable depth cameras, bio-acoustics, and infrared reflectance (IR) sensors, these systems do not instrument the gesturing finger, do not easily support multiple body locations, and have not been evaluated with visually impaired users (our target). In this paper, we introduce TouchCam, a finger wearable to support location-specific, on-body interaction. TouchCam combines data from infrared sensors, inertial measurement units, and a small camera to classify body locations and gestures using supervised learning. We empirically evaluate TouchCam's performance through a series of offline experiments followed by a realtime interactive user study with 12 blind and visually impaired participants. In our offline experiments, we achieve high accuracy (>96%) at recognizing coarse-grained touch locations (e.g., palm, fingers) and location-specific gestures (e.g., tap on wrist, left swipe on thigh). The follow-up user study validated our real-time system and helped reveal tradeoffs between various on-body interface designs (e.g., accuracy, convenience, social acceptability). Our findings also highlight challenges to robust input sensing for visually impaired users and suggest directions for the design of future on-body interaction systems.
Money Drives
Authors: Cohen Yehoshua Shuki , Shmueli Erez
Published: January 2018

This paper examines the effectiveness of monetary incentives based on real-time monitoring as means to improve driving behavior of company car drivers. We conducted a 5-months 60-drivers field study with one of the largest public transportation companies in Israel. Driving behavior was measured continuously using In-Vehicle Data Recorders (IVDR) that were pre-installed in the vehicles, enabling naturalistic, objective and concise measurements. The driving behavior measurements were then used to examine two different monetary incentive schemes: (1) a simple individual incentive scheme where each driver was rewarded based on his own improvement in driving behavior, and (2) a peer-reward scheme where each driver was rewarded based on the improvement of his peers. Drivers were also provided with daily feedback about their improvement and the reward they gained using text messages and a dedicated smartphone app. We find that the two incentive schemes presented an average improvement of 25% in driving behavior, whereas the control group (that did not use any monetary incentive) presented no improvement at all. Surprisingly and in contrast to the reported superiority of the peer-reward scheme in previous studies, we find the individual scheme to perform better in our setting (31% vs. 15% improvement). Finally, we find that the monetary incentive schemes were able to reduce fuel consumption significantly, suggesting that such incentives can serve as a sustainable mechanism for improving driving behavior in real-world applications.
Mathematical analysis of circadian disruption and metabolic re-entrainment of hepatic gluconeogenesis: the intertwining entraining roles of light and feeding
Authors: Bae Seul-A , Androulakis Ioannis P.
Published: January 2018

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host’s health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.
Fixed Point Theorems for Generalized θ-ϕ-Contractions in G-Metric Spaces
Authors: Zheng Dingwei
Published: January 2018

We introduce the notion of generalized θ-ϕ contraction and establish some new fixed point theorems for this contraction in the setting of complete G-metric spaces. The results presented in the paper improve, extend, and unify some known results. Finally, we give an example to illustrate them.
The Role of Scanning Electron Microscopy in the Direct Diagnosis of Onychomycosis
Authors: Yue Xueping , Wang Aiping , Li Qing
Published: January 2018

Purpose. The purpose of this study was to evaluate the role of scanning electron microscopy (SEM) in the direct diagnosis of suspected onychomycosis with negative mycological test results.Methods. Outpatients diagnosed with suspected onychomycosis with negative mycological test results, including direct microscopic examination with 10% potassium hydroxide (KOH) and fungal culture, on 3 separate occasions were recruited. A small piece of infected nail was obtained for SEM examination.Results. Among the 48 suspected onychomycosis samples, SEM revealed that 18 (37.5%) were positive for fungal structures, including 10 (20.8%) cases of hyphae and 8 (16.7%) cases of yeast blastospores or budding.Conclusion. SEM represents an effective method to diagnose suspected onychomycosis when the traditional mycological methods were negative. Therefore, this technique could be used in clinical practice.