Determining how impactful an anomaly is on the viewing experience of an audience is important to production studios, content creators, and content distributors. However, judging the impact of each anomaly is a highly manual and subjective task. To support automation of this, we propose the use of noticeability and introduce a method of prediction. We employed a psychophysical experimental method to capture the impact of various anomalies across various images. We then developed a multi-scale context aggregation model, trained on that data, to predict the noticeability of anomalies on novel images. This notice ability prediction can then be used to prioritize anomalies and ensure that remediation efforts are spent where it would most benefit the audience experience.

Predicting image saliency has many potential useful applications across several industries, including film production, creative marketing content, product design, and quality control in manufacturing. Although image saliency prediction, in general, has made substantial progress with the advent of deep learning on benchmark datasets, it still has room to improve when applied to novel scenarios. This paper presents our experimental findings in dataset building and model development for predicting saliency of cinematic images in a real-world context. To evaluate the proposed model, we conducted experiments on both benchmark datasets (i.e., SALICON, MIT1003, MIT300, CAT2000) as well as a private dataset. The results demonstrate the superior performance of our proposed method. We highlight a film production use case, but the model and methods explored here may also generalize to other areas relevant to image saliency.

Wearable glasses are on the rising edge of development with great user popularity. However, user data stored on these devices bring privacy risks to the owner. To better protect the owner's privacy, a continuous authentication system is needed. In this paper, we propose a continuous and noninvasive authentication system for wearable glasses, named GlassGuard. GlassGuard discriminates the owner and an impostor with behavioral biometrics from six types of touch gestures (single-tap, swipe forward, swipe backward, swipe down, two-finger swipe forward, and two-finger swipe backward) and voice commands, which are all available during normal user interactions. With data collected from 32 users on Google Glass, we show that GlassGuard achieves 99% detection rate and 0.5% false alarm rate after 3.5 user events on average when all types of user events are available with equal probability. Under five typical usage scenarios, the system has a detection rate above 93% and a false alarm rate below 3% after less than five user events.

WiFi is a major source of energy consumption on smartphones. Unfortunately, a non-negligible portion of the WiFi energy consumption is spent for frames that are useless to the smartphone. For example, energy is wasted to receive WiFi broadcast frames that are not needed by any smartphone application. What's worse, in order to process the broadcast frames received, a smartphone in suspend mode switches from suspend mode to high power active mode and stays there for a while. As such, additional energy is wasted to do the processing. In this paper, we design a system, namely HIDE, to reduce smartphone energy wasted on useless WiFi broadcast traffic. With our system, smartphones in suspend mode do not receive useless broadcast frames or wake up to process useless broadcast frames. Our trace-driven simulation shows that the HIDE system saves 34%-75% energy for Nexus One when 10% of the broadcast frames are useful to the smartphone. Our overhead analysis demonstrates that our system has negligible impact on network capacity and packet round-trip time.

Our study reveals that facilitating warm launch of just five smartphone applications is extremely expensive, using up to 36 percent of memory. Further investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Therefore, we present iRAM, a personalized system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation on memory hungry applications indicates that iRAM reduces application crashes by up to 14 percent, and reduces launch delays by up to 78.2 percent. In addition, the results confirm that iRAM increases free memory levels by up to 4.8 times. This performance gain comes with 3.5 percent of CPU overhead and 0.9 percent of power overhead.

Mobile video chat apps offer users an approachable way to communicate with others. As high-speed 4G networks are being deployed worldwide, the number of mobile video chat app users increases. However, video chatting on mobile devices brings users financial concerns, since streaming video demands high bandwidth and can use up a large amount of data in dozens of minutes. Lowering the bandwidth usage of mobile video chats is challenging since video quality may be compromised. In this paper, we attempt to tame this challenge. Technically, we propose a context-aware frame rate adaption framework, named low-bandwidth video chat (LBVC). It follows a sender-receiver cooperative principle that smartly handles the tradeoff between lowering bandwidth usage and maintaining video quality. We implement LBVC by modifying an open-source app–Linphone– and evaluate it with both objective experiments and subjective studies.

Existing research has demonstrated that inter-body sensor network (inter-BSN) information sharing among coexisting BSNs can enhance applications' performance and save energy. However, how to achieve effective inter-BSN information sharing through wireless communication is a challenging task. On one hand, a BSN should be able to discover neighboring BSNs and establish inter-BSN links with quality of service (QoS) assurances. On the other hand, a BSN should be able to prevent the QoS of intraand inter-BSN links from being degraded by multiple BSNs' mutual interference. In this paper, we propose BuddyQoS, a framework that provides network throughput assurances for coexisting and shared buddy BSNs. In particular, BuddyQoS accurately estimates and adaptively schedules wireless resources to meet the throughput requirements of all interand intra-BSN links. Our trace-driven experiment results demonstrate that BuddyQoS outperforms the default CSMA solution in the standard TinyOS-2.x releases in terms of providing throughput assurances.

Smartphones save energy by entering a low power suspend mode (120mW). As a result, power consumption increases dramatically. To improve energy efficiency, some phones employ a hardware broadcast filter in the WiFi driver. All UDP broadcast frames other than Multicast DNS frames are blocked, thus none is received by upper layer applications. So, we have a dilemma of handling WiFi broadcast traffic during smartphone suspend mode: either receive all of them suffering high power consumption, or receive none of them sacrificing functionalities. In this paper, we propose Software Broadcast Filter (SBF) to address the dilemma. SBF is smarter than the hardware broadcast filter as it only filters out useless broadcast frames and does not impair functionalities of applications. SBF is also more energy efficient than the “receive all” method. Our trace driven evaluation shows that SBF can save up to 52% energy consumption than the “receive all” method.

Video chat apps enable users to stay in touch with their family, friends and colleagues. However, they consume a lot of bandwidth and hence can quickly use up a monthly data plan quota, which is a high-cost resource on smartphones. In this paper, we propose LBVC (Low-bandwidth Video Chat), a user-guided vibration-aware frame rate adaption framework. LBVC takes a sender-receiver cooperative approach and reduces bandwidth usage as well as alleviates video quality degradation for video chat apps on smartphones. We implement LBVC on the Android platform and evaluate its performance on a series of experiments and user study with 21 pairs of subjects. Compared to the default solution, LBVC decreases bandwidth usage by 35% and at the same time maintains good video quality without introducing extra power consumption under typical video chat scenarios.

The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this paper, we investigate the behavior of reads and writes in smartphones. We conduct the first large-scale measurement study on the Android I/O delay using the data collected from our Android application running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. Additionally, we show the asymmetry of the slowdown of one I/O type due to another, and elaborate the speedup of concurrent I/Os over serial ones. We use this obtained knowledge to design and implement a system prototype called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO issues I/Os with optimized concurrency parameters. The system is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.

No abstract available.

We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.

WiFi effectively has two extremes: low power consumption and high latency, or low latency and high power consumption. WiFi Power Save Mode saves energy by trading added latency for less power consumption. Minimal latency but maximum power, on the other hand, is consumed with WiFi Active Mode. While research has advanced in mitigating these extremes, certain types of network traffic such as constant bitrate streaming make the contrast unavoidable. We introduce Bluesaver, which provides low latency and low energy by maintaining a Bluetooth and WiFi connection simultaneously. Bluesaver is designed at the MAC layer and is able to opportunistically select the most efficient connection for packets while still assuring acceptable latency. We implement Bluesaver on an Android phone and Access Point and show that we can save more than 25% energy over existing solutions and attain the capability of quickly adapting to changes in network traffic.

No abstract available.

The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an application, leading to poor user experience. This video features our study of storage impact on smartphone application delay. We conduct the first large-scale measurement study on the I/O delay of Android using the data collected from our application running on 1009 devices within 130 days. We observe that Android devices spend up to 58% of their CPU active time waiting for storage I/Os to complete. This negatively affects the smartphone's overall application performance, and results in slow response time. Further investigation, among others, reveals that reads experience up to a 626% slowdown in the presence of concurrent writes. The obtained knowledge is used to design and implement a system called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results from the 20 researched applications demonstrate that SmartIO reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.

Continuous authentication modalities allow a device to authenticate users transparently without interrupting them or requiring their attention. This is especially important on smartphones, which are more prone to be lost or stolen than regular computers, and carry plenty of sensitive information. There is a multitude of signals that can be harnessed for continuous authentication on mobile devices, such as touch input, accelerometer, and gyroscope, etc. However, existing public datasets include only a handful of them, limiting the ability to do experiments that involve multiple modalities. To fill this gap, we performed a large-scale user study to collect a wide spectrum of signals on smartphones. Our dataset combines more modalities than existing datasets, including movement, orientation, touch, gestures, and pausality. This dataset has been used to evaluate our new behavioral modality named Hand Movement, Orientation, and Grasp (H-MOG). This poster reports on the data collection process and outcomes, as well as preliminary authentication results.

Wireless sensor network applications, such as those for natural disaster warning, vehicular traffic monitoring, and surveillance, have stringent accuracy requirements for detecting or classifying events and demand long system lifetimes. Through quantitative study, we show that existing event detection approaches are challenged to explore the sensing capability of a deployed system and choose the right sensors to meet user-specified accuracy. Event detection systems are also challenged to provide a generic system that efficiently adapts to environmental dynamics and works easily with a range of applications, machine learning approaches, and sensor modalities. Consequently, we propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user-specified detection accuracy during runtime while significantly reducing energy consumption. Watchdog can use different machine learning techniques to learn the sensing capability of a heterogeneous sensor deployment and meet accuracy requirements. To address environmental dynamics and ensure energy savings, Watchdog wakes up and puts to sleep sensors as needed to meet user-specified accuracy. Through evaluation with real vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user-specified detection accuracy, energy savings, and environmental adaptability.

In this paper, to our best knowledge, we are first to provide an experimental study on how storage techniques affect power levels in smartphones and introduce energy-efficient approaches to reduce energy consumption. We evaluate power degradation at several layers of block I/O, focusing on the block layer and device driver. At each level, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. The system tracks the run-time I/O pattern of a smartphone that is then matched with the closest pattern from the benchmark table. After having obtained the optimal parameters, it dynamically configures storage parameters to reduce energy consumption. We evaluate our prototype by using the 20 most popular Android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques.

Video conferencing attempts to convey subtle cues of face-to-face interaction (F2F), but it is generally believed to be less effective than F2F. We argue that careful design based on an understanding of non-verbal communication can mitigate these differences. In this paper, we study the effects of video image framing in one-on-one meetings on empathy formation. We alter the video image by framing the display such that, in one condition, only the head is visible while, in the other condition, the entire upper body is visible. We include a F2F control case. We used two measures of dyad empathy and found a significant difference between head-only framing and both upper-body framing and F2F, but no significant difference between upper-body framing and F2F.

Video conferencing is still considered a poor alternative to face-to-face meetings. In the business setting, where these systems are most prevalent, the misuse of video conferencing systems can have detrimental results, especially in high-stakes communications. Prior work suggests that spatial distortions of nonverbal cues, particularly gaze and deixis, negatively impact many aspects of effective communication in dyadic communications. However, video conferencing systems are often used for group-to-group meetings where spatial distortions are exacerbated. Meanwhile, its effects on the group dynamic are not well understood. In this study, we examine the effects that spatial distortions of nonverbal cues have on inter-group trust formation. We conducted a large (169 participant) study of group conferencing under various conditions. We found that the use of systems that introduce spatial distortions negatively affect trust formation patterns. On the other hand, these effects are essentially eliminated by using a spatially faithful video conferencing system.

Distributed wireless systems (DWSs) are emerging as the enabler for next-generation wireless applications. There is a consensus that DWS-based applications, such as pervasive computing, sensor networks, wireless information networks, and speech and data communication networks, will form the backbone of the next technological revolution. Simultaneously, with great economic, industrial, consumer, and scientific potential, DWSs pose numerous technical challenges. Among them, two are widely considered as crucial: autonomous localized operation and minimization of energy consumption. We address the fundamental problem of how to maximize the lifetime of the network using only local information, while preserving network connectivity. We start by introducing the care-free sleep (CS) Theorem that provides provably optimal conditions for a node to go into sleep mode while ensuring that global connectivity is not affected. The CS theorem is the basis for an efficient localized algorithm that decides which nodes will go to into sleep mode and for how long. We have also developed mechanisms for collecting neighborhood information and for the coordination of distributed energy minimization protocols. The effectiveness of the approach is demonstrated using a comprehensive study of the performance of the algorithm over a wide range of network parameters. Another important highlight is the first mathematical and Monte Carlo analysis that establishes the importance of considering nodes within a small number of hops in order to preserve energy.

In this paper, we develop an architecture for principal component analysis (PCA) to be used as an outlier detection method for high-speed network intrusion detection systems (NIDS). PCA is a common statistical method used in multivariate optimization problems in order to reduce the dimensionality of data while retaining a large fraction of the data characteristic. First, PCA is used to project the training set onto eigenspace vectors representing the mean of the data. These eigenspace vectors are then used to predict malicious connections in a workload containing normal and attack behavior. Our simulations show that our architecture correctly classifies attacks with detection rates exceeding 99 % and false alarms rates as low as 1.95%. For next generation NIDS, anomaly detection methods must satisfy the demands of Gigabit Ethernet. FPGAs are an attractive medium to handle both high throughput and adaptability to the dynamic nature of intrusion detection. Using hardware parallelism and extensive pipelining, our architecture is implemented on FPGAs to achieve Gigabit link speeds. 1.

MultiView is a new video conferencing system that supports collaboration between remote groups of people. MultiView accomplishes this by being spatially faithful. As a result, MultiView preserves a myriad of nonverbal cues, includ-ing gaze and gesture, in a way that should improve com-munication. Previous systems fail to support many of these cues because a single camera perspective warps spatial char-acteristics in group-to-group meetings. In this paper, we present a formal definition of spatial faithfulness. We then apply a metaphor-based design methodology to help us spec-ify and evaluate MultiView's support of spatial faithfulness. We then present results from a low-level user study to mea-sure MultiView's effectiveness at conveying gaze and ges-ture perception. MultiView is the first practical solution to spatially faithful group-to-group conferencing, one of the most common applications of video conferencing.