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Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework. [ABSTRACT FROM AUTHOR]

As one of the important parameters of the satellite transponder, the group delay of the transponder not only determines the magnitude of the signal transmission delay that is generated by the system or network but is also closely related to the signal transmission distortion, which has a substantial impact on the signal transmission quality. When the satellite is in orbit, it is difficult to measure this index due to the particularity of its position. To overcome this problem, this paper proposes a method for measuring the group delay of an on-orbit satellite transponder. By using two sets of ground station antenna systems that are installed in parallel to conduct the observation of the transponders to be measured simultaneously in the mode of “receiving its own station-disseminated signal,” the measurement results of the group delay of the transponders can be obtained after the calculation. Based on the orbit determination by the transfer tracking system of the National Time Service Center, a group time delay measurement experiment of the C5B transponder of the APSTAR-7 satellite in orbit was conducted. The results of an analysis of the data demonstrate that this method can effectively measure the group delay of a satellite transponder and that the root mean square of the two measurement results is less than 0.2 ns, which provides a useful reference for further optimization and improvement of the orbit determination accuracy of the system and provides a performance evaluation service for satellite companies. [ABSTRACT FROM AUTHOR]

Two-way satellite time and frequency transfer (TWSTFT) using satellites in inclined geo-synchronous orbit (IGSO) is fundamentally more difficult than using satellites in geostationary orbit (GEO), due to the fact that IGSO satellites are constantly moving relative to a ground station, which brings in large residual effects. In this paper, we designed a protocol and an algorithm to correct several factors that affect the precision of TWSTFT using IGSO satellites. We used two ground stations, each equipped with sending and receiving channels, to conduct real-time measurements of the distance and the speed of an IGSO satellite, and data collected were used to make appropriate corrections. We tested this concept by conducting TWSTFT experiments using an IGSO satellite (BEIDOU I1-S), and compared the results with that obtained using a GEO satellite (CHINASAT 12). Indeed, the individual components that contribute to the residual effects of TWSTFT measurements using the IGSO satellite were several orders of magnitude greater than that of the GEO satellite. By using our modified protocol, TWSTFT results with an IGSO satellite were well accorded with that of using a GEO satellite; the standard deviation of residuals was less than 0.4 ns, well within the limit for the measurement uncertainty of TWSTFT. Therefore, we have expanded the use of IGSO satellites for TWSTFT. In principle, it should also be applicable to medium earth orbit and other satellites. [ABSTRACT FROM AUTHOR]

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps—Ookla Speedtest and MobiPerf—have their RTT measurements inflated. We build three test apps for three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete trace of packets and their timestamps using multiple sniffers and frame-based synchronization. Our multi-layer analysis reveals that the delay inflation can be introduced both in the user space and kernel space. The long path of subfunction invocations accounts for the majority of the delay overhead in the Android runtime (both Dalvik VM and ART), and the sleeping functions in the drivers are the major source of the delay overhead between the kernel and physical layer. We propose and implement a native measurement app to mitigate the delay overhead in the Android runtime, and the resulted delay inflation in the user space can be kept under 1.5 ms for almost all cases. [ABSTRACT FROM AUTHOR]

QoE crowdtesting is increasingly popular among researchers to conduct subjective assessments of network services. Experimenters can easily access a huge pool of human subjects through crowdsourcing platforms. Without any supervision, low-quality workers, however, can threaten the reliability of the assessments. One of the approaches in classifying the quality of workers is to analyze their behavior during the experiments, such as mouse cursor trajectory. However, existing works analyze the trajectory coarsely, which cannot fully extract the imbedded information. In this paper, we propose a novel method to detect low-quality workers in QoE crowdtesting by analyzing the worker behavior. Our approach is to construct a predictive model by using supervised learning algorithms. A quality score is computed by applying existing anti-cheating techniques and human inspections to label the workers. We define a set of ten worker behavior metrics, which quantifies different types of worker behavior, including finer-grained cursor trajectory analysis. A multiclass Naïve Bayes classifier is applied to train a model to predict the quality of workers from the metrics. We have conducted video QoE assessments on Amazon Mechanical Turk and CrowdFlower to collect the worker behavior. Our results show that the error rates of the model trained from four metrics are equal or less than 30%. We further find that combining the predictions from the four different 5-point Likert scale rating methods can improve the success rate in detecting low-quality workers to around 80%. Finally, our method is 16.5% and 42.9% better in precision and recall than CrowdMOS. [ABSTRACT FROM PUBLISHER]

Many HTTP streaming video systems have been developed and widely deployed in recent years. Previous efforts were mainly spent on improving the caching of videos or proposing mid-stream measurement methods to update the best bitrate. However, since the video length is often short, the mid-stream measurement may not even converge to the best bitrate due to insufficient bandwidth estimates. On the other hand, because of diversified Web infrastructure, estimating the actual network quality at the pre-stream stage is increasingly challenging for video service providers. In this paper, we propose IRate, which enables video service providers to proactively profile clients’ streaming performance by carrying out pre-stream measurement in the Content Delivery Network (CDN). With the measurement results, the video stream can start at the best video quality at the onset of streaming. This is especially beneficial to short video clips, which are very popular in the Internet today. IRate is composed of a probe kit and a quality oracle. The probe kit utilizes the pre-stream time window (e.g., user’s think time and pre-roll advertisement) for measuring network quality by running a lightweight measurement script on the Web page to induce probe packets from the IRate middlebox on the server side. With the measurement results, the quality oracle estimates the clients’ streaming performance by determining the highest initial bitrate with a pre-trained decision tree. Our testbed results show that IRate is able to achieve 80% accuracy in determining the bitrate within 10s. By having a better estimate of the best initial bitrate, the buffering time and rebuffering events are significantly reduced in HTTP streaming. Furthermore, the stability and the efficiency in dynamic adaptive streaming over HTTP streaming are also improved by about 40% and 36%, respectively. Our user quality of experience (QoE) experiment further validates that IRate can improve the QoE by more than 6% and the perceived quality of initial quality by 24% in the actual Internet environment. [ABSTRACT FROM PUBLISHER]