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The purpose of this study was to determine the quality of service, price, and promotion of customer satisfaction at Pegipegi.com in making hotel room bookings in Makassar City. Data obtained by distributing questionnaires to Pegipegi.com customers who have made hotel bookings in Makassar. This study used 506 respondents using purposive sampling. The results showed that price and promotion significantly affected customer satisfaction, but service quality had no significant impact. This study concludes that responses to guest complaints can affect the relationship between service quality and guest satisfaction.

440 Background: Cancer-related fatigue is one of the most distressing symptoms of cancer patients. Finding a patient's fatigue often requires the use of a subjective assessment scale, such as the Brief Fatigue Inventory (BFI). There are few objective ways to measure fatigue. The aim of this study was to evaluate whether objective cancer-related fatigue (CRF) diagnostic criteria can differentiate cancer from cancer-free participants and to explore the relationship between subjective and objective fatigue. Methods: In this study, we used a photoplethysmography (PPG) smartband device to collect the PPG information from the patients. During PPG measurements, the heart rate variability (HRV) signal was included in the data collection process for subsequent calculation of the LF/HF ratio. Participants completed a self-report measure to assess demographics, fatigue levels, and brief sleep diaries for the next 7 days. At the same time, the participants wore a wristband to collect HRV signals, which triggered HRV every hour over a 24-hour period for 7 consecutive days. Results: Cancer patients (n = 71) and cancer-free controls (n = 75) were studied. Study participants completed CRF questionnaires and heart rate variability (HRV) records. The smartband can be worn continuously for 120 hours as an objective measure of activity phase, sleep phase and HRV. Compared with controls, cancer patients were more fatigued and were more likely to be disturbed by fatigue (t = -3.73, p < 0.001), especially general activity (t = -2.93, p < 0.001), walking ability (t = -3.6, p < 0.001) and normal work (t = -2.18, p = 0.03). Wearing the bracelet can get the LF/HF value of HRV in the time domain. The LF to HF and LF/HF disorder ratios were further divided into active and sleep phases. During sleep phase, the LF/HF disorder ratio was higher in the cancer group than in the control group regardless of whether fatigue was mild or moderate (t = -2.5, p = 0.01; t = -2.8, p = 0.01, respectively). The LF/HF disorder ratios was higher in cancer patients with mild fatigue than in cancer-free participants during sleep phase (40% vs 20%, respectively). Cancer patients with moderate fatigue had higher incidence of LF/HF disorder ratios than cancer-free participants (50% vs 20%, respectively). Conclusions: It can be concluded that the diagnostic criteria of subjective and objective CRF can distinguish cancer and cancer-free cases. Using this objective fatigue device could provide an indicator for further clinical monitoring. Clinical trial information: NCT04300842.

Human behavior analysis is the process that consists of activity monitoring and behavior recognition and has become the core component of intelligent applications such as security surveillance and fall detection. Generally, the techniques involved in behavior recognition include sensor and vision-based processing. During the process, the activity information is typically required to ensure a good recognition performance. On the other hand, the privacy issue attracts much attention and requires a limited range of activity monitoring accordingly. We study behavior analysis for such privacy-oriented applications. A local object tracking (LOT) technique based on an infrared sensor array is developed in a limited monitoring range and is further realized to a practical bed-exit system in the clinical test environment. The experimental results show a correct recognition rate of 99% for 6 bedside activities. In addition, 89% of participants in a satisfaction survey agree on its effectiveness.

BACKGROUND Cancer-related fatigue is a serious side effect of cancer, and its treatment can disrupt the quality of life of patients. Clinically, the standard method for assessing cancer-related fatigue relies on subjective experience retrieved from patient self-reports, such as the Brief Fatigue Inventory (BFI). However, most patients do not self-report their fatigue levels. OBJECTIVE In this study, we aim to develop an objective cancer-related fatigue assessment method to track and monitor fatigue in patients with cancer. METHODS In total, 12 patients with lung cancer who were undergoing chemotherapy or targeted therapy were enrolled. We developed frequency-domain parameters of heart rate variability (HRV) and BFI based on a wearable-based HRV measurement system. All patients completed the BFI-Taiwan version questionnaire and wore the device for 7 consecutive days to record HRV parameters such as low frequency (LF), high frequency (HF), and LF-HF ratio (LF-HF). Statistical analysis was used to map the correlation between subjective fatigue and objective data. RESULTS A moderate positive correlation was observed between the average LF-HF ratio and BFI in the sleep phase (ρ=0.86). The mapped BFI score derived by the BFI mapping method could approximate the BFI from the patient self-report. The mean absolute error rate between the subjective and objective BFI scores was 3%. CONCLUSIONS LF-HF is highly correlated with the cancer-related fatigue experienced by patients with lung cancer undergoing chemotherapy or targeted therapy. Beyond revealing fatigue levels objectively, continuous HRV recordings through the photoplethysmography watch device and the defined parameters (LF-HF) can define the active phase and sleep phase in patients with lung cancer who undergo chemotherapy or targeted chemotherapy, allowing a deduction of their sleep patterns. CLINICALTRIAL

Forward error correction (FEC) is a common error control technique to improve the quality of video streaming over unreliable channels by recovering packet losses during transmission. The traditional frame-level FEC adopts the independent FEC coding method, regarding each video frame as one coding block, and protects each block with its respective FEC data according to the principle of unequal error protection (UEP). In contrast, emerging content-dependent FEC coding schemes explore the performance improvement provided by the expandable block structure. For the frame-level FEC with an expandable block structure, a high-priority frame is involved in the FEC processing of its following frame, and thus the frames with a dependency relation in the video coding stage are also correlated in the FEC coding stage. In this paper, an analytical content-dependent FEC model is developed to estimate video streaming quality in terms of playable frame rate. Based on this model, optimized FEC configuration can be found given the inputs of the video and transmission channel parameters. A comprehensive performance study is conducted for the proposed model to observe the redundancy assignment behavior, the reconstructed video quality and the FEC coding complexity, under the various network situations. The performance results show that the expanding-block FEC can be superior in balancing the trade-off between FEC complexity and loss recovery capacity.

Forward error correction (FEC) is a common error control technique to improve the quality of video streaming over lossy channels. To optimize the data recovery performance, frame-level FEC schemes have been proposed for streaming video to maximize playable frame rate (PFR) within transmission rate constraint on a random binary symmetric channel (BSC). However, burst loss is a commonplace in current Internet architecture, and the FEC efficacy c an be degraded since the burst data losses easily exceed the error correction capacity of FEC. Accordingly, an estimated video quality model over burst loss channels is proposed in this paper to evaluate the impact of burst loss for FEC-based video applications. In addition to th e model analysis, the simulation experiments on the NS-2 network simulator are conducted at a given estimate of the packet loss probability and average burst length. The results suggest a useful reference in designing the FEC scheme for video applications, and as the video coding and channel parameters are given, the proposed model can provide the optimal FEC solution to achieve a better reconstructed video quality than the FEC model based on a random BSC channel.

By adding the redundant packets into source packet block, cross-packet forward error correction FEC scheme performs error correction across packets and can recover both congestion packet loss and wireless bit errors accordingly. Because cross-packet FEC typically trades the additional latency to combat burst losses in the wireless channel, this paper presents a FEC enhancement scheme using the small-block interleaving technique to enhance cross-packet FEC with the decreased delay and improved good-put. Specifically, adopting short block size is effective in reducing FEC processing delay, whereas the corresponding effect of lower burst-error correction capacity can be compensated by deliberately controlling the interleaving degree. The main features include i the proposed scheme that operates in the post-processing manner to be compatible with the existing FEC control schemes and ii to maximize the data good-put in lossy networks; an analytical FEC model is built on the interleaved Gilbert-Elliott channel to determine the optimal FEC parameters. The simulation results show that the small-block interleaved FEC scheme significantly improves the video streaming quality in lossy channels for delay-sensitive video. Copyright © 2013 John Wiley & Sons, Ltd.

Summary Forward error correction (FEC) techniques are widely used to recover packet losses over unreliable networks in real-time video streaming applications. Traditional frame-level FEC encodes 1 video frame in each FEC coding window. By contrast, in the expanding-window FEC scheme, high-priority frames are included in the FEC processing of the following frames, so as to construct a larger coding window. In general, expanding-window FEC improves the recovery performance of FEC, because the high-priority frame can be protected by multiple windows and the use of a larger coding window increases the efficiency. However, the larger window size also increases the complexity of the coding and the memory space requirements. Consequently, expanding-window FEC is limited in terms of practical applications. Sliding-window FEC adopts a fixed window size in order to approximate the performance of the expanding-window FEC method, but with a reduced complexity. Previous studies on sliding-window FEC have generally adopted an equal error protection (EEP) mechanism to simplify the analysis. This paper considers the more practical case of an unequal error protection (UEP) strategy. An analytical model is derived for estimating the playable frame rate (PFR) of the proposed sliding-window FEC scheme with a Reed-Solomon erasure code for real-time non-scalable streaming applications. The analytical model is used to determine the optimal FEC configuration which maximizes the PFR value under given transmission rate constraints. The simulation results show that the proposed sliding-window scheme achieves almost the same performance as the expanding-window scheme, but with a significantly lower computational complexity.

Summary Burst packet loss is a common problem over wired and wireless networks and leads to a significant reduction in the performance of packet-level forward error correction (FEC) schemes used to recover packet losses during transmission. Traditional FEC interleaving methods adopt the sequential coding-interleaved transmission (SCIT) process to encode the FEC packets sequentially and reorder the packet transmission sequence. Consequently, the burst loss effect can be mitigated at the expense of an increased end-to-end delay. Alternatively, the reversed interleaving scheme, namely, interleaved coding-sequential transmission (ICST), performs FEC coding in an interleaved manner and transmits the packets sequentially based on their generation order in the application. In this study, the analytical FEC model is constructed to evaluate the performance of the SCIT and ICST schemes. From the analysis results, it can be observed that the interleaving delay of ICST FEC is reduced by transmitting the source packets immediately as they arrive from the application. Accordingly, an Enhanced ICST scheme is further proposed to trade the saved interleaving time for a greater interleaving capacity, and the corresponding packet loss rate can be minimized under a given delay constraint. The simulation results show that the Enhanced ICST scheme achieves a lower packet loss rate and a higher peak signal-to-noise-ratio than the traditional SCIT and ICST schemes for video streaming applications.

This paper presents a Multiple-Instance Decision Based Neural Networks (MI-DBNN) based image retrieval system. Without precisely image segmentation, the image retrieval problem is considered as a Multiple-Instance Learning problem. A set of exemplar images are selected, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. Then, the MI-DBNN is trained to learn the user's preferred image concept from the positive and negative examples. The proposed system is built and located on http://210.240.226.146/MIL/. Experimental results show that our method can significantly improve the retrieving performance from 68.4% to 80.7%, which outperforms to the results of some leading image retrieval methods.

Forward error correction (FEC) is a common error control technique to recover transmission losses with low delay overhead. However, enhancing or optimizing the loss recovery capability for video streaming results in the increased FEC processing latencies to violate the stringent delay constraints of video communications. Accordingly, a low-delay FEC scheme is proposed in this paper to minimize the additional FEC processing delay while achieving nearly the same video quality that can be obtained by the optimal FEC. To support the online FEC rate allocation, the proposed FEC scheme extends the optimal frame-level FEC with the frame size prediction technique to imitate the optimal allocation parameters of previous video data. The simulation results show that the proposed prediction-based FEC scheme can maintain low end-to-end delay and significantly improves the video streaming quality for delay-sensitive video.

Concurrent Multipath Transmission (CMT) mechanisms have been proposed to improve the transmission performance by means of concurrently transmitting data packets via two or more network interfaces. Based on the wireless cognitive radio, that can dynamically adjust the transmission parameters of the specified device, the required CMT mechanism in the wireless environment differs from that in the wired one since the multirate capacity which the data transmission can take place at a number of different rates according to channel conditions, is characterized in wireless networks to cater for the error-prone problem. Accordingly, this paper extends previous CMT mechanisms to propose Multirate-aware Concurrent Multipath (MCM) transmission mechanism to maximize the data good-put in wireless networks. Compared with the previous CMT mechanisms, the MCM mechanism has a higher data throughput and obtains the better transmission performances in terms of end-to-end delay, packet loss rate, decodable frame rate, and peak signal to noise ratio.

Multiple Instance Learning (MIL) is a variation of supervised learning, where the training set is composed of many bags, each of which contains many instances. If a bag contains at least one positive instance, it is labelled as a positive bag; otherwise, it is labelled as a negative bag. The labels of the training bags are known, but that of the training instances are unknown. In this paper, a Multiple Instance Decision Based Neural Networks (MI-DBNN) is proposed for MIL, which employs a novel discriminate function to capture the nature of MIL. The experiments were performed on MUSK1 and MUSK2 data sets. In comparison with other methods, MI-DBNN demonstrates competitive classification accuracy on MUSK1 and MUSK2 data sets, which are 97.8% and 98.4%, respectively.

In end-to-end networks, the burst losses caused by network congestion and wireless channel errors reduce the efficiency of the forward error correction (FEC) scheme. Although different control techniques are designed to enhance the loss recovery ability, the resulting FEC processing latencies may increase the end-to-end delay to violate the stringent constraints of delay sensitive video communications. Therefore, this paper presents a FEC enhancement scheme to achieve a tradeoff between end-to-end delay and the FEC loss recovery performance. The proposed scheme consists of 1) an adaptive block size control mechanism to reduce the FEC processing delay and 2) the interleaving technique to improve the error correction capacity of small block sizes. The experimental results show that the proposed scheme significantly improves the video streaming quality in lossy channels for delay-sensitive video.

This paper presents a self-regulated redundancy control scheme for high-bit-rate video transmission using packet-level forward-error-correction (FEC) codes over error-prone wireless networks. The effectiveness of FEC on video is obtained by regulating its redundancy cost to balance that tradeoff of bandwidth utilization and FEC efficiency based on channel status, temporal dependency of video frames, and network congestion level. Experimental results show the proposed scheme maintains the desired quality-of-service (QoS) control over unloaded networks with the realistically measured wireless loss trace in 802.11b WLAN and over loaded networks, the adverse effect of FEC efficiency due to blindly increasing redundancy can be reduced to achieve the better video quality.

Delivering MPEG-4 real-time video over the Internet requires the use of rate control schemes in both the compression and the transport domains. This paper discusses the practical difficulties involved in integrating MPEG-4 scalable rate control (SRC) in the compression domain with TCP-friendly rate control (TFRC) in the transport domain. In addition to the requirement for rate matching between the two schemes to ensure TCP-friendliness, users demand a high-quality, smooth media output under the varying network conditions. In this paper, an integrated rate control scheme is proposed for MPEG-4 video transported by a TFRC protocol. The proposed scheme comprises a rate smoothing control mechanism to meet the rate matching with the controlled transmission buffering delay, and an adaptive rate control for the I-frame designed to reduce the frame skipping. Through the successful integration of the two schemes, the experimental results show that the proposed scheme better preserves the motion smoothness and improves the overall perceptual quality for TCP-friendly MPEG-4 video transmissions to obtain a smooth, high visual quality video experience.

IntServ and DiffServ networks are both proposed to promote quality of service (QoS) for multimedia applications in the Internet. However, the complexity of communication between diverse applications and underlying QoS architectures leads to one of the deployment problems which decreases the utility of QoS provisioning. Without modifying legacy applications, the paper presents a transparent QoS mechanism (TQM) to communicate with underlying QoS architectures and also provide swappable modules to support different QoS setups for diverse applications. Currently, TQM is implemented on the UNIX platform, and some important issues of applying TQM in IntServ/DiffServ networks have been examined.

With the wide deployment of wireless networks and the rapid integration of various emerging networking technologies nowadays, Internet video applications must be updated on a sufficiently timely basis to support high end-to-end quality of service (QoS) levels over heterogeneous infrastructures. However, updating the legacy applications to provide QoS support is both complex and expensive since the video applications must communicate with underlying architectures when carrying out QoS provisioning, and furthermore, should be both aware of and adaptive to variations in the network conditions. Accordingly, this paper presents a transparent loss recovery scheme to transparently support the robust video transmission on behalf of real-time streaming video applications. The proposed scheme includes the following two modules: (i) a transparent QoS mechanism which enables the QoS setup of video applications without the requirement for any modification of the existing legacy applications through its use of an efficient packet redirection scheme; and (ii) an instant frame-level FEC technique which performs online FEC bandwidth allocation within TCP-friendly rate constraints in a frame-by-frame basis to minimize the additional FEC processing delay. The experimental results show that the proposed scheme achieves nearly the same video quality that can be obtained by the optimal frame-level FEC under varying network conditions while maintaining low end-to-end delay.