nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid temporal-SNR fine-granular scalability for Internet video

Transmission of video over bandwidth varying networks like the Internet requires a highly scalable solution capable of adapting to the network condition in real-time. To address this requirement, scalable video-coding schemes with multiple enhancement layers have been proposed. However, under this multiple-layer paradigm, the transmission bit-rate of each layer has to be predetermined at encoding time. Consequently, the range of bit-rates that can be covered with these compression schemes is limited and often lower than, or different from, the desired range required at transmission time. In this paper, a novel scalable video-coding framework and a corresponding compression method for Internet video streaming are introduced. Building upon the MPEG-4 SNR fine-granular-scalability (FGS) approach, the proposed framework provides a new level of abstraction between the encoding and transmission process by supporting both SNR and temporal scalability through a single enhancement layer. Therefore, our proposed approach enables streaming systems to support full SNR, full temporal, and hybrid temporal-SNR scalability in real-time depending on the available bandwidth, packet-loss patterns, user preferences, and/or receiver complexity. Moreover, our experiments revealed that the presented FGS temporal-SNR scalability has similar or better PSNR performance than the multilayer scalability schemes. Subsequently, an Internet video streaming system employing the proposed hybrid FGS-temporal scalability structure is introduced, together with a very simple, yet effective, rate-control that performs the tradeoffs between individual image quality (SNR) and motion-smoothness in real-time. The hybrid temporal-SNR scalability presented in this paper has been adopted in the MPEG-4 standard to support video-streaming applications.
Network coding with multi-generation mixing

Connectivity, losses, and buffering are factors that directly affect the performance of network coding. These factors affect the ability of intermediate nodes to generate useful encodings; these are encodings that contribute in propagating and recovering data at receiver node(s). It has been shown that in particular scenarios network coding performance can be improved by simply increasing the generation size (k); however, this leads to increasing transmission overhead, encoding complexity and (more importantly) buffer sizes at intermediate nodes. In this paper, we propose a new network coding approach where we employ Multi-Generation Mixing (MGM). MGM eliminates the need to increasing buffer sizes while improving the performance of network coding. Under MGM, we define a mixing set of size m generations that can be network coded (mixed) together. Within each MGM mixing set, a new set of generation packets are mixed with previously transmitted (network coded) generations. This generalized approach provides a great deal of resilience against losses when compared with traditional generation-based network coding. Our analysis of the performance of MGM-based network coding demonstrates significant reduction in required overhead for a given recovery performance at the receivers. We also illustrate the performance of MGM using extensive simulations that provide a useful insight into the viability of MGM-based network coding.
The contourlet transform for image denoising using cycle spinning

A new method for image denoising based on the contourlet transform, which has been recently introduced is presented in this paper. Image denoising by means of the contourlet transform introduces many visual artifacts due to the Gibbs-Iike phenomena. Due to the lack of translation invariance of the contourlet transform, we employ a cycle-spinning-based technique to develop translation invariant contourlet denoising scheme. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. Our experiments show that the proposed approach outperforms the translation invariant wavelets both visually and in terms of the PSNR values, especially for the images that include mostly fine textures and contours.
On the Impact of Ignoring Markovian Channel Memory on the Analysis of Wireless Systems

Recent wireless measurement studies have revealed the presence of high-order memory in wireless bit-error channels. However, most wireless studies continue to employ the memory-less or 1st order Gilbert bit-error channels to design, analyze and verify wireless protocols and systems. The inaccuracies incurred by ignoring high-order channel memory are largely unexplored. This paper quantifies inaccuracies incurred by ignoring bit-level wireless channel memory in the context of two simple and commonly-used protocol metrics: (i) packet good put of an abstract unreliable protocol and (ii) number of retransmissions per packet for an abstract reliable protocol. We analytically derive expected values of these metrics in terms of the parameters of four models with varying levels of memory. We then train the models using actual 802.11b bit-error traces and use the analytical expressions to estimate the metrics. Comparison of the model-based estimates with actual values of the metrics derived from the traces shows that the memory-less and 1st order models incur significant inaccuracies. The remaining two models that capture high-order memory are very accurate in their estimates of the crucial good put and retransmission metrics.
Practical Distributed Video Coding based on source rate estimation

In Distributed Video Coding (DVC) the encoder compresses frames at source rates that depend on the statistical dependency between the Wyner-Ziv (WZ) and side information frames. An important issue that we address in this paper is providing the encoder with a mechanism to identify the source rate to be used in encoding a WZ frame. One possible solution is to follow a feedback approach; the encoder starts by sending a small amount of data (low source rate). In case the decoder fails to recover the compressed frame, it provides the encoder with a feedback requesting more bits. This solution results in using source rates that are ideal in the sense that they are the minimal rates that lead to successful decoding. Nevertheless, this solution might not be practical for visual sensor networks as it may exhaust limited bandwidth and energy resources. We propose a mechanism to estimate ideal source rates without the need to exchanging feedback messages. The proposed mechanism uses conditional entropy (entropy of a WZ source conditioned on a side information source) to estimate ideal source rates. Further, we show that by estimating the source rates (using optimal estimators) we can achieve a video quality and compression performance that is close to that achieved when feedback messages are exchanged. Moreover, we show that by avoiding incremental transmission and feedback messages, the proposed estimation-based approach can demonstrate lower energy consumption for a range of video quality compared with the feedback approach when both are deployed over a real visual sensor platform, namely, the imote2/IMB400.
Adaptive motion-compensation fine-granular-scalability (AMC-FGS) for wireless video

Transmission of video over wireless and mobile networks requires a scalable solution that is capable of adapting to the varying channel conditions in real-time (bit-rate scalability). Furthermore, video content needs to be coded in a scalable fashion to match the capabilities of a variety of devices (complexity scalability). These two scalability properties provide the flexibility that is necessary to satisfy the "anywhere, anytime and anyone" network paradigm of wireless systems. MPEG-4 fine-granular-scalability (FGS) is a flexible low-complexity solution for video streaming over heterogeneous networks (e.g., the Internet and wireless networks) and is highly resilient to packet losses. However, the flexibility and packet-loss resilience come at the expense of decreased coding efficiency compared with nonscalable coding. A novel scalable video-coding framework and corresponding compression methods for wireless video streaming is introduced. Building on the FGS approach, the proposed framework, which we refer to as adaptive motion-compensation FGS (AMC-FGS), provides improved video quality of up to 2 dB. Furthermore, the new scalability structures provide the FGS framework with the flexibility to provide tradeoffs between resilience, higher coding efficiency and terminal complexity for more efficient wireless transmission.
Fast smooth rank approximation for tensor completion

In this paper we consider the problem of recovering an N-dimensional data from a subset of its observed entries. We provide a generalization for the smooth Shcatten-p rank approximation function in  to the N-dimensional space. In addition, we derive an optimization algorithm using the Augmented Lagrangian Multiplier in the N-dimensional space to solve the tensor completion problem. We compare the performance of our algorithm to state-of-the-art tensor completion algorithms using different color images and video sequences. Our experimental results showed that the proposed algorithm converges faster (approximately half the execution time), and at the same time it achieves comparable performance to state-of-the-art tensor completion algorithms.
Tensor video coding

In this paper, we exploit the intrinsic tensor-nature of video and propose a Tensor Video Coding (TVC) framework that is based on tensor decomposition. We develop a Progressive Canonical-decomposition Parallel-factor (PCP) framework that is tailored for video representation and coding. Our simulation results show that the proposed TVC approach outperforms state-of-the-art video coding methods that do not rely on motion estimation or compensation.
Network embedded FEC (NEF) for video multicast in presence of packet loss correlation

Network embedded forward error correction (NEF) framework is a paradigm shift from a conventional approach of providing forward error correction (FEC) only on an end-to-end basis. Previous work on NEF has shown promise in greatly improving the decodable probability, message throughput and video quality available to the end receiver. However the utility of NEF for video applications in presence of packet loss correlation has not been evaluated as yet. In practice, packet losses are often correlated and occur in bursts. The distortion in video can be sensitive to the bursty nature of the losses. Thus in this paper we analyze the performance of NEF within random multicast distribution trees that exhibit losses-with-memory over their branches. We utilized the Gilbert model for packet losses over each link. We quantify the performance improvement of NEF over conventional end-to-end FEC in terms of improvement in video quality in presence of packet loss correlation. Embedding NEF codecs can impact the sensitivity of video quality to packet loss correlation. We explicitly evaluate as to how NEF can alter the dependence of video quality on packet loss burstiness. Finally we show that in addition to an average improvement in video quality, NEF can improve the performance in terms of quality guarantees also.
Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.
Compressed video seamless switching using variable splicing parameters

We propose a new approach for creating splice points in compressed video streams. The resulting streams can be switched seamlessly, and therefore no decoder underflow or overflow events occur. The proposed variable splicing parameters (VSP) method eliminates major encoder buffer constraints and related video encoding issues associated with the standard approach for creating seamless splice points. These advantages are achieved at the expense of a minor increase in the splicing operation complexity. When using VSP splice points, the splicer has to wait a specific period of time (equal to the difference between the out point and in point decoding delays) before switching into the new stream (i.e. the stream with the in point).
Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack
Packet-loss resilient Internet video using MPEG-4 fine granular scalability

Fine granular scalability (FGS) has been adopted by the MPEG-4 standard as the core video-compression method for streaming applications. From its inception, the FGS scalability structure was designed to be packet-loss resilient especially under unequal packet-loss protection (UPP). However, since the introduction of FGS, there has not been a comprehensive study evaluating its packet-loss resilience under unrecoverable packet losses that are common in Internet streaming applications. We evaluate two important aspects of FGS packet-loss resilience. First, we study the impact of applying UPP between the base- and enhancement-layers of FGS-based streams, and we compare equal packet-loss protection (EPP) with UPP scenarios. Second, we introduce the notion of fine grained loss protection (FGLP), which is suitable for the FGS enhancement-layer, and we develop an analytical framework for evaluating FGLP bounds. Based on these bounds, we show the impact of applying fine-grained protection to the FGS enhancement-layer for different types of video sequences and over a wide range of bit-rates and packet-loss ratios. As illustrated by our extensive simulation results, applying (a) UPP between the base- and enhancement layers and (b) fine grained loss protection for the FGS enhancement-layer can provide significant resilience under moderate-to-high packet-loss ratios (e.g. 5-10%).
Truncated and smoothed schatten-p function for robust tensor recovery

In this paper, we consider the robust tensor recovery problem in which we recover the low rank and sparse tensors from an observed data that is formed by the superposition of the two tensors. Our main contribution in this paper is deriving the truncated and smoothed schatten-p function to solve the robust tensor recovery problem using the Augmented Lagrangian Multiplier (ALM) optimization algorithm. Further, we compare the performance of our proposed algorithm against state-of-the-art robust tensor recovery algorithms using a variety of corrupted image and video signals. The experimental results show that our algorithm achieves an average of 2 dB performance improvement in PSNR; and at the same time it needs less number of iterations to converge when compared to state-of-the-art algorithms that tackle this problem.
End-to-end rate-based congestion control: convergence properties and scalability analysis

In this paper, we study several properties of binary-feedback congestion control in rate-based applications. We first derive necessary conditions for generic binary-feedback congestion control to converge to fairness monotonically (which guarantees asymptotic stability of the fairness point) and show that AIMD is the only TCP-friendly binomial control with monotonic convergence to fairness. We then study steady-state behavior of binomial controls with n competing flows on a single bottleneck. Our main result here shows that combined probing for new bandwidth by all flows results in significant overshoot of the available bandwidth and rapid (often super-linear as a function of n) increase in packet loss. We also show that AIMD has the best scalability and lowest packet-loss increase among all TCP-friendly binomial schemes. We conclude the paper by deriving the conditions necessary to achieve constant packet loss regardless of the number of competing flows n and examine one new scheme with such constant packet loss called ideally scalable congestion control in both simulation and streaming experiments.
Key frame extraction from consumer videos using epitome

Key frame extraction algorithms select a subset of the most informative frames from videos. Key frame extraction finds applications in several broad areas of video processing research such as video summarization, video indexing, and prints from video. In this paper, an image epitome [1][2] based method to extract key frames from unstructured consumer videos is presented. In the proposed approach, we exploit image epitome to measure dissimilarity between frames of the input video. The dissimilarity scores are further analyzed using a min-max approach to extract the desired number of key frames from the input video. The proposed approach does not require shot(s) detection, segmentation, or semantic understanding. A comparison of the results obtained by this method with the ground truth agreed by multiple judges clearly indicates the feasibility of the proposed approach.
A QoS-based Web Service Dynamic Composition Framework

With more and more applications are developed by Web services, the importance of QoS for Web services combination are becoming increasingly. According to the problems about process of developing Web services and QoS global optimization of Web services composition, the paper design and implement a QoS-based Web service combination framework which supports the visualization modeling and the process of dynamic Web service selection. The paper introduces the overall structure and operating mechanism of the framework, and focus on the implementation of the Web services publish module, process design module. An improved QoS global optimization algorithm, including the methods and characteristics of the realization are introduced in the paper. Finally, a simple Prototype system is also presented.
Current-source three phase high-power PFC converter with optimum PWM strategy

This paper describes a new method to select the optimum sinewave PWM patterns of a large-capacity current source-fed active PWM AC/DC converter, its performance evaluations and a practical procedure to design a low-pass filter which is used to suppress higher harmonics flowing into the utility-grid power source. A feasible test is carried out using a 500 kVA active PWM converter which is designed and operated for unique considerations mentioned above. It is verified that these new considerations are more effective to minimize higher harmonic current components flowing into the utility-grid AC line. Furthermore, this experimental work provides highly efficient AC/DC converter characteristics under a unity power factor correction and sinewave line current shaping schemes.
LDLT Decomposition Based Spectrum Sensing in Cognitive Radio Using Hard Decision Criterion

Inspired by random matrix theory, a quantity of eigenvalue based cooperative spectrum sensing methods have been proposed. The results are based on the asymptotical assumptions in need of large numbers of users and samples, which result in inferior performance with a few users. In this paper, sensing methods based on maximum eigenvalue and minimum eigenvalue of LDLT decomposition are proposed respectively with a view to improve the accuracy of decision threshold by means of hard decision criterion. The corresponding expressions of false alarm probability are also derived. Finally, both theoretical analyses and simulations demonstrate that the proposed two methods perform better than the existing eigenvalue based sensing methods for accurate decision threshold.
Random access for a cognitive radio transmitter with RF energy harvesting

In this paper, we investigate the random access for an energy harvesting secondary user (SU) in a cognitive radio system, in which the SU harvests energy from radio frequency (RF) radiation of the primary user (PU). With multipacket reception channel model, the SU can increase its throughput through not only utilizing the idle periods of the PU, but also opportunistically sharing the PU spectrum with some probability when the PU is active. By choosing the appropriate random access probability, we maximize the throughput of the SU under the constraint of the primary queue stability. We also define the energy-limited region and spectrum-limited region to specify the tradeoff between the SU performance and the PU activity. Furthermore, we investigate the effect of the primary queueing delay constraint on the throughput performance of the SU.
Equivalent channel Eigen-values product quantization criterion for limited feedback beamforming in MIMO systems

Beamforming is one of the key techniques for the multiple-input multiple-output (MIMO) wireless communication systems, which enables the system to achieve higher performance. In this paper, the Grassmann codebooks are employed to quantize channel state information (CSI) in the MIMO downlink for Long Term Evolution (LTE). Further more, a novel beamforming (BF) scheme based on the proposed equivalent channel Eigen-values product (EVP) quantizing criterion is developed. The proposed BF scheme jointly optimizes the system channel capacity and bit error rate (BER) performance. Hence, the proposed scheme outperforms the traditional quantizing criteria, where only one factor is concerned. The extensive simulation results verify that the proposed BF scheme obtains better performance in terms of both system capacity and BER.
Single Range Matching Filtering for Space Debris Radar Imaging

In the recent literature, only single-range Doppler interferometry (SRDI) has been considered for radar imaging of space debris with dimensions smaller than the radar range resolution. Considering the typical trajectories and dynamics of space debris, a single-range matching filtering (SDMF) approach is proposed in this letter. SDMF permits obtaining a 2-D image of space debris by using range unit cross-range echo data and by matched filtering the signals with different turning radii. The analysis and simulations show that, compared with SRDI, the proposed approach has less computational load and better images, particularly, for targets with smaller size.