nils-herrmann/clean_scirepeval huggingface dataset:
On measuring memory length of the error rate process in wireless channels

Bit errors are orders of magnitude more frequent in wireless channels than in wired channels. Design of network protocols and other architectural components of wireless networking systems entail a better understanding of the process that introduces errors into transmissions. A key characteristic of the error process operating on a channel is its memory length which determines the degree of similarity and clustering of errors. In this paper we demonstrate the inadequacy of correlation coefficient based analysis used in determining memory length of bit level errors to the packet-level bit error rate (BER) process. To overcome this problem we propose an alternate means of measuring channel memory, called the relative mutual information (RMI) and a lower complexity variant, the pairwise RMI. We demonstrate the use of RMI and pairwise RMI to sets of residual bit-error traces collected in IEEE 802.15.4 low rate-wireless personal area network (LR-WPAN) and IEEE 802.11b wireless local area network (WLAN) channels.
Open-loop rate control for real-time video streaming: analysis of binomial algorithms

Emerging real-time streaming applications often rely on rate-based flow control. However, congestion control for rate-based applications is typically dismissed as being not viable due to the common notion that "open-loop" congestion control is simply "difficult." This paper sheds new light on the performance of binomial NACK-based (i.e., rate-based) congestion control and measures the amount of "difficulty" inherently present in such protocols. Even though previous work proposed several new congestion control methods for real-time streaming, our analysis shows that traditional additive-increase, multiplicative-decrease (AIMD) schemes possess the best packet-loss scalability among all proposed TCP-friendly schemes, especially when used in rate-based applications. We further confirmed our analytical results in a number of experiments using MPEG-4 Fine-Granular Scalable (FGS) streaming over a Cisco testbed.
New guarantees for Blind Compressed Sensing

Blind Compressed Sensing (BCS) is an extension of Compressed Sensing (CS) where the optimal sparsifying dictionary is assumed to be unknown and subject to estimation (in addition to the CS sparse coefficients). Since the emergence of BCS, dictionary learning, a.k.a. sparse coding, has been studied as a matrix factorization problem where its sample complexity, uniqueness and identifiability have been addressed thoroughly. However, in spite of the strong connections between BCS and sparse coding, recent results from the sparse coding problem area have not been exploited within the context of BCS. In particular, prior BCS efforts have focused on learning constrained and complete dictionaries that limit the scope and utility of these efforts. In this paper, we develop new theoretical bounds for perfect recovery for the general unconstrained BCS problem. These unconstrained BCS bounds cover the case of overcomplete dictionaries, and hence, they go well beyond the existing BCS theory. Our perfect recovery results integrate the combinatorial theories of sparse coding with some of the recent results from low-rank matrix recovery. In particular, we propose an efficient CS measurement scheme that results in practical recovery bounds for BCS. Moreover, we discuss the performance of BCS under polynomial-time sparse coding algorithms.
FAST: A channel access protocol for wireless video (and non-video) traffic

This paper presents the design of a new paradigm for a content-aware wireless MAC layer that is optimized for wireless video (first and foremost) while targeting fairness and stability among competing video traffic, and among video and non-video traffic. Hence, we refer to the proposed MAC framework as the FAST (Fair And STable) protocol. FAST employs two parameters for each packet, a quality value and a time-to-live value. Based on these parameters, FAST is designed on a multiclass priority queuing system that classifies the incoming traffic according to the content of each traffic flow and further identifies different priorities within each video content. We develop analytical frameworks to formulate channel allocation based on video/non-video fairness and video stability requirements as a joint bandwidth maximization and scheduling optimization problem. We incorporate these frameworks to design and simulate a content-aware channel access mechanism, which utilizes video traffic content classifications and users demand in conjunction with stability and fairness requirements at the MAC layer to allocate wireless channels to individual wireless users. Our simulation results show that FAST provides significant improvements in packet-loss-ratio, delay, overall fairness, and stability parameters when compared with leading access control mechanisms over 4G/LTE environment.
ITU-T Standardization of Audiovisual Communication Systems in ATM and LAN Environments

This paper presents the ITU-T Study Group 15 development of H-series Recommendations that allow interworking between different audiovisual communication terminals manufactured by different equipment providers. The paper focuses on H.310 and H.321 systems for broad-band ATM environments and H.322 and H.323 systems for LAN environments where the quality of service may or may not be guaranteed. The paper first lists the Recommendations developed by the ITU-T for audiovisual communication systems and the network environments in which they may be used. It then describes the design philosophy, the network specific characteristics, and hardware trials for each system. Then it describes the communication control protocol defined in H.245 which is used commonly by different audiovisual communication systems. Finally, the paper discusses interworking scenarios for communication between the different types of terminal on different networks.
Analysis and modeling of errors at the 802.11b link layer

In this paper, we analyze the errors observed at the link layer of an 802.11b network. Our analysis at all supported bitrates (i.e., 2, 5.5. and 11 Mbps) establishes that the error patterns are not memoryless, and therefore, they exhibit a certain level of temporal dependencies. Thus, we evaluate the suitability of a two-state Markov model to capture the channel behavior. Non-stationarity of the error patterns renders such a simplistic model inadequate, and hence, we consider higher order models. This formulates a key contribution of this paper, and that is, a hierarchical Markov model, which captures the non-stationarity of the channel while employing real-time application-specific considerations to determine state-transition probabilities.
Improved seam carving for image resizing

As displays become cheaper and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed ([1][2][4][6][7]) which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose an improved seam carving algorithm which incorporates antialiasing and thresholding techniques. Experiments have demonstrated superior performance over the current seam carving methods.
Image Super-Resolution via Local Self-Learning Manifold Approximation

This letter proposes a novel learning-based super-resolution method rooted in low dimensional manifold representations of high-resolution (HR) image-patch spaces. We exploit the input image and its different down-sampled scales to extract a set of training sample points using a min-max algorithm. A set of low dimensional tangent spaces is estimated from these samples using the l1 norm graph-based technique to cluster these samples into a set of manifold neighborhoods. The HR image is then reconstructed from these tangent spaces. Experimental results on standard images validate the effectiveness of the proposed method both quantitatively and perceptually.
Advancements in cross-layer wireless multimedia communications

Despite their low-complexity and (perceived) efficiency, many traditional wireless communication and networking approaches for handling errors and losses are far from being optimum. For example, popular wireless LANpsilas, such as 802.11 systems, heavily rely on retransmission based schemes for handling corrupted packets. These schemes waste precious wireless bandwidth when discarding packets that contain mostly good data. In this tutorial, new advancements in the development of cross-layer wireless communications that are particularly tailored for multimedia applications will be covered. New cross-layer frameworks for handling errors and losses over wireless channels will be described. In particular, a framework that is based on a cross-layer design with side information (CLDS) will be described. The benefits and limitations of cross-layer side information for combating errors in a wireless environment will be discussed. Capacity improvements (in information-theoretic sense) due to CLDS protocols will be highlighted and compared with the capacity of current popular schemes (802.11 based MAC protocols). Unlike traditional wireless link-layer methods that shift reliability and stability functions to other layers, new approaches for reliable and stable link-layer wireless channels are being developed. The impact of these approaches on both realtime video applications and non-realtime TCP-based services will be highlighted.
Increase-decrease congestion control for real-time streaming: scalability

Typically, NACK-based congestion control is dismissed as being not viable due to the common notion that "open-loop" congestion control is simply "difficult." Emerging real-time streaming applications, however, often rely on rate-based flow control and would benefit greatly from scalable NACK-based congestion control. This paper sheds new light on the performance of NACK-based congestion control and measures the amount of "difficulty" inherently present in such protocols. We specifically focus on increase-decrease (I-D) congestion control methods for real-time, rate-based streaming. First, we introduce and study several new performance measures that can be used to analyze the class of general I-D congestion control methods. These measures include monotonicity of convergence to fairness and packet-loss scalability (explained in the paper). Second, under the assumptions that the only feedback from the network is packet loss, we show that AIMD is the only TCP-friendly method with monotonic convergence to fairness. Furthermore, we find that AIMD possesses the best packet-loss scalability among all TCP-friendly binomial schemes and show how poorly all of the existing methods scale as the number of flows is increased. Third, we show that if the flows can obtain the knowledge of an additional network parameter (i.e., the bottleneck bandwidth), the scalability of AIMD can be substantially improved. We conclude the paper by studying the performance of a new scheme, called ideally-scalable congestion control (ISCC), both in simulation and a NACK-based MPEG-4 streaming application over a Cisco testbed.
Motion-compensation fine-granular-scalability (MC-FGS) for wireless multimedia

The MPEG-4 fine-granular-scalability (FGS) has been introduced as a flexible low-complexity solution for video streaming over heterogeneous networks (eg, the Internet). A novel scalable video-coding framework and corresponding compression methods for wireless video streaming are introduced. Building upon the FGS approach, the proposed framework, which we refer to as motion-compensation FGS (MC-FGS), provides improved video quality of up to 2 dB.
A KLT-inspired node centrality for identifying influential neighborhoods in graphs

We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.
Network completion with node similarity: A matrix completion approach with provable guarantees

This paper investigates the network completion problem, where it is assumed that only a small sample of a network (e.g., a complete or partially observed subgraph of a social graph) is observed and we would like to infer the unobserved part of the network. In this paper, we assume that besides the observed subgraph, side information about the nodes such as the pairwise similarity between them is also provided. In contrast to the original network completion problem where the standard methods such as matrix completion is inapplicable due the non-uniform sampling of observed links, we show that by effectively exploiting the side information, it is possible to accurately predict the unobserved links. In contrast to existing matrix completion methods with side information such as shared subsapce learning and matrix completion with transduction, the proposed algorithm decouples the completion from transduction to effectively exploit the similarity information. This crucial difference greatly boosts the performance when appropriate similarity information is used. The recovery error of the proposed algorithm is theoretically analyzed based on the richness of the similarity information and the size of the observed submatrix. To the best of our knowledge, this is the first algorithm that addresses the network completion with similarity of nodes with provable guarantees. Experiments on synthetic and real networks from Facebook and Google+ show that the proposed two-stage method is able to accurately reconstruct the network and outperforms other methods.
Common and Innovative Visuals: A Sparsity Modeling Framework for Video

Efficient video representation models are critical for many video analysis and processing tasks. In this paper, we present a framework based on the concept of finding the sparsest solution to model video frames. To model the spatio-temporal information, frames from one scene are decomposed into two components: (1) a common frame, which describes the visual information common to all the frames in the scene/segment and (2) a set of innovative frames, which depicts the dynamic behaviour of the scene. The proposed approach exploits and builds on recent results in the field of compressed sensing to jointly estimate the common frame and the innovative frames for each video segment. We refer to the proposed modeling framework by common and innovative visuals (CIV). We show how the proposed model can be utilized to find scene change boundaries and extend CIV to videos from multiple scenes. Furthermore, the proposed model is robust to noise and can be used for various video processing applications without relying on motion estimation and detection or image segmentation. Results for object tracking, video editing (object removal, inpainting), and scene change detection are presented to demonstrate the efficiency and performance of the proposed model.
Hierarchical handoff schemes over wireless LAN/WAN networks for multimedia applications

The objective of this paper is to study the performance of handoff procedures for an integrated wireless LAN/WAN infrastructure, both qualitatively and quantitatively. We make use of a proposed hierarchical mobility management scheme to evaluate the performance of a handoff procedure with a hierarchy level of one. Also, we propose a design and perform the analysis for a two-level hierarchy architecture that is applicable for integrated 802.11-based WLAN/WAN scenarios. The focus of our analysis is on performance measures that are suitable for real-time multimedia applications. In particular, we observe and analyze the improvement in handoff latencies and packet loss ratios that are obtained by employing hierarchical mobility schemes for UDP based traffic sessions under different network scenarios. We also study the scalability of the proposed schemes by evaluating the impact of the number of mobile nodes on the above performance measures.
Temporal-SNR rate-control for Fine-Granular Scalability

To compensate for the unpredictability and variability in bandwidth between sender and receiver(s) over the Internet, a new scalable coding tool has recently been introduced in MPEG-4: Fine-Granular-Scalability (FGS). The FGS framework is very flexible and can adapt in real-time to the Internet bandwidth variations by supporting both SNR and temporal scalability through a single pre-encoded fine-granular stream. This paper presents a novel Internet video streaming system employing the MPEG-4 FGS temporal-SNR scalability structure. The proposed streaming system performs the trade-off between temporal-resolution and SNR improvements at transmission time, depending on the available bandwidth and possible user preferences, and not at encoding time. An important feature of the proposed system is the temporal-SNR rate-control that ensures a high visual quality for each client, depending on the available bandwidth. The proposed rate-allocation mechanism is based on available compressed-domain information (ie, motion-vectors and texture) and/or pre-stored quality (PSNR) tags determined at encoding time.
Error concealment of still image and video streams with multi-directional recursive nonlinear filters

A novel approach for error concealment in block-based image and video coding systems over the ATM networks is presented. This new approach aims at reconstructing the lost pixels in the intra-coded frames by spatial interpolation of the nearest undamaged pixels with a recursive multi-directional nonlinear filtering scheme. The lost interframe information are also reconstructed by using robust nonlinear filtering of the adjacent and previous motion vector blocks. We assume that the important synchronization and header information of the compressed bit streams are packed into high priority ATM cells and an integral number of macroblocks are packed into normal cells for efficient transmission. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.
Identifying Leaders and Followers in Online Social Networks

Identifying leaders and followers in online social networks is important for various applications in many domains such as advertisement, community health campaigns, administrative science, and even politics. In this paper, we study the problem of identifying leaders and followers in online social networks using user interaction information. We propose a new model, called the Longitudinal User Centered Influence (LUCI) model, that takes as input user interaction information and clusters users into four categories: introvert leaders, extrovert leaders, followers, and neutrals. To validate our model, we first apply it to a data set collected from an online social network called Everything2. Our experimental results show that our LUCI model achieves an average classification accuracy of up to 90.3% in classifying users as leaders and followers, where the ground truth is based on the labeled roles of users. Second, we apply our LUCI model on a data set collected from Facebook consisting of interactions among more than 3 million users over the duration of one year. However, we do not have ground truth data for Facebook users. Therefore, we analyze several important topological properties of the friendship graph for different user categories. Our experimental results show that different user categories exhibit different topological characteristics in the friendship graph and these observed characteristics are in accordance with the expected ones based on the general definition of the four roles.
Adaptive dictionaries for compressive imaging

Compressive imaging reconstructs the original signal by searching through the feasible space for the solution with maximum compactness under a known frame or dictionary. With the extent of available optimization tools, the recovery performance mainly relies on the power of dictionary to sparsely represent the data. Universal dictionaries can be trained from a corpus of natural images or they can be designed through mathematical modeling. However, a problem with universal dictionaries is that they are suboptimal for individual classes of images. To mitigate this suboptimality, we explore ways of adapting the dictionary after the image is sensed using local and non-overlapping sampling matrices. We demonstrate that to prevent the dictionary from becoming biased under the deterministic sensor structure, sampling matrices should have diversity across different locations of the image. The proposed dictionary adaptation along with varying sampling matrices improves the recovery over state-of-the-art universally learned dictionaries of different sizes.
A Channel Model for the Bit Error Rate Process in 802.15.4 LR-WPAN Wireless Channels

In this work we analyze the nature of the error process in IEEE 802.15.4 low rate-wireless personal area network (LR-WPAN) channels experiencing varying levels of interference from nearby 802.11b/g networks co-habiting the unlicensed 2.4 GHz industrial-scientific-medical (ISM) band. The entire analysis is performed on a set of residual bit error traces, the data collection methodology for which is described in detail. These traces allow us to identify individual bit positions in a received packet that were in error. We use lagged phase space analysis, a technique commonly used in the analysis of chaotic systems. The analysis uncovers the stable nature of the BER process during short bursts. This simple behavior of the bit error rate (BER) can be captured by three parameters; the burst length of consecutive good packets, bad packets, and the distribution with which different BERs occur. Using collected residual bit error traces, we model the BER process by estimating the probability distributions of its parameters using maximum likelihood estimates (MLE).