nils-herrmann/clean_scirepeval huggingface dataset:
Transitivity based community analysis and detection

This paper extends our previous effort in employing transitivity attributes of graphs for social network analysis. Specifically, here we focus on the problem of network community detection. We propose spectral analysis of the transitivity gradient matrix and compare our framework to the modularity based community detection that attracted many network researchers' attention recently. Previously, we showed that the transitivity attributes of social networks can be analyzed within the resolution of individual links, helping analysts in bridging concepts from the micro- and macro- levels of social network analysis. In this paper, we show that for the problem of network community detection, a key advantage of using transitivity is that it quantifies the degree of community structure independent of the number and sizes of clusters or communities within the network. We employ a Gaussian mixture model in the spectral domain of the proposed transitivity gradient matrix for modeling communities. Performance of the proposed method is compared to the state-of-the-art modularity based community detection over randomly generated networks with social network characteristics such as scale-free degree distributions and high clustering coefficients.
Design and analysis of Generalized LT-codes using colored ripples

Research has shown that fluid limits of Markov processes can be used to obtain closed form expressions for the evolution of the ripple-size. In this work we extend the above analysis to generalized LT (GLT) codes, which can be used to represent LT encoding (with priorities) over multiple data segments. In our analysis, we segregate the ripple into multiple colored ripples, where each color corresponds to a segment. We derive closed form expressions for the size of each ripple. We utilize these expressions to design GLT distributions, optimized for a desired intermediate and unequal recovery.
On unequal error protection with low density parity check codes in scalable video coding

In this work, we propose an unequal error protection (UEP) scheme for scalable video coding using Low Density Parity Check (LDPC) codes. LDPC codes are renowned for their near-capacity performance. In the proposed framework, a video sequence is coded based on Scalable Video Coding (SVC) H.264/AVC standard document. We divide the SVC coded video stream into different sub-streams in terms of time and quality scalability. These sub-streams are channel-coded using Low Density Parity Check (LDPC) codes for robustness against channel impairments. LDPC codes with different coding rates are applied for different sub-streams according to the significance level of each sub-stream in order to provide UEP. We develop a probability model for decoding failure using LDPC codes in order to evaluate the bit error rate under different signal-to-noise ratios and delay constraints for an AWGN channel. Further motivated by optimally allocating the bits to maximize the quality of reconstructed video at the decoder against channel induced corruptions, we propose an Optimal Bit Allocation Algorithm (OBAA) that optimally allocates the channel coding rates using a dynamic programming approach. We use the proposed probability model in OBAA in order to obtain rate-distortion characteristics of LDPC coded SVC video. The results achieved clearly establish that the proposed UEP scheme can optimally allocate LDPC codes with different coding rates to different sub-streams that significantly decreases the end-to-end distortion of the system as compared to equal error protection (EEP) scheme resulting in greater system throughputs
End-to-end Internet video traffic dynamics: statistical study and analysis

We analyze the dynamics of a seven-month real-time streaming experiment, which was conducted between a number of unicast dialup clients, connecting to the Internet through access points in more than 600 major US cities, and a backbone video server. During the experiment, the clients streamed low-bitrate MPEG-4 video sequences from the server over paths with more than 5,000 distinct Internet routers. We describe the methodology of the experiment and the architecture of our NACK-based streaming application, study end-to-end dynamics of 16 thousand ten-minute sessions (85 million packets), and analyze the behavior of the following network parameters: packet loss, round-trip delay, and packet reordering. We also study the impact of these parameters on the quality of real-time streaming (i.e., the number of underflow events).
On Link Layer Prioritization for Wireless Communication

In this paper, we develop Prioritized Adaptive Code-Enhanced (PACE) link-layer protocol to achieve preferred data recovery order across connections, while maintaining stable and reliable data flow over a wireless network. We classify link-layer traffic arrivals into different priorities based on delay constraint and distortion associated with that traffic. We formulate the link-layer buffer as a multiclass M/G/1 priority queuing system and the decoding process by a nonhomogeneous Geometric density function. This formulation enables the determination of an optimal dynamic decoder scheduling for heterogeneous link-layer traffic. PACE employs rate-adaptive Low Density Parity Check (LDPC) codes for error recovery. We demonstrate experimentally that PACE reduces the throughput-delay cost by 20%-70% in comparison with the IEEE802.11 ARQ and Hybrid ARQ (HARQ) protocols. Further, it achieves 20%-60% throughput and 2-10dB PSNR improvements in channel bandwidth utilization and realtime video playback quality.
Increasing Network Lifetime Of An IEEE 802.15.4 Wireless Sensor Network By Energy Efficient Routing

In a multi-hop 802.15.4 Wireless Sensor Network the volume of traffic that is processed by intermediate nodes increases considerably as data approaches the PAN coordinator. This problem is referred to as the reachback problem. In this work we focus on a more fundamental implication of reachback on operational network lifetime of 802.15.4-based wireless sensor networks. This paper proposes the joint minimization of two network lifetime metrics, previously proposed for independent use, to more accurately characterize the degree of longevity of wireless sensor networks. The method uses the k-shortest simple path algorithm and a dynamic programming method rooted in operational rate-distortion (RD) theory to increase the operational lifetime of wireless sensor networks.
INPoD: In-Network Processing over Sensor Networks based on Code Design

In this paper, we develop a joint network coding (NC)-channel coding error-resilient sensor-network approach that performs in-network processing based on channel code design (INPoD). INPoD represents a major development of an underlying framework for designing code-on-network-graph (CNG). CNG (and hence INPoD) maps variable nodes of low density parity check (LDPC) codes onto sensor nodes, and consequently translates check equations (used in linear algebraic LDPC codes) into in-network processing. INPoD/CNG codes not only improve capacity, but are also resilient to errors in noisy environments. In absence of INPoD, basic CNG employs standard LDPC codes while assuming the underlying sensor network is capable of supporting the degree distribution dictated by these codes. In practice, however, we usually have the network topology pre-determined by the placement of the sensors, and hence we are constrained to map a code onto a given topology. In this paper, we specifically address this problem, and propose the INPoD framework which enables the use of LDPC design tools in the design of CNG for a given sensor network topology. We formulate the design of CNG, as a convex optimization problem, which determines the best codes to be used, given the underlying network connectivity and channel conditions. Specifically, we use density evolution to design degree distributions which controls the performance of the joint NC-channel code CNG. We also give a code construction algorithm, which achieves a designed degree distribution. We show that well-designed INPoD provide gains of 1.5 to 2.5 dB, when compared with the best known erasure codes - LT codes.
A Multi-Tier Model for BER Prediction over Wireless Residual Channels

Bit-error rate (BER) modeling and prediction over residual wireless channels, which represent errors not corrected by the physical layer, has emerged as an active research area. Recently, it has been shown that signal to noise ratio (SNR) is a useful side-information that could be employed for BER prediction. In this paper, we propose a novel and accurate three-tier model that leverages a received packet's SNR and checksum side-information to predict BER in future packets over a wireless residual channel. We first observe that direct inference of BER from SNR results in optimistic estimates because of the relatively large amounts of error-free data (in comparison with corrupted data) received on viable wireless networks. Consequently, we propose a model that separates packet-and bit-error prediction. At the first tier, we employ a high-order packet-level Markov model which predicts whether or not a packet is in error. The second tier model is invoked only when a corrupted packet is predicted. The second tier consists of conditional probabilities that predict future SNR values based on the current packet's SNR. Once the SNR is predicted, a third-tier provides the BER estimate for that SNR using a binary-symmetric channel model. We use 802.11b traces collected over an operational 802.11b LAN to compare the performance of the proposed predictor with state-of-the-art predictors. We show that at all three 802.11b data rates (2, 5.5 and 11 Mbps) the proposed model has higher BER prediction accuracy than the optimum Yule-Walker and finite-state Markov chain predictors.
A rate-distortion empirical model for rate adaptive wireless scalable video

Resent studies (L. Larzon, et al., June 1999)-(S. A. Khayam, et al., Aug, 2003) have indicated that a significant improvement in wireless video throughput can be achieved by cross layer design with side-information (CLDS) protocols. In (Y. Cho, et al.), for real-time video streaming over wireless LANs the optimal rate prediction architecture under CLDS ( ORPACLDS ) was developed. In (Y. Cho, et al.), we showed that an accurate source and channel coding rate prediction can be achieved by utilizing the video quality models and the distribution of channel prediction error process under CLDS. To optimally utilize ORPACLDS , the video quality function for rates exceeding the channel capacity (i.e., video rate-distortion function) should be taken into consideration. To this end, in this paper we propose an empirical model for video rate-distortion. To deduce the generic video rate- distortion model, actual scalable video coding (SVC) and low density parity check (LDPC) code were used. The outstanding performance of ORPACLDS in accordance with the video rate-distortion model is exhibited by using a comprehensive set of IEEE 802.11b wireless traces.
Compressive Framework for Demosaicing of Natural Images

Typical consumer digital cameras sense only one out of three color components per image pixel. The problem of demosaicing deals with interpolating those missing color components. In this paper, we present compressive demosaicing (CD), a framework for demosaicing natural images based on the theory of compressed sensing (CS). Given sensed samples of an image, CD employs a CS solver to find the sparse representation of that image under a fixed sparsifying dictionary Ψ. As opposed to state of the art CS-based demosaicing approaches, we consider a clear distinction between the interchannel (color) and interpixel correlations of natural images. Utilizing some well-known facts about the human visual system, those two types of correlations are utilized in a nonseparable format to construct the sparsifying transform Ψ. Our simulation results verify that CD performs better (both visually and in terms of PSNR) than leading demosaicing approaches when applied to the majority of standard test images.
Breaching IM session privacy using causality

The breach of privacy in encrypted instant messenger (IM) service is a serious threat to user anonymity. Performance of previous de-anonymization strategies was limited to 65%. We perform network de-anonymization by taking advantage of the cause-effect relationship between sent and received packet streams and demonstrate this approach on a data set of Yahoo! IM service traffic traces. An investigation of various measures of causality shows that IM networks can be breached with a hit rate of 99%. A KCI Causality based approach alone can provide a true positive rate of about 97%. Individual performances of Granger, Zhang and IGCI causality are limited owing to the very low SNR of packet traces and variable network delays.
Measuring Packet-Level Memory Length of 802.15.4 Wireless Channels with Relative Mutual Information

Measurement of a wireless channel's memory length is of great, fundamental interest to designers of communication systems. The principal tool used for measuring the memory length of wireless channels has been the correlation function which has been shown to work well for the measurement of channel memory at the level of individual bits and symbols. However, as we demonstrate, applied to the packet-level bit-error process of 802.15.4 wireless channels, the correlation function fails to provide us with a clear answer even in a wide variety of channels. To overcome this limitation we propose the use of a new information theoretic measure. We introduce the use of relative mutual information and demonstrate its application for measuring the memory length of the BER process in sensor 802.15.4 wireless channels.
Kovalenko's full-rank limit and overheads as lower bounds of error-performances of LDPC and LT codes over binary erasure channels

We present Kovalenko's full-rank limit as a tight probabilistic lower bound for error performances of LDPC codes and LT codes over BEC. From the limit, we derive a full-rank overhead as a tight lower-bound for stable overheads for successful maximum-likelihood decoding of the codes.
Using Session-Keystroke Mutual Information to Detect Self-Propagating Malicious Codes

In this paper, we propose an endpoint-based joint network-host anomaly detection technique to detect self- propagating malicious codes. Our proposed technique is based on the observation that on any endpoint there exists very high correlation between benign network sessions and the keystrokes that trigger these sessions. Specifically, users generally use a few keystrokes to trigger most of the benign network sessions. On the other hand, malicious sessions originating from a compromised endpoint will not have the session-keystroke correlation. We lever-age this observation in a novel information-theoretic framework that characterizes the session-keystroke correlation in terms of their mutual information. Changes in session-keystroke mutual information are used to detect malicious codes in an automated and real-time fashion. To evaluate the proposed anomaly detector, we use actual traffic and keystroke data collected on benign and infected endpoints. We show that the proposed anomaly detector provides almost 100% detection with negligible false-alarm rates and significantly surpasses the accuracy of existing techniques.
Optimal rate control methods for fine granularity scalable video

In this paper we present two optimum (in a rate-distortion -RD- sense) rate-control algorithms for FGS scalable video. The proposed methods, which could compliment the proposed RD FGS algorithms for bit allocation among different FGS frames, are targeted for optimum rate allocation among the different macroblock bitplanes within each FGS frame. The proposed methods are also designed to be very low in complexity to enable real-time streaming and fast off-line processing. Simulations with a variety of video sequences have been conducted and some of these simulations are presented in this paper. Based on these results, the proposed RD based rate-control algorithms provide a gain of 0.25 - 0.6 dB gain when compared with the traditional raster-scan bitplane FGS rate-control algorithm.
A novel MPEG-4 based hybrid temporal-SNR scalability for Internet video

Transmission of video over bandwidth-varying networks (e.g. the Internet) requires a highly scalable solution that is 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 bitrate of each layer has to be predetermined at encoding time. Consequently, the range of bitrates 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 is introduced. Building upon the MPEG-4 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. Consequently, our proposed approach enables streaming systems to support full SNR, full temporal, and hybrid SNR-temporal scalability in real-time depending on available bandwidth, packet-loss patterns, user preferences, and/or receiver complexity.
A topologically-aware worm propagation model for wireless sensor networks

Internet worms have repeatedly revealed the susceptibility of network hosts to malicious intrusions. Recent studies have proposed to employ the underlying principles of worm propagation to disseminate security-critical information in a network. Wireless sensor networks can benefit from a thorough understanding of worm propagation over sensor networks to defend from worms and to efficiently disseminate security-critical information. In this paper, we develop a topologically-aware worm propagation model (TWPM) for wireless sensor networks. In addition to simultaneously capturing both time and space propagation dynamics, the TWPM also incorporates physical, MAC and network layer considerations of practical sensor networks. Simulation results show that the proposed model follows actual propagation dynamics quite closely.
Image denoising using translation-invariant contourlet transform

The contourlet transform, one of the recent geometrical image transforms, lacks the feature of translation invariance due to subsampling in its filter bank (FB) structure. In this paper, we develop a translation-invariant (TI) scheme of a general multi-channel multidimensional FB and apply our findings to the contourlet transform to obtain a TI contourlet transform (TICT). Further, we employ the proposed TICT for image denoising, where we show that a significant improvement in the PSNR values as well as visual results is gained. Moreover, we demonstrate that this proposed denoising scheme outperforms the TI wavelet denoising approach for most experiments. We also introduce a less-redundant variety of the TICT, where we merely make the first stage of contourlets, translation invariant. We show that this transform, which we call semi-TICT (STICT), achieves a performance near that of the TICT in image denoising.
A new family of channel coding schemes for real-time visual communications

In this paper, we extend our work in [Karande, S. S. and Radha, H., 2003] which employed partial Reed-Solomon (PRS) codes at coding rates near channel capacity on a binary erasure channel (BEC). We demonstrated that an appropriately designed PRS code outperforms the classical Reed-Solomon (RS) code for a performance criterion tailored for realtime applications. This paper extends this analysis for a general BEC with varying channel conditions (under/above channel capacity). We illustrate that PRS codes exhibit a graceful degradation in erasure recovery performance and, hence, are suitable for multimedia communication. Our video simulation results outline that the enhanced erasure recovery yields a profound improvement in the perceived media quality. Finally we investigate the performance of the dividend rendered by PRS codes operating above channel capacity. In particular we define a paradigm for a unique "fixed rate" adaptive FEC scheme based on PRS codes.
Rate-constrained adaptive FEC for video over erasure channels with memory

Current adaptive FEC schemes used for video streaming applications alter the redundancy in a block of message packets to adapt to varying channel conditions. However, for many popular streaming applications, both the source-rate and the available bandwidth are constrained. In this paper, we present FEC codes that can adapt in real-time to provide higher source-packets recovery without changing the FEC block (N, K) pair constraint. The FEC code profile is changed as function of the number of losses to facilitate an improved data recovery even under severe channel conditions (e.g., number of losses within an N-packet FEC block is larger than N-K). We present a feedback based adaptive FEC scheme, which can adapt in a rate-constrained manner. We also illustrate the utility of this scheme for video streaming applications by analyzing the results of extensive video simulations and comparing our performance to adaptive Reed Solomon FEC schemes. We consider a variety of video sequences and use actual packet traces from WLAN (802.11b) and wired Internet environments. Comparison between the two schemes is conducted on the basis of message packet recovery, PSNR, model based perceptual evaluation and visual subjective evaluation. It is shown that the proposed scheme can significantly improve the video quality and in particular reduce the jerkiness in the received video.
HopCaster: A network coding-based hop-by-hop reliable multicast protocol

Intra-flow network coding (NC) is an innovative technique that has potential to improve multicast performance in wireless mesh networks (WMNs) by allowing intermediate forwarding nodes (FNs) to use coding and overhearing to reduce the number of required transmissions. However the benefits of the NC technology are limited unless there are protocols to exploit its capabilities. The existing intra-flow NC-based multicast protocols are all based upon the conventional end-to-end transport principle. By such a principle, intermediate FNs are unable to accurately determine the minimum number of coded packets they should transmit in order to ensure successful data delivery to the destinations, and hence redundant packets can be injected into the network, leading to performance degradation. Furthermore, the existing protocols cannot handle the bandwidth heterogeneity of multicast receivers very well. We argue that a receiver-driven hop-by-hop transport approach is more suitable for intra-flow NC and these two techniques can create synergy by enabling cooperation among the FNs. In this paper we propose HopCaster, a novel protocol that incorporates intra-flow NC with hop-by-hop transport to achieve high-throughput reliable multicast and to solve the heterogeneous receiver issue. It completely eliminates the need for estimating the number of coded packets to be transmitted by a FN and avoids transmission of redundant packets, as well as simplifies multicast management and congestion control. Moreover, HopCaster employs a cross-layer rate adaptation mechanism that optimizes radio transmission rate in hop-by-hop multicast by taking into consideration next-hop node population changes. Our evaluations show that HopCaster outperforms the existing NC-based reliable multicast protocol.