nils-herrmann/clean_scirepeval huggingface dataset:
OPERA: An Optimal Progressive Error Recovery Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) require robustness against channel induced errors while retransmission based schemes prove too costly for energy constrained sensor nodes. Channel coding with low rates can ensure increased reliability, and it can eliminate the need for costly retransmissions. However, low channel coding rates on an end-to-end basis (between a source sensor and a destination base-station) requires a large number of transmissions of redundant data. In this paper, we propose an Optimal Progressive Error Recovery Algorithm (OPERA) over WSNs. Under OPERA, individual intermediate sensors, which are relaying data toward the base station, partially and optimally channel-decode the incoming packets while employing a progressive decrease in parity bits as data reaches the final destination. OPERA requires significantly lesser processing than would be required for complete decoding or full decoding/encoding at the sensor nodes; and OPERA significantly reduces the total number of transmissions when compared to optimal end-to-end channel coding schemes. We use iteratively decodeable LDPC codes for this purpose. OPERA not only provides a partial processing framework, but also an algorithm to optimally map the decoding iterations over the multi hop network. We provide a comparison between our iteration assignment scheme and random iteration assignment, and show that our scheme performs considerably better. Finally, while being naturally motivated by the sensor reachback problem, we further develop a fairness-based OPERA scheme for the allocation of channel-decoding LDPC iterations to sensor nodes taking into consideration the life-expectancy of each sensor.
A Distributed Codec Placement Algorithm for Network-Embedded FEC

Network-embedded FEC (NEF) for overlay and peer-to-peer (p2p) multicast networks was introduced, and the impact of packet loss correlation on the performance of NEF was further analyzed and evaluated. In these previous works, a centralized codec placement algorithm and a greedy optimization algorithm to place a specified number of NEF codecs in a multicast distribution network were developed and analyzed. In the centralized codec placement algorithm, it has been assumed that the network topology and the loss rate on each branch of the network were known. In reality, it is very difficult to know the loss rate of each branch on the multicast tree beforehand. Further, the topology of a multicast session changes over time as users join and leave the session randomly. In this paper, we design and implement a distributed algorithm to place NEF codecs in the intermediate nodes of a multicast distribution network without knowledge of the network topology; the algorithm can also cope with the dynamics of the network very well. Our simulation results, based on the popular network simulator 2 (ns2), show that the proposed distributed NEF placement algorithm provides very competitive performance when compared to the centralized algorithm and it always outperforms traditional end-to-end FEC.
Regular Hybrid Wavelets and Directional Filter Banks: Extensions and Applications

In a previous work, we proposed a new family of nonredundant geometrical image transforms using hybrid wavelets and directional filter banks (HWD). In this paper we further develop and examine the proposed family and provide an efficient realization utilizing regular filters. Furthermore, we extend and employ the new proposed HWD transforms in two key image processing applications, coding and denoising, and demonstrate their promising results. For coding, an SPIHT-like algorithm is developed for HWD; and for denoising, a translation-invariant HWD (TIHWD) transform is constructed. Our simulations illustrate significant improvements under the proposed transforms when compared with other transform variations.
The unequal protection of Network Coding with multi-generation mixing

In this paper, we study and analyze the unequal protection characteristic of Network Coding (NC) with Multi-generation Mixing (MGM). MGM supports unequal protection by providing different levels of reliable communication for different groups of sender packets. MGM is a generalized framework for practical network coding that enhances its performance by improving the reliability of delivering sender packets . By employing MGM, generations are created from sender packets. At the same time a mixing set consists of a fixed number of generations. In this paper we analyze the capability of MGM to support unequal protection of MGM mixing set generations.
Bounding A Statistical Measure Of Network Lifetime For Wireless Sensor Networks

The inherent many-to-one flow of traffic in wireless sensor networks produces a skewed distribution of energy consumption rates leading to the early demise of those sensors that are critical to the ability of surviving nodes to communicate their readings to the data collection center. Numerous previous approaches aimed at balancing the consumption of energy in wireless networks are based on a linear programming-based minmax formulation that seeks to maximize the minimum lifetime of sensors in a network. However, this approach fails to provide a clear picture of the cost at which this optimization is achieved and focuses attention on a single sensor, the minimum lifetime sensor. This paper makes two contributions: it puts forward a new understanding of sensor network lifetime based on statistical measures, mean and variance, of node power consumption rates that provides a more inclusive view of the consumption rates, and it provides an optimal quadratic programming (QP) formulation that provides an upper bound on the lifetime under a given set of topological and energy budgetary constraints. Our results demonstrate that the QP formulation has the ability to provide a soft trade-off between the mean and variance of power consumption rates.
Detecting national political unrest on Twitter

The popular uprisings in a number of countries in the Middle East and North Africa in the Spring of 2011 were broadcasted live and enabled by local populations' access to social networking services such as Twitter and Facebook. The goal of this paper is to study the flow characteristics of the information flow of these broadcasts on Twitter. We have used language independent features of Twitter traffic to identify differences in information flows on Twitter mentioning countries experiencing some form of unrest, compared to traffic mentioning countries with peaceful political situations. We used these features to identify countries with political unstable situation. For empirical analysis, we collected several data sets of countries that were experiencing political unrest, as well as a set of countries in a control group that were not subject to such socio-political condition. Several different methods are used to model the flow of information between Twitter users in data sets as graphs, called information cascades. By using the dynamic properties of information cascades, naïve Bayes and SVM classifiers both achieve true positives rates of 100%, with false positives rates of 3% and 0%, respectively.
The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP

Real-time streaming of audiovisual content over the Internet is emerging as an important technology area in multimedia communications. Due to the wide variation of available bandwidth over Internet sessions, there is a need for scalable video coding methods and (corresponding) flexible streaming approaches that are capable of adapting to changing network conditions in real time. In this paper, we describe a new scalable video-coding framework that has been adopted recently by the MPEG-4 video standard. This new MPEG-4 video approach, which is known as Fine-Granular-Scalability (FGS), consists of a rich set of video coding tools that support quality (i.e., SNR), temporal, and hybrid temporal-SNR scalabilities. Moreover, one of the desired features of the MPEG-J FGS method is its simplicity and flexibility in supporting unicast and multicast streaming applications over IF.
An Information-Theoretic Combining Method for Multi-Classifier Anomaly Detection Systems

Recent studies have shown that standalone anomaly classifiers used by network anomaly detectors are unable to provide acceptable accuracies in real-world deployments. To achieve higher accuracies, Network Anomaly Detection Systems (NADSs) now use multiple classifiers whose outputs are combined to formulate an aggregate anomaly score. Judicious methods of combining these classifiers' outputs are largely unexplored. In this paper, we propose a novel information-theoretic combining method which caters for the individual classifiers' accuracies in a multi-classifier NADS. We first show that existing combining schemes designed for or adapted to the problem of multi-classifier NADS combining do not provide good accuracies because they do not use individual classifiers' detection and false alarm rates in the combining process. Furthermore, we reveal that an accurate multi-classifier NADS, in addition to catering for the mean accuracy rates, must also consider the classifiers' variances during combining. Therefore, we propose a Standard Deviation normalized Entropy of Accuracy (SDnEA) method for classifier combining. Using 9 prominent classifiers operating on two publicly-available traffic datasets, we show that around 3%-10% increase in detection rate and a 40% decrease in false alarm rate over existing combining techniques can be provided by the proposed information-theoretic NADS combining technique.
Single image super resolution via manifold linear approximation using sparse subspace clustering

This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence property of low resolution (LR) and high resolution (HR) patches via dictionary learning. In this paper, we propose a novel approach based on local linear approximation of the HR patch space using a sparse subspace clustering (SSC) algorithm. Our approach exploits the underlying HR patches' non-linear space by considering it as a low dimensional manifold in a high dimensional Euclidean space, and by employing each training HR patch as a sample from the manifold. We utilize the SSC algorithm to create the set of low dimensional linear spaces that are considered, approximately, as tangent spaces at the HR samples. Based on the obtained approximated tangent spaces, we examine the structure of the underlying HR manifold that allows locating the co-occurrence HR patch for a given LR one. The proposed approach requires a small number of training HR samples (about 1000 patches), without any prior assumption about the LR images. A comparison of the obtained results with other state-of-the-art methods clearly indicates the viability of the proposed approach.
Image processing performance evaluation for DSP based parallel computers with distributed frame buffers

Performance evaluation criteria are presented to provide a useful tool for comparing current digital signal processing (DSP)-based parallel machines used in image processing (IP) applications. Additionally, these criteria can be considered as a step toward a unified computation model for IP which may influence the design of future parallel architectures using distributed frame buffers. As an example, it is shown how to determine the criteria for a parallel machine with a mesh connected architecture and 2-D interleaving scheme. Benchmarks are derived as a function of key architectural parameters (e.g. the number of processors) and image parameters. The use of the criteria in determining task performance is explained.<<ETX>>
Sensitivity analysis in RIPless compressed sensing

Sensitivity analysis in optimization theory explores how the solution to a particular optimization problem changes as the objective function or constraints of such optimization problem perturb. A recent and yet important class of optimization problems is the framework of compressed sensing where the objective is to find the sparsest solution to an under-determined and possibly noisy system of linear equations. In this paper, we show that by utilizing some tools in sensitivity analysis, namely Invariant Support Sets (ISS), one can improve certain developed results in the field of compressed sensing. More specifically, we show that in a noiseless and RIP-less setting , the recovery process of a compressed sensing framework is a binary event in the sense that either all vectors with the same support and sign pattern can be recovered from their compressive samples or none can be estimated correctly. However, in a noisy and RIP-less setting, recovering only one signal from its limited noisy samples guarantees that there exist signals (possibly even with different supports and sign patterns) and noise vectors that shall be recovered with good accuracies by using Lasso.
The utility of hybrid error-erasure LDPC (HEEL) codes for wireless multimedia

Traditional wireless communication protocols do not relay corrupted packets towards the application layer and neither do they forward such packets over multiple hops. Such an approach can lead to a significant number of packet drops and thus a severe deterioration in performance of high bandwidth applications. Cross-layer protocols which do relay and forward corrupted packets have exhibited substantial promise to mitigate the above problem and thus their utility for wireless multimedia needs to be explored further. Moreover, there is a need to identify efficient channel coding methods for the cross-layer channel. Unlike the traditional schemes, where the channel observed at the application layer is a pure erasure channel, in the cross-layer schemes the application layer channel exhibits hybrid erasure-error impairments. Thus in this paper, we use a rather abstract link-layer model on the basis of which we compare the performance of cross-layer and conventional schemes. We identify the modifications required to be made to RS and LDPC based FEC schemes in order to use them over hybrid erasure-error channels. Finally we compare the considered schemes in terms of video quality using the emerging H.264 video standard. Our video analysis is based on employing a hybrid error-erasure channel coding FEC for the cross-layer schemes versus employing erasure recovery FEC for the traditional protocols. We show that cross-layer schemes can lead to a significant improvement in video quality.
End-to-End Channel Capacity of a Wireless Sensor Network Under Reachback

Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the effects of reachback, spatial correlation of sensor data has enabled the use of Slepian-Wolf coding as a compression technique to reduce the volume of traffic in sensor networks. Within a cluster, Slepian-Wolf coding is used to compress the sensor readings. Clusterheads then transmit the compressed sensor readings over the overlay network which is formed by all clusterheads. We develop an expression for the end-to-end (sensor-to-base station) channel capacity of such a wireless sensor network and observe the effect of variations in the number of clusterheads on end-to-end capacity.
EPIQ video streaming: Packet design and performance analysis

In this paper, we introduce a new video-streaming framework that is based on a novel packet design in conjunction with low-complexity active queue management within the network. Our proposed framework, which we call Erasable Packets within Internet Queues (EPIQ), exploits the inherent multi-priority nature of video to deliver optimal quality-of-experience to users, yet without requiring any additional overhead in terms of redundant packets or network resources such as multiple queues or maintaining state information about flows. The novelty of EPIQ is rooted at its packetization. Each EPIQ packet consists of multiple segments and where each segment carries different priority video content. Under congestion, a network node (ideally a router) can simply partially erase only small portions of the EPIQ packets instead of dropping complete packets. We show through an analytical model and extensive simulations that this new paradigm in video streaming provides improvements over multi-queue systems. These improvements are achieved while maintaining low-complexity within the network.
Optimally Mapping an Iterative Channel Decoding Algorithm to a Wireless Sensor Network

Retransmission based schemes are not suitable for energy constrained wireless sensor networks. Hence, there is an interest in including parity bits in each packet for error control. From an information-theoretic perspective the most efficient usage of network capacity can be achieved by performing full encoding/decoding at each node and using a variable rate in accordance with the link-quality. However, such an approach represents a major burden on power-constrained sensors. In this paper, we propose a more practical approach that is based on optimally distributing iterative channel decoding over sensor networks. In such a paradigm, the guarantee with which the base station, or collector, gets the data from a sensor is a function of the processing within the intermediate nodes between source and destination (in-network processing). There are two extreme cases: a) Complete channel decoding at each hop and b) decoding only at the final destination. In this paper, we present a novel scheme in which intermediate nodes conduct partial decoding of LDPC coded packets. In this scheme each node is assigned some number of decoding iterations. The relay node conducts LPDC decoding for that number of iterations and forwards the packet, without ensuring a complete error correction. We show that such partial processing is sufficient to improve the end-to-end reliability significantly. Additionally, we show that it is feasible to tradeoff complexity/energy usage with distortion/reliability by varying the assignment of number of iterations. Finally, we present a low-complexity dynamic programming algorithm that optimally assigns iterations within the network to facilitate operation along an optimal energy-distortion curve.
Sparse Decoding of Low Density Parity Check Codes Using Margin Propagation

One of the key factors underlying the popularity of Low-density parity-check (LDPC) code is its iterative decoding algorithm that is amenable to efficient hardware implementation. Even though different variants of LDPC iterative decoding algorithms have been studied for its error-correcting properties, an analytical basis for evaluating energy efficiency of LDPC decoders has not been reported. In this paper, we present a framework of a parameterized LDPC decoding algorithm that can be optimized to produce sparse representation of communication messages used in iterative decoding. The sparsity of messages is determined by its differential entropy and has been used as a theoretical metric for determining the energy efficiency of an iterative LDPC decoder. At the core of the proposed algorithm is margin propagation (MP) which approximates the log-sum-exp function used in conventional sum-product (SP) decoders by a piecewise linear (PWL) function. Using Monte-Carlo simulations, we demonstrate that the MP decoding leads to a significant reduction in message entropy compared to a conventional SP decoder, while incurring a negligible performance penalty (less than 0.03dB). The proposed work therefore lays the foundation for design of parameterized LDPC decoders whose bit-errorrate performance can be effectively traded-off with respect to different energy efficiency constraints as required by different set of applications.
Fast Single-Image Super-Resolution Via Tangent Space Learning of High-Resolution-Patch Manifold

Manifold assumption has been used in example-based super-resolution (SR) reconstruction from a single frame. Previous manifold-based SR approaches (more generally example-based SR) mainly focus on analyzing the co-occurrence properties of low-resolution and high-resolution patches. This paper develops a novel single-image SR approach based on linear approximation of the high-resolution-patch space using a sparse subspace clustering algorithm. The approach exploits the underlying high-resolution patches nonlinear space by considering it as a low-dimensional manifold in a high-dimensional Euclidean space, and by considering each training high-resolution-patch as a sample from the manifold. We utilize the sparse subspace clustering algorithm to create the set of low-dimensional linear spaces that are considered, approximately, as tangent spaces at the high-resolution samples. Furthermore, we consider and analyze each tangent space as one point in a Grassmann manifold, which helps to compute geodesic pairwise distances among these tangent spaces. An optimal subset of these tangent spaces is then selected using a min-max algorithm. The optimal subset reduces the computational cost in comparison with using the full set of tangent spaces while still preserving the quality of the high-resolution image reconstruction. In addition, we perform hierarchical clustering on the optimal subset based on the geodesic distance, which helps to further achieve much faster SR algorithm. We also analytically prove the validity of the geodesic distance based clustering under the proposed framework. A comparison of the obtained results with other related methods in both high-resolution image quality and computational complexity clearly indicates the viability of the proposed framework.
Multipath multi-stream distributed reliable video delivery in Wireless Sensor Networks

Reliably transporting captured video content over Wireless Sensor Networks (WSNs) poses several challenges due to the unique energy constraints presented by individual sensor nodes. The nature and amount of video content is another challenge that further complicates WSN data reliability problem. Motivated by the aforementioned factors, in this work, we propose an architecture that promises to enhance received video quality along-with sensor network lifetime maximization using a three prong strategy: splitting source coded video into prioritized streams, use path diversity to route video packets and partially and progressively decode the data packets as they traverse the multi-hop network. Further intrigued by quality enhancement for received video, we apply our proposed framework to an actual video sequence and compare the benefits that can be achieved using multipath multi-stream distributed reliability (MMDR) framework that we propose. We show that with little bit of processing within the network, significant enhancements in quality of video can be achieved for same level of energy spent to deliver video over the end-to-end path. We propose selective budgeting of network energy resources for processing within the network. We use low density parity check (LDPC) codes for channel coding the video sensor data. We show that the proposed architecture outperforms prevalent end-to-end channel coding schemes by considerable margins. Further, based on the results achieved, we discuss the associated rate-distortion tradeoffs in design of video sensor networks.