nils-herrmann/clean_scirepeval huggingface dataset:
On the Detection of Multiplicative Watermarks for Speech Signals in the Wavelet and DCT Domains

Blind multiplicative watermarking schemes for speech signals using wavelets and discrete cosine transform are presented. Watermarked signals are modeled using a generalized Gaussian distribution (GGD) and Cauchy probability model. Detectors are developed employing generalized likelihood ratio test (GLRT) and locally most powerful (LMP) approach. The LMP scheme is used for the Cauchy distribution, while the GLRT estimates the gain factor as an unknown parameter in the GGD model. The detectors are tested using Monte Carlo simulation and results show the superiority of the proposed LMP/Cauchy detector in some experiments
Graph entropy rate minimization and the compressibility of undirected binary graphs

With the increasing popularity of complex network analysis through the use of graphs, a method for computing graph entropy has become important for a better understanding of a network's structure and for compressing large complex networks. There have been many different definitions of graph entropy in the literature which incorporate random walks, degree distribution, and node centrality. However, these definitions are either computationally complex or seemingly ad hoc. In this paper we propose a new approach for computing graph entropy with the intention of quantifying the compressibility of a graph. We demonstrate the effectiveness of our measure by identifying the lower bound of the entropy rate for scale-free, lattice, star, random, and real-world networks.
Compressive dictionary learning for image recovery

In this paper, we tackle real-time learning of a dictionary D from compressive measurements Y of an image X. Existing dictionary learning algorithms are inapplicable because compressive samples Y = ΦX are incomplete and can be arbitrary linear combinations of different pixels. Our strategy is to learn a dictionary of the form D = ΨΘ, which represents compressible dictionaries with respect to the base dictionary Ψ. We show that our method for learning dictionaries during compressive image recovery can improve the recovery results by up to 3 dBs for general random sampling matrices.
Reducing Packet Losses in Networks of Commodity IEEE 802.15.4 Sensor Motes Using Cooperative Communication and Diversity Combination

This paper presents the 'Poor Man's SIMO System' (PMSS) which combines two ideas, cooperative communication and diversity combination, to reduce packet losses over links in Wireless Sensor Networks (WSN). The work is based on the IEEE 802.15.4 standard and is distinct from previous works that apply the same concepts because it foregoes the need for any changes to mote hardware. We describe a Poor Man's SIMO System protocol that governs the cooperation between receivers. Three diversity combination methods are evaluated including selection diversity, equal gain and maximal ratio combining. The latter relies on a model of the instantaneous Bit Error Rate (BER) driven by Channel State Information (CSI), i.e. Received Signal Strength Indication (RSSI) and Link Quality Indication (LQI). First, we demonstrate the PMSS on residual bit error traces in a fully reproducible manner. This is followed by an implementation of PMSS in C# on the .NET Micro Framework edition of the recently released Imote2 WSN mote platform. Both, trace based analysis and implementation demonstrate significant improvements over the single receiver baseline configuration. We deliberately verified PMSS by residual bit error traces and implementation to avoid the use of simulators that depend on abstract models of wireless channels.
A multistage camera self-calibration algorithm

We present a new camera self-calibration algorithm that uses a low-complexity multistage approach. We derive a polynomial optimization function with respect to the camera intrinsic parameters, based on the equal singular value property of the essential matrix. In terms of the stability analysis of the intrinsic parameters, we propose a multistage procedure to refine the estimation. Experimental results with both synthetic and real images show the accuracy and robustness of our method.
Randomness-in-Structured Ensembles for compressed sensing of images

Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., Gaussian or random Fourier matrices). For a given m, perfect reconstruction usually requires high complexity methods, such as Basis Pursuit (BP), which has complexity O(n3). Meanwhile, low-complexity greedy algorithms do not achieve the same level of performance (as BP) in terms of the quality of the reconstructed signal for the same m. In this paper, we introduce a new CS framework, which we refer to as Randomness-in-Structured Ensemble (RISE) projection. RISE projection matrices enable compressive sampling of image coefficients from random locations within the k-sparse image vector while imposing small structured overlaps. We prove that RISE-based compressed sensing requires only m = ck samples (where c is not a function of n) to perfectly recover a k-sparse image signal. For the case of n ≤ O(k2), the complexity of our solver is O(nk) which is less than the complexity of the popular greedy algorithm Orthogonal Matching Pursuit (OMP). Moreover, in practice we only need m = 2k samples to reconstruct the signal. We present simulation results that demonstrate the RISE framework's ability to recover the original image with higher than 50 dB PSNR, whereas other leading approaches (such as BP) can achieve PSNR values around 30 dB only.
The Impact of Receive Diversity in the Presence of Correlation in Wireless Sensor Networks

We demonstrate the benefits of incorporating receive diversity into wireless sensor network (WSN) applications that require high data fidelity and resolution upon event triggering. Such an approach is particularly applicable to active sensing scenarios, where the distribution of active sensors across the network varies or can be adaptively varied. By integrating spatial correlation in wireless sensor networks with receive diversity, we show that the field estimation accuracy can be significantly improved while ensuring energy and spectrum efficiency. The underlying physical phenomenon is modeled as a spatially correlated joint Gaussian random process where the sensor correlation is a function of the separation or node density. We abstract the overall system as a multiple input multiple output (MIMO) system, since we consider joint (multiple and simultaneous) sensor transmissions and multiple receive antennas at the base station. The optimum estimator for the overall system is derived and the mean square error (MSE) is analyzed as a function of equivalent MIMO system parameters. We investigate the combined effects of the data correlation, node density, and multi-user and receive diversity on the overall estimation accuracy.
Seamlets: Content-aware nonlinear wavelet transform

With the rise of mobile media devices, resizing an image or video to fit a screen of arbitrary size has become an important topic. In general, arbitrary resizing does not preserve the original image aspect ratio, and hence, can introduce significant visual distortion. Content-aware retargeting methods have been proposed to preserve salient features of an image or video ([1][3][5][6]) even under arbitrary changing of the aspect ratio. While these are useful tools, little attention has been given to the development of an efficient retargeting-driven representation of images. Such representation, for example, can lead to efficient distribution of media destined for retargeting at a variety of devices with different aspect ratios. We propose a new framework, seamlets, which utilizes the discrete wavelet transform (DWT) to perform content-aware arbitrary media resizing. In essence, seamlets provide an efficient multi-resolution representation for retargeting applications. The seamlet transform elegantly generalizes the DWT and seam carving, which is an intriguing retargeting method. The result is a new image framework, seamlets, that inherits the benefits of retargeting and wavelets.
Compression of image ensembles using tensor decomposition

In this paper we address the problem of compressing a collection of still images of the same type, such as an image database of faces or any other visual objects of similar shapes. We refer to such collection of images as an image ensemble. The compression of image ensembles presents a unique set of requirements such as random access to any image within the collection without the need to reconstruct other images in the same ensemble. Such requirement is readily met by any still image compression standard, simply by encoding each image in isolation of other images within the same ensemble. However, traditional approaches of still image compression do not exploit the strong correlation that might exist among images within a given ensemble. In this paper we argue that a tensor-decomposition framework can achieve both: (a) random access to any image within an ensemble and (b) the exploitation of the correlation among all images within the same ensemble. To that end, we propose a progressive tensor-factorization approach that decomposes an image ensemble into a set of block-wise rank-one tensors. We derive and encode the rank-one tensors of an image ensemble using an optimization method for rank allocation among the original block tensors. Our simulation results show the viability of the proposed tensor framework when applied to image ensembles of faces.
The influence mobility model: a novel hierarchical mobility modeling framework

Practical mobile ad hoc systems include heterogeneous classes of mobile nodes, such as people and vehicles. The simultaneous presence and movement of these classes influence the mobility pattern of their members in a variety of random and deterministic ways. In addition, there is an inherent hierarchical and multi-resolution structure to the mobility patterns. We present a novel hierarchical mobility framework which incorporates the fact that the movement of certain classes of mobile nodes (e.g., people) is affected by their surroundings and the movement of other forms of mobile nodes. The proposed model takes into account that a mobile node's movement is neither completely random, nor a mere function of its routing decision and/or the trip's source and destination points. The proposed model is neither classified as microscopic nor macroscopic. Instead, it is categorized as a multi-scale mobility framework capable of modeling mobility scenarios of different scales, i.e. it is equally capable of modeling the movement of mobile nodes in a street intersection as it is to model the movement of nodes between a number of population centers. The proposed mobility framework integrates and extends an influence model and hierarchical graph-based representations of the mobility area to achieve this goal. Our simulation results show that this framework accurately captures the influences among different groups of mobile nodes and the constraints imposed by their surroundings.
A statistical receiver-based approach for improved throughput of multimedia communications over wireless LANs

Delay-sensitivity of real-time applications stipulates resilience against errors and losses in multimedia content. Such resilience is particularly important for bandwidth-constrained and error-prone wireless networks. Recent wireless studies have highlighted that significant improvements in bandwidth utilization and multimedia quality can be achieved if the decision to retain or drop "corrupted" packets is made at the application layer instead of medium access, network or transport layers. Such a strategy, however, necessitates that the maximum number of (good and bad) packets are relayed to the application layer while minimizing any modifications (if any) to the widely-deployed UDP/IP protocol stack. Previous studies have proposed that, while ignoring corruptions in packet payload, only packets with errors in the headers should be dropped. We introduce a receiver-based scheme which uses the history of a multimedia session to correct packet header errors, thereby improving the throughput of real-time applications over wireless local area networks (LANs). The proposed scheme is truly receiver-based and, therefore, does not require any modifications to the source or any intermediate network node. Only minor modifications are required to the protocol stack of the multimedia receiver. Specifically, the proposed scheme generates statistics based on the history of "critical" protocol header fields for active multimedia sessions. These statistics are in turn employed to (a) correct errors in the critical fields and (b) determine if the relevant packets belong to the receiver's session of interest.
Image Watermarking using Hybrid Wavelets and Directional Filter Banks

We recently proposed a new family of nonredundant directional image transforms using hybrid wavelets and directional filter banks (HWD). In this paper we develop and analyze an HWD family algorithm for blind multiplicative watermarking. We approximate the watermarked coefficients by a generalized Gaussian distribution probability model and use a locally optimal detector. Then, we compare the performance of the HWD family to that of wavelet-based image watermarking.
IM session identification by outlier detection in cross-correlation functions

The identification of encrypted Instant Messaging (IM) channels between users is made difficult by the presence of variable and high levels of uncorrelated background traffic. In this paper, we propose a novel Cross-correlation Outlier Detector (CCOD) to identify communicating end-users in a large group of users. Our technique uses traffic flow traces between individual users and IM service provider's data center. We evaluate the CCOD on a data set of Yahoo! IM traffic traces with an average SNR of  (data set includes ground truth). Results show that our technique provides 88% true positives (TP) rate, 3% false positives (FP) rate and 96% ROC area. Performance of the previous correlation-based schemes on the same data set was limited to 63% TP rate, 4% FP rate and 85% ROC area.
Denoising of network graphs using topology diffusion

Partial Differential Equation (PDE) based diffusion has been utilized for image denoising for more than two decades. It is known that the process of diffusion preserves the edges and object boundaries making it a suitable preprocessing step for edge detection. Synergetic to these efforts, in this work, we apply diffusion to network graphs leading to an efficient algorithm for removing anomalous structures from the network topology. The driving force for the diffusion process in our work is the clustering propensity that exists in real social networks. The proposed diffusion enhances the boundaries of communities of which makes it a suitable step prior to community detection.
Hybrid Erasure-Error Protocols for Wireless Video

Many recently proposed cross-layer protocols for wireless video, have advocated the relay of corrupted packet to higher layers. Such protocols lead to both errors and erasures at the compressed video application layer. We generically refer to such schemes as hybrid erasure-error protocols (HEEPs). In this paper, we analyze the utility of HEEPs for efficient transmission of video over wireless channels. In order to maintain the generic nature of the deductions in this paper, we base our analysis on two (rather abstract) communication schemes for wireless video: hybrid error-erasure cross-layer design (CLD) and hybrid error-erasure cross-layer design with side-information (CLDS). We make a comparative analysis of the channel capacities of these schemes over single and multi-hop wireless channels to identify the conditions under which the HEEPs can provide improved performance over conventional (CON) protocols. In addition, we employ Reed Solomon (RS) and low-density parity check (LDPC)-code-based forward-error correction (FEC) schemes to illustrate that the improvement in capacity can easily enable an FEC scheme employed in conjunction with a HEEP to provide improved throughput. Finally we compare the performance of CON, CLD, and CLDS in terms of video quality using the H.264 video standard. The simulation results show a significant advantage for the HEEPs
Performance evaluation of error control protocols over finite-state Markovian channels

Reliable transmission is a challenging task over wireless links because wireless channels are susceptible to noise and interference. To address this problem various error control schemes have been introduced which are broadly categorized into automatic repeat request (ARQ)-based and forward error correction (FEC)-based techniques. In (S. Soltani et al., 2007), we introduced a channel adaptive error control protocol (PEEC) which utilized a buffering mechanism and the receiver feedback for optimal error recovery. In this paper, we are interested in evaluating the performances of ARQ-, FEC-based error control schemes and PEEC protocol over a finite-state Markov channel (FSMC).
Does relay of corrupted packets increase capacity?

Cross-layer protocols that are designed for wireless exhibit both errors and erasures. The nature of these error/erasure impairments and their impact on capacity is a function of the particular cross-layer scheme used. In this paper, we consider two (rather abstract) communication schemes, cross layer design (CLD) and cross layer design with side-information (CLDS). We make a comparative analysis of the channel capacities of these schemes over single and multi-hop wireless channels to identify the conditions under which the cross-layer protocols/channels can provide improved performance over traditional (pure) erasure channels. We show that cross-layer schemes like CLD lead to a capacity increase in most realistic channel scenarios (especially over a single hop) and schemes such as CLDS always lead to a capacity increase.
Improving Wireless Multimedia Quality using Header Detection with Priors

Recent wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lesser delay than retransmissions. To facilitate FEC decoding at a wireless receiver, it is desirable to relay maximum number of (error-free and corrupted) packets to the receiver's application layer. To that end, most cross-layer multimedia schemes perform partial checksum only on packet headers. However, even with a partial checksum, bursty wireless errors introduce frequent header corruptions, thereby causing considerable packet drops. In this paper, we extend our work in , which proposed receiver-based schemes to correct a packet's critical (and corrupted) header fields.  (a) poses header detection as the well-known decision theoretic problem of detecting known parameters in noise, (b) evaluates two detectors using bit-error traces collected over an 802.11b network, (c) provides throughput results for the detectors, and (d) uses video in conjunction with FEC as an example to highlight the efficacy of the proposed schemes. We show that header detection provides significant improvements in throughput and video quality over the conventional UDP/IP protocol stack.
On Modeling of 802.1lb Residue Errors

Errors that are observed at the MAC layer of a wireless protocol are known as residue errors due to the failure of the physical layer to correct them. In this paper, based on experimentation with actual 802.11b wireless LANs (WLANs), it is shown that the 802.11b residue error process is a highly non-stationary process; and hence error models derived on the basis of observations at one time/environment may not be applicable at/in another. Thus MAC-layer modeling/prediction techniques need to adapt on the basis of some channel state information (CSI). Consequently, in this work, we characterize the variation in the behavior of the residue error process in accordance to the radio-link signal to silence ratio (SSR) indications (SSRI). The residue error process is treated as a piecewise stationary process, where each piece corresponds to a PHY/MAC packet. In particular, we construct a unified Markov framework to obtain SSR aware Markov models for residue errors. Further, it is shown that the proposed SSR aware Markov framework provides significant performance benefits and can characterize the residue error traces even across varied WLAN setups. This leads to the design of non-homogenous Markov models that can vary the transition matrix on the basis of using SSR indication as side-information. We deploy such a non-homogenous Markov model and show that it can characterize the error process significantly better than Markov models that are derived by ignoring SSR values.
Rate-distortion modeling of scalable video coders

After the emergence of numerous Internet streaming applications, rate-distortion (R-D) modeling of scalable video encoders has become an important issue. In this paper, we examine the performance of existing R-D models in scalable coders by using the example of MPEG-4 FGS and PFGS and propose a novel R-D model based on approximation theory. Experimental results demonstrate that the proposed model is very accurate and significantly outperforms those in previous work.
Disparity dependent segmentation based stereo image coding

In this paper, we propose a novel rate-distortion (R-D) optimized disparity based coding scheme for stereo images. This new scheme efficiently integrates the coding of the disparity field with the residual image obtained via disparity estimation/compensation (DE/DC) in an R-D framework. The scheme first performs a quadtree decomposition of the target image and computes the disparity information along with the residual image for each node in the tree. An R-D based algorithm is then used for optimum bit allocation among the different quadtree nodes. The proposed scheme further jointly encodes the neighboring nodes with similar disparity information to attain higher coding gains. We present simulation results for the proposed scheme and compare these results with the performance of a fixed block size DE/DC based JPEG2000 stereo image coder. Our simulations show that the proposed scheme outperforms the fixed block size based disparity compensated JPEG2000 coder by more than 0.5 dB.
A Distributed Algorithm for Identifying Information Hubs in Social Networks

This paper addresses the problem of identifying the top-k information hubs in a social network. Identifying top-k information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. Existing solutions are centralized and require time stamped information about pair-wise user interactions and can only be used by social network owners as only they have access to such data. Existing distributed algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. Our method can identify hubs without requiring a central entity to access the complete friendship graph. We achieve this by fully distributing the computation using the Kempe-McSherry algorithm, while addressing user privacy concerns. We evaluate the effectiveness of our proposed technique using three real-world data set; The first two are Facebook data sets containing about 6 million users and more than 40 million friendship links. The third data set is from Twitter and comprises of a little over 2 million users. The results of our analysis show that our algorithm is up to 50% more accurate than existing algorithms. Results also show that the proposed algorithm can estimate the rank of the top-k information hubs users more accurately than existing approaches.