nils-herrmann/clean_scirepeval huggingface dataset:
Using signal processing techniques to model worm propagation over wireless sensor networks

This paper describes how a topologically aware worm propagation model (TWPM) for wireless sensor networks is formulated and derived. The paper defines worm propagation characteristics that are specific to sensor networks. It also parameterize the effects of physical channel conditions, medium access control (MAC) layer contention, network layer routing, and transport layer protocol on worm propagation in sensor networks. The basic model formulation results in a partial differential equation, which is solved in the frequency domain to yield a closed-form solution for the TWPM. It is shown that in the spatial domain, the TWPM spread function is low-pass filtered by a two-dimensional isotropic Gaussian filter, thereby providing an intuitive feel for the dependence of the model on its underlying parameters. Simulation results demonstrate that the TWPM provides an effective and accurate worm propagation model for sensor networks
A reliability framework for visual sensor networks

In this work, we present a distributed framework for provision of reliability to visual data in Wireless Multimedia Sensor Networks (WMSNs). Individual sensor nodes cooperate, in a pair-wise fashion, to allocate processing resources within the network which are then used to partially decode the incoming data as it traverses the multi-hop network. We show that the proposed framework outperforms the conventional end-to-end channel coding by considerable margin in terms of energy efficiency for desired multimedia content delivery.
RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video data set, without considering the underlying low-rank structure of the data set. Other algorithms exploit the low-rank component only, ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on robust principal component analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that the RPCA decomposes an input data into: 1) a low-rank component that reveals the systematic information across the elements of the data set and 2) a set of sparse components each of which containing distinct information about each element in the same data set. The two information types are combined into a single $\ell _{1}$ -norm-based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer and other types of videos. A comparison of the results obtained by our method with the ground truth and with related state-of-the-art algorithms clearly illustrates the viability of the proposed RPCA-based framework.
Complex sparse projections for compressed sensing

Sparse projections for compressed sensing have been receiving some attention recently. In this paper, we consider the problem of recovering a k-sparse signal (x) in an n-dimensional space from a limited number (m) of linear, noiseless compressive samples (y) using complex sparse projections. Our approach is based on constructing complex sparse projections using strategies rooted in combinatorial design and expander graphs. We are able to recover the non-zero coefficients of the k-sparse signal (x) iteratively using a low-complexity algorithm that is reminiscent of well-known iterative channel decoding methods. We show that the proposed framework is optimal in terms of sample requirements for signal recovery (m = O (k log(n/k))) and has a decoding complexity of O (m log(n/m)), which represents a tangible improvement over recent solvers. Moreover we prove that using the proposed complex-sparse framework, on average 2k ≪ m ≤ 4k real measurements (where each complex sample is counted as two real measurements) suffice to recover a k-sparse signal perfectly.
Heterogeneity Image Patch Index and Its Application to Consumer Video Summarization

Automatic video summarization is indispensable for fast browsing and efficient management of large video libraries. In this paper, we introduce an image feature that we refer to as heterogeneity image patch (HIP) index. The proposed HIP index provides a new entropy-based measure of the heterogeneity of patches within any picture. By evaluating this index for every frame in a video sequence, we generate a HIP curve for that sequence. We exploit the HIP curve in solving two categories of video summarization applications: key frame extraction and dynamic video skimming. Under the key frame extraction framework, a set of candidate key frames is selected from abundant video frames based on the HIP curve. Then, a proposed patch-based image dissimilarity measure is used to create affinity matrix of these candidates. Finally, a set of key frames is extracted from the affinity matrix using a min-max based algorithm. Under video skimming, we propose a method to measure the distance between a video and its skimmed representation. The video skimming problem is then mapped into an optimization framework and solved by minimizing a HIP-based distance for a set of extracted excerpts. The HIP framework is pixel-based and does not require semantic information or complex camera motion estimation. Our simulation results are based on experiments performed on consumer videos and are compared with state-of-the-art methods. It is shown that the HIP approach outperforms other leading methods, while maintaining low complexity.
Constant-Complexity Models for Wireless Channels

High-order full-state Markov (FSM) chains have been employed to model errors and losses in many wireless studies. The complexity of this modeling paradigm is an exponential function of a random process’ memory-length and hence the viability of FSM chains in resource-constrained wireless environments is severely limited. In this paper, we address an important yet unsettled question: What characteristics of high-order FSM chains should be captured by a low-complexity approximate model? We analytically derive vital guidelines for accurate approximation of an FSM chain of arbitrary memory-length. These guidelines lead to a novel constant-complexity model (CCM), which always comprises of five states irrespective of a process’ memory-length. Our trace-driven evaluations for 802.11b and GSM channels demonstrate that the 5 state CCM, while providing orders of magnitude reduction in complexity, is comparable to FSM chains and better than linear-complexity models.
A Maximum-Likelihood Decoding Algorithm of LT Codes with a Small Fraction of Dense Rows

We design a new form of the Maximum Likelihood Decoding Algorithm (MLDA) in  for LT codes  with a small fraction of dense rows. In particular, we design degree distributions using the Robust Soliton Distribution (RSD) for the proposed MLDA based decoding of LT codes. We also estimate the computational complexity of the proposed LT based MLDA. Simulation results, which show the viability of the proposed MLDA decoding of LT codes, are also presented.
A New Family of Nonredundant Transforms Using Hybrid Wavelets and Directional Filter Banks

We propose a new family of nonredundant geometrical image transforms that are based on wavelets and directional filter banks. We convert the wavelet basis functions in the finest scales to a flexible and rich set of directional basis elements by employing directional filter banks, where we form a nonredundant transform family, which exhibits both directional and nondirectional basis functions. We demonstrate the potential of the proposed transforms using nonlinear approximation. In addition, we employ the proposed family in two key image processing applications, image coding and denoising, and show its efficiency for these applications
Worm Detection at Network Endpoints Using Information-Theoretic Traffic Perturbations

In this paper, we propose an endpoint-based anomaly detection scheme that detects computer worms by comparing the current traffic patterns of each host to the corresponding benign traffic profile of the host. To detect deviations in the traffic patterns, we employ the information-theoretic Kullback-Leibler (K-L) divergence measure which estimates the distance between the distribution of source/destination ports engaged in current communication and that observed in the legitimate host traffic collected earlier. We use a small subset of traces obtained from endpoints in home, university, and office environments to build benign traffic profiles of studied endpoints. Endpoint traces are then infected with both real and simulated worms to examine the performance of our detection mechanism. To perform automated, real-time worm detection, we use Support Vector Machines (SVMs) that are trained using the K-L divergence values. Our results show that the proposed worm detector provides almost 100% detection with negligible false- alarm rates and significantly surpasses the accuracy of existing anomaly detectors.
Statistical analysis and distortion modeling of MPEG-4 FGS

In this paper, we analyze statistical and rate-distortion (R-D) properties of MPEG-4 fine-granular scalability (FGS), which has recently become an important scalable compression framework and a de-facto standard for Internet video streaming. We first propose a novel statistical model of DCT residue that accurately captures the properties of the input to the MPEG-4 FGS enhancement layer. Our results show that FGS residue concentrates a lot of probability mass near zero and cannot be accurately modeled by Gaussian or Laplacian distributions. We then model the distortion of each bitplane based on the proposed statistical framework and further demonstrate that our R-D model significantly outperforms current distortion models.
Complexity reduction using power-law based scheduling for exploiting spatial correlation in distributed video coding

In pixel-domain distributed video coding (DVC), due to the largely translational nature of motion, residue errors in the side-information frame are often clustered together. These clusterings can be exploited to reduce the number of syndrome bits required to successfully perform low density parity check (LDPC) decoding, and therefore improve the overall rate-distortion performance. We shall see that using alternate iterations of LDPC syndrome decoding and Baum-Welch channel estimation proves to be an efficient scheme for exploiting the spatial clustering of errors in pixel-domain DVC. In this paper we demonstrate that a sparser power-law based scheduling of the channel estimation iteration leads to significant reduction in estimation complexity (around 83% reduction) for a small loss in rate-distortion performance (less than 0.75 dB). This sparser scheduling of channel estimation iterations can potentially improve decoding delays.
Optimal Progressive Error Recovery for Wireless Sensor Networks using Irregular LDPC Codes

We study the problem of providing reliable data transmission in energy constrained wireless sensor networks (WSNs). Low rate channel coding can increase reliability and eliminate the need of costly retransmissions for sensor data. However, low rate channel coding on end to end basis puts a considerable burden in terms of transmit energy on resource constrained sensor nodes. We propose a scheme that progressively provides error resilience as information reaches the final destination. Precisely, we present a novel framework for processing within the network (in-network) using irregular low density parity check (LDPC) codes for channel coding, in which nodes progressively decode the information at intermediate nodes. We not only present the in-network processing setup, but also an optimal progressive error recovery algorithm (OPERA) that optimally maps the decoding iterations to attain maximum throughput at the destination node. We use density evolution algorithm for belief propagation decoding of LDPC codes to cast the optimization problem and use dynamic programming to reach the solution. We compare the performance of our scheme with end to end channel coding and establish the efficiency of proposed solution for a given energy budget. Finally, we give a comparison between our scheme and random iteration assignment for decoding at intermediate nodes, and show that our scheme performs considerably better.
Who are you talking to? Breaching privacy in encrypted IM networks

We present a novel attack on relayed instant messaging (IM) traffic that allows an attacker to infer who's talking to whom with high accuracy. This attack only requires collection of packet header traces between users and IM servers for a short time period, where each packet in the trace goes from a user to an IM server or vice-versa. The specific goal of the attack is to accurately identify a candidate set of top-k users with whom a given user possibly talked to, while using only the information available in packet header traces (packet payloads cannot be used because they are mostly encrypted). Towards this end, we propose a wavelet-based scheme, called COmmunication Link De-anonymization (COLD), and evaluate its effectiveness using a real-world Yahoo! Messenger data set. The results of our experiments show that COLD achieves a hit rate of more than 90% for a candidate set size of 10. For slightly larger candidate set size of 20, COLD achieves almost 100% hit rate. In contrast, a baseline method using time series correlation could only achieve less than 5% hit rate for similar candidate set sizes.
On Multicast-Based Binding Update Scheme for NEMO Environments

NEMO basic support (NBS) protocol ensures session continuity for all the nodes in a mobile network, however, there exists a serious pinball routing problem. To overcome this one, there are many route optimization (RO) solutions such as bi-directional tunneling (BT) mechanism, aggregation and surrogate (A&S) mechanism, recursive Approach, etc. The A&S RO mechanism is known to outperform the other RO mechanisms, except for the binding update (BU) cost. Although improved prefix delegation (IPD) reduces the cost problem of Prefix Delegation (PD), a well-known A&S protocol, the BU cost problem still presents. In this paper, a solution to reduce the cost is proposed using a multicast mechanism instead of unicasting such as the traditional BU of the RO. The performance of the proposed multicast-based BU scheme is examined with an analytical model which shows that the BU cost enhancement is up to about 32% over IPD-based, hence, it is feasible to predict that the proposed scheme could benefit in other NEMO RO protocols.
Deep learning algorithm for autonomous driving using GoogLeNet

In this paper, we consider the Direct Perception approach for autonomous driving. Previous efforts in this field focused more on feature extraction of the road markings and other vehicles in the scene rather than on the autonomous driving algorithm and its performance under realistic assumptions. Our main contribution in this paper is introducing a new, more robust, and more realistic Direct Perception framework and corresponding algorithm for autonomous driving. First, we compare the top 3 Convolutional Neural Networks (CNN) models in the feature extraction competitions and test their performance for autonomous driving. The experimental results showed that GoogLeNet performs the best in this application. Subsequently, we propose a deep learning based algorithm for autonomous driving, and we refer to our algorithm as GoogLenet for Autonomous Driving (GLAD). Unlike previous efforts, GLAD makes no unrealistic assumptions about the autonomous vehicle or its surroundings, and it uses only five affordance parameters to control the vehicle as compared to the 14 parameters used by prior efforts. Our simulation results show that the proposed GLAD algorithm outperforms previous Direct Perception algorithms both on empty roads and while driving with other surrounding vehicles.
A distributed and privacy preserving 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 and privacy preserving algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. The intuition is that highly connected users tend to have more interactions with their neighbors than less connected users. 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 to address user privacy concerns. To the best of our knowledge, the proposed algorithm represents an arguably first attempt that (1) uses friendship graphs (instead of interaction graphs), (2) employs a truly distributed method over friendship graphs, and (3) maintains user privacy by not requiring them to disclose their friend associations and interactions, for identifying information hubs in social networks. We evaluate the effectiveness of our proposed technique using a real-world Facebook data set containing about 3.1 million users and more than 23 million friendship links. The results of our experiments show that our algorithm is 50% more accurate than existing distributed algorithms. Results also show that the proposed algorithm can estimate the rank of the top-k information hubs users more accurately than existing approaches.
Network coding with multi-generation mixing for scalable and non-scalable video communication

Network coding with multi-generation mixing (MGM) has been proposed as a generalized approach for practical network coding [6]. Our goal in this paper is to evaluate the improvements achieved by MGM network coding on the quality of recovered video in packet loss networks. The improvements of network coding are clear in wireless networks due to the opportunities of enhancing bandwidth utilization. This makes MGM network coding appropriate for video communication over wireless networks. In this paper MGM is applied on scalable as well as non-scalable video. MGM deployment in the case of scalable video differs from that in the case of non-scalable video. After explaining the way MGM is applied, we evaluate the performance with extensive simulations in a wireless network setup. Results indicate major improvements in network coding decodable rates and enhancement in the quality of recovered video with MGM.
Smoothed rank approximation algorithms for matrix completion

We consider using smooth rank approximation functions to solve the matrix completion problem. Our main contribution in this paper is deriving two robust algorithms using the accelerated proximal gradient (APG) and the alternating direction method of multipliers (ADM). Further, we compare both algorithms against each other and against the iterative reweighted least squares (IRLS-1) algorithm using a variety of noisy images. The experiments show that using ADM achieves approximately 1.5 dB SNR improvement over irls-1, while it needs comparable execution time to IRLS-1. Meanwhile, using APG saves about 50% of IRLS-1's computation time with lower SNR than ADM.
Transitivity matrix of social network graphs

Transitivity in friendship graphs has been well known as a key property of social networks. In this paper, we extend the graph transitivity index by introducing a new characteristic quantity of graphs, namely the transitivity matrix. The transitivity matrix measures the microscopic impact that each link has on the global transitivity index of the graph. We argue that the new transitivity metric is synergetic with key aspects of the social science literature on social network theory, and we show that it can be used as a tool for locating bridges as well as redundant links. This work represents a major departure from the traditional graph transitivity index, which provides a coarse measure for how transitive the overall network is.
Effects of channel delays on underflow events of compressed video over the Internet

This paper presents an extensive statistical study and analysis of the effects of channel delays in the current (best-effort) Internet on underflow events in MPEG-4 video streaming. Two types of network delays are considered: end-to-end round-trip delays and delay jitter. Our data were collected in a seven-month real-time streaming experiment, which was conducted between a number of unicast dialup clients in more than 600 major USA cities and a backbone video server. Among other findings, our analysis shows that startup delays approximately 15-20 times the average roundtrip time (RTT) are required for the client to avoid 90% of late packets caused by delay jitter. Meanwhile, startup delays of only 3-4 times the average RTT are needed to achieve lost-packet recovery rates of 90% or more. Hence, a key finding of our study is that delay jitter represents a more challenging problem for video streaming applications than round-trip delays. We also show that the probability density function (PDF) of RTT samples, which are associated with retransmitted video packets, can be modeled using a Pareto distribution. This observation indicates that the upper tail of the RTT PDF decays slower than reported in earlier studies.
Representative selection for big data via sparse graph and geodesic Grassmann manifold distance

This paper addresses the problem of identifying a very small subset of data points that belong to a significantly larger massive dataset (i.e., Big Data). The small number of selected data points must adequately represent and faithfully characterize the massive Big Data. Such identification process is known as representative selection [19]. We propose a novel representative selection framework by generating an ℓ1 norm sparse graph for a given Big-Data dataset The Big Data is partitioned recursively into clusters using a spectral clustering algorithm on the generated sparse graph. We consider each cluster as one point in a Grassmann manifold, and measure the geodesic distance among these points. The distances are further analyzed using a min-max algorithm  to extract an optimal subset of clusters. Finally, by considering a sparse subgraph of each selected cluster, we detect a representative using principal component centrality [11]. We refer to the proposed representative selection framework as a Sparse Graph and Grassmann Manifold (SGGM) based approach. To validate the proposed SGGM framework, we apply it onto the problem of video summarization where only few video frames, known as key frames, are selected among a much longer video sequence. A comparison of the results obtained by the proposed algorithm with the ground truth, which is agreed by multiple human judges, and with some state-of-the-art methods clearly indicates the viability of the SGGM framework.