nils-herrmann/clean_scirepeval huggingface dataset:
Key distribution for secure multimedia multicasts via data embedding

The problem of controlling access to multimedia multicasts requires the distribution and maintenance of keying information. The conventional approach to distributing keys is to use a channel independent of the multimedia content. We propose a second approach that involves the use of a data-dependent channel, and can be achieved for multimedia by using data embedding techniques. Using data embedding to convey rekeying messages can provide an additional layer of security when compared with the traditional approach. We then introduce multicast key distribution, and employ a recent tree-based key distribution scheme to exhibit the factors involved in transmitting keys using data embedding.
DCT-based subpixel motion compensation and fully DCT-based video coder

The feedback loop for temporal prediction of traditional implementations of an MPEG-compliant video coder requires conversion of images from the spatial domain to the transform domain and then back to the spatial domain due to the restriction of motion estimation schemes performed only in the spatial domain, severely limiting the throughput of the coder. However, it was shown that the low-complexity DCT-based motion estimation scheme (DXT-ME) can remove this bottleneck and thus reduce the overall coder complexity. In this paper, we develop efficient DCT-based motion compensation algorithms to complete the fully DCT-based motion compensated video coder. We demonstrate that the performance of this new coder structure is comparable to the conventional one while the overall coder complexity is lower.
Fast Toeplitz-Hankel orthogonalization for high-resolution spectral estimation

The authors proposed a fast algorithm to perform the QR decomposition of the FBLP (forward-backward linear prediction) matrix. It is based on exploring the special Toeplitz-Hankel form of the FBLP matrix. The computational complexity is then reduced to 10n/sup 2/+4mn+O(n) The fast algorithm, can also be easily implemented onto a linear systolic array. The number of time steps required is further reduced to 2m+n-1 by using the parallel implementation.<<ETX>>
Robust connectivity-aware energy-efficient routing for wireless sensor networks

In this paper, we consider a class of energy-aware routing algorithm that explicitly takes into account the connectivity of the remaining sensor network. In typical sensor network deployments, some nodes may be more important than other nodes because the failure of these nodes causes the network disintegration, which results in early termination of information delivery. To mitigate this problem, we propose a class of routing algorithms called keep-connect algorithms, that use computable measures of network connectivity in determining how to route packets. The proposed algorithms embed the importance of the nodes in the routing cost/metric. The importance of a node is characterized by the algebraic connectivity of the remaining graph when that node fails. We prove several properties of the proposed routing algorithm including the energy consumption upper bound. Using extensive simulations, we demonstrate that the proposed algorithm achieves significant performance improvement compared to the existing routing algorithms. More importantly, we show that our proposed algorithm is more robust in terms of algebraic network connectivity compared to the existing algorithm. Finally, we present the distributed implementation of our proposed algorithm.
Learning characteristics for general class of adaptive blind equalizer

This paper presents a theoretical analysis of the static and dynamic convergence behavior (in PAM digital communication systems) for a general class of adaptive blind equalizers. We first study the properties of prediction error functions of blind equalization algorithms, and then we use these properties to analyze the static and dynamic convergence behavior based on the independence assumption. We prove that with a small step-size, the ensemble average of equalizer coefficients will converge to the minimum of the cost function near the channel inverse. However, the convergence is not consistent. The correlation matrix of equalizer coefficients at equilibrium is determined by a Lyapunov equation. According to our analysis results, for a given channel and step-size, there is an optimal length for an equalizer to minimize the intersymbol interference. This result implies that a longer-length blind equalizer does not necessarily outperform a shorter one, as contrary to what is conventionally conjectured. The theoretical analysis results are confirmed by computer simulations.
Multi-Stage Pricing Game for Collusion-Resistant Dynamic Spectrum Allocation

In order to fully utilize scarce spectrum resources, dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. However, the collusion among selfish network users may seriously deteriorate the efficiency of dynamic spectrum sharing. The network users' behaviors and dynamics need to be taken into consideration for efficient and robust spectrum allocation. In this paper, we model the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. In order to combat user collusion, we propose a pricing-based collusion-resistant dynamic spectrum allocation approach to optimize overall spectrum efficiency, while not only keeping the participating incentives of the selfish users but also combating possible user collusion. The simulation results show that the proposed scheme achieves high efficiency of spectrum usage even with the presence of severe user collusion.
Outage probability for two-way solar-powered relay networks with stochastic scheduling

An optimal relay transmission policy by exploiting a stochastic energy harvesting (EH) model is proposed for EH two-way relay (TWR) networks, wherein a solar-powered relay with a finite-sized battery adopts an amplify-and-forward protocol for helping relaying signals. The relay transmission power is optimized to minimize the long-term outage probability by considering the causal EH information, battery energy and random channel status. The design framework is formulated as a Markov decision process (MDP), in which a monotonic structure for the long-term reward values and a threshold property for the optimal relay transmission are revealed. Furthermore, an interesting saturation structure of the outage performance is uncovered, which means the expected outage probability eventually approaches to the relay's battery empty probability. Simulation results are demonstrated to verify the theoretical analysis and prove that the proposed optimal policy outperforms other myopic policies.
Channel aware unequal error protection for image transmission over broadband wireless LAN

Orthogonal frequency division multiplexing (OFDM) has been adopted in many broadband wireless communication standards. In OFDM systems, channel estimation is a crucial module in coherent signal detection and receiver diversity combining. In this paper, we investigate the leakage effect of pilot-symbol-assisted channel estimation techniques and propose a channel-aware unequal error protection (UEP) scheme for multimedia communications in OFDM systems. The proposed method exploits the variations of (he bit error rate in different OFDM subchannels caused by the leakage effect, and provides UEP by transmitting the more important data through the subchannels experiencing lower channel estimation error. Compared with the systems that do not jointly consider the channel estimation in the transmission of multimedia data, the proposed scheme can improve the peak-signal-to-noise-ratio (PSNR) of reconstructed images by up to 3 dB.
Time-reversal indoor positioning with centimeter accuracy using multi-antenna WiFi

In this paper, we propose an indoor positioning system (IPS) that achieves centimeter accuracy in a complex indoor environment using time-reversal (TR) technique with a single pair of off-the-shelf multi-antenna WiFi devices. The proposed IPS can work under both line-of-sight (LOS) and non-line-of-sight (NLOS) environment. Leveraging the spatial diversity on the multi-antenna WiFi device, the proposed IPS creates a large effective bandwidth to mitigate the ambiguity between different locations due to the limited bandwidth in WiFi systems. The proposed IPS obtains channel frequency responses (CFRs) from different antenna links at locations-of-interest in the offline phase. In the online phase, it captures instantaneous CFR which is compared against CFRs in the offline phase by fusing the time-reversal resonating strength (TRRS) calculated at each link. Finally, the location is determined according to the TRRS. Extensive experiment results in an office environment demonstrate true positive rates of 99.93% and 100% while only incurring false positive rates of 1.56% and 1.80% under LOS and NLOS conditions, respectively.
Defense of trust management vulnerabilities in distributed networks

Establishing trust among distributed network entities has been recognized as a powerful tool to secure distributed networks such as MANETs and sensor networks. Similar to most security schemes, trust establishment methods themselves can be vulnerable to attacks. In this article we investigate the benefits of introducing trust into distributed networks, the vulnerabilities in trust establishment methods, and the defense mechanisms. Five attacks against trust establishment methods are identified, and defense techniques are developed. Effectiveness of the attacks and the defense is demonstrated in the scenarios of securing routing protocols and detecting malicious nodes in MANETs.
Radio Biometrics: Human Recognition Through a Wall

In this paper, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity Wi-Fi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using the time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with  $3 \times 3$  multi-in multi-out (MIMO) transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the proposed system shows the promise for future low-cost low-complexity reliable human identification applications based on radio biometrics.
Trust modeling and evaluation in ad hoc networks

The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed information theoretic framework, trust is a measure of uncertainty with its value represented by entropy. We develop Axioms that address the basic rules for trust propagation. Based on these Axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the Axioms. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. Simulations show that the proposed framework can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks.
Understanding Popularity Dynamics: Decision-Making With Long-Term Incentives

With the explosive growth of big data, human's attention has become a scarce resource to be allocated to the vast amount of data. Numerous items such as online memes, videos are generated everyday, some of which go viral, i.e., attract lots of attention, whereas most diminish quickly without any influence. The recorded people's interactions with these items constitute a rich amount of popularity dynamics, e.g., hashtags mention count dynamics, which characterize human behaviors quantitatively. It is crucial to understand the underlying mechanisms of popularity dynamics in order to utilize the valuable attention of people efficiently. In this paper, we propose a game-theoretic model to analyze and understand popularity dynamics. The model takes into account both the instantaneous incentives and long-term incentives during people's decision-making process. We theoretically prove that the proposed game possesses a unique symmetric Nash equilibrium (SNE), which can be computed via a backward induction algorithm. We also demonstrate that, at the SNE, the interaction rate first increases and then decreases, which confirms with the observations from real data. Finally, by using simulations as well as experiments based on real-world popularity dynamics data, we validate the effectiveness of the theory. We find that our theory can fit the real data well and also predict the future dynamics.
Resource-aware conference key establishment for heterogeneous networks

The Diffie-Hellman problem is often the basis for establishing conference keys. In heterogeneous networks, many conferences have participants of varying resources, yet most conference keying schemes do not address this concern and place the same burden upon less powerful clients as more powerful ones. The establishment of conference keys should minimize the burden placed on resource-limited users while ensuring that the entire group can establish the key. In this paper, we present a hierarchical conference keying scheme that forms subgroup keys for successively larger subgroups en route to establishing the group key. A tree, called the conference tree, governs the order in which subgroup keys are formed. Key establishment schemes that consider users with varying costs or budgets are built by appropriately designing the conference tree. We then examine the scenario where users have both varying costs and budget constraints. A greedy algorithm is presented that achieves near-optimal performance, and requires significantly less computational effort than finding the optimal solution. We provide a comparison of the total cost of tree-based conference keying schemes against several existing schemes, and introduce a new performance criterion, the probability of establishing the session key (PESKY), to study the likelihood that a conference key can be established in the presence of budget constraints. Simulations show that the likelihood of forming a group key using a tree-based conference keying scheme is higher than the GDH schemes of Steiner et al.. Finally, we study the effect that greedy users have upon the Huffman-based conference keying scheme, and present a method to mitigate the detrimental effects of the greedy users upon the total cost.
Distributed Space-Frequency Coding over Amplify-and-Forward Relay Channels

In this paper, the design of distributed space- frequency codes (DSFCs) for wireless relay networks employing the amplify-and-forward (AAF) protocol is considered. The term distributed comes from the fact that the space-frequency code is distributed among randomly located relay nodes. DSFCs are designed to achieve the multi-path (frequency) and cooperative diversities of the wireless relay channels. We derive sufficient conditions for the proposed code design to achieve full diversity based on minimizing the pairwise error probability (PEP). We prove that the proposed DSFC can achieve full diversity of order LN, where L is the number of paths of the channel and N is the number of relay nodes, for any N and for the cases of L = 1 (flat, frequency-nonselective fading channel) and L = 2 (two-ray fading channel).
Collusion-resistant fingerprinting for multimedia

Digital fingerprinting is a technology for enforcing digital rights policies whereby unique labels, known as digital fingerprints, are inserted into content prior to distribution. For multimedia content, fingerprints can be embedded using conventional watermarking techniques that are typically concerned with robustness against a variety of attacks mounted by an individual. These attacks, known as multiuser collusion attacks, provide a cost-effective method for attenuating each of the colluder's fingerprints and poses a real threat to protecting media data and enforcing usage policies. In this article, we review some major design methodologies for collusion-resistant fingerprinting of multimedia and highlight common and unique issues of different fingerprinting techniques. It also provides detailed discussions on the two major classes of fingerprinting strategies, namely, orthogonal fingerprinting and correlated fingerprinting.
Sequential multi-channel access game in distributed cognitive radio networks

In distributed cognitive radio networks, due to the negative network externality, rational secondary users tend to avoid accessing same vacant primary channels with others. Moreover, they usually make channel access decisions in a sequential manner to avoid collisions. The characteristic of negative network externality and the structure of sequential decision making make the multi-channel access problem challenging, which has not been well studied by existing literatures. In this paper, we propose a multi-channel access game, which not only considers the negative network externality, but also takes into account their sequential decision making structure. We design a recursive best response algorithm to find the subgame perfect Nash equilibria. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed methods.
Decentralized Sparse Multitask RLS Over Networks

Distributed adaptive signal processing has attracted much attention in the recent decade owing to its effectiveness in many decentralized real-time applications in networked systems. Because many natural signals are highly sparse with most entries equal to zero, several decentralized sparse adaptive algorithms have been proposed recently. Most of them is focused on the single task estimation problems, in which all nodes receive data associated with the same unknown vector and collaborate to estimate it. However, many applications are inherently multitask oriented and each node has its own unknown vector different from others. The related multitask estimation problem benefits from collaborations among the nodes as neighbor nodes usually share analogous properties and thus similar unknown vectors. In this paper, we study the distributed sparse multitask recursive least squares (RLS) problem over networks. We first propose a decentralized online alternating direction method of multipliers algorithm for the formulated RLS problem. The algorithm is simplified for easy implementation with closed-form computations in each iteration and low storage requirements. Convergence analysis of the algorithm is presented. Moreover, to further reduce the complexity, we propose a decentralized online subgradient method with low computational overhead. We theoretically establish its mean square stability by providing upper bounds for the mean square deviation and the excess mean square error. A related distributed online proximal gradient method is presented and extension to clustered multitask networks is also provided. The effectiveness of the proposed algorithms is corroborated by numerical simulations and an accuracy-complexity tradeoff between the proposed algorithms is highlighted.
A simple and unified proof of dyadic shift invariance and the extension to cyclic shift invariance

A simple and unified proof of the dyadic shift invariance and an extension to cyclic shift invariance are presented. First, the concept of the dyadic shift invariance (DSI) and cyclic shift invariant (CSI) functions is proposed. Basic properties of the DSI and CSI functions are considered. Then it is shown that the Walsh-Hadamard transform (WHT) and discrete Fourier transform (DFT) are, in fact, special cases of the DSI and CSI functions, respectively. Many properties of the WHT and DFT can then be obtained easily from DSI and CSI points of view. The proposed unified approach is simple and rigorous. It is also shown that the properties of the WHT and DFT are the consequence of the basic principles of the DSI and CSI functions.
A secure multicast scheme for anti-collusion fingerprinted video

In networked video applications, to protect the multimedia content after decryption, digital fingerprinting can be used to trace the illegal redistribution of multimedia by uniquely labelling each distributed copy. It is crucial to efficiently distribute the uniquely fingerprinted copies without disclosing the secrecy of the embedded fingerprints. This paper investigates the bandwidth efficient transmission of fingerprinted video. To reduce the communication cost, we explore the special structure of the fingerprint design and propose a joint fingerprint design and distribution scheme, where some fingerprinted coefficients that are shared by a subgroup of users are securely multicast to them. From the simulations, the proposed scheme reduces the bandwidth requirement by 61% to 87%, depending on the number of users and the characteristics of video sequences.
A class of cooperative communication protocols for multi-node wireless networks

In this paper, a class of cooperative communication protocols with arbitrary N-relay nodes is proposed for wireless networks, in which each relay coherently combines the signals received from the source and m (1/spl les/m/spl les/N-1) previous relays. Exact symbol-error-rate (SER) expressions for an arbitrary N-node network employing MPSK or QAM modulation are derived. Also an approximate expression for the SER, that is tight at high SNR, is provided. Furthermore, an optimal power allocation problem based on minimizing the asymptotically tight SER expression is formulated. Closed form solutions for the optimal power allocation problem are provided for some network topologies.
Spectrum Auction Games for Multimedia Streaming Over Cognitive Radio Networks

Cognitive radio technologies have become a promising approach to efficiently utilize the spectrum. Although many works have been proposed recently in the area of cognitive radio for data communications, little effort has been made in content-aware multimedia applications over cognitive radio networks. In this paper, we study the multimedia streaming problem over cognitive radio networks, where there is one primary user and N secondary users. The uniquely scalable and delay-sensitive characteristics of multimedia data and the resulting impact on users' viewing experiences of multimedia content are explicitly involved in the utility functions, due to which the primary user and the secondary users can seamlessly switch among different quality levels to achieve the largest utilities. Then, we formulate the spectrum allocation problem as an auction game and propose three distributively auction-based spectrum allocation schemes, which are spectrum allocation using Single object pay-as-bid Ascending Clock Auction (ACA-S), spectrum allocation using Traditional Ascending Clock Auction (ACA-T), and spectrum allocation using Alternative Ascending Clock Auction (ACA-A). We prove that all three algorithms converge in a finite number of clocks. We also prove that ACA-S and ACA-A are cheat-proof while ACA-T is not. Moreover, we show that ACA-T and ACA-A can maximize the social welfare while ACA-S may not. Therefore, ACA-A is a good solution to multimedia cognitive radio networks since it can achieve maximal social welfare in a cheat-proof way. Finally, simulation results are presented to demonstrate the efficiency of the proposed algorithms.