nils-herrmann/clean_scirepeval huggingface dataset:
Information Theoretical Limit of Media Forensics: The Forensicability

While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this paper, we explore the fundamental limit of operation forensics by proposing an information theoretical framework. In particular, we consider a general forensic system of estimating operations' hypotheses based on extracted features from the multimedia content. In this system, forensicability is defined as the maximum forensic information that features contain about operations. Then, due to its conceptual similarity with mutual information in an information theory, forensicability is measured as the mutual information between features and operations' hypotheses. Such a measurement gives the error probability lower bound of all practical estimators, which use these features to detect the operations' hypotheses. Furthermore, it can determine the maximum number of hypotheses that we can theoretically detect. To demonstrate the effectiveness of our proposed information theoretical framework, we apply this framework on a forensic example of detecting the number of JPEG compressions based on normalized discrete cosine transform (DCT) coefficient histograms. We conclude that, when subband (2, 3) is used in detection and the size of the testing database is <;20000, the maximum number of JPEG compressions that we can expectedly perfectly detect using normalized DCT coefficient histogram features is four. Furthermore, we obtain the optimal strategies for investigators and forgers based on the fundamental measurement of forensicability.
Super-resolution of musical signals using approximate matching pursuit

Super-resolution (SR) is a well-studied problem in signal processing, particularly with regard to image and video applications. SR techniques are useful because unlike simple interpolation, they create a high-resolution signal from a low-resolution input by generating new information that was not previously present. A growing body of research shows progress in development of SR techniques using dictionary learning. We propose a method for SR of musical signals through matrix factorization and use of a known musical dictionary. The approximate matching pursuit (AMP) algorithm is used to query the dictionary and perform factorization, making the overall process efficient and scalable. By approximating the frequency information of a low-resolution input spectrogram as the linear combination of entries in the musical dictionary, we are able to closely match the input signal's missing high frequency information and thereby create a high-resolution output signal.
Algorithm-based low-power transform coding architectures

In most low-power VLSI designs, the supply voltage is usually reduced to lower the total power consumption. However, the device speed will be degraded as the supply voltage goes down. In this paper, we propose new algorithmic-level techniques for compensating the increased delays based on the multirate approach. We will show how to compute most of the discrete sinusoidal transforms through the decimated low-speed sequences with reasonable linear hardware overhead. For the case where the decimation factor is equal to two, the overall power consumption can be reduced to about one-third of the original design. The resulting multirate low-power architectures are regular, modular, and free of global communications. Such properties are very suitable for VLSI implementations. The proposed architectures can also be applied to very high-speed block transforms where only low-speed operators are required.
An adaptive ESPRIT based on URV decomposition

ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Its computational burden makes it unsuitable for real-time processing of signals with time-varying directions of arrival. The authors develop a new implementation of ESPRIT that has potential for real-time processing. It is based on a rank-revealing URV decomposition, rather than the eigendecomposition or singular value decomposition (SVD) used in previous ESPRIT algorithms. Its performance is demonstrated on simulated data representing both constant and time-varying signals. It is shown that the URV-based ESPRIT algorithm is effective for estimating time-varying directions-of-arrival at considerable computational savings over the SVD-based algorithm.<<ETX>>
Systematic design of complex orthogonal space-time block codes with high rates

In this paper, we develop a systematic design method to generate high-rate space-time block codes (STBCs) from complex orthogonal designs for any number of transmit antennas. The resulting complex orthogonal STBCs have the best known rates, which are conjectured "optimal". Two constructions with rates 2/3 and 5/8 are further illustrated for 6 and 7 transmit antennas, respectively.
Anti-collusion of group-oriented fingerprinting

Digital fingerprinting of multimedia data involves embedding information in the content, and offers protection to the digital rights of the content by allowing illegitimate usage of the content to be identified by authorized parties. One potential threat to fingerprints is collusion, whereby a group of adversaries combine their individual copies in an attempt to remove the underlying fingerprints. Former studies indicate that collusion attacks based on a few dozen independent copies can confound a fingerprinting system that employs orthogonal modulation. However, since an adversary is more likely to collude with some users than other users, we propose a group-based fingerprinting scheme where users likely to collude with each other are assigned correlated fingerprints. We evaluate the performance of our group-based fingerprints by studying the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints. We compare the results to those of fingerprinting systems employing orthogonal modulation.
Near-optimal reinforcement learning framework for energy-aware sensor communications

We consider the problem of average throughput maximization per total consumed energy in packetized sensor communications. Our study results in a near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and the channel condition. We investigate the point-to-point and multinode communication scenarios. Many solutions of the previous works require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to propose and utilize a class of learning algorithms [called reinforcement learning (RL)] to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multinode scenario. For comparison purpose, we develop the stochastic models to obtain the optimal strategy in the point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multinode scenario by independent learning. We compare the learned policy to a simple policy, where the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The proposed learning algorithm achieves more than twice the throughput per energy compared with the simple policy, particularly, in high packet arrival regime. Beside the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.
High Resolution Carrier Frequency Offset Estimation in Time-Reversal Wideband Communications

Time-reversal (TR) wideband communication systems enjoy the spatial–temporal focusing effect in a rich-scattering environment. However, the performance degrades in the presence of carrier frequency offset (CFO). The impact of CFO can be mitigated by compensating the estimated CFO values obtained using CFO estimators. Yet, CFO estimators in literature cannot work well in wideband TR systems due to the fact that the normalized CFO values are very small and thus cannot be estimated accurately using conventional schemes. To address this issue, we propose four CFO estimators which are capable of accurate CFO estimations for wideband TR systems. The theoretical performances of the proposed estimators are analyzed. Additionally, realizing that phase wrapping might introduce severe bias into CFO estimations, we present the conditions on the system parameters so that phase wrapping can be avoided. Extensive simulations and experimental results demonstrate the superiority of the proposed methods.
WLC41-4: Stackelberg Game for Distributed Resource Allocation over Multiuser Cooperative Communication Networks

In this paper, we propose a Stackelberg game theoretic framework for distributive resource allocation over multiuser cooperative communication networks to improve the system performance and stimulate cooperation. Two questions of who should relay and how much power for relaying are answered, by employing a two-level game to jointly consider the benefits of source nodes as buyers and relay nodes as sellers in cooperative communication. From the derived results, the proposed game not only helps the source smartly find relays at relatively better locations but also helps the competing relays ask reasonable prices to maximize their own utilities. From the simulation results, the relays in good locations or good channel conditions can play more important roles in increasing source node's utility, so the source would like to buy power from these preferred relays. On the other hand, because of competition from other relays and selections from the source, the relays have to set proper prices to attract the source's buying so as to optimize their utility values.
Data-Driven Optimal Throughput Analysis for Route Selection in Cognitive Vehicular Networks

To meet the dramatically increasing demands for vehicular communications, cognitive vehicular networks have been proposed to broaden the vehicular communication bandwidth by using cognitive radio technology. Meanwhile, the nationwide Super Wi-Fi project that allows the TV white space frequencies to be used for free, makes the concept of cognitive vehicular networks realistic. Recently, lots of technical issues of cognitive vehicular networks have been studied from the network designers' perspective, e.g., vehicular spectrum sensing and access, applications with different vehicular QoS, etc. Different from the existing works, in this paper, we consider from the vehicular users' perspective by optimizing throughput via route selection in cognitive vehicular networks using TV white space. By employing the attainable data rate as route selection metric, we propose two schemes: instantaneous route selection and long-term route selection. To evaluate the expected data rate on the route, we analyze the cognitive vehicular network throughput under two spectrum sharing models: spectrum overlay and spectrum underlay. In the experiments, we use Google spectrum dataset to estimate the intensity of TV base stations in the United States and evaluate the cognitive vehicular network throughput performance, which shows that the spectrum overlay model is more suitable for most of states in current United States, except New Jersey, Delaware and Utah. Moreover, we conduct a case study regarding the route I-88E and I-90E selection between Cortland and Schenectady in New York State. The traffic intensities and traffic intensity transition probabilities of these two routes are estimated using the real-world traffic volume dataset of New York State. Based on the estimated traffic information, we calculate the attainable instantaneous and long-term data rates of each vehicular user, which shows that route I-88E is preferable to route I-90E in most cases.
Minimization of rekeying cost for contributory group communications

While contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of rekeying overhead. In this paper we introduce a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needs O(1) rounds of two-party Diffie-Hellman upon any single user join event and O(log n) rounds of two-party Diffie-Hellman upon any single user leave event. Both theoretical bound analysis and simulation studies show that the proposed scheme achieves much lower rekeying cost than the existing tree-based contributory group key agreement schemes.
OFDM channel estimation based on time-frequency polynomial model of fading multipath channel

Orthogonal frequency division multiplexing (OFDM) scheme gains growing interests in broadband data communications for wireless communications because of its great immunity to fast fading and intersymbol interference. The channel estimation is a crucial aspect in the design of OFDM systems. In this work, we propose a channel estimation algorithm based on polynomial approximation of the channel parameters in both time and frequency domains. The method exploits both the time and frequency correlation of the channel parameters. The estimator is robust and needs a little prior knowledge about the delay and fading properties of the channel. It can even adjust itself to follow the variation of the channel statistics. Our simulation shows it has more than 5 dB improvement over the existing method under practical channel conditions.
Time-Reversal Division Multiple Access over Multi-Path Channels

The multi-path effect makes high speed broadband communications a very challenging task due to the severe inter-symbol interference (ISI). By concentrating energy in both the spatial and temporal domains, time-reversal (TR) transmission technique provides a great potential of low-complexity energy-efficient communications. In this paper, a novel concept of time-reversal division multiple access (TRDMA) is proposed as a wireless channel access method based on its high-resolution spatial focusing effect. It is proposed to use TR structure in multi-user downlink systems over multi-path channels, where signals of different users are separated solely by TRDMA. Both the single-transmit-antenna scheme and its enhanced version with multiple transmit antennas are developed and evaluated in this paper. The system performance is investigated in terms of its effective signal-to-interference-plus-noise ratio (SINR), the achievable sum rate and the achievable rates with outage. And some further discussions regarding its advantage over conventional rake receivers and the impact of spatial correlations between users are given at the end of this paper. It is shown in both analytical and simulation results that desirable properties and satisfying performances can be achieved in the proposed TRDMA multi-user downlink system, which makes TRDMA a promising candidate for future energy-efficient low-complexity broadband wireless communications.
On analysis of two-way relaying with network-coded ARQ

In this work, we study the throughput of two-way relay channel (TWRC) with different automatic repeat-request (ARQ) strategies. Beyond orthogonal relaying, we propose two network-coded ARQ schemes, where the packets intended for different end terminals could be combined at the relay nodes. By considering the per-hop and end-to-end maximum number of transmissions constraint, we derive the closed-from throughput expression. We demonstrate that network coding can greatly improve the system throughput, but the throughput gain is upper bounded. Besides, we come up with a near-optimum power allocation scheme to maximize the throughput. Compared to equal power allocation, the proposed power allocation scheme can greatly improve the throughput especially when the network topology is highly asymmetric.
WSN03-3: Dynamic Pricing Approach for Spectrum Allocation in Wireless Networks with Selfish Users

Dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. In this paper, we consider the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. A dynamic pricing approach is proposed to optimize overall spectrum efficiency while keeping the participating incentives of the users based on double-auction rules. Moreover, a belief system is developed to assist selfish users to dynamically update their strategies adaptive to the network dynamics and substantially decrease the pricing overhead. The simulation results show that our proposed scheme not only approaches optimal outcomes but also has low overhead.
Establishment of conference keys in heterogeneous networks

In order to secure communication amongst members of a conference, a secret shared by all group members must be established. The Diffie-Hellman problem is often the basis for generating keys in two-party communication, and can also be used to establish conference keys. In heterogeneous networks, many conferences have participants of varying computational power and resources. 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 try to minimize the burden placed on more resource-limited users while ensuring that the entire group can establish the group secret. In this paper, we present a scheme for establishing a conference key using the two-party Diffie-Hellman scheme. The scheme is hierarchical, forming subgroup keys for successively larger subgroups en route to establishing the group key. A full, binary 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 designed by appropriately choosing the conference tree. The tree that minimizes the total group cost is produced via the Huffman algorithm. A criterion is presented for the existence of a conference tree when users have varying budgets, and a greedy algorithm is presented that minimizes the total length of the conference tree under budget constraints.
An Activity-Subspace Approach for Estimating the Integrated Input Function and Relative Distribution Volume in PET Parametric Imaging

Dynamic positron emission tomography (PET) imaging technique enables the measurement of neuroreceptor distributions corresponding to anatomic structures, and thus, allows image-wide quantification of physiological and biochemical parameters. Accurate quantification of the concentration of neuroreceptor has been the objective of many research efforts. Compartment modeling is the most widely used approach for receptor binding studies. However, current compartment-model-based methods often either require intrusive collection of accurate arterial blood measurements as the input function, or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function. We propose a novel concept of activity subspace, and estimate the input function by the analysis of the intersection of the activity subspaces. Then, the input function and the distribution volume (DV) parameter are refined and estimated iteratively. Thus, the underlying parametric image of the total DV is obtained. The proposed method is compared with a blind estimation method, iterative quadratic maximum-likelihood (IQML) via simulation, and the proposed method outperforms IQML. The proposed method is also evaluated in a brain PET dataset.
Adaptive Channel Estimation Using Pilot-Embedded Data-Bearing Approach for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems employing coherent receivers crucially require channel state information (CSI). Since the multipath delay profile of channels is arbitrary in the MIMO-OFDM systems, an effective channel estimator is needed. In this paper, we first develop a pilot-embedded data-bearing (PEDB) approach for joint channel estimation and data detection, in which PEDB least-square (LS) channel estimator and maximum-likelihood (ML) data detection are employed. Then, we propose an LS fast Fourier transform (FFT)-based channel estimator by employing the concept of FFT-based channel estimation to improve the PEDB-LS one via choosing a certain number of significant taps for constructing a channel frequency response. The effects of model mismatch error inherent in the proposed LS FFT-based estimator when considering noninteger multipath delay profiles and its performance analysis are investigated. The relationship between the mean-squared error (MSE) and the number of chosen significant taps is revealed, and hence, the optimal criterion for obtaining the optimum number of significant taps is explored. Under the framework of pilot embedding, we further propose an adaptive LS FFT-based channel estimator employing the optimum number of significant taps to compensate the model mismatch error as well as minimize the corresponding noise effect. Simulation results reveal that the adaptive LS FFT-based estimator is superior to the LS FFT-based and PEDB-LS estimators under quasi-static channels or low Doppler's shift regimes
Content-Aware Cooperative Multiple Access Protocol for Packet Speech Communications

A novel cooperative multiple access protocol for packet speech communications is proposed. Cooperation is achieved through a relay node and by exploiting the silence periods during speech communication. The relay forwards packets for the active calls during some of the time slots that are free from those users which are silent. Therefore, no extra channel resources are needed for cooperation and the system encounters no bandwidth losses. A Markov model characterizing the network operation is built and thoroughly analyzed. The throughput and delay performance of the proposed protocol are characterized and compared to a similar non-cooperative protocol for speech communications.
Diffusion analysis of distributed adaptive networks with graphical evolutionary game

Most existing distributed adaptive filtering algorithms focus on de- signing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. However, due to the natural selfishness and rationality, nodes in a distributed network may not be willing to follow a predefined rule. Instead, they tend to follow some nature evolutionary rule. Therefore, it is ample of importance to study the information diffusion problem in a distributed network from the evolutionary game theoretic perspective. In this paper, we analyze the information diffusion over the adaptive net- work with regular graph and derive the close-form expression for the diffusion probability using graphical evolutionary game theory. Simulation are conducted to verify the effectiveness of our analysis.