nils-herrmann/clean_scirepeval huggingface dataset:
Image Source Coding Forensics via Intrinsic Fingerprints

In this digital era, digital multimedia contents are often transmitted over networks without any protection. This raises serious security concerns since the receivers/subscribers do not know what processes have been applied to multimedia data, and neither do they know whether this copy comes from a trusted source. Therefore, it is critical to provide forensic tools to identify the history of operations applied to multimedia data. In this paper, we focus on the identification of source coding techniques applied to multimedia, and we investigate the forensic analysis of transform based coding (both DCT and DWT based), subband coding, and linear predictive coding. Using the intrinsic fingerprints as trace of evidences, we construct an image source coding forensic system that analyzes which source encoder is used to compress the image and provides confidence measurements. Our simulation results show that the proposed system provides trustworthy performance: the probability of detecting the correct source encoder is 0.82 when PSNR = 40 dB, and it can correctly identify the source encoder with probability 0.98 with PSNR = 20dB.
A fully pipelined parallel CORDIC architecture for half-pel motion estimation

Based on the concept of pseudo-phase and the sinusoidal orthogonal principles, we design a novel low-complexity and high throughput CORDIC (COordinate Rotation Digital Computer) architecture for half-pel motion estimation. The proposed multiplier-free and fully-pipelined parallel architecture works solely at DCT domain without interpolation of input images to meet the needs of high-quality high-bit rate video communications. In addition, the DCT-based nature enables us to efficiently combine the DCT and motion estimator into one single component of relatively low complexity. Its regular and modular structure along with only local interconnection provides a low-complexity solution for MPEG-2 and H.263 compatible video codec design on a dedicated single chip.
Channel-aware priority transmission scheme using joint channel estimation and data loading for OFDM systems

This paper presents a data-loading technique that jointly considers the effect of channel estimation and the property of encoded multimedia data in Orthogonal Frequency Division Multiplexing (OFDM) systems. We observe that OFDM subchannels experience different average bit error rate (BER) due to channel estimation inaccuracy. The leakage effect in the fast Fourier transform (FFT)-based channel estimation method or the model mismatch in the polynomial-based channel estimation method results in a variation on the decoded BER across different OFDM subchannels. Thus, we are motivated to design the Priority Transmission (PT) scheme, which utilizes this BER variation across different OFDM subchannels and provides unequal error protection (UEP) for multimedia transmission. In addition, since OFDM has been adopted in many multimedia transmission standards, we compare the different channel estimation techniques, which were compared only for generic data transmission before, in the context of multimedia transmission with the PT scheme. In particular, we extend the polynomial-based channel estimation that was previously designed for a decision-directed scenario to a pilot-symbol-assisted (PSA) channel estimation scenario. Then, we investigate the channel estimation mean square error (MSE) and BER performance of individual OFDM subchannels for both the FFT-based and the polynomial-based channel estimation. Furthermore, we design the PT scheme that achieves significant gain in peak-signal-to-noise ratio (PSNR) of the reconstructed images for both channel estimation methods. Finally, we compare different OFDM channel estimation techniques for multimedia transmission. It is shown that for generic data transmission, the polynomial-based PSA channel estimation outperforms the FFT-based method in realistic channel conditions, and both types of channel estimation have similar performance when using the proposed PT scheme for multimedia transmission.
Employing cooperative diversity for performance enhancement in UWB communication systems

Due to limitation on transmitted power level, any UWB system faces major design challenges in achieving wide coverage while assuring an adequate system performance. In this paper, an employment of cooperative communications in UWB is proposed to enhance the performance and the coverage of UWB by exploiting the broadcasting nature of wireless channels and the cooperation among UWB devices. Symbol-error-rate (SER) performance analysis and optimum power allocation are provided for cooperative UWB multiband OFDM systems with decode-and-forward cooperative protocol. To capture the multipath-clustering phenomenon of UWB channels, the SER performance is characterized in terms of cluster and ray arrival rates. An optimum power allocation is determined based on two different objectives, namely minimizing the overall transmitted power and maximizing the system coverage. Furthermore, an improved cooperative UWB multiband OFDM scheme is proposed to take advantage of unoccupied subbands. Simulation results are shown to validate the theoretical analysis
Privacy or Utility in Data Collection? A Contract Theoretic Approach

With the growing popularity of data mining, privacy has become an issue of growing importance. Privacy can be seen as a special type of goods, in a sense that it can be traded by the owner for incentives. In this paper, we consider a private data collecting scenario where a data collector buys data from multiple data owners and employs anonymization techniques to protect data owners' privacy. Anonymization causes a decline of data utility; therefore, the data owner can only sell his data at a lower price if his privacy is better protected. Can one pursue higher data utility while maintaining acceptable privacy? How to balance the trade-off between privacy protection and data utility is an important question for big data. Considering that different data owners treat privacy differently, and their privacy preferences are unknown to the collector, we propose a contract theoretic approach for data collector to deal with the trade-off. By designing an optimal contract, the collector can make rational decisions on how to pay the data owners, and more importantly, how he should protect the owners' privacy. We show that when the collector requires a large amount of data, he should ask data owners who care privacy less to provide as much as possible data. We also find that whenever the collector requires higher utility of data or the data becomes less profitable, the collector should provide a stronger protection of the owners' privacy. Performance of the proposed contract is evaluated by both numerical simulations and real data experiments.
Simultaneous estimation of kinetic parameters and the input function from DCE-MRI data: theory and simulation

DCE-MRI is a noninvasive functional imaging technique capable of assessing tumor microvasculature clinically. Major limitations associated with conventional region-of-interest (ROI) based compartmental methods include the requirement of invasive acquisition of the input function and labor-intensive identification of the ROIs. We propose a novel blind system identification approach for quantitative imaging by simultaneously estimating the input function and the kinetic parameters. New statistical model is on the pixel domain, whose parameters are initialized using a sub-space based algorithm, and further refined by an iterative maximum likelihood estimation procedure. The performances of the proposed scheme is examined extensively via simulations. The real breast tumor DCE-MRI data are examined by determining the time activity curves and the underlying factor images.
An iterative auction mechanism for data trading

In the big data era, it is vital to allocate the vast amount of data to various users efficiently. However, the data agents (data owners, collectors and users) are selfish and seek to maximize their own utilities instead of the overall system efficiency. In this paper, the data trading problem of a data market with multiple data owners, collectors and users is formulated and an iterative auction mechanism is proposed to coordinate the data trading. The proposed mechanism guilds the selfish data agents to trade data efficiently and avoids direct access of the agents' private information. We theoretically prove that the proposed mechanism can achieve the socially optimal operation point. Moreover, we demonstrate that the mechanism satisfies appealing economic properties such as individual rationality and weakly balanced budget. Simulations as well as real data experiments validate the theoretical properties of the mechanism.
A scalable collusion-resistant multi-winner cognitive spectrum auction game

Dynamic spectrum access (DSA), enabled by cognitive radio technologies, has become a promising approach to improve efficiency in spectrum utilization, and the spectrum auction is one important DSA approach, in which secondary users lease some unused bands from primary users. However, spectrum auctions are different from existing auctions studied by economists, because spectrum resources are interference-limited rather than quantity-limited, and it is possible to award one band to multiple secondary users with negligible mutual interference. To accommodate this special feature in wireless communications, in this paper, we present a novel multi-winner spectrum auction game not existing in auction literature. As secondary users may be selfish in nature and tend to be dishonest in pursuit of higher profits, we develop effective mechanisms to suppress their dishonest/collusive behaviors when secondary users distort their valuations about spectrum resources and interference relationships. Moreover, in order to make the proposed game scalable when the size of problem grows, the semi-definite programming (SDP) relaxation is applied to reduce the complexity significantly. Finally, simulation results are presented to evaluate the proposed auction mechanisms, and demonstrate the complexity reduction as well.
Resistance of orthogonal Gaussian fingerprints to collusion attacks

Digital fingerprinting is a means to offer protection to digital data by which fingerprints embedded in the multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We propose a likelihood-based approach to estimate the number of colluders, and introduce the thresholding detector for colluder identification. We first analyze the collusion resistance of a system to the average attack by considering the probability of a false negative and the probability of a false positive when identifying colluders. Lower and upper bounds for the maximum number of colluders K/sub max/ are derived. We then show that the detectors are robust to different attacks. We further study different sets of performance criteria.
Design of space-time trellis codes for full spatial diversity

Space-time codes have been developed to answer the need for high data rates in future wireless communication systems. In this work, we propose a systematic code construction method that provides full diversity advantages for any number of transmit antennas, any number of encoder states, and any constellation. Our approach is to exploit the properties of the state transitions in the trellis to force the desired structure on the channel symbol difference matrix. The design rules do not specify the space-time codes completely, leaving room for further optimization for coding advantage.
Algorithm-based low-power DSP system design: methodology and verification

We present a low-power design methodology based on the multirate approach for DSP systems. Since the data rate in the resulting multirate implementation is M-times slower (where M is a positive integer) than the original data rate while maintaining the same throughput rate, we can apply this feature to either the low-power implementation, or the speed-up of the DSP systems. This design methodology provides VLSI designers with a systematic tool to design low-power DSP systems at the algorithmic/architectural level. The proposed low-power multirate design scheme is verified by the implementation of two FIR VLSI chips with different architectures: one is the normal pipelined design and the other is the multirate FIR design with downsampling rate equal to two. Using the CMOS power dissipation model, we can predict that the multirate FIR chip consumes only 29% power of the normal FIR chip given the same throughput rate. The predicted results will be verified by measuring real power consumption of both chips.
Attack-resistant cooperation strategies in P2P live streaming social networks

Multimedia social networks have become an emerging research area, in which analysis and modeling of the behavior of users who share multimedia are of ample importance in understanding the impact of human dynamics on multimedia systems. In peer-to-peer live-streaming social networks, users cooperate with each other to provide a distributed, highly scalable and robust platform for live streaming applications. However, every user wishes to use as much bandwidth as possible to receive a high-quality video, while full cooperation cannot be guaranteed. This paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in peer-to-peer live streaming. We first analyze the Nash equilibrium and the Pareto optimality of 2-person game and then extend to multiuser case. We also take into consideration selfish users' cheating behavior and malicious users' attacking behavior. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free, attack resistance and help to provide reliable services.
A self-learning repeated game framework for optimizing packet forwarding networks

For networks with packet forwarding, distributed control to enforce cooperation for node packet forwarding probabilities is essential to maintain the connectivity. In this paper, we propose a novel self-learning repeated game framework to optimize packet forwarding probabilities of distributed users. The framework has two major steps: first, an adaptive repeated game scheme ensures the cooperation among users for the current cooperative packet forwarding probabilities; second, a self-learning scheme tries to find better cooperation probabilities. Some special cases are analyzed to evaluate the proposed framework. From the simulation results, the proposed framework demonstrates the near optimal solutions in both symmetrical and asymmetrical networks.
Downlink beamforming for DS-CDMA mobile radio with multimedia services

Downlink beamforming is a promising technique for DS-CDMA systems with multimedia services to effectively reduce strong interferences induced by high data rate users. In this paper, we first proposed a new downlink beamforming algorithm which converts the downlink beamforming problem to a virtual uplink one and takes into account the data rate information of all users. Since the main complexity of this algorithm is due to existence of the multipath effect, a simplified algorithm is suggested using an equivalent one-path channel vector (EOCV) to replace multipath channel vectors. Computer simulation results are given to evaluate downlink capacity of DS-CDMA systems using a base station antenna array and the new algorithms proposed in this paper.
Performance analysis of time hopping and direct sequence UWB space-time systems

In this paper, we analyze the performance of ultra-wideband (UWB) multiple-input multiple-output (MIMO) systems employing various modulation and multiple access schemes, including time hopping (TH) binary pulse position modulation (BPPM), TH binary phase shift keying (BPSK), and direct sequence (DS) BPSK. We quantify the performance merits of UWB space-time (ST) systems regardless of the specific coding technique. We introduce a framework that enables us to compare UWB-MIMO systems with conventional UWB single-antenna systems in terms of diversity and coding gains. We show that the combination of ST coding and Rake architecture is capable of exploiting spatial diversity as well as multipath diversity, richly inherent in UWB environments. We find the upper bound on the average pairwise error probability (PEP) under the hypothesis of quasi-static Nakagami-m frequency selective fading channels. Finally, simulation results are presented to support the theoretical analysis.
Anti-Jamming Games in Multi-Channel Cognitive Radio Networks

Crucial to the successful deployment of cognitive radio networks, security issues have begun to receive research interests recently. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks. Secondary users can exploit the flexible access to multiple channels as the means of anti-jamming defense. We first investigate the situation where a secondary user can access only one channel at a time and hop among different channels, and model it as an anti-jamming game. Analyzing the interaction between the secondary user and attackers, we derive a channel hopping defense strategy using the Markov decision process approach with the assumption of perfect knowledge, and then propose two learning schemes for secondary users to gain knowledge of adversaries to handle cases without perfect knowledge. In addition, we extend to the scenario where secondary users can access all available channels simultaneously, and redefine the anti-jamming game with randomized power allocation as the defense strategy. We derive the Nash equilibrium for this Colonel Blotto game which minimizes the worst-case damage. Finally, simulation results are presented to verify the performance.
Analysis and Protection of Dynamic Membership Information for Group Key Distribution Schemes

In secure group-oriented applications, key management schemes are employed to distribute and update keys such that unauthorized parties cannot access group communications. Key management, however, can disclose information about the dynamics of group membership, such as the group size and the number of joining and departing users. This is a threat to applications with confidential group membership information. This paper investigates techniques that can stealthily acquire group dynamic information from key management. We show that insiders and outsiders can successfully obtain group membership information by exploiting key establishment and key updating procedures in many popular key management schemes. Particularly, we develop three attack methods targeting tree-based centralized key management schemes. Further, we propose a defense technique utilizing batch rekeying and phantom users, and derive performance criteria that describe security level of the proposed scheme using mutual information. The proposed defense scheme is evaluated based on the data from MBone multicast sessions. We also provide a brief analysis on the disclosure of group dynamic information in contributory key management schemes
A game-theoretic framework for multi-user multimedia rate allocation

How to efficiently and fairly allocate data rate among different users is a key problem in the field of multiuser multimedia communication. However, most of the existing optimization-based methods, such as minimizing the weighted sum of the distortions or maximizing the weighted sum of the PSNRs, have their weights heuristically determined. Moreover, those approaches mainly focus on the efficiency issue while ignoring the fairness issue. In this paper, we address this problem by proposing a game-theoretic framework, in which the utility/payoff function of each user/player is jointly determined by the characteristic of the transmitted video sequence and the allocated bitrate. We show that with the proportional fairness criterion, the game has a unique Nash equilibrium, according to which the controller can efficiently and fairly allocate the available network bandwidth to the users. Finally, we show several experimental results on real video data to verify the proposed method.
Femto-macro co-channel interference coordination via pricing game

Recently, intercell interference coordination in het-erogenous networks attracts great attention. This paper presents an analytical framework to evaluate time mute scheme in closed access femto and macro co-existent networks. We use stochastic geometry to model the downlink scenario and derive the coverage probability of indoor macro users and femto users. Considering the selfishness of the owners of femtos, we formulate the two-tier interference coordination as pricing game, and obtain the closed-form of Nash equilibrium (NE). Simulation results demonstrate the influences of different parameters on the coverage probability of macro users achieved at the NE of the pricing game.
Robust Median Filtering Forensics Using an Autoregressive Model

In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering forensic technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.
Wireless network cocast: location-aware cooperative communications with linear network coding

In wireless networks, reducing aggregate transmit power and in many cases, having even power distribution increase the network lifetime. Conventional direct transmission (DTX) scheme results in high aggregate transmit power and uneven power distribution. In this paper, we consider location-aware cooperation-based schemes namely immediate-neighbor cooperation (INC), maximal cooperation (MAX), and wireless network cocast (WNC) that achieve spatial diversity to reduce aggregate transmit power and even power distribution. INC utilizes twouser cooperative communication, resulting in good reduction of aggregate transmit power and low transmission delay; however, power distribution is still uneven. MAX utilizes multi-node cooperative communication, providing incremental diversity to achieve even power distribution and substantial reduction in aggregate transmit power. Transmission delay in MAX, however, grows quadratically with network sizes. As a result, the novel WNC is proposed to achieve incremental diversity as in MAX and low transmission delay as in INC. In WNC, mobile units acting as relays form unique linearly-coded signals from overheard signals and transmit them to the destination, where a multiuser detector jointly detects the symbols from all received signals. Performance evaluation in uniformly distributed networks shows that INC, MAX, and WNC substantially reduce aggregate transmit power while MAX and WNC also provide even power distribution.