nils-herrmann/clean_scirepeval huggingface dataset:
Behavior forensics for scalable multiuser collusion: fairness versus effectiveness

Multimedia security systems involve many users with different objectives and users influence each other's performance. To have a better understanding of multimedia security systems and offer stronger protection of multimedia, behavior forensics formulate the dynamics among users and investigate how they interact with and respond to each other. This paper analyzes the behavior forensics in multimedia fingerprinting and formulates the dynamics among attackers during multi-user collusion. In particular, this paper focuses on how colluders achieve the fair play of collusion and guarantee that all attackers share the same risk (i.e., the probability of being detected). We first analyze how to distribute the risk evenly among colluders when they receive fingerprinted copies of scalable resolutions due to network and device heterogeneity. We show that generating a colluded copy of higher resolution puts more severe constraints on achieving fairness. We then analyze the effectiveness of fair collusion. Our results indicate that the attackers take a larger risk of being captured when the colluded copy has higher resolution, and they have to take this tradeoff into consideration during collusion. Finally, we analyze the collusion resistance of the scalable fingerprinting systems in various scenarios with different system requirements, and evaluate the maximum number of colluders that the fingerprinting systems can withstand
Many-to-many communications via space-time network coding

The mutual cooperative communication between multiple nodes in a wireless network is efficiently achieved through a novel concept of Space-Time Network Coding (STNC). Unlike the conventional point-to-point cooperative communications between two nodes with N relay nodes deployed in between, simultaneous transmissions from the different N nodes acting as source/relay nodes are performed within 2N time-slots. In particular, the communication is split into two phases: 1) Broadcasting Phase and 2) Cooperation Phase. In the Broadcasting Phase, each node broadcasts its data symbol to the other nodes in the network in its own time-slot, alternatively; while in the Cooperation Phase, in each time-slot, a set of (N-1) nodes transmit while a single destination node receives the other nodes' transmissions. Specifically, each node employing a selective Decode-and-Forward (DF) cooperative protocol, performs a linear combination of the other nodes data symbols and all the (N-1) nodes simultaneously transmit their signals to a single receiving node; which then performs joint multiuser detection to separate the different nodes' symbols. Exact symbol-error-rate (SER) expressions for arbitrary order M-ary Phase Shift Keying (M-PSK) modulation are derived. In addition, an asymptotic SER approximation is also provided which is shown to be tight at high signal-to-noise ratio (SNR). Finally, the analytical results confirm that for a network of N nodes, a full diversity order of (N-1) per node is achieved by the proposed STNC cooperative communication scheme.
The hierarchical timing pair model

We present a new model for representing timing information for functions in High-Level Synthesis (HLS). We identify shortcomings of the conventional timing model, which is a very simple model derived from the combinational logic model, and show that our new model overcomes many of these defects. In particular, we are able to provide a unified timing model that describes hierarchical combinational and iterative circuits and provides a compact representation of the information, that can be used to streamline system performance analysis. We present experimental results that demonstrate the effectiveness of our new approach, and describe an efficient algorithm to easily compute the required timing parameters from a description of the graph.
Mitigating Error Propagation for Wireless Network Coding

Network coding has been widely used in wireless uplink to improve spectral efficiency. However, the relay decoding error may propagate to the intended receiver and thus severely degrade the diversity performance. So in this work, we develop two power scaling schemes at the relay side and two detection schemes at the receiver side, respectively, to mitigate error propagation and thus achieve full diversity for the two-user multiple access relay network. For the soft power scaling based link adaptive relaying, we develop a virtual source-relay-destination channel model and demonstrate that the relay power should be such to balance the signal-to-noise ratios of the source-relay channel and relay-destination channel. As for the hard power scaling based ON-OFF relaying, we first design a decision rule based on total pairwise error probability, and then simplify it to the threshold-based relaying strategy. At the receiver side, we show that the weighted minimum distance detection with the weight being determined by the relative link quality of source-relay channel and relay-destination channel can achieve full diversity once the global channel state information is available, otherwise the maximum likelihood detection should be employed to achieve full diversity when the receiver only knows the local channel state information.
A Subspace Signal Processing Technique for Concealed Weapons Detection

Concealed weapons detection is one of the greatest challenges facing national security nowadays. Recently, it has been shown that each weapon can have a unique fingerprint, which is a set of electromagnetic (EM) resonant frequencies determined by its size, shape, and physical composition. Extracting the resonant frequencies of each weapon is one of the major tasks of any detection system. In this paper, we model the reflected signal from each object as a summation of sinusoidal signals, each at certain frequency equal to one of the object's resonant frequencies. Using this model, we propose a detection approach that is based on a modified version of the multiple signal classification (MUSIC) algorithm. We show by simulations that each object can be represented using a two-dimensional vector, which consists of its two major resonant frequencies.
Non-cooperative resource competition game by virtual referee in multi-cell OFDMA networks

In this paper, a distributive non-cooperative game is proposed to perform sub-channel assignment, adaptive modulation, and power control for multi-cell multi-user orthogonal frequency division multiplexing access (OFDMA) networks. Each individual user's goal is to minimize his/her own transmitted power in a distributed manner under the constraints that the desirable rate is achieved and the transmitted power is bounded. The pure non-cooperative game may result in non-convergence or some undesirable Nash Equilibriums with low system and individual performances. To enhance the performances, a virtual referee is introduced to the networks and is in charge of monitoring and improving the outcome of non-cooperative competition for resources among the distributed users. If the game outcome is not desirable, either the required transmission rates should be reduced or some users should be prevented from using some radio resources such as sub-channels, so that the rest of users can share the limited resources more efficiently. Moreover, it can be shown that the introduction of the virtual referee does not increase the complexity of the networks. From the simulation results in a two-cell case, the proposed scheme reduces the transmitted power by 80% and 25% compared with the fixed channel assignment algorithm and the iterative water-filling algorithm in the literature, respectively. The achievable rate can be improved by 10%. In a multi-cell case, the proposed scheme can have up to 40% power reduction compared with the iterative water-filling algorithm when the co-channel interferences are severe.
Maximum connectivity and maximum lifetime energy-aware routing for wireless sensor networks

In this paper, we consider the energy-aware routing algorithm that explicitly takes into account the connectivity of the sensor networks. In typical sensor network deployment, some nodes may be more important than other nodes because the death of these nodes cause the network disintegration. The network disintegration causes early termination of information delivery. To overcome this problem, we propose a routing algorithm called keep connect algorithm that explicitly considers the connectivity of the network while making the routing decision. The algorithm can be used along with the existing routing algorithms. When doing the routing decision, the keep connect algorithm embeds the importance of the nodes in the routing cost. The importance of a node is quantified by how severe the remaining network becomes disconnected/disintegrated when that particular node dies. In particular, the importance of a node is characterized by the algebraic connectivity of the remaining graph. Compared to the existing routing algorithms, the proposed method achieves up to two times improvement in terms of the network lifetime and the number of successfully delivered packets in the special grid network. In random network, the proposed algorithm achieves 20% improvement of network lifetime, 30% less energy per packet, and 33% more successfully delivered packets.
Evolutionary game for joint spectrum sensing and access in cognitive radio networks

Many spectrum sensing and access algorithms have been proposed to improve secondary users' (SUs') opportunities of utilizing primary spectrums. However, most of them have separated the analysis of spectrum sensing and access. In this paper, we propose to integrate the design of spectrum sensing and access algorithms by taking into account the mutual influence of them. Due to selfish natures, SUs tend to access the primary channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among SUs, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for SUs to converge to the ESS. With the proposed algorithm, each SU senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of selfish SUs.
Forensic identification of compressively sensed images

Due to the ease with which digital images can be forged, a great deal of work has been done in the field of digital image forensics. A particularly important problem is to forensically determine an image's acquisition and storage history. Recent research has shown that a new acquisition technique known as compressive sensing can be used to capture a digital image. Images acquired by compressive sensing can not be easily forensically distinguished from the images captured using standard digital cameras, nor from those which have undergone Discrete Wavelet Transform (DWT) based compressions. In this paper, we propose a two step detection scheme to identify the images acquired by compressive sensing. The first step identifies unaltered images captured using standard digital cameras and the second step separates compressively sensed images from ones compressed using DWT-based techniques. Our simulations show the first step of detection can achieve a probability of detection (Pd) of 100% for probability of false alarm (Pf) less than 4%, and the second step gets Pd nearly 90% with Pf of 10%.
Blind adaptive spatial-temporal equalization algorithms for wireless communications using antenna arrays

Antenna arrays are used for diversity reception so as to improve the quality of wireless communication systems. To make full use of the information contained in each sensor, a spatial-temporal equalizer is used to remove intersymbol interference and mitigate the additive channel noise. This equalizer can be used in the wireless communication systems to improve the performance. This article introduces two blind adaptive algorithms for spatial-temporal equalization. Computer simulation demonstrates that the new algorithms converge faster than fractionally spaced constant-modulus algorithm (FS-CMA).
Algorithm-based low-power transform coding architectures: the multirate approach

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 to compensate the increased delays based on the multirate approach. We apply the technique of polyphase decomposition to design low-power transform coding architectures, in which the transform coefficients are computed through decimated low-speed input sequences. Since the operating frequency is M-times slower than the original design while the system throughput rate is still maintained, the speed penalty can be compensated at the architectural level. We start with the design of low-power multirate discrete cosine transform (DCT)/inverse discrete cosine transform (IDCT) VLSI architectures. Then the multirate low-power design is extended to the modulated lapped transform (MLT), extended lapped transform (ELT), and a unified low-power transform coding architecture. Finally, we perform finite-precision analysis for the multirate DCT architectures. The analytical results can help us to choose the optimal wordlength for each DCT channel under required signal-to-noise ratio (SNR) constraint, which can further reduce the power consumption at the circuit level. The proposed multirate architectures can also be applied to very high-speed block discrete transforms in which only low-speed operators are required.
Compressive Sensing Forensics

Identifying a signal's origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal's acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed. This is an important problem since compressive sensing techniques have seen increased popularity in recent years. In this paper, we propose a set of forensic techniques to identify signals acquired by compressive sensing. We do this by first identifying the fingerprints left in a signal by compressive sensing. We then propose two compressive sensing detection techniques that can operate on a broad class of signals. Since compressive sensing fingerprints can be confused with fingerprints left by traditional image compression techniques, we propose a forensic technique specifically designed to identify compressive sensing in digital images. In addition, we propose a technique to forensically estimate the number of compressive measurements used to acquire a signal. Through a series of experiments, we demonstrate that each of our proposed techniques can perform reliably under realistic conditions. Simulation results show that both our zero ratio detector and distribution-based detector yield perfect detections for all reasonable conditions that compressive sensing is used in applications, and the specific two-step detector for images can at least achieve probability of detection of 90% for probability of false alarm <;10%. In addition, our estimator for the number of compressive measurements can well reflect the real number.
Cooperation enforcement and learning for optimizing packet forwarding in autonomous wireless networks

In wireless ad hoc networks, autonomous nodes are reluctant to forward others' packets because of the nodes' limited energy. However, such selfishness and noncooperation deteriorate both the system efficiency and nodes' performances. Moreover, the distributed nodes with only local information may not know the cooperation point, even if they are willing to cooperate. Hence, it is crucial to design a distributed mechanism for enforcing and learning the cooperation among the greedy nodes in packet forwarding. In this paper, we propose a self- learning repeated-game framework to overcome the problem and achieve the design goal. We employ self-transmission efficiency as the utility function of individual autonomous node. The self transmission efficiency is defined as the ratio of the power for self packet transmission over the total power for self packet transmission and packet forwarding. Then, we propose a framework to search for good cooperation points and maintain the cooperation among selfish nodes. The framework has two steps: First, an adaptive repeated game scheme is designed to ensure the cooperation among nodes for the current cooperative packet forwarding probabilities. Second, self-learning algorithms are employed to find the better cooperation probabilities that are feasible and benefit all nodes. We propose three learning schemes for different information structures, namely, learning with perfect observability, learning through flooding, and learning through utility prediction. Starting from noncooperation, the above two steps are employed iteratively, so that better cooperating points can be achieved and maintained in each iteration. From the simulations, the proposed framework is able to enforce cooperation among distributed selfish nodes and the proposed learning schemes achieve 70% to 98% performance efficiency compared to that of the optimal solution.
Time-Reversal Wireless Paradigm for Green Internet of Things: An Overview

In this paper, we present an overview of the time-reversal (TR) wireless paradigm for green Internet of Things (IoT). It is shown that the TR technique is a promising technique that focuses signal waves in both time and space domains. The unique asymmetric architecture significantly reduces the cost of the terminal devices, the total number of which is expected to be very large for IoT. The focusing effect of the TR technique can harvest the energy of all the multi-paths at the receiver, which improves the energy efficiency of the wireless transmission and thus the battery life of terminal devices in IoT. Facilitated by the high-resolution spatial focusing, the TR division multiple access scheme leverages the uniqueness of the multi-path profiles in the rich-scattering environment and maps them into location-specific signatures, so that spatial multiplexing can be achieved for multiple users operating on the same spectrum. In addition, the TR system can easily support heterogeneous terminal devices by providing various quality-of-service (QoS) options through adjusting the waveform and rate backoff factor. Finally, the unique location-specific signature in TR system can provide additional physical-layer security and thus can enhance the privacy and security of customers in IoT. All the advantages show that the TR technique is a promising paradigm for IoT.
Joint Design of Spectrum Sensing and Channel Access in Cognitive Radio Networks

Spectrum sensing is an essential functionality of cognitive radio networks. However, the effect of errors in the spectrum sensing process on the performance of the multiple access layer of both primary and secondary networks has not gained much attention. This paper aims at bridging the gap between the study of spectrum sensing and the multiple access of cognitive radio networks. To achieve this goal we pose and answer the question how the spectrum sensing errors affects the performance of cognitive radio networks from a multiple access protocol design point of view. The negative effects of the spectrum sensing errors on the throughput of both primary and secondary networks are characterized through queuing theory analysis of both networks. To alleviate these negative effects a novel joint design of the spectrum sensing and channel access mechanisms is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistics could be used as a confidence measure for the test outcome. Therefore, this value will be used to define different channel access probabilities for secondary users. Results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks by virtue of the proposed technique.
Cooperation and Coalition in Multimedia Fingerprinting Colluder Social Networks

Users in multimedia social networks actively interact with each other. It is crucial to study the complex user dynamics and analyze its impact on the performance of multimedia social networks. This paper uses multimedia fingerprinting as an example and studies user dynamics in colluder social networks. During collusion, a group of attackers collectively attack multimedia fingerprinting system and use multimedia content illegally. This paper analyzes the incentives of cooperation among attackers and investigates how colluders form their coalitions to maximize their payoffs. We present a game-theoretic framework to model the complex dynamics among colluders, analyze when attackers cooperate with each other, and investigate how a colluder selects his/her fellow attackers to maximize his/her own payoff. We analyze multiuser collusion in two scenarios: when all attackers receive fingerprinted copies of the same resolution, and when they have copies of different resolutions. The proposed framework considers both the colluders' risk of being detected by the digital rights enforcer and the reward received from illegal usage of multimedia content. Our analysis shows that in both scenarios, colluding with more attackers does not always increase an attacker's utility, and attackers may not always want to cooperate with each other. We first examine the necessary conditions for attackers to collude together, and study how they select the collusion parameters such that cooperation benefits all colluders. We then study how the number of colluders affects each attacker's utility, and investigate the optimum strategy that an attacker should use to select fellow attackers and to form a coalition in order to maximize his or her own payoff.
Cooperative multimedia communications: joint source coding and collaboration

Cooperative diversity exploits the broadcast nature of wireless channels by allowing users to relay information for each other so as to create multiple signal paths. This paper analyzes what is the best strategy from the viewpoint of a resource allocation protocol, to match source coding with cooperation diversity for conversational multimedia communications by studying the distortion performance for different schemes. The results show that the best performance is obtained when all layers of a layered-coded source are sent with user cooperation (using decode-and-forward in most cases) if the source-destination channel is bad, and with no user cooperation, if the source-destination channel is good. The results also show that the gains from cooperative diversity outweigh the loss due to the sacrifice in overall bandwidth and that cooperation performance is sensitive to the proportion of communication capacity allocated for cooperation.