nils-herrmann/clean_scirepeval huggingface dataset:
Road vehicle speed estimation from a two-microphone array

A novel car speed estimation technique from a pair of omnidirectional microphones is presented. This approach is based on the maximum likelihood principle and it directly estimates the speed without any assumptions on the acoustic signal emitted by the vehicle. This has the advantages of bypassing troublesome intermediate delay estimation steps as well as eliminating the need for an accurate yet general enough acoustic model. An analysis of the estimate for the particular case of a narrowband source is provided. The estimation algorithm is well suited to DSP implementation and performs well with preliminary field data.
An Adaptive Feedback Canceller for Full-Duplex Relays Based on Spectrum Shaping

Although full-duplex relaying schemes are appealing in order to improve spectral efficiency, simultaneous reception and transmission in the same frequency results in self-interference, distorting the retransmitted signal and making the relay prone to oscillation. Current feedback cancellation techniques by means of adaptive filters are hampered by the fact that the useful and interference signals are highly correlated. We present a new adaptive algorithm which effectively and blindly restores the spectral shape of the desired signal. In contrast with previous schemes, the novel adaptive feedback canceller has low complexity, does not introduce additional delay in the relay station, and partly compensates for multipath propagation.
Performance analysis of existing and new methods for data hiding with known-host information in additive channels

A considerable amount of attention has been paid lately to a number of data hiding methods based in quantization, seeking to achieve in practice the results predicted by Costa (1983) for a channel with side information at the encoder. With the objective of filling a gap in the literature, this paper supplies a fair comparison between significant representatives of both this family of methods and the former spread-spectrum approaches that make use of near-optimal ML decoding; the comparison is based on measuring their probabilities of decoding error in the presence of channel distortions. Accurate analytical expressions and tight bounds for the probability of decoding error are given and validated by means of Monte Carlo simulations. For dithered modulation (DM), a novel technique that allows us to obtain tighter bounds to the probability of error is presented. Within the new framework, the strong points and weaknesses of both methods are distinctly displayed. This comparative study allows us to propose a new technique named "quantized projection" (QP), which, by adequately combining elements of those previous approaches, produces gains in performance.
Revealing the true achievable rates of scalar Costa scheme

By abandoning the assumption of an infinite document to watermark ratio, we recompute the achievable rates for Egger's scalar Costa scheme (SCS, also known as scalar distortion compensated dither modulation) and show, as opposed to the results reported by Eggers, that the achievable rates of SCS are always larger than those of spread spectrum (SS). Moreover, we show that for small watermark to noise ratios, SCS equivalent to a two-centroid problem, thus revealing interesting relations with SS and with Malvar's improved spread spectrum (ISS). We also show an interesting behavior for the optimal distortion compensation parameter. All these results aim at filling an existing gap in watermarking theory and have important consequences for the design of efficient decoders for data hiding problems.
Statistical analysis of watermarking schemes for copyright protection of images

In this paper, we address the problem of the performance analysis of image watermarking systems that do not require the availability of the original image during ownership verification. We focus on a statistical approach to obtain models that can serve as a basis for the application of decision theory to the design of efficient detector structures. Special attention is paid to the possible nonexistence of a statistical description of the original image. Different modeling approaches are proposed for the cases when such a statistical characterization is known and when it is not. Watermarks may encode a message, and the performance of the watermarking system is evaluated using as a measure the probability of false alarm, the probability of detection when the presence of the watermark is tested, and the probability of error when the information that it carries is extracted. Finally, the modeling techniques studied are applied to the analysis of two watermarking schemes, one of them defined in the spatial domain, and the other in the direct cosine transform (DCT) domain. The theoretical results are contrasted with empirical data obtained through experimentation covering several cases of interest. We show how choosing an appropriate statistical model for the original image can lead to considerable improvements in performance.
A Least Squares approach to user profiling in pool mix-based anonymous communication systems

Deployed high-latency anonymous communication systems conceal communication patterns using pool mixes as building blocks. These mixes are known to be vulnerable to Disclosure Attacks that uncover persistent relationships between users. In this paper we study the performance of the Least Squares Disclosure Attack (LSDA), an approach to disclosure rooted in Maximum Likelihood parameter estimation that recovers user profiles with greater accuracy than previous work. We derive analytical expressions that characterize the profiling error of the LSDA with respect to the system parameters for a threshold binomial pool mix and validate them empirically. Moreover, we show that our approach is easily adaptable to attack diverse pool mixing strategies.
Hyperstable adaptive IIR algorithms with polyphase structures: analysis and design

We consider adaptive identification algorithms based on hyperstability concepts with polyphase structures. The SPR condition required for convergence of these schemes can be always met by using a sufficiently high polyphase expansion factor M. The degree of persistent excitation required for parameter convergence is obtained. If some a priori knowledge about the system to identify is available, a compensating filter can be designed to avoid the need for a high M.
Fingerprinting Tor's hidden service log files using a timing channel

Hidden services are anonymously hosted services that can be accessed over Tor, an anonymity network. In this paper we present an attack that allows an entity to prove, once a machine suspect to host a hidden server has been confiscated, that such machine has in fact hosted a particular content. Our solution is based on leaving a timing channel fingerprint in the confiscated machine's log file. In order to be able to fingerprint the log server through Tor we first study the noise sources: the delay introduced by Tor and the log entries due to other users. We then describe our fingerprint method, and analytically determine the detection probability and the rate of false positives. Finally, we empirically validate our results.
Analysis of the prewhitened constant modulus cost function

We provide an analysis of the constant modulus (CM) cost function under the assumption of a white equalizer input. This can be achieved by means of an adaptive prewhitening all-pole filter and has been suggested in previous works as a means for both MSE improvement and DFE cold start-up. For white inputs, it is seen that CM -optimizing the spherical component of the equalizer parameter vector is equivalent to minimizing the fourth moment of its output, regardless of the value of the radial component. This leads to an eigenvector interpretation of prewhitened CM receivers, and to a blind initialization procedure from an eigenvector of the quadri-covariance matrix of the whitened data. Connections with iterative eigenvector-based schemes are also explored.
Improving data hiding performance by using quantization in a projected domain

The quantization of a linear projective transformation first proposed by Chen and Wornell is shown to allow for much better performance figures than those yielded by previous approaches. The procedure to achieve this improvement is explained through the proposal and analysis of an improved data hiding method called quantized projection (QP), based in the quantization of a statistic similar to those used at detection in spread-spectrum algorithms. Both the theoretical analysis and the empirical validation show that projection-based methods exhibit huge performance improvements over existing ones under the same conditions - i.e. same degree of diversity and level of random additive attacking distortion.
The flow fingerprinting game

Linking two network flows that have the same source is essential in intrusion detection or in tracing anonymous connections. To improve the performance of this process, the flow can be modified (fingerprinted) to make it more distinguishable. However, an adversary located in the middle can modify the flow to impair the correlation by delaying the packets or introducing dummy traffic. We introduce a game-theoretic framework for this problem, that is used to derive the Nash Equilibrium. As obtaining the optimal adversary delays distribution is intractable, some approximations are done. We study the concrete example where these delays follow a truncated Gaussian distribution. We also compare the optimal strategies with other fingerprinting schemes. The results are useful for understanding the limits of flow correlation based on packet timings under an active attacker.
A Least Squares Approach to the Static Traffic Analysis of High-Latency Anonymous Communication Systems

Mixes, relaying routers that hide the relation between incoming and outgoing messages, are the main building block of high-latency anonymous communication networks. A number of so-called disclosure attacks have been proposed to effectively deanonymize traffic sent through these channels. Yet, the dependence of their success on the system parameters is not well-understood. We propose the least squares disclosure attack (LSDA), in which user profiles are estimated by solving a least squares problem. We show that LSDA is not only suitable for the analysis of threshold mixes, but can be easily extended to attack pool mixes. Furthermore, contrary to previous heuristic-based attacks, our approach allows us to analytically derive expressions that characterize the profiling error of LSDA with respect to the system parameters. We empirically demonstrate that LSDA recovers users' profiles with greater accuracy than its statistical predecessors and verify that our analysis closely predicts actual performance.
Quantized projection data hiding

We propose a novel data hiding procedure called quantized projection (QP), that combines elements from quantization (i.e. quantization index modulation, QIM) and spread-spectrum methods. The method is based on quantizing a diversity projection of the host signal, inspired in the statistic used for detection in spread-spectrum algorithms. We carry out a theoretical analysis of QP together with its empirical validation to show rigorously that it offers an excellent performance; QP features probabilities of decoding error several orders of magnitude lower than the aforementioned families of methods for the same dimensionality (diversity) and attacking distortion level. In addition, we introduce a Costa-based improvement (see Costa, M.H.M.,  1983) of the basic QP method named distortion compensated QP.
Filter design for delay-based anonymous communications

In this work, we address the problem of designing delay-based anonymous communication systems. We consider a timed mix where an eavesdropper wants to learn the communication pattern of the users, and study how the mix must delay the messages so as to increase the adversary's estimation error. We show the connection between this problem and a MIMO system where we want to design the coloring filter that worsens the adversary's estimation of the MIMO channel matrix. We obtain theoretical solutions for the optimal filter against short-term and long-term adversaries, evaluate them with experiments, and show how some properties of filters can be used in the implementation of timed mixes. This opens the door to the application of previously known filter design techniques to anonymous communication systems.
Overlay Cognitive Transmission in a Multicarrier Broadcast Network with Dominant Line of Sight Reception

The insertion of a secondary transmitter in a multicarrier broadcast single frequency network is studied. The secondary information is overlaid on top of the primary waveform, which is also reinforced by the secondary transmitter. The degradation of the primary service due to the presence of echoes in a strong line of sight environment is taken into account, and mitigated with an appropriate filtering at the secondary transmitter. The transmit rate of the secondary system is maximized while keeping the original primary coverage area, defined as a function of a BER bound. The analytical results are verified by means of software simulations and hardware tests, where the importance of the proposed filtering is clearly shown.
Overlay spectrum reuse in a multicarrier broadcast network: Coverage analysis

A secondary cognitive user overlaying its message in a broadcast multicarrier network is studied. The secondary user exploits the primary message knowledge to convey its own information while preserving the primary user coverage area, determined by a bound on the BER, and taking into account the degradation due to the insertion of an echo in a dominant line of sight environment. The results are compared with those obtained when the coverage area is defined in capacity terms, which do not consider the practical degradation caused by the secondary replica of the primary message.
Angle QIM: a novel watermark embedding scheme robust against amplitude scaling distortions

Quantization index modulation (QIM) watermarking has received a great deal of attention ever since the rediscovery of Costa's result on codes with host-interference rejecting properties. While such embedding schemes exhibit considerable improvement in watermark capacity over their earlier predecessors, (e.g. spread-spectrum), their fragility to even the simplest attacks soon became apparent. Among such attacks, amplitude scaling has received special attention. We introduce a quantization scheme, named angle QIM (AQIM), that is provably insensitive to amplitude scaling attacks. Instead of embedding information by quantizing the amplitude of pixel values, AQIM works by quantizing the angle formed by the host-signal vector with the origin of a hyperspherical coordinate system. Hence, AQIM's invariance to amplitude scaling can be shown by construction. Experimental results are presented for the bit error rate performance of AQIM under additive white Gaussian noise attacks.
Criteria for decision-making in transportation logistics function

This paper presents the first phase of a methodology for making transportation decisions in the Supply Chain (SC), in the first part of this article are the importance of transactional costs in transportation and their decision levels. Then interrelationships and dependencies of the criteria in Transportation Logistics Function (TLF). Finally presents design of the Multicriteria Decision Network (MCDN), used to support decision-making by managers of goods and transportation companies for modes or carriers selection in the Supply Chain, together with theirs concepts for to standardize the criteria from the literature review.
Fully Private Noninteractive Face Verification

Face recognition is one of the foremost applications in computer vision, which often involves sensitive signals; privacy concerns have been raised lately and tackled by several recent privacy-preserving face recognition approaches. Those systems either take advantage of information derived from the database templates or require several interaction rounds between client and server, so they cannot address outsourced scenarios. We present a private face verification system that can be executed in the server without interaction, working with encrypted feature vectors for both the templates and the probe face. We achieve this by combining two significant contributions: 1) a novel feature model for Gabor coefficients' magnitude driving a Lloyd-Max quantizer, used for reducing plaintext cardinality with no impact on performance; 2) an extension of a quasi-fully homomorphic encryption able to compute, without interaction, the soft scores of an SVM operating on quantized and encrypted parameters, features and templates. We evaluate the private verification system in terms of time and communication complexity, and in verification accuracy in widely known face databases (XM2VTS, FERET, and LFW). These contributions open the door to completely private and noninteractive outsourcing of face verification.
Improving the performance of RDM watermarking by means of trellis coded quantisation

We propose a method to improve the performance of the recently introduced rational dither modulation (RDM) watermarking scheme. The improvement is obtained by modifying the essentially scalar nature of RDM with the introduction of a vector quantiser. The vector quantiser is based on a properly modified version of classical trellis coded quantisation, thus leading to the new trellis-coded RDM scheme (TC-RDM). Due to the infinite memory of the trellis TC-RDM relies on, the standard Viterbi algorithm is no longer optimum, hence we introduce a new suboptimal encoding algorithm that keeps the computational complexity reasonably low while ensuring a minimal loss with respect to the optimal scheme. The simulations we carried out on synthetic sequences show that TC-RDM permits improvement of the performance of RDM by 2–4 dB in terms of robustness against Gaussian noise addition.
Automatic delay selection in blind channel equalization: a prewhitening + eigenvector approach

We consider the problem of blindly equalizing a single input single output communication channel, assuming that the tap input vector to the equalizer has mutually uncorrelated components. This can be achieved if the received signal is preprocessed by an adaptive all-pole filter (which has been suggested as a means for both MSE improvement and DFE initialization), or by a standard lattice predictor. In the former case, it has been observed recently that an eigenvector of the (prewhitened) input quadricovariance matrix /spl Psi/ may provide a good initial estimate for the equalizer. Analytical justification for this fact is provided and show that if the MSE of a Wiener equalizer for a given delay is small, then this Wiener equalizer is close to an eigenvector of /spl Psi/. The corresponding eigenvalue decreases with the MSE, and thus picking the smallest eigenvalue automatically provides blind delay optimization.
Subband hyperstable adaptive IIR filters

Two subband implementations of hyperstability based adaptive infinite-impulse response (IIR) filtering algorithms are presented. These schemes are based on the polyphase decomposition of the adaptive filter and do not require cross filters to avoid aliasing. With this configuration, the larger the number of subbands, the more likely the SPR condition that this class of algorithms requires for convergence is to be satisfied. In addition, it is possible to assign different weights and/or compensating filters across the subbands, thus providing the designer with additional flexibility.
Misconvergence and stabilization of adaptive IIR lattice filters

We take a new look to adaptive IIR lattice filters in order to devise algorithms that preserve the stability of the corresponding direct form schemes. By analyzing the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride and SHARF algorithms is presented. By contrast, it is shown that previous lattice versions of these algorithms may fail to preserve stability of stationary points.
Privacy-preserving data aggregation in smart metering systems: an overview

Growing energy needs are forcing governments to look for alternative resources and ways to better manage the energy grid and load balancing. As a major initiative, many countries including the United Kingdom, United States, and China have already started deploying smart grids. One of the biggest advantages of smart grids compared to traditional energy grids is the ability to remotely read fine-granular measurements from each smart meter, which enables the grid operators to balance load efficiently and offer adapted time-dependent tariffs. However, collecting fine-granular data also poses a serious privacy threat for the citizens as illustrated by the decision of the Dutch Parliament in 2009 that rejects the deployment of smart meters due to privacy considerations. Hence, it is a must to enforce privacy rights without disrupting the smart grid services like billing and data aggregation. Secure signal processing (SSP) aims at protecting the sensitive data by means of encryption and provides tools to process them under encryption, effectively addressing the smart metering privacy problem.
Efficient protocols for secure adaptive filtering

The field of Signal Processing in the Encrypted Domain (SPED) has emerged in order to provide efficient and secure solutions for preserving privacy of signals that are processed by untrusted agents. In this work, we study the privacy problem of adaptive filtering, one of the most important and ubiquitous blocks in signal processing nowadays. We examine several use cases along with their privacy characteristics, constraints and requirements, that differ in several aspects from those of the already tackled linear filtering and classification problems. Due to the impossibility of using a strategy based solely on current homomorphic encryption systems, we propose novel secure protocols for a privacy-preserving execution of the BLMS (Block Least Mean Squares) algorithm, combining different SPED techniques, and paying special attention to the trade-off between computational complexity, bandwidth, and the error produced due to finite-precision implementations.
A new model for Gabor coefficients' magnitude in face recognition

Gabor filters have demonstrated their effectiveness in automatic face recognition, which can greatly benefit from an accurate statistical model for Gabor-based face representations. Previous approaches have modeled real and imaginary parts independently as Generalized Gaussians (GG). Since most Gabor-based face recognition systems discard coefficients' phase, we propose a novel statistical model for the magnitude of Gabor coefficients that accounts for the dependence between real and imaginary parts, assuming they are circularly symmetric and marginally GG distributed. The quality of the fit for our model is assessed using the Kullback-Leibler divergence, and optimal quantization of Gabor coefficients is shown as one of its applications.