nils-herrmann/clean_scirepeval huggingface dataset:
Environmental sound recognition using MP-based features

Defining suitable features for environmental sounds is an important problem in an automatic acoustic scene recognition system. As with most pattern recognition problems, extracting the right feature set is the key to effective performance. A variety of features have been proposed for audio recognition, but the vast majority of the past work utilizes features that are well-known for structured data, such as speech and music, and assumes this association will transfer naturally well to unstructured sounds. In this paper, we propose a novel method based on matching pursuit (MP) to analyze environment sounds for their feature extraction. The proposed MP-based method utilizes a dictionary from which to select features, resulting in a representation that is flexible, yet intuitive and physically interpretable. We will show that these features are less sensitive to noise and are capable of effectively representing sounds that originate from different sources and different frequency ranges. The MP- based feature can be used to supplement another well-known audio feature, i.e. MFCC, to yield higher recognition accuracy for environmental sounds.
Extension of Non-Local Means (NLM) algorithm with Gaussian filtering for highly noisy images

The denoising performance of the Non-Local Means (NLM) method decreases as the variance of additive white Gaussian noise becomes higher. In this paper, we explain this phenomenon and propose a modified version of the Non-Local Means (NLM) method, called the Enhanced-Weights NLM (EWNLM) algorithm, to denoise highly noisy images. The EWNLM algorithm evaluates weights from a pre-filtered image using the Gaussian kernel, which in turn result in more robust weight contributions from similar pixels in the search window. Experimental results are given to demonstrate the superior performance of the EWNLM scheme when the standard deviation of the additive white Gaussian noise (AWGN) is greater than 20.
Rate control for low-bit-rate video via variable-encoding frame rates

A novel rate control algorithm with a variable-encoding frame rate method is proposed for low-bit-rate video coding. Most existing rate control algorithms for low-bit-rate video focus on bit allocation at the macroblock level under a constant frame-rate assumption. The proposed rate control algorithm is able to adjust the encoding frame rate at the expense of tolerable time-delay. The new rate control algorithm attempts to achieve a good balance between spatial quality and temporal quality to enhance the overall human perceptual quality at low-bit-rates. It is demonstrated that the rate control algorithm achieves higher coding efficiency at low-bit-rates, with a low additional computational cost. The proposed variable-encoding frame rate method is compatible with the bit-stream structure of H.263+.
A multi-metric fusion approach to visual quality assessment

This paper presents a new methodology for objective visual quality assessment with multi-metric fusion (MMF). The current research is motivated by the observation that there is no single metric that gives the best performance scores in all situations. To achieve MMF, we adopt a regression approach. First, we collect a large number of image samples, each of which has a score labeled by human observers and scores associated with different metrics. The new MMF score is set to be the nonlinear combination of multiple metrics with suitable weights obtained by a training process. Furthermore, we divide image distortions into groups and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. It is shown by experimental results that the proposed MMF metric outperforms all existing metrics by a significant margin.
Removal of blocking artifacts of DCT transform by classified space-frequency filtering

Image compression techniques based on block discrete cosine transform (BDCT) are widely used today. However, visually annoying blocking artifacts appear when the compression ratio is high. We propose an iterative method to remove these artifacts using block classification and space-frequency filtering. The new method is better than POCS (projections onto convex sets) and regularization methods in visual appearance as well as objective error measures.
Cross-layer QoS Analysis of Opportunistic OFDM-TDMA and OFDMA Networks

Performance analysis of multiuser orthogonal frequency division multiplexing (OFDM-TDMA) and orthogonal frequency division multiple access (OFDMA) networks in support of multimedia transmission is conducted in this work. We take a cross-layer approach and analyze several quality-of-service (QoS) measures that incilude the bit rate and the bit error rate (BER) in the physical layer, and packet average throughput/delay and packet maximum delay in the link layer. The authors adopt a cross-layer QoS framework similar to that in IEEE 802.16, where service classification, flow control and opportunistic scheduling with different subcarrier/bit allocation schemes are implemented. In the analysis, the Rayleigh fading channel in the link layer is modeled by a finite-state Markov chain, and the channel state information (CSI) is assumed to be available at the base station. With the M/G/1 queueing model and flow control results, the analysis provides important insights into the performance difference of these two multiaccess systems. The derived analytical results are verified by extensive computer simulation. It is demonstrated by analysis and simulation that OFDMA outperforms OFDM-TDMA in QoS metrics of interest. Thus, OFDMA has higher potential than OFDM-TDMA in supporting multimedia services.
Dependent R/D Modeling Techniques and Joint T-Q Layer Bit Allocation for H.264/SVC

We investigate dependent rate/distortion (R/D) modeling techniques for H.264/SVC videos. We introduce a self-domain (S-domain) analysis method for characterizing the dependent R/D behaviors, where the R/D characteristics of a base layer are employed as the observation domain for those of dependent layers. Based on S-domain observations, we propose empirical dependent R/D models and analyze physical implications of the proposed models. As an application of the proposed R/D models, we examine a joint temporal-quality layer bit allocation algorithm formulated as a Lagrange optimization problem. The proposed R/D models enable us to derive an analytical solution to the joint optimization problem. Finally, it is demonstrated by experimental results that our bit allocation algorithm outperforms JSVM benchmark by a significant margin (10%-20%) at various bit rates.
Error resilience analysis of multihypothesis motion compensated prediction for video coding

The relationship between the error propagation effect and the hypothesis coefficients of the multihypothesis motion compensated prediction (MHMCP) is analyzed in this work. MHMCP enhances the error resilience of compressed video but demands a higher bit rate for the motion information. We study the rate-distortion performance of MHMCP in an error prone environment and then perform extensive experiments to conform the analytical results. Several design guidelines for MHMCP coders are drawn based on the analytical and experimental results.
Progressive lossless 3D mesh encoder with octree-based space partitioning

A progressive lossless 3D mesh encoder using the octree-based space partitioning of vertex data is proposed. Given a 3D mesh, the quantized 3D vertices are first partitioned into an octree structure, which is then traversed from the root and gradually to the leaves. During the traversal, each 3D cell in the tree front is subdivided into eight child cells. For each cell subdivision, both the local geometry and connectivity changes are encoded. Furthermore, selective cell subdivision is performed in the tree front to provide better rate-distortion performance. It is shown, by experimental results, that the geometry coding costs of the proposed codec are 4.2 and 14.5 bits per vertex (bpv) for 8-bit and 12-bit coordinate quantizations, respectively, and the connectivity coding cost is 3.3 bpv on the average, which provides state-of-the-art coding performance.
Block-based image steganalysis for a multi-classifier

Traditional image steganalysis techniques for classification of steganograhic algorithms are conducted with respect to the entire image. In this work, we aim to design a multi-classifier which classifies stego images depending on their steganographic algorithms in addition to distinguishing stego images from cover images. This classification is based on steganalysis results of decomposed image blocks. As a natural image often consists of heterogeneous regions, its decomposition will lead to smaller image blocks, each of which is more homogeneous. We classify these image blocks into multiple classes and find a classifier for each class to decide whether a block is from a cover image or a stego image with a specific steganographic algorithm. Consequently, the steganalysis of the whole image can be conducted by fusing weighted steganalysis results of all image blocks through a voting process. Experimental results will be given to show the advantage of using the proposed block-based image steganalysis for a multi-classifier.
Learning a Combined Model of Visual Saliency for Fixation Prediction

A large number of saliency models, each based on a different hypothesis, have been proposed over the past 20 years. In practice, while subscribing to one hypothesis or computational principle makes a model that performs well on some types of images, it hinders the general performance of a model on arbitrary images and large-scale data sets. One natural approach to improve overall saliency detection accuracy would then be fusing different types of models. In this paper, inspired by the success of late-fusion strategies in semantic analysis and multi-modal biometrics, we propose to fuse the state-of-the-art saliency models at the score level in a para-boosting learning fashion. First, saliency maps generated by several models are used as confidence scores. Then, these scores are fed into our para-boosting learner (i.e., support vector machine, adaptive boosting, or probability density estimator) to generate the final saliency map. In order to explore the strength of para-boosting learners, traditional transformation-based fusion strategies, such as Sum, Min, and Max, are also explored and compared in this paper. To further reduce the computation cost of fusing too many models, only a few of them are considered in the next step. Experimental results show that score-level fusion outperforms each individual model and can further reduce the performance gap between the current models and the human inter-observer model.
Focus of attention (FOA) identification from compressed video for automatic target recognition (ATR)

We examine an approach to perform pattern recognition on compressed video such as MPEG data streams. The current effort has focused on the identification of the region of interest (ROI) which contains the target, which is also known as the focus of attention (FOA) module. Two techniques are studied, i.e. motion field analysis and DCT transform coefficient analysis. The performance of the proposed algorithms is demonstrated with experimental results.
Seamless high-velocity handover support in mobile WiMAX networks

The IEEE 802.16e standard (i.e., mobile WiMAX) has been proposed to provide connectivity in wireless networks for mobile users (including users at a vehicular speed). It is shown in our analysis that the probability of a successful handover decreases significantly when the user moves at a higher speed. Then, we propose a scheme that combines adaptive forward error correction (FEC) with retransmission to offer extra protection for handover signaling messages to enhance the probability of a successful handover, especially at a higher velocity. It is demonstrated by computer simulation that the proposed scheme provides a higher successful handover probability at various velocities.
Design of Energy-Efficient Video Sharing Servers with Delay Constraints

With the continually growing popularity of online video sharing, energy consumption in video sharing servers has become an pivotal issue. Energy savings in video servers can be achieved by utilizing low power modes in disks, but this causes enhanced delay which is not desirable for video sharing services. In this paper, we present a new energy management scheme that jointly optimizes energy and delay for the video storage system. There are two salient characteristics of the proposed scheme. First, it exploits the unique workload characteristics of video sharing services. Second, it includes a new model that efficiently facilitates the selection of power modes for disks. Experiments show that our scheme achieves considerably more energy savings under the same delay level compared to the traditional threshold-based energy management scheme.
Fast Mode Decision Based on Mode Adaptation

This paper proposes an efficient algorithm for fast mode decision in H.264/advanced video coding by adaptively predicting the optimal mode for each macroblock (MB) to be coded. Firstly, encoding modes are projected as points onto a 2-D map, and an optimal 2-D point of the MB to be coded is predicted based on the encoding information of spatial-temporal neighboring blocks. Then, a priority-based mode candidate list with a descending order to be the best mode is constructed based on the optimal 2-D point. Finally, mode decision is performed according to the priority-based mode candidate list in the checking order, from the most important mode to the least one, with early termination conditions. Extensive experimental results demonstrate that the proposed algorithm is superior to three recent fast mode decision algorithms, with the entire encoding time being reduced by about 60% for quarter common intermediate format/common intermediate format/standard-definition sequences on average and the rate distortion performance being kept almost intact.
Multi-stage MMSE/MOE receivers for frequency selective fading channels in DS-CDMA systems

A multi-stage minimum mean square error filter bank (MS-MMSE-FB) and a minimum output energy filter bank (MS-MOE-FB) for the detection of DS-CDMA (Direct-Sequence Code-Division Multi-Access) signals under a dynamic multi-path fading environment are presented. The output signal-to-interference plus noise ratios (SINRs) for both MS-MMSE-FB and MS-MOE-FB are derived. The analytical results reveal how the output SINRs of reduced-rank MS-MMSE-FB and MS-MOE-FB vary with the rank of filters. The findings coincide with results of Honig and Goldstein (see  2000), namely, the rank needed to achieve a target performance does not scale with the system loading in an additive white Gaussian noise (AWGN) channel. The derivation shows that, with the aid of desired user's channel information, the rank of the receivers can be drastically dropped without affecting the steady state SINR, an important factor for interference suppression in a dynamic fading channel with adaptive MS-MMSE-FB/MS-MOE-FB detections.
Fast linearized energy allocation for multimedia loading on multicarrier systems

Efficient near-optimal energy allocation for multimedia data transmission using multicarrier modulation is investigated. The optimal method typically relies on an iterative process that is computationally intensive, and it does not provide insight into the allocation process for higher order M-quadrature amplitude modulation (QAM) constellations. A sequence of two fast energy allocation algorithms are proposed in this work. First, a basic energy allocation algorithm is developed by solving a simple set of linear equations directly. It is shown to be near-optimal for almost the entire signal-to-noise ratio range. Based on this direct method, we can analyze asymptotic limits of the approximation error, carrier energy, and carrier rate conveniently. Then, we present an advanced algorithm that enhances the performance of the basic algorithm via iteration. The convergence of the advanced algorithm is studied, and an estimate of its computational complexity is derived. It is worthwhile to point out that, while limits for the special case with uniform noise and binary phase-shift keying modulation were studied previously, the derived asymptotic performance here works for a more general case with nonuniform noise and M-QAM constellations. Finally, we examine these limits with several types of multimedia and nonmultimedia sources, and show that our algorithms perform well regardless of the source type.
Fragile speech watermarking based on exponential scale quantization for tamper detection

A fragile speech watermarking scheme of low complexity is proposed in this research as an effective way to detect malicious content alterations while tolerating content preserving operations. Since no auxiliary data are required, the authentication process is transparent to agents that perform content preserving operations such as transcoding during the transmission process. The proposed scheme is based on the modified odd/even modulation scheme with exponential scale quantization and a localized frequency masking model while assuring no mismatch between quantization steps used in watermark embedding and detection. The noise introduced by watermarking is shown to be smaller than that of speech coders. It is shown by experimental results that the proposed system is able to distinguish malicious alterations from resampling, white noise pollution, G.711 speech coding and G.721 speech coding with very low error probabilities.
Frequency-Domain Pre-Equalization for Single-Carrier Space-Division Multiple-Access Downlink Transmissions

A frequency-domain pre-equalization scheme for single-carrier space-division multiple-access (SC-SDMA) downlink transmissions is proposed in this work. In the system of our interest, both the base station (BS) and mobile terminals (MTs) are equipped with multiple antennas. It operates in a time-division-duplex (TDD) mode and the channel reciprocity between alternative uplink and downlink transmissions is exploited to feed the channel state information (CSI) back to the transmit side. Pre-filtering coefficients are designed so as to minimize the sum of mean-squared-errors at all MTs under an overall transmit power constraint. Several strategies are investigated for linearly combining the received signals at each MT in the frequency domain. Simulation results are provided to demonstrate the effectiveness of the proposed scheme
Adaptive variable length Markov chain for non-stationary fading channel modeling

A novel adaptive mapping from the measurements of a non-stationary wireless environment to a variable length Markov chain (VLMC) model is proposed in this research. This scheme consists of two main components: the estimation of channel signal-to-noise ratio (SNR) distribution and discrete VLMC modeling. To obtain the channel SNR distribution, a kernel density estimation algorithm is used to track local changes of channel statistics resulting from varying mobile environments. With the estimated channel SNR distribution, an iterative partitioning mechanism is performed to construct the VLMC model, which yields a much larger and structurally richer class of models than ordinary higher order Markov chains. The application of the derived VLMC channel model to the available throughput of a non-stationary wireless channel is examined with the feedback channel state information from the mobile terminal to the base station. The performance of the proposed adaptive mapping scheme and throughput estimation is demonstrated via simulation in a micro-cell non-stationary wireless environment.
An interactive approach to image retrieval using multiple seed images

The concept, procedure and tools for interactive image retrieval with multiple seed images are investigated in this research. Specifically, we consider an interactive query process in which the query can be refined so that the meaning of "similarity": defined by a specific user for a particular application can be approached gradually. This idea is implemented by performing adaptive filtering with multiple low level indexing features based on user's feedback. The proposed approach is demonstrated by a testbed with flexible query formation, efficient initial guess and further refinement tools.
Latent fingerprint segmentation with adaptive total variation model

Image segmentation is an important step in automatic fingerprint identification systems. While tremendous progress has been made in rolled and plain fingerprint segmentation, the segmentation of latent fingerprints is still a difficult problem. Features used for rolled and plain fingerprint images fail to work properly on latent images due to the poor quality in ridge information and the presence of multiple types of strong structured noise. In this work, we present an adaptive total variation (TV) model to achieve effective latent fingerprint segmentation. The proposed solution can remove various types of structured noise existing in a single latent image and automatically locate the region of interest (ROI), which contains primarily the latent fingerprint. Then, the following tasks such as fingerprint feature extraction and matching can be conducted in the ROI only. In the proposed TV-based image model, one can adaptively adjust the weight coefficient of the fidelity term in L1-norm depending on the background noise level, which is estimated via TV-based texture analysis. We apply the proposed TV-based segmentation algorithm to the NIST SD27 latent fingerprint database to demonstrate its superior performance.
Cross-Layer Interaction of TCP and Ad Hoc Routing Protocols in Multihop IEEE 802.11 Networks

In this research, we first investigate the cross-layer interaction between TCP and routing protocols in the IEEE 802.11 ad hoc network. On-demand ad hoc routing protocols respond to network events such as channel noise, mobility, and congestion in the same manner, which, in association with TCP, deteriorates the quality of an existing end-to-end connection. The poor end-to-end connectivity deteriorates TCP's performance in turn. Based on the well-known TCP-friendly equation, we conduct a quantitative study on the TCP operation range using static routing and long-lived TCP flows and show that the additive-increase, multiplicative-decrease (AIMD) behavior of the TCP window mechanism is aggressive for a typical multihop IEEE 802.11 network with a low-bandwidth-delay product. Then, to address these problems, we propose two complementary mechanisms, that is, the TCP fractional window increment (FeW) scheme and the Route-failure notification using BUIk-losS Trigger (ROBUST) policy. The TCP FeW scheme is a preventive solution used to reduce the congestion-driven wireless link loss. The ROBUST policy is a corrective solution that enables on-demand routing protocols to suppress overreactions induced by the aggressive TCP behavior. It is shown by computer simulation that these two mechanisms result in a significant improvement of TCP throughput without modifying the basic TCP window or the wireless MAC mechanisms.