nils-herrmann/clean_scirepeval huggingface dataset:
A dictionary approach to repetitive pattern finding in music

A dictionary-based approach for extracting repetitive patterns in music aiming at music feature extraction and indexing for audio database management is proposed. In this system, segmentation is achieved with the tempo information, and a music score is decomposed into bars. Each bar is indexed to construct a bar index table. Then, an adaptive dictionary-based compression algorithm known as Lempel Ziv 78 (LZ-78) is applied to the barrepresented music scores to extract repetitive patterns. Finally, pruning is applied to this dictionary to remove non-repeating patterns and to combine shorter repeating patterns into a longer one. The LZ78 algorithm is slightly modified to achieve better results in the current application context. Experiments performed on a popular music database of MIDI files demonstrated that the proposed algorithm extracts repeating melodies effectively with a speed of four times faster compared to the traditional linear search approach.
Robust signal extrapolation using wavelets

A new approach for signal extrapolation based on wavelet representation: known as scale-time limited extrapolation and a denoising process is investigated in this research. We first examine a new signal modeling technique using wavelets and the corresponding scale-time limited signal extrapolation algorithm. Then, the sensitivity of the algorithm to noise is discussed, and a denoising algorithm based on the time-localization property of the wavelet transform is proposed. By integrating the denoising process and the iterative scale-time limited extrapolation algorithm, we obtain a very robust signal extrapolation algorithm for noisy data. A simulation result of signal extrapolation from noisy observed data is presented to illustrate the performance of the proposed robust signal extrapolation algorithm.<<ETX>>
Comparison of Power Control Schemes for Relay Sensor Networks

Three power control schemes for the space-time coded amplify-and-forward (AF) relaying scheme targeting at wireless sensor network applications are examined and compared. The opportunistic scheme performs the best by considering the signal-to-noise-ratio (SNR) of the received signal. However, if the power for the relay is limited, the performance of the opportunistic scheme degrades due to the loss of active relay nodes that have better channel conditions. Since the battery lifetime of nodes for wireless sensor networks is limited and the loss of relay nodes is critical to system performance, we propose an SNR-constrained power reduction scheme to prolong the relay lifetime for the opportunistic scheme. It is demonstrated by computer simulation that the opportunistic scheme with SNR-constrained power reduction is power efficient and the relay lifetime of dense relay networks can be significantly prolonged.
Improved H.263+ rate control via variable frame rate adjustment and hybrid I-frame coding

A novel rate control algorithm consisting of two major components, i.e. a variable encoding frame rate method and a hybrid DCT/wavelet I-frame coding scheme, 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. Furthermore, an R-D optimized hybrid DCT/wavelet scheme is used for effective I-frame coding. 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 variable frame rate method and the hybrid I-frame coding scheme are compatible with the bitstream structure of H.263+.
TCP-friendly Internet video with smooth and fast rate adaptation and network-aware error control

A solution for Internet video streaming with two major components is examined in this paper. First, a new rate adaptation mechanism called the smooth and fast rate adaptation mechanism (SFRAM) is developed based on the transmission control protocol (TCP) throughput equation. SFRAM not only achieves TCP-friendliness but also provides a smooth and fast rate adaptation for transmitted video. By adaptively averaging measurements such as the round-trip time and the packet loss rate over a suitable window, SFRAM mitigates fluctuation that is undesirable for video transmission. The adopted weighting scheme enables the response in a fast manner only for distinct network variations so that the overall network utilization is improved while sustaining end-to-end video quality. When integrated with active routing support such as random early detection and explicit congestion notification, SFRAM provides an even better performance. Second, an adaptive network-aware error control is proposed to alleviate error propagation due to packet loss. Based on SFRAM's capturing of distinct network variations, various source-level error control options such as reference picture selection, video redundancy coding and I-MB refresh are dynamically selected. Extensive experiments by using both the ns-2 network simulator and the real Internet environment are performed to evaluate the dynamic behavior of SFRAM.
Performance study of robust wireless video transmission over fading channels

We study the robust transmission of video over wireless cellular channels. The source video is coded based on the H.263 standard for low bit rate coding where the transmission rate is limited to 64 kb/s and below. Due to the nature of burst errors in fading channels, the video communication system needs to be robustly designed. Unnecessary overheads should be prevented because of the limited bandwidth of wireless channels. The techniques of automatic repeat request (ARQ) and forward error correction (FEC) are employed. It is shown that to obtain the same video quality, the ARQ system requires less overhead than FEC. Our simulation gives the results in favor of using ARQ for error control rather than FEC at a bit error rate of 10/sup -3/ under the channel environment stated in the IS-95 standard.
Improving Object Classification Performance via Confusing Categories Study

As the number of object categories continues to increase in object classification, it is inevitable to have certain categories that are more confusing than others due to the proximity of their samples in the feature space. In this work, we conduct a detail analysis on confusing categories and propose a confusing categories identification and resolution (CCIR) scheme, which can be applied to any CNNbased object classification baseline method to further improve its performance. In the CCIR scheme, we first present a procedure to cluster confusing object categories together to form a confusion set automatically. Then, a binary-treestructured (BTS) clustering method is adopted to split a confusion set into multiple subsets. A classifier is subsequently learned within each subset to enhance its performance. Experimental results on the ImageNet ILSVRC2012 dataset show that the proposed CCIR scheme can offer a significant performance gain over the AlexNet and the VGG16.
Adaptive Directional Total-Variation Model for Latent Fingerprint Segmentation

A new image decomposition scheme, called the adaptive directional total variation (ADTV) model, is proposed to achieve effective segmentation and enhancement for latent fingerprint images in this work. The proposed model is inspired by the classical total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed ADTV model decomposes a latent fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g., structured noise) while the texture layer mainly consists of the latent fingerprint. This cartoon-texture decomposition facilitates the process of segmentation, as the region of interest can be easily detected from the texture layer using traditional segmentation methods. The effectiveness of the proposed scheme is validated through experimental results on the entire NIST SD27 latent fingerprint database. The proposed scheme achieves accurate segmentation and enhancement results, leading to improved feature detection and latent matching performance.
Environmental Sound Recognition With Time–Frequency Audio Features

The paper considers the task of recognizing environmental sounds for the understanding of a scene or context surrounding an audio sensor. A variety of features have been proposed for audio recognition, including the popular Mel-frequency cepstral coefficients (MFCCs) which describe the audio spectral shape. Environmental sounds, such as chirpings of insects and sounds of rain which are typically noise-like with a broad flat spectrum, may include strong temporal domain signatures. However, only few temporal-domain features have been developed to characterize such diverse audio signals previously. Here, we perform an empirical feature analysis for audio environment characterization and propose to use the matching pursuit (MP) algorithm to obtain effective time-frequency features. The MP-based method utilizes a dictionary of atoms for feature selection, resulting in a flexible, intuitive and physically interpretable set of features. The MP-based feature is adopted to supplement the MFCC features to yield higher recognition accuracy for environmental sounds. Extensive experiments are conducted to demonstrate the effectiveness of these joint features for unstructured environmental sound classification, including listening tests to study human recognition capabilities. Our recognition system has shown to produce comparable performance as human listeners.
A robust technique for latent fingerprint image segmentation and enhancement

A robust technique for latent fingerprint image segmentation and enhancement is presented. In contrast with most state-of-the-art methods, our approach does not rely on the information of local gradients, which are sensitive to structured and unstructured background noise. Thus, the proposed technique is robust against gradient deviations. It also provides robust estimates to orientations and frequencies of fingerprints in a local region to allow effective Gabor filtering for fingerprint ridge/valley pattern enhancement. Results of latent fingerprint segmentation and enhancement along with orientation estimations on real data are presented to demonstrate the superior performance of the proposed technique.
ECG-based biometrics using recurrent neural networks

In this paper, we propose the use of recurrent neural networks (RNNs) to develop an effective solution to two problems in electrocardiogram (ECG)-based biometrics: identification/classification and authentication. Different RNN architectures with various parameter settings were evaluated, including traditional, long short-term memory (LSTM), gated recurrent unit (GRU), unidirectional, and bidirectional networks. Unlike many existing methods, the RNN-based method does not require any feature extraction. The method was evaluated using two publicly available datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). For the identification problem, nearly 100% classification accuracy on the ECG-ID dataset was achieved, and similar results were observed for the MITDB dataset. For the authentication problem, an RNN was trained and the hidden state at the final time step was extracted to make a decision. We evaluated the effect of the training size on the equal error rate (EER), and showed that the EER drops from approximately 3.5% to nearly 0% as we increased the percentage of subjects used for training from approximately 15% to 80%.
Classification and retrieval of sound effects in audiovisual data management

We present a method for the classification of sound effects which exploits time-frequency analysis of audio signals and uses the hidden Markov model as the classifier. The proposed approach can be used to retrieve audio/video segments in studios, audiovisual libraries, and family entertainment applications. For example, video scenes of a gun fight can be retrieved by searching for sounds of shooting or explosion. In addition, it will have applications in surveillance by recognizing sounds related to criminal activities. An accuracy rate of 86% for sound effects classification is achieved with the proposed method. Also, a query-by-example retrieval approach for sound effects is proposed on top of the archiving scheme, which is proved to be highly efficient and effective.
Multimedia data encryption via random rotation in partitioned bit streams

A new method to encrypt a compressed multimedia bit stream, which is the output of any compression system using an entropy coder in the last stage, is proposed in this research. The basic idea is to partition a coded bit stream into random-sized blocks and perform a random rotation in each block. The proposed encryption scheme demands low computational overhead and does not increase the size of compressed bit stream. Security analysis to both the ciphertext-only attack and the known/chosen-plaintext attack shows that the proposed algorithm achieves high security.
Adaptive delay concealment for Internet voice applications with packet based time-scale modification

Much effort has been involved in packet-level error control and delay jitter concealment for Internet audio applications. A packet-based time-scale modification scheme for speech signals is developed to provide adaptive delay concealment, at the receiver for an Internet voice session in this work. The adaptive playout algorithm strives to minimize packet droppings for late-arrival packets and premature packets while maintaining the end-to-end delay constraint. By stretching the length of voice segments and incorporating silence intervals, the proposed algorithm is able to accommodate fluctuating delays including delay spikes quickly. It is verified by experiments that the proposed adaptive playout algorithm improves the received speech intelligence under a tightly bounded average playout delay.
On scale-limited extrapolation

We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals.<<ETX>>
Robust Image Segmentation Using Contour-Guided Color Palettes

The contour-guided color palette (CCP) is proposed for robust image segmentation. It efficiently integrates contour and color cues of an image. To find representative colors of an image, color samples along long contours between regions, similar in spirit to machine learning methodology that focus on samples near decision boundaries, are collected followed by the mean-shift (MS) algorithm in the sampled color space to achieve an image-dependent color palette. This color palette provides a preliminary segmentation in the spatial domain, which is further fine-tuned by post-processing techniques such as leakage avoidance, fake boundary removal, and small region mergence. Segmentation performances of CCP and MS are compared and analyzed. While CCP offers an acceptable standalone segmentation result, it can be further integrated into the framework of layered spectral segmentation to produce a more robust segmentation. The superior performance of CCP-based segmentation algorithm is demonstrated by experiments on the Berkeley Segmentation Dataset.
Understanding CNN via deep features analysis

Two quantitative metrics are proposed for evaluating trained deep features at different convolution layers in this work. We first show mathematically that the Gaussian confusion measure (GCM) can be used to identify the discriminative ability of an individual feature. Next, we generalize this idea, introduce another measure called the cluster purity measure (CPM), and use it to analyze the discriminative ability of multiple features jointly. The discriminative ability of the trained Convolution Neural Network (CNN) features is confirmed by experiments. Further studies utilizing GCM and CPM as tools offer important insights into the CNN, such as understanding the behavior of trained CNN features and the good detection performance of some object classes that were considered difficult in the past. Finally, the trained deep feature representation is compared between different CNN structures to validate the superiority of deeper networks.
Robust video transmission over wideband wireless channel using space-time coded OFDM systems

A space-time coded orthogonal frequency-division multiplexed (STC-OFDM) system is proposed to transmit layered video signals over wireless channels. The space-time coding can provide a diversity gain for the multiple-antenna system while OFDM can alleviate the frequency-selective fading effect. An input video sequence is compressed and partitioned into layers with different priorities. Then, an unequal error protection scheme based on the product Reed-Solomon (RS) codes is employed to provide different levels of protection to different layers. At the receiver, a minimum mean square error (MMSE) detector with interference cancellation (IC) is integrated with the space-time decoder to reconstruct the desired signal effectively. We derive an analytic bound for the error probability, and conduct experiments for the transmission of the H.263 video bitstream. It is shown that the proposed scheme enhances the PSNR performance significantly in the environment with a moderate signal-to-noise ratio.
A Novel Two-Stage Rate Control Scheme for H.264

A new rate control algorithm for the H.264 encoder is developed in this work. There are several unique features in the proposed rate control scheme. First, a two-stage encoding mechanism is used to resolve the problem of inter-dependency between R-D optimization (RDO) and rate control (RC) in H.264. Second, it is emphasized that the bits of the header information may occupy a larger portion of the total bit budget especially when encoding at low bit rates in H.264. To address this issue, a rate model for the header information is established so as to estimate header bits more accurately. Third, a new source rate model is proposed to estimate the quantization parameter (QP) from the residual signal. Built upon the above ideas, a rate control algorithm is developed for the H.264 baseline-profile encoder under the constant-bit-rate (CBR) constraint. It is shown by experimental results that the proposed scheme can control bit rates accurately with the R-D performance better than that of the rate control algorithm implemented in H.264 JM8.1a
Motion-compensated frame interpolation scheme for H.263 codec

A fast block-based motion-compensated frame interpolation (FMCI) scheme was proposed in our previous work. FMCI does not require any motion search in the decoder so that the complexity is low enough to achieve frame rate increase in real time. To further improve the visual quality, we propose two add-on schemes to enhance FMCI in this research. They are the adaptive frame skip (AFS) scheme applied at the encoder and the hybrid frame interpolation (HFI) scheme which incorporates both frame repetition and FMCI at the decoder. By adopting these two add-on features, the enhanced FMCI method is made more robust to allow smooth playback.
Unitary modulation for secrecy enhancement in multi-antenna wireless systems with only CSIT

This work proposes the use of a unitary modulation scheme to enhance physical layer secrecy in multi-antenna wireless systems. The proposed scheme takes advantage of having only channel state information at the transmitter (CSIT) but not at the receiver and the eavesdropper. With CSIT, the transmitter can first take the LQ or singular value decomposition of the channel matrix and then replace its unitary component by an information-bearing unitary matrix symbol. When the SNR is sufficiently high, the legitimate receiver is able to extract the unitary matrix by exploiting the uniqueness of the matrix decomposition. But the eavesdropper is incapable of doing so due to the ambiguity caused by the unknown unitary rotation on its channel, which ensures zero information leakage to the eavesdropper when the channels are independent. A detection scheme based on the chordal distance is proposed, and the achievable secrecy rate is derived and shown to depend only on the mutual information of the main channel. The simulation results demonstrate the effectiveness and secrecy advantages of our proposed unitary modulation scheme.
Multicell OFDMA Downlink Resource Allocation Using a Graphic Framework

A novel practical low-complexity multicell orthogonal frequency-division multiple access (OFDMA) downlink channel-assignment method that uses a graphic framework is proposed in this paper. Our solution consists of two phases: 1) a coarse-scale intercell interference (ICI) management scheme and 2) a fine-scale channel-aware resource-allocation scheme. In the first phase, state-of-the-art ICI management techniques such as ICI coordination (ICIC) and base-station cooperation (BSC) are incorporated in our framework. In particular, the ICI information is acquired through inference from the diversity set of mobile stations and is presented by an interference graph. Then, ICIC or BSC is mapped to the MAX k-CUT problem in graph theory and is solved in the first phase. In the second phase, channel assignment is accomplished by taking instantaneous channel conditions into account. Heuristic algorithms are proposed to efficiently solve both phases of the problem. Extensive simulation is conducted for various practical scenarios to demonstrate the superior performance of the proposed solution compared with the conventional OFDMA allocation scheme. The proposed scheme can be used in next-generation cellular systems such as the 3GPP Long-Term Evolution and IEEE 802.16 m.
Visual Tempo Analysis for MTV-Style Home Video Authoring

Automatic visual tempo analysis and adaptive shot detection are studied for MTV-style home video authoring. Based upon the significant variation caused by object and background independently, spatial and temporal analysis are integrated for region of interest(ROI) detection in video sequences. To further detect homogeneous shots, visual tempos are proposed to describe the variation. Then, an adaptive segmentation method is presented to automatically detect homogeneous shots using visual tempo cues. A baby video is used as an illustrative example, and the experimental results are encouraged.