nils-herrmann/clean_scirepeval huggingface dataset:
Sparse human movement representation and recognition

In this paper a novel method for human movement representation and recognition is proposed. A movement type is regarded as a unique combination of basic movement patterns, the so-called dynemes. The fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space and allow the expression of a posture in terms of these dynemes. In the so-called dyneme space, the sparse posture representations of a movement are combined to represent the movement as a single point in that space, and linear discriminant analysis (LDA) is further employed to increase movement type discrimination and compactness of representation. This method allows for simple Mahalanobis or cosine distance comparison of movements, taking implicitly into account time shifts and internal speed variations, and, thus, aiding the design of a real-time movement recognition algorithm.
Summarization of human activity videos using a salient dictionary

Video summarization has become more prominent during the last decade, due to the massive amount of available digital video content. A video summarization algorithm is typically fed an input video and expected to extract a set of important key-frames which represent the entire content, convey semantic meaning and are significantly more concise than the original input. The most wide-spread approach relies on video frame clustering and extraction of the frames closest to the cluster centroids as key-frames. Such a process, although efficient, offloads the burden of semantic scene content modelling exclusively to the employed video frame description/representation scheme, while summarization itself is approached simply as a distance-based data partitioning problem. This work focuses on videos depicting human activities (e.g., from surveillance feeds) which display an attractive property, i.e., each video frame can be seen as a linear combination of elementary visual words (i.e., basic activity components). This is exploited so as to identify the video frames containing only the elementary visual building blocks, which ideally form a set of independent basis vectors that can linearly reconstruct the entire video. In this manner, the semantic content of the scene is considered by the video summarization process itself. The above process is modulated by a traditional distance-based video frame saliency estimation, biasing towards more spread content coverage and outlier inclusion, under a joint optimization framework derived from the Column Subset Selection Problem (CSSP). The proposed algorithm results in a final key-frame set which acts as as salient dictionary for the input video. Empirical evaluation conducted on a publicly available dataset suggest that the presented method outperforms both a baseline clustering-based approach and a state-of-the-art sparse dictionary learning-based algorithm.
Video summarization based on Subclass Support Vector Data Description

In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.
Information Clustering Using Manifold-Based Optimization of the Bag-of-Features Representation

In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset. The proposed method improves both the internal and the external clustering criteria for two different clustering algorithms: 1) the  ${k}$ -means and 2) the spectral clustering. Also, the optimized histogram space can be used to directly assign a new object to its cluster, instead of using the spectral space (which requires reapplying the spectral clustering algorithm or using incremental spectral clustering techniques). Finally, the learned representation is also evaluated using an information retrieval setup and it is demonstrated that improves the retrieval precision over the baseline BoF representation.
Minimum Class Variance Extreme Learning Machine for Human Action Recognition

In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing at spatiotemporal locations of interest in a video. Action representation involves fuzzy vector quantization, while action classification is performed by a feedforward neural network. A novel classification algorithm, called minimum class variance extreme learning machine, is proposed in order to enhance the action classification performance. The proposed method can successfully operate in situations that may appear in real application scenarios, since it does not set any assumption concerning the visual scene background and the camera view angle. Experimental results on five publicly available databases, aiming at different application scenarios, denote the effectiveness of both the adopted action recognition approach and the proposed minimum class variance extreme learning machine algorithm.
De-identifying facial images using projections on hyperspheres

A major issue that arises from mass visual media distribution in modern video sharing, social media and cloud services, is the issue of privacy. Malicious users can use these services to track the actions of certain individuals and/or groups thus violating their privacy. As a result the need to hinder automatic facial image identification in images and videos arises. In this paper we propose a method for de-identifying facial images. Contrary to most de-identification methods, this method manipulates facial images so that humans can still recognize the individual or individuals in an image or video frame, but at the same time common automatic identification algorithms fail to do so. This is achieved by projecting the facial images on a hypersphere. From the conducted experiments it can be verified that this method is effective in reducing the classification accuracy under 10%. Furthermore, in the resulting images the subject can be identified by human viewers.
Image authentication techniques for surveillance applications

In automatic video surveillance (VS) systems, the issue of authenticating the video content is of primary importance. Given the ease with which digital images and videos can be manipulated, practically they do not have any value as legal proof, if the possibility of authenticating their content is not provided. In this paper, the problem of authenticating video surveillance image sequences is considered. After an introduction motivating the need for a watermarking-based authentication of VS sequences, a brief survey of the main watermarking-based authentication techniques is presented and the requirements that an authentication algorithm should satisfy for VS applications, are discussed. A novel algorithm which is suitable for VS visual data authentication is also presented and the results obtained by applying it to test data are discussed.
Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning

In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy vector quantization and linear discriminant analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition, and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
Multi modal verification for teleservices and security applications (M2VTS)

The paper presents the European ACTS project "M2VTS" which stands for Multi Modal Verification for Teleservices and Security Applications. The primary goal of this project is to address the issue of secured access to local and centralised services in a multimedia environment. The main objective is to extend the scope of application of network based services by adding novel and intelligent functionalities, enabled by automatic verification systems combining multimodal strategies (secured access based on speech, image or other information). The objectives of the project are also to show that limitations of individual technologies (speaker verification, frontal face authentication, profile identification...) can be overcome by relying on multi modal decisions (combination or fusion of these technologies).
Wideband DOA estimation in spherical harmonic domain using sparse Bayesian learning

This paper focuses on the direction-of-arrival (DOA) estimation of wideband signals using a spherical array which has many advantages in 3D space. We propose two DOA estimation methods in spherical harmonic domain. First of all, spherical Fourier transform (SFT) is used to construct the array signal model. The first method exploits the joint sparsity of all the frequency bins and the second method transforms multiple frequency information into only one measure by high order singular value decomposition (HOSVD). Sparse Bayesian learning (SBL) is then utilized to estimate the DOAs. Simulation results verify the effectiveness of the proposed methods.
Integrated MAP detector with statistical channel information for vector OFDM systems

Vector OFDM (V-OFDM) for single transmit antenna is a general transmission scheme that can bridge the gap between OFDM and SC-FDE (Single-Carrier Frequency Domain Equalization). Because of the attractive properties it has, such as controllable PAPR, capability to exploit the signal space diversity, etc., V-OFDM has drawn more and more attention recently. At the receiver end, the conventional coherent receiver usually assumes separated detector and channel estimator, which limits the system performance. The integrated MAP (IMAP) receiver, proposed by Luo and Huang for V-BLAST and OFDM, combines the detector and channel estimator and is shown to have better performance than the separated ones. In this paper, we exploit the principal of I MAP, and develop the receiving algorithm for V-OFDM. By utilizing the special structure of the V-OFDM signal, we propose some methods to simplify the IMAP receiver. In the simulations, we show that the developed IMAP receiver performs much better than the receiver composed of separated conventional MAP (CMAP) detector and MMSE channel estimator, where both of them assume the same knowledge of the channel correlation statistical information and noise variance. Further, it is shown that when the sub-block size of V-OFDM is 4 and using 2 sub-blocks as pilots, the performance gap between the IMAP receiver, which has only statistical channel information, and the CMAP receiver with perfect instantaneous channel state information is less than
Three-dimensional sound field reproduction based on multi-resolution sparse representation

In this paper, a multi-resolution sparse representation method is proposed to reproduce three-dimensional (3D) Higher Order Ambisonics (HOA) sound fields. The multi-resolution dictionaries are constructed from low to high spatial angle resolution. Based on these dictionaries, the positions of virtual plane wave sources are estimated by complex least absolute shrinkage and selection operator (CLASSO) algorithm. The proposed method can effectively localize primary sources and reduce the computation complexity. Finally the signals of virtual plane waves are adjusted by a debiasing step to derive proper loudspeaker weights. Simulation results show that the proposed method can improve the 3D reproduction performance with a few active loudspeakers and enlarge the size of the spatial sweet spot.
Localization of multiple narrowband acoustic sources in spherical harmonic domain

This paper presents a new method for the localization of multiple narrowband acoustic sources using spherical microphone arrays. The space-time array signal model is transformed into spherical harmonic domain by spherical Fourier transform (SFT). Then subspace decomposition technique is used to obtain signal and noise subspace in the domain. Relying on the recursive relationship of spherical harmonics, signal subspace can possess the displacement invariances which could be used to estimate both the azimuth and elevation angles of acoustic sources impinging on spherical arrays. Simulations demonstrated that this algorithm has a high accuracy of estimating multiple acoustic sources with low computational complexity. Moreover, spherical microphone arrays have the advantage of spherical symmetrical structure, facilitating 3-D direction-of-arrival (DOA) estimation, which is superior to uniform circular arrays (UCA).
DOA Estimation Using Block Variational Sparse Bayesian Learning

In Direction-of-arrival (DOA) estimation, the real-valued sparse Bayesian algorithm degrades the estimation performance by decomposing the complex value into real and imaginary components and combining them independently.We directly use complex probability density functions to model the noise and complex-valued sparse direction weights. Based on the Multiple measurement vectors (MMV), block sparse structure for the direction weights is integrated into the variational Bayesian learning to provide accurate source direction estimates. The proposed algorithm can be used for arbitrary array geometries and does not need the prior information of the incident signal number. Simulation results demonstrate the better performance of the proposed method compared with the real-valued sparse Bayesian algorithm, the Orthogonal matching pursuit (OMP) and l1 norm based complexvalued methods.
A method for improving the accuracy of predicting protein localization

In this paper, a system based on the novel Maximum Variance Projection (MVP) is proposed to improve the performance of protein subcellular localization prediction. Firstly, the protein sequences are quantized into a high dimension space using a new representation approach Position-Specific Score Matrix (PSSM). However, the problems caused by such representation are computation complexity and complicated classifier design. To sort out this problem, a new dimension reduction algorithm, MVP, is introduced. It extracts the essential features from the high dimension feature space. Then, K-Nearest Neighbor (K-NN) classifier is employed to recognize the subcellular localization of proteins according to the new features after dimension reduction. A good experimental result is obtained based on the jackknife dataset.
Evolutionary Discovery of Co-Movement Patterns Among Foreign Currencies

Changes in currency exchange rates can bring significant impact to international macroeconomics and vice versa. Discovering co-movement patterns of currencies in a specific period can help explain economic and financial relationships between the countries under the flexible exchange rate system. In order to discover various co-movement patterns of foreign exchange rates, we propose an evolutionary biclustering algorithm to search for the biclusters each of which has a subset of rows (time points) and a subset of columns (currencies), indicating a group of currencies co-moved within a specific period. We test this algorithm on the historical monthly data of 15 currencies, and successfully detect a number of comovement patterns. We also find that the discovered patterns will be useful as a guide for foreign currency investment.
Multi-beam steering for 3D audio rendering in linear phased loudspeaker arrays

In order to improve flexibility of surrounding stereo system, a novel system for 3D audio rendering is proposed in this paper based on multi-beam steering, which is composed of five beam units using linear loudspeaker arrays. By controlling accurately time delay or phase of loudspeakers, beam can be formed in a desired direction. The simulation results show that the proposed method is reasonable and effective.
A quantitative evaluation model of teachers based on the consumer behavior theory

The study of educational evaluation is by now mostly limited within the realm of the science of education itself, and is frequently found to depend on qualitative analysis. Nevertheless, the study of science is apt to combine several subjects and focus on the accuracy and comparability of the analysis. In this project, a quantitative study of educational evaluation is conducted in the framework of theories and models of economics. This thesis is designed to construct a new quantitative educational evaluation model on teachers based on the theory of consumer behavior, which “is the description of how consumers allocate incomes among different goods and services to maximize their well-being” (Richard, 2001, p. 54). This quantitative model will provide a more efficient and humane evaluation result on teachers and more accurate results are provided for the sake of comparison. In addition, this model is a tentative study to combine education science and economics.
A two-stage method for DOA estimation using spherical arrays

Spherical arrays have become a popular research topic in recent years because of their advantages in direction-of-arrival (DOA) estimation. In this paper, we develop a two-stage method (TSM) based on spherical arrays to estimate DOAs of source signals. First, we propose a novel spherical harmonics estimation of signal parameter via rotational invariance technique that independently obtains the elevation using one recurrence relation of spherical harmonics (SHESPRIT-ELE). Second, we utilize spherical harmonics root-MUSIC, which can only obtain the azimuth with a determined elevation, to estimate the azimuth based on the elevation obtained in the first stage (SHRMUSIC-AZI). The proposed TSM owns high estimation accuracy and low computational complexity. The simulation results verify the performance of the proposed method.
A Hand-Held Indentation System for the Assessment of Mechanical Properties of Soft Tissues In Vivo

Quantitative assessment of the mechanical properties of soft tissues in vivo is required in both clinical and research fields. This paper introduces a hand-held indentation system that employed an electromagnetic spatial sensor as a displacement transducer. The system was pen-sized, portable, and easy to control. The accuracy and reliability of the system were investigated. The effect of indentation rate on the variation of the values of the measured effective Young's modulus was also studied. A series of elastomers with different Young's modulus (which ranged from 13.08 to 36.19 kPa) were assessed with both the hand-held indentation system and a Hounsfield material testing machine. Intraindividual and interindividual variations of the system were tested by five independent operators. The hand-held indentation system was applied to quantitatively assess the effective Young's modulus of human body parts in vivo. Twenty healthy female subjects aged 21.1 plusmn 1.8 years old were included for the in vivo test. The system was shown to be highly accurate (R 2 = 0.99) compared with the results obtained by the mechanical testing machine and had good reliability (intraindividual variation = 5.43%, and interindividual variation = 5.99%). The average effective Young's moduli of the region of umbilicus were 11.31 and 12.65 kPa for two different sites, respectively. It is believed that this hand-held indentations system was an accurate reliable tool for rapidly assessing the mechanical properties of human body tissues in vivo.
Comparison of sonomyography and electromyography of forearm muscles in the guided wrist extension

Electromyography (EMG) and ultrasonography have been widely used for skeletal muscle assessment. The muscle thickness change detected using ultrasound has been recently defined as sonomyography. In this study, the performances of one dimensional sonomyography (1-D SMG) and surface EMG signal in tracking the guided patterns of wrist extension were compared, and the potential of 1-D SMG for skeletal muscle assessment and prosthesis control was investigated. Sixteen normal subjects were instructed to perform the wrist extension under the guidance of displayed sinusoidal, square and triangular waveforms at movement rates of 20, 30, 50 cycles per minute. SMG and EMG root mean squares (RMS) were collected from the extensor carpi radialis respectively and their RMS errors in relation to the guiding signals were compared. Paired t-test showed that the RMS errors of SMG tracking were significantly smaller than those of EMG. The results suggest that SMG signal has great potential to be an alternative method of EMG to evaluate muscle function and control prosthesis.
AR-Based Bayesian Speech Enhancement for Nonstationary Environments

A new technique for enhancing audio signal from a noisy nonstationary environment is presented in the paper. Autoregressive (AR) model is used to efficiently exploit the temporally correlated information of audio and noise signals during a short stationary frame. The temporal models of signals and noisy process are combined to construct a state space. The state space appropriately describes that the observed noisy signal is generated from two underlying sources which evolve with Markovian dynamics across successive step times. In the state space, the clean speech and the noise are two hidden source signals. The recovery of clean speech and the estimation of all the model parameters are carried out within the variational Bayesian framework. The original speech can be estimated as a state using a variational Kalman smoother. The experimental results show that our approach can obtain better performance in terms of signal-to-noise ratio (SNR) measure.