nils-herrmann/clean_scirepeval huggingface dataset:
IMPART: Big media data processing and analysis for film production

A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). The EU project IMPART (impart.upf.edu) has been researching solutions that improve the integration and understanding of the quality of the multiple data sources to support creative decisions onset or near it, and an enhanced post-production as well. The main results covered in this paper are: a public multisource production dataset made available for research purposes, monitoring and quality assurance of multicamera set-ups, multisource registration, anthropocentric visual analysis for semantic content annotation, acceleration of 3D reconstruction, and integrated 2D-3D web visualization tools.
Face authentication using variants of elastic graph matching based on mathematical morphology that incorporate local discriminant coefficients

Two novel variants of dynamic link architecture that are based on mathematical morphology and incorporate local coefficients which weigh the contribution of each node according to its discriminatory power in elastic graph matching are proposed, namely, the morphological dynamic link architecture and the morphological signal decomposition-dynamic link architecture. They are tested for face authentication in a cooperative scenario where the candidates claim an identity to be checked. Their performance is evaluated in terms of their receiver operating characteristics and the equal error rate achieved in the M2VTS database. An equal error rate of 6.6%-6.8% is reported.
One class classification applied in facial image analysis

In this paper, we apply One-Class Classification methods in facial image analysis problems. We consider the cases where the available training data information originates from one class, or one of the available classes is of high importance. We propose a novel extension of the One-Class Extreme Learning Machines algorithm aiming at minimizing both the training error and the data dispersion and consider solutions that generate decision functions in the ELM space, as well as in ELM spaces of arbitrary dimensionality. We evaluate the performance in publicly available datasets. The proposed method compares favourably to other state-of-the-art choices.
Maximum Margin Projection Subspace Learning for Visual Data Analysis

Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.
Bag of Embedded Words learning for text retrieval

The word embedding models are capable of capturing the semantic content of the textual words. The process of extracting a set of word embedding vectors from a text document is similar to the feature extraction step of the Bag-of-Features pipeline, which is usually used in computer vision tasks. That gives rise to the Bag-of-Embedded Words (BoEW) model. In this paper a novel learning technique that optimizes both the word embedding and the codebook of the BoEW model towards text retrieval is proposed. The proposed method adheres to the cluster hypothesis that states that points in the same cluster are likely to fulfill the same information need and it is demonstrated, using two text datasets, that can significantly increase the retrieval precision. Finally, the proposed technique uses smaller representations than the competitive representation methods, that allows to reduce both the retrieval time and the storage requirements.
Progressive neural network training for the Open Racing Car Simulator

In this paper a novel methodology for training neural networks as car racing controllers is proposed. Our effort is focused on finding a new fast and effective way to train neural networks that will avoid stacking in local minima and can learn from advanced bot-teachers to handle the basic tasks of steering and acceleration in The Open Racing Car Simulator (TORCS). The proposed approach is based on Neural Networks that learn progressively the driving behaviour of other bots. Starting with a simple rule-based decision driver, our scope is to handle its decisions with NN and increase its performance as much as possible. In order to do so, we propose a sequence of Neural networks that are gradually trained from more dexterous drivers, as well as, from the simplest to the most skillful controller. Our method is actually, an effective initialization method for Neural Networks that leads to increasingly better driving behavior. We have tested the method in several tracks of increasing difficulty. In all cases the proposed method resulted in improved bot decisions.
View-Invariant Action Recognition Based on Artificial Neural Networks

In this paper, a novel view invariant action recognition method based on neural network representation and recognition is proposed. The novel representation of action videos is based on learning spatially related human body posture prototypes using self organizing maps. Fuzzy distances from human body posture prototypes are used to produce a time invariant action representation. Multilayer perceptrons are used for action classification. The algorithm is trained using data from a multi-camera setup. An arbitrary number of cameras can be used in order to recognize actions using a Bayesian framework. The proposed method can also be applied to videos depicting interactions between humans, without any modification. The use of information captured from different viewing angles leads to high classification performance. The proposed method is the first one that has been tested in challenging experimental setups, a fact that denotes its effectiveness to deal with most of the open issues in action recognition.
Movie shot selection preserving narrative properties

Automatic shot selection is an important aspect of movie summarization that is helpful both to producers and to audiences, e.g., for market promotion or browsing purposes. However, most of the related research has focused on shot selection based on low-level video content, which disregards semantic information, or on narrative properties extracted from text, which requires the movie script to be available. In this work, semantic shot selection based on the narrative prominence of movie characters in both the visual and the audio modalities is investigated, without the need for additional data such as a script. The output is a movie summary that only contains video frames from selected movie shots. Selection is controlled by a user-provided shot retention parameter, that removes key-frames/key-segments from the skim based on actor face appearances and speech instances. This novel process (Multimodal Shot Pruning, or MSP) is algebraically modelled as a multimodal matrix Column Subset Selection Problem, which is solved using an evolutionary computing approach.
Human identification from human movements

In this paper a multi-modal method for human identification that exploits the discrimination power of several movement types performed from the same human is proposed. Utilizing a fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) based algorithm, an unknown movement is first classified, and, then, the person performing the movement is recognized from a movement specific person classifier. In case that the unknown person performs more than one movements, a multi-modal algorithm combines the results of the individual classifiers to yield the final decision for the id of the unknown human. Using a publicly available database, we provide promising results regarding the discrimination power of the different movements for the human identification task, as well as we indicate that the combination of the individual classifiers may increase the robustness of the human recognition algorithm.
Watermarking of 3D models using principal component analysis

A novel method for 3D model watermarking, robust to geometric distortions such as rotation, translation and scaling, is proposed. A ternary watermark is embedded in the vertex topology of a 3D model. A transformation of the model to an invariant space is proposed prior to watermark embedding. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks giving very good results.
Stereoscopic video description for human action recognition

In this paper, a stereoscopic video description method is proposed that indirectly incorporates scene geometry information derived from stereo disparity, through the manipulation of video interest points. This approach is flexible and able to cooperate with any monocular low-level feature descriptor. The method is evaluated on the problem of recognizing complex human actions in natural settings, using a publicly available action recognition database of unconstrained stereoscopic 3D videos, coming from Hollywood movies. It is compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Experimental results denote that the proposed approach outperforms them and achieves state-of-the-art performance.
Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.
Improving the detection reliability of correlation-based watermarking techniques

The performance of watermarking schemes based on correlation detection is closely related to the frequency characteristics of the watermark sequence. In order to improve both detection reliability and robustness against attacks, embedding of watermarks with high-frequency spectrum, in the low frequencies of the DFT domain, is introduced in this paper and theoretical analysis of correlation based watermarking techniques with multiplicative embedding is performed. The proposed watermarking framework is successfully applied to audio signals, demonstrating its superiority with respect to both robustness and inaudibility. Experiments are conducted, in order to verify the validity of the theoretical analysis results.
Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction

In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods.
Robust multibit audio watermarking in the temporal domain

In this paper, a technique for temporal embedding of a multibit watermark on an audio signal is proposed. The method is based on generating a chaotic sequence which is used for modifying the audio samples. A chaotic sequence generates the watermark, while the parameters of the embedding procedure are chosen so as to minimize the perceivable distortion of the initial signal, while preserving the power of the watermark at a detectable level. The watermark is resistant to lowpass manipulation attacks, such as filtering or compression, and cropping attacks as well.
Class-specific nonlinear subspace learning based on optimized class representation

In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems, where its performance is compared with that of the standard class-specific subspace learning approach, as well as other nonlinear discriminant subspace learning techniques. Experimental results denote the effectiveness of this new approach, since it consistently outperforms the standard one and outperforms other nonlinear discriminant subspace learning techniques in most cases.
Distributed, MapReduce-Based Nearest Neighbor and E-Ball Kernel k-Means

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is a state of the art clustering algorithm, which employs the kernel trick, in order to perform clustering on a higher dimensionality space, thus overcoming the limitations of classic k-Means regarding the non linear separability of the input data. Kernel k-Means typically computes the kernel matrix, which contains the results of the kernel function for every possible sample combination. This matrix can be viewed as the weight matrix of a full graph, where the samples are the vertices and the edges are weighed according to the similarity between the samples they connect, according to the kernel function. In this context, it is possible to work on the Nearest Neighbor graph, where each sample is only connected to some of its closest samples, or only using information from samples that are sufficiently close to each other, referred to as ε-ball. Doing so reduces the size of the kernel matrix and can provide improved clustering results. In this paper, we present a MapReduce based distributed implementation of Nearest Neighbor and ε-ball Kernel k-Means.
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks

In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated patterns of the price movements. Being able to detect them in advance, allows them to take profitable positions or avoid anomalous events in the financial markets. In this work we proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. The dataset that we use contains more than 4 million limit order events and our comparison with other methods, like Multilayer Neural Networks and Support Vector Machines, shows that CNNs are better suited for this kind of task.
Performance analysis of correlation-based watermarking schemes employing Markov chaotic sequences

In this paper, theoretical performance analysis of watermarking schemes based on correlation detection is undertaken, leading to a number of important observations on the watermarking system detection performance. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are investigated. Correlation/spectral properties of such sequences are easily controllable, which is a fact that reflects on the watermarking system performance. A family of chaotic maps, namely the skew tent map family, is used to verify the theoretical analysis. Skew tent chaotic sequences are compared against the widely used pseudorandom sequences, indicating the superiority of the former in watermarking applications. The minimum number of samples required for reliable watermark detection is also investigated. Experiments using audio data are conducted to verify the theoretical analysis results.
Nearest Class Vector Classification for Large-Scale Learning Problems

In this paper, we describe a method for combined metric learning and classification, that is based on logistic discrimination for the determination of a low-dimensional feature space of increased discrimination power. An iterating optimization process is applied to this end, where the probability of correct classification rate is increased at each optimization step. Extensions of the method that allow richer class representations and non-linear feature space determination and classification are also described. The described optimization schemes are solved by following (stochastic or mini-batch) gradient descent optimization, which is well suited for large-scale learning problems.
Multi-bit image watermarking robust to geometric distortions

A novel method for multi-bit image watermarking robust to geometric distortions is proposed. A binary watermark is embedded in a grayscale or a color host image. The ability of progressive watermark detection enables fast and robust watermark detection even after several geometric distortions of the watermarked image. The embedding of a multi-bit message and its robust decoding after the watermark detection is also discussed. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks.
Dimensionality Reduction Using Similarity-Induced Embeddings

The vast majority of dimensionality reduction (DR) techniques rely on the second-order statistics to define their optimization objective. Even though this provides adequate results in most cases, it comes with several shortcomings. The methods require carefully designed regularizers and they are usually prone to outliers. In this paper, a new DR framework that can directly model the target distribution using the notion of similarity instead of distance is introduced. The proposed framework, called similarity embedding framework (SEF), can overcome the aforementioned limitations and provides a conceptually simpler way to express optimization targets similar to existing DR techniques. Deriving a new DR technique using the SEF becomes simply a matter of choosing an appropriate target similarity matrix. A variety of classical tasks, such as performing supervised DR and providing out-of-sample extensions, as well as, new novel techniques, such as providing fast linear embeddings for complex techniques, are demonstrated in this paper using the proposed framework. Six data sets from a diverse range of domains are used to evaluate the proposed method and it is demonstrated that it can outperform many existing DR techniques.
Using Particle Swarm Optimization for scaling and rotation invariant face detection

Common face detection algorithms exhaustively search in all possible locations in the image for precisely located, frontal faces. In this paper, a novel face detection algorithm based on Particle Swarm Optimization (PSO) method for searching in the image is proposed. The algorithm uses a linear Support Vector Machine (SVM) as fast and accurate classifier and searches for a face in four dimensions: plane, orientation of the face, size of the face. Using PSO, the exhaustive search in all possible combinations of the 4D coordinates can be avoided, saving time and decreasing the computational complexity. Moreover, linear SVMs are proved to be a powerful and fast classifier for demanding applications. Experimental results under real recording conditions in the BioID and VALID database are very promising and indicate the potential use of the proposed approach to real applications.
Face detection based on deep convolutional neural networks exploiting incremental facial part learning

Deep learning methods are powerful approaches but often require expensive computations and lead to models of high complexity which need to be trained with large amounts of data. In this paper, we consider the problem of face detection and we propose a light-weight deep convolutional neural network that achieves a state-of-the-art recall rate at the challenging FDDB dataset. Our model is designed with a view to minimize both training and run time and outperforms the convolutional network used in  for the same task. Our model consists only of 113.864 free parameters whereas the previously proposed CNN for face detection had 60 million parameters. We propose a new training method that gradually increases the difficulty of both negative and positive examples and has proved to drastically improve training speed and accuracy. Our second approach, involves training a separate deep network to detect individual facial features whilst creating a model that combines the outputs of two different networks. Both methods are able to detect faces under severe occlusion and unconstrained pose variation and meet the difficulties and the large variations of real-world face detection.
Facial image clustering in stereo videos using local binary patterns and double spectral analysis

In this work we propose the use of local binary patterns in combination with double spectral analysis for facial image clustering applied to 3D (stereoscopic) videos. Double spectral clustering involves the fusion of two well known algorithms: Normalized cuts and spectral clustering in order to improve the clustering performance. The use of local binary patterns upon selected fiducial points on the facial images proved to be a good choice for describing images. The framework is applied on 3D videos and makes use of the additional information deriving from the existence of two channels, left and right for further improving the clustering results.