nils-herrmann/clean_scirepeval huggingface dataset:
Investigating Topic Models' Capabilities in Expression Microarray Data Classification

In recent years a particular class of probabilistic graphical models-called topic models-has proven to represent an useful and interpretable tool for understanding and mining microarray data. In this context, such models have been almost only applied in the clustering scenario, whereas the classification task has been disregarded by researchers. In this paper, we thoroughly investigate the use of topic models for classification of microarray data, starting from ideas proposed in other fields (e.g., computer vision). A classification scheme is proposed, based on highly interpretable features extracted from topic models, resulting in a hybrid generative-discriminative approach; an extensive experimental evaluation, involving 10 different literature benchmarks, confirms the suitability of the topic models for classifying expression microarray data.
Statistical classification of raw textile defects

The problem of classification of defects occurring in a textile manufacture is addressed. A new classification scheme is devised in which different features, extracted from the gray level histogram, the shape, and cooccurrence matrices, are employed. These features are classified using a support vector machines (SVM) based framework, and an accurate analysis of different multiclass classification schemes and SVM parameters has been carried out. The system has been tested using two textile databases showing very promising results.
Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition

Characterizing an image region by its feature intercorrelations is a modern trend in computer vision. In this paper, we introduce a new image descriptor that can be seen as a natural extension of a standard covariance descriptor with the advantage of capturing nonlinear and nonmonotone dependencies. Inspired from the recent advances in mathematical statistics of Brownian motion, we can express highly complex structural information in a compact and computationally efficient manner. We show that our Brownian covariance descriptor can capture richer image characteristics than the covariance descriptor. Additionally, a detailed analysis of the Brownian manifold reveals that opposite to the classical covariance descriptor, the proposed descriptor lies in a relatively flat manifold, which can be treated as a Euclidean. This brings significant boost in the efficiency of the descriptor. The effectiveness and the generality of our approach is validated on two challenging vision tasks, pedestrian classification, and person reidentification. The experiments are carried out on multiple datasets achieving promising results.
A confidence-based approach to enhancing underwater acoustic image formation

This paper describes a flexible technique to enhance the formation of short-range acoustic images so as to improve image quality and facilitate the tasks of subsequent postprocessing methods. The proposed methodology operates as an ideal interface between the signals formed by a focused beamforming technique (i.e., the beam signals) and the related image, whether a two-dimensional (2-D) or three-dimensional (3-D) one. To this end, a reliability measure has been introduced, called confidence, which allows one to perform a rapid examination of the beam signals and is aimed at accurately detecting echoes backscattered from a scene. The confidence-based approach exploits the physics of the process of image formation and generic a priori knowledge of a scene to synthesize model-based signals to be compared with actual backscattered echoes, giving, at the same time, a measure of the reliability of their similarity. The objectives that can be attained by this method can be summarized in a reduction in artifacts due to the lowering of the side-lobe level, a better lateral resolution, a greater accuracy in range determination, a direct estimation of the reliability of the information acquired, thus leading to a higher image quality and hence a better scene understanding. Tests on both simulated and actual data (concerning both 2-D and 3-D images) show the higher efficiency of the proposed confidence-based approach, as compared with more traditional techniques.
Auditory dialog analysis and understanding by generative modelling of interactional dynamics

In the last few years, the interest in the analysis of human behavioral schemes has dramatically grown, in particular for the interpretation of the communication modalities called social signals. They represent well defined interaction patterns, possibly unconscious, characterizing different conversational situations and behaviors in general. In this paper, we illustrate an automatic system based on a generative structure able to analyze conversational scenarios. The generative model is composed by integrating a Gaussian mixture model and the (observed) influence model, and it is fed with a novel kind of simple low-level auditory social signals, which are termed steady conversational periods (SCPs). These are built on duration of continuous slots of silence or speech, taking also into account conversational turn-taking. The interactional dynamics built upon the transitions among SCPs provide a behavioral blueprint of conversational settings without relying on segmental or continuous phonetic features. Our contribution here is to show the effectiveness of our model when applied on dialogs classification and clustering tasks, considering dialogs between adults and between children and adults, in both flat and arguing discussions, and showing excellent performances also in comparison with state-of-the-art frameworks.
Covariances in Computer Vision and Machine Learning

Covariance matrices play important roles in many areas of mathematics, statistics, and machine learning, as well as their applications. In computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications. In this book, we begin by presenting an overview of the {\it finite-dimensional covariance matrix} representation approach of images, along with its statistical interpretation. In particular, we discuss the various distances and divergences that arise from the intrinsic geometrical structures of the set of Symmetric Positive Definite (SPD) matrices, namely Riemannian manifold and convex cone structures. Computationally, we focus on kernel methods on covariance matrices, especially using the Log-Euclidean distance. We then show some of the latest developments in the generalization of the finite-dimensional covariance matrix representation to the {\it infinite-dimensional covariance operator} representati...
Focused beamforming for underwater imaging: depth of field and range resolution optimization

Addresses two problems that characterize the formation of underwater acoustic images (3D maps) by focused beamforming. When usual parameters are chosen to achieve an accurate lateral resolution, the depth of field provided by focused beamforming is insufficient to build a flexible imaging system. Moreover, the accuracy of range determination is directly proportional to the signal bandwidth. The authors propose an evaluation methodology, namely, confidence measure, which, is calculated on the beam signal profile, and permits one to determine the exact position of an attenuated pulse in time by a comparison with an expected model of the beam signal. The confidence measure makes it possible to improve the precision of range determination and to generate an algorithm that, without any a priori knowledge of a scene, succeeds in focusing each beam signal correctly. Finally, they compare, in terms of image quality, sensitivity to noise, and processing time, the results obtained by the proposed algorithm with those obtained by fixed and dynamic focalizations.<<ETX>>
Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification

Capturing the essential characteristics of visual objects by considering how their features are inter-related is a recent philosophy of object classification. In this paper, we embed this principle in a novel image descriptor, dubbed Heterogeneous Auto-Similarities of Characteristics (HASC). HASC is applied to heterogeneous dense features maps, encoding linear relations by co variances and nonlinear associations through information-theoretic measures such as mutual information and entropy. In this way, highly complex structural information can be expressed in a compact, scale invariant and robust manner. The effectiveness of HASC is tested on many diverse detection and classification scenarios, considering objects, textures and pedestrians, on widely known benchmarks (Caltech-101, Brodatz, Daimler Multi-Cue). In all the cases, the results obtained with standard classifiers demonstrate the superiority of HASC with respect to the most adopted local feature descriptors nowadays, such as SIFT, HOG, LBP and feature co variances. In addition, HASC sets the state-of-the-art on the Brodatz texture dataset and the Daimler Multi-Cue pedestrian dataset, without exploiting ad-hoc sophisticated classifiers.
On-line adaptive background modelling for audio surveillance

We investigate the problem of automatic audio surveillance. This aspect of the surveillance, which extends the more investigated area of video surveillance, can be very informative to solve many problems in real situations. Similarly to video surveillance, also in this case it is necessary to build a background (BG) model, so that it is immediate to discover foreground (FG) events. To this end, we first introduce the concepts of audio BG and FG in an automated surveillance scenario. Subsequently, we propose a novel audio BG system able to build in real time an adaptive model of the audio scene BG, and to promptly detect unexpected FG auditory events. The method is based on the probabilistic modelling of the audio data stream using separate sets of adaptive Gaussian mixture models, working on the audio-frequency spectrum. This approach is also characterized by the use of only one microphone and on-line functioning, so that it can be directly used in real situations, also to support a video surveillance system. Preliminary results show the effectiveness of the approach to discover different FG audio situations.
Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature

A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed. A likelihood function is derived from this map and fed to a particle filter yielding the target location estimation on the acoustic image. The method is tested on two real environments, addressing both speaker and vehicle tracking. The comparison with a couple of trackers, relying on the acoustic map only, shows a sharp improvement in performance, paving the way to the application of audio tracking in real challenging environments.
Violence detection in crowded scenes using substantial derivative

This paper presents a novel video descriptor based on substantial derivative, an important concept in fluid mechanics, that captures the rate of change of a fluid property as it travels through a velocity field. Unlike standard approaches that only use temporal motion information, our descriptor exploits the spatio-temporal characteristic of substantial derivative. In particular, the spatial and temporal motion patterns are captured by respectively the convective and local accelerations. After estimating the convective and local field from the optic flow, we followed the standard bag-of-word procedure for each motion pattern separately, and we concatenated the two resulting histograms to form the final descriptor. We extensively evaluated the effectiveness of the proposed method on five benchmarks, including three standard datasets (Violence in Movies, Violence In Crowd, and BEHAVE), and two new video-survelliance sequences downloaded from Youtube. Our experiments show how the proposed approach sets the new state-of-the-art on all benchmarks and how the structural information captured by convective acceleration is essential to detect violent episodes in crowded scenarios.
Background Initialization in Cluttered Sequences

In this paper we propose a technique to robustly estimate the background in a cluttered sequence, i.e., a sequence where occluding objects persist in the same position for a considerable portion of time. As pixel-level heuristic are not sufficient in this case, we introduce spatial support. First the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then the background is grown incrementally by selecting at each step the best continuation of the current background, according to the principles of visual grouping. The method rests on sound principles in all its stages, and only few, intelligible parameters are needed. Experiments with real sequences illustrate the approach.
Temporal Poselets for Collective Activity Detection and Recognition

Detection and recognition of collective human activities are important modules of any system devoted to high level social behavior analysis. In this paper, we present a novel semantic-based spatio-temporal descriptor which can cope with several interacting people at different scales and multiple activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded environments - the scenario most difficult to analyse because of occlusions. In particular, we extend the Pose let detector approach by defining a descriptor based on Pose let activation patterns over time, named TPOS. We will show that this descriptor can effectively tackle complex real scenarios allowing to detect humans in the scene, to localize (in space-time) human activities, and perform collective group activity recognition in a joint manner, reaching state-of-the-art results.
Audio-Visual Event Recognition in Surveillance Video Sequences

In the context of the automated surveillance field, automatic scene analysis and understanding systems typically consider only visual information, whereas other modalities, such as audio, are typically disregarded. This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage and coupled with an audio BG/FG modelling scheme. These processes permit one to detect separate audio and visual patterns representing unusual unimodal events in a scene. The integration of audio and visual data is subsequently performed by exploiting the concept of synchrony between such events. The audio-visual (AV) association is carried out online and without need for training sequences, and is actually based on the computation of a characteristic feature called audio-video concurrence matrix, allowing one to detect and segment AV events, as well as to discriminate between them. Experimental tests involving classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by employing the single modalities and without considering the synchrony issue
A statistical signature for automatic dialogue classification

In the last few years, there has been a certain attention to the problem of human-human communication, trying to devise artificial systems able to mediate a conversational setting between two or more people. In this paper, we designed an automatic system based on a generative structure able to classify hard dialog acts. The generative model is composed by integrating a hierarchical Gaussian mixture model and the Influence Model, originating a brand new method able to deal with such difficult scenarios. The method has been tested on a set of conversational settings involving dialogues between adults and children and adults, in flat and arguing discussions, proving very accurate classification results.
Uncalibrated interpolation of rigid displacements for view synthesis

In this paper we present a method for novel view synthesis from two uncalibrated reference views. Snapshots of a scene are created as if they were taken from a different "virtual" viewpoint. The relative affine structure is used to describe the geometry of the scene and then to extrapolate and interpolate novel views. The contribution of this paper is an automatic method for specifying the virtual viewpoint in an uncalibrated setting, based on the interpolation and extrapolation of the epipolar geometry linking the reference views. Experimental results using synthetic and real images are shown.
Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers

A score function induced by a generative model of the data can provide a feature vector of a fixed dimension for each data sample. Data samples themselves may be of differing lengths (e.g., speech segments or other sequential data), but as a score function is based on the properties of the data generation process, it produces a fixed-length vector in a highly informative space, typically referred to as “score space.” Discriminative classifiers have been shown to achieve higher performances in appropriately chosen score spaces with respect to what is achievable by either the corresponding generative likelihood-based classifiers or the discriminative classifiers using standard feature extractors. In this paper, we present a novel score space that exploits the free energy associated with a generative model. The resulting free energy score space (FESS) takes into account the latent structure of the data at various levels and can be shown to lead to classification performance that at least matches the performance of the free energy classifier based on the same generative model and the same factorization of the posterior. We also show that in several typical computer vision and computational biology applications the classifiers optimized in FESS outperform the corresponding pure generative approaches, as well as a number of previous approaches combining discriminating and generative models.
Learning with dataset bias in latent subcategory models

Latent subcategory models (LSMs) offer significant improvements over training linear support vector machines (SVMs). Training LSMs is a challenging task due to the potentially large number of local optima in the objective function and the increased model complexity which requires large training set sizes. Often, larger datasets are available as a collection of heterogeneous datasets. However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias. In this paper, we present a model which jointly learns an LSM for each dataset as well as a compound LSM. The method provides a means to borrow statistical strength from the datasets while reducing their inherent bias. In experiments we demonstrate that the compound LSM, when tested on PASCAL, LabelMe, Caltech101 and SUN09 in a leave-one-dataset-out fashion, achieves an average improvement of over 6.5% over a previous SVM-based undoing bias approach and an average improvement of over 8.5% over a standard LSM trained on the concatenation of the datasets.
A Probabilistic Approach to the Coupled Reconstruction and Restoration of Underwater Acoustic Images

Describes a probabilistic technique for the coupled reconstruction and restoration of underwater acoustic images. The technique is founded on the physics of the image-formation process. Beamforming, a method widely applied in acoustic imaging, is used to build a range image from backscattered echoes, associated point by point with another type of information representing the reliability (or confidence) of such an image. Unfortunately, this kind of images is plagued by problems due to the nature of the signal and to the related sensing system. In the proposed algorithm, the range and confidence images are modeled as Markov random fields whose associated probability distributions are specified by a single energy function. This function has been designed to fully embed the physics of the acoustic image-formation process by modeling a priori knowledge of the acoustic system, the considered scene, and the noise-affecting measures and also by integrating reliability information to allow the coupled and simultaneous reconstruction and restoration of both images. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed range image map and the restored confidence image are obtained by minimizing the energy function using simulated annealing. Experimental results show the improvement of the processed images over those obtained by other methods performing separate reconstruction and restoration processes that disregard reliability information.
Real-time adaptive regulation of a visual camera for automatic investigation of changing environments

An adaptive strategy for regulating the intrinsic parameters of a CCD visual camera is proposed. The parameter regulation procedure is based on the computation of the "quality" of an acquired signal by means of a set of functions which estimate the goodness of the datum. Each function performs a quantitative evaluation by computing several features derived from the high-frequency content of an image and from the histogram analysis. The regulation strategy uses such values to compute the actual values of the camera parameters, i.e., focusing distance, aperture diameter, electronic gain, and black level. Moreover, the same strategy can be applied both to the whole image and to smaller subareas, so providing a focus-of-attention mechanism. This is useful for the automatic investigation and control of environments subject to dynamically changing conditions (e.g., the illumination and the structure of a scene), whenever it is not possible to interact directly with such environments. The system has been implemented on a Sun Spark Station II linked with a CCD camera (with the capability of computer-controlled parameters) and with an image-acquisition system, which permits real time performances.<<ETX>>
Wavelet-based Processing of EEG Data for Brain-Computer Interfaces

Brain-Computer Interfaces based on non-invasive electroencephalographic (EEG) signals were recently made practical through sophisticated algorithms and clever systems, in such a way that the dream of effortlessly translating volition into action is coming true, albeit in a limited way. However, a low signal-to-noise ratio and the presence of frequent artefacts, such as eye blinks, contaminate the recordings and make the recognition of the underlying mental processes difficult. In this study, a novel waveletbased signal processing technique, ContinuousWavelet Regression, has been applied to refine EEG data in a wellknown setting. The recordings of spontaneous (i.e., asynchronous) signals of subjects performing highly different cognitive tasks have been processed by our algorithm, and then analyzed and classified, obtaining very promising results as compared with those obtained by previous studies.