nils-herrmann/clean_scirepeval huggingface dataset:
Central Subspace Dimensionality Reduction Using Covariance Operators

We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.
Multi-cue Structure Preserving MRF for Unconstrained Video Segmentation

Video segmentation is a stepping stone to understanding video context. Video segmentation enables one to represent a video by decomposing it into coherent regions which comprise whole or parts of objects. However, the challenge originates from the fact that most of the video segmentation algorithms are based on unsupervised learning due to expensive cost of pixelwise video annotation and intra-class variability within similar unconstrained video classes. We propose a Markov Random Field model for unconstrained video segmentation that relies on tight integration of multiple cues: vertices are defined from contour based superpixels, unary potentials from temporally smooth label likelihood and pairwise potentials from global structure of a video. Multi-cue structure is a breakthrough to extracting coherent object regions for unconstrained videos in absence of supervision. Our experiments on VSB100 dataset show that the proposed model significantly outperforms competing state-of-the-art algorithms. Qualitative analysis illustrates that video segmentation result of the proposed model is consistent with human perception of objects.
Impact of dynamic model learning on classification of human motion

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and analysis of figure motion has employed either generic or highly specific hand-tailored dynamic models superficially coupled with hidden Markov models (HMMs) of motion regimes. Recently, an alternative class of learned dynamic models known as switching linear dynamic systems (SLDSs) has been cast in the framework of dynamic Bayesian networks (DBNs) and applied to analysis and tracking of the human figure. In this paper we further study the impact of learned SLDS models on analysis and tracking of human motion and contrast them to the more common HMM models. We develop a novel approximate structured variational inference algorithm for SLDS, a globally convergent DBN inference scheme, and compare it with standard SLDS inference techniques. Experimental results on learning and analysis of figure dynamics from video data indicate the significant potential of the SLDS approach.
Enhancement of Hopfield neural networks using stochastic noise processes

Hopfield neural networks (HNN) are a class of densely connected single layer nonlinear networks of perceptrons. The network's energy function is defined through a learning procedure so that its minima coincide with states from a predefined set. However, because of the network's nonlinearity a number of undesirable local energy minima emerge from the learning procedure. This has shown to significantly effect the network's performance. In this work we present a stochastic process enhanced bipolar HNN. Presence of the stochastic process in the network enables us to describe its evolution using the Markov chains theory. When faced with a fixed network topology, the desired final distribution of states can be reached by modulating the network's stochastic process. Guided by the desired final distribution, we propose a general L/sup 2/ norm error density function optimization criterion for enhancement of the Hopfield neural network performance. This criterion can also be viewed in terms of stability intervals associated with the desired and non-desired stable states of the network. Because of the complexity of the general criterion we relax the optimization to the set of non-desired states. We further formulate a stochastic process design based on the stability intervals, which satisfies the optimization criterion and results in enhanced performance of the network. Our experimental simulations confirm the predicted improvement in performance.
On the Role of Local Matching for Efficient Semi-supervised Protein Sequence Classification

Recent studies in protein sequence analysis have leveraged the power of unlabeled data. For example, the profile and mismatch neighborhood kernels have shown significant improvements over classifiers estimated under the fully supervised setting. In this study, we present a principled and biologically motivated framework that more effectively exploits the unlabeled data by only utilizing regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias kernel estimations that rely on unlabeled data, we also propose a method to remove this bias and improve performance of resulting classifiers.Combined with a computationally efficient sparse family of string kernels, our proposed framework achieves state-of-the-art accuracy in semi-supervised protein remote homology detection on three large unlabeled databases.
SDLDS—System for Digital Logic Design and Simulation

This paper presents the basic features of a software system developed to support the teaching of digital logic, as well as the experience of using it in the Digital Logic course taught at the School of Electrical Engineering, University of Belgrade, Serbia. The system has been used for several years, both by students for self-learning and laboratory work, and by teachers to automate the assessment and verification of students' work. The system allows users to design and simulate a switching circuit. It also collects data on all student activities and transfers these to the school's information system. Finally, the paper gives figures demonstrating the overall benefits of the system.
Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification

A mixture of Bayesian Network Classifiers(BNC) has a potential to yield superior classification and generative performance to a single BNC model. We introduce novel discriminative learning methods for mixtures of BNCs. Unlike a single BNC model where the discriminative learning resorts to a gradient search, we can exploit the properties of a mixture to alleviate the complex learning task. The proposed method adds mixture components recursively via functional gradient boosting while maximizing the conditional likelihood. This method is highly efficient as it reduces to generative learning of a base BNC model on weighed data. The proposed approach is particularly suited to sequence classification problems where the kernels in the base model are usually too complex for effective gradient search. We demonstrate the improved classification performance of the proposed methods in an extensive set of evaluations on time-series sequence data, including human motion classification problems.
Fast Motif Selection for Biological Sequences

We consider the problem of identifying motifs, recurring or conserved patterns, in the sets of biological sequences. To solve this task, we present new deterministic and exact algorithms for finding patterns that are embedded as exact or inexact instances in all or most of the input strings. The proposed algorithms (1) improve search efficiency compared to existing exact algorithms by focusing search on a selected set of potential motif instances, and (2) scale well with the input length and the size of alphabet.Our algorithms are orders of magnitude faster than existingexact algorithms for common pattern identification. We evaluate our algorithms on benchmark motif finding problemsand real applications in biological sequence analysis and show that they exhibit significant running time improvements compared to the state-of-the-art approaches.
Multimodal speaker detection using error feedback dynamic Bayesian networks

Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous multi-sensory data such as video and sound. Temporal fusion of multiple sensors has been efficiently formulated using dynamic Bayesian networks (DBNs) which allows the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately simple learning methods can cause such appealing models to fail when the data exhibits complex behavior. We formulate a learning framework for DBNs based on error-feedback and statistical boosting theory. We apply this framework to the problem of audio/visual speaker detection in an interactive kiosk environment using "off-the-shelf" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). Detection results obtained in this setup demonstrate superiority of our learning framework over that of the classical ML learning in DBNs.
Discriminative Learning of Dynamical Systems for Motion Tracking

We introduce novel discriminative learning algorithms for dynamical systems. Models such as conditional random fields or maximum entropy Markov models outperform the generative hidden Markov models in sequence tagging problems in discrete domains. However, continuous state domains introduce a set of constraints that can prevent direct application of these traditional models. Instead, we suggest to learn generative dynamic models with discriminative cost functionals. For linear dynamical systems, the proposed methods provide significantly lower prediction error than the standard maximum likelihood estimator, often comparable to nonlinear models. As a result, the models with lower representational capacity but computationally more tractable than nonlinear models can be used for accurate and efficient state estimation. We evaluate the generalization performance of our methods on the 3D human pose tracking problem from monocular videos. The experiments indicate that the discriminative learning can lead to improved accuracy of pose estimation with no increase in computational cost of tracking.
3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image features to 3D human pose estimates. PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics. We apply the PLSA approach to motion tracking by extending it to a sequential setting where the latent variables describe intrinsic motion semantics linking human figure appearance to 3D pose estimates. This dynamic PLSA (DPLSA) approach is in contrast to many current methods that directly learn the often high-dimensional image-to-pose mappings and utilize subspace projections as a constraint on the pose space alone. As a consequence, such mappings may often exhibit increased computational complexity and insufficient generalization performance. We demonstrate the utility of the proposed model on the synthetic dataset and the task of 3D human motion tracking in monocular image sequences with arbitrary camera views. Our experiments show that the proposed approach can produce accurate pose estimates at a fraction of the computational cost of alternative subspace tracking methods.
Embedded Profile Hidden Markov Models for Shape Analysis

An ideal shape model should be both invariant to global transformations and robust to local distortions. In this paper we present a new shape modeling framework that achieves both efficiently. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is then built on such descriptors to represent a class of similar shapes. PHMMs are a particular type of Hidden Markov Models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions, and missing parts. The sparseness of the PHMM structure provides efficient inference and learning algorithms for shape modeling and analysis. To capture the global characteristics of a class of shapes, the PHMM parameters are further embedded into a subspace that models long term spatial dependencies. The new framework can be applied to a wide range of problems, such as shape matching/registration, classification/recognition, etc. Our experimental results demonstrate the effectiveness and robustness of this new model in these different settings.
Face tracking and recognition with visual constraints in real-world videos

We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework. The generative term conforms the particles to the space of generic face poses while the discriminative one ensures rejection of poorly aligned targets. This leads to a tracker that significantly improves robustness against abrupt appearance changes and occlusions, critical for the subsequent recognition phase. Identity of the tracked subject is established by fusing pose-discriminant and person-discriminant features over the duration of a video sequence. This leads to a robust video-based face recognizer with state-of-the-art recognition performance. We test the quality of tracking and face recognition on real-world noisy videos from YouTube as well as the standard Honda/UCSD database. Our approach produces successful face tracking results on over 80% of all videos without video or person-specific parameter tuning. The good tracking performance induces similarly high recognition rates: 100% on Honda/UCSD and over 70% on the YouTube set containing 35 celebrities in 1500 sequences.
Digital Logic Simulator

This paper describes a visual simulator developed at the School of Electrical Engineering, University of Belgrade for the purpose of teaching topics in the field of digital logic. The simulator makes it possible to carry out the interactive and visual design and simulation of switching circuits and standard switching modules. The predefined and user created modules can be further used to design new more complex modules. The visual simulation of any created module can be carried out interactively in a user friendly simulation environment. The simulator is implemented using Java programming language.
Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity

Joint modeling of the intensity of multiple facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of suitable data the models on. For this reason, majority of the methods resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. To this end, we propose the Copula regression approach for modeling multivariate ordinal variables. Our model accounts for $ordinal$ordinal structure in output variables and their $non$non-$linear$linear dependencies via copula functions modeled as cliques of a conditional random fields. The copula ordinal regression model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We demonstrate the effectiveness of our approach on three challenging datasets of naturalistic facial expressions and we show that the estimation of target AU intensities improves especially in the case of (a) noisy image features, (b) head-pose variations and (c) imbalanced training data. Lastly, we show that the proposed approach consistently outperforms (i) independent modeling of AU intensities and (ii) the state-of-the-art approach for the target task and (iii) deep convolutional neural networks.
Spatial Representation for Efficient Sequence Classification

We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multi-scale representation of strings. The new representation leads to discovery of very efficient algorithms for string comparison, independent of the alphabet size. We show that these algorithms can be generalized to handle a wide gamut of sequence classification problems in diverse domains such as the music and text sequence classification. The presented algorithms offer low computational cost and highly scalable implementations across different application domains. The new method demonstrates order-of-magnitude running time improvements over existing state-of-the-art approaches while matching or exceeding their predictive accuracy.
Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data

Accurate computational prediction of protein functions increasingly relies on network-inspired models for the protein function transfer. This task can become challenging for proteins isolated in their own network or those with poor or uncharacterized neighborhoods. Here, we present a novel probabilistic chain-graph-based approach for predicting protein functions that builds on connecting networks of two (or more) different species by links of high interspecies sequence homology. In this way, proteins are able to “exchange” functional information with their neighbors-homologs from a different species. The knowledge of interspecies relationships, such as the sequence homology, can become crucial in cases of limited information from other sources of data, including the protein-protein interactions or cellular locations of proteins. We further enhance our model to account for the Gene Ontology dependencies by linking multiple but related functional ontology categories within and across multiple species. The resulting networks are of significantly higher complexity than most traditional protein network models. We comprehensively benchmark our method by applying it to two largest protein networks, the Yeast and the Fly. The joint Fly-Yeast network provides substantial improvements in precision, accuracy, and false positive rate over networks that consider either of the sources in isolation. At the same time, the new model retains the computational efficiency similar to that of the simpler networks.
A hybrid face recognition method using Markov random fields

We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's is then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method.
SHAPE ANALYSIS USING CURVATURE-BASED DESCRIPTORS AND PROFILE HIDDEN MARKOV MODELS

This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A profile hidden Markov model (PHMM) is then built on such descriptors to represent a class of similar shapes. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions, and missing parts. The sparseness of the PHMM structure also provides efficient inference and learning algorithms for shape modeling and analysis. Our experimental results on corpus callosum images show the effectiveness and robustness of this new framework.
Boosting and structure learning in dynamic Bayesian networks for audio-visual speaker detection

Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Earlier work has demonstrated that boosted parameter learning could be used to improve the performance of Bayesian network, classifiers for complex multi-modal inference problems such as speaker detection. In speaker detection, the goal is to use video and audio cites to infer when a person is speaking to a user interface. In this paper we introduce a new boosted structure learning algorithm based on AdaBoost. Given labeled data, our algorithm modifies both the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameter learning on a fixed structure. We present results for speaker detection and for the UCI "chess" dataset.