nils-herrmann/clean_scirepeval huggingface dataset:
Haptic human-computer interaction : first International Workshop, Glasgow, UK, August 31 - September 1, 2000 : proceedings

Teleoperator Controls.- Haptic feedback: a brief history from telepresence to virtual reality.- Haptic Interfaces for Blind People.- Design principles for tactile interaction.- The haptic perception of texture in virtual environments: an investigation with two devices.- Haptic display of mathematical functions for teaching mathematics to students with vision disabilities: design and proof of concept.- Haptic graphs for blind computer users.- Web-based touch display for accessible science education.- Collaborative  Communicating with feeling.- Improved precision in mediated collaborative manipulation of objects by haptic force feedback.- Hand-shaped force interface for human-cooperative mobile robot.- Psychological Issues and Measurement.- Can the efficiency of a haptic display be increased by short-time practice in exploration?.- Implicit accuracy constraints in two-fingered grasps of virtual objects with haptic feedback.- Interaction of visual and haptic information in simulated environments: texture perception.- The effective combination of haptic and auditory textural information.- Cursor trajectory analysis.- What impact does the haptic-stereo integration have on depth perception in stereographic virtual environment? a preliminary study.- A shape recognition benchmark for evaluating usability of a haptic environment.- Applications of  A horse ovary palpation simulator for veterinary training.- Tactile navigation display.- Tactile information presentation in the cockpit.- Scaleable SPIDAR: a haptic interface for human-scale virtual environments.- The sense of object-presence with projection-augmented models.- Virtual space computer games with a floor sensor control - human centred approach in the design process.- Sensing the fabric: to simulate sensation through sensory evaluation and in response to standard acceptable properties of specific materials when viewed as a digital image.
NONLINEAR STRUCTURE IDENTIFICATION :

This paper investigates new ways of inferring nonlinear dependence parsimoniously from measured data. The existence of unique linear and nonlinear sub-spaces which are structural invariants of general nonlinear maps is established for the first time. Necessary and sufficient conditions determining these sub-spaces are derived. In addition to being of considerable interest in their own right, the importance of these invariants in an identification context is that they provide a tractable framework for minimising the dimensionality of the nonlinear modelling task. Specifically, once the linear/nonlinear subspaces are known, by definition the explanatory variables may be transformed to form two disjoint sub-sets spanning, respectively, the linear and nonlinear sub-spaces. The nonlinear modelling task is confined to the latter sub-set, which will typically have a smaller number of elements than the original set of explanatory variables. Constructive algorithms are proposed for inferring the linear and nonlinear sub-spaces from noisy data.
Proceedings of the First International Workshop on Haptic Human-Computer Interaction

From the Publisher: 
"Haptic, human-computer interaction is interaction between a human computer user and the computer user interface based on the powerful human sense of touch. Haptic hardware has been discussed and explored for some time, in particular in the context of computer games. Haptic technology is now maturing and migrating from research labs into real-world applications and commercial products. However, so far, little attention has been paid to the general principles of haptic HCI and the systematic use of haptic devices for improving efficiency, effectiveness, and satisfaction in HCI." This book is the first one to focus on haptic human-computer interaction. It is based on a workshop held in Glasgow, UK, in August/September 2000.
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples.
On the interpretation of local models in blended multiple model structures.

The construction of non-linear dynamics by means of interpolating the behaviour of locally valid models offers an attractive and intuitively pleasing method of modelling non-linear systems. The approach is used in fuzzy logic 
modelling, operating regime based models, and nonlinear statistical models. The model structure suggests that the composite local models can be used to interpret, in some appropriate manner, the overall non-linear dynamics. In this 
paper we demonstrate that the interpretation of these local models, in the context of multiple model structures, is not as straightforward as it might initially appear. We argue that the blended multiple model system can be interpreted in 
two ways – as an interpolation of linearisations, or as a full parameterisation of the system. The choice of interpretation affects experiment design, parameter 
identification, and model validation. We then show that, in some cases, the local models give insight into full model behaviour only in a very small region of state space. More alarmingly, we demonstrate that for off-equilibrium behaviour, subject to some approximation error, a non-unique parameterisation of the model dynamics exists. Hence, qualitative conclusions drawn from the behaviour of an identified local model, e.g. regarding stable, unstable, nodal or complex behaviour, must be treated with extreme caution. The example of muscle modelling is used to illustrate these points clearly.
On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures

The use of multiple-model techniques has been reported in a variety of control and signal processing applications. However, several theoretical analyses have recently appeared which outline fundamental limitations of these techniques in certain domains of application. In particular, the identifiability and interpretability of local linear model parameters in transient operating regimes is shown to be limited. Some modifications to the basic paradigm are suggested which overcome a number of the problems. As an alternative to parametric identification of blended multiple model structures, nonparametric Gaussian process priors are suggested as a means of providing local models, and the results compared to a multiple-model approach in a Monte Carlo simulation on some simulated vehicle dynamics data.
Robot Docking Using Mixtures of Gaussians

This paper applies the Mixture of Gaussians probabilistic model, combined with Expectation Maximization optimization to the task of summarizing three dimensional range data for a mobile robot. This provides a flexible way of dealing with uncertainties in sensor information, and allows the introduction of prior knowledge into low-level perception modules. Problems with the basic approach were solved in several ways: the mixture of Gaussians was reparameterized to reflect the types of objects expected in the scene, and priors on model parameters were included in the optimization process. Both approaches force the optimization to find 'interesting' objects, given the sensor and object characteristics. A higher level classifier was used to interpret the results provided by the model, and to reject spurious solutions.
Modelling human control behaviour with a Markov-chain switched bank of control laws

A probabilistic model of human control behaviour is described. It assumes that human behaviour can be represented by switching among a number of relatively simple behaviours. The model structure is closely related to the Hidden Markov Models (HMMs) commonly used for speech recognition. An HMM with context-dependent transition functions switching between linear control laws is identified from experimental data. The applicability of the approach is demonstrated in a pitch control task for a simplified helicopter model.
Multiple Model Approaches to Modelling and Control

1. Basic Principles: The Operating Regime Approach 2. Modelling: Fuzzy Set Methods for Local Modelling Identification 3. Modelling of Electrically Stimulated Muscle 4. Process Modelling Using a Functional State Approach 5. Markov Mixtures of Experts 6. Active Learning With Mixture Models 7. Local Learning in Local Model Networks 8. Side Effects of Normalising Basic Functions 9.  Heterogeneous Control Laws 10. Local Laguerre Models 11. Multiple Model Adaptive Control 12. H Control Using Multiple Linear Models 13. Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno Fuzzy Models
Side effects of Normalising Radial Basis Function Networks

Normalisation of the basis function activations in a Radial Basis Function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisation of the basis functions can lead to other effects which are sometimes less desirable for modelling applications. This paper describes some side effects of normalisation which fundamentally alter properties of the basis functions, e.g. the shape is no longer uniform, maxima of basis functions can be shifted from their centres, and the basis functions are no longer guaranteed to decrease monotonically as distance from their centre increases--in many cases basis functions can 'reactivate', i.e. re-appear far from the basis function centre. This paper examines how these phenomena occur, discusses their relevance for non-linear function approximation and examines the effect of normalisation on the network condition number and weights.
Extending the functional equivalence of radial basis function networks and fuzzy inference systems

We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.
Local Model Architectures for Nonlinear Modelling and Control

Local Model Networks are learning systems which are able to model and control unknown nonlinear dynamic processes from their observed input-output behaviour. Simple, locally accurate models are used to represent a globally complex process. The framework supports the modelling process in real applications better than most artificial neural network architectures. This paper shows how their structure also allows them to more easily integrate knowledge, methods and a priori models from other paradigms such as fuzzy logic, system identification and statistics. Algorithms for automatic parameter estimation and model structure identification are given.
Feature-based pronunciation modeling for automatic speech recognition

Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech. One approach to handling this variation consists of expanding the dictionary with phonetic substitution, insertion, and deletion rules. Common rule sets, however; typically leave many pronunciation variants unaccounted for and increase word confusability due to the coarse granularity of phone units. 
We present an alternative approach, in which many types of variation are explained by representing a pronunciation as multiple streams of linguistic features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or to acoustic or perceptual categories. By allowing for asynchrony between features and per-feature substitutions, many pronunciation changes that are difficult to account for with phone-based models become quite natural. Although it is well-known that many phenomena can be attributed to this "semi-independent evolution" of features, previous models of pronunciation variation have typically not taken advantage of this. 
In particular, we propose a class of feature-based pronunciation models represented as dynamic Bayesian networks (DBNs). The DBN framework allows us to naturally represent the factorization of the state space of feature combinations into feature specific factors, as well as providing standard algorithms for inference and parameter learning. We investigate the behavior of such a model in isolation using manually transcribed words. Compared to a phone-based baseline, the feature-based model has both higher coverage of observed pronunciations and higher recognition rate for isolated words. We also discuss the ways in which such a model can be incorporated into various types of end-to-end speech recognizers and present several examples of implemented systems, for both acoustic speech recognition and lipreading tasks.
Production domain modeling of pronunciation for visual speech recognition

Articulatory feature models have been proposed in the automatic speech recognition community as an alternative to phone-based models of speech. In this paper, we extend this approach to the visual modality. Specifically, we adapt a recently proposed feature-based model of pronunciation variation to visual speech recognition (VSR) using a set of visually-salient features. The model uses a dynamic Bayesian network (DBN) to represent the evolution of the feature streams. A bank of SVM feature classifiers, with outputs converted to likelihoods, provides input to the DBN. We present preliminary experiments on an isolated-word VSR task, comparing feature-based and viseme-based units and studying the effects of modeling inter-feature asynchrony.
Visual speech recognition with loosely synchronized feature streams

We present an approach to detecting and recognizing spoken isolated phrases based solely on visual input. We adopt an architecture that first employs discriminative detection of visual speech and articulate features, and then performs recognition using a model that accounts for the loose synchronization of the feature streams. Discriminative classifiers detect the subclass of lip appearance corresponding to the presence of speech, and further decompose it into features corresponding to the physical components of articulate production. These components often evolve in a semi-independent fashion, and conventional viseme-based approaches to recognition fail to capture the resulting co-articulation effects. We present a novel dynamic Bayesian network with a multi-stream structure and observations consisting of articulate feature classifier scores, which can model varying degrees of co-articulation in a principled way. We evaluate our visual-only recognition system on a command utterance task. We show comparative results on lip detection and speech/non-speech classification, as well as recognition performance against several baseline systems
Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines (SVM); dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an ASR, current theories of human speech perception and phonology. All systems begin with a high-D multiframe acoustic-to-distinctive feature transformation, implemented using SVMs trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the SVMs are then integrated using one of 3 pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a 1st pass recognizer, and the resulting combination score is used to compute a 2nd-pass speech recognition output.
Feature-based pronunciation modeling with trainable asynchrony probabilities

We report on ongoing work on a pronunciation model based on explicit representation of the evolution of multiple linguistic feature streams. In this type of model, most pronunciation variation is viewed as the result of asynchrony between features and changes in feature values. We have implemented such a model using dynamic Bayesian networks. In this paper, we extend our previous work with a mechanism for learning feature asynchrony probabilities from data. We present experimental results on a word classification task using phonetic transcriptions of utterances from the Switchboard corpus.
Feature-based Pronunciation Modeling for Speech Recognition

We present an approach to pronunciation modeling in which the evolution of multiple linguistic feature streams is explicitly represented. This differs from phone-based models in that pronunciation variation is viewed as the result of feature asynchrony and changes in feature values, rather than phone substitutions, insertions, and deletions. We have implemented a flexible feature-based pronunciation model using dynamic Bayesian networks. In this paper, we describe our approach and report on a pilot experiment using phonetic transcriptions of utterances from the Switchboard corpus. The experimental results, as well as the model's qualitative behavior, suggest that this is a promising way of accounting for the types of pronunciation variation often seen in spontaneous speech.
Proceedings of the Student Research Workshop at HLT-NAACL 2004, Boston, Massachusetts, USA, May 2-7, 2004

We are pleased to present the 10 papers accepted for presentation at the HLT/NAACL 2004  Boston on May 2. The main mission of the workshop is to provide feedback for students' work in progress. Each presentation will be followed by comments on the work from a panel of senior researchers. 
 
This year, we received 12 submissions representing 4 countries.
Hidden feature models for speech recognition using dynamic Bayesian networks

In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features, such as articulatory or other phonological features, for automatic speech recognition. In previous work using the idea of hidden features, the representation has typically been implicit, relying on a single hidden state to represent a combination of features. We present a class of DBN-based hidden feature models, and show that such a representation can be not only more expressive but also more parsimonious. We also describe a way of representing the acoustic observation model with fewer distributions using a product of models, each corresponding to a subset of the features. Finally, we describe our recent experiments using hidden feature models on the Aurora 2.0 corpus.
Structurally discriminative graphical models for automatic speech recognition - results from the 2001 Johns Hopkins Summer Workshop

In recent years there has been growing interest in discriminative parameter training techniques, resulting from notable improvements in speech recognition performance on tasks ranging in size from digit recognition to Switchboard. Typified by Maximum Mutual Information training, these methods assume a fixed statistical modeling structure, and then optimize only the associated numerical parameters (such as means, variances, and transition matrices). In this paper, we explore the significantly different methodology of discriminative structure learning. Here, the fundamental dependency relationships between random variables in a probabilistic model are learned in a discriminative fashion, and are learned separately from the numerical parameters. Tn order to apply the principles of structural discriminability, we adopt the framework of graphical models, which allows an arbitrary set of variables with arbitrary conditional independence relationships to be modeled at each time frame. We present results using a new graphical modeling toolkit (described in a companion paper) from the recent 2001  These results indicate that significant gains result from discriminative structural analysis of both conventional MFCC and novel AM-FM features on the Aurora continuous digits task.
Discriminatively Structured Graphical Models for Speech Recognition The Graphical Models Team JHU 2001 Summer Workshop

In recent years there has been growing interest in discriminative parameter training techniques, resulting from notable improvements in speech recognition performance on tasks ranging in size from digit recognition to Switchboard. Typified by Maximum Mutual Information (MMI) or Minimum Classification Error (MCE) training, these methods assume a fixed statistical modeling structure, and then optimize only the associated numerical parameters (such as means, variances, and transition matrices). Such is also the state of typical structure learning and model selection procedures in statistics, where the goal is to determine the structure (edges and nodes) of a graphical model (and thereby the set of conditional independence statements) that best describes the data. This report describes the process and results from the 2001 Johns Hopkins summer workshop on graphical models. Specifically, in this report we explore the novel and significantly different methodology of discriminative structure learning. Here, the fundamental dependency relationships between random variables in a probabilistic model are learned in a discriminative fashion, and are learned separately and in isolation from the numerical parameters. The resulting independence properties of the model might in fact be wrong with respect to the true model, but are made only for the sake of optimizing classification performance. In order to apply the principles of structural discriminability, we adopt the framework of graphical models, which allows an arbitrary set of random variables and their conditional independence relationships to be modeled at each time frame. We also, in this document, describe and present results using a new graphical modeling toolkit (GMTK). Using GMTK and discriminative structure learning heuristics, the results presented herein indicate that significant gains result from discriminative structural analysis of both conventional MFCC and novel AM-FM features on the Aurora continuous digits task. Lastly, we also present results using GMTK on several other tasks, such as on an IBM audio-video corpus, preliminary results on the SPINE-1 data set using hidden noise variables, on hidden articulatory modeling using GMTK, and on the use of interpolated language models represented by graphs within GMTK.