Modeling and Representation

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Efficient modeling and representation of agreement in interval valued data

Efficient modeling and representation of agreement in interval valued data

Abstract—Recently, there has been much research into effective representation and analysis of uncertainty in human responses, with applications in cyber-security, forest and wildlife manage- ment, and product development, to name a few. Most of this research has focused on representing the response uncertainty as intervals, e.g., “I give the movie between 2 and 4 stars.” In this paper, we extend upon the model-based interval agreement approach (IAA) for combining interval data into fuzzy sets and propose the efficient IAA (eIAA) algorithm, which enables effi- cient representation of and operation on the fuzzy sets produced by IAA (and other interval-based approaches, for that matter). We develop methods for efficiently modeling, representing, and aggregating both crisp and uncertain interval data (where the interval endpoints are intervals themselves). These intervals are assumed to be collected from individual or multiple survey respondents over single or repeated surveys; although, without loss of generality, the approaches put forth in this paper could be used for any interval-based data where representation and analysis is desired. The proposed method is designed to minimize loss of information when transferring the interval-based data into fuzzy set models and then when projecting onto a compressed set of basis functions. We provide full details of eIAA and demonstrate it on real-world and synthetic data.
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Robust Visual Tracking via Appearance Modeling and Sparse Representation

Robust Visual Tracking via Appearance Modeling and Sparse Representation

A good appearance model will obtain good tracking results [3].To achieve an effective appearance models, we should consider several factors which could affect the tracking results. In the process of tracking, target appearance will be changed due to non-rigid deformation, target moving, illumination change or occlusion, etc. In order to adapt to the variations of target appearance, we often employ a linear combination of multiple templates to represent the target appearance. Recently, a class of appearance modeling techniques named sparse representation [4-6] has been shown to give state-of-the- art robustness against various disturbances. The methods based on sparse representation attempt to approximate the target state by finding a sparse linear combination over a basis library that contains target templates and trivial templates.
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Foundational Block Modeling Structures for Computer Systems Representation

Foundational Block Modeling Structures for Computer Systems Representation

Abstract — This work explores and explains the use of basic block modeling structures, their function for representing computer systems and computer systems structures. The main use of block diagrams and notations is to represent system configurations. Traditionally this has been done using different notations and structures. One of the major problems is precisely that there exist a myriad of ways how to do this. This work challenges the traditional approaches by using a particular method for connecting systems, sub-systems and components together. An exhaustive identification of possible configurations is given. The structures described here are represented both graphically and formally. This approach supports formal representation and description of systems. A reduced form of the system structures can be developed. Various examples and a practical case study are explained along with results, observations and a conclusion.
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Business process redesign : an optimal process modeling representation for knowledge intensive organizations

Business process redesign : an optimal process modeling representation for knowledge intensive organizations

Employees within OHRA will evaluate the three process modeling representation approaches through a questionnaire. This research is a field experiment; A Field setting is almost always preferred over laboratory for reasons of better external validity, though sacrificing some level of control of internal validity. As the research question states, the focus will be on three approaches: the Communication flow representation, the Activity Flow representation and a combination of the two approaches in which an Activity diagram with information flow will be used. The data type required is quantitative. This research is theory testing, in which hypothesis on the use of different process modeling representations will be tested. Quantitative research results in answers that are useful in accepting or rejecting the
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Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions

Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions

Recently several large pre-trained language mod- els using transformation architecture, like BERT (Devlin et al., 2018), or bidirectional LSTM with attention, such as ELMo (Peters et al., 2018), have achieved state of the art results across a variety of NLP tasks. We opted not to experiment with any of these pre-trained language models for our task. The LSTM architecture of our LMs is far simpler, which facilitates testing the contribution of explicit feature representation to correlation in the acceptability prediction task, and perplexity for the language modeling task.

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HIGH-SPEED LOCOMOTIVE XBOM ONTOLOGY MODELING RESEARCH SUPPORTING MRO SEMANTIC KNOWLEDGE REPRESENTATION

HIGH-SPEED LOCOMOTIVE XBOM ONTOLOGY MODELING RESEARCH SUPPORTING MRO SEMANTIC KNOWLEDGE REPRESENTATION

MRO knowledge representation is a kind of description of MRO knowledge and the first step of the MRO knowledge management. At present, the main method of knowledge representation can be classified into three categories. [9] : The first kind is the unconstructed knowledge representation; The second kind is the construction knowledge representation; The third kind is the knowledge representation method of semantic Web. The emergence and development of semantic Web technology will greatly promote the innovation and development of resource knowledge management technology. [10] At present, in the definition of ontology, the most popular is Gruber .T R given: “Ontology is an explicit specification of the conceptual model.” [11] , He also puts forward five rules of ontology modeling including clarity and objectivity, consistency, completeness, maximum monotonic scalability, and minimal commitment. In addition, foreign universities and research institutions have developed a number of ontology construction tools including Onto Edit, KAON, Web ODE and Protégé, among them, the Protégé tool developed by Stanford University is the most powerful and widely used. Ontology is a conceptual model for formal description of resources on the semantic and knowledge level and is the core of the semantic Web, has become an important way of XBOM semantic modeling for MRO services.
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A Novel Inheritance Mechanism for Modeling Knowledge Representation Systems

A Novel Inheritance Mechanism for Modeling Knowledge Representation Systems

Owing to the algorithm developed for prefixing names of the components being inher- ited, also entailing virtual inheritance modes, a designer is unconstrained in naming com- ponents, without any concerns pertaining to ambiguity related to uniqueness of names. At the same time, multiple inheritance is permissible, since by no means does it cause any ambiguity or any limitations as to names of components or inheritance structures. The only limitation, structurally imposed upon inheritance, is absence of cycles, which is detected and controlled by an association-oriented database using internal algorithms en- suring coherence detection as well as correctness of structures and data. The inheritance mechanism functioning in the AODB Metamodel is of major importance for modeling productivity, and its complete integration with other mechanisms exerts very significant influence on the model’s power of expression. The proposed solution could make a benefit for modeling ontologies [12] and other knowledge representation systems, as shown on the case studies of OCM SKB and ESNM SKB .
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Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique

Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique

A markup language is a tool for adding information to the documents. Semantic markup is expected to have universal expressive power, syntactic and semant ic interoperability. The XML (eXtensible Markup Language) does not provide semantic interoperability. Using XML as a base , a number of new markup languages have been deve loped to meet semantic interoperability, these are RDF(Resource Description Framework), RDFS, SHOE, DAML+OIL and recently OWL (Daconta Michael, 2003). The work done earlier in domain knowledge representation using software engineering modeling techniques was concentrated towards developing the mapping of the software engineering models to the RDF, RDFS and other markup languages (Cranefield, 2001). At present the researchers are trying to map the domain models to the OWL representation (Djuric, Gasevic & Devedzic, 2004).
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From Conceptual Modeling to Logical Representation of Trajectories in DBMS-OR and DW Systems

From Conceptual Modeling to Logical Representation of Trajectories in DBMS-OR and DW Systems

In [Marketos et al. 2008], data originated from GPS sensors were loaded into a trajectory warehouse by using a set of ETL (Extract, Transform and Load) procedures. In addition, mechanisms for per- forming spatio-temporal aggregations analytical queries over trajectory data are detailed in [Leonardi et al. 2009]. Moreover, based on the trajectory conceptual model proposed in [Spaccapietra et al. 2008], a conceptual model schema for TDW that is aimed at supporting the monitoring of athletes’ biochemical data is discussed in [Porto et al. 2010]. However, these biochemical data are not organized into dimensional data structures, through the use of fact and dimension tables and the representation of different levels of aggregation. In fact, traditional data warehouse techniques and current analytical tools do not satisfy the requirements of TDW applications because the representation and aggrega- tion of trajectories imply the need for the modeling and aggregation of varied and interrelated data types, such as geometries, context information and spatiotemporal objects. Consequently, there is still no consensus about how trajectories are modeled multidimensionally, organized in different levels of aggregation and used for answering aggregation queries of OLAP systems.
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Quantitative modeling of the neural representation of adjective noun phrases to account for fMRI activation

Quantitative modeling of the neural representation of adjective noun phrases to account for fMRI activation

In this study, we represented the meaning of both adjectives and nouns in terms of their co- occurrences with 5 sensory verbs. While this type of representation might be justified for con- crete nouns (hypothesizing that their neural rep- resentations are largely grounded in sensory- motor features), it might be that a different repre- sentation is needed for adjectives. Further re- search is needed to investigate alternative repre- sentations for both nouns and adjectives. More- over, the composition models that we presented here are overly simplistic in a number of ways. We look forward to future research to extend the intermediate representation and to experiment with different modeling methodologies. An al- ternative approach is to model the semantic rep- resentation as a hidden variable using a genera- tive probabilistic model that describes how neu- ral activity is generated from some latent seman-
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Neural network based representation learning and modeling for speech and speaker recognition

Neural network based representation learning and modeling for speech and speaker recognition

The most common ASR feature extraction method applies auditory-inspired Mel-filter banks to the squared magnitude of the short-time Fourier transform of the windowed speech signal. Filter parameters are inspired by knowledge of the human speech perception. Filter bank outputs are then used to train an acoustic model (AM). These perceptually motivated filter- bank features are not always the best choice in statistical modeling frameworks such as ASR acoustic modeling, where the end goal is phone classification. This motivates the data-driven learning schemes for joint feature learning from audio signals and acoustic modeling. Audio signals can be represented in both the time and frequency domains. Applying the Discrete Fourier Transform (DFT) on time-domain waveforms, can normalize the more variant wave- form signals (due to phase shifts and temporal distortions) and generate equivalent feature representation (complex DFT features), to make it easier to train acoustic models, such as neural-network based models.
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Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences

Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences

The spatial autocorrelation issue is now well established, and it is almost impossible to deal with spatial data without considering this reality. In addition, recent developments have been devoted to developing methods that deal with spatial autocorrelation in panel data. However, little effort has been devoted to dealing with spatial data (cross-section) pooled over time. This paper endea- vours to bridge the gap between the theoretical modeling development and the application based on spatial data pooled over time. The paper presents a schematic representation of how spatial links can be expressed, depending on the nature of the variable, when combining the spatial mul- tidirectional relations and temporal unidirectional relations. After that, a Monte Carlo experiment is conducted to establish the impact of applying a usual spatial econometric model to spatial data pooled over time. The results suggest that neglecting the temporal dimension of the data generat- ing process can introduce important biases on autoregressive parameters and thus result in the inaccurate measurement of the indirect and total spatial effect related to the spatial spillover effect.
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Shape Modeling and Analysis for Object Representation, Reconstruction, and Recognition

Shape Modeling and Analysis for Object Representation, Reconstruction, and Recognition

The majority of 2D shape modeling techniques operate on a smaller subset of sim- ple closed shapes. In Chapter 5, we presented a novel method for modeling 2D shapes of arbitrary topology. To the best of our knowledge, it is the first time 2D shape modeling has been addressed in a Morse theoretic framework. The power of this method is that it encodes both the topological and the geometric properties in a single skeletal representa- tion, which was shown to be a complete and compact shape representation. Completeness and uniqueness properties of the model were demonstrated by reconstructing shapes from models. Although this method bears some similarities with shock graph based methods, it operates on a wider class of shapes with much less computational complexity, since it does not employ any grammar rules.
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Modeling Relation Paths for Representation Learning of Knowledge Bases

Modeling Relation Paths for Representation Learning of Knowledge Bases

Recent years have witnessed great advances of modeling multi-relational data such as social net- works and KBs. Many works cope with rela- tional learning as a multi-relational representation learning problem, encoding both entities and re- lations in a low-dimensional latent space, based on Bayesian clustering (Kemp et al., 2006; Miller et al., 2009; Sutskever et al., 2009; Zhu, 2012), energy-based models (Bordes et al., 2011; Chen et al., 2013; Socher et al., 2013; Bordes et al., 2013; Bordes et al., 2014), matrix factorization (Singh and Gordon, 2008; Nickel et al., 2011; Nickel et al., 2012) . Among existing representation mod- els, TransE (Bordes et al., 2013) regards a relation as translation between head and tail entities for optimization, which achieves a good trade-off be- tween prediction accuracy and computational effi- ciency. All existing representation learning meth- ods of knowledge bases only use direct relations between entities, ignoring rich information in re- lation paths.
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A generative adversarial strategy for modeling relation paths in knowledge base representation learning

A generative adversarial strategy for modeling relation paths in knowledge base representation learning

In the approach to be outlined, although we shall follow the broad modeling approach of [8], rather than training RNN on single-hop paths, we will train the RNN on mh-RPs by using GANs. This development restricts the likelihood of the RNN generating candidate entities that are optimal at a given instant but sub-optimal with respect to the path as a whole. It also enables the RNN to produce natural or human-like reasoning paths. Relevant to our study is the research area concerned with learning rules from KBs. However, contrary to the modeling of reasoning pathways, rule learning seeks to emulate an inference procedure. Commonly, these methods represent facts using vectors or tensors and employ RNNs, such as memory networks, to model transitivity relations between facts in order to perform inferences [10, 11]; other studies approach the learning of representations of entities and relations via a transitivity structure [12]. Neuro-symbolic computing is another relevant domain dealing with integrating and reasoning over symbolic knowledge utilizing neural networks; some notable recent efforts include [13, 14].
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Sequence-to-sequence modeling for graph representation learning

Sequence-to-sequence modeling for graph representation learning

In the unsupervised regime, we leverage the LSTM sequence-to-sequence learning framework of (Sutskever et al. 2014), which uses one LSTM to encode the input sequence into a vector and another LSTM to generate the output sequence from that vector. In some variations of our models, we use the same sequence as both input and output, mak- ing this a sequence-to-sequence LSTM autoencoder (Li et al. 2015a). We consider several types of substructures including random walks, shortest paths, and breadth-first search, with various levels of node neighborhood granularity to prepare the input to our mod- els. An unsupervised graph representation approach can be used not only in processing labeled data, such as in graph classification in bioinformatics, but can be also used in many practical applications, such as anomaly detection in social networks or streaming data, as well as in exploratory analysis and scientific hypothesis generation. An unsupervised method for learning graph representations provides a fundamental capability to analyze graphs based on their intrinsic properties. Figure 1 shows the result of using our unsuper- vised approach to embed the graphs in the Proteins dataset (Borgwardt et al. 2005) into a 100-dimensional space, visualized with t-SNE (Maaten and Hinton 2008). Each point is one of the 1113 graphs in the dataset. The two class labels were not used when learn- ing the graph representations, but we still generally find that graphs of the same class are clustered in the learned space.
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Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation

Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation

represent topography. Because the coverage of high-accuracy DEM data (e.g., with elevation errors less than 1 m) is lim- ited, hydrologic modeling at regional or larger scales uses DEM data obtained by spaceborne sensors. The Shuttle Radar Topography Mission (SRTM) DEM data have been widely used for hydrologic modeling, but some factors limit their accuracy. In forested regions such as the Amazon Basin, primary biases in the SRTM DEM data were caused by veg- etation cover because the radar signal was not able to pene- trate the vegetation canopy (Sanders, 2007). Previous studies in the Amazon Basin adopted various approaches to alleviate the vegetation-caused biases embedded in the SRTM data. In some modeling studies, elevation values were lowered by a constant in forested areas of the entire basin, so the spa- tial variability of vegetation heights was ignored (Coe et al., 2008; Paiva et al., 2013a). In a few hydrodynamic modeling studies for the central Amazon Basin, the vegetation-caused biases in the SRTM elevations were derived from spatially varying vegetation heights. For example, Wilson et al. (2007) estimated the vegetation height distribution based on their surveyed heights of various vegetation types, and a map of vegetation types by Hess et al. (2003) and Baugh et al. (2013) utilized a global dataset of spatially distributed vegetation heights developed by Simard et al. (2011). These two studies estimated the vegetation-caused biases as products of spa- tially varying vegetation heights and a fixed percentage. In this study, we used the HydroSHEDS DEM data which were derived from the SRTM data and inherited the vegetation- caused biases. To alleviate those biases, we used a method similar to that of Baugh et al. (2013). Besides the vegeta- tion height map by Simard et al. (2011), we also used a land cover dataset for wetlands of the lowland Amazon Basin de- veloped by Hess et al. (2003, 2015a). A “bare-earth” DEM of the Amazon Basin was created and employed in the hydro- logic modeling for the entire basin. To our knowledge, this was the first time that the spatial variability of vegetation- caused biases in the DEM data was explicitly considered in hydrologic modeling for the entire Amazon Basin.
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Sensitivity analysis and probabilistic re-entry modeling for debris using high dimensional model representation based uncertainty treatment

Sensitivity analysis and probabilistic re-entry modeling for debris using high dimensional model representation based uncertainty treatment

The objective of this study was to gain an understanding for the effects of uncertainties on the re-entry trajectory and impact location and to in- troduce a novel and efficient methodology for probabilistic modeling of the atmospheric re-entry. Current re-entry modeling tools perform the analysis in a deterministic sense and do not include any uncertainty treatment. This work presents progress towards incorporating uncertainty treatment into the modeling of atmospheric re-entry of space debris using a recently developed novel high-dimensional derivative based uncertainty quantification approach. Validation of the results obtained from the High Dimensional Model Repre- sentation methodology with a Monte Carlo analysis is also presented.
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Modeling the Maturation of Grip Selection Planning and Action Representation: Insights from Typical and Atypical Motor Development

Modeling the Maturation of Grip Selection Planning and Action Representation: Insights from Typical and Atypical Motor Development

In general terms, motor planning reflects the process of selecting a movement plan from an infinite number of solutions in order to achieve a desired end-state (Rosenbaum et al., 1992). Though often implicit, the nervous system must make this decision with consideration of the surrounding environment, involvement of external agents and the biomechanical constraints of relevant limbs. Indeed, neuro-computational modeling suggests that the intended movement, including its terminal location, may be implicitly simulated via internal (neural) representations of the forthcoming action prior to movement execution. In doing so, the most appropriate movement plan can be selected with consideration of a hierarchy of cost constraints to ensure that the goal of the forthcoming action is achieved in a comfortable and efficient manner (Rosenbaum et al., 1995, 2001, 2014; Flanagan and Wing, 1997; Johnson, 2000; Johnson et al., 2002; Glover, 2004; Johnson-Frey et al., 2004). Specifically, to simulate the intended action, the central nervous system is thought to use an efferent copy of the impending motor command (i.e., internal action representations) to predict the sensory consequences of an action given the current and the desired end- state of the limb (Wolpert and Kawato, 1998; Johnson, 2000; Wolpert and Ghahramani, 2000; Glover, 2004). At a neural level, this view is supported by recent imaging data which suggests that this process may call upon similar systems that are active during movement preparation, including parietal- cerebellar structures and premotor cortices (see Hétu et al., 2013 for a review).
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Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

Data fusion can be achieved through multimodal representation learn- ing. The general idea is to learn a shared knowledge representation and con- struct the semantic correspondence between modalities through a shared rep- resentation. An intuitive approach is to apply matrix factorization technique for the case of multiple modalities, where a single objective function can be developed to optimize each data modality [159]. A link between data modal- ities is constructed through a newly-learned latent space that represents la- tent semantics. This achieves the feature-level data fusion, which provides a more systematic framework than decision-stage data integration [160] since the latter involves tuning the weights of decisions from different modalities. Moreover, various regularization terms (in Section 2.3.2) can be directly added in the objective function to achieve respective learning goals. Existing online update rules and visualization techniques for NMF can also be easily used for developing interactive frameworks [104, 62] (See Section 2.3.2 and Chapter 5). We have developed a data fusion algorithm based on matrix factorization to integrate multimodal data at an early stage (feature-level fusion) for our data (see Section 4.4). Besides the NMF-based learning framework, other represen- tation learning approaches could also be extended for data fusion [161], which can also incorporate sparsity constraints [162, 163].
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