Everyday huge amount of data is being captured and stored. This can either be due to several social initiatives, technological advancement or by smart devices. This involves the release of data which differs in format, language, schema and standards from various types of user communities and organizations. The main challenge in this scenario lies in the inte- gration of such diverse data and on the generator of knowledge from the existing sources. Various methodology for data modeling has been proposed by different research groups, under different approaches and based on the scenarios of the different domain of application. However, a few method- ology elaborates the proceeding steps. As a result, there is lack of clarifi- cation how to handle different issues which occurs in the different phases of domainmodeling. The aim of this research is to presents a scalable, in- teroperable, effective framework and a methodology for data modeling. The backbone of the framework is composed of a two-layer, schema and lan- guage, to tackle diversity. An entity-centric approach has been followed as a main notion of the methodology. A few aspects which have especially been emphasized are: modeling a flexible data integration schema, dealing with the messy data source, alignment with an upper ontology and implementa- tion. We evaluated our methodology from the user perspective to check its practicability.
This paper applies BERT to ad hoc document retrieval on news articles, which requires ad- dressing two challenges: relevance judgments in existing test collections are typically pro- vided only at the document level, and docu- ments often exceed the length that BERT was designed to handle. Our solution is to ag- gregate sentence-level evidence to rank docu- ments. Furthermore, we are able to leverage passage-level relevance judgments fortuitously available in other domains to fine-tune BERT models that are able to capture cross-domain notions of relevance, and can be directly used for ranking news articles. Our simple neural ranking models achieve state-of-the-art effec- tiveness on three standard test collections.
In this paper, an innovative modeling approach is ap- plied to impulsive noises which are henceforth studied directly at their sources outputs. Noise at receiver would be simply the noise model at source convolved by pow- erline Channel Transfer Function (CTF). CTF model examples can be found in  and .
contacts 0.091). The main reason for the different performance of these two targets could be related to the larger deviation of the modeled α-repeat eGFP-binder A protein with respect to the bound structure in T96 as compared to that of the α-repeat eGFP-binder C protein in T97 (interface RMSD 5 Å and 2 Å, respectively). This larger deviation in T96 subunit could be due either to modeling issues or to conformational rearrangement upon binding. From a posteriori analysis of our initial sets of decoys, we found that in T97 there were many more acceptable solutions than in T96, both as predictors and as servers, which suggests that the large deviation in T96 subunit had some kind of effect in sampling. In the case of scorers, we also obtained better results for T97. Interestingly, in the initial set of scorers provided by the organizers there were only two acceptable poses in T96, as compared to 18 acceptable poses in T97, which again points to the existence of global sampling difficulties in T96. In general, our performance in these two targets was consistent with the results of the rest of the CAPRI participants, which showed that T96 was a more difficult target than T97.
This thesis aimed at improving the current practices of Software Language Engineering with a particular focus on domain specific modeling languages and review-based valida- tion. To reach this objective, we investigated the use of Alloy in the design of DSMLs. The use of the formal language Alloy in this exercise was motivated by (1) its high-level minimalist syntax allowing us to focus on system abstractions in a platform independent fashion and (2) by the ability of its accompanying tool, the Alloy analyzer, to generate instances from any Alloy specification, hence allowing seamless review-based validation. Those investigations had several outcomes, first of which is a novel approach to the definition of DSMLs based exclusively on Alloy. More precisely, we define how each com- ponent of a DSML definition (abstract syntax, concrete syntax and semantics) can be expressed by models and model transformations specified in Alloy, respectively. Based on this approach to DSMLs specification we introduced a design process tailored to enable the involvement of domain experts in their validation.
Contracts allow an explicit description of the language constructs and primitives in a human- readable format. Conforming to the analysis of B. A. Nardi concerning task-specific languages, it becomes possible to package business knowledge in a set of well-described primitives and then present them to experts in a neat and structured manner (cf. e.g. [GLS02]). Contracts can also serve for guiding experts at modeling and model fine-tuning at execution time. For instance, it becomes possible to automatically identify that the Calculate Daily Interest primitive requires two arguments of types Money and Percentage. By coupling a type system to Dart, it becomes possible to filter choices and to avoid type mismatches when selecting arguments. Contracts allow further automating the generation of graphical interfaces for editing primitive instances. Material for online help can also be associated to contracts. At runtime, contracts help automating type checking on effective arguments and produced results. They can also automate the selection of better execution strategies according to the primitive’s resource consumptions and the actual execution environment. We currently take advantage of this feature in developing ambient systems [RMSAP06]. Modeling steps by combining part holders with procedures (instead of uniquely procedures) brings several new possibilities. It allows developing modeling environments with a spreadsheet look & feel, well-known for their accessibility to domain experts [Nardi93]. Domain experts can model complex behavior by simply (1) selecting amongst the contracts, the primitive to instantiate, (2) selecting the grid cell to which the instance of the primitive should be attached, and (3) selecting the arguments for the primitive amongst other cells in the sheet. We have successfully tested this idea by developing a Web-based and Dart- compliant graphical interface for a research prototype called AmItalk [CRZ05].
Another means to raise the effectiveness is editor support. The skeleton of a motion primitive, shown in Fig. 6.8a, for example is created every time a new primitive is created in the language, so that the user only has to fill out the remaining blanks and variation points. Fig. 6.8 shows a DSL example with several of the introduced concepts involved. The snippet shows an Adaptive Component (red) with a mo- tion primitive resp. an Adaptive Module, Inverse Kinematics Mapping, a Criterion, together forming a Reaching Controller to reach a certain pose and check convergence. The expressiveness of the Dynamical System DSL is largely driven by the easy integration of the formula expressing the dynamics of a motion primitive with its Inputs and Outputs. Tying data streams of a complex system to algorithmic code is usually a tedious task in GPL code, but very compact in the Dynamical System DSL while still being formulated on an abstract level and therefore platform and technology independent. Its notation is the natural notation of the domain expert, mathematical expressions, who can easily specify its connection to the surrounding system, yet do not need to be concerned with the technical details that realize its proper execution and technical integration.
ated successfully in several projects (Karow et al., 2008; Matzner et al., 2009) and through experi- ments (Becker et al., 2009a). Therefore, it of- fers a good basis for the language of DSLs4BPM. Our framework language has the same structural ap- proach to process modeling as PICTURE, but encom- passes only those core elements of PICTURE that are domain-independent. As PICTURE has been suc- cessfully transferred to other domains, it is a viable basis for the framework language. PICTURE mod- els for public administrations have been successfully transformed to BPMN (Heitkoetter, 2011). This sug- gests that it will also be possible to transform models based on a framework language that has been derived and generalized from PICTURE’s structural parts. 4 DESIGN OF FRAMEWORK Figure 3 gives an overview of our framework for the integrated creation of process modeling DSLs and transformations. DSLs4BPM consists of a generic PML (À in figure 3) and a transformation (Á) map- ping the generic concepts to BPMN 2 (Ä). Language and transformation provide the basic structure and are explicitly designed to be extended. Domain-specific languages can be derived from the framework by ex- tending the generic language at predefined extension points with domain-specific constructs (Â). At the same time, the transformation should be adapted to the new language elements and their semantics by overloading specified rules (Ã). The new rules should transform the domain-specific constructs into corre- sponding elements from BPMN. They can also adapt the behavior of the general transformation where nec- essary. These partial transformations are seamlessly integrated into the general transformation by the prin- ciple of Inversion of Control.
In recent years, software development meets many problems that are caused by increase in scale the complexity of software design, the reduction of development schedule and cost and so on. Domain specific modeling (DSM) as a method to solve these problems has been attracting methods. Domain-specific modeling (DSM) is a higher level of CASE process, a way to model data structures and logic in domain concepts independent from programming languages and thus also include syntax details. The final source code in a desired programming language is derived automatically from these high concept models by using exact language generators. This is possible, because both the modelling concepts and language generators are defined by users of DSM solution software. The basis for DSM is Language Engineering allowing to define and to use various Domain Specific Languages to implement. The whole process of Meta- modeling in the MetaEdit+ tool rotates around the Meta types represented together as GOPPRR. It stands of Graph, Object, Property, Port, Relationship and Role. Meta modeling starts with defining objects and its properties. After objects are generated a relevant diagram is assigned
Traditionally, the process of creating models using DSMLs is manual. In this manual process, the modeler must possess pre-requisite knowledge about the target domain before creating models within the domain that address their application needs. Modelers can also leverage other options to assist with creating models like constraint solvers [6–8] and model guidance [9, 10]. Though, at first, constraint solvers and model guidance help the modeler, the modeling effort increases as the number of modeling elements and their constraints in the domain increase. Moreover, the modeler must track of the structure, functionalities, state and implementation of each of the modeling elements . For example, Platform Independent Component Modeling Language (PICML)  is a large-scale DSML that contains approximately 930 modeling elements and 130 constraints, and has high modeling effort  due to the aforementioned reasons.
The extracellular calcium-sensing receptor (CaR) on the parathyroid cell surface negatively regulates secretion of parathyroid hormone (PTH). CaR has an important rule in diseases. Cinacalcet hydrochloride, an allosteric agonist of this receptor was approved by FDA in 2004 for treatment of secondary hyperparathyroidism. Cinacalcet is indicated for the treatment of hypercalcemia in patients with parathyroid carcinoma or for secondary hyperparathyroidism in patients with chronic kidney disease who require dialysis. This drug is an allosteric agonist which is bind to transmembrane domain of CaR. Studies showed that the ligand binding region of CaR, as expected, is located at the amino-terminal domain (extracellular domain). There is no crystal structure or model is available for this domain of CaR so we constructed the model and found putative ligand binding site. This model will be useful for finding new CaR orthosteric ligands and designing new drugs.
In this paper an alternative numerical scheme based on the boundary domain integral method (BDIM) is presented for the solution of a general two-phase two-component flow motion problem. This is definitely the first attempt to implement the BDIM and velocity-vorticity formulation for modeling two- phase flows. The two-fluid model (TFM) is used to derive two sets of modified Navier-Stokes equations. The velocity-vorticity formulation of the physical conservation laws of mass and momentum then follows. The advantages of this approach lie with the numerical separation of kinematic and kinetic aspects of both phases motion from the thermodynamic pressure and the solids pressure computation. Particular attention is given to the drag between the phases, which is described by the interphase momentum exchange coefficient.
The disparity in performance between in- domain and out-of-domain tests is by no means restricted to SRL. Past research in a variety of NLP tasks has shown that parsers (Gildea, 2001), chunkers (Huang and Yates, 2009), part-of-speech taggers (Blitzer et al., 2006), named-entity tag- gers (Downey et al., 2007a), and word sense dis- ambiguation systems (Escudero et al., 2000) all suffer from a similar drop-off in performance on out-of-domain tests. Numerous domain adapta- tion techniques have been developed to address this problem, including self-training (McClosky et al., 2006) and instance weighting (Bacchiani et al., 2006) for parser adaptation and structural corre- spondence learning for POS tagging (Blitzer et al., 2006). Of these techniques, structural correspon- dence learning is closest to our technique in that it is a form of representation learning, but it does not learn features for word spans. None of these tech- niques have been successfully applied to SRL.
Baselines We consider the following baselines: (a) AE-SCL-SR (ZR17). We also experimented with the more basic AE-SCL but, like in ZR17, we got lower results in most cases; (b) SCL with pivot features selected using the mutual informa- tion criterion (SCL-MI, (Blitzer et al., 2007)). For this method we used the implementation of ZR17; (c) MSDA (Chen et al., 2012), with code taken from the authors’ web page; (d) The MSDA-DAN model (Ganin et al., 2016) which employs a do- main adversarial network (DAN) with the MSDA vectors as input. The DAN code is taken from the authors’ repository; (e) The no domain adapta- tion case where the sentiment classifier is trained in the source domain and applied to the target do- main without adaptation. For this case we consider three classifiers: logistic regression (denoted NoSt as it is not aware of its input’s structure), as well as LSTM and CNN which provide a control for the importance of the structure aware task classifiers in PBLM models. To further control for this effect we compare to the PBLM-NoSt model where the PBLM output vectors (h t vectors generated after
Another advantage of learning from real-world open domain documents is improved generaliza- tion ability. We conduct zero-shot evaluations on the DUC-2001 news KPE datasets (Wan and Xiao, 2008b), where neural KPE systems are evaluated without seeing any labels from their news articles. BLING-KPE trained on OpenKP is the only neu- ral method that outperforms traditional non-neural KPE methods, while neural KPE systems trained on the scientific documents do not generalize well to the news domain due to the domain differences. 2 Related Work
The performance of the contourlet domain denoising method is evaluated by conducting experiments using set of images obtained from , and then compared to that of the many of the state-of-the-art techniques. The experiments are performed on images corrupted with Gaussian noise of standard deviation, σ η varying from 10 to 40. The noisy images are decomposed by the contourlet transform into three scales with eight directions in each scale. Note that any further decomposition beyond these levels does not produce a significant increase in the denoising performance. We use the 9-7 bi- orthogonal filters for both the multi-scale and multi-directional decomposition stages. Since the contourlet transform is not shift-invariant, the denoised image is affected by the pseudo-Gibbs phenomena, resulting in artifacts in smooth regions and ringing effect around the edges. To overcome this problem, as discussed in Section 3.2, we employ the cycle spinning mode by averaging the result of the contourlet shrinkage method over all the circulant shifts of the input noisy image. The PSNR, in decibels, and MSSIM index measure are used to provide quantitative evaluations of the algorithm. It should be noted that for a particular noise level, the PSNR value is calculated by repeating the experiment ten times and then averaging over these values.