A Brief History of the Object-Oriented Approach

Unlike other fads, the object-oriented paradigm is here to stay. The road towards an object-oriented approach is described and several object-oriented programming languages are reviewed. Since the object-oriented paradigm promised to revolutionize software development, in the 1990s, demand for object-oriented software systems increased dramatically; consequently, several methodologies have been proposed to support software development based on thatparadigm. Also presented are a survey and a classification schemefor object-oriented methodologies

Distribution of the Object Oriented Databases. A Viewpoint of the MVDB Model's Methodology and Architecture

In databases, much work has been done towards extending models with advanced tools such as view technology, schema evolution support, multiple classification, role modeling and viewpoints. Over the past years, most of the research dealing with the object multiple representation and evolution has proposed to enrich the monolithic vision of the classical object approach in which an object belongs to one hierarchy class. In particular, the integration of the viewpoint mechanism to the conventional object-oriented data model gives it flexibility and allows one to improve the modeling power of objects. The viewpoint paradigm refers to the multiple descriptions, the distribution, and the evolution of object. Also, it can be an undeniable contribution for a distributed design of complex databases. The motivation of this paper is to define an object data model integrating viewpoints in databases and to present a federated database architecture integrating multiple viewpoint sources following a local-as-extended-view data integration approach.object-oriented data model, OQL language, LAEV data integration approach, MVDB model, federated databases, Local-As-View Strategy.

Supporting a Multi-hierarchical Classifcation in the Object-Oriented Paradigm

This research and the resulting prototype system show that the object-oriented paradigm is an appropriate mechanism for supporting a complex, multi-hierarchical controlled vocabulary and the resulting classification when applied to a data base. In addition to supporting such a classification, it allows the searching and browsing of both the data base items and the assigned vocabulary terms

A model-based approach to hypermedia design.

This paper introduces the MESH approach to hypermedia design, which combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In addition, the data model's support for a context-based navigation paradigm, as well as a platform-independent implementation framework, are briefly discussed.Data; Model; Specifications; Classification;

Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava

Since the introduction of object-oriented programming few programming languages have attempted to provide programmers with more than objects and classes, i.e., more than two levels. Those that did, almost exclusively aimed at describing language properties—i.e., their metaclasses exert linguistic control on language concepts and mechanisms— often in order to make the language extensible. In terms of supporting logical domain classification levels, however, they are still limited to two levels. In this paper we conservatively extend the object-oriented programming paradigm to feature an unbounded number of domain classification levels. We can therefore avoid the introduction of accidental complexity into programs caused by accommodating multiple domain levels within only two programming levels. We present a corresponding language design featuring “deep instantiation ” and demonstrate its features with a running example. Finally, we outline the implementation of our compiler prototype and discuss the potentials of further developing our language design

MESH: an object-oriented approach to hypermedia modeling and navigation.

This paper introduces the MESH approach to hypermedia modeling and navigation, which aims at relieving the typical drawbacks of poor maintainability and user disorientation. The framework builds upon two fundamental concepts. The data model combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In the context-based navigation paradigm, conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. These specifications are captured in a high-level, platform independent implementation framework.Data; Model; Specifications; Classification; Information; Requirements;

Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain

Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.Comment: 7 pages, 6 figures. Accepted to ICRA202