User-defined Class Research Articles

MACnet: Mask augmented counting network for class-agnostic counting

Class agnostic counting involves counting the instances of any user-defined class. It is also usually phrased as a matching problem wherein the model finds all the matching objects in a query image given exemplar patches containing the target object. Typically, users define exemplar patches by placing bounding boxes around the target object. However, defining exemplars using bounding boxes inevitably captures both the target object (foreground) and its surrounding background. This would unintentionally match patches similar to the background, leading to an inaccurate count. Moreover, objects poorly represented by a bounding box (e.g., non-axis aligned, irregular, or non-rectangular shapes) may capture a significantly disproportionate amount of background relative to the foreground within the exemplar patch, leading to poor matches. In this paper, we propose to utilize segmentation masks to separate target objects from their background. We derived these segmentation masks from extreme points, which requires no additional annotation effort from the user compared to annotating bounding boxes. Moreover, we designed a module that learns the mask features as residual to the object features, allowing the network to learn how to better incorporate the segmentation masks. Our model improves upon state-of-the-art methods by up to 3.7 MAE points on the FSC-147 benchmark dataset, showing the effectiveness of our masking approach.

로봇용 정보 모델 및 정보모델 편집기

In the robot and automation domain, interface-related standards have been studied from the viewpoint of interoperability and the concept of an information model has recently begun to be used instead of the term “interface”. This paper introduces the concepts of the information model used in some application domains such as building and manufacturing, which have been developed based on object-oriented approach. In addition, the international standard related to information models currently under development in the robotics domain is introduced and an editor to easily edit these information models is proposed. The proposed editor for information model first collects data for setting the module ID and then provides the different editor-screen according to the data so that users does not input unnecessary data. It also provides a way for users to add the user-defined class types and the enumeration types that the editor does not provide. The proposed editor is implemented and verified for the information model.

Nonlinear Model Reduction by Moment-Matching for a Point Absorber Wave Energy Conversion System

This paper presents a data-driven model reduction by moment-matching approach to construct control-oriented models for a point absorber device. The methodology chosen and developed generates models which are input-to-state linear, with any nonlinear behaviour confined to the output map. Such a map is the result of a data-driven approximation procedure, where the so-called moment of the point absorber system is estimated via a least-squares procedure. The resulting control-oriented model can inherently preserve steady-state properties of the target WEC system for a user-defined class of input signals of interest, with the computation only dependent upon a suitably defined set of input-output data.

PEF: Python Error Finder

PeerCheck [1] is a lightweight library-based approach to implement symbolic execution over object-oriented languages without modifying the analyzed code. Unlike traditional symbolic execution engines, the symbolic semantics is built as an external library that runs together with the target program. They can accomplish this task thanks to special features of the python programming language which allows one to implement symbolic values with class objects. However, their approach is limited to symbolic execution on primitive types. In this work, we show an extension that enables one to perform symbolic execution with user-defined class objects. In addition, we build a new verification tool, called PEF, which implements the described technique, and our extension. This tool can be used to verify programs written in the Python programming language, and we developed in python on top of the Z3 SMT solver. We used the tool to verify its own code, showing its potential and usefulness to test programs in production. We also evaluate the tool for testing well known algorithms and data structures.

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Semantic-Aware Automatic Parallelization of Modern Applications Using High-Level Abstractions

Automatic introduction of OpenMP for sequential applications has attracted significant attention recently because of the proliferation of multicore processors and the simplicity of using OpenMP to express parallelism for shared-memory systems. However, most previous research has only focused on C and Fortran applications operating on primitive data types. Modern applications using high-level abstractions, such as C++ STL containers and complex user-defined class types, are largely ignored due to the lack of research compilers that are readily able to recognize high-level object-oriented abstractions and leverage their associated semantics. In this paper, we use a source-to-source compiler infrastructure, ROSE, to explore compiler techniques to recognize high-level abstractions and to exploit their semantics for automatic parallelization. Several representative parallelization candidate kernels are used to study semantic-aware parallelization strategies for high-level abstractions, combined with extended compiler analyses. Preliminary results have shown that semantics of abstractions can help extend the applicability of automatic parallelization to modern applications and expose more opportunities to take advantage of multicore processors.

Object-oriented classic data structures for CS2 in C#

For many years there has been substantial agreement that the content of the CS2 course should be a study of the classic data structures including, but not limited to, stacks, queues, trees, and tables. In recent years we have observed a move to the object-oriented programming paradigm for the CS2 course. At this point there is significant divergence as to the best programming language to use -- C++, Eiffel, Java, Oberon, Python, or Scheme, to name a few. Although many agree that to be truly object-oriented, a programming language must support (1) encapsulation of attributes and behavior in a user-defined class specification, (2) instantiation of objects of predefined and user-defined classes, (3) polymorphism, and (4) inheritance. From a survey of recent texts for the CS2 course, it appears that there is heavy emphasis on encapsulation and instantiation with some treatment of inheritance and little treatment of polymorphism. There is also significant treatment of generic container classes supported by template classes if the supporting language is C++. If the supporting language is Java, then the container typically manages objects of the type Object. This paper makes a case that the new Microsoft C# ((c)2001 Microsoft Corporation) language could be considered a viable candidate for the CS2 course since it supports user-defined class specifications with the encapsulation of attributes and behavior, instantiation of objects of predefined and user-defined classes, polymorphism and inheritance. Additionally, there is full support for exception handling using the now, well-known, try/catch/finally semantics. Further, there is support for dynamic memory allocation and deallocation. The language also makes the distinction between pass by reference and pass by value function/method parameters by obvious keywords in both the formal declarations and in the call. The language also supports namespaces for the organization of software components. And finally, the student or developer has full support for visual, event-driven, net-centric programming paradigm.This paper presents an implementation of a stack container ADT that is fully generic, that is, it may manage a collection of arbitrary objects that demonstrate polymorphic behavior.

Object-oriented classic data structures for CS 2 in visual basic .net

For many years there has been substantial agreement that the content of the CS2 course should be a study of the classic data structures including, but not limited to, stacks, queues, trees, and tables. In recent years we have observed a move to the object-oriented programming paradigm for the CS2 course. At this point there is significant divergence as to the best programming language to use - C++, Eiffel, Java, Oberon, Python, or Scheme, to name a few. Although many agree that to be truly object-oriented, a programming language must support (1) encapsulation of attributes and behavior in a user-defined class specification, (2) instantiation of objects of pre-defined and user-defined classes, (3) polymorphism, and (4) inheritance. From a survey of recent texts for the CS2 course, it appears that there is heavy emphasis on encapsulation and instantiation with some treatment of inheritance and little treatment of polymorphism. There is also significant treatment of generic container classes supported by template classes if the supporting language is C++. If the supporting language is Java, then the container typically manages objects of the type Object. This paper makes a case that Visual Basic .NET could be considered a viable candidate for the CS2 course since it supports user-defined class specifications with the encapsulation of attributes and behavior, instantiation of objects of pre-defined and user-defined classes, polymorphism and inheritance. Additionally, there is full support for exception handling using the now, well-known, try/catch/finally semantics. Further, there is support for dynamic memory allocation and deallocation. And finally, the student or developer has full support for visual, event-driven, net-centric programming paradigm.This paper is a follow-up to a series of papers several years ago by the same author. These papers made a case for the use of Visual Basic in CS2 even though, at the time, Visual Basic was not yet fully object-oriented. This paper will present an implementation of a stack container ADT that is fully generic, that is, it may manage a collection of arbitrary objects that demonstrate polymorphic behavior.

Ad hoc classification of radiology reports.

The task of ad hoc classification is to automatically place a large number of text documents into nonstandard categories that are determined by a user. The authors examine the use of statistical information retrieval techniques for ad hoc classification of dictated mammography reports. The authors' approach is the automated generation of a classification algorithm based on positive and negative evidence that is extracted from relevance-judged documents. Test documents are sorted into three conceptual bins: membership in a user-defined class, exclusion from the user-defined class, and uncertain. Documentation of absent findings through the use of negation and conjunction, a hallmark of interpretive test results, is managed by expansion and tokenization of these phrases. Classifier performance is evaluated using a single measure, the F measure, which provides a weighted combination of recall and precision of document sorting into true positive and true negative bins. Single terms are the most effective text feature in the classification profile, with some improvement provided by the addition of pairs of unordered terms to the profile. Excessive iterations of automated classifier enhancement degrade performance because of overtraining. Performance is best when the proportions of relevant and irrelevant documents in the training collection are close to equal. Special handling of negation phrases improves performance when the number of terms in the classification profile is limited. The ad hoc classifier system is a promising approach for the classification of large collections of medical documents. NegExpander can distinguish positive evidence from negative evidence when the negative evidence plays an important role in the classification.