Showing papers on "Object-oriented design published in 2015"

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Abstract: . The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

Best of both worlds: Human-machine collaboration for object annotation

Abstract: The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.

Executable virtual objects associated with real objects

Abstract: Embodiments for interacting with an executable virtual object associated with a real object are disclosed. In one example, a method for interacting with an executable virtual object associated with a real object includes receiving sensor input from one or more sensors attached to the portable see-through display device, and obtaining information regarding a location of the user based on the sensor input. The method also includes, if the location includes a real object comprising an associated executable virtual object, then determining an intent of the user to interact with the executable virtual object, and if the intent to interact is determined, then interacting with the executable object.

Dynamic content activation for automated analysis of embedded objects

Abstract: According to one embodiment, a threat detection platform is integrated with at least one virtual machine that automatically performs a dynamic analysis of a received document object and monitors the processing during the dynamic analysis. The dynamic analysis includes a detection of embedded objects and may automatically process the embedded objects, while maintaining a context of the embedding, within the virtual machine processing the document object. The virtual machine may monitor the processing of both the document object and the embedded object. The results of the processing may be analyzed to determine whether the document object includes malware and/or a threat level of the document object.

Object hallmarks: identifying object users using wearable wrist sensors

Abstract: In order for objects to perform personalized or contextual functions based on identity, they must solve what we call the object user identification problem: understanding who is actually using them. In this paper, we propose a new technique that uses data from wearable wrist sensors to perform object user identification. We hypothesize that objects have unique hallmarks that are imprinted in the hand gestures of its users. By detecting the presence of an object's hallmark in the wrist sensor data, we can identify who used the object. We evaluate this concept with a smart home application: recognizing who is using an object or appliance in a multi-person home by combining smart meter data and wearables. We conduct three different studies with 10 participants: 1) a study with scripted object use 2) a study with high-level tasked activities and unscripted object use, and 3) a 5-day in-situ study. These studies indicate that our approach performs object user identification with an average accuracy of 85--90%.

Object-Oriented Design and Programming: An Investigation of Novices’ Conceptions on Objects and Classes

Abstract: The Object-Oriented Programming (OOP) technique is nowadays the most popular programming technique among tertiary education institutions. However, learning OOP is a cognitively demanding task for undergraduate students. Several difficulties and misconceptions have been recorded in the literature for both OOP concepts and languages, mainly Java. This article focuses on reviewing and advancing research on the most fundamental OOP concepts, namely, the concepts of “object” and “class” and their role during program execution. The results of a long-term investigation on the subject are presented, focusing on a study exploring undergraduate students’ conceptions on “objects” and “classes.” The study advances related research on categories of conceptions on “objects” and “classes” by providing quantitative results, in addition to qualitative results, regarding the frequency of the recorded conceptions. Nearly half the students seem to comprehend the modeling and staticsdynamic aspects of the concepts “object” and “class.” Implications for achieving a deep conceptual understanding of text, action, and modeling aspects of these fundamental concepts are also discussed. Information regarding the programming environments utilized in the course and key features of the applied teaching approach are presented, in order to facilitate both a better understanding of the context and a better employment of the results of the presented study. Finally, proposals for enhancing the contribution of this and similar studies are made.

RGB-D object modelling for object recognition and tracking

Abstract: This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that unlike other modelling tools, our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions—each individual session presenting the object of interest in different configurations that reveal occluded parts of the object — that are automatically merged together to reconstruct a full 3D model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities to complex applications requiring these functionalities (e.g. human-object interaction analysis, robot grasping, etc.). The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or turn-table setups with static camera). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.

Learning View-Model Joint Relevance for 3D Object Retrieval

Abstract: 3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graph-based framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method.

Interactive Open-Ended Learning for 3D Object Recognition: An Approach and Experiments

Abstract: 3D object detection and recognition is increasingly used for manipulation and navigation tasks in service robots. It involves segmenting the objects present in a scene, estimating a feature descriptor for the object view and, finally, recognizing the object view by comparing it to the known object categories. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, “open-ended” implies that the set of object categories to be learned is not known in advance. The training instances are extracted from on-line experiences of a robot, and thus become gradually available over time, rather than at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D scenes in an open-ended manner? (2) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? This approach starts with a pre-processing step to remove irrelevant data and prepare a suitable point cloud for the subsequent processing. Clustering is then applied to detect object candidates, and object views are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to predict the categories of the detected objects. A leave-one-out cross validation algorithm is used to compute precision and recall, in a classical off-line evaluation setting, for different system parameters. Also, an on-line evaluation protocol is used to assess the performance of the system in an open-ended setting. Results show that the proposed system is able to interact with human users, learning new object categories continuously over time.

Source code and design conformance, design pattern detection from source code by classification approach

Abstract: Nowadays, software designers attempt to employ design patterns in software design phase, but design patterns may be not used in the implementation phase. Therefore, one of the challenging issues is conformance checking of source code and design, i.e., design patterns.In addition, after developing a system, usually its documents are not maintained, so, identifying design pattern from source code can help to achieve the design of an existing system as a reverse engineering task.The variant implementations (i.e., different source codes) of a design pattern make hard to detect the design pattern instances from the source code. To address this issue, in this paper, we propose a new method which aims to map the design pattern detection problem into a learning problem.The proposed design pattern detector is made by learning from the information extracted from design pattern instances which normally include variant implementations.To evaluate the proposed method, we applied it on open source codes to detect six different design patterns. The experimental results show that the proposed method is promising and effective. Nowadays, software designers attempt to employ design patterns in software design phase, but design patterns may be not used in the implementation phase. Therefore, one of the challenging issues is conformance checking of source code and design, i.e., design patterns. In addition, after developing a system, usually its documents are not maintained, so, identifying design pattern from source code can help to achieve the design of an existing system as a reverse engineering task. The variant implementations (i.e., different source codes) of a design pattern make hard to detect the design pattern instances from the source code. To address this issue, in this paper, we propose a new method which aims to map the design pattern detection problem into a learning problem. The proposed design pattern detector is made by learning from the information extracted from design pattern instances which normally include variant implementations. To evaluate the proposed method, we applied it on open source codes to detect six different design patterns. The experimental results show that the proposed method is promising and effective.

3D object class detection in the wild

Abstract: Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-of-the-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ [50] dataset.

Systems and method of interacting with a virtual object

Abstract: The technology disclosed relates to a method of interacting with a virtual object. In particular, it relates to referencing a virtual object in an augmented reality space, identifying a physical location of a device in at least one image of the augmented reality space, generating for display a control coincident with a surface of the device, sensing interactions between at least one control object and the control coincident with the surface of the device, and generating data signaling manipulations of the control coincident with the surface of the device.

Exploring Person Context and Local Scene Context for Object Detection

Abstract: In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).

Revised Use Case Point (Re-UCP) Model for Software Effort Estimation

Abstract: At present the most challenging issue that the software development industry encounters is less efficient management of software development budget projections. This problem has put the modern day software development companies in a situation wherein they are dealing with improper requirement engineering, ambiguous resource elicitation, uncertain cost and effort estimation. The most indispensable and inevitable aspect of any software development company is to form a counter mechanism to deal with the problems which leads to chaos. An emphatic combative domain to deal with this problem is to schedule the whole development process to undergo proper and efficient estimation process, wherein the estimation of all the resources can be made well in advance in order to check whether the conceived project is feasible and within the resources available. The basic building block in any object oriented design is Use Case diagrams which are prepared in early stages of design after clearly understanding the requirements. Use Case Diagrams are considered to be useful for approximating estimates for software development project. This research work gives detailed overview of Re-UCP (revised use case point) method of effort estimation for software projects. The Re-UCP method is a modified approach which is based on UCP method of effort estimation. In this research study 14 projects were subjected to estimate efforts using Re-UCP method and the results were compared with UCP and e-UCP models. The comparison of 14 projects shows that Re-UCP has significantly outperformed the existing UCP and e-UCP effort estimation techniques.

Generation of synthetic context frameworks for dimensionally constrained hierarchical synthetic context-based objects

Abstract: A processor-implemented method, system, and/or computer program product derives and utilizes a context object to generate a synthetic context-based object. A context object for a non-contextual data object is derived by contextually searching a document that contains multiple instances of the non-contextual data object. The non-contextual data object is associated with the derived context object to define a synthetic context-based object, where the non-contextual data object ambiguously relates to multiple subject-matters, and where the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object. The synthetic context-based object is then associated with at least one specific data store, which includes data that is associated with data contained in the non-contextual data object and the context object. A dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is then constructed for handling requests for data stores.

Digital object library management system for machine learning applications

Abstract: Digital object library management systems and methods for machine learning applications are taught herein. Such a method includes populating a digital object library with a number of machine readable digital objects, modifying the digital objects to include additional machine readable data about the digital objects or other digital objects and the relationships among existing digital objects, generating lists of objects for use in construction and verification of machine learning models used to classify unknown objects into one or more categories, building queries to generate object lists, initiating model generation, in which a machine learning model used to classify unknown objects into one or more categories is generated, initiating model evaluation, storing models, object lists, evaluation results, and associations among these objects, generating a visual display of object metadata, lists, relational information, and evaluation results and running distributable algorithms across the library of digital objects.

Flexibility: A Key Factor to Testability

Abstract: Testability is an important software quality factor that is ineffective if it is not available at an early stage in the development life-cycle. It becomes more essential in the case of object oriented design. Flexibility is an important key factor to testability analysis and measurement for delivering high class testable and maintainable software. Flexibility is a criterion of crucial significance to software developers, designers and the quality controllers. It constantly guides and supports to avoid wastage of resources as well as enable the designers for continuous improvement in the development process. Flexibility is concerned with building high quality and reliable software within the constraints of cost and time. It greatly influences cost, quality and reliability at software evolution process. Despite the fact flexibility is vital and highly significant aspect for software development processes, it is poorly managed. This paper focuses the need and importance of flexibility early at design phase. A model has been proposed for flexibility measurement of object oriented design by establishing multiple linear regression. Finally the proposed model has been validated using experimental tryout.

Dimensionally constrained synthetic context objects database

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects. A non-contextual data object is associated with a context object to define a synthetic context-based object, where the non-contextual data object ambiguously relates to multiple subject-matters, and where the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object. The synthetic context-based object is associated with at least one specific data store, which includes data that is associated with data contained in the non-contextual data object and the context object. A dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is constructed for handling requests for data stores. Synthetic context-based objects within a same dimension of the object library share data from a same context object and disparate data from different non-contextual data objects.

A systematic composite service design modeling method using graph-based theory.

Abstract: The composite service design modeling is an essential process of the service-oriented software development life cycle, where the candidate services, composite services, operations and their dependencies are required to be identified and specified before their design. However, a systematic service-oriented design modeling method for composite services is still in its infancy as most of the existing approaches provide the modeling of atomic services only. For these reasons, a new method (ComSDM) is proposed in this work for modeling the concept of service-oriented design to increase the reusability and decrease the complexity of system while keeping the service composition considerations in mind. Furthermore, the ComSDM method provides the mathematical representation of the components of service-oriented design using the graph-based theoryto facilitate the design quality measurement. To demonstrate that the ComSDM method is also suitable for composite service design modeling of distributed embedded real-time systems along with enterprise software development, it is implemented in the case study of a smart home. The results of the case study not only check the applicability of ComSDM, but can also be used to validate the complexity and reusability of ComSDM. This also guides the future research towards the design quality measurement such as using the ComSDM method to measure the quality of composite service design in service-oriented software system.

Quantifying Reusability of Object Oriented Design: A Testability Perspective

Abstract: The quality factor of class diagram is critical because it has a significant influence on overall quality of the product, delivered finally. Testability analysis, when done early in the software creation process, is a criterion of critical importance to software quality. Reusability is an important quality factor to testability. Its early measurement in object oriented software especially at design phase, allows a design to be reapplied to a new problem without much extra effort. This research paper proposes a research framework for quantification process and does an extensive review on reusability of object oriented software. A metrics based model “Reusability Quantification of Object Oriented Design” has been proposed by establishing the relationship among design properties and reusability and justifying the correlation with the help of statistical measures. Also, “Reusability Quantification Model” is empirically validated and contextual significance of the study shows the high correlation for model acceptance. This research paper facilitates to software developers and designer, the inclusion of reusability quantification model to access and quantify software reusability for quality product.

A Critical Analysis on Software Fault Prediction Techniques

Abstract: 2 Abstract: This paper presents the results of a systematic review conducted to collect evidence on software fault prediction techniques. Different models, methods, algorithms and approaches were studied and conclusion was drawn. The review was conducted by studying the different set of parameters at class level, component level and other software fault prediction techniques considering object oriented design approach. The information was collected from various research papers related to fault prediction and out of 577 mainly 15 studies, which were found most relevant are analyzed. Our results shows that there are few metrics which helps in predicting early fault prediction in software and reduces testing cost, increases reliability, quality of software, helps development managers in decision making to identify and make strategic planning regarding most faulty modules.

Self-demonstrating object features and/or operations in interactive 3d-model of real object for understanding object's functionality

Abstract: Method, system and computer program product are disclosed for self demonstration of particular functionality of the 3D model of a object, wherein functionality of the 3D model is demonstrated by automatic parallel or sequential operation of the part/s of the 3D model in multiple steps after receiving an user input which comprises one or more extrusive interaction, intrusive interactions, time bound change based interaction, real environment mapping based interactions command which further in response to the identified command/s, render corresponding interaction to 3D model of object with or without sound output using texture data, computer graphics data and by selectively using sound data of the 3D model of object to display the corresponding interaction to 3D model in virtual electronic display.

Automated object identification and processing based on digital imaging and physical attributes

Abstract: A method and associated systems for object identification and subsequent processing based on digital imaging and physical attributes. An object-identification system receives, in a materials-handling environment, a digital image and physical attributes that characterize an unidentified object. An attempt is made to identify the object by matching the image and attributes to those of known objects stored in an image database, an attribute database, or another external source. The object is associated with a label that identifies the actual object, associates the object with a similar object that may be substituted for the actual object in a desired application, or designates the object as unidentifiable. The digital image, label, and external sources used to identify the object may be updated by associating them with metadata gathered during the identification process. Subsequent processing is governed by business rules that operate as functions of the label data.

Software for Simplifying Embedded System Design Based on Event-Driven Method

Abstract: Complexity of embedded system application increases along with the escalation of market demand. Embedded system design process must be enhanced to face design complexity problem. One of challenges in designing embedded system is speed, accuracy, and flexibility. The design process is usually conducted recursively to fulfill requirement of user and optimization. To solve this problem, it needs a system design that is flexible for adaptation. One of solutions is by optimizing all or some of the design steps. This paper proposes a design framework with an automatic framework code generator with of event driven approach. This software is a part of a design flow which is flexible and fast. Tron game and simple calculator are presented as a case study. Test result shows that this framework generator can increase speed of design’s flexibility.

System and method for intelligent task management and routing

Abstract: Systems and methods are shown for routing task objects to multiple agents that involve analyzing content of each task object in an input buffer to determine a classification relevant to the content of the task object that is added to task object metadata, which is placed in a second buffer. Objects in the second buffer are analyzed and the classification in the object metadata used to search workforce management data representing agent characteristics to identify agents who match the classification. A routing strategy is applied to the object to select an agent and the object is routed to the agent's workbin. Another aspect involves organizing workbin tasks objects by priority, according to recent system conditions excluding objects that cannot presently be processed based on a workflow strategy or status data and presenting remaining objects based on order of priority, or re-arranging objects between workbins based on recent status info.

Evaluating effectiveness factor of object oriented design : a testability perspective

Abstract: Effectiveness is important quality factor to testability measurement of object oriented software at an initial stage of software development process exclusively at design phase for high quality product. It will help developer’s design capability to achieve the specified functionalities, characteristics, better design quality and behavior using appropriate object oriented design (OOD) concepts and procedures. Metric based model for ‘Effectiveness Quantification Model of Object Oriented Design’ has been proposed by establishing the correlation between effectiveness and OOD constructs. Later ‘Effectiveness Quantification Model’ is empirically validated and statistical significance of the study considers the high correlation for model acceptance. The aim of this research work is to encourage researchers and developers for inclusion of the effectiveness quantification model to access and quantify software effectiveness quality factor at design time.

Testability Measurement Model for Object Oriented Design (TMMOOD)

Abstract: Measuring testability early in the development life cycle especially at design phase is a criterion of crucial importance to software designers, developers, quality controllers and practitioners. However, most of the mechanism available for testability measurement may be used in the later phases of development life cycle. Early estimation of testability, absolutely at design phase helps designers to improve their designs before the coding starts. Practitioners regularly advocate that testability should be planned early in design phase. Testability measurement early in design phase is greatly emphasized in this study; hence, considered significant for the delivery of quality software. As a result, it extensively reduces rework during and after implementation, as well as facilitate for design effective test plans, better project and resource planning in a practical manner, with a focus on the design phase. An effort has been put forth in this paper to recognize the key factors contributing in testability measurement at design phase. Additionally, testability measurement model is developed to quantify software testability at design phase. Furthermore, the relationship of Testability with these factors has been tested and justified with the help of statistical measures. The developed model has been validated using experimental tryout. Finally, it incorporates the empirical validation of the testability measurement model as the authors most important contribution.

Testability Measurement Model for Object Oriented Design (Tmmood)

Abstract: Measuring testability early in the development life cycle especially at design phase is a criterion of crucial importance to software designers, developers, quality controllers and practitioners. However, most of the mechanism available for testability measurement may be used in the later phases of development life cycle. Early estimation of testability, absolutely at design phase helps designers to improve their designs before the coding starts. Practitioners regularly advocate that testability should be planned early in design phase. Testability measurement early in design phase is greatly emphasized in this study; hence, considered significant for the delivery of quality software. As a result, it extensively reduces rework during and after implementation, as well as facilitate for design effective test plans, better project and resource planning in a practical manner, with a focus on the design phase. An effort has been put forth in this paper to recognize the key factors contributing in testability measurement at design phase. Additionally, testability measurement model is developed to quantify software testability at design phase. Furthermore, the relationship of Testability with these factors has been tested and justified with the help of statistical measures. The developed model has been validated using experimental tryout. Finally, it incorporates the empirical validation of the testability measurement model as the author’s most important contribution.

System including a data repository and a data importing component

Abstract: A computer system comprises a data repository configured to store data objects. A task object generation component is implemented at least partially by computing hardware and is configured to generate task objects and cause the task objects to be stored in the data repository. An object linking component is implemented at least partially by computing hardware and is configured to attach particular data objects stored in the data repository to particular task objects generated by the task object generation component. A data importing component is implemented at least partially by computing hardware and is configured to identify one or more first fields of a first task object, of the task objects, that corresponds to one or more second fields of a first data object, of the data objects, the first data object having been attached to the first task object, each of the one or more second fields storing a particular value. The data importing component is further configured to assign the particular value of the second field of the first data object to the first field of the first task object. The computer system can be more efficient as less memory needs to be allocated to individual searches.

Incremental learning framework for object detection in videos

Abstract: Techniques disclose an incrementally expanding object detection model An object detection tool identifies, based on an object detection model, one or more objects in a sequence of video frames The object detection model provides an object space including a plurality of object classes Each object class includes one or more prototypes Each object is classified as being an instance of one of the object classes Each identified object is tracked across at least one of the frames The object detection tool generates a measure of confidence for that object based on the tracking Upon determining that the measure of confidence exceeds a threshold, the object detection tool adds a prototype of the instance to the object detection model