Frame-based Representation Research Articles

Фреймовая репрезентация концепта DIGITAL ECONOMY в англоязычной дискурсивной практике

The aim of the study is to identify the content minimum of the DIGITAL ECONOMY concept in the English-language discursive practice. The paper considers the theoretical and methodological approaches of scientists to the frame-based representation of the concept in scientific literature; identifies and presents a graphical and content minimum of the DIGITAL ECONOMY concept; proposes a thematic categorisation of the DIGITAL ECONOMY concept. The scientific novelty of the work is expressed in the fact that the application of theoretical and methodological approaches to the frame-based representation of the concept made it possible to determine the subframe and slots of the DIGITAL ECONOMY concept and highlight the thematic categories of the concept. As a result of the study, the subframe and slots of the DIGITAL ECONOMY concept, including the lexical units and phrases “digital technology”, “internet”, “information and communication technology”, “data”, have been identified; 15 thematic categories of the DIGITAL ECONOMY concept have been outlined; the largest thematic categories, such as “Artificial Intelligence”, “Technology” and “Digital Money and Transactions”, have been determined. The identification of these components will contribute to the objectification of the linguistic world image in the English-language discursive practice.

Knowledge Base Reuse With Frame Representation In Artificial Intelligence Applications

The creation and upkeep of knowledge bases have emerged as the primary issue in artificial intelligence development. The knowledge domain is expanding, and application interoperability is urgently demanded. To accommodate this modification, the knowledge base, which serves as the foundation of the artificial intelligence architecture, must be updated. XML (extensible markup language), RDF (resources description framework), DAML (DARPA agent markup language), OIL (ontology inference layer), and OKBC are just a few of the standards that have been introduced as a result of the development of the internet (Open Knowledge-Base Connectivity). One type of knowledge representation that can store information and facts about a subject is frame-based representation. The OKBC, which can help with knowledge base interoperability, supports this approach. In this work, the main issues covered are the comprehension and fundamentals of knowledge base creation with frame representation. To better understand how frame representation functions as a reusable component, a case study from a news publication were used as an example.

Frame-Based Representation for Event Detection on Twitter

Frame-Based Representation for Event Detection on Twitter

A random forest classifier for detecting rare variants in NGS data from viral populations

We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of k-mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies k-mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of k-mers. Frames are sets of non-orthogonal basis functions that were traditionally used in signal processing for noise removal. We define discrete spatial signals for genomes and sequenced reads, and show that k-mers of a given size constitute a frame.We evaluate MultiRes on simulated and real viral population datasets, which consist of many low frequency variants, and compare it to the error detection methods used in correction tools known in the literature. MultiRes has 4 to 500 times less false positives k-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their de-novo assembly. It has high recall of the true k-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs) and fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is available freely from the GitHub link https://github.com/raunaq-m/MultiRes.

A frame-based approach for theoretical concepts

According to a seminal paper by Barsalou (Frames, fields, and contrasts, 1992), frames are attribute-value-matrices for representing exemplars or concepts. Frames have been used as a tool for reconstructing scientific concepts as well as conceptual change within scientific revolutions (Andersen and Nersessian, in Philos Sci 67:224–241, 2000; Chen and Barker, in Philos Sci 67:208–223, 2000; Chen, in Philos Sci 70:962–974, 2003; Barker et al., in Thomas Kuhn, 2003; Andersen et al., in The cognitive structure of scientific revolutions, 2006; Votsis and Schurz, in Stud Hist Philos Sci 43:105–114, 2012, in Concept types and frames. Application in language, cognition, and science, 2014). In the frame-based representations of scientific concepts developed so far the semantic content of concepts is (partially) determined by a set of attribute-specific values. This way of representing semantic content works best for prototype concepts and defined concepts of a conceptual taxonomy satisfying the no-overlap principle. In addition to the semantic content of prototype concepts and defined concepts, frames can also contain empirical knowledge that is represented as constraints between the values of the frame. Beside prototype concepts and defined concepts, theoretical concepts that are multiply operationalized play an important role in science. However, so far no frame-based representation of theoretical concepts has been proposed. In this paper, it will be shown that theoretical concepts can be represented by frames and that frame-based representations of prototype concepts and defined concepts have another structure than frame-based representations of theoretical concepts. In order to explicate this difference, we will develop a frame-based method for representing all three kinds of concepts by means of mathematical graph-theory. One important consequence will be that the constraints of a frame representing a theoretical concept are entailed by the structure of the frame, as opposed to a frame representing defined or prototype concepts.

Concept Algebra and Frame-based Representation for Event and Event Class

Concept Algebra and Frame-based Representation for Event and Event Class

The Prediction Model of Case-Based Reasoning about Project Evaluation of Xiamen City

This paper was based on construction cost indexes of Xiamen, 11 feature attributes were extracted, including engineering purposes, engineering categories, and 3 predictors including square metre cost. Frame-based representation was used to represent cases. Access was used to build engineering case library. Visual Basic language was adopted to develop object-oriented design. Combined with knowledge guidance strategy and the nearest neighbor algorithm, hybrid retrieval strategy was adopted in basic retrieval and advanced retrieval. When built on case-based reasoning, engineering evaluation model provids a fast and effective method for determining the investment target of project planning and feasibility studies.

Knowledge-based Links for Automatic Interaction with Programming Online Judges

Programming online judges are computing resources with pre-designed test data. Recently, an increasing attention has been paid on integration of online judges into a tutoring system. However, it is difficult for a local system to automatic interaction with remote online judges on the web to share their computation, because these computing resources are designed for human users only. To address such issue, a novel link is proposed here, called framed link, which consists of a frame-based representation for the knowledge about how to interact with the computing resource that the link points to. With the framed link, three remote online judges including their internal test data are integrated successfully into a local system for programming courses.

A frame-based representation for a bedside ventilator weaning protocol

We describe the use of a frame-based knowledge representation to construct an adequately-explicit bedside clinical decision support application for ventilator weaning. The application consists of a data entry form, a knowledge base, an inference engine, and a patient database. The knowledge base contains database queries, a data dictionary, and decision frames. A frame consists of a title, a list of findings necessary to make a decision or carry out an action, and a logic or mathematical statement to determine its output. Frames for knowledge representation are advantageous because they can be created, visualized, and conceptualized as self-contained entities that correspond to accepted medical constructs. They facilitate knowledge engineering and provide understandable explanations of protocol outputs for clinicians. Our frames are elements of a hierarchical decision process. In addition to running diagnostic and therapeutic logic, frames can run database queries, make changes to the user interface, and modify computer variables.

On Pose Estimation for Human-Robot Symbiosis

This paper presents a vision based pose estimation system using knowledge based approach for human-robot symbiosis. The system is based on visual information of the face by connected component analysis of the skin color segmentation of images in HSV color model and is commenced with the face recognition and pose classification scheme using subspace PCA based pattern-matching strategies. With the knowledge of the known user's profile, face poses are then classified by multilayer perceptron. Based on the frame-based knowledge representation approach, face poses are being interpreted using the Software Platform for Agent and Knowledge (SPAK) management. On face pose recognition, robot is then instructed to perform some specific tasks by issuing pose commands. Experimental results demonstrate that the subspace method is better than that of the standard PCA method for face pose classification. The system has been demonstrated with the implementation of the algorithm to interact with an entertainment robot named, AIBO for human-robot symbiotic relationship.

Knowledge model-based adaptive intelligent control of robots for a symbiotic human robot system

This paper proposes a novel knowledge model-based adaptive intelligent control of robots for a symbiotic human-robot system. In this method, a knowledge model is first defined by the frame-based knowledge representation. It contains various frames for describing different users, features of multiple robots as well as robot behaviours for human-robot interaction and performing various tasks. According to this knowledge model, the intelligent control of robots in a symbiotic human-robot system can be implemented by means of a software platform, called Software Platform for Agents and Knowledge Management (SPAK). In addition, a kind of learning function is developed and integrated into the SPAK in order that the system can autonomously learn new knowledge by human-robot interaction and generate new control strategy for robots. Hence, the symbiotic human-robot system can adapt to various situations with different human requests and realise high-autonomous intelligent control of robots. In this paper, the effectiveness of the proposed method is verified by the experiment using actual robots.

A Nonparametric Adaptive Tracking Algorithm Based on Multiple Feature Distributions

This paper presents an object tracking framework based on the mean-shift algorithm, which is a nonparametric technique that uses statistical color distribution of objects. Tracking objects through highly similar-colored background is one of the problems that need to be addressed. In various cases where object and background color distributions are very similar, the color distribution obtained from single frame alone is not sufficient to track objects reliably. To deal with this problem, the proposed algorithm utilizes an adaptive statistical background and foreground modeling to detect the change due to motion using kernel density estimation techniques based on multiple recent frames. The use of multiple frames supplies more information than single frame and thus it provides more accurate modeling of both background and foreground. In addition to color distribution, this statistical multiple frame-based motion representation is integrated into a modified mean-shift algorithm to create more robust object tracking framework. The use of motion distribution provides additional discriminative power to the framework. The superior performance with quantitative results of the framework has been validated using experiments on synthetic and real sequence of images

Knowledge model-based heterogeneous multi-robot system implemented by a software platform

In order to build a heterogeneous multi-robot system that can be regarded as a primitive prototype of a future symbiotic autonomous human-robot system, this paper presents a knowledge model-based heterogeneous multi-robot system implemented by a software platform. With using frame-based knowledge representation, a knowledge model is constructed to describe the features of heterogeneous robots as well as their behaviors according to human requests. The required knowledge for constructing a heterogeneous multi-robot system can be therefore integrated in a single model and shared by robots and users. Based on the knowledge model, the heterogeneous multi-robot system is defined in the Software Platform for Agents and Knowledge Management (SPAK) by use of XML format. With the use of SPAK, the cooperative operation of heterogeneous multi-robot system can be flexibly carried out. The proposed system not only can integrate heterogeneous robots and various techniques for robots, but also can automatically perform human-robot interaction and plan robot behaviors taking into account different intelligence of robots corresponding to human requests. In this paper, an actual heterogeneous multi-robot system comprised by humanoid robots (Robovie, PINO), mobile robot (Scout) and entertainment robot dog (AIBO) is built and the effectiveness of the proposed system is verified by experiment.

Correlation Migration Using Gaussian Beams of Scattered Teleseismic Body Waves

Correlation migration for structural imaging using Gaussian beams is described for the inversion of passively recorded teleseismic waves. Gaussian beam migration is based on an overcomplete frame-based representation of the seismic wave field and uses localized slant-stack windows of the data. Paraxial Gaussian beams are then utilized for the backpropagation of the seismic waves into the me- dium. The method can provide stable imaging of seismic data in smoothly varying background media where caustics and triplicated arrivals exist. We develop Gaussian beam migration for structural imaging, which allows for incident teleseismic waves from beneath the structure, using directly scattered or surface-reflected phases. The method is applied to synthetic data computed for a collisional-zone model, and the inversions show that the Gaussian beam migration can image structure using passive teleseismic data. The method is next applied to synthetic data that have been con- volved with an observed pulse from the 1993 Cascadia experiment. The autocorre- lation is computed and the autocorrelated data are migrated by using Gausian beams for direct P-to-S scattered waves and surface-reflected waves. Although the migra- tions of the autocorrelation data with the source pulse included have more noise than the migrations of the impulsive source data, the subduction zone structure can still be clearly identified in the migrated results without removal of the source pulses.

Description logic-based methods for auditing frame-based medical terminological systems

Medical terminological systems (TSs) play an increasingly important role in health care by supporting recording, retrieval and analysis of patient information. As the size and complexity of TSs are growing, the need arises for means to audit them, i.e. verify and maintain (logical) consistency and (semantic) correctness of their contents. This is not only important for the management of TSs but also for providing their users with confidence about the reliability of their contents. Formal methods have the potential to play an important role in the audit of TSs, although there are few empirical studies to assess the benefits of using these methods. In this paper we propose a method based on description logics (DLs) for the audit of TSs. This method is based on the migration of the medical TS from a frame-based representation to a DL-based one. Our method is characterized by a process in which initially stringent assumptions are made about concept definitions. The assumptions allow the detection of concepts and relations that might comprise a source of logical inconsistency. If the assumptions hold then definitions are to be altered to eliminate the inconsistency, otherwise the assumptions are revised. In order to demonstrate the utility of the approach in a real-world case study we audit a TS in the intensive care domain and discuss decisions pertaining to building DL-based representations. This case study demonstrates that certain types of inconsistencies can indeed be detected by applying the method to a medical terminological system. The added value of the method described in this paper is that it provides a means to evaluate the compliance to a number of common modeling principles in a formal manner. The proposed method reveals potential modeling inconsistencies, helping to audit and (if possible) improve the medical TS. In this way, it contributes to providing confidence in the contents of the terminological system.

Video summaries and cross-referencing through mosaic-based representation

We present an approach for compact video summaries that allows fast and direct access to video data. The video is segmented into shots and, in appropriate video genres, into scenes, using previously proposed methods. A new concept that supports an hierarchical representation of video is presented, and is based on physical setting and camera locations. We use mosaics to represent shots and then scenes. We use a novel method for mosaic comparison which is robust against changes in viewpoint and illumination. In contrast to approaches to video indexing which rely on a frame-based representation, our efficient mosaic-based representation allows fast clustering of scenes into physical settings, a new conceptual form grounded in the recognition of real-world backgrounds. We employ a technique for choosing representative mosaics for each physical setting, for a more compact representation and faster comparison between settings. This compact representation and comparison method runs in real time and allows fast and accurate summaries and comparison of scenes across different videos, and serves as a basis for indexing video libraries. We demonstrate our work using situation comedies (sitcoms), where each half-hour episode is well structured by rules governing background use. Consequently, browsing, indexing, and comparison across videos by physical setting is very fast. Further, we show that physical settings lead to a higher-level contextual identification of the main plots in each video. We demonstrate these contributions with a browsing tool whose top-level single page displays the settings of several episodes. In sports videos where settings are not as well defined, our approach allows classifying shots for characteristic event detection.

Pushing the envelope: challenges in a frame-based representation of human anatomy

One of the main threads in the history of knowledge-representation formalisms is the trade-off between the expressiveness of first-order logic on the one hand and the tractability and ease-of-use of frame-based systems on the other hand. Frame-based systems provide intuitive, cognitively easy-to-understand, and scalable means for modeling a domain. However, when a domain model is particularly complex, frame-based representation may lead to complicated and sometimes awkward solutions. We have encountered such problems when developing the Digital Anatomist Foundational Model, an ontology aimed at representing comprehensively the physical organization of the human body. We show that traditional frame-based techniques such as is-a hierarchies, slots (roles) and role restrictions are not sufficient for a comprehensive model of this domain. The diverse modeling challenges and problems in this project required us to use such knowledge-representation techniques as reified relations, metaclasses and a metaclass hierarchy, different propagation patterns for template and own slots, and so on. We posit that even though the modeling structure imposed by frame-based systems may sometimes lead to complicated solutions, it is still worthwhile to use frame-based representation for very large-scale projects such as this one.

An extended unified framework for the rapid development of automated manufacturing systems

The design of today’s manufacturing systems involves the integration of specialised components with specific properties and functionality. Indeed, it has been a formidable task for the manufacturing community to develop a useable system economically with enhanced efficiency. In view of the technological advances in developing complex manufacturing systems, a unified framework had been proposed by the authors of this paper to formalise the procedures for the development of such systems. This paper presents an extension to this framework that automates the simulation of system behaviour at the design level. This extended framework incorporates a frame-based representation of the abstract classes that are generated from the unified framework, so that an expert system shell can be used to generate the expected behaviour of higher level designs prior to subsequent developments. The advantages of the extended framework include a shortened cycle for the product design and greater ease for system designers to produce manufacturing systems that better conform to their original specifications. A pharmaceutical system is chosen as a case example to illustrate the extended framework’s capability.

An object-oriented prototyping tool for automated manufacturing systems

System prototyping is essential in the development of automated manufacturing systems (AMSs), and the value of prototyping depends largely on the effectiveness of the prototyping tool to represent a system design and also the prototyping results. This paper presents a windows-based prototyping tool that adopts a frame-based representation approach to build a prototype from an object-oriented system design specification. The tool provides a platform capable of executing the prototype under different constraints and operating conditions, and of presenting the prototyping results to facilitate the study of the behaviour of the AMS. Properties, such as the total cycle time, resource utilisation, potential conflicts, errors in design, and abnormal behaviours, can be readily derived. The tool's effectiveness is illustrated by prototyping a flexible manufacturing system, and examining the results obtained. Indeed, the prototyping tool proposed in this paper is shown to provide a convenient means for the design of AMSs.

An integrated intelligent approach to process diagnosis in process industries

An integrated intelligent approach to enhancing process diagnosis automation (IIPD) is presented. A parallel hierarchical architecture is developed for IIPD, where various subsystems are coordinated by a meta-system. As the kernel of IIPD, the meta-system is endowed with a better understanding of diagnostic problem-solving and aims at large scale knowledge integration. Subsystems include domain expert systems and numerical routines that may be written in different languages or programming tools and may be used separately in different stages of the diagnostic process. An object-oriented knowledge language is also developed for organizing both meta-knowledge and domain diagnostic knowledge. This frame-based representation facilitates a hybrid utilization of diagnostic knowledge at both shallow and deep levels. Preliminary results have shown the potential of applying the integrated intelligent approach to complex process diagnosis.