SeSFJava harness: service and assertion checking for protocol implementations

Many formal specification languages and associated tools have been developed for network protocols. Ultimately, formal language specifications have to be compiled into a conventional programming language and this involves manual intervention (even with automated tools). This manual work is often error prone because the programmer is not familiar with the formal language. So our goal is to verify and test the ultimate implementation of a network protocol, rather than an abstract representation of it. We present a framework, called services and systems framework (SeSF), in which implementations and services are defined by programs in conventional languages, and mechanically tested against each other. SeSF is a markup language that can be integrated with any conventional language. We integrate SeSF into Java, resulting in what we call SeSFJava. We present a service-and-assertion checking harness for SeSFJava, called SeSFJava harness, in which distributed SeSFJava programs can be executed, and the execution checked against services and any other correctness assertions. The harness can test the final implementation of a concurrent system. We present an application to a data transfer service and sliding window protocol implementation. SeSFJava and the harness has been used in networking courses to specify and test transmission control protocol-like transport protocols and service.     

Novel infrastructures for supporting mixed-reality experiences

This article focuses on the challenge of managing the large amount of heterogeneous spatiotemporal mixed-reality data (such as audio/video files, GPS logs, and text messages) generated in a distributed asynchronous fashion that must be indexed, annotated, synchronized, and replayed in the postproduction phase. As mixed-reality technologies grow and mature, this process becomes increasingly difficult. We face a vast amount of mixed-reality data as well as a vast number and diversity of services and tools. Producing meaningful content requires a systematic and integrated approach to managing audio and video streams rather than sophisticated tools or large development teams. We need an infrastructure to support the provenance and context of (often) transient resources (for example, data or tools and services being changed and updated) that could support richer user interaction with the resources. More specifically, we identify key grid and peer-to-peer capabilities that can bring us closer to such a large-scale generic record-and-reuse infrastructure for mixed-reality experiences.     

State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Rapid advances in optical instrumentation, high-speed cameras, and fluorescent probes have spurred tremendous growth in the volume of biomolecular imaging data. Various optical imaging modalities are used for probing biological systems in vivo and in vitro. These include traditional two-dimensional imaging, three-dimensional confocal imaging, time-lapse imaging, and multispectral imaging. Many applications require a combination of these imaging modalities, which gives rise to huge data sets. However, lack of powerful information extraction and quantitative analysis tools poses a major hindrance to exploiting the full potential of the information content of these data. In particular, automated extraction of semantic information from multimodality imaging data, crucial for understanding biological processes, poses unique challenges. Information extraction from large sets of biomolecular imaging data requires modeling at multiple levels of detail to allow not only quantitative analysis but also interpretation and extraction of high-level semantic information. In this paper, we survey the state of the art in the area of information extraction and automated analysis tools for in vivo and in vitro biomolecular imaging. The modeling and knowledge extraction for these data require sophisticated image processing and machine learning techniques, as well as formalisms for information extraction and knowledge management. Development of such tools has the potential to significantly improve biological discovery and drug development processes.     

BAAQ: an infrastructure for application integration and knowledge discovery in bioinformatics.

The emerging grid computing technologies enable bioinformatics scientists to conduct their researches in a virtual laboratory, in which they share public databases, computational tools as well as their analysis workflows. However, the development of grid applications is still a nightmare for general bioinformatics scientists, due to the lack of grid programming environments, standards and high-level services. Here, we present a system, which we named Bioinformatics: Ask Any Questions (BAAQ), to automate this development procedure as much as possible. BAAQ allows scientists to store and manage remote biological data and programs, to build analysis workflows that integrate these resources seamlessly, and to discover knowledge from available resources. This paper addresses two issues in building grid applications in bioinformatics: how to smoothly compose an analysis workflow using heterogeneous resources and how to efficiently discover and re-use available resources in the grid community. Correspondingly an intelligent grid programming environment and an active solution recommendation service are proposed. Finally, we present a case study applying BAAQ to a bioinformatics problem.     

Future ATC automation aids based upon AI technology

The authors discuss two knowledge-based systems that are currently under development by the US Federal Aviation Administration, namely a simulation system for air traffic controller training and an automated problem resolution capability that is the central feature of the Automated En Route Air Traffic Control System (AERA 2). For each of these systems, the manner in which knowledge-based system technology is applied is discussed, along with the knowledge bases. The process for building, refining, and evaluating the knowledge bases is also presented. In the air traffic controller training system, ATCoach, the representation of ATC knowledge has been shown to be highly beneficial for both ATC training and airspace modeling. In AERA 2 automated problem resolution (APR) evaluations, the AI/knowledge-based system technology has proven to be indispensable in the representation of the experts' knowledge and the effective factoring of the knowledge into APR. It is concluded that these applications offer great potential for success and, consequently, represent only the first step in applying AI principles to ATC     

Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.     

Knowledge-based system for boundary detection of four-dimensional cardiac magnetic resonance image sequences

A strategy for a knowledge-based system to detect the interior and exterior boundaries of the left ventricle from time-varying cross-sectional images obtained by ECG-gated magnetic resonance imaging (MRI) is discussed. The system uses both fuzzy set theory and Dempster and Shafer theory to manage the knowledge and to control the flow of system information for more efficient use of limited computational resources and memory space. The key to the approach is that it performs edge detection on images through integration and unification of knowledge and information from edge candidates on all the slices and phases of the acquired cardiac MRI dataset. The analysis system does not base decisions on individual measurements, but on consensus opinions by combining many knowledge sources, some of which may not be completely accurate.     

Semantic-driven grid-enabled computer-aided engineering of aperture-antenna arrays

In this paper, we propose a novel architecture for computer-aided engineering (CAE) of rectangular aperture arrays that takes advantage of semantic grid computing technologies. This allows the implementation of the CAE environment in a service-oriented framework, where CAE applications are built up at run time, by exploiting remote services through the grid. The identification and localization of remote services and their orchestration is simplified by semantic facilities, centered around a CAE ontology. This provides a structured, conceptual representation of hardware and software resources, including CAE services, which is understandable both by humans and software agents. The implemented prototype demonstrates how semantic grids satisfy the strong need for cooperation tools in the CAE of microwave circuits and antennas, and represents an effective pathway towards the automatic generation of design tools, attained by integrating electromagnetic software available through the Web.     

面向语义Web的逻辑描述原语扩展

语义Web旨在利用知识工程中成熟的技术为数据和知识交换提供语义上的互操作性,为了针对Web这种标记语言环境描述一阶命题逻辑公式、Horn子句和推理规则,本文对OIL/RDFS进行了扩展,定义了相关的逻辑描述原语.通过典型的命题规则表示示例,说明了本文的原语扩展为命题公式、推理规则表示提供了可行的方法.     

Workflow-enabled distributed component-based information architecture for digital medical imaging enterprises

Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.     

Using Automated Negotiation for Grid Services

The popularity of grid services has widened their application to numerous domains and increased the utilization of computational resources. In order to create more incentives for the resources owners to lease their resources and prevent users from wasting the resources, the introduction of a market-oriented grid is inevitable. However, the issues for the negotiation between service provider and consumer over the supply and demand of resources can be complex, with highly interdependent issues. In this research, a simulated automated negotiation mechanism including a co-evolutionary mechanism and a modified game theory approach is proposed, to assist them in reaching an agreement over the conflicting issues. In the proposed architecture, the co-evolution process is able to reduce the multiple dimensional search space into a two-dimension search space and identify the appropriate negotiation strategies for the negotiating agents to form a payoff matrix which can be used for the game theory related stage of their interaction. The multiple stage negotiation process is introduced to improve the negotiation result. In this paper, an application which requires a large amount of computational resources to process the data generated from mobile devises is used to demonstrate that the proposed system is able to resolve the conflicts and obtain a valid solution.     

Interoperability among healthcare organizations acting as certification authorities

One of the main problems in public key infrastructures (PKI) is currently the lack of interoperability at international level, which is greatly dependent on the automation of the cross-certification procedure using certificate policies (CP). This paper addresses the aforementioned need by presenting a method for the automated development and comparison of CPs, with main emphasis on healthcare environments. The basic elements of this method include standardization of the CP content for healthcare, a prototype decision-making algorithm for CPs comparison, representation of CPs in extensible markup language, as well as a JAVA-based CP comparison tool. The final aim of the paper is to contribute toward the technical implementation of an on-line automated cross-certification service, yielding PKI interoperability and promoting information exchange between healthcare establishments.     

Towards a knowledge-based system for automated geophysical interpretation of seismic data (AGIS)

Geophysical seismic interpretation is part of geophysical oil prospecting. It evaluates and analyzes seismic reflection data aiming at the detection of the position of hydrocarbon reservoirs. This work requires considerable experience and knowledge and must be done by skillful interpreters. The aim of our research is to develop an expert system, which incorporates some of this geophysical knowledge and which can partly perform a knowledge-based automated geophysical interpretation. In its present state, the system developed can recognize various patterns useful in interpretation. It can also work interactively with an interpreter for improved performance.     

A new cost effective 3D measurement audit and model comparison system for verification tasks

A new unified system application for the production audit in an aerospace industry is presented in this paper which comprises two key application tools such as (a) 3D PAMT (production audit measurement tool) and (b) 3D PACT (production audit compare tool). In spite of the facts that above functionalities are modular wise independent, commonly they are related in terms of assisting the production audit task. 3D PAMT facilitates the verification of manufactured parts to be within a pre-defined threshold range using a calibrated stereo camera with the safety test engineer interaction in order to select the matching disparity points. The distance between datum points with or without reference to a planar reference surface model can be obtained. We describe the system flow, plus validate the technique via a number of experimental datasets. 3D PACT allows the identification of discrepancies between a computed 3D point cloud model and the corresponding digital mock-up point cloud model. Usually, the computer aided geometry model is built before an actual installation. This knowledge about the components of an installation assembly is available as semantic information in an extendable markup language (XML) format of the CATIA model. We have provided an use case study of a sample assembly with components such as cube, pyramid, rectangular prism and triangular prism. The proposed cost-effective and robust framework for 3D measurement audit and model comparison is based on the input available from a digital camera and the semantic metadata knowledge available from geometry models which can be used for verification tasks.     

Attention-based contextual interaction asymmetric network for RGB-D saliency prediction

Saliency prediction on RGB-D images is an underexplored and challenging task in computer vision. We propose a channel-wise attention and contextual interaction asymmetric network for RGB-D saliency prediction. In the proposed network, a common feature extractor provides cross-modal complementarity between the RGB image and corresponding depth map. In addition, we introduce a four-stream feature-interaction module that fully leverages multiscale and cross-modal features for extracting contextual information. Moreover, we propose a channel-wise attention module to highlight the feature representation of salient regions. Finally, we refine coarse maps through a corresponding refinement block. Experimental results show that the proposed network achieves a performance comparable with state-of-the-art saliency prediction methods on two representative datasets.     

Fast Non-Local Attention network for light super-resolution

Although convolutional neural network-based methods have achieved significant performance improvement for Single Image Super-Resolution (SISR), their vast computational cost hinders real-world environment application. Thus, the interest in light networks for SISR is rising. Since existing SISR light models mainly focus on extracting fine local features using convolution operation, they have a limitation in that networks hardly capture global information. To capture the long-range dependency, Non-Local (NL) attention and Transformers have been explored in the SISR task. However, they are still suffering from a balancing problem between performance and computational cost. In this paper, we propose Fast Non-Local attention NETwork (FNLNET) for a super light SISR, which can capture the global representation. To acquire global information, we propose The Fast Non-Local Attention (FNLA) module that has low computational complexity while capturing global representation that reflects long-distance relationships between patches. Then, FNLA requires only 16 times lower computational cost than conventional NL networks while improving performance. In addition, we propose a powerful module called Global Self-Intension Mining (GSIM) that fuses the multi-information resources such as local, and global representation. Our FNLNET shows outstanding performance with fewer parameters and computational costs in the experiments on the benchmark datasets against state-of-the-art light SISR models.     

Knowledge structures for communications networks design and sales

The authors present the architectural principles and generic concepts that they have identified in their chosen sales domain and subsequently modeled in the expert network selector (ENS), ENS is a knowledge-based tool that is currently being developed to assist Bell Canada networks-sales agents, who may not possess some of the in-depth technical knowledge required to operate conventional design tools, in their daily operations. It uses a multiparadigm knowledge representation scheme to translate a customer's communications and networking requirements into fully-configured solutions. ENS is implemented in Prolog and combines frames and rules