BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts. RESULTS: Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model's robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST'16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models. CONCLUSIONS: We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model's performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications.

BACKGROUND: Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together. RESULTS: We have developed ChemEx, a chemical information extraction system. ChemEx processes both text and images in publications. Text annotator is able to extract compound, organism, and assay entities from text content while structure image recognition enables translation of chemical raster images to machine readable format. A user can view annotated text along with summarized information of compounds, organism that produces those compounds, and assay tests. CONCLUSIONS: ChemEx facilitates and speeds up chemical data curation by extracting compounds, organisms, and assays from a large collection of publications. The software and corpus can be downloaded from http://www.biotec.or.th/isl/ChemEx.

SUMMARY: LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps; (ii) compare functional compositions within and among the datasets using TreeMaps; (iii) analyze the statistically enriched pathways across the datasets; (iv) build the pathway-to-pathway networks for each dataset; (v) explore potential interaction/reaction paths between pathways; and (vi) identify common pathway-to-pathway networks across the datasets. AVAILABILITY: LinkinPath is freely available to all interested users at http://www.biotec.or.th/isl/linkinpath/.

Ubiquitous computing promises a computing environment that seamlessly and pervasively delivers applications to the user, despite changes of resources, devices, and locations. However, few ubiquitous multimedia applications (UMAs) exist up-to-date. One of the main reasons lies in the fact that it is difficult and error-prone to build a UMA which is mobile and deployable in different ubiquitous environments, and still provides acceptable application-specific Quality-of-Service (QoS) guarantees. In this paper, we present the design and implementation of a novel programming framework, called 'QCompiler" to address the challenges. The framework includes (1) a high-level application specification for the application developer to easily write a UMA with specific quality, mobility, and ubiquity supports, (2) a meta-data compilation, which provides automated consistency checks, translations, and substitutions, to relieve the application developer from dealing with complex programming related to quality, mobility, and ubiquity, (3) a binding, which prepares a quality-aware specification to be executable, in a specific deployment environment, and (4)a run-time meta-data execution, utilizing the meta-data compilation's results, to manage and control a quality-aware multimedia application. As a case study, we apply the programming framework to build a mobile Video-on-Demand (VoD) application. The experimental results show tradeoffs between easiness and flexibility to develop and deploy UMA, and overheads during UMA instantiation and adaptation.

Global computational grids bring together distributed computation/communication resources. Beyond this, we envision the emergence of global 'service grids', which provide a 'market' of application-level distributed services for clients to discover and to request. We study the issue of wide-area service discovery in service grids. We start with an existing basic wide-area service discovery framework. The framework adopts a scalable architecture consisting of a hierarchy of Discovery Servers. We then identify problems with the basic framework, and propose our enhancement of query responsiveness and QoS awareness. The key techniques we introduce include: (1) the addition of QoS feedback capability to clients; and (2) the caching and propagation of discovery results with QoS feedback in the discovery server hierarchy. With these techniques, the enhanced service discovery framework will be faster in finding qualified service providers. Furthermore, it will select a 'good' (with respect to the QoS to be delivered) service provider for each querying client, based on QoS feedback.

The provision of distributed multimedia services is becoming mobile and ubiquitous. Different multimedia services require application-specific Quality of Service (QoS). In this paper, we present QoSTalk, a unified component-based programming environment that allows application developers to specify different application-specific QoS requirements easily. In QoSTalk, we adopt a hierarchical approach to model application configuration graphs for different distributed multimedia services. We design and implement the XML-based Hierarchical QoS Markup Language, called HQML, to describe the hierarchical configuration graph as well as other application-specific QoS requirements and policies. QoSTalk promotes the separation of concerns in developing QoS-aware ubiquitous multimedia applications and thus enables easy programming of QoS-aware applications, running on top of a unified QoS-aware middleware framework. We have prototyped the QoSTalk in Java and CORBA. Our case studies with several multimedia applications show that QoSTalk effectively fills the gap for application developers between the very general facilities provided by the QoS-aware middleware and different kinds of distributed multimedia applications.

Presents a QoS and contention-aware multi-resource reservation algorithm to provide end-to-end QoS guarantees for distributed and component-based services. We study a reservation-enabled environment, where each type of resource can be reserved. However, the goals of: (1) achieving the best end-to-end QoS for each client, and (2) increasing the overall success rate of resource reservations for different service requests, are in conflict with each other. Our algorithm provides a solution to alleviate this conflict. For each service request, the algorithm computes an end-to-end multi-resource reservation plan which (1) achieves the highest level of end-to-end QoS under the constraint of current resource availability, and (2) tends to incur low bottleneck resource contention among all feasible reservation plans for this service request. Our initial simulation results show the excellent performance of this algorithm.

Widely deployed multimedia services are expected to accommodate clients in a highly heterogeneous environment. Clients of a multimedia service may vary greatly in processing and communication capabilities. In addition, due to workload, location, and service time differences, the availability of end-to-end resources between a client and a server may also vary. Current solutions tend to focus on either the qualitative heterogeneity (in client and resource types) or the quantitative heterogeneity (in resource availability) problem. We present a framework for dynamic end-to-end multimedia service configuration and reservation: an integrated solution to both aspects of the heterogeneity problem. Service configuration is responsible for choosing appropriate service components to compose a customized service deliver to each client; while service reservation is responsible for reserving the end-to-end resources in a coordinated manner and providing the best possible quality within the chosen configuration. We have implemented a prototype of this framework as part of the 2K operating system and tested it by building a proof-of-concept video streaming service on top of it. Our experiments show the soundness of this framework.