EST-PAC HPC - a web portal for high-throughput EST annotation and protein sequence prediction

Expressed Sequence Tags (ESTs) are short DNA sequences generated by sequencing the transcribed cDNAs coming from a gene expression. They can provide significant functional, structural and evolutionary information and thus are a primary resource for gene discovery. EST annotation basically refers to the analysis of unknown ESTs that can be performed by database similarity search for possible identities and database search for functional prediction of translation products. Such kind of annotation typically consists of a series of repetitive tasks which should be automated, and be customizable and amenable to using distributed computing resources. Furthermore, processing of EST data should be done efficiently using a high performance computing platform. In this paper, we describe an EST annotator, EST-PACHPC, which has been developed for harnessing HPC resources potentially from Grid and Cloud systems for high throughput EST annotations. The performance analysis of EST-PACHPC has shown that it provides substantial performance gain in EST annotation.<br /

Modeling the online health information seeking process: Information channel selection among university students

This study investigates the influence of individual and information characteristics on university students' information channel selection (that is, search engines, social question & answer sites, online health websites, and social networking sites) of online health information (OHI) for three different types of search tasks (factual, exploratory, and personal experience). Quantitative data were collected via an online questionnaire distributed to students on various postgraduate programs at a large UK university. In total, 291 responses were processed for descriptive statistics, Principal Component Analysis, and Poisson regression. Search engines are the most frequently used among the four channels of information discussed in this study. Credibility, ease of use, style, usefulness, and recommendation are the key factors influencing users' judgments of information characteristics (explaining over 62% of the variance). Poisson regression indicated that individuals' channel experience, age, student status, health status, and triangulation (comparing sources) as well as style, credibility, usefulness, and recommendation are substantive predictors for channel selection of OHI

ДОСЛІДЖЕННЯ ПЕРСПЕКТИВ ВИКОРИСТАННЯ ТА ПРИНЦИПІВ ПОБУДОВИ МУЛЬТИАГЕНТНОЇ ПОШУКОВОЇ СИСТЕМИ

У роботі досліджено принципи функціонування систем інформаційного пошуку та, зокрема, мультиагентної пошукової системи. Відповідно, проаналізовано ряд наукових досліджень у сфері інформаційного пошуку. В ході дослідження встановлено перспективність використання мультиагентності стосовно вдосконалення пошукових методів та, зокрема, при побудові систем інформаційного пошуку. Були визначені переваги побудови розподіленої мультиагентної пошукової системи в порівнянні з централізованими системами пошуку. Також наголошено, що організація мультиагентного пошуку дозволяє об’єднати в собі різні підходи до вирішення завдання інтелектуалізації та персоналізації пошукової видачі.In conditions of the information society development, there is one of the most important tasks remains - to solve the problem of effective search and collection of the information. This is crucially important due to a growing diversity of information sources focused on developing different areas of human activities. Thus, there is a demand for new methods to ensure the effective information search.In this paper, the principles of functioning of information search systems and, in particular, of multiagent search engine was analyzed. Accordingly, a number of scientific works in the field of information search have been analyzed. During the analysis of the principles of the functioning of information search systems and the lot of scientific research in the field of information search, the prospect of using the distributed multiagent system in the framework of the improvement of search methods was established and the feasibility of using it to improve the accuracy of document evaluation was emphasized. The study established the prospect of using multiagency in the improvement of search methods, and in particular, in the construction of information search systems. The advantages of building a distributed multi-agent search engine over centralized search systems were identified. It is also emphasized that multi-agent search can combine different approaches to solve the problem of search engine intellectualization and personalization.It was summarized that using the methodology of building a distributed multiagent system in the framework of improving search methods and, in particular, in the construction of information search systems, it is possible to ensure that the search engine first finds documents containing the necessary information. In addition, the basic principles of construction for the development of multiagent structure within the organization of information search were highlighted.The findings and suggestions of this study can be used in research and teaching. In particular, the results obtained from this study can be used to further analyze and refine information search methods

Cross-display attention switching in mobile interaction with large displays

Mobile devices equipped with features (e.g., camera, network connectivity and media player) are increasingly being used for different tasks such as web browsing, document reading and photography. While the portability of mobile devices makes them desirable for pervasive access to information, their small screen real-estate often imposes restrictions on the amount of information that can be displayed and manipulated on them. On the other hand, large displays have become commonplace in many outdoor as well as indoor environments. While they provide an efficient way of presenting and disseminating information, they provide little support for digital interactivity or physical accessibility. Researchers argue that mobile phones provide an efficient and portable way of interacting with large displays, and the latter can overcome the limitations of the small screens of mobile devices by providing a larger presentation and interaction space. However, distributing user interface (UI) elements across a mobile device and a large display can cause switching of visual attention and that may affect task performance. This thesis specifically explores how the switching of visual attention across a handheld mobile device and a vertical large display can affect a single user's task performance during mobile interaction with large displays. It introduces a taxonomy based on the factors associated with the visual arrangement of Multi Display User Interfaces (MDUIs) that can influence visual attention switching during interaction with MDUIs. It presents an empirical analysis of the effects of different distributions of input and output across mobile and large displays on the user's task performance, subjective workload and preference in the multiple-widget selection task, and in visual search tasks with maps, texts and photos. Experimental results show that the selection of multiple widgets replicated on the mobile device as well as on the large display, versus those shown only on the large display, is faster despite the cost of initial attention switching in the former. On the other hand, a hybrid UI configuration where the visual output is distributed across the mobile and large displays is the worst, or equivalent to the worst, configuration in all the visual search tasks. A mobile device-controlled large display configuration performs best in the map search task and equal to best (i.e., tied with a mobile-only configuration) in text- and photo-search tasks

The construction of mental models of information-rich web spaces: the development process and the impact of task complexity

This study investigated the dynamic process of people constructing mental models of an information-rich web space during their interactions with the system and the impact of task complexity on model construction. In the study, subjects' mental models of MedlinePlus were measured at three time points: after subjects freely explored the system for 5 minutes, after the first search session, and after the second search session. During the first search session, the 39 subjects were randomly divided into two groups; one group completed 12 simple search tasks and the other group completed 3 complex search tasks. During the second search session, all subjects completed a set of 4 simple tasks and 2 complex tasks. Measures of the subjects' mental models included a concept listing protocol, a semi-structured interview, and a drawing task. The analysis revealed that subjects' mental models were a rich representation of the cognitive and emotional processes involved in their interaction with information systems. The mental models consisted of three dimensions (structure, evaluation and emotion, and (expected) behaviors); the structure and evaluation/emotion dimensions consisted of four components each: system, content, information organization, and interface. The construction of mental models was a process coordinated by people's internal cognitive structure and the external sources (the system, system feedback, and tasks) and a process distributed through time, in the sense that earlier mental models impacted later ones. Task complexity also impacted the construction of mental models by influencing what objects in the system were perceived and represented by the user, the specificity of the representations, and the user's feelings about the objects. Based on the study results, recommendations for employing mental models as a tool to assist designers in constructing user models, eliciting user requirements, and performing usability evaluations are put forward

Information seeking behaviour of mathematicians : scientists and students

Introduction. The paper presents original research designed to explore and compare selected aspects of the information seeking behaviour of mathematicians (scientists and students) on the Internet. Method. The data were gathered through a questionnaire distributed at the end of 2011 and in January 2012. Twenty-nine professional mathematicians and 153 students of mathematics from the Institute of Mathematics of the Jagiellonian University in Kraków, Poland, were surveyed. Analysis. The gathered data were analysed in a quantitative manner and then interpreted comparatively to find similarities and differences between the behaviour of professional mathematicians and students. Results. Students, as opposed to scientists, often declared searching for reference works and multimedia objects and comparatively rarely for journal papers and information about sources unavailable on the Web. They more willingly use social networking sites while scientists more often search discipline-oriented portals or library Websites. Scientists use, first of all, the author's name or the publication titles to formulate queries, students prefer keyword searching. While scientists trust their own ability to determine the scientific character of information or treat journals as determinants of the scientific quality, students do not. Conclusions. The research revealed some significant differences between the information seeking behaviour of those two groups of mathematicians. It could be the result of different levels of experience in scientific work, distinct tasks undertaken within the academic environment, and the change in the general paradigm of information searching

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

Coupling analysis in educational data

University of Technology Sydney. Faculty of Engineering and Information Technology.Educational data analysis refers to techniques, tools, and research designed to automatically extract meaning from large repositories of data generated by or related to people’s learning activities in educational environments. It is a research field which focus on helping policymakers and administrators understand how analytics and data mining can be applied for the purposes of educational improvement. Unfortunately, most research on educational data only by applying the existing machine learning or data mining algorithms, very few publications have discussed the character of the data itself. Traditional data mining algorithms have disadvantages, in that most of them assume the independent and identically distributed (IID) of data objects, attributes, and values. However, real world data usually contains strong couplings among values, attributes and data objects, and this represents a considerable challenge to existing methods and tools. This thesis focuses on utilizing coupling analysis in educational data analysis tasks. In particular, it focuses on two educational data analysis tasks: student performance prediction, and student social media sentiment analysis. The student performance prediction task is firstly examined. This thesis begins with the most straightforward method which integrates coupling similarities as the distance for a weighted k-nearest centroid classifier. This method considers not only the intra-coupled similarity within an attribute but also the inter-coupled similarity between attributes. Computational cost is high for coupling analysis. Hence, a more efficient method is proposed that selects the centroid objects instead of all objects in the nearest neighbor search process. Furthermore, integrating support vector machines with coupled similarity. The original SVMs is designed for numerical data. This thesis develops a novel pairwise SVMs that use the coupled similarity metric as a kernel between data objects with nominal attributes. The experiment result shows the two proposed methods outperform the traditional SVMs and other popular classification methods on various public data sets, and the student performance prediction task. Secondly, the student social media sentiment analysis is examined. Unlike linguistic methods, this thesis learns how to classify student sentiment by applying data mining on the labeled historical data. Most previous research employs the vector-space model for text representation and analysis, however, the vector-space model does not utilize the information about the term to term relationships. In other words, the traditional text mining techniques assume the relations between term to term are independent and identically distributed (IID). This thesis introduces a novel term representation by involving coupling relations between neighbors. This coupling representation provide much richer information which enables us to create a coupled similarity metric from document to document, and a coupling document similarity based k-nearest centroid classifier applied to the classification task. Experiments verify that the proposed approach outperforms the classic vector-space based classifier and displays distinct advantages and richness in terms of student social media sentiment analysis tasks. Finally, due to the complexity of the proposed algorithm and the enormous amount of the educational related data source, a scalable educational data mining platform is in great demand. Hence, with the help of the Spark cluster, a novel coupling similarity based learning approach has been proposed to cater for the big data learning problem by parallelizing the coupled similarity calculation process. Further, the parallel k-NN for classification and k-Means for the clustering task has been proposed. Compared to the original algorithms, the experimental results show that the proposed methods not only outperforms the clustering and classification performance of the baselines, but also represent a huge improvement on the data scale in terms of the time efficiency. Accordingly, the proposed framework has already been implemented, a scalable educational data analysis platform with coupling analysis will serve to meet a host of future challenges

The Canadian consortium for arctic data interoperability : an emerging polar information network

Established in 2015, the Canadian Consortium for Arctic Data Interoperability (CCADI) is an emerging initiative to develop an integrated Canadian arctic data anagement system that will facilitate information discovery, establish metadata and data sharing standards, enable interoperability among existing data infrastructures, and that will be accessible to a broad audience of users. Key to the CCADI vision are: standards and mechanisms for metadata interoperability and semantic interoperability; a distributed data exchange platform; streamlined data services with common entry, access, search, match, analysis, visualization and output tools; an intellectual property and sensitive data service; and data stewardship capacity. This will be a particularly challenging set of tasks given that the data planned for inclusion is multidisciplinary, in multiple types that range from sensor data to material artifacts, and, in some cases, confidential.publishedVersio

Centralized and distributed cognitive task processing in the human connectome

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectomes (FC) . A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straight-forward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated to different functional brain networks, and use the proposed measure to infer changes in the information processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well grounded mathematical quantification of connectivity changes associated to cognitive processing in large-scale brain networks.Comment: 22 pages main, 6 pages supplementary, 6 figures, 5 supplementary figures, 1 table, 1 supplementary table. arXiv admin note: text overlap with arXiv:1710.0219