A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Technology-enabled Learning (TEL): YouTube as a Ubiquitous Learning Aid.

The use of social networks such as Facebook, Twitter, and YouTube in the society has become ubiquitous. The advent of communication technologies alongside other unification trends and notions such as media convergence and digital content allow the users of the social network to integrate these networks in their everyday life. There have been several attempts in the literature to investigate and explain the use of social networks such as Facebook and WhatsApp by university students in the Arab region. However, little research has been done on how university students utilise online audiovisual materials in their academic activities in the UAE. This research aims to elucidate the use of YouTube as a learning aid for university students in the UAE. We adopt the technology acceptance model (TAM) as the theoretical framework for this investigation. A quantitative methodology is employed to answer the research question. Primary data consisting of 221 correspondents were analysed, covering patterns of using YouTube as an academic audiovisual learning aid. Statistical techniques including descriptive, correlations, regression tests were used to analyse the data. The study concluded that students use YouTube as a learning tool for their academic studies and enriching their general knowledge; and there is a positive relationship between the use of YouTube videos in academic settings and the students’ overall performance. This study can shed light for teachers, curriculum designers, government entities, and other stakeholders on how to best utilise and integrate the online technology — YouTube — as a learning aid

Current Practices for Product Usability Testing in Web and Mobile Applications

Software usability testing is a key methodology that ensures applications are intuitive and easy to use for the target audience. Usability testing has direct benefits for companies as usability improvements often are fundamental to the success of a product. A standard usability test study includes the following five steps: obtain suitable participants, design test scripts, conduct usability sessions, interpret test outcomes, and produce recommendations. Due to the increasing importance for more usable applications, effective techniques to develop usable products, as well as technologies to improve usability testing, have been widely utilized. However, as companies are developing more cross-platform web and mobile apps, traditional single-platform usability testing has shortcomings with respect to ensuring a uniform user experience. In this report, a new strategy is proposed to promote a consistent user experience across all application versions and platforms. This method integrates the testing of different application versions, e.g., the website, mobile app, mobile website. Participants are recruited with a better-defined criterion according to their preferred devices. The usability session is conducted iteratively on several different devices, and the test results of individual application versions are compared on a per-device basis to improve the test outcomes. This strategy is expected to extend on current practices for usability testing by incorporating cross-platform consistency of software versions on most devices

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Knowledge based text indexing and retrieval utilizing case based reasoning

Information retrieval systems for documents normally rely on the use of keywords that describe the text in some fashion or another, or are contained in the text itself, for indexing and searching. These keywords may be associated with standard boolean operators, where presence or absence in the text or text description is used as the truth value, or other oper ators indicating their proximity to one another in the text. Another emerging approach is the use of content or knowledge based indexing and retrieval. In this approach the text is not represented or treated as a collection keywords, rather its meaning or semantic content is abstracted and the meaning is used to search for the text desired. This approach may have several advantages over the standard keyword approach. Both precision and recall of the search may be improved, increasing the likelihood that relevant texts will be found while decreasing the probability of finding irrelevant ones. The knowl edge based approach may also allow more sophisticated query techniques, for instance queries based on the purpose for which the text will be used. This thesis will explore the possibility and usefulness of applying case based reasoning to the problem of text search and retrieval. An easy-to-use expert system for information retrieval that utilizes case-based reasoning to improve, over time, its capability to find those items that are relevant and useful, and only those items that are relevant and useful will be implemented. It will support formulation of a search in an intuitive manner that avoids complicated command syntax and occult operators. It will present retrieved docu ments to the user in a logical, useful way and will allow the user to easily refine his search criteria based on a selection of documents from his original results that he has judged to be good examples of what he is searching for

THE ABSENCE OF IMPORTANT CAREWORDS AMONG UNIVERSITY WEBSITES: A PRELIMINARY STUDY ON WEB USABILITY

Most universities have a website with one of prevalent basic purpose is to provide an information to current and prospective students. The university web developers quite often neglect the process to incorporate customers’ voice during the development process, as suggested by the web usability, an area within humancomputer interaction (HCI) research. As many web users rely on a search engine to seek information, the inclusion of words that are most likely used by them (called carewords), in a web site are very critical. This paper aims to present a preliminary investigation of usability aspects in university websites, by focusing on the web content. This study applies Internet research methods. A simulation of a hypothetical case in which a prospective university student intends to study in Surabaya is performed. Search-words “kuliah di Surabaya”, “universitas di Surabaya”, and “kuliah teknik industri” are applied in Yahoo and Google search engines. The top 20 links obtained from each searching task are analyzed. Secondary data from EPSBED website was supplied to support the analysis. Furthermore, a content analysis using was conducted to selected websites of universities located in Surabaya. Overall, the findings emphasize that mismatch between the terms used by audiences and those presented in the web content could reduce the visibility of the website. Finally, this study suggests the university web developers to be more intensive applying usability principles to make their websites visible and accessible to potential students, as well as usable to other intended stakeholders

Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects