Compact gml: merging mobile computing and mobile cartography

The use of portable devices is moving from "Wireless Applications", typically implemented as browsing-on-the-road, to "Mobile Computing", which aims to exploit increasing processing power of consumer devices. As users get connected with smartphones and PDAs, they look for geographic information and location-aware services. While browser-based approaches have been explored (using static images or graphics formats such as Mobile SVG), a data model tailored for local computation on mobile devices is still missing. This paper presents the Compact Geographic Markup Language (cGML) that enables design and development of specific purpose GIS applications for portable consumer devices where a cGML document can be used as a spatial query result as well

Digital Forensic Tools & Cloud-Based Machine Learning for Analyzing Crime Data

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between two forensic tools; Paraben E3: DS and Autopsy. At the end of the study, it was concluded that most of the activities performed on a rooted android device can be found in its internal memory. In the second study, the Snapchat application was analyzed on a rooted Android device to see how well it handles privacy issues. The result of the study shows that some of the predefined activities performed on the Snapchat application as well as user information can be retrieved using Paraben E3: DS forensic tool. The third study, machine learning services on Microsoft Azure and IBM Watson were used in performing predictive analysis to uncover their performance. At the end of the experiments, the Azure machine learning studio was seen to be more user friendly and builds models faster compared to the SSPS Modeler in the IBM Watson Studio. This research is important as data needs to be analyzed in order to generate insights that can aid organizations or police departments in making the best decisions when analyzing crime data

Collaborative yet independent: Information practices in the physical sciences

In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data. This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research. Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies

Spatially Aware Computing for Natural Interaction

Spatial information refers to the location of an object in a physical or digital world. Besides, it also includes the relative position of an object related to other objects around it. In this dissertation, three systems are designed and developed. All of them apply spatial information in different fields. The ultimate goal is to increase the user friendliness and efficiency in those applications by utilizing spatial information. The first system is a novel Web page data extraction application, which takes advantage of 2D spatial information to discover structured records from a Web page. The extracted information is useful to re-organize the layout of a Web page to fit mobile browsing. The second application utilizes the 3D spatial information of a mobile device within a large paper-based workspace to implement interactive paper that combines the merits of paper documents and mobile devices. This application can overlay digital information on top of a paper document based on the location of a mobile device within a workspace. The third application further integrates 3D space information with sound detection to realize an automatic camera management system. This application automatically controls multiple cameras in a conference room, and creates an engaging video by intelligently switching camera shots among meeting participants based on their activities. Evaluations have been made on all three applications, and the results are promising. In summary, this dissertation comprehensively explores the usage of spatial information in various applications to improve the usability

Selected Computing Research Papers Volume 7 June 2018

Contents Critical Evaluation of Arabic Sentimental Analysis and Their Accuracy on Microblogs (Maha Al-Sakran) Evaluating Current Research on Psychometric Factors Affecting Teachers in ICT Integration (Daniel Otieno Aoko) A Critical Analysis of Current Measures for Preventing Use of Fraudulent Resources in Cloud Computing (Grant Bulman) An Analytical Assessment of Modern Human Robot Interaction Systems (Dominic Button) Critical Evaluation of Current Power Management Methods Used in Mobile Devices (One Lekula) A Critical Evaluation of Current Face Recognition Systems Research Aimed at Improving Accuracy for Class Attendance (Gladys B. Mogotsi) Usability of E-commerce Website Based on Perceived Homepage Visual Aesthetics (Mercy Ochiel) An Overview Investigation of Reducing the Impact of DDOS Attacks on Cloud Computing within Organisations (Jabed Rahman) Critical Analysis of Online Verification Techniques in Internet Banking Transactions (Fredrick Tshane

Applying touch gesture to improve application accessing speed on mobile devices.

The touch gesture shortcut is one of the most significant contributions to Human-Computer Interaction (HCI). It is used in many fields: e.g., performing web browsing tasks (i.e., moving to the next page, adding bookmarks, etc.) on a smartphone, manipulating a virtual object on a tabletop device and communicating between two touch screen devices. Compared with the traditional Graphic User Interface (GUI), the touch gesture shortcut is more efficient, more natural, it is intuitive and easier to use. With the rapid development of smartphone technology, an increasing number of data items are showing up in users’ mobile devices, such as contacts, installed apps and photos. As a result, it has become troublesome to find a target item on a mobile device with traditional GUI. For example, to find a target app, sliding and browsing through several screens is a necessity. This thesis addresses this challenge by proposing two alternative methods of using a touch gesture shortcut to find a target item (an app, as an example) in a mobile device. Current touch gesture shortcut methods either employ a universal built-in system- defined shortcut template or a gesture-item set, which is defined by users before using the device. In either case, the users need to learn/define first and then recall and draw the gesture to reach the target item according to the template/predefined set. Evidence has shown that compared with GUI, the touch gesture shortcut has an advantage when performing several types of tasks e.g., text editing, picture drawing, audio control, etc. but it is unknown whether it is quicker or more effective than the traditional GUI for finding target apps. This thesis first conducts an exploratory study to understand user memorisation of their Personalized Gesture Shortcuts (PGS) for 15 frequently used mobile apps. An experiment will then be conducted to investigate (1) the users’ recall accuracy on the PGS for finding both frequently and infrequently used target apps, (2) and the speed by which users are able to access the target apps relative to GUI. The results show that the PGS produced a clear speed advantage (1.3s faster on average) over the traditional GUI, while there was an approximate 20% failure rate due to unsuccessful recall on the PGS. To address the unsuccessful recall problem, this thesis explores ways of developing a new interactive approach based on the touch gesture shortcut but without requiring recall or having to be predefined before use. It has been named the Intelligent Launcher in this thesis, and it predicts and launches any intended target app from an unconstrained gesture drawn by the user. To explore how to achieve this, this thesis conducted a third experiment to investigate the relationship between the reasons underlying the user’s gesture creation and the gesture shape (handwriting, non-handwriting or abstract) they used as their shortcut. According to the results, unlike the existing approaches, the thesis proposes that the launcher should predict the users’ intended app from three types of gestures. First, the non-handwriting gestures via the visual similarity between it and the app’s icon; second, the handwriting gestures via the app’s library name plus functionality; and third, the abstract gestures via the app’s usage history. In light of these findings mentioned above, we designed and developed the Intelligent Launcher, which is based on the assumptions drawn from the empirical data. This thesis introduces the interaction, the architecture and the technical details of the launcher. How to use the data from the third experiment to improve the predictions based on a machine learning method, i.e., the Markov Model, is described in this thesis. An evaluation experiment, shows that the Intelligent Launcher has achieved user satisfaction with a prediction accuracy of 96%. As of now, it is still difficult to know which type of gesture a user tends to use. Therefore, a fourth experiment, which focused on exploring the factors that influence the choice of touch gesture shortcut type for accessing a target app is also conducted in this thesis. The results of the experiment show that (1) those who preferred a name-based method used it more consistently and used more letter gestures compared with those who preferred the other three methods; (2) those who preferred the keyword app search method created more letter gestures than other types; (3) those who preferred an iOS system created more drawing gestures than other types; (4) letter gestures were more often used for the apps that were used frequently, whereas drawing gestures were more often used for the apps that were used infrequently; (5) the participants tended to use the same creation method as the preferred method on different days of the experiment. This thesis contributes to the body of Human-Computer Interaction knowledge. It proposes two alternative methods which are more efficient and flexible for finding a target item among a large number of items. The PGS method has been confirmed as being effective and has a clear speed advantage. The Intelligent Launcher has been developed and it demonstrates a novel way of predicting a target item via the gesture user’s drawing. The findings concerning the relationship between the user’s choice of gesture for the shortcut and some of the individual factors have informed the design of a more flexible touch gesture shortcut interface for ”target item finding” tasks. When searching for different types of data items, the Intelligent Launcher is a prototype for finding target apps since the variety in visual appearance of an app and its functionality make it more difficult to predict than other targets, such as a standard phone setting, a contact or a website. However, we believe that the ideas that have been presented in this thesis can be further extended to other types of items, such as videos or photos in a Photo Library, places on a map or clothes in an online store. What is more, this study also leads the way in tackling the advantage of a machine learning method in touch gesture shortcut interactions

Role of Middle Managers in Mitigating Employee Cyberloafing in the Workplace

Companies in the United States are concerned about the indeterminate effectiveness of corporate cyberloafing mitigation efforts leading to the persistence of employee cyberloafing behavior. Although middle managers are the driving force behind the transformational influences that guide employee productivity and could proffer practical solutions, a lack of clarity surrounds the middle manager\u27s role in the overall cyberloafing mitigation efforts within organizations. The central research question for this transcendental phenomenological research study explored the lived experiences of middle managers regarding their roles in mitigating employee cyberloafing at higher education institutions in Florida. This study used a social constructivist-interpretive framework that draws from the multiple realities constructed through social interactions and lived experiences. Participants included 7 middle managers with experience mitigating cyberloafing at higher education institutions in Florida. Four major themes emerged from an inductive analysis of the data, including managing employee performance, proximity matters, cyberloafing interventions, and understanding employee online technology use. The results and recommendations of this study provide implications for social change. Business organizations may modify cyberloafing mitigation strategies and policies from a better understanding of manager/employee interactions, transformational managerial influences used to mitigate employee cyberloafing, and managerial knowledge of employee appropriation of online technology

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption