Out of Sight, Out of Mind: Turnover Intentions through an eLeadership Lens

The basis for this research is founded on the emerging eLeadership theory and the need for retention of qualified leadership in organizations that either are required to or elect to adopt information communication technology (ICT). Development of the eLeadership theory is still in its nascent stage and thus the use of theoretical foundations it is built upon are used to measure and understand the influencing factors of an eLeader\u27s turnover intention. The researchers in this study propose a research model of eLeader’s turnover intention and develop propositions using eLeader’s relationship with followers, transformational leadership ability, technology self-efficacy, and organizational engagement as its core constructs. Data from this research is proposed to be collected using qualified surveys and findings. This research is expected to make a significant contribution and enrich the developing body of eLeadership literature so that more empirical data is available for future researchers interested in measuring constructs related to eLeadershi

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future researc

Knowledge Management in Software Development

Today’s business environment is extremely dynamic and competitive. In order to sustain the pressure and gain a competitive edge, it is imperative for organizations to be creative in their software development efforts. Agile software development has huge potential for nurturing creativity. However, little research has examined creativity in the context of software development projects, particularly those using agile practices. The objective of this paper is to articulate a model that elucidates the relationship between agile practices and creativity. Further, the model tries to provide an understanding of how Knowledge Integration mediates the Relationship between agile practices and team creativity

l-dyno: framework to learn consistent visual features using robot's motion

Historically, feature-based approaches have been used extensively for camera-based robot perception tasks such as localization, mapping, tracking, and others. Several of these approaches also combine other sensors (inertial sensing, for example) to perform combined state estimation. Our work rethinks this approach; we present a representation learning mechanism that identifies visual features that best correspond to robot motion as estimated by an external signal. Specifically, we utilize the robot's transformations through an external signal (inertial sensing, for example) and give attention to image space that is most consistent with the external signal. We use a pairwise consistency metric as a representation to keep the visual features consistent through a sequence with the robot's relative pose transformations. This approach enables us to incorporate information from the robot's perspective instead of solely relying on the image attributes. We evaluate our approach on real-world datasets such as KITTI & EuRoC and compare the refined features with existing feature descriptors. We also evaluate our method using our real robot experiment. We notice an average of 49% reduction in the image search space without compromising the trajectory estimation accuracy. Our method reduces the execution time of visual odometry by 4.3% and also reduces reprojection errors. We demonstrate the need to select only the most important features and show the competitiveness using various feature detection baselines.Comment: 7 pages, 6 figure

Methods and Systems for Indoor Navigation

Patent relating to methods and systems for indoor navigation

Email shape analysis

Email has become an integral part of everyday life. Without a second thought we receive bills, bank statements, and sales promotions all to our inbox. Each email has hidden features that can be extracted. In this paper, we present a new mechanism to characterize an email without using content or context called Email Shape Analysis. We explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering, and social-context based finger printing. Our in-depth analysis of botnet detection leads to very high accuracy of tracing templates and spam campaigns. However, when it comes to spam filtering we do not propose new method but rather a complementing method to the already high accuracy Bayesian spam filter. We also look at its ability to classify individual senders in personal email inbox’s

Behavior based adaptive call predictor

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people’s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller’s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user’s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user’s calling behavior and reciprocity. We show that the recent trend of the caller/user’s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user’s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder