We propose a human-robot interaction approach for social robots that attracts and controls the attention of a target person depending on her/his current visual focus of attention. The system detects the person's current task (attention) and estimates the level by using the "task-related contextual cues" and "gaze pattern." The attention level is used to determine the suitable time to attract the target person's attention toward the robot. The robot detects the interest or willingness of the target person to interact with it. Then, depending on the level of interest of the target person, the robot generates awareness and establishes a communication channel with her/him. To evaluate the performance, we conducted an experiment using our static robot to attract the target human's attention when she/he is involved in four different tasks: reading, writing, browsing, and viewing paintings. The proposed robot determines the level of attention of the current task and considers the situation of the target person. Questionnaire measures confirmed that the proposed robot outperforms a simple attention control robot in attracting participants' attention in an acceptable way. It also causes less disturbance and establishes effective eye contact. We implemented the system into a commercial robotic platform (Robovie-R3) to initiate interaction between visitors and the robot in a museum scenario. The robot determined the visitors' gaze points and established a successful interaction with a success rate of 91.7%.Index Terms-Gaze pattern, human-robot interaction, taskrelated contextual cues, visual focus of attention (VFOA).

In the era of “big data,” a huge number of people, devices, and sensors are connected via digital networks and the cross‐plays among these entities generate enormous valuable data that facilitate organizations to innovate and grow. However, the data deluge also raises serious privacy concerns which may cause a regulatory backlash and hinder further organizational innovation. To address the challenge of information privacy, researchers have explored privacy‐preserving methodologies in the past two decades. However, a thorough study of privacy preserving big data analytics is missing in existing literature. The main contributions of this article include a systematic evaluation of various privacy preservation approaches and a critical analysis of the state‐of‐the‐art privacy preserving big data analytics methodologies. More specifically, we propose a four‐dimensional framework for analyzing and designing the next generation of privacy preserving big data analytics approaches. Besides, we contribute to pinpoint the potential opportunities and challenges of applying privacy preserving big data analytics to business settings. We provide five recommendations of effectively applying privacy‐preserving big data analytics to businesses. To the best of our knowledge, this is the first systematic study about state‐of‐the‐art in privacy‐preserving big data analytics. The managerial implication of our study is that organizations can apply the results of our critical analysis to strengthen their strategic deployment of big data analytics in business settings, and hence to better leverage big data for sustainable organizational innovation and growth. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Fundamental Concepts of Data and Knowledge > Big Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts

Physical therapy is a common treatment for the rehabilitation of hemiparesis, or the weakness of one side of the body. Stroke is a common cause of hemiparesis. Stroke survivors regularly struggle with motivation and engagement, especially in-between sessions when the therapist is absent from the exercising process. As a solution, we have developed a robotic tablet gaming system to facilitate post-stroke hand function rehabilitation. Healthy subject pilot studies have been completed to verify that this system increases engagement and is capable of encouraging specific therapeutic motions. In the future, a learning model algorithm will be added to the system to assess the patient's progress and optimize the recovery time.

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