The rapid evolution of data science forced individuals with different backgrounds to adapt to statistical analysis. We hypothesize that conversational agents are better suited for statistical analysis than traditional graphical user interfaces (GUI). In this work, we propose a novel conversational agent, StatZ, for statistical analysis. We evaluate StatZ relative to statistical software—SPSS, SAS, Stata, and JMP—in terms of accuracy, task completion time, user experience, and user satisfaction. We experimented with 51 participants from diverse backgrounds with proposed analysis question.Our design assessed each participant’s ability to perform statistical tasks using GUI-based tools and our conversational agent. Results indicate that conversational agents significantly outperform GUI statistical software in all assessed metrics, including quantitative (task completion time, accuracy, and user experience), and qualitative (user satisfaction). Our findings underscore the potential of conversational agents to enhance statistical analysis, reducing cognitive load and learning curves enabling data analysis capabilities.

Blockchain is a technology to enable decentralized collaboration among un-trusted entities. Academia and industry are rushing to uncover its potential for their field of interest. Due to the novelty of the technology and its diverse applications, there are some ambiguities in approaches and trends. In this paper, we analyze the scientific publications and patents from the past five years to identify the trends for blockchain integration with healthcare. For this purpose, we have adopted a quantitative (clustering) and qualitative (theme extraction) approach to discover themes and temporal dynamics in academia and industry. Our results shed light on the potential challenges and vision for future works.

Abstract Despite large investments in smartwatch development, the market growth remains smaller than forecasted. The purpose of smartwatch use remains unclear, indicated by the lack of large-scale adoption. Thus, we aim to better understand the early adoption and everyday smartwatch use. We investigate a diverse usage data of smartwatches logged over a period of up to 14 months from 79 individuals between December 2015 and March 2017, one of the largest wearable datasets collected. First, we identify both explorative and accepted behaviours that users exhibit and further investigate how the individual usage traits and features differ between the two categories. Our analysis offers an insightful perspective on how smartwatch use evolves organically. Our results improve our shared understanding of smartwatch use and users adapting their use of smartwatch over time to match the capabilities of the technology by validating numerous findings from previous literature.

We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD). The interface is intended to enable private conversation practice anywhere, anytime using a web-browser. Users converse informally with a virtual agent, receiving feedback on nonverbal cues in realtime, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using a hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers. Through a thematic analysis of post-experiment interviews, identified several key design considerations, notably:

Noise and missing data are intrinsic characteristics of real-world data, leading to uncertainty that negatively affects the quality of knowledge extracted from the data. The burden imposed by missing data is often severe in sensors that collect data from the physical world, where large gaps of missing data may occur when the system is temporarily off or disconnected. How can we reconstruct missing data for these periods? We introduce an accurate and efficient algorithm for missing data reconstruction (imputation), that is specifically designed to recover off-period segments of missing data. This algorithm, Ghost, searches the sequential dataset to find data segments that have a prior and posterior segment that matches those of the missing data. If there is a similar segment that also satisfies the constraint - such as location or time of day - then it is substituted for the missing data. A baseline approach results in quadratic computational complexity, therefore we introduce a caching approach that reduces the search space and improves the computational complexity to linear in the common case. Experimental evaluations on five real-world datasets show that our algorithm significantly outperforms four state-of-the-art algorithms with an average of 18 percent higher F-score.

Advanced cancer patients sometimes spend their final days in unnecessary distress while receiving aggressive cancer treatment that is unlikely to work. Part of this problem stems from patients having incorrect understanding of their prognosis. Although studies have identified that effective doctor-patient communication is associated with better patient outcomes, most cancer patients misunderstand their prognosis. We applied computational language analysis tools (word category and language sentiment) to identify gender-specific communication characteristics associated with improved patient prognosis understanding. Analysis of 382 conversations between oncologists and patients identified that for female doctors, discussing feelings, using positive sentiment language, and speaking in shorter turns were strongly associated with better patient prognosis understanding. For male doctors, allowing patients to speak more, discussing the future, and not focusing heavily on religion or death were important. Synchrony between the doctors and patients usage of positive sentiment language was shown to be relevant only for female doctors.

We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

An essential part of the social media ecosystem is user-generated comments. However, not all comments are useful to all people as both authors of comments and readers have different intentions and perspectives. Consequently, the development of automated approaches for the ranking of comments and the optimization of viewers' interaction experiences are becoming increasingly important. This work proposes an adaptive faceted ranking framework which enriches comments along multiple semantic facets (e.g., subjectivity, informativeness, and topics), thus enabling users to explore different facets and select combinations of facets in order to extract and rank comments that match their interests. A prototype implementation of the framework has been developed which allows us to evaluate different ranking strategies of the proposed framework. We find that adaptive faceted ranking shows significant improvements over prevalent ranking methods which are utilized by many platforms such as YouTube or The Economist. We observe substantial improvements in user experience when enriching each element of a comment along multiple explicit semantic facets rather than in a single topic or subjective facets.

With continuing advances in the development of low-power electronics, including sensors and actuators, we anticipate a rapid expansion of pervasive computing. Wearable devices, in particular, require new modes for interaction -- many have no keyboard or touchscreen. In this work, we focus on user authentication on wearable devices. For an entertainment device, such as a VR headset, it can recognize the user and load the right game profile or music playlist. For a house climate-control system, it can adjust the environment to the wearer's preference. Most compellingly, for a health-monitoring device, it can label the sensor data with the correct identity so that the data can be stored in the correct health record. (A mix-up of sensor data could lead to incorrect decisions, with harm to the patient.) Because not all devices are personal devices -- my phone, your fitness sensor -- many devices will need to automatically recognize their wearer. They may have no interface for user identification (or PIN or password for authentication). Thus, we need a simple, wearable biometric technique to identify the user -- which could be embedded in one authentication device that shares the identity with a body-area network of other devices (earlier confirmed to be on the same body). This device should be trained once, for each user that might wear it, but thenceforth be completely automatic. Although a wristband could use a physiological biometric to recognize its wearer; we seek an alternative biometric, notably, one that might work for devices mounted on the head, neck, or chest.

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e., privacy, and the network costs will also be removed.

We are living in an era of social media such as online communities and social networking sites. Exposing or sharing personal information with these communities has risks as well as benefits and there is always a trade off between the risks versus the benefits of using these technologies. Life-logs are pervasive tools or systems which sense and capture contextual information from the user's environment in a continuous manner. A life-log produces a dataset, which consists of continuous streams of sensor data. Sharing this information has a wide range of advantages for both user and society. On the other hand, in terms of individual privacy, life-log information is very sensitive. Although social media enable users to share their information, due to life-log data structure, current sharing models are not capable of handling life-log information while maintaining user privacy. Our approach here is to describe the sharing of life-log information with society based on the identification of associated risks and benefits. Subsequently, based on the identified risks, we propose a data model for sharing life-log information. This data model has been designed to reduce the potential risks of life-logs. Furthermore, ethics for providing and using life-logs will be discussed. These ethics focus on reducing risks as much as possible while sharing life-log information.

Sharing personal information benefits both data providers and data consumers in many ways. Recent advances in sensor networks and personal archives enable users to record personal information including emails, social networking activities, or life events (life logging). These information objects are usually privacy sensitive and thus need to be protected adequately when being shared. In this work, we present a lightweight pseudonymization framework which allows users to benefit from sharing their personal information while still preserving their privacy. Furthermore, this approach increases the data owners' awareness of what information they are sharing, thus rendering data publishing more transparent.

In this paper we describe an approach for preserving personal information, stored on pervasive devices, using a reliable platform. Nowadays, pervasive devices such as mobile phones or personal multimedia players, provide software and hardware capabilities that allow individuals to read and create digital contents. Therefore it is important to provide the ability to archive these contents for later retrieval and use on other platforms. To address this need we introduce an archiving framework and present a working prototype based on this framework. The prototype runs on the Android operating system and enables individuals to archive their pervasive device information for long-term preservation.