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Patient-Reported Outcome (PRO) surveys are used to monitor patients’ symptoms during and after cancer treatment. Acute symptoms refer to those experienced during treatment and late symptoms refer to those experienced after treatment. While most patients experience severe symptoms during treatment, these usually subside in the late stage. However, for some patients, late toxicities persist negatively affecting the patient’s quality of life (QoL). In the case of head and neck cancer patients, PRO surveys are recorded every week during the patient’s visit to the clinic and at different follow-up times after the treatment has concluded. In this paper, we model the PRO data as a time-series and apply Long-Short Term Memory (LSTM) neural networks for predicting symptom severity in the late stage. The PRO data used in this project corresponds to MD Anderson Symptom Inventory (MDASI) questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. Our experiments show that the LSTM model also outperforms other machine learning models and time-series prediction models for these data.

We compared two common techniques of controller-based locomotion (teleportation and steering locomotion) in virtual reality (VR) in terms of the cybersickness they produce. Participants had to continuously navigate a commercial VR application for 16 minutes using each technique, while standing and seated. While teleportation produced less cybersickness than steering locomotion on average, a number of participants reported teleportation to be more sickening. These ‘telesick’ participants were found to have greater medio/lateral positional variability in their spontaneous postural sway than ‘steersick’ participants prior to VR exposure. We conclude that different individuals may require unique techniques to comfortably locomote in VR.

Process skills (also known as professional skills, lifelong learning skills, workplace skills, transferable skills, or soft skills) are an important aspect of computer science education. Learning objectives for computer science courses often include the development of process skills like critical thinking, problem solving, and teamwork, but many instructors struggle with how to encourage and measure the development of these skills. This special session will introduce detailed rubrics for assessing process skills in a manner that will model POGIL (Process Oriented Guided Inquiry Learning) facilitation techniques. Presenters will demonstrate how to use one set of these rubrics in an active learning classroom (i.e., POGIL) to provide students with feedback. The other set of rubrics assess process skills on student work and can be used with any classroom. Attendees will leave the session with a better understanding of key process skills in computer science, how to assess student interactions and student work for these skills, and how a POGIL classroom facilitates the development of critical thinking, information processing, and teamwork. This special session will appeal to any K-12 teacher or college instructor interested in developing students' process skills in a computer science class.

One approach to analysis of private data is e-differential privacy, a randomization-based approach that protects individual data items by injecting carefully limited noise into results. A challenge in applying this to private data analysis is that the noise added to the feature parameters is directly proportional to the number of parameters learned. While careful feature selection would alleviate this problem, the process of feature selection itself can reveal private information, requiring the application of differential privacy to the feature selection process. In this paper, we analyze the sensitivity of various feature selection techniques used in data mining and show that some of them are not suitable for differentially private analysis due to high sensitivity. We give experimental results showing the value of using low sensitivity feature selection techniques. We also show that the same concepts can be used to improve differentially private decision trees.

As a form of active learning, POGIL (Process Oriented Guided Inquiry Learning) engages students by grouping them in teams to work collaboratively through activities and construct their own understanding of computer science content. POGIL activities are carefully designed to foster student learning by guiding students through a learning cycle of exploration, concept invention and application. Computer science assignments typically emphasize application questions and problems, where students are expected to apply the new concept to different applications (e.g., writing multiple programs involving while-loops). In contrast, POGIL activities require students to first explore a model, possibly by asking critical thinking questions about some code and its output, before leading students to recognize patterns in the model, thereby helping them to form a better understanding of the new CS concept. Only after students have explored and developed an understanding of the concept, do the POGIL activities proceed to more traditional application questions. Studies of POGIL's effectiveness in STEM classrooms have generally found that (1) attrition is lower for POGIL than for traditional courses; (2) content mastery is greater than for traditional instruction; (3) students prefer POGIL over traditional methods; (4) students have more positive attitudes about the course and the instructors; and (5) learning skills appear to improve during the course. These advantages have drawn many Computer Science instructors to adopt POGIL in their classrooms, but very few instructors are developing POGIL activities for Python, even though Python lends itself well to learning cycle questions as an interpreted language. All CS instructors, but especially Python instructors, can benefit from adding exploration and concept invention questions to assignments before asking application questions.The growing popularity of Jupyter Notebooks makes it even easier for instructors to embed learning cycle questions in Python (or any other Jupyter supported programming language). This special session will provide SIGCSE attendees the opportunity to experience a CS POGIL activity for themselves, before introducing attendees to the learning cycle. Attendees will be view several examples of exploration and concept invention questions, which should be helpful to anyone creating CS assignments, not just POGIL instructors. The presenters will provide a short tour of two collections of POGIL activities in Python before the question and answer period.

As shown in many large-scale and longitudinal studies, spatial ability is strongly associated with STEM (science, technology, engineering, and mathematics) learning and career success. At the same time, a growing volume of research connects cognitive science theories with tangible/embodied interactions (TEI) and virtual reality (VR) to offer novel means to support spatial cognition. But very few VR-TEI systems are specifically designed to support spatial ability, nor are they evaluated with respect to spatial ability. In this paper, we present the background, approach, and evaluation of TASC (Tangibles for Augmenting Spatial Cognition), a VR-TEI system built to support spatial perspective taking ability. We tested 3 conditions (tangible VR, keyboard/mouse, control; n=46). Analysis of the pre/post-test change in performance on a perspective taking test revealed that only the VR-TEI group showed statistically significant improvements. The results highlight the role of tangible VR design for enhancing spatial cognition.

A growing body of empirical evidence from the cognitive sciences shows that physical experience can enhance cognition in areas that involve spatial thinking. At the same time, virtual environments provide opportunities to engage learners with novel spatial tasks that cannot be achieved in the real world. Yet combining virtual worlds with tangible interfaces to engage spatial cognition is still not a well-explored area. This paper describes the TASC (Tangibles for Augmenting Spatial Cognition) system, which combines movement tracking and tangible objects in order to create a strong sense of embodiment in a virtual environment for spatial puzzle solving, designed to engage perspective taking ability. We describe the motivation, design process, and development of TASC. We also report the results from our user study, showing the participants' positive experiences, linking to future research opportunities.

Current touch devices are adept at tracking finger touches, but cannot distinguish if multiple touches are caused by different fingers on a single hand, by fingers from both hands of a single user, or by different users. This limitation significantly reduces the possibilities for interaction techniques in touch interfaces. We present GhostID, a capacitive sensor that can differentiate the origins of multiple simultaneous touches. Our approach analyzes the signal ghosting, already present as an artifact in a frequency-division touch controller, to differentiate touches from the same hand or different hands of a single user (77% reliability at 60 fps) or from two different users (95% reliability at 60 fps). In addition to GhostID, we also develop a framework of user-differentiation capabilities for touch input devices, and illustrate a set of interaction techniques enabled by GhostID.

Process Oriented Guided Inquiry Learning (POGIL) is an evidence-based instructional strategy used across Science, Technology, Engineering, and Mathematics (STEM) disciplines. In POGIL, student teams work on classroom activities specifically designed to guide them to construct understanding of key concepts and to develop key process skills, with active facilitation by a teacher. A pattern language for POGIL should help the POGIL community to more easily create, review, and revise POGIL activities, and use them effectively in the classroom. A previous paper described POGIL, some advantages of using patterns with POGIL, and patterns for some of the structure and elements of POGIL activities, including models used in learning cycles. This paper describes nine patterns to help teachers organize teams, lead discussions, and guide students to learn and reflect. These patterns should help teachers to use POGIL more effectively, and to better understand elements of POGIL that can be adapted to other settings.

Multimedia lessons have the potential to foster deeper understanding and knowledge. The way a robot instructor presents material and uses gestures likely impacts the perception students have toward the robot. We present preliminary data of students' (N=30) perceptions (i.e., social presence, personality, and method of instruction) of a robot instructor that applies spatial contiguity through the use of gestures. We conducted a between-subjects study with three conditions (no robot, robot without gestures, and robot with gestures). Students had the more positive perceptions of the robot with gestures and the less positive perceptions of the robot without gestures. Future work will include a larger sample size and investigate the impact on learning and retention.

This paper presents an analysis of CS faculty perceptions of the benefits of POGIL, the obstacles to POGIL adoption, and opportunities for professional development. Participants strongly agreed that with POGIL, students are more engaged and active, develop communication and teamwork skills, and have better learning outcomes. The largest perceived obstacle was lack of preparation time; other obstacles included availability of relevant POGIL activities and pressure to cover more content. Participants expressed a desire for further training and mentoring beyond workshops. Our data analysis also considers bivariate associations and interactions. The results should help to improve professional development for CS faculty adopting evidence-based strategies, and thereby help more CS students to be successful.

We report on the Latency Hammer, a low-cost yet highaccuracy and high-precision automated tool that measures the interface latency of touchscreen devices. The Hammer directly measures latency by triggering a capacitive touch event on a device using an electrically actuated touch simulator, and a photo sensor to monitor the screen for a visual response. This allows us to measure the full end-toend latency of a touchscreen system exactly as it would be experienced by a user. The Hammer does not require human interaction to perform a measurement, enabling the acquisition of large datasets. We present the operating principles of the Hammer, and discuss its design and construction; full design documents are available online. We also present a series of tools and equipment that were built to assess and validate the performance of the Hammer, and demonstrate that it provides reliable latency measurements.

Employer surveys and other sources indicate that CS students need process skills such as communication, teamwork, critical thinking, and problem solving. Further, the AP CS Principles practices include communicating and collaborating. POGIL (Process Oriented Guided Inquiry Learning) can be used to teach these process skills in high school or college courses. Instead of sitting in lecture or working together with minimal guidance, student teams work through POGIL activities to discover concepts on their own, while the instructor circulates and facilitates learning of key concepts and the development of process skills. Structured roles help students learn to communicate and work in teams more effectively. In this special session, attendees will experience a POGIL activity, learn how process skills are developed and reinforced by POGIL (through a POGIL meta-activity), and see the range of activities available at http://cspogil.org. We will share POGIL activities and discuss how POGIL can transform CS classes at all levels, from high school to graduate-level classes, from small schools to large universities.

Regular array languages for high performance computing based on aggregate operations provide a convenient parallel programming model, which enables the generation of efficient code for SIMD architectures, such as GPUs. However, the data sets that can be processed with current implementations are severely constrained by the limited amount of main memory available in these architectures. In this paper, we propose an extension of the embedded array language Accelerate with a notion of sequences, resulting in a two level hierarchy which allows the programmer to specify a partitioning strategy which facilitates automatic resource allocation. Depending on the available memory, the runtime system processes the overall data set in streams of chunks appropriate to the hardware parameters. In this paper, we present the language design for the sequence operations, as well as the compilation and runtime support, and demonstrate with a set of benchmarks the feasibility of this approach.

This paper reports on two experiments designed to further our understanding of users' perception of latency in touch- based systems. The first experiment extends previous efforts to measure latency perception by reporting on a unified study in which direct and indirect form-factors are compared for both tapping and dragging tasks. Our results show significant effects from both form-factor and task, and inform system designers as to what input latencies they should aim to achieve in a variety of system types. A follow-up experiment investigates peoples' ability to perceive small improvements to latency in direct and indirect form-factors for tapping and dragging tasks. Our results provide guidance to system designers of the relative value of making improvements in latency that reduce but do not fully eliminate lag from their systems.

POGIL (Process Oriented Guided Inquiry Learning) is based on the principle that students learn more when they construct their own understanding. Instead of attending lecture, student teams work through POGIL activities to discover concepts on their own, while instructors circulate and facilitate learning. Students learn the material better, and this constructivist approach also teaches them important process skills, including critical thinking, team work, and leadership. In this special session, SIGCSE attendees will experience a POGIL activity for themselves, learn about the structure of POGIL activities (through a POGIL meta-activity), and hear perspectives from teachers who recently adopted POGIL activities available at http://cspogil.org. We will share classroom-tested guided inquiry activities and discuss how POGIL can transform CS classes at all levels, from high school to graduate-level classes, from small schools to large universities.

Museums are exploring new ways of using emerging digital technologies to enhance the visitor experience. In this context, our research focuses on designing, developing and studying interactions for museum exhibits that introduce cultural concepts in ways that are tangible and embodied. We introduce here a tangible tabletop installation piece that was designed for a museum exhibition contrasting Western and African notions of mapping history and place. Inspired by the Lukasa board, a mnemonic device used by the Luba peoples in Central Africa, the tabletop piece enables visitors to learn and understand symbolic and nonlinguistic mapping concepts that are central to the Lukasa by creating and sharing stories with each other. In this paper we share our design process, a user study focusing on children and learning, and design implications on how digital and tangible interaction technologies can be used for cultural learning in museum exhibits.

The "Universal Threshold Object" is a tangible device for television-like interactive narratives based on the TV show American Horror Story. The project uses gestural interactions with a tangible controller that provides haptic feedback as an interaction strategy to augment the narrative pleasures of immersion and dramatic agency. We leverage a branching scenario and story-driven gestural interaction with haptic feedback to provide limited sets of interactivity suitable for a television platform. From our research, design goals, and design process, we provide design implications for interactive narratives that employ gestural and haptic interactions.

The computer science community has had a growing research focus in Privacy over the last decade. Much of this has really focused on confidentiality: Anonymization, computing on encrypted data, access control policy, etc. This talk will look at a variety of research results in this area, including "weaker" approaches than the absolutes typically considered in the security community, and how they all come down to the same basic concept of providing confidentiality. Privacy is much more complex. People are often willing to allow use of their data -- but not just for anything. This talk will look at such other privacy issues, such as harm to individuals and society from the fear of disclosure or misuse of private data. The talk will conclude with ideas for new research directions in privacy.

A method of reducing the perceived latency of touch input by employing a model to predict touch events before the finger reaches the touch surface is proposed. A corpus of 3D finger movement data was collected, and used to develop a model capable of three granularities at different phases of movement: initial direction, final touch location, time of touchdown. The model is validated for target distances >= 25.5cm, and demonstrated to have a mean accuracy of 1.05cm 128ms before the user touches the screen. Preference study of different levels of latency reveals a strong preference for unperceived latency touchdown feedback. A form of 'soft' feedback, as well as other uses for this prediction to improve performance, is proposed.

We present "Fast Multi-Touch" (FMT), an extremely high frame rate and low-latency multi-touch sensor based on a novel projected capacitive architecture that employs simultaneous orthogonal signals. The sensor has a frame rate of 4000 Hz and a touch-to-data output latency of only 40 microseconds, providing unprecedented responsiveness. FMT is demonstrated with a high-speed DLP projector yielding a touch-to-light latency of 110 microseconds.

Researchers have long known that aggregate estimations built from the collected opinions of a large group of people often outperform the estimations of individual experts. This phenomenon is generally described as the "Wisdom of Crowds". This approach has shown promise with respect to the task of accurately forecasting future events. Previous research has demonstrated the value of utilizing meta-forecasts (forecasts about what others in the group will predict) when aggregating group predictions. In this paper, we describe an extension to meta-forecasting and demonstrate the value of modeling the familiarity among a population's members (its social network) and applying this model to forecast aggregation. A pair of studies demonstrates the value of taking this model into account, and the described technique produces aggregate forecasts for future events that are significantly better than the standard Wisdom of Crowds approach as well as previous meta-forecasting techniques.

In this paper, I describe the motivation for and the background necessary to develop and evaluate an early set of design guidelines for creating tangible and embodied interfaces that focus on engaging, augmenting and improving spatial ability. I then briefly analyze prior tangible and embodied interactive systems (TEIs) from a spatial ability perspective and discuss the next steps in the development of my proposed guidelines.

Adolescent mental health cannot be ignored, and psychological pressure is one of the prominent problems of current teenagers. Micro-blog, as the most important information exchange and broadcast tool in the current society, is becoming an important channel for teenagers' information acquisition, inter-interaction, self-expression, emotion release due to its unique equality, freedom, fragmentation, individuality characteristics. This poster envisions a micro-blog platform, aiming to (1) sense psychological pressures through teenagers' tweets, and (2) assist teenagers to release their stress through micro-blog. A method for timely detecting psychological pressures from teenagers' tweets is particularly described. Our preliminary experimental results on real data demonstrate the validity of the approach. We also discuss ways to assist teenagers to release their pressures through micro-blog at the end of the poster.

We introduce operations to safely update an anatomized database. The result is a database where the view of the server satisfies standards such as k-anonymity or l-diversity, but the client is able to query and modify the original data. By exposing data where possible, the server can perform value-added services such as data analysis not possible with fully encrypted data, while still being unable to violate privacy constraints. Update is a key challenge with this model; naive application of insertion and deletion operations reveals the actual data to the server. This paper shows how data can be safely inserted, deleted, and updated. The key ideas are that data is inserted or updated into an encrypted temporary table until enough data is available to safely decrypt, and that sensitive information of deleted tuples is left behind to ensure privacy of both deleted and undeleted individuals. This approach is proven effective in maintaining the privacy constraint against an adversarial server. The paper also gives empirical results on how much data remains encrypted, and the resulting quality of the server's (anatomized) view of the data, for various update and delete rates.

This workshop is for anyone who teaches CS, and introduces process-oriented guided inquiry learning (POGIL) in computer science. POGIL is based on learning science, and shares characteristics with other forms of active, discovery, and inquiry learning. In a POGIL classroom, teams of 3-5 learners work on instructor-facilitated activities. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and meta-cognition, learners develop process skills and individual responsibility. Studies show that POGIL can significantly improve student performance. POGIL has particular potential for CS education. Software development is largely a team-based problem-solving activity, and POGIL helps students to learn from each other and develop problem-solving abilities as well as important team process skills. POGIL has been developed and validated over the last 15 years in a range of STEM disciplines.The workshop consists primarily of hands-on team activities. Participants will work through CS activities and a set of meta-activities to explore POGIL practices and activity structures. Participants will also begin to draft parts of their own activities. More information and materials are available at http://cspogil.org and http://pogil.org, including sample activities for topics in a variety of CS courses. Laptops optional. This material is based upon work supported by the National Science Foundation under grant DUE-1044679.