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1. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
2. Exploring Virtual Environments by Visually Impaired Using a Mixed Reality Cane Without Visual Feedback

   https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00028  

   Zhang, Lei ; Wu, Klevin ; Yang, Bin ; Tang, Hao ; Zhu, Zhigang ( November 2020 , ISMAR 2020 - International Symposium on Mixed and Augmented Reality)

   Though virtual reality (VR) has been advanced to certain levels of maturity in recent years, the general public, especially the population of the blind and visually impaired (BVI), still cannot enjoy the benefit provided by VR. Current VR accessibility applications have been developed either on expensive head-mounted displays or with extra accessories and mechanisms, which are either not accessible or inconvenient for BVI individuals. In this paper, we present a mobile VR app that enables BVI users to access a virtual environment on an iPhone in order to build their skills of perception and recognition of the virtual environment and the virtual objects in the environment. The app uses the iPhone on a selfie stick to simulate a long cane in VR, and applies Augmented Reality (AR) techniques to track the iPhone’s real-time poses in an empty space of the real world, which is then synchronized to the long cane in the VR environment. Due to the use of mixed reality (the integration of VR & AR), we call it the Mixed Reality cane (MR Cane), which provides BVI users auditory and vibrotactile feedback whenever the virtual cane comes in contact with objects in VR. Thus, the MR Cane allowsmore »BVI individuals to interact with the virtual objects and identify approximate sizes and locations of the objects in the virtual environment. We performed preliminary user studies with blind-folded participants to investigate the effectiveness of the proposed mobile approach and the results indicate that the proposed MR Cane could be effective to help BVI individuals in understanding the interaction with virtual objects and exploring 3D virtual environments. The MR Cane concept can be extended to new applications of navigation, training and entertainment for BVI individuals without more significant efforts.« less
3. Work in Progress: An Exploration of Students’ Conceptualization of Research after Participating in an Undergraduate Research Experience

   https://doi.org/10.18260/1-2--32361  

   Tise, J. ; Zappe, S.E. ; Gomez, E.W. ; Kumar, M. ; Croninger, R.M.V. ; Cutler, S. ( January 2019 , American Society for Engineering Education)

   This Work-in-Progress paper investigates how students participating in a chemical engineering (ChE) Research Experience for Undergraduates (REU) program conceptualize and make plans for research projects. The National Science Foundation has invested substantial financial resources in REU programs, which allow undergraduate students the opportunity to work with faculty in their labs and to conduct hands-on experiments. Prior research has shown that REU programs have an impact on students’ perceptions of their research skills, often measured through the Undergraduate Research Student Self-Assessment (URSSA) survey. However, few evaluation and research studies have gone beyond perception data to include direct measures of students’ gains from program participation. This work-in-progress describes efforts to evaluate the impact of an REU on students’ conceptualization and planning of research studies using a pre-post semi-structured interview process. The construct being investigated for this study is planning, which has been espoused as a critical step in the self-regulated learning (SRL) process (Winne & Perry, 2000; Zimmerman, 2008). Students who effectively self-regulate demonstrate higher levels of achievement and comprehension (Dignath & Büttner, 2008), and (arguably) work efficiency. Planning is also a critical step in large projects, such as research (Dvir & Lechler, 2004). Those who effectively plan their projects make consistentmore »progress and are more likely to achieve project success (Dvir, Raz, & Shenhar, 2003). Prior REU research has been important in demonstrating some positive impacts of REU programs, but it is time to dig deeper into the potential benefits to REU participation. Many REU students are included in weekly lab meetings, and thus potentially take part in the planning process for research projects. Thus, the research question explored here is: How do REU participants conceptualize and make plans for research projects? The study was conducted in the ChE REU program at a large, mid-Atlantic research-oriented university during the summer of 2018. Sixteen students in the program participated in the study, which entailed them completing a planning task followed by a semi-structured interview at the start and the end of the REU program. During each session, participants read a case statement that asked them to outline a plan in writing for a research project from beginning to end. Using semi-structured interview procedures, their written outlines were then verbally described. The verbalizations were recorded and transcribed. Two members of the research team are currently analyzing the responses using an open coding process to gain familiarity with the transcripts. The data will be recoded based on the initial open coding and in line with a self-regulatory and project-management framework. Results: Coding is underway, preliminary results will be ready by the draft submission deadline. The methods employed in this study might prove fruitful in understanding the direct impact on students’ knowledge, rather than relying on their perceptions of gains. Future research could investigate differences in students’ research plans based on prior research experience, research intensity of students’ home institutions, and how their plans may be impacted by training.« less
4. Virtual Reality Laboratory Experiences for Electricity and Magnetism Courses

   Ilie, R. ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access)

   A solid understanding of electromagnetic (E&M) theory is key to the education of electrical engineering students. However, these concepts are notoriously challenging for students to learn, due to the difficulty in grasping abstract concepts such as the electric force as an invisible force that is acting at a distance, or how electromagnetic radiation is permeating and propagating in space. Building physical intuition to manipulate these abstractions requires means to visualize them in a three-dimensional space. This project involves the development of 3D visualizations of abstract E&M concepts in Virtual Reality (VR), in an immersive, exploratory, and engaging environment. VR provides the means of exploration, to construct visuals and manipulable objects to represent knowledge. This leads to a constructivist way of learning, in the sense that students are allowed to build their own knowledge from meaningful experiences. In addition, the VR labs replace the cost of hands-on labs, by recreating the experiments and experiences on Virtual Reality platforms. The development of the VR labs for E&M courses involves four distinct phases: (I) Lab Design, (II) Experience Design, (III) Software Development, and (IV) User Testing. During phase I, the learning goals and possible outcomes are clearly defined, to provide context for themore »VR laboratory experience, and to identify possible technical constraints pertaining to the specific laboratory exercise. During stage II, the environment (the world) the player (user) will experience is designed, along with the foundational elements, such as ways of navigation, key actions, and immersion elements. During stage III, the software is generated as part of the course projects for the Virtual Reality course taught in the Computer Science Department at the same university, or as part of independent research projects involving engineering students. This reflects the strong educational impact of this project, as it allows students to contribute to the educational experiences of their peers. During phase IV, the VR experiences are played by different types of audiences that fit the player type. The team collects feedback and if needed, implements changes. The pilot VR Lab, introduced as an additional instructional tool for the E&M course during the Fall 2019, engaged over 100 students in the program, where in addition to the regular lectures, students attended one hour per week in the E&M VR lab. Student competencies around conceptual understanding of electromagnetism topics are measured via formative and summative assessments. To evaluate the effectiveness of VR learning, each lab is followed by a 10-minute multiple-choice test, designed to measure conceptual understanding of the various topics, rather than the ability to simply manipulate equations. This paper discusses the implementation and the pedagogy of the Virtual Reality laboratory experiences to visualize concepts in E&M, with examples for specific labs, as well as challenges, and student feedback with the new approach. We will also discuss the integration of the 3D visualizations into lab exercises, and the design of the student assessment tools used to assess the knowledge gain when the VR technology is employed.« less
5. Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices

   Chen, Jiangong ; Qian, Feng ( July 2022 , IEEE ICDCS)

   Virtual Reality (VR), together with the network infrastructure, can provide an interactive and immersive experience for multiple users simultaneously and thus enables collaborative VR applications (e.g., VR-based classroom). However, the satisfactory user experience requires not only high-resolution panoramic image rendering but also extremely low latency and seamless user experience. Besides, the competition for limited network resources (e.g., multiple users share the total limited bandwidth) poses a significant challenge to collaborative user experience, in particular under the wireless network with time-varying capacities. While existing works have tackled some of these challenges, a principled design considering all those factors is still missing. In this paper, we formulate a combinatorial optimization problem to maximize the Quality of Experience (QoE), defined as the linear combination of the quality, the average VR content delivery delay, and variance of the quality over a finite time horizon. In particular, we incorporate the influence of imperfect motion prediction when considering the quality of the perceived contents. However, the optimal solution to this problem can not be implemented in real-time since it relies on future decisions. Then, we decompose the optimization problem into a series of combinatorial optimization in each time slot and develop a low-complexity algorithm that canmore »achieve at least 1/2 of the optimal value. Despite this, the trace-based simulation results reveal that our algorithm performs very close to the optimal offline solution. Furthermore, we implement our proposed algorithm in a practical system with commercial mobile devices and demonstrate its superior performance over state-of-the-art algorithms. We open-source our implementations on https://github.com/SNeC-Lab-PSU/ICDCS-CollaborativeVR.« less