More Like this

1. The Generativity of Remixing: Understanding Knowledge Reuse Process for Innovation in Online Communities

   Han, Y. ; Nickerson, J.V. 2017. ( December 2017 , Proceedings of the International Conference on Information Systems)

   Remixing, a method to harness collective intelligence, is used in many online innovation communities. It is also an important form of online engagement. What actions lead to a remix that is generative? This paper addresses this question by using a knowledge reuse process model previously applied in offline settings as the basis for a series of hypotheses about online communities. An empirical study is performed to examine the relationship between three major actions in the knowledge reuse process model and the generativity of the remix created. An analysis of the reuse of proposals in an online innovation community, Climate CoLab, shows that those including prevalent topics and metaknowledge about integration are more generative. These findings provide insights to strategies and tools that can support knowledge reuse for innovation in online communities.
2. 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
3. Developing Humanitarian Engineering Perspectives Among Underrepresented Scholars Through Engagement with the Sustainable Development Goals in Global Contexts

   Tull, R. G. ; Hester, S. ; Medina, Y. ; Williams, D. N. ; Medina, H. ; Aparaka, E. T. ( June 2018 , ASEE Annual Conference proceedings)

   Opportunities to participate in international engagement experiences broaden students’ perspectives and perceptions of real world problems. A strong sense of “global engineering identity” can emerge when students are part of international teams that consider solutions to humanitarian challenges. To encourage retention in engineering among undergraduate and graduate students from underrepresented groups, a multi-campus team of faculty and administrators developed a plan expose students to humanitarian engineering perspectives within global contexts. Through a federally-funded program, the leaders took students to international conferences that fostered global team-based approaches to the National Academy of Engineering’s (NAE) 14 Grand Challenges, and the United Nations’ 17 Sustainable Development Goals (SDGs). Students attended international conferences on three continents in 2016 and 2017. The conferences introduced students to the NAE’s Grand Challenges in plenary sessions, and the SDGs in smaller group sessions, with a charge to transform the world. Students from across the globe developed action plans to potentially address problems within their communities. Students were encouraged to consider real-life scenarios of their choice that could be further refined and potentially implemented upon return to their home countries. The structure of the small group sessions allowed students to be members of an international team, agree upon amore »problem to tackle, conduct early research, and propose a concrete path toward addressing one of the SDGs. Data for this project was collected through crowd-sourcing, using online student reflections. Students blogged throughout a one-week period for each of three conferences. There were 28 respondents, across the three events. Content analysis was used to disaggregate data and group similarities. Data showed that the students from the federally-funded delegation demonstrated a clear need to assist the global community. They were particularly interested in working on problems related to industry innovation, infrastructure, gender equality, sustainable cities, and communities. Students realized that approaches to solutions could not be centralized to their own country, and that their proposals had to be feasible and logical for other parts of the world. As an example, challenges with bringing clean water to remote regions and approaches to sanitation required a need to take time to learn from peers from other countries. Students were asked to provide ubiquitous solutions to the problems. They were asked to consider themselves as part of the respective communities as a means of assessing the practicality of potential approaches. Students’ perspectives changed throughout the course of the conference, as they reflected on their ability to bring global contexts to their research. After participating in these conferences, students experienced a greater awareness of sustainability. They were also inspired to experience different cultures, cultivating greater appreciation for the need to engage with the international community when sharing research. The exposure to humanitarian engineering perspectives influenced global STEM identity, while appreciating disciplines outside of engineering, e.g, psychology, social behaviors. Further, students learned that strides can be made toward solving global problems when collaborations and relationships are formed and fostered.« less
4. NSF RED: Supporting Convergence Development through Structural Changes to an ECE Program

   Cheville, R. A. ; Appelhans, S. E. ; Thomas. R. ; Thomas, S. ; Nickel, R. M. ; Thompson, M. S. ( January 2022 , American Society for Engineering Education Annual Conference and Exhibition)

   This NSF Grantees poster discusses an early phase Revolutionizing Engineering Departments (RED) project which is designed to address preparing engineering students to address large scale societal problems, the solutions of which integrate multiple disciplinary perspectives. These types of problems are often termed “convergent problems”. The idea of convergence captures how different domains of expertise contribute to solving a problem, but also the value of the network of connections between areas of knowledge that is built in undertaking such activities. While most existing efforts at convergence focus at the graduate and post-graduate levels, this project supports student development of capabilities to address convergent problems in an undergraduate disciplinary-based degree program in electrical and computer engineering. This poster discusses some of the challenges faced in implementing such learning including how to decouple engineering topics from societal concerns in ways that are relevant to undergraduate students yet retain aspects of convergence, negotiations between faculty on ways to balance discipline-specific skills with the breadth required for systemic understanding, and challenges in integrating relevant projects into courses with different faculty and instructional learning goals. One of the features of the project is that it builds on ideas from Communities of Transformation by basing activities onmore »a coherent philosophical model that guides theories of change. The project has adopted Amartya Sen’s Development as Freedom or capabilities framework as the organizing philosophy. In this model the freedom for individuals to develop capabilities they value is viewed as both the means and end of development. The overarching goal of the project is then for students to build personalized frameworks based on their value systems which allow them to later address complex, convergent problems. Framework development by individual students is supported in the project through several activities: modifying grading practices to provide detailed feedback on skills that support convergence, eliciting self-narratives from students about their pathways through courses and projects with the goal of developing reflection, and carefully integrating educational software solutions that can reduce some aspects of faculty workload which is hypothesized to enable faculty to focus efforts on integrating convergent projects throughout the curriculum. The poster will present initial results on the interventions to the program including grading, software integration, projects, and narratives. The work presented will also cover an ethnographic study of faculty practices which serves as an early-stage baseline to calibrate longer-term changes.« less
5. Systems Thinking to Protect Coral Reefs: A Case Study Rooted in Community Partnerships

   Prouty, C ; Garcia, L ; Carne, L ; Trotz, M. ( July 2019 , 12 th Natural Resource Management & Research Symposium “BELIZE, ‘THROUGH THE BOTTLENECK’”)

   Environmental impacts associated with inefficient and ineffective land-based wastewater treatment have direct implications for regional governments and local communities in the Caribbean due to the links between environmental quality of coastal areas (e.g. coral reefs) and socioeconomic activities (e.g. tourism, commercial fishing, cultural heritage, recreation). In Placencia, Belize an interdisciplinary team of students and community members investigate the tradeoffs that exists amid a food-energy-water systems (FEWS) case study, in order to co-create sustainable solutions. This work partners with Fragments of Hope and EcoFriendly Solutions to take a systems approach to consider the dynamic and interrelated factors and leverage points (e.g. technological, regulatory, organizational, social, economic) related to the adoption and sustainability of wastewater innovations at cayes where coral restoration work is occurring. This technology can improve water quality issues in sensitive marine ecosystems and productively reuse water and nutrients to grow food. Results show that marketing and technical strategies contributed to incremental improvements in the system's sustainability, while changing community behaviors (i.e. reporting the correct number of users and reclaiming resources – water and nutrients – for food production), was the more significant way to influence the sustainable management of the wastewater resources and to protect the coastal environment. Themore »work is situated within the deeper context of graduate student research and training where the University of South Florida is partnering with the Caribbean Community Climate Change Center to raise up a new generation of globally competent science, technology, engineering, and math (STEM) students. These students develop interdisciplinary and 21st century skills, as well as technical and methodological flexibility to address the complexity inherent in “wicked problems”. To accomplish this, the partners provide resources and training for interdisciplinary and systems-based teaching and research that results in original and impactful solutions developed alongside community members to locally and globally focused challenges.« less