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1. An Analysis of Low-Scoring Blind and Low Vision Individuals; Selected Answers on a Tactile Spatial Ability Instrument

   Kane, D. ; Shaheen, N. L. ; and Goodridge, W. H. ( June 2023 , 2023 ASEE Annual Conference & Exposition)

   Spatial ability has been shown through numerous studies to be a strong predictor of student success in STEM fields. Beyond the classroom, professionals demonstrating higher levels of spatial ability are also more likely to be successful in their STEM careers than their peers with lower spatial ability. Research has also shown that spatial ability is a malleable skill that can be strengthened through targeted intervention and leads to better retention in rigorous STEM fields. For this reason, spatial ability has been a significant focus of engineering education research. Despite the focus on spatial ability in engineering education research, members of the blind and low vision (BLV) population have largely been omitted from research in this area, likely due to the lack of a nonvisually accessible instrument for measuring spatial ability in a tactile format. This work utilizes the Tactile Mental Cutting Test (TMCT), a fully accessible adaptation of the commonly used multiple-choice Mental Cutting Test (MCT) spatial ability instrument which requires participants to identify cross sectional outlines from a three-dimensional object with a cut through it. This paper explores data collected from BLV participants who completed a TMCT test at National Federation of the Blind (NFB) sponsored summer programs for BLV youth, blindness training centers, and state and national NFB conventions. Raw scores from each TMCT participant were analyzed and ranked into high, medium, and low performing groups to help identify main characteristics of each group. In this study we examined patterns in the selected answer choices of the low scoring group to determine frequency of participant selection of distractors for each item of the TMCT. Analysis of the low-performer scores indicate that the majority of low scoring participants select incorrect answer choices that represent a side view or top view of the TMCT object as opposed to the true cross-sectional shape. Furthermore, the results suggest that certain answer choices may be overly difficult to distinguish between due to the tactile format of the exam. Results from this study can inform academia of the inherent differences between tactile and traditional spatial ability instruments and aid in the design of new tactile instruments. 

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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))

   null (Ed.)

   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 data 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. 

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3. A proposed standard for quantifying 3‐D hindlimb joint poses in living and extinct archosaurs

   https://doi.org/10.1111/joa.13635  

   Gatesy, Stephen M. ; Manafzadeh, Armita R. ; Bishop, Peter J. ; Turner, Morgan L. ; Kambic, Robert E. ; Cuff, Andrew R. ; Hutchinson, John R. ( February 2022 , Journal of Anatomy)

   The last common ancestor of birds and crocodylians plus all of its descendants (clade Archosauria) dominated terrestrial Mesozoic ecosystems, giving rise to disparate body plans, sizes, and modes of locomotion. As in the fields of vertebrate morphology and paleontology more generally, studies of archosaur skeletal structure have come to depend on tools for acquiring, measuring, and exploring three‐dimensional (3‐D) digital models. Such models, in turn, form the basis for many analyses of musculoskeletal function. A set of shared conventions for describing 3‐D pose (joint or limb configuration) and 3‐D kinematics (change in pose through time) is essential for fostering comparison of posture/movement among such varied species, as well as for maximizing communication among scientists. Following researchers in human biomechanics, we propose a standard methodological approach for measuring the relative position and orientation of the major segments of the archosaur pelvis and hindlimb in 3‐D. We describe the construction of anatomical and joint coordinate systems using the extant guineafowl and alligator as examples. Our new standards are then applied to three extinct taxa sampled from the wider range of morphological, postural, and kinematic variation that has arisen across >250 million years of archosaur evolution. These proposed conventions, and the founding principles upon which they are based, can also serve as starting points for measuring poses between elements within a hindlimb segment, for establishing coordinate systems in the forelimb and axial skeleton, or for applying our archosaurian system more broadly to different vertebrate clades.

    

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4. Quality of knee strengthening exercises performed at home deteriorates after one week

   https://doi.org/10.1186/s12891-022-05120-3  

   Mitchell, Ulrike H. ; Lee, Hyunwook ; Dennis, Hayden E. ; Seeley, Matthew K. ( December 2022 , BMC Musculoskeletal Disorders)

   Abstract Background To compare the performance (as determined by lower extremity kinematics) of knee exercises in healthy middle-aged and older individuals immediately after instruction and one week later. Methods This is a cross-sectional study in a laboratory setting. Nineteen healthy volunteers (age [y] 63.1 ± 8.6, mass [kg] 76.3 ± 14.7, height [m] 1.7 ± 0.1) participated in this study. High speed video and reflective markers were used to track motion during four exercises. The exercises were knee flexion, straight leg raise, and “V “in supine position, and hip abduction in side lying position. All participants received verbal and tactile cues during the training phase and the therapist observed and, if necessary, corrected the exercises. Upon return a week later the participants performed the same exercises without any further instructions. Knee and hip sagittal and rotational angles were extracted from the motion capture. A repeated measures t-test was used to compare the motions between two visits. Results Participants demonstrated more knee flexion during straight leg raise and “V in” exercises at the 2nd visit compared to the 1st visit (both p  <  0.05). During the “V out” exercise, they performed more external rotation ( p  <  0.05) while they showed more internal rotation during the “V in” exercise at the 2nd visit compared to the 1st visit. Conclusions Exercise performance declined significantly in healthy middle-aged and older individuals one week after instruction. This decline occurred despite an instructional exercise sheet being given to every participant. Other approaches designed to help individuals retain the ability to perform rehabilitative exercises correctly need to be explored. 

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5. Exploring Parallel Computing with OpenMP on the Raspberry Pi

   https://doi.org/10.1145/3287324.3287535  

   Matthews, Suzanne J. ; Adams, Joel C. ; Brown, Richard A. ; Shoop, Elizabeth ( February 2019 , Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE ’19))

   The ACM/IEEE CS 2013 report recommends fifteen hours of parallel & distributed computing (PDC) education for every undergraduate. This workshop illustrates the use of the Raspberry Pi as an inexpensive, multicore platform for teaching shared-memory parallel programming. The inexpensive and tactile nature of the Raspberry Pi enables each student to experience her own parallel multiprocessor through sight and touch. In this hands-on workshop, we will teach attendees how they can leverage the Raspberry Pi and the OpenMP library to teach shared-memory parallel concepts in their own classrooms. All CS educators who are interested in learning about the Raspberry Pi, shared memory parallelism, and OpenMP are encouraged to attend. In Part I of the workshop, each participant will connect to and learn about the Raspberry Pi's multicore capabilities. In Part II, each participant will engage in self-paced, hands-on exploration of basic parallel computing concepts using the OpenMP "patternlets" from CSinParallel.org. In Part III, participants will investigate more complex applications, such as numeric integration and drug design and study how these applications can be parallelized using OpenMP. We will conclude the workshop with a series of lightning talks discussing how the Raspberry Pi has been used to teach parallel computing concepts at different institutions. We will also present a summary of student perceptions of the Raspberry Pi. All materials from this workshop will be freely available from CSinParallel.org. Space is limited to 20 participants. A laptop is required. 

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