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1. Unconstrained Face Detection and Open-Set Face Recognition Challenge

   https://doi.org/10.1109/BTAS.2017.8272759  

   Gunther, M.; Hu, P.; Herrmann, C.; Chan, C. H.; Jiang, M.; Yang, S.; Dhamija, A. R.; Ramanan, D.; Beyerer, J.; Kittler, J.; et al (October 2017, International Joint Conference on Biometrics)

   Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention. 

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2. Face Liveness Detection Competition (LivDet-Face) - 2021

   https://doi.org/10.1109/IJCB52358.2021.9484359  

   Purnapatra, Sandip; Smalt, Nic; Bahmani, Keivan; Das, Priyanka; Yambay, David; Mohammadi, Amir; George, Anjith; Bourlai, Thirimachos; Marcel, Sebastien; Schuckers, Stephanie; et al (August 2021, 2021 IEEE International Joint Conference on Biometrics (IJCB))

   Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners. 

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3. Differences in thermal expansion and motion ability for herringbone and face-to-face π-stacked solids

   https://doi.org/10.1107/S2052252521009593  

   Ding, Xiaodan; Zahid, Ethan; Unruh, Daniel K.; Hutchins, Kristin M. (January 2022, IUCrJ)

   A series of aromatic organic molecules functionalized with different halogen atoms (I/ Br), motion-capable groups (olefin, azo or imine) and molecular length were designed and synthesized. The molecules self-assemble in the solid state through halogen bonding and exhibit molecular packing sustained by either herringbone or face-to-face π-stacking, two common motifs in organic semiconductor molecules. Interestingly, dynamic pedal motion is only achieved in solids with herringbone packing. On average, solids with herringbone packing exhibit larger thermal expansion within the halogen-bonded sheets due to motion occurrence and molecular twisting, whereas molecules with face-to-face π-stacking do not undergo motion or twisting. Thermal expansion along the π-stacked direction is surprisingly similar, but slightly larger for the face-to-face π-stacked solids due to larger changes in π-stacking distances with temperature changes. The results speak to the importance of crystal packing and intermolecular interaction strength when designing aromatic-based solids for organic electronics applications. 

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4. A Comparative Analysis of Online and Face-to-Face Professional Development Models for CS Education

   https://doi.org/10.1145/3017680.3017784  

   Webb, David C.; Nickerson, Hilarie; Bush, Jeffrey B. (January 2017, 10.1145/3017680.3017784)

   This paper compares student outcomes from 75 K-12 teachers who participated in either online, blended, or face-to-face professional development design to support teacher implementation of a programming curriculum during the regular school day. The results are based on survey responses collected over two years from 4,832 students. With only one exception, the results showed no negative student outcomes when comparing student survey results from teachers who participated in online professional development compared to students of teachers who participated in face-to-face professional development. Students who had teachers who participated in face-to-face professional development, however, expressed stronger interest in designing their own games at home. These results suggest that online professional development that is designed to support K-12 teacher classroom implementation of CS education curricula is a viable model with respect to student outcomes. Recommendations for the design of online curricula for CS education are discussed. 

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5. Impact of Video-Based Analysis-of-Practice Professional Learning: Comparing Online and Face-to-face Models

   Kowalski, S. M.; Belcastro, A.; Hvidsten, C.; Ollison, G.; Askinas, K.; De Vaul, R.; Roehrig, G. (September 2022, Annual Meeting for the Society for Research on Educational Effectiveness)

   Video-based analysis of practice models have gained prominence in mathematics and science teacher education inservice professional learning. There is a growing body of evidence that these intensive professional learning (PL) models lead to positive impacts on teacher knowledge, classroom instructional practice, and student learning (Roth et al., 2018; Taylor et al., 2017), but they are expensive and difficult to sustain. An online version would have several benefits, allowing for greater reach to teachers and students across the country, but if online models were substantially less effective, then lower impacts would undercut the benefits of greater accessibility. We designed and studied a fully online version of the face-to-face Science Teachers Learning from Lesson Analysis (STeLLA) PL model (Roth, et al., 2011; Roth et al., 2018; Taylor et al., 2017). We conducted a quasi-experimental study comparing online STeLLA to face-to-face STeLLA. Although we found no significant difference in elementary student learning between the online and face-to-face versions ( p = .09), the effect size raises questions. Exploratory analyses suggest that the impact of online STeLLA on students is greater than the impact of a similar number of hours of traditional, face-to-face content deepening PL, but less than the impact of the full face-to-face STeLLA program. Differences in student populations, with higher percentages of students from racial and ethnic groups underserved by schools in the online STeLLA program, along with testing of the online STeLLA model during the pandemic, complicates interpretation of the findings. 

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