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1. Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

   https://doi.org/10.5281/zenodo.6304194  

   Jacovic, Marko ; Rivas, Xaime Rey ( January 2022 , Zenodo)

   Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.

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   All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'

   • 'ModeSelect.py': class used to defined the antenna state selection algorithm
   • 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
   • 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
   • 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only

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   Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

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   Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

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   classifier_training: files used for training classifiers that are integrated into SDR platform

   • 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
   • 'classTrain.py' is the main parser for training the classifiers
   • 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'

   post_processing_classifier: contains logs of online classifier outputs and processing script

   • 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
   • 'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'

   post_processing_mgen: contains MGEN receiver logs and parser

   • 'configs' contains JSON files to be used with parser for each experiment
   • 'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'

   ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

   • Directory contains detailed 'readme.txt' for understanding.
   • Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. 
     • S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.

   results: contains results obtained from study

   • 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
   • 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
   • 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'

   tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study

   • 'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.
   • 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.

    

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2. Increasing learners' self‐efficacy beliefs and curiosity through a Frankenstein‐themed transmedia storytelling experience

   https://doi.org/10.1111/bjet.13202  

   Nagy, Peter ; Mawasi, Areej ; Eustice, Kristi ; Cook‐Davis, Alison ; Finn, Ed ; Wylie, Ruth ( February 2022 , British Journal of Educational Technology)

   Using Mary Shelley'sFrankensteinas inspiration, this paper presents a Frankenstein‐themed transmedia storytelling experience, which encompasses simple hands‐on activities and an online narrative experience that allows students to model scientific work and engage in various science activities. The study aimed to test whether students can develop higher science and creative self‐efficacy beliefs, and a stronger science curiosity, by engaging in the transmedia experience that combined hands‐on and online narrative activities compared with participating in only hands‐on or online narrative experiences. Our paper presents findings from two classroom studies using survey findings. Results show that all three conditions (hands‐on, online game experience and transmedia) had a significant positive impact on learners' self‐efficacy beliefs and curiosity, but there was no additional benefit for the transmedia condition. Nevertheless, our work has various implications for learning sciences about the potential benefits and drawbacks of transmedia storytelling experiences. Our findings can help educators and researchers design and run transmedia storytelling projects.

   What is already known about this topic

   Transmedia storytelling is a popular and adaptable learning application.

   Transmedia storytelling can be beneficial due to transfer of learning.

   Transmedia storytelling may foster learners' engagement and knowledge acquisition.

   What this paper adds

   The paper presents a Frankenstein‐themed transmedia experience that combines digital and hands‐on activities and borrows several themes from Mary Shelley'sFrankenstein; or, the Modern Prometheus.

   Findings from this study show that transmedia storytelling can boost learners' science and creative self‐efficacy beliefs and science curiosity.

   However, transmedia storytelling combining digital and hands‐on experiences is not more effective in bolstering self‐efficacy beliefs and curiosity than digital or hands‐on experiences alone.

   Implications for practice and/or policy

   Transmedia storytelling might have unintended consequences for learning because it may exhaust learners' cognitive resources.

   Learners' transliteracy skills and competencies may influence what benefits they gain from partaking in transmedia storytelling experiences.

   Educators need to take learners' transliteracy skills into consideration when they wish to design and/or use transmedia storytelling experiences for learning purposes.

    

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3. ASYMPTOTIC SIZE OF KLEIBERGEN’S LM AND CONDITIONAL LR TESTS FOR MOMENT CONDITION MODELS

   https://doi.org/10.1017/S0266466616000347  

   Andrews, Donald W.K. ; Guggenberger, Patrik ( October 2017 , Econometric Theory)

   An influential paper by Kleibergen (2005, Econometrica 73, 1103–1123) introduces Lagrange multiplier (LM) and conditional likelihood ratio-like (CLR) tests for nonlinear moment condition models. These procedures aim to have good size performance even when the parameters are unidentified or poorly identified. However, the asymptotic size and similarity (in a uniform sense) of these procedures have not been determined in the literature. This paper does so. This paper shows that the LM test has correct asymptotic size and is asymptotically similar for a suitably chosen parameter space of null distributions. It shows that the CLR tests also have these properties when the dimension p of the unknown parameter θ equals 1. When p ≥ 2, however, the asymptotic size properties are found to depend on how the conditioning statistic, upon which the CLR tests depend, is weighted. Two weighting methods have been suggested in the literature. The paper shows that the CLR tests are guaranteed to have correct asymptotic size when p ≥ 2 when the weighting is based on an estimator of the variance of the sample moments, i.e., moment-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic. The paper also determines a formula for the asymptotic size of the CLR test when the weighting is based on an estimator of the variance of the sample Jacobian. However, the results of the paper do not guarantee correct asymptotic size when p ≥ 2 with the Jacobian-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic, because two key sample quantities are not necessarily asymptotically independent under some identification scenarios. Analogous results for confidence sets are provided. Even for the special case of a linear instrumental variable regression model with two or more right-hand side endogenous variables, the results of the paper are new to the literature. 

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4. Smart paper transformer: new insight for enhanced catalytic efficiency and reusability of noble metal nanocatalysts

   https://doi.org/10.1039/c9sc05287a  

   Jin, Qijie ; Ma, Lei ; Zhou, Wan ; Shen, Yuesong ; Fernandez-Delgado, Olivia ; Li, XiuJun ( March 2020 , Chemical Science)

   Although noble metal nanocatalysts show superior performance to conventional catalysts, they can be problematic when balancing catalytic efficiency and reusability. In order to address this dilemma, we developed a smart paper transformer (s-PAT) to support nanocatalysts, based on easy phase conversion between paper and pulp, for the first time. The pulp phase was used to maintain the high catalytic efficiency of the nanocatalysts and the transformation to paper enabled their high reusability. Herein, as an example of smart paper transformers, a novel chromatography paper-supported Au nanosponge (AuNS/pulp) catalyst was developed through a simple water-based preparation process for the successful reduction of p -nitrophenol to demonstrate the high catalytic efficiency and reusability of the noble metal nanocatalyst/pulp system. The composition, structure, and morphology of the AuNS/pulp catalyst were characterized by XRD, TGA, FE-SEM, ICP, TEM, FT-IR, and XPS. The AuNS/pulp catalyst was transformed into the pulp phase during the catalytic reaction and into the paper phase to recover the catalysts after use. Owing to this smart switching of physical morphology, the AuNS/pulp catalyst was dispersed more evenly in the solution. Therefore, it exhibited excellent catalytic performance for p -nitrophenol reduction. Under optimal conditions, the conversion rate of p -nitrophenol reached nearly 100% within 6 min and the k value of AuNS/pulp (0.0106 s −1 ) was more than twice that of a traditional chromatography paper-based catalyst (0.0048 s −1 ). Additionally, it exhibited outstanding reusability and could maintain its high catalytic efficiency even after fifteen recycling runs. Accordingly, the unique phase switching of this smart paper transformer enables Au nanosponge to transform into a highly efficient and cost-effective multifunctional catalyst. The paper transformer can support various nanocatalysts for a wide range of applications, thus providing a new insight into maintaining both high catalytic efficiency and reusability of nanocatalysts in the fields of environmental catalysis and nanomaterials. 

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5. Machine learning for the educational sciences

   https://doi.org/10.1002/rev3.3310  

   Hilbert, Sven ; Coors, Stefan ; Kraus, Elisabeth ; Bischl, Bernd ; Lindl, Alfred ; Frei, Mario ; Wild, Johannes ; Krauss, Stefan ; Goretzko, David ; Stachl, Clemens ( November 2021 , Review of Education)

   Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.

   In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.

   The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.

   The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.

   Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

    

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