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1. Graphery : interactive tutorials for biological network algorithms

   https://doi.org/10.1093/nar/gkab420  

   Zeng, Heyuan; Zhang, Jinbiao; Preising, Gabriel A; Rubel, Tobias; Singh, Pramesh; Ritz, Anna (May 2021, Nucleic Acids Research)

   null (Ed.)

   Abstract Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/. 

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2. Automated Assessment of Quality of Jupyter Notebooks Using Artificial Intelligence and Big Code

   https://doi.org/10.32473/flairs.v34i1.128560  

   Oli, Priti; Banjade, Rabin; Tamang, Lasang Jimba; Rus, Vasile (May 2021, The International FLAIRS Conference Proceedings)

   We present in this paper an automated method to assess the quality of Jupyter notebooks. The quality of notebooks is assessed in terms of reproducibility and executability. Specifically, we automatically extract a number of expert-defined features for each notebook, perform a feature selection step, and then trained supervised binary classifiers to predict whether a notebook is reproducible and executable, respectively. We also experimented with semantic code embeddings to capture the notebooks' semantics. We have evaluated these methods on a dataset of 306,539 notebooks and achieved an F1 score of 0.87 for reproducibility and 0.96 for executability (using expert-defined features) and an F1 score of 0.81 for reproducibility and 0.78 for executability (using code embeddings). Our results suggest that semantic code embeddings can be used to determine with good performance the reproducibility and executability of Jupyter notebooks, and since they can be automatically derived, they have the advantage of no need for expert involvement to define features. 

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3. FuncADL: Functional Analysis Description Language

   https://doi.org/10.1051/epjconf/202125103068  

   Proffitt, Mason; Watts, Gordon (January 2021, EPJ Web of Conferences)

   Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A. (Ed.)

   The traditional approach in HEP analysis software is to loop over every event and every object via the ROOT framework. This method follows an imperative paradigm, in which the code is tied to the storage format and steps of execution. A more desirable strategy would be to implement a declarative language, such that the storage medium and execution are not included in the abstraction model. This will become increasingly important to managing the large dataset collected by the LHC and the HL-LHC. A new analysis description language (ADL) inspired by functional programming, FuncADL, was developed using Python as a host language. The expressiveness of this language was tested by implementing example analysis tasks designed to benchmark the functionality of ADLs. Many simple selections are expressible in a declarative way with FuncADL, which can be used as an interface to retrieve filtered data. Some limitations were identified, but the design of the language allows for future extensions to add missing features. FuncADL is part of a suite of analysis software tools being developed by the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP). These tools will be available to develop highly scalable physics analyses for the LHC. 

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4. Leave It to Weaver

   https://doi.org/10.1145/3289239.3291459  

   Mahoney, William; Franco, Joseph; Hoff, Greg; McDonald, J. Todd (December 2018, Proceedings of the 8th Software Security, Protection, and Reverse Engineering Workshop (SSPREW))

   Malware authors make use of several techniques to obfuscate code from reverse engineering tools such as IdaPro. Typically, these techniques tend to be effective for about three to six instructions, but eventually the tools can properly disassemble the remaining code once the tool is again synchronized with the operation codes. But this loss of synchronization can be used to hide information within the instructions – steganography. Our research explores an approach to this by presenting “Weaver”, a framework for executable steganography. “Weaver” differs from other techniques in how it hides malicious instructions: the hiding instructions are prepared by generating an assembly listing of the program and finding candidate hiding locations, the steganography instructions are prepared by creating an assembly listing of the program to obtain the operation codes to be hidden, and the “weaving” process merges the two. This “weaving” attempts to place all the steganography instructions into candidate locations found in the hiding instructions. 

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5. Interactive Editing of Circuits in a Step-Based Tutoring System

   Skromme, B; Redshaw, C.; Gupta, A; Gupta, S; Andrei, P; Erives, H; Bailey, A; Thompson, W; Bansal, S. (July 2020, American Society for Engineering Education Annual Conference)

   Step-based tutoring systems are known to be more effective than traditional answer-based systems. They however require that each step in a student’s work be accepted and evaluated automatically to provide effective feedback. In the domain of linear circuit analysis, it is frequently necessary to allow students to draw or edit circuits on their screen to simplify or otherwise transform them. Here, the interface developed to accept such input and provide immediate feedback in the Circuit Tutor system is described, along with systematic assessment data. Advanced simplification methods such as removing circuit sections that are removably hinged, voltage-splittable, or current-splittable are taught to students in an interactive tutorial and then supported in the circuit editor itself. To address the learning curve associated with such an interface, ~70 video tutorials were created to demonstrate exactly how to work the randomly generated problems at each level of each of the tutorials in the system. A complete written record or “transcript” of student’s work in the system is being made available, showing both incorrect and correct steps. Introductory interactive (multiple choice) tutorials are now included on most topics. Assessment of exercises using the interactive editor was carried out by professional evaluators for several institutions, including three that heavily serve underrepresented minorities. Both quantitative and qualitative methods were used, including focus groups, surveys, and interviews. Controlled, randomized, blind evaluations were carried out in three different course sections in Spring and Fall 2019 to evaluate three tutorials using the interactive editor, comparing use of Circuit Tutor to both a commercial answer-based system and to conventional textbook-based paper homework. In Fall 2019, students rated the software a mean of 4.14/5 for being helpful to learn the material vs. 3.05/5 for paper homework (HW), p < 0.001 and effect size d = 1.11σ. On relevant exam questions that semester, students scored significantly (p = 0.014) higher with an effect size of d = 0.64σ when using Circuit Tutor compared to paper HW in one class section, with no significant difference in the other section. 

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