Reviewerly/exHarmony huggingface dataset:
Digital Technologies for Gun Control

In the paper, we analyze (1) Russian legislation on weapon control, (2) developing e-government, and (3) public services in Russia. Based on this analysis, the paper proposes a scenario for the provision of the public service entitled Monitoring the circulation of civil, service, and premium weapons and ammunition in electronic form and the technical condition of military hand-held small arms and service weapons, temporarily used by citizens and organizations, as well as compliance by citizens and organizations with the legislation of the Russian Federation in the field of arms trafficking, which, according to Russian law, is an obligatory annual administrative procedure for citizens who own civilian weapons. Verification of the conditions for storing weapons and ammunition owned by citizens (conditions for ensuring the safety of weapons and ammunition) is currently carried out in the traditional form, requiring the direct presence of an official of the government body responsible for controlling civilian weapons in a citizen’s home. This administrative procedure is quite complicated, as it requires significant investment from the government, and inconvenient for citizens, as it is associated with the restriction of human rights to the inviolability of the home. The paper substantiates this conclusion and proposes a model of effective government control over civilian weapons using an e-application for the validation of the owner of the weapon, collection, analysis, and systematization of geodata and information about the safety of weapons.
Creating and Modifying Existing Surveys to Fit Your CS Education Research Needs

Have a CS education research project that needs a survey? If you're an education researcher who wants to learn more about survey instrumentation in order to expedite and improve your research, this session is for you! Come learn how to find, remix, and use existing surveys by delving into the process of taking openly shared resources and repurposing them for your projects. In this workshop, we'll cover basics of survey creation and modification, as well as touch upon some additional useful tips like piloting surveys, how to avoid bots attacking your survey and protecting your participants' privacy. We'll also look at how to choose a survey that fits your needs. You will be actively engaged in hands-on activities. Key takeaways from this workshop will include clear steps you can take to choose an instrument that addresses your research questions and study design, the role that validity and reliability play in instrument design, how to modify an instrument (if needed) to answer your research question, and how to add your instruments to the corpus of instruments at the K-12 CS Education Research Resource Center. Laptop is needed for this workshop.
Evaluating Solo vs Pair Programming in an Online Setting for Introductory Programming Students

Many studies have shown the efficacy of pair programming for students learning to program. However, most of these studies have taken place in an in-person environment, where the driver and navigator are physically sharing a keyboard and screen and can communicate verbally and non-verbally. With the increase in online learning, especially during the COVID-19 pandemic. It is important to know whether these results generalize to an online environment.
Developing a Modular and Interactive Blockchain Learning Tool for Undergraduate Computer Science Programs

With blockchain technology being increasingly adopted by significant segments in industry and government, it is important to expose more students to blockchain concepts in undergraduate curricula. Among the challenges with this task are the inherent complexity of blockchain based designs and the limited resources in both time and personnel in a computer science program. The purpose of this project is to design and implement a modular, publicly available learning tool that will facilitate teaching fundamental computer science concepts using a blockchain environment. We started by successfully embedding blockchain elements in two courses at the freshman and sophomore levels as week-long modules in spring 2022. Our goal is to cover the most important aspects of a blockchain by injecting material in 7-8 existing computer science courses.
Impact of Group Member Prerequisite Grades on Problem Set and Test Grades

In introductory CS theory courses, instructors sometimes give students the option to work in groups when completing assignments so they can discuss problems and gain teamwork skills. It is important, then, to ensure that diverse student populations, including those with different academic abilities, can all benefit from this optional group work. In this work, we investigate how lower- and higher-ability students, as measured by prerequisite grades, perform in self-selected, 1-3 person groups of different abilities. We find that lower-ability students can benefit from being in groups that are heterogeneous with respect to student ability.
Incremental TypeScript

Less than nine years since its initial release, TypeScript has become the world's third most popular programming language. A modern imperative language, TypeScript balances the pedagogical benefits of Python and Java, blending the simplicity and brevity of the former with the greater safety of the latter. Furthermore, its type system obviates many of JavaScript's notorious papercuts, making it arguably easier to teach than its dynamically-typed cousin. Yet all presently-available TypeScript curricula miss this opportunity, exclusively targeting programmers already familiar with JavaScript.
Equitable Grading Best Practices

Why do instructors have the grading policies they do (e.g., rigid assignment deadlines, curved grading, exams with no "clobber" where a high performance on a later exam overwrites an earlier poor score, etc.)? Chances are, it's because that was the way (a) they were graded as a student, (b) the department expected, or (c) the previous instructor did it before they took over.
Creating Algorithmically Generated Questions Using a Modern, Open-sourced, Online Platform

PrairieLearn is an open source, extensible online assessment platform built on modern web technologies. In this workshop, we will focus on how PrairieLearn can be used to improve student learning in undergraduate computer science classes. However, the platform is also more than suitable for use as an assessment engine in a variety of courses including the humanities, social, physical, and life sciences. In the first part of the workshop, we will showcase multiple question styles that highlight PrairieLearn's abilities as an online platform, including deploying automatically and manually graded questions at scale in large classes. In the second part of the workshop, we will discuss the anatomy of a PrairieLearn question, create several custom questions, and design assessments in PrairieLearn. In the third part, we will share strategies on adopting PrairieLearn at your institution. In particular, how algorithmically generated questions can be used in support of alternative grading schemes such as Mastery- or Specifications-Grading. Finally, we will share how PrairieLearn can be extended to support other coding languages and paradigms with custom and external autograders. There will be plenty of opportunities for questions throughout the workshop, and we intend to leave plenty of time for additional 1:1 support and training. Attendees will be able to attend the session virtually and are recommended to bring a web-connected computing device. By the end of the session, attendees will know enough to run a whole class on PrairieLearn including designing questions appropriate for homework, labs, and tests.
Survey Says

Despite the need for computing professionals and high salaries, the majority of Computer Science (CS) majors at Colorado School of Mines in a pre-service teaching methods class (CS Teaching Techniques) reported interest in becoming K-12 teachers. Get The Facts Out survey results from a customized myth-busting presentation about K-12 teaching careers confirm that, even among students pursuing a teaching minor, K-12 teaching satisfaction, salaries, and pensions are misunderstood in comparison to industry careers. More students may be attracted to CS teaching careers utilizing Get The Facts Out state-by-state presentation materials.
Teaching Database Security in an Undergraduate Database Administration Course Serving Computer Science, Information Technology and Cybersecurity Students

With increasing cyberattacks in today's data-driven world, the demand for database security professional is increasing. Teaching an advanced topic such as database security at undergraduate level can be challenging. This work reports our experience teaching database security topics in a recently developed database administration course. Different methods to ensure database security such as data encryption, authentication, authorization, and change tracking are covered. Relevant hands-on demonstrations are developed to help students better understand the concepts and gain real-world insights into database security.
Linking Learning Fundamental Reinforcement Learning Concepts with Being Physically Active

In this paper, we define a learning activity for an elementary physical education classroom that simultaneously engages students in physical activity while introducing students to basic principles of reinforcement learning. Reinforcement learning is a sub-domain of machine learning in which an independent agent (in our activity, a student) takes some action or series of actions and receives a reward for the chosen action(s). While reinforcement learning intuitively maps to many activities in our daily lives, our learning activity involves a spy game. Students create sequences of spy moves that generate rewards based on their component moves and the orders in which they are performed. Students then iteratively expand their spy moves in an attempt to receive the maximum reward. The construction of the game will demonstrate that the rewards, while deterministic, do not always follow a greedy pattern, introducing students to basic algorithmic principles. Such an approach that combines physical activity with reinforcement learning connects artificial intelligence education within the broader scope of computing and students' everyday lives.
QC.py

Quantum computing (QC) is a rapidly growing field linking the disciplines of mathematics, physics, and computer science. CS students and software developers are a natural talent pool for the future QC workforce, but existing QC simulation and visualization tools are not designed with their background in mind. We present QC.py, a full-featured, open-source Python simulation and visualization tool for quantum circuits, with a feature set tailored to the background of CS students and software practitioners.
The Urgency of Using Teaching Materials Based on E-Book Problem-Based Learning

The development of technology is running very rapidly in all aspects of education, including in the world of education.Technology and education today are two things that cannot be separated and go hand in hand.This study aims to outline the urgency of using e-book-based problem-based learning teaching materials specifically in civic education.This research uses a quantitative approach with a descriptive method.Data collection was carried out by observation, interviews, questionnaires and documentation.The respondents in this study were students who took civic education courses at Sriwijaya University.The results showed that the use of e-book-based teaching materials is important to be developed in civic education learning.Furthermore, this teaching material should be designed with a problem-based learning-based basis to make it more interesting for students.Problem-based learning will build the critical spirit of students and increase students' understanding of the material provided.
Educative Game-Based Edutainment Model Design in Social Studies Learning

This research is based on the importance of developing the creativity of social studies teachers in creating fun learning while at the same time accommodating the achievement of student competencies.This study seeks to examine and describe edutainment-based social studies learning innovations at the junior high school level in Bandung City, West Java, Indonesia.The method used in this research is the Classroom Action Research method by adopting the Kemmis and McTaggart model.The design of this model has several stages, namely planning, implementation, observation, and reflection, as improvements for the next cycle.The study results indicate that the application of edutainment can be developed through the game method.Games in learning will provide opportunities for students to manipulate, repeat, discover for themselves, explore, practice, and get countless concepts and understandings.This is where students can make decisions, choose, determine, express opinions, solve problems completely, cooperate with friends, and experience various feelings.Games that can be developed in social studies learning with an edutainment approach, including games snake ladders, puzzles, and bingo.Several research cycles have been developed to produce a -based edutainment model design educational game following the characteristics of social studies learning without forgetting the development of students' thinking skills.
The Development of the Hybrid Learning Method with the Open Broadcaster Software (OBS) Application

Along with the progress of the 21st century which is marked by the era of the Industrial Revolution 4.0 as a century of openness and globalization, there have been many fundamental changes.In the world of education, technology is crucial in facilitating the learning process to keep up with the advancements of the fourth Industrial Revolution.This study aims to promote the understanding and need for hybrid learning using the Open Broadcaster Studio (OBS) recorder and streaming application.This research was conducted with a research and development (R&D) approach.Preliminary studies conducted on 19 respondents consisting of 12 primary teachers at PKBM Homeschooling RSI Indonesia and seven teachers at SMPIT Harapan Mulia Palembang.The results of the study show that the need for hybrid learning is necessary in overcoming problems in learning during a pandemic with limited face-to-face learning.Teachers need methods that can support hybrid learning and can present solutions if students experience obstacles during learning.The results of this research also show that recording and streaming media can help provide solutions to the aforementioned problems.So, it is concluded that it is necessary to develop a learning approach that can complement educators in the optimal application of hybrid learning practices.This research is expected to provide an overview and instruction for educators to continue to develop hybrid learning optimally and to additionally become a resource for further hybrid learning development research.
Representable and Diagonally Representable Weakening Relation Algebras

A binary relation defined on a poset is a weakening relation if the partial order acts as a both-sided compositional identity. This is motivated by the weakening rule in sequent calculi and closely related to models of relevance logic. For a fixed poset the collection of weakening relations is a subreduct of the full relation algebra on the underlying set of the poset. We present a two-player game for the class of representable weakening relation algebras akin to that for the class of representable relation algebras. This enables us to define classes of abstract weakening relation algebras that approximate the quasivariety of representable weakening relation algebras. We give explicit finite axiomatisations for some of these classes. We define the class of diagonally representable weakening relation algebras and prove that it is a discriminator variety. We also provide explicit representations for several small weakening relation algebras.
Towards a Theory of Conversion Relations for Prefixed Units of Measure

Units of measure with prefixes and conversion rules are given a formal semantic model in terms of categorial group theory. Basic structures and both natural and contingent semantic operations are defined. Conversion rules are represented as a class of ternary relations with both group-like and category-like properties. A hierarchy of subclasses is explored, each with better algebraic behavior than the preceding, culminating in a direct efficient conversion-by-rewriting algorithm.
Optimizing Public Grievance Detection Accuracy Through Hyperparameter Tuning of Random Forest and Hybrid Model

Machine Learning provides an extensive range of supervised learning binary classifiers; Picking up the correct model for your application and dataset is an extremely challenging task as each classifier performs differently for a given dataset. Researchers put their very intense efforts and perform wide range of experiments with various techniques to get the best learner and performer model. In this paper, we are focusing on a binary classification task, to be performed on Indian Railway tweets to identify whether the piece of text is a grievance or not. From our prior work, we found that Random Forest with the Word2Vec word embedding technique perform all around well against 5 other classifiers – Support Vector Machine, Logistic Regression, Decision Tree and K Nearest Neighbor. The core objective of this paper is to study the prediction performance of Random Forest classifier with hyperparameter tuning versus the hybrid model built using the best of all the binary classifiers.
Intrinsic Use of Genetic Optimizer in CNN Towards Efficient Image Classification

The inception of genetic algorithms in the 80’s laid a strong foundation in the theory of optimization. Numerous engineering applications are rewarded with wings of optimal and faster solutions through suitable genetic modelling. So far, a handful of evolutionary algorithms and modelling have been introduced by researchers. This has led to vivid applications in numerous domains. In this paper a customized evolutionary framework is proposed that is blended with deep learning mechanism. The widely used convolutional neural networks (CNN) model has been customized whereby optimally informative features are selected through intermittent genetic optimization. The inherent convolution layer outcomes are subjected to the optimizer module that in turn results in optimized set of feature points. The pooling process is abandoned for the purpose; thus, getting rid of uniform feature selection. Now, with this model the feature selection inhibits dynamic process of optimized feature selection. Case study on the usage of the same is shown on classification of facial expression images. Performance of the proposed mechanism is further compared with the simulated outcomes of the generic CNN model. Nevertheless, to say the results show promising rate of efficiency.
Meetei Mayek Natural Scene Character Recognition Using CNN

Recognition of characters present in natural scene images is a nascent and challenging area of research in computer vision and pattern recognition. This paper proposes a convolutional neural network (CNN) based natural scene character recognition system for Meetei Mayek. Meetei Mayek text present in natural scene images have been detected using maximally stable extremal regions (MSER), geometrical properties, strokewidth and distance. The extracted and manually cropped characters have been used to create a small database. The experiments of the proposed CNN have been conducted on the isolated characters of the Meetei Mayek natural scene character database. The proposed system has been compared with different combinations of feature descriptors, extracted using pretrained CNNs - Alexnet, VGG16, VGG19 and Resnet18 employing three classifiers - support vector machine (SVM), multilayer perceptron (MLP) and k-nearest neighbour (K-NN). The proposed system has achieved better performance with a classification accuracy of 97.57%.
Completeness and the Finite Model Property for Kleene Algebra, Reconsidered

Abstract Kleene Algebra (KA) is the algebra of regular expressions. Central to the study of KA is Kozen’s (1994) completeness result, which says that any equivalence valid in the language model of KA follows from the axioms of KA. Also of interest is the finite model property (FMP), which says that false equivalences always have a finite counterexample. Palka (2005) showed that, for KA, the FMP is equivalent to completeness. We provide a unified and elementary proof of both properties. In contrast with earlier completeness proofs, this proof does not rely on minimality or bisimilarity techniques for deterministic automata. Instead, our approach avoids deterministic automata altogether, and uses Antimirov’s derivatives and the well-known transition monoid construction. Our results are fully verified in the Coq proof assistant.
A General Method for Representing Sets of Relations by Vectors

We present a general method for computing vector representations $$\mathfrak {r}$$ of sets of relations. This method is used for obtaining $$\mathfrak {r}$$ from an inclusion $$\mathfrak {R}\subseteq \mathfrak {S}$$ , where $$\mathfrak {R}$$ and $$\mathfrak {S}$$ are relation-algebraic expressions over a relation rather than its vector representation. The core of the method is a theorem that shows how $$\mathfrak {r}$$ can be obtained from $$\mathfrak {R}\subseteq \mathfrak {S}$$ in one step. As applications we consider some problems concerning kernels of relations.
RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection

The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation Artificial Intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable Artificial Intelligence driven intrusion detection approach, based on formal logic calculations. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic.
Dependences Between Domain Constructions in Heterogeneous Relation Algebras

We show the following dependences between relational domain constructions in the framework of heterogeneous relation algebras. If all power sets and subsets exist and objects are comparable, then all sums exist. If all sums exist and atoms are rectangular, then all products exist. If all atoms are rectangular, then all subsets exist if and only if all quotients exist. We give models with rectangular atoms which rule out further dependences between these constructions.