Reviewerly/exHarmony huggingface dataset:
Hybrid Encryption Scheme for Hospital Financial Data Based on Noekeon Algorithm

The previous encryption methods of hospital financial data have the problem of overburden. Therefore, a research study on hybrid encryption of hospital financial data based on Noekeon algorithm is proposed. From the basic principles of the Noekeon algorithm and the application and implementation of the Noekeon algorithm, a hybrid encryption scheme for hospital financial data based on the Noekeon algorithm is designed. In order to improve the security of the encryption system, the RSA algorithm is used to encrypt the encrypted content twice. The hybrid algorithm realizes the hybrid encryption of the hospital's financial data. Finally, a hybrid encryption system for hospital financial data based on Noekeon algorithm is designed. Experimental results show that this method has a higher success rate and better comprehensive performance. It not only improves the encryption efficiency of hospital financial data but also enhances the security of hospital financial data, which has greater application value.
Comparing the impact of informal physics program on undergraduate versus graduate student facilitators

Recent studies have begun to explore the connection between physics identity development and career skill development with the facilitation of informal physics programs by college and university students.Informal physics programs, or outreach programs, provide pathways for less structured, voluntary engagement with the field, beyond the formal spaces of a classroom or laboratory, where students can explore their passions within the field.This work narrows the focus of a prior study to explore the differences in experiences between graduate and undergraduate students who facilitated at least one of
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS ANDROID PADA MATA PELAJARAN IPA POKOK BAHASAN PERKEMBANGBIAKAN HEWAN UNTUK SISWA KELAS VI SD

The aim of this research was to obtain a valid and practical android-based interactive learning media for science subjects on animal reproduction for sixth grade elementary school students. This research was a development research which was carried out by using the ADDIE (Analyze, Design, Development, Implementation, Evaluation) model in five steps, namely: (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. The implementation step was not carried out in this research. The subject of this research are expert of subject content and instructional media, teacher, as well as a sixth grade elementary school student. The data collection method used was questionnaire method by providing assessment sheet to 2 lecturers as expert of subject content and instructional media and 3 teachers as the user (practitioner), and 10 students in a small-group trial which aims to obtain data on the level of validity and practicality of the media. The instrument used to measure the level of validity and practicality of android-based interactive learning media was rating scale of media assessment sheet by the material experts, media experts, and students. The obtained data then analyzed by using a mean formula to determine the average score validity and practicality of learning media as a result of development. The results of data analysis on android-based interactive learning media obtained an average validation score by expert of subject content was 4.8 from a maximum score of 5 with "Very Good" category, then expert of instructional media obtained an average score of 4.8 from a maximum score of 5 with "Very Good" category. Furthermore, the score on the small-group trial obtained an average of 4.5 from a maximum score of 5 which is in the "Very Good" category. Thus the validation results from material experts, media experts, and small-scale trials state that the android-based learning media development product is valid and practical. The results of this study provide insight to the Education study program that the use of technology that follows the times will have a positive impact in the field of education.
Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data.
Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. This option is useful in cases of uncertainty, where the learner does not feel confident enough on the entire label set. Adopting a decision-theoretic perspective, we propose a formal framework of MLC with partial abstention, which builds on two main building blocks: First, the extension of underlying MLC loss functions so as to accommodate abstention in a proper way, and second the problem of optimal prediction, that is, finding the Bayes-optimal prediction minimizing this generalized loss in expectation. It is well known that different (generalized) loss functions may have different risk-minimizing predictions, and finding the Bayes predictor typically comes down to solving a computationally complexity optimization problem. In the most general case, given a prediction of the (conditional) joint distribution of possible labelings, the minimizer of the expected loss needs to be found over a number of candidates which is exponential in the number of class labels. We elaborate on properties of risk minimizers for several commonly used (generalized) MLC loss functions, show them to have a specific structure, and leverage this structure to devise efficient methods for computing Bayes predictors. Experimentally, we show MLC with partial abstention to be effective in the sense of reducing loss when being allowed to abstain.
INDONESIAN STUDENTS' EXPERIENCES IN STUDYING FOREIGN LANGUAGE USING TECHNOLOGY

The existence of technology in education has impact tremendously in learning. This greatly facilitates the experience of students in order not to be monotonous and boring. The usefulness of the media learning app is very accepted by the public because it allows users, especially for Students. Advantages in the use of media applications can add new knowledge to a lesson. To facilitate the learning experience of the student, to learning to speak German can use the app Quizizz. Features. Quizizz is very easy for students to understand and explore a learning material. This research was conducted by interviewing 10 students of education German language ever use the app Quizizz with the aim to see the opinion of the experience of students who learn using the application. Based on the results of this research, many of the students of the educational program of the German language, said that the experience of learning with the application quizizz is very easy to understand the material of the German language and is also very effective in explore and evaluate his understanding.
Detection of Semantic Changes in Russian Nouns with Distributional Models and Grammatical Features

The paper presents the models detecting the degree of semantic change in Russian nouns developed by the team aryzhova within the RuShiftEval competition of the Dialogue 2021 conference.We base our algorithms mostly on unsupervised distributional models and additionally test a model that uses vectors representing morphological preferences of the words in question.The best results are obtained by the model built on the ELMo architecture with a small window, while the quality of performance of the "grammatical" model is comparable to that of the models based on much more sophisticated algorithms.
Lifted Inference with Tree Axioms

We consider the problem of weighted first-order model counting (WFOMC): given a first-order sentence ϕ and domain size n ∈ ℕ, determine the weighted sum of models of ϕ over the domain {1, ..., n}. Past work has shown that any sentence using at most two logical variables admits an algorithm for WFOMC that runs in time polynomial in the given domain size (Van den Broeck 2011; Van den Broeck, Meert, and Darwiche 2014). In this paper, we extend this result to any two-variable sentence ϕ with the addition of a tree axiom, stating that some distinguished binary relation in ϕ forms a tree in the graph-theoretic sense.
On the Maximal Number of Complete Extensions in Abstract Argumentation Frameworks

Abstract argumentation frameworks are by now a major research area in knowledge representation and reasoning. Various aspects of AFs have been extensively studied over the last 25 years. Contributing to understanding the expressive power of AFs, researchers found lower and upper bounds for the maximal number of extensions, that is, acceptable points of view, in AFs. One of the classical and most important concepts in AFs are so-called complete extensions. Surprisingly, the exact bound for the maximal number of complete extensions in an AF has not yet been formally established, although there is a reasonable conjecture tracing back at least to 2015. Recently the notion of modularization was introduced and it was shown that this concept plays a key role for the understanding of relations between semantics as well as intrinsic properties. In this paper, we will use this property to give a formal proof of the conjecture regarding complete semantics.
THE CAPACITY OF PRAGMATICS IN ESL TEACHING OF ENGLISH STUDENTS IN BULELENG REGENCY

This study is focused on the capacities of pragmatics in ESL teaching and limited to three essential discussions; first, the investigation of teaching pragmatics relevancy stated on pragmatics elements, second, the presentation of the effective ways in teaching pragmatics by applying SURE steps (Brock and Nagaska, 2005), and the elaboration of pragmatics effects towards ESL learners. It is necessary to conduct a research of pragmatics capacities in ESL teaching. Due to the problems discussed, the theories suggested in this research include pragmatics definition, pragmatic competence, pragmatic elements and SURE method. Then, the method applied in this research is descriptive qualitative in nature and the data are from the 3rd semester of STKIP Agama Hindu Singaraja students. The data analysis is performed to investigate the relevancy of pragmatics in ESL teaching, the pragmatics teaching methods, and the effect of pragmatics towards ESL learners. This study finds pragmatics is a relevant point to be taught in the language classroom due to language is an arbitrary. Through SURE methods, students are expected to comprehend the language used in contexts. Pragmatics affects the ESL learners to be sensitive about the surroundings, other cultural interactions, also how they must behave towards circumstances as ESL learners.&#x0D; &#x0D; Abstrak&#x0D; Penelitian ini difokuskan pada kapasitas pragmatik dalam pengajaran ESL dan dibatasi pada tiga diskusi esensial; pertama, investigasi relevansi pengajaran pragmatik yang tertuang pada unsur-unsur pragmatik, kedua, penyajian cara efektif dalam pembelajaran pragmatik dengan menerapkan metode SURE (Brock dan Nagaska, 2005), dan penjabaran efek pragmatik terhadap peserta didik ESL. Untuk itu perlu dilakukan penelitian tentang kapasitas pragmatik dalam pengajaran ESL. Berdasarkan permasalahan yang dibahas, maka teori yang disarankan dalam penelitian ini meliputi pengertian pragmatik, kompetensi pragmatis, unsur pragmatis dan metode SURE. Metode yang digunakan dalam penelitian ini adalah metode deskriptif kualitatif dengan data dari mahasiswa-mahasiswa STKIP Agama Hindu Singaraja semester 3. Analisis data dilakukan untuk mengetahui relevansi pragmatik dalam pembelajaran ESL, metode pembelajaran pragmatik, dan pengaruh pragmatik terhadap peserta didik ESL. Studi ini menemukan bahwa pragmatik adalah poin yang relevan untuk diajarkan di kelas bahasa karena bahasa bersifat sewenang-wenang. Melalui metode SURE, mahasiswa diharapkan memahami bahasa yang digunakan dalam konteks. Pragmatik mempengaruhi mahasiswa ESL untuk peka terhadap lingkungan sekitar, interaksi budaya lainnya, juga bagaimana mereka harus berperilaku terhadap keadaan sebagai peserta didik ESL.
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression

On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods.Nevertheless, their huge model size and low inference speed have hindered the deployment on resource-limited devices in practice.In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.Specifically, to enhance the model capability and transferability, we leverage the idea of metalearning and set up domain-relational graphs to capture the relational information across different domains.And to dynamically select the most representative prototypes for each domain, we propose a hierarchical compareaggregate mechanism to capture hierarchical relationships.Extensive experiments on public multi-domain datasets demonstrate the superior performance of our HRKD method as well as its strong few-shot learning ability.For reproducibility, we release the code at https: //github.com/cheneydon/hrkd.
Alternative Computer Assisted Communicative Task-based Language Testing: New Communicational and Interactive Online Skills

Computer-assisted language learning knowledge tests should no longer be designed on traditional skills to measure individual competence through traditional skills such as reading, comprehension and writing, but instead, it should diagnose interactive and communication skills in foreign lan-guages. In recent years in online education, it has been necessary to review the concept of interactive competence in digital environments in a comple-mentary way to its traditional use. It is important to promote a new typolo-gy of alternative tasks and items in tests where examinees can prove a real interactive performance in communication and interaction through the digi-tal scenario. This should be done through tools that facilitate oral negotia-tion, the management and understanding of the information extracted from online repositories, the search for suitable online digital material, and the use of new modes of audio-visual communication. Although some of these tasks have been used in a complementary way in the design of language tests previously: it is true that they have not been applied in a coherent way to be used as an assessment tool. A first approach was made by Miguel Ál-varez, García Laborda &amp; Magal-Royo (2021) in the development of oral ne-gotiation skills through the use of interactive tools. The current online as-sessment models analyzed by García Laborda &amp; Álvarez Fernández (2021) indicate the need to seek new ways of assessing foreign languages through the design of tests that fit in the current digital and interactive world.
Measures related to social and human factors that influence productivity in software development teams

Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team's performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts.
Existential Abstraction on Argumentation Frameworks via Clustering

Argumentation in Artificial Intelligence (AI) builds on formal approaches to reasoning argumentatively. Common to many such approaches is to use argumentation frameworks (AFs) as reasoning engines, with AFs being composed of arguments and attacks between arguments, which are instantiated from knowledge bases in a principle-based manner. While representing what can be argued for in an AF provides a conceptually clean way, this process can face challenges arising from generating a large number of arguments, which can act as a barrier to explainability. Inspired by successful approaches to model checking where the state explosion is mitigated by applying existential abstraction, we study an adaption of existential abstraction in form of clustering arguments in an AF to address an associated "argument explosion". In this paper, we provide a foundational investigation of this form of existential abstraction by defining semantics of the resulting clustered AFs, which balance two inherent aspects of existential abstractions: abstracting from concrete AFs and not permitting too much spuriousness (i.e., conclusions that hold on the abstraction but not on the original AF). Moreover, we show properties of clustered AFs, including complexity results, discuss use of clusterings for explaining results of reasoning tasks, and employ the recently introduced methodology of abstraction in answer set programming (ASP) for obtaining and reasoning over clustered AFs.
Toward Social Situation Awareness in Support Agents

Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approaches for developing support agents that are social situation aware. We identify key requirements for a support agent to be social situation aware and propose steps to realize those requirements. These steps are presented through a conceptual architecture centered on two key ideas: (1) conceptualizing social situation awareness as an instantiation of `general' situation awareness, and (2) using situation taxonomies for such instantiation. This enables support agents to represent a user's social situation, comprehend its meaning, and assess its impact on the user's behavior. We discuss empirical results supporting the effectiveness of the proposed approach and illustrate how the architecture can be used in support agents through two use cases.
A Prototype of a Decision Support System for Equine Cardiovascular Diseases Diagnosis and Management

Proper diagnosis and management of equine cardiac diseases require a broad experience and a specialization in the field, but acquisition of specific knowledge is difficult, due, among other reasons, to the limited literature in this field. Therefore, we have designed, developed, and implemented (on a computer algebra system) a Decision Support System (DSS) for equine cardiovascular diseases diagnosis and management based on clinical practise. At this step it is appropriate for equine science teaching, but this work paves the way for a clinical decision support system that facilitated equine clinicians the management of horses with cardiac diseases, allowing improving health care in this species. The latter would require extensive testing prior to its use. The novelty of this work relies on the organization of the equine cardiology workflow in mathematical logic form, that allowed designing, develop and implement a DSS in this new field. An innovation of this work is the part of the DSS devoted to data completion (motivated by the possible lack of specialization of the users—the veterinarians).
Node copying: A random graph model for effective graph sampling

There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various existing techniques either rely on restrictive assumptions, fail to preserve topological properties within the samples or are prohibitively expensive for larger graphs. In this work, we introduce the node copying model for constructing a distribution over graphs. Sampling of a random graph is carried out by replacing each node’s neighbors by those of a randomly sampled similar node. The sampled graphs preserve key characteristics of the graph structure without explicitly targeting them. Additionally, sampling from this model is extremely simple and scales linearly with the nodes. We show the usefulness of the copying model in three tasks. First, in node classification, a Bayesian formulation based on node copying achieves higher accuracy in sparse data settings. Second, we employ our proposed model to mitigate the effect of adversarial attacks on the graph topology. Last, incorporation of the model in a recommendation system setting improves recall over state-of-the-art methods.
Sentence simplification with ruGPT3

This paper describes our solution for the RuSimpleSentEval shared task on sentence simplification held together with Dialogue 2021 сonference.Our approach was to filter the provided dataset, finetune the pretrained ruGPT3 model on it and select generated simple candidates based on cosine similarity and ROUGEL with a complex sentence as an input.The system achieved SARI 38.49 and took third place in the competition.We have reviewed and analyzed examples of simplified sentences produced by the model.The analysis showed that the sentences produced by the system lose the original meaning of the input sentence in about half of the cases.