Reviewerly/exHarmony huggingface dataset:
APPROACHES TO INCREASING MOTIVATION FOR LEARNING ACTIVITIES OF MEDICAL UNIVERSITY STUDENTS

Introduction. This article is devoted to the study of the influence of the teacher’s activity on the level of motivation of students of higher educational institutions.&#x0D; Purpose: to study the characteristics of the motivation of students studying in the specialty 33.05.01. “Pharmacy”.&#x0D; Materials and methods. The study is based on a preliminary study and analysis of data obtained by domestic scientists and presented in the sources of psychological and pedagogical literature. The implementation of the tasks set was carried out in the course of a sociological survey. The results were processed using the methods of mathematical statistics.&#x0D; Results. A technology has been developed to increase the motivational level of students in the form of an algorithm, introduced into the work of the teaching staff of the Department of Chemistry of the Faculty of Pharmacy in the preparation of future pharmacists.&#x0D; Scope. The algorithm of professional orientation of students developed by the authors can be used when teaching students in a higher educational institution. The applicability of the obtained statistically significant results as a basis for further work on increasing students’ motivation for the implementation of educational activities is substantiated.&#x0D; Conclusion. It was revealed that changes in the deep motives of students occurred when the teaching staff used new methods that make it possible to modernize the standard learning process, taking into account the interests and psychological characteristics of future pharmacists.
Tokenized Carbon Credits

Blockchains are well-suited for tokenizing, trading, and retiring voluntary carbon credits. However, tokenized carbon credits are a heterogeneous body of tokens on the blockchain, which hampers some key goals of the industry. We give a detailed exposition of the current state of tokenized carbon credits and the surrounding blockchain-based ecosystem, with the goal of clarifying current impediments to token interoperability and trading with high liquidity.
A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem

The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully.
Secret extraction attacks against obfuscated IQP circuits

Quantum computing devices can now perform sampling tasks which, according to complexity-theoretic and numerical evidence, are beyond the reach of classical computers. This raises the question of how one can efficiently verify that a quantum computer operating in this regime works as intended. In 2008, Shepherd and Bremner proposed a protocol in which a verifier constructs a unitary from the comparatively easy-to-implement family of so-called IQP circuits, and challenges a prover to execute it on a quantum computer. The challenge problem is designed to contain an obfuscated secret, which can be turned into a statistical test that accepts samples from a correct quantum implementation. It was conjectured that extracting the secret from the challenge problem is NP-hard, so that the ability to pass the test constitutes strong evidence that the prover possesses a quantum device and that it works as claimed. Unfortunately, about a decade later, Kahanamoku-Meyer found an efficient classical secret extraction attack. Bremner, Cheng, and Ji very recently followed up by constructing a wide-ranging generalization of the original protocol. Their IQP Stabilizer Scheme has been explicitly designed to circumvent the known weakness. They also suggested that the original construction can be made secure by adjusting the problem parameters. In this work, we develop a number of secret extraction attacks which are effective against both new approaches in a wide range of problem parameters. The important problem of finding an efficient and reliable verification protocol for sampling-based proofs of quantum supremacy thus remains open.
Divertirsi con la lingua: Il Doge di Aldo Palazzeschi

In 1967 Aldo Palazzeschi (1885-1974), at the venerable age of 82, published this novel (however not the last)in which critics wanted to see a return to the lazzi or experimentalism of the young futurist. The plot is soonstated: "without characters and without direct dialogue, it is the novel of a teeming and charlatan mass" (Tellini)who waits for a few days for the appearance of a Doge, who will never appear. Obviously, therefore, the settingin Venice is teeming with tourists, famous and less famous, accompanied by a flood of suitcases, pretentiousin their ignorance when they talk about Dante (Alighieri!), famous polar explorer. A novel in which "a crowdis crowded or a mass is crowded", in Arbasino's words. The focus of this contribution, however, is on thelinguistic expression of the novel. The reader is immediately struck and dazed by the long sections (of 17 linesor more) and the risky syntactic structure, rhetorically constructed ("Mentre gli intransigenti … Altri invece, diparer contrario sempre"). The criticism was rather severe in evaluating all this, speaking of 'lava flow' (MarcoMarchi), 'lutulent verbal river' (Tellini), 'abnormal syntactic regime' (De Maria). Here we propose, on the onehand, to verify the suggestion of De Maria: "that Palazzeschi intends to parody the 'periodoni', canonical ofour literature, of the History of Italy of Guicciardini", associating examples from other writers of the Italiansixteenth century. On the other hand, it is to rediscover this Palazzeschian passion for syntax in works ofprevious periods of his literary production, where language is fully an opportunity to have fun. As I have triedto demonstrate elsewhere, in fact, the tripartition in watertight bulkheads between an avant-garde period, asubsequent return to order and therefore a resumption of the experimentalist vein, risks leading one astray. Wehave here, returning to what Luciano De Maria said well: "Mass society interpreted, therefore, as a society ofuniversal gossip. The Venetian 'ciacola' as a symbol of a world parlerie perpetuated by newspapers, radio,television". What a topicality! We can only imagine what Palazzeschi would have written in our years of thetriumph of bloggers and mobile phones!
&lt;b&gt;Systematic Review of Semantic Analysis Methods&lt;/b&gt;

NLP (natural language processing) is a very broad subcategory of computer science that focuses on human language processing in computers. Each different NLP processing technique focuses on different parts of linguistics, with semantics being the main focus of this manuscript. This manuscript aims to utilize a systematic review of several published papers ranging from 1998 to the current day to review and summarize critical analysis regarding the three main models that this paper focuses on: Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Neural Network based models. This manuscript compares these models against each other, weighs their advantages and disadvantages, and provides their uses. The selected studies in this review were analyzed and examined to ensure that they meet the quality and standards of the proposed research methodology. The results show that Neural network-based solutions are the most popular Semantic Analysis model in Academia (doubling the number of results of ESA and LSA combined), and they are usually the best in most tasks. However, there are specific scenarios and circumstances in which relying on the older LSA and ESA models could be beneficial.
The perfect participle and the supine in two chronolects of Danish

Modern Standard Danish distinguishes between a perfect participle and a supine. The perfect participle is an adnominal non-finite form of the verb, ascribing properties to a referent (as do the prototypical adjectives) and morpho-syntactically agreeing with this referent. The supine is an indeclinable non-finite form of a verb, ending in -t, used as a component in periphrastic verb forms. Outside the attributive position, the perfect participle is used in complement constructions with the copula verbs være ‘be’ and blive ‘be’/‘become’; the supine is used in perfective constructions and periphrastic passives with the auxiliaries have ‘have’, få ‘get’, være ‘be’ and blive ‘be’/‘become’. In Modern Standard Danish, the perfect participle has restricted use (it is the marked member of the paradigm perfect participle vs. supine); the supine has a wider domain of usage (it is the unmarked member of the paradigm). In the nineteenth century, this was different. Back then, the perfect participle was the unmarked form with a wide usage domain, whereas the supine had a more restricted use. This paper presents a study of these verb forms in two corpora representing different chronolects of Danish, one corpus consisting of texts from the nineteenth century, one of texts from Modern Standard Danish.
Patienters sprogbrug i e-konsultationer med deres praktiserende læge: Hvordan kommunikerer patienter relationel og emotionel information?

In Denmark, the e-mail consultation has been an important part of general practice for more than a decade. This consultation format has several advantages, but general practitioners have been concerned that they cannot, for example, interpret patients’ emotional state through the written medium. In this article, we examine what characterizes those parts of patients’ language use in e-mail consultations that deviate from standard written language. The background is the importance of understanding how digital patients communicate in writing, and what possibilities they have of expressing themselves. Based on 646 e-mail consultations from 38 patients from four Danish general practitioners’ offices, we have analyzed the linguistic devices which deviate from standard written language. Our purpose is to characterize the patients’ use of language, including deviations, alternative sign use, context keywords, and orthographic variation. We argue that patients’ use of these forms makes it possible to communicate relational and emotional information which in a face-to-face meeting would typically be communicated non-verbally. In addition, this language use creates an informal tone with their general practitioner. Our analyses reflect how the e-mail consultation format places new demands on patients, who must be able to express themselves in writing, but who also have to be creative with their keyboard use if they want to express something that is not normally communicated in writing.
Di che cosa parliamo quando siamo infedeli? Alcune puntualizzazioni sulle valenze di rinvio: anafora, catafora ed esofora

In my paper, I draw an overall picture of the "referential valencies" of a text, i.e. of what we could call the text's referential skeleton. Based on Italian evidence, I give examples of the many kinds of anaphoric, cataphoric and exophoric reference, including elliptical null forms, and I pay special attention to a phenomenon that has been very little debated in the literature, viz "unfaithful" (i.e. lexically varied, e.g. hyperonymic) anaphors, trying to answer the question "What do we talk about when we are unfaithful?". Not surprisingly, the use of unfaithful anaphors depends considerably on the text type, such anaphors being e.g. more frequent in argumentative and narrative texts than in technical texts, and on the degree of text elaboration. This section of my paper is empirically based on three typologically different text corpora.
Fundamental Elements of a Blockchain-Based Tax System – When to Use Blockchain for Tax?

This article aims to explore the fundamental elements of a blockchain-based tax system by approaching the research question of when to use blockchain for tax. The authors, after introducing the basics of blockchain technology, address the conceptual theoretical perspective by identifying the preconditions under which blockchain can concretely represent a valuable opportunity for tax. In this respect, the analysis starts from the systematic literature review and also covers the different points of view from which to consider the development of a blockchain-based tax system, including the tax administration’s perspective, the taxpayer’s perspective, and the ecosystem perspective. Furthermore, the article addresses the empirical analysis of the current blockchain pilot projects in the tax domain; in this respect, the objective is to verify whether and how each use case concretely addresses the above preconditions. The authors also discuss some future possibilities for more blockchain-based use cases having regard to revenue sourcing rules under the OECD Pillar One proposal and transfer pricing control. In the conclusions, the authors argue that, to comply with the principle of tax efficiency, blockchain-based use cases for tax should always comply with the preconditions identified under the present study.
ProtoUDA: Prototype-based Unsupervised Adaptation for Cross-Domain Text Recognition

Text recognition reads from real scene text or handwritten text, facilitating many real-world applications such as driverless cars, visual Q&A, and image-based machine translation. Although impressive results have been achieved in single-domain text recognition, it still suffers from great challenges in cross-domain due to the domain gaps among the synthetic text, the real scene text, and the handwritten text. Existing standard unsupervised domain adaptation (UDA) methods struggle to solve the text recognition task since they view a domain or a text image (containing a character sequence) as a whole, ignoring the subunits that make up the sequence. In the paper, we present a Prototyped-based Unsupervised Domain Adaptation method for text recognition (ProtoUDA), where the class prototypes are computed from the source domain, target domain, and the mixed (source-target) domain, respectively. Technically, ProtoUDA initially extracts pseudo-labeled character features under word-level supervised information. Further, based on these character features, we propose two parallel and complementary modules to perform class-level and instance-level alignment, which explicitly transfer the knowledge learned in the source domain to the target domain. Among them, class-level alignment is to close the distance between the similar source prototypes and target prototypes. The instance-level alignment is based on contrastive learning, making the character instances of the mixed domain close to the corresponding class mixed prototype while staying away from other class mixed prototypes. To our knowledge, we are the first to adopt contrastive learning in UDA-based text recognition tasks. Extensive experiments on several benchmark datasets show the superiority of our method over state-of-the-art methods.
Learning Gaps in Project-based Requirements Engineering Education - A Case Study of Student Projects

[Context and motivation] Learning Requirements Engineering is one of the key activities in teaching Software Engineering. Training computer science students to appreciate the importance of requirement engineering in practice is essential but also a difficult task. [Question/problem] In software requirements engineering education courses, project-based practice is commonly adopted as the pedagogical approach. However, feedback shows that projects taken in the class resulted in poorly translated requirements engineering skill sets required in the industry. [Principal ideas/results] This paper is to investigate and illustrate the student learning gaps observed in project-based requirements engineering education. Data containing software requirements were collected from 72 Computer Science student projects. The grounded theory approach had been systematically adopted, and keywords classified by requirements engineering activities were extracted from assessment comments. Based on the analysis of the comment keywords and report details, the issues exposed in student projects have been observed and summarized. Integrating with existing requirements engineering research, we discuss the causes from the teachers’ perspective. The leading causes of students’ underperformance in requirements engineering education learning outcomes are the lack of the industry environment, the lack of domain knowledge, and the lack of problem-solving skills. [Contribution] This paper contributes to the current educational effort in preparing students for the requirements engineering and software engineering community.
PGN: A Perturbation Generation Network Against Deep Reinforcement Learning

Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the agent. Our proposed model can achieve both targeted attacks and untargeted attacks. Considering the specificity of deep reinforcement learning, we propose the action consistency ratio as a measure of stealthiness, and a new measurement index of effectiveness and stealthiness. Experiment results show that our method can ensure the effectiveness and stealthiness of attack compared with other algorithms. Moreover, our methods are considerably faster and thus can achieve rapid and efficient verification of the vulnerability of deep reinforcement learning.
Developing Cuboid Recognition-based AR Application for Preschool Marine Education

Augmented Reality (AR) is widely used in education because of its ability to deepen learning interactions by overlaying virtual objects with the real world. However, there is a lack of empirical studies on the impact of using AR in preschool marine education. To address this issue, this paper developed a cuboid recognition-based AR application and applied it to preschool marine courses, and further investigated its impact on students’ learning achievement and motivation. We adopted a quasi-experimental approach, using a pretest and posttest to measure learning achievement and scales to measure motivation and technology acceptance. A total of 60 students in the middle grades of kindergarten were randomly assigned to an experimental group and a control group. Students in the experimental group learned about the knowledge using the iPad AR application, while students in the control group learned the same material using traditional teaching methods. The results showed that the students in the experimental group scored higher than the students in the control group in terms of learning achievement and motivation (attention, confidence, relevance, and satisfaction). In addition, students in the experimental group had a high level of technology acceptance for teaching with the AR application. Our study provides an empirical basis for the application of AR in preschool marine education. In addition, this paper illustrates how the cuboid recognition-based AR application can be developed for formal educational purposes.
Enabling Go Program Analysis in Rascal

In this paper, we present early work on the foundation of a program analysis framework for the Go programming language. This framework, named Go AiR (Analysis in Rascal), currently provides support for extracting abstract syntax trees from Go source code, working with multiple Go projects and multiple versions of a single Go project (based on Git version history), and code exploration through the use of Rascal features for working with representations of source code. We discuss the structure of the framework, describe the mapping from Go to Rascal and how this was tested, present sample code showing how the framework can be used to explore Go source code, and discuss future extensions to the framework to support program analysis and verification.
Do Code Quality and Style Issues Differ Across (Non-)Machine Learning Notebooks? Yes!

The popularity of computational notebooks is rapidly increasing because of their interactive code-output visualization and on-demand non-sequential code block execution. These notebook features have made notebooks especially popular with machine learning developers and data scientists. However, as prior work shows, notebooks generally contain low quality code. In this paper, we investigate whether the low quality code is inherent to the programming style in notebooks, or whether it is correlated with the use of machine learning techniques. We present a large-scale empirical analysis of 246,599 opensource notebooks to explore how machine learning code quality in Jupyter Notebooks differs from non-machine learning code, thereby focusing on code style issues. We explored code style issues across the Error, Convention, Warning, and Refactoring categories. We found that machine learning notebooks are of lower quality regarding PEP-8 code standards than non-machine learning notebooks, and their code quality distributions significantly differ with a small effect size. We identified several code style issues with large differences in occurrences between machine learning and non-machine learning notebooks. For example, package and import-related issues are more prevalent in machine learning notebooks. Our study shows that code quality and code style issues differ significantly across machine learning and non-machine learning notebooks.
CIGAR: Contrastive Learning for GitHub Action Recommendation

GitHub Actions was introduced in 2019 as an integrated solution for CI/CD to automate software development workflow. Since then, it has gained tremendous popularity among developers. In a GitHub Actions workflow, actions refer to custom applications for performing complex but frequently repeated tasks. Actions can be typically found in GitHub Marketplace or public GitHub repositories. Prior studies have already disclosed that developers often reuse actions to reduce double work and improve productivity. However, it is not trivial for developers, especially novices, to figure out which action to reuse due to the large number of actions available and the limited search functionality GitHub Marketplace provides. To address this issue, we propose CIGAR (ContrastIve learning for GitHub Action Recommendation). Given the textual description of a task developers want to execute, CIGAR will recommend the most relevant actions. CIGAR exploits a pre-trained RoBERTa model to convert sequences of words into high-dimensional vector representations, and is fine tuned through a contrastive learning objective. The performance of CIGAR was evaluated on a novel dataset curated based on prior research, and the results demonstrate that CIGAR can reliably recommend actions needed by developers and significantly outperforms the GitHub Marketplace search engine. Our study indicates the promise of employing contrastive learning for GitHub action recommendation. The promising performance achieved can potentially drive a wider adoption of GitHub Actions and facilitate the automation of software development workflows.