Reviewerly/exHarmony huggingface dataset:
Postdigital health practices: new directions in medical humanities

Digitalisation has changed the way we understand and practice health. The recent pandemic has accelerated some of the developments in digital health and brought about modifications in public access to information. Taking this into consideration, this programmatic paper sets the stage for and conceptualises postdigital health practices as a possible field of inquiry within medical humanities. While delineating some central aspects of said practices, I draw attention to their significance in contemporary strategies of knowledge production. Spotlighting online environments as the point of ingress for the analysis of these practices, I propose three possible foci of critical and methodological engagement. By spotlighting the serialisation, multimodality, and transmediality of such environments, I argue, we have a chance to both augment and go beyond the field’s long-standing preoccupation with narrative, attend to various strategies of communicating illness experience, and re-frame them within larger questions of systemic inequalities. On this basis, and taking as examples COVID-19 and Long COVID, I sketch some of the directions that future strands of medical humanities may take and some of the questions we still have to ask for the field to overcome its own biases and blind spots.
Functional Reasoning of System Architecture in the System Modeling Language (SysML) With XML Representation

Abstract Formal and structured modeling of system or software behavior enables automated reasoning early in the design process. This permits checking the correctness of the flow of information, consistency among the interacting components, and dependencies among components, to mention a few potential automated reasoning. Presently, research efforts have emphasized the integration of formal analytical capabilities with systems modeling methods. But, the approaches lack formal representations for function-based modeling and reasoning. Prior research in function-based design has produced formal languages, graphical representations, and reasoning algorithms that support model consistency checks, physics-based verification of model correctness, natural-language and qualitative physics-based grammars that grow and decompose given function models, and formal representation of system states that support causal analysis within the function modeling context. We posit that by integrating these capabilities into the system modeling languages, we could significantly augment the languages’ capability to support formal function-based reasoning, which was not possible so far. In this paper, we address this gap with the creation of domain-specific language for function-based modeling. This is illustrated with a coffeemaker example designed in a systems engineering framework along with XML-based reasoning.
Intelligent Healthcare: Evaluation Potential Implications of Metaverse in Healthcare Based on Mathematical Decision-Making Framework

The Metaverse has the ability to restructure and change the manner in which individuals connect with one another as well as the activities that they carry out on a daily basis. One definition of a Metaverse describes it as "a virtual, digital, and three-dimensional universe formed by the integration of various cutting-edge technologies and virtual places." In this piece, we will explore the potential applications of the Metaverse in the medical field. Our conversations on the notion of the Metaverse and the primary technologies that make it possible are both thought-provoking and in-depth. In this article, we study and analyze the possible uses of the Metaverse in the healthcare industry, including telemedicine and telehealth, medical education and training, medical marketing, healthcare supply chain, healthcare facilities, as well as fitness and wellness. The primary obstacles that prevent the broad implementation of the Metaverse in healthcare are discussed in this paper. Finally, evaluating implications of leveraging Metaverse in healthcare is discussed. Best Worst Method (BWM) is deployed with Triangular Neutrosophic Sets (TriNSs) to estimate criteria’s weights where five criteria have been determined for alternatives of Metaverse. These alternatives are evaluated and ranked through weighted sum model (WSM) with help of TriNSs. The findings of this process demonstrated that Metaverse alternative 1 (MeT1) Health Surveillance is optimal according to global score. Antithesis Metaverse alternative 3 (MeT3) Virtual surgical is least one.
Dynamic Separation Logic

This paper introduces a dynamic logic extension of separation logic. The assertion language of separation logic is extended with modalities for the five types of the basic instructions of separation logic: simple assignment, look-up, mutation, allocation, and de-allocation. The main novelty of the resulting dynamic logic is that it allows to combine different approaches to resolving these modalities. One such approach is based on the standard weakest precondition calculus of separation logic. The other approach introduced in this paper provides a novel alternative formalization in the proposed dynamic logic extension of separation logic. The soundness and completeness of this axiomatization has been formalized in the Coq theorem prover.
Senior High School English Textbooks and Religious Aspects

Indonesia is a diverse country consisting of various cultures and also religions. Currently, there are six official religions recognized by the Indonesian government, which are Islam, Protestant, Catholic, Hindu, Buddhi, and Confucianism. Thus, it is important for English book writers to design an English textbook that not only accommodates the needs of the students but also contains learning content which are representing all those varied religions and cultures in order to represent the background of the students. This study focuses on the portrayal of religious content found in the six Indonesian English textbooks published by the government and private sector. The purpose of the study is to assess, whether the six Indonesian English textbooks fairly distribute religious content through the textbooks’ visual illustrations or not. Visual content analysis was used as the methodology of the study to answer the research question. Based on the findings of the study, it was revealed that all the six Indonesian textbooks did not fairly distribute the religious contents. Some religions were having little to no representation in the textbooks. The religions portrayed throughout the contents were Islam, Protestantism, Hinduism, Buddhism, and Confucianism.
Synchronous micromechanically resonant programmable photonic circuits

Abstract Programmable photonic integrated circuits (PICs) are emerging as powerful tools for control of light, with applications in quantum information processing, optical range finding, and artificial intelligence. Low-power implementations of these PICs involve micromechanical structures driven capacitively or piezoelectrically but are often limited in modulation bandwidth by mechanical resonances and high operating voltages. Here we introduce a synchronous, micromechanically resonant design architecture for programmable PICs and a proof-of-principle 1×8 photonic switch using piezoelectric optical phase shifters. Our design purposefully exploits high-frequency mechanical resonances and optically broadband components for larger modulation responses on the order of the mechanical quality factor Q m while maintaining fast switching speeds. We experimentally show switching cycles of all 8 channels spaced by approximately 11 ns and operating at 4.6 dB average modulation enhancement. Future advances in micromechanical devices with high Q m , which can exceed 10000, should enable an improved series of low-voltage and high-speed programmable PICs.
Approximating Online Human Evaluation of Social Chatbots with Prompting

With conversational models becoming increasingly available to the general public, developing scalable and robust evaluation metrics is crucial to minimize potential social and psychological risks for the users. Existing evaluation metrics aim to automate offline user evaluation and approximate human judgment of pre-curated dialogs. However, they are limited in their ability to capture subjective perceptions of users who actually interact with the chatbots and might not generalize to real-world settings. To address this limitation, we propose an approach to approximate online human evaluation, leveraging large language models (LLMs) from the GPT-family. We introduce a new Dialog system Evaluation framework based on Prompting (DEP), which enables a fully automatic evaluation pipeline that replicates live user studies and achieves an impressive correlation with human judgment (up to Pearson r=0.95 on a system level). The DEP approach involves collecting synthetic chat logs of evaluated bots with an LLM in the other-play setting, where the LLM is carefully conditioned to follow a specific scenario. We further explore different prompting approaches to produce evaluation scores with the same LLM. The best-performing prompts, which contain few-shot demonstrations and instructions, show outstanding performance on the tested dataset and demonstrate the ability to generalize to other dialog corpora.
Molecular Symmetry in VQE: A Dual Approach for Trapped-Ion Simulations of Benzene

Understanding complex chemical systems—such as biomolecules, catalysts, and novel materials—is a central goal of quantum simulations. Near-term strategies hinge on the use of variational quantum eigensolver (VQE) algorithms combined with a suitable ansatz. However, straightforward application of many chemically-inspired ansatze yields prohibitively deep circuits. In this work, we employ several circuit optimization methods tailored for trapped-ion quantum devices to enhance the feasibility of intricate chemical simulations. The techniques aim to lessen the depth of the unitary coupled cluster with singles and doubles (uCCSD) ansatz's circuit compilation, a considerable challenge on current noisy quantum devices. Furthermore, we use symmetry-inspired classical post-selection methods to further refine the outcomes and minimize errors in energy measurements, without adding quantum overhead. Our strategies encompass optimal mapping from orbital to qubit, term reordering to minimize entangling gates, and the exploitation of molecular spin and point group symmetry to eliminate redundant parameters. The inclusion of error mitigation via post-selection based on known molecular symmetries improves the results to near milli-Hartree accuracy. These methods, when applied to a benzene molecule simulation, enabled the construction of an 8-qubit circuit with 69 two-qubit entangling operations, pushing the limits for variational quantum eigensolver (VQE) circuits executed on quantum hardware to date. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-000R22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan(https://energy.gov/doe-public-access-plan).
Using online constructor as a means of increasing motivation to study literature

The aim of this paper - presents the experience in creating a layout of an electronic literature textbook using an online constructor. The “Arctic Robinsonade” plot served as the material for the textbook. The methodological apparatus of the research involves the synthesis of such approaches as hermeneutics and comparative studies. The present investigation is of teaching theory and techniques and digital technologies. The questionnaire surveying method was used to collect the empirical material. Mathematical methods of processing, analysis and interpretation of the pedagogical experiment results were applied in the course of the work. The methods of analyzing differences between independent samples, ranking method, method of testing significance of zero and alternative hypotheses were used for statistic information processing in MS Excel. The results: the capabilities of online constructor Tilda to work on the design of multimedia study aid are determined and described; the model of the chapter in multimedia textbook in native literature is developed; the sections of multimedia study aid are described, the examples of the tasks are given, the efficiency of applying the service of creating websites (online constructor) is established. The developed multimedia product is useful for school teachers and students.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format

Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2023.
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell. Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning. 2023.
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

Nearest Neighbor Machine Translation (kNN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we comprehensively analyze kNN-MT through theoretical and empirical studies. Initially, we provide new insights into the working mechanism of kNN-MT as an efficient technique to implicitly execute gradient descent on the output projection layer of NMT, indicating that it is a specific case of model fine-tuning. Subsequently, we conduct multi-domain experiments and word-level analysis to examine the differences in performance between kNN-MT and entire-model fine-tuning. Our findings suggest that: (i) Incorporating kNN-MT with adapters yields comparable translation performance to fine-tuning on in-domain test sets, while achieving better performance on out-of-domain test sets; (ii) Fine-tuning significantly outperforms kNN-MT on the recall of in-domain low-frequency words, but this gap could be bridged by optimizing the context representations with additional adapter layers.
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles

We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs). The CLASS framework empowers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem-solving strategies, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS assists ITS in facilitating natural language interactions, fostering engaging student-tutor conversations. The CLASS framework also provides valuable insights into ITS’s internal decision-making process which allows seamless integration of user feedback, thus enabling continuous refinement and improvement. We also present a proof-of-concept ITS, referred to as SPOCK, which is trained using the CLASS framework with a focus on introductory college level biology content. A carefully constructed protocol was developed for SPOCK’s preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK’s capability to break down questions into manageable subproblems and provide encouraging responses to students.
Improving Summarization with Human Edits

Recent work has shown the promise of learning with human feedback paradigms to produce human-determined high-quality text. Existing works use human feedback to train large language models (LLMs) in general domain abstractive summarization and have obtained summary quality exceeding traditional likelihood training. In this paper, we focus on a less explored form of human feedback – Human Edits. We propose Sequence Alignment (un)Likelihood Training (SALT), a novel technique to use both the human-edited and model-generated data together in the training loop. In addition, we demonstrate simulating Human Edits with ground truth summaries coming from existing training data – Imitation edits, along with the model-generated summaries obtained after the training, to reduce the need for expensive human-edit data. In our experiments, we extend human feedback exploration from general domain summarization to medical domain summarization. Our results demonstrate the effectiveness of SALT in improving the summary quality with Human and Imitation Edits. Through additional experiments, we show that SALT outperforms the conventional RLHF method (designed for human preferences) – DPO, when applied to human-edit data. We hope the evidence in our paper prompts researchers to explore, collect, and better use different human feedback approaches scalably.
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation

In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model’s weaknesses and foster a tailored learning experience by generating targeted exercises aligned with educational science principles, such as knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math tutor and run two steps iteratively: 1) assessing the student model’s current learning status on a GPT-generated exercise book, and 2) improving the student model by training it with tailored exercise samples generated by GPT-3. Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and PaLM) in accuracy across three distinct benchmarks while employing significantly fewer parameters. Furthermore, we provide a comprehensive analysis of the various components within our methodology to substantiate their efficacy.
clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents

Recent work has proposed a methodology for the systematic evaluation of “Situated Language Understanding Agents” — agents that operate in rich linguistic and non-linguistic contexts — through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs), if suitably set up, can be understood as (simulators of) such agents. A connection suggests itself, which this paper explores: Can LLMs be evaluated meaningfully by exposing them to constrained game-like settings that are built to challenge specific capabilities? As a proof of concept, this paper investigates five interaction settings, showing that current chat-optimised LLMs are, to an extent, capable of following game-play instructions. Both this capability and the quality of the game play, measured by how well the objectives of the different games are met, follows the development cycle, with newer models generally performing better. The metrics even for the comparatively simple example games are far from being saturated, suggesting that the proposed instrument will remain to have diagnostic value.
The right to research as guarantor for sustainability, innovation and justice in EU copyright law

Research is essential for scientific, cultural, and social advancement and will be crucial for economic and societal recovery in a post-pandemic world. Restrictions to access and use of information contained in copyright-protected expression, however, can constitute significant hindrances to conducting research efficiently, especially since modern research methods rely on accessing, storing and processing large amounts of digitised data. Over the last decade, copyright in the European Union (EU) has undergone a process of constitutionalisation, which has seen the growing importance of fundamental rights arguments in policy- and law-making, as well as in the jurisprudence of the Court of Justice of the European Union. However, research - as an activity that is indispensable to achieving the aims and objectives of the Union to enable technological, scientific, and cultural progress and work towards a sustainable future - has insufficiently featured in this constitutional discourse. The digital environment and its tremendous potential to enable new forms of research have accentuated the urgency of addressing the issue from a constitutional perspective under the heading of ‘digital constitutionalism’. It is therefore argued that a ‘right to research’ derived from international and European human and fundamental rights law can play an important role in the future to remove copyright barriers to research activities and to inform reforms towards more sustainable and research-friendly copyright laws in the EU. Although a ‘right to research’ is not expressly included in any of the relevant human rights and fundamental rights instruments, it is present implicitly: indeed, the seeds of a right to research are already contained in a variety of fundamental rights at European and international level and in the aims and objectives of the Union’s constitutional order. Based on the relevant fundamental rights, this chapter tries to identify the substance of the right to research, arguing that there is a constitutional imperative to create a paradigmatic shift in EU copyright law towards a copyright system that can help to achieve the programmatic goals of the Union, such as sustainable development, innovation and justice, that form the core of a regulated market economy. In order to help in positioning research as a core priority of the EU, this chapter further proposes the introduction of a specific right to research in the Charter of Fundamental Rights of the EU as a precondition for protection of the moral and material interests of creators, thus mirroring the international human rights justifications of copyright protection.
PROFESSIONAL TRAINING OF FUTURE TEACHERS

Professional pedagogical training of future vocational teachers refers to the process of preparing individuals to become effective and competent vocational teachers. This type of training includes the acquisition of both theoretical knowledge and practical skills necessary for teaching in a vocational education and training setting. The article on this topic covers a range of issues, including pedagogical technologies, development of professional competence, and policies and practices that support the professional development of teachers. The article presents the points of view of authors from all over the world, including Russian authors, who provide a unique insight into teacher training in the Russian context. In general, the article emphasizes the importance of quality professional training for teachers to ensure effective professional education and training.