Reviewerly/exHarmony huggingface dataset:
Structural Complexity of Rational Interactive Proofs

In 2012 P. D. Azar and S. Micali introduced a new model of interactive proofs, called “Rational Interactive Proofs”. In this model the prover is neither honest nor malicious, but rational in terms of maximizing his expected reward. In this article we explore the connection of this area with classic complexity results. In the first part of this article we revise the ties between the counting hierarchy and the hierarchy of constant-round rational proofs. We prove that a polynomial-time machine with oracle access to $$\textbf{DRMA}[k]$$ decides exactly languages in $$\textbf{DRMA}[k]$$ , a coincidence unknown for levels of the counting hierarchy. In the second part we study communication complexity of single-round rational proofs. We show that the class defined by logarithmic-communication single-round rational proofs coincides with $$\textbf{PP}$$ . We also show that single-round rational protocols that treat problems in $$\oplus \textbf{P}$$ as black-box samplers of a random variable require at least a linear number of bits of communication.
Immersive Virtual Work Integrated Learning: A Scoping Review

Undergraduate students were not spared by the negative effect of covid 19, some could not access placement, and some were dismissed from doing their work integrated learning (WIL) curriculum. The application of Virtual Reality (VR) in education training, has lately shown a tremendous interest, several authors have investigated the benefits and applications of this technology in various scenarios. However, fewer scoping review studies presently exist on how researchers have applied immersive virtual reality (IVR) for work integrated learning purposes in higher education institutions (HEI). A total of 25 published studies in the period of 2015-2022 were selected for screening and selection to analyze the topic. This scoping review present how IVR supported instructional design methods, development tools and evaluation criteria for virtual work integrated learning (VWIL). The result of the review highlights that IVR has greatly aided in providing solution to the scarcity of placement-based WIL, the negative effects of Covid 19 and has exposed the need to adopt industry 4.0 technology in academia. It furthermore inspires researchers on the path to follow in conducting relevant immersive virtual applications that benefits the undergraduate students, higher institutions, and industry. Future work is to incorporate soft skills and collaborative engagements in ensuring VWIL is effective and efficient in this era.
Research on the Self-improvement Path of Teachers’ Information Literacy from the Perspective of Multimodal Teaching

Multimodal teaching is one of the ways to accelerate the development of information education and intelligent education.In order to meet the requirements of multimodal teaching on teachers' own abilities, teachers need to improve their information literacy from both practical and cognitive levels: first, teachers should pay attention to the self-training of their multi-literacy, including basic ICT practical skills and the critical use of multimodal technology tools.Second, teachers have to solve their own cognitive challenges in information literacy, by raising their understanding level and autonomy consciousness of multimodal meaningmaking supported by information technology on the basis of multi-literacy skills across disciplines.
A Proof-Theoretic Analysis of Weak Crossover

Dependent type semantics (DTS) is a framework for proof-theoretic semantics of natural language based on Martin-Löf type theory. In this paper, I first show that DTS has an empirical problem regarding the quantificational weak crossover. I then suggest that the definition of underspecified terms in DTS shall be replaced by the underspecified types. Finally, I claim that the suggested setting not only successfully solves the problem, but also allows us to naturally define the mechanism of local accommodation in DTS.
Critical Appraisal of Using Digital Human Model, Virtual Human, Human Digital Twin and Digital Twin

This paper addresses the need to define and discuss the relationship between the digital human model, the human digital twin and the digital twin. Nowadays, in scientific literature, the new terms human digital twin or digital twin are mentioned in different fields of applications, omitting the concept digital human model. However, a model is not the same as a twin. Simultaneously, the question of whether a digital human model can be a digital twin and what the relationships are between a digital twin and vice versa arises. Or how do the terms virtual humans, human digital twins, and digital human models interact? Is there a distinction between virtual and digital human models? All these questions are discussed and evaluated in this critical appraisal based on scientific literature research.
An NLP-based framework for extracting the catalysts involved in Hydrogen production from scientific literature

Hydrogen production as a renewable fuel source is an active area for the industry and researchers. NLP has been growing for the last few years as the need for extracting non-structural text data is accelerating with time. Here, in this work, we focused on the peer review articles on “Hydrogen production from alcohol" as a keyword for search in an Elsevier database. The abstracts are retrieved with the help of the “Elsevier API” key. We mainly focus on the article's abstract because it contains crucial points. The collated text data set combines two broad classes, i.e., relevant abstracts and non-relevant abstracts. The relevant abstract class stands for hydrogen production from alcohol-based abstracts. Other left-over abstracts will belong to the non-relevant class. The relevant abstracts are followed by the task of “Question and Answering” (Q&A), where we have selected a question, e.g. (a) What is the catalyst material used? Transfer learning implemented on the emerging model "BERT" will be utilized for these tasks. The finetuned BERT model is renamed H2-BERT. The H2-BERT performs well, with an accuracy of 0.980 for the train set and 0.974 for the test dataset for the classification task. For the task of Q&A, the data is prepared, fed to the “BERT” model and retrained the last layer weights. This downstream task gave outstanding results with 1.000 accuracy for the train set and an accuracy of 0.823 for the test dataset for accurately predicting the answer to their question based on the given context. This model will help readers extract the utilized catalyst name from the given text and make the process less time-consuming.
Feature Selection on Imbalanced Domains: A Stability-Based Analysis

A large body of literature has shown the beneficial impact of feature selection on the efficiency, interpretability, and generalization ability of machine learning models. Most of the existing studies, however, focus on the effectiveness of feature selection algorithms in identifying small subsets of predictive features, often neglecting the stability of the selection process, i.e., its robustness with respect to sample variation, which can be crucial for the actual exploitation of the results. In particular, little research has so far investigated the stability of feature selection methods in class-imbalanced domains, where some classes are underrepresented and any perturbation in the set of training records can strongly affect the final selection outcome. This work aims to investigate this important issue by studying the stability of different selection algorithms across high-dimensional datasets that present different levels of class imbalance. To this end, a methodological pipeline is discussed which allows a joint evaluation of the selection outcome both in terms of stability and final predictive performance. Although not exhaustive, our experiments provide very useful insight into which methods can be more stable on imbalanced data while still ensuring good generalization results.
Presuppositions and Comparison Classes in Japanese hodo-equatives

This paper proposes an analysis of a Japanese equative marker hodo in terms of a delineation theory of gradable adjectives. Hodo-equatives are associated with a norm-related presupposition where the complement of hodo exceeds a degree of standard, but the norm-relatedness is flipped when mo “even” is appended to hodo. We propose that hodo takes a comparison class as one of its arguments and argue that this delineation-based analysis of hodo successfully captures the flipped norm-related implication.
Investigating the Effects of Pre-trained BERT to Improve Sparse Data Recommender Systems

Recommender systems play an important role with many applications in natural language processing such as in e-commerce services. Matrix factorization (MF) is a powerful method in recommender systems, but a main issue is the sparse data problem. In order to overcome the problem, some previous models use neural networks to represent additional information such as product item reviews to enhance MF-based methods, and obtain improvement in recommender systems. However, these models use conventional pre-trained word embeddings, which raise a question whether recent powerful models such as BERT can improve these MF-based methods enhanced by item reviews. In this work, we investigate the effect of utilizing BERT model to improve some previous models, especially focusing on several specific sparse data settings. Experimental results on the MovieLens dataset show that our model has successfully utilized BERT to represent item reviews and outperformed the previous probabilistic MF-based model which does not use item reviews. We also conducted intensive analyses on several settings related to sparse data and obtained some promising findings related to the lengths of review texts, which may open directions to improve this on-going model to solve the problem of sparse data in MF-based recommender systems.
Closed-loop optimization of high-throughput robotic platforms for reproducible bioprocess development

To confidently accelerate bioprocess development using high-throughput robotic platforms, parallel experiments should be reproducible whereas the generated data must be FAIR. To this aim, higher levels of automation for planning, redesigning, executing, and monitoring of related experimental-computational workflows are needed. In this work, an automation hierarchy for closed-loop optimization based on Bayesian update, belief propagation and Thompson sampling is proposed. For online redesign of parallel dynamic experiments, asynchronous rolling out beliefs about beliefs based on simulated data is used for counterfactual assessment of the effect of alternative (re)design decisions on the distributions of model parameters. A computational implementation of the Bayesian automation hierarchy using Apache Airflow is described. Simulation results obtained using an E. coli case study are presented to argue that parallel dynamic experiments made using the proposed Bayesian hierarchy are reproducible whereas experimental-computational workflows and data are FAIR by design.
Learning from Successes and Failures: An Exploration of a Case-Based Reasoning Technique

Usually, existing works on adaptation in case-based reasoning assume that the case base holds only successful cases, i.e., cases having solutions believed to be appropriate for the corresponding problems. However, in practice, the case base could hold failed cases, resulting from an earlier adaptation process but discarded by the revision process. Not considering failed cases would be missing an interesting opportunity to learn more knowledge for improving the adaptation process. This paper proposes a novel approach to the adaptation process in the case-based reasoning paradigm, based on an improved barycentric approach by considering the failed cases. The experiment performed on real data demonstrates the benefit of the method considering the failed cases in the adaptation process compared to the classical ones that ignore them, thus, improving the performance of the case-based reasoning system.
Ultrasonic image segmentation of thyroid nodules-relevant multi-scale feature based h-shape network

面向甲状腺结节超声图像分割的多尺度特征融合 "h" 形网络Dice 相似系数 (Dice similarity coefficients， DSC) 、 豪斯多夫距离 (Hausdorff dis• tance， HD) 、 灵敏度 (sensitivity， SEN) 和特异度 (specificity， SPE) 分别为 0. 872 1、 0. 935 6、 0. 879 7 和 0. 997 3， 在公开 数据集 DDTI (digital database thyroid image) 上， DSC 和 SPE 分别为 0. 758 0 和 0. 977 3， 在数据集 TN3K (thyroid nodule 3 thousand) 上的重要指标 DSC 和 HD 分别为 0. 781 5 和 4. 472 6， 皆优于其他模型。结论 该网络模型以较低的参数 量提升了甲状腺超声图像结节的分割效果， 增强了泛化性能。
Time rescaling of nonadiabatic transitions

Applying time-dependent driving is a basic way of quantum control. Driven systems show various dynamics as its time scale is changed due to the different amount of nonadiabatic transitions. The fast-forward scaling theory enables us to observe slow (or fast) time-scale dynamics during moderate time by applying additional driving. Here we discuss its application to nonadiabatic transitions. We derive mathematical expression of additional driving and also find a formula for calculating it. Moreover, we point out relation between the fast-forward scaling theory for nonadiabatic transitions and shortcuts to adiabaticity by counterdiabatic driving.
Probing the quantitative–qualitative divide in probabilistic reasoning

This paper explores the space of (propositional) probabilistic logical languages, ranging from a purely ‘qualitative’ comparative language to a highly ‘quantitative’ language involving arbitrary polynomials over probability terms. While talk of qualitative vs. quantitative may be suggestive, we identify a robust and meaningful boundary in the space by distinguishing systems that encode (at most) additive reasoning from those that encode additive and multiplicative reasoning. The latter includes not only languages with explicit multiplication but also languages expressing notions of dependence and conditionality. We show that the distinction tracks a divide in computational complexity: additive systems remain complete for NP, while multiplicative systems are robustly complete for ∃R. We also address axiomatic questions, offering several new completeness results as well as a proof of non-finite-axiomatizability for comparative probability. Repercussions of our results for conceptual and empirical questions are addressed, and open problems are discussed.
VPN: Variation on Prompt Tuning for Named-Entity Recognition

Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity recognition (NER), due to the sophisticated design of entity-related templates. Note that the nature of prompt tuning makes full use of the parameters of the mask language model (MLM) head, while previous methods solely utilized the last hidden layer of language models (LMs) and the power of the MLM head is overlooked. In this work, we discovered the characteristics of semantic feature changes in samples after being processed using MLMs. Based on this characteristic, we designed a prompt-tuning variant for NER tasks. We let the pre-trained model predict the label words derived from the training dataset at each position and fed the generated logits (non-normalized probability) to the CRF layer. We evaluated our method on three popular datasets, and the experiments showed that our proposed method outperforms the state-of-the-art model in all three Chinese datasets.
A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror

As the demand for 3D information continues to grow in various fields, technologies are rapidly being used to acquire such information. Laser-based estimation and multi-view images are popular methods for sensing 3D information, while deep learning techniques are also being developed. However, the former method requires precise sensing equipment or large observation systems, while the latter relies on substantial prior information in the form of extensive learning datasets. Given these limitations, our research aims to develop a method that is independent of learning and makes it possible to capture a wide range of 3D information using a compact device. This paper introduces a novel approach for estimating the 3D information of an observed scene utilizing a monocular image based on a catadioptric imaging system employing an omnidirectional camera and a spherical mirror. By employing a curved mirror, it is possible to capture a large area in a single observation. At the same time, using an omnidirectional camera enables the creation of a simplified imaging system. The proposed method focuses on a spherical or spherical cap-shaped mirror in the scene. It estimates the mirror’s position from the captured images, allowing for the estimation of the scene with great flexibility. Simulation evaluations are conducted to validate the characteristics and effectiveness of our proposed method.
Complying with the New NIH Data Management and Sharing Policy

Abstract The National Institutes of Health (NIH) implemented a new Data Management and Sharing Policy on January 25, 2023. This policy replaces the 2003 document and affirms the NIH’s support for open science and data sharing. Almost all proposals for research that will produce scientific data are now required to include a Data Management and Sharing Plan, which lays out how the research team will collect and preserve data to ensure long-term discoverability and access. Researchers must approach their projects with data sharing in mind, to the extent that is ethically, legally, and technically possible.
Unsupervised Feature Extraction for Various Computer-Aided Diagnosis Using Multiple Convolutional Autoencoders and 2.5-Dimensional Local Image Analysis

There are growing expectations for AI computer-aided diagnosis: computer-aided diagnosis (CAD) systems can be used to improve the accuracy of diagnostic imaging. However, it is not easy to collect large amounts of disease medical image data with lesion area annotations for the supervised learning of CAD systems. This study proposes an unsupervised local image feature extraction method running without such disease medical image datasets. Local image features are one of the key determinants of system performance. The proposed method requires only a normal image dataset that does not include lesions and can be collected easier than a disease dataset. The unsupervised features are extracted by applying multiple convolutional autoencoders to analyze various 2.5-dimensional images. The proposed method is evaluated by two kinds of problems: the detection of cerebral aneurysms in head MRA images and the detection of lung nodules in chest CT images. In both cases, the performance is high, showing an AUC of more than 0.96. These results show that the proposed method can automatically learn features that are useful for lesion recognition from lesion-free normal data, regardless of the type of image or lesion.
Agile Machine Learning Model Development Using Data Canyons in Medicine: A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement

Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Business-Driven Data Recommender System: Design and Implementation

Self-Service Business Intelligence (SSBI) increases decision-making reactivity of companies by facilitating the data use by non-IT experts. An important SSBI dimension is data querying where businesspeople create their own queries by reducing the technical complexity of formal languages like SQL. However, existing solutions ignore two other key challenges of data querying identified in the literature: the databases technical jargon and the data overload. In this paper, we propose, following the Design Science Research methodology, a framework (i.e. DatAssistant) to complement existing querying solutions with two new theoretical artifacts. The first bridges the semantic gap between technical databases and businesspeople via a business-aware ontology of the Data Warehouse mapped to the business Data Catalog. The second artifact filters data overload by mobilizing a hybrid recommender engine combining semantic systems and business rules. This paper then demonstrates the validity and applicability of the framework through its technical implementation in a real-world environment.
Remove ambiguous zones using floodfill algorithm in digital photoelasticity

Digital photoelasticity is a widely used technique for the full-field measurement of stress/strain distributions. However, ambiguous zones are present in the isochromatic phase map obtained with the commonly used ten-step phase shifting technique. Obtaining accurate isochromatic phase maps without wrapping can be challenging due to the presence of ambiguous zones. Existing methods to address this issue are complex and not fully automated. In this study, a novel approach is proposed to automatically and accurately remove the ambiguous zones using the Floodfill algorithm, an advanced digital image processing algorithm. The validity and robustness of the proposed method is demonstrated through benchmark tests on a disc and a ring subjected to diametral compression and a four-point bent beam, as well as a model with a complex geometry. The proposed method enhances the automation and simplicity of full-field stress/strain measurement in terms of the removal of ambiguous zones.