Reviewerly/exHarmony huggingface dataset:
Accurate determination of the elastic moduli of optimized cantilever beams by efficient time-averaged ESPI system

Abstract Elastic moduli, including Young’s modulus, shear modulus, bulk modulus, etc, are key parameters that are used to characterize the ability of a solid material to resist various types of deformation. The moduli can be extracted from the natural frequencies of a cantilever beam. In this paper, the relationships between moduli and natural frequencies, for the first time, are quantified by the finite element method. The optimized three-dimensional proportion of the cantilever beam is selected to be implemented simple error compensation. Experimentally, to precisely obtain the natural frequencies of the cantilever beam, an efficient time-averaged electronic speckle pattern interferometry(ESPI) system has been developed. The efficiency and precision are reflected in the following aspects: firstly, according to the slender character of the cantilever beam, a large shear optical path arrangement is designed to facilitate isolation from environmental interference; secondly, a resonance search method, based on the moiré effect is employed to recognize the natural frequencies accurately and efficiently; thirdly, a novel dynamic phase-shifting method is proposed based on the arrangement of the large shear optical path for clearer visualization of the mode shape of the cantilever beam. The proposed methods are verified by three kinds of common materials. The results suggest that Young’s modulus and shear modulus derived from natural frequencies are higher than the known value, and the error compensation can significantly reduce the calculation error. Furthermore, the experiments carried out on the woven carbon fiber reinforced plastic laminates illustrate the potential of the proposed methods in the evaluation of elastic moduli of composites. Given that the exciter attached to the specimen surfaces can be replaced with some special counterparts, the proposed ESPI system has considerable potential to test the objects loaded in some extreme environments, e.g. at high temperatures or underwater, where contact detection methods are difficult to be implemented.
Learning Domain‐specific Semantic Representation from Weakly Supervised Data to Improve Research Dataset Retrieval

Abstract Along with the development of the data‐driven research paradigm, there are exponentially increasing datasets, which bring challenges to researchers in the efficient retrieval of relevant datasets. Previous studies mainly focused on query expansion methods based on sparse retrieval models to improve the accuracy and recall in retrieval. We investigated the use of semantically rich information to retrieve relevant datasets and the benefits of using domain‐specific dense vector representation as opposed to general representation. First, we used pairs of metadata fields that have semantic relevance to construct the domain‐specific weakly supervised training data. Then, a pre‐trained transformer‐based deep learning model is fine‐tuned on the training data using the contrastive learning method. Finally, dense vector representations of the queries and datasets are obtained based on the fine‐tuned model. The relevance of a dataset to a query is measured by the similarity between the vectors. To evaluate the performance of the proposed model, we collected 104,683 datasets from 13 research data repositories, recruited volunteers to design research‐oriented queries, and annotated the retrieval results. The experimental results show that compared with the domain‐independent fine‐tuned model, our proposed method can improve the NDCG@10 score by about 5%.
Mind the gap: The 4M Bridge Between 4E-Cognition and Movement-Based Design

Along with technology trends like extended reality, wearables, IoT, and exergames, new design approaches have emerged, focusing on full-body interactions by actively working with the lived body’s capacity to sense, feel, and create. Thus, designers are recommended to use movement as part of the design activity when designing for and of movement, regardless of the targeted application domain. However, designing for bodily experiences is challenging. We have identified a gap of no movement-based design framework available, including the moving body as the centre part and core material of the design processes. We recognise the human body is more than a physical object in the world, but a feeling, perceptualising body, that creates meaning in interaction with the environment. It thus frustrates and challenges us to reach a bodily grounded design process embracing the lived body. A common framework informed by the theoretical aspect of embodied cognition and the practical element of movement design can be a starting point for embodied design research. Recognising these challenges, we see a need for creating a bridge between practice and theory. Based on the bridging concept from Dalsgaard and Dindler, this paper presents a movement-based design framework to bridge the abstract idea of embodied cognition theory with the 4E perspectives of embodied, embedded, enactive and extended and concrete movement-based design practices. We created a movement-based design framework structuring the movement-based methods of different perspectives. The 4M model we propose contains three types of facilitator-mediated methods: 1) Mood-setters stimulating a creative body being, 2) Movement-based design methods for creating immersion in creative bodily activities, and 3) Movement concepts as knowledge and evidence for developing and validating movement artefacts. Besides the facilitator cards, the participants have access to Modifiers that can be used in conjunction with the other methods as creative inspiration for exploring, trying, or performing new movement possibilities.
Exploiting Interdisciplinary Research Design for Temporally Complex Big Data: Discussion of a<scp>Case‐Study</scp>Using on Heterogenous Bibliographic Big Data

Abstract Artificial Intelligence (AI) methods are valued for their ability to predict outcomes from dynamically complex data. Despite this virtue, AI is widely criticized as a “black box” i.e., lacking mechanistic explanations to accompany predictions. We introduce a novel interdisciplinary approach that balances the predictive power of data‐driven methods with theory‐driven explanatory power by presenting a shared use case from four disciplinary perspectives. The use case examines scientific career trajectories through temporally complex, heterogeneous bibliographic big data. Topics addressed include: data representation in complex problems, trade‐offs between theoretical, hypothesis‐driven, and data‐driven approaches, AI trustworthiness, model fairness, algorithm explainability and AI adoption/usability. Panelists and audience members will be prompted to discuss the value of approach presented versus other ways to address the challenges raised by the panel, and to consider their limitations and remaining challenges.
Landmark tracking in 4D ultrasound using generalized representation learning

In this study, we present and validate a novel concept for target tracking in 4D ultrasound. The key idea is to replace image patch similarity metrics by distances in a latent representation. For this, 3D ultrasound patches are mapped into a representation space using sliced-Wasserstein autoencoders.A novel target tracking method for 4D ultrasound is presented that performs tracking in a representation space instead of in images space. Sliced-Wasserstein autoencoders are trained in an unsupervised manner which are used to map 3D ultrasound patches into a representation space. The tracking procedure is based on a greedy algorithm approach and measuring distances between representation vectors to relocate the target . The proposed algorithm is validated on an in vivo data set of liver images. Furthermore, three different concepts for training the autoencoder are presented to provide cross-patient generalizability, aiming at minimal training time on data of the individual patient.Eight annotated 4D ultrasound sequences are used to test the tracking method. Tracking could be performed in all sequences using all autoencoder training approaches. A mean tracking error of 3.23 mm could be achieved using generalized fine-tuned autoencoders. It is shown that using generalized autoencoders and fine-tuning them achieves better tracking results than training subject individual autoencoders.It could be shown that distances between encoded image patches in a representation space can serve as a meaningful measure of the image patch similarity, even under realistic deformations of the anatomical structure. Based on that, we could validate the proposed tracking algorithm in an in vivo setting. Furthermore, our results indicate that using generalized autoencoders, fine-tuning on only a small number of patches from the individual patient provides promising results.
Geometrically interpretable Variance Hyper Rectangle learning for pattern classification

Many current intrinsically interpretable machine learning models can only handle the data that are linear, low-dimensional, and relatively independent attributes and often with discrete attribute values, while the models that are capable of handling high-dimensional nonlinear data, like deep learning, have very poor interpretability. Based on the geometric characteristics, a new idea of accurately wrapping the data region with minimum-volume geometry is proposed for pattern classification. The Variance Hyper Rectangle (VHR) model presented in this paper is a realization of the idea. The VHR model uses the minimum-volume hyper rectangles, obtained through projection variance calculation, to wrap the regions occupied by a category of data, hence it has strong and clear geometric interpretability. In addition, the VHR model is well suited for large data volume, as it approaches the linear complexity in both time and space. Extensive qualitative and quantitative experiments are performed on seven real-world data sets, demonstrating that VHR outperforms the state-of-the-art interpretable methods while running quickly.
Improving Reading Comprehension of Novels in Class 1x SMPN 1 Pringsewu using Metacognitive Strategies and Reading Retention Techniques

The purpose of this study is to describe the learning process of novel reading comprehension skills, describe the improvement of novel reading comprehension skills, describe changes in student behavior, and describe students' responses to novel reading comprehension learning using metacognitive strategies and reading retention techniques. The action process in Cycle I and Cycle II includes planning, action, observation, and reflection. The instruments used are test and non-test instruments. Data analysis techniques in this study used quantitative and qualitative techniques. The results of the research on reading comprehension skills of students of class IX B SMP Negeri 1 Pringsewu experienced an increase. Cycle I reached 66.8 and cycle II to 77 increased by 10.2 or 13.24%.
SCTB-V2: the 2nd version of the Chinese treebank in the scientific domain

Word segmentation, part-of-speech (POS) tagging, and syntactic parsing are three fundamental Chinese analysis tasks for Chinese language processing, which are also crucial for various downstream tasks such as machine translation and information extraction. To achieve high accuracy for these tasks, treebanks that contain sentences manually annotated with word segmentation, part-of-speech tags, and phrase structures are essential. Although there are large-scale Chinese treebanks in the news domain, such treebanks are unavailable in the scientific domain. This significantly limits the performance of Chinese language processing for scientific text. To address this problem, we annotate the 2nd version of the Chinese treebank in the scientific domain (SCTB-V2). SCTB-V2 contains 12,175 sentences annotated with word segmentation, part-of-speech tags, and phrase structures. We conducted Chinese analyses and machine translation experiments on SCTB-V2. The results show the effectiveness of SCTB-V2. We release this treebank to promote scientific Chinese language processing research http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?A%20Chinese%20Treebank%20 in%20Scientific%20Domain%20%28SCTB%29 .
Inside the Membrane; The Percent Hebbian-LMS Algorithm

A presynaptic neuron and postsynaptic neuron are coupled by a synapse. The synaptic weight is proportional to the number of receptors of the synapse. Learning is effected by increase and decrease in the number of receptors under the control of an adaptive algorithm, very likely the Hebbian-LMS algorithm. A “mechanism” implements the algorithm and is located in the membrane and cell body of the postsynaptic neuron. In the synaptic cleft, increase and decrease is accomplished by the mechanism undergoing “receptor traffic.” To do its work, the mechanism implementing Hebbian-LMS needs an input X-vector component and an error signal. It senses an error signal from the membrane potential. It senses its X-component from the neurotransmitter in the cleft via the receptors. The X-component is proportional to the product of the presynaptic neuron’s signal as reflected in the neurotransmitter concentration in the cleft, and the number of receptors that act like antennas sticking into the fluid of the cleft. Accounting for this, the Hebbian-LMS algorithm is modified and becomes the percent Hebbian-LMS, making the weights with the largest values adapt fastest.
Ancient History

Original photos and drawings from my laboratory in the early to mid-1960s, at the dawn of the field of artificial neural networks, are shown in this chapter. The photos depict apparatus that we built and experiments we had done in developing what today would be called machine learning. Shown are the original ADALINE (adaptive linear neuron), and MADALINE (multiple adaline), a small two-layer network of ADALINEs. Adaptation was done with the LMS (least mean squares) algorithm. An electronically controlled synapse called the memistor (resistor with memory), based on electroplating, was developed. With it an electronic neuron was possible and was demonstrated. Applications were to trainable systems. One of these was the now famous “broom balancer”. In addition, an IBM1620 computer was used to simulate small networks.
The Hebbian-LMS Algorithm

In order to effect adaptation and learning, a neuron must have inputs and a desired response. The objective is the difference between the desired response and the sum (SUM). With unsupervised learning, no external desired response is given. Bootstrap learning manufactures a desired response from the sum (SUM), from its own output. Bootstrap learning is the basis of the Hebbian-LMS algorithm. It is shown how the Hebbian-LMS algorithm implements the extended Hebbian learning rules and in addition, exhibits homeostasis.
Serendipity-Oriented Clustering Method for Recommender Systems Based on Collaborative Filtering

Online shopping is becoming more common, and customers need a recommendation from the system for their needs. Providing accurate predictions is of utmost importance for the success of recommender systems. Various factors may affect the quality of the prediction. Besides the quality of prediction, efficiency is also vital for the success of recommender systems. A similarity metric is used to create a neighborhood in a dataset. In this study, we investigated the effects of binary similarity metrics on the accuracy and efficiency of the prediction. Additionally, we compared how the number of neighborhoods impacts the result and the quality of prediction. We conduct various real data-based experiments to determine the optimal number of neighborhoods and the optimal binary similarity measure. Our experimental outcomes show that the Jaccard similarity metric provides the best result when compared to the Ochiai and Kulczynski. These similarity measurements are implemented with a serendipity approach in mind. The Serendipity approach contains novelty, unexpectedness, and relevance components. In this study, we implemented the above-mentioned similarity measurements to calculate the relevance component whereas most of the researchers used cosine and Pearson similarity measurements.
Kalman Filtering in the Car Navigation Using Mobile Application GPSLogger

Kalman filtering, as the algorithm that uses a series of measurements observed over time, has numerous applications in navigation. A mobile device such as a smartphone has become common for navigation using signals from Global Navigation Satellite Systems – GNSS since there are plenty of applications available. The paper is focused on the experimental study of the effect of linear Kalman filtering on navigation data obtained by the GPSLogger mobile application. The experiment involved collecting a sample of 50 measurements on a trajectory of the car inside which the mobile device was located, which enabled filtering with 49 iterations in MATLAB. The study showed that the values of Kalman gain decreased with an increasing number of iterations. At the same time, this meant that measurement errors increased with time, which was confirmed by the created model of prediction of positioning accuracy. The model was created by the combination of time series analysis by the method of moving averages and regression analysis and it had a slightly positive trend. Kalman filtering reduced the measurement uncertainties by an average of 55%, i.e., the filtering process brought the measurements closer to the predicted values for the mentioned percentage. The Kalman filtering procedure proved to be effective in the data collected by the mobile application, and the negative trend of the Kalman gain confirmed the normalcy of the method in the conducted experiment.
Analysis of Neuroevolution for Feedforward Neural Networks

This paper presents a mathematical model for a layer-less feed-forward neural network defined as an acyclic weighted directed graph, which allows for easy manipulation of the network components (neurons, connections, and activation functions). In contrast, layered feed-forward neural networks have only two ways of simulating the addition/removal of network elements such as adding/removing whole layers of neurons or using a dropout technique. The main contribution of this paper is about the reconfigurability of a layer-less feed-forward neural network whereas new elements (neurons and connections) can be added and removed at will as long as those modifications keep the target network acyclic with the ability to perform propagation of values properly. Additionally, every neuron can modify its activation function, which results in a variety of different network behaviors and characteristics. A novel propagation method is introduced which allows the input data to conform with the network structure via vector representation. Based on the principle of conforming to the input dataset, the mathematical model is defined as a recursive non-linear equation. Once the mathematical description for a given network is obtained another concept is introduced, that is network sensitivity which has the goal of measuring the impact of genetic operations (such as mutations) on the network functionality. There are four major mutation types, which are discussed in detail in the paper. The method allows for the definition of functional equivalence between networks and the analysis of change in functional similarity based on the impact of each type of mutation. Applying this analytical method it can be shown that the functional equivalence between applying different types of mutations on a target network. Our future work will focus on generalizing the results of the current paper.
JGRCAN: A Visual Question Answering Co-Attention Network via Joint Grid-Region Features

In recent years, region features extracted from target detection networks have played an important role in visual question answering. The region features only extract the areas that are related to the target, but they lose a lot of nontarget context information and fine-grained details. On the contrary, the grid feature does not lose the details of nontargets but is not conducive to the recognition of the counting question of multiple small targets in the image. To solve this problem, this paper proposes a visual question answering network via joint grid-region features (JGRCAN), which consists of a feature extraction layer, co-attention layer, and fusion layer. The feature extraction layer includes extracting grid features and region features from the image and text features from the question and extracting multivisual feature representation and question feature representation through the co-attention layer to output attention weight and attention feature representation, respectively. The proposed approach effectively integrates grid features and region features, realizes the complementary advantages of region features and grid features, and is able to accurately focus on areas of the image that are relevant to the answer to the question. The results show that the overall classification accuracy of the algorithm on the test-dev and test-std subsets of VQA-v2 is 70.87% and 71.18%, respectively. Compared with baseline models, our proposed JGRCAN has good performance.
Notes on Models of (Partial) Kripke–Feferman Truth

Abstract This article investigates models of axiomatizations related to the semantic conception of truth presented by Kripke (J Philos 72(19):690–716, 1975), the so-called fixed-point semantics . Among the various proof systems devised as a proof-theoretic characterization of the fixed-point semantics, in recent years two alternatives have received particular attention: classical systems (i.e., systems based on classical logic) and nonclassical systems (i.e., systems based on some nonclassical logic). The present article, building on Halbach and Nicolai (J Philos Log 47(2):227–257, 2018), shows that there is a sense in which classical and nonclassical theories (in suitable variants) have the same models.
Spacecraft Diagnostic Generation from Remote Sensing for OSAM Missions

On-orbit servicing, assembly, and manufacturing (OSAM) missions have been developed with a presupposed understanding of what faults have occurred and what responses are necessary. In the future, OSAM missions may not know what faults have occurred on a servicing target until the servicer reaches the target. An onboard spacecraft diagnostic system will be needed to diagnose faults in the servicing target. Here we investigate the necessary sensing modalities, processing, and architecture to synthesize such a report.