Reviewerly/exHarmony huggingface dataset:
Promoting systems thinking through perspective taking when using an online modeling tool

Introduction Disagreements between people on different sides of popular issues in STEM are often rooted in differences in “mental models,” which include both rational and emotional cognitive associations about the issue; especially given these issues are systemic in nature. Methods In the research described here, we employ the fuzzy cognitive mapping software MentalModeler (developed by one of the authors) 1 as a tool for articulating implicit and explicit assumptions about one’s knowledge of both the environmental and social science and values underpinning complex system related issues. More specifically, we test the assumption that this pedagogical approach will foster certain aspects of perspective taking that can be traced with cognitive development and systems thinking as students not only articulate their own understanding of an issue, but also articulate the view of others. Results and discussion Results are discussed with respect to systems thinking that is developed through this type of modeling.
FPGA versus GPU for accelerating homomorphic encryption in federated learning

As a new learning paradigm preserving data privacy in distributed machine learning， federated learning becomes increasingly attractive，in which Homomorphic Encryption (HE) technology is utilized to encrypt the private intermediate data during computation. However, the homomorphic operation in HE impose significant computing overhead on federated learning. A hardware solution is important to speed up the training process in federated learning. As current two popular hardware accelerating platform, both GPU based and FPGA based accelerators are introduced into this area, but which is a better choice? We device and customize a FPGA implementation of the homomorphic encryption, as well as a GPU version. The experiment results demonstrate that GPU is more efficient for PHE computations in most case, because GPU version outperforms its counterpart on performance in terms of throughputs. However in some cases FPGA version achieves better performance than the GPU, with far lower clock frequency.
Image retrieval based on asymmetric supervised deep pairwise hashing

Effective storage and retrieval for large-scale images is made possible based on the binary representation used in hashing. Variable hash code (HC) lengths reflect the swap between retrieval speed and accuracy necessary for creating a hashing framework in practical applications. Considering all this, the present hashing algorithms must train several frameworks for various HC lengths, decreasing hashing flexibility and increasing training time costs. Considering that several HCs of varying lengths may be used to describe a sample, there are helpful correlations that can enhance the efficiency of hashing techniques. Nevertheless, the hashing techniques do not entirely use these connections. We suggest a novel method, Asymmetric Supervised Deep Pairwise Hashing (ASDPH), for discriminative learning and to concurrently train HCs of various lengths to overcome the identified issues. Three pieces of information are obtained in this proposed ASDPH approach from HCs of multiple sizes. The samples' original characteristics and labels are used for hash learning. To validate the proposed module, we evaluated the method's performance on 16, 32, 64, and 128 bits for NUSWIDE, CIFAR-10, and MSCOCO datasets by achieving 2%, 7%, and 12% improved mean average precision than other state-of-the-art methods.
Trust, perceived usefulness, and intentions to adopt robotic health advisors for physical and relational health issues

AI and robots are continuously being used in a variety of health contexts. To better understand this emerging phenomenon of employing machines in healthcare, the present research investigates trust in a robotic health advisor and intentions to adopt it by comparing a robotic health advisor designed to assist with physical health vs. relational health. An online experiment that employed a two-group comparison with a between-subjects design was conducted among 284 undergraduate students in the United States. The primary findings indicate that people report greater trust in a robotic health advisor for physical health compared to relational health, and this relationship is due to the perceived usefulness of a robotic health advisor. Additionally, trust in a robotic health advisor positively predicts intentions to adopt it. The study’s findings provide meaningful implications and contributions for research and practice.
Exploring spatial–temporal features fusion model for Deepfake video detection

The rapid development of Deepfake technology has posed significant challenges in detecting fake videos. In response to the existing problems in reference frame selection, spatial–temporal feature mining, and fusion in face-swapping video detection techniques, we propose a face-swapping video detection model based on spatial–temporal feature fusion. First, key frame sequences are selected using interframe facial edge region differences. Then, the key frame sequences are separately input into the spatial branch to extract hidden artifacts and the temporal branch to extract inconsistent information. Finally, the spatial–temporal features are fused using a self-attention mechanism and input into a classifier to achieve detection results. To validate the effectiveness of the proposed model, we conducted experiments on the Faceforensics++ and Celeb-DF open-source Deepfake datasets. The experimental results demonstrate that the proposed model achieves better detection accuracy and higher-ranking generalization performance than state-of-the-art competitors.
Beyond images: data visualization through headline analysis in historical newspaper with computer vision

This paper introduces an innovative method that combines Computer Vision and Deep Learning to extract headlines from a historical newspaper. Through the illustrations from historical newspapers, one of our goals is to use these extracted headlines to support digital humanities. The research goes beyond traditional image analysis by exploring how new digital technologies can facilitate the understanding of newspaper content by visualizing through time and place. The experimental results reveal that our recommended approaches, which involve Optical Character Recognition (OCR) with scraping and Deep Learning Object Detection models, can successfully obtain the required information for more advanced analytics. Due to the distinctive historical and humanities values, we chose "The Hongkong News" from the Hong Kong Early Tabloid Newspaper collection to illustrate the efficacy of our methodology. In addition, we constructed several visualization applications to demonstrate the viability of our suggested approaches.
Quantum optimal control without arbitrary waveform generators

Simple, precise, and robust control is demanded for qubit operations on large quantum information processors. However, existing routes to high-fidelity quantum control heavily rely on arbitrary waveform generators that are difficult to scale up. Here, we show that the arbitrary control of a quantum system can be achieved by simply turning the control fields on and off in a proper sequence. The switching instances can be designed using conventional quantum optimal control algorithms, while the required computational resources for matrix exponentials can be substantially reduced. We demonstrate the flexibility and robustness of the resulting control protocol, and illustrate it in the context of superconducting quantum circuits. We expect this proposal to be achievable with current semiconductor and superconductor technologies, which offers a significant step towards scalable quantum computing.
Real‐Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR‐GNSS and Deep Learning

Abstract Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.
Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods

In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual understanding, domain-specific adaptation, robustness and noise tolerance, and scalability and extensibility. The approach aims to leverage techniques like convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, and single visual models for scene text recognition. While focusing on English, Hungarian, and Japanese as representative languages, the proposed methodology can be extended to any existing or even synthesized languages. The development of accurate, efficient, and versatile OCR systems is at the core of this research, offering societal benefits by bridging global communication gaps, ensuring reliability in diverse environments, and demonstrating the adaptability of AI to evolving needs. This work not only mirrors the state of the art in the field but also paves new paths for future innovation, accentuating the importance of sustained research in advancing AI’s potential to shape societal development.
Pegmatites

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSWe thank Dr. Larry Stifler, Mary McFadden, and the Maine Mineral & Gem Museum, Bethel, Maine, for their continued support of the MP2 research lab. We thank Carl Francis, Mark I. Jacobson, and Mike Wise for their thorough reviews that improved the manuscript.Additional informationNotes on contributorsWilliam B. SimmonsPegmatite experts Dr. William B. Simmons, Dr. Karen L. Webber, and Alexander U. Falster are the founders of the MP2 Research Group (Mineralogy, Petrology, Pegmatology), begun thirty years ago at the Department of Earth and Environmental Sciences, University of New Orleans. Professor Emeritus Simmons and his wife, Webber, have retired but continue their research with Falster, experimental chemist and researcher, at the Maine Mineral & Gem Museum, Bethel, Maine, where their research laboratory was relocated in 2014. They are organizers of the annual Maine Pegmatite Workshop and principal authors of the book Pegmatology.Karen L. WebberPegmatite experts Dr. William B. Simmons, Dr. Karen L. Webber, and Alexander U. Falster are the founders of the MP2 Research Group (Mineralogy, Petrology, Pegmatology), begun thirty years ago at the Department of Earth and Environmental Sciences, University of New Orleans. Professor Emeritus Simmons and his wife, Webber, have retired but continue their research with Falster, experimental chemist and researcher, at the Maine Mineral & Gem Museum, Bethel, Maine, where their research laboratory was relocated in 2014. They are organizers of the annual Maine Pegmatite Workshop and principal authors of the book Pegmatology.Alexander U. FalsterPegmatite experts Dr. William B. Simmons, Dr. Karen L. Webber, and Alexander U. Falster are the founders of the MP2 Research Group (Mineralogy, Petrology, Pegmatology), begun thirty years ago at the Department of Earth and Environmental Sciences, University of New Orleans. Professor Emeritus Simmons and his wife, Webber, have retired but continue their research with Falster, experimental chemist and researcher, at the Maine Mineral & Gem Museum, Bethel, Maine, where their research laboratory was relocated in 2014. They are organizers of the annual Maine Pegmatite Workshop and principal authors of the book Pegmatology.
The prefabs lexicon of Lhasa Tibetan in continuous speech

In this study, we propose a method for constructing a Lhasa Tibetan prosodic lexicon based on a continuous speech database, which leads to significant improvements in speech synthesis performance for low-resource and complex languages. The experiment begins by utilizing a 3.95-hour speech database of a Lhasa Tibetan speaker, focusing on the prosodic feature of “tone sandhi” to investigate the phonological features and grammatical functions of Lhasa Tibetan. Drawing inspiration from the “Usage-Based Theory” in cognitive linguistics, we extract prefabs (prefabricated chunks) from 2,526 utterances. According to the prosodic features and grammatical structure of these prefabs, we construct a Prefabs Lexicon consisting of 175 thousand entries. In the comparative experiment, we employ a sequence-to-sequence speech synthesis approach and automatically segment the input sequence using both the Prefabs Lexicon and the conventional Tibetan lexicon. To evaluate the performance, a 56-minute dataset from another professional Lhasa broadcaster is used as a test set. Compared to the conventional Tibetan lexicon, the Prefabs Lexicon achieves an improved 𝐹<sub>1</sub> − 𝑠𝑐𝑜𝑟𝑒 of 0.92. Additionally, in the synthesis experiment for the toneless Amdo Tibetan, the Mean Opinion Score (MOS) increases to 4.17, indicating the universal applicability of the Prefabs Lexicon across dialects.
SSGCN: a graph convolutional neural network model based on syntactic structure and similarity features for relation extraction in shipbuilding industry

The construction of the standard knowledge graph is to realize the reorganization of the internal knowledge of the traditional standard texts and achieve the purpose of innovating knowledge storage and the practice of knowledge supply. Technical requirement extraction is a typical relation extraction task in the construction process of a standard knowledge graph. However, the existing relation extraction models can not achieve ideal performance due to standard texts' unique organizational forms and special writing characteristics. Therefore, This paper proposes a Graph Convolutional Neural Network model based on Syntactic structure and Similarity features (SSGCN), integrating expert knowledge in syntactic pruning, dynamically interfering with the weight matrix by the pruning strategy based on attention, and making full use of supervised relation label semantic similarity features. The experiment in this paper compares the large language models (LLMs) such as GPT-3, and our model achieves better experimental performance than other relation extraction models on specialized datasets in the field, providing a related solution for standard technical requirement extraction tasks.
Cubature Kalman filter based on generalized minimum error entropy with fiducial point

In practical applications, factors such as pulse interference, sensor malfunctions, and other factors can lead to observation noise exhibiting a non-Gaussian distribution, which will impair the performance of the classical cubature Kalman filter (CKF) algorithm. The existing CKF algorithm exhibits some limitations in handling complex non-Gaussian noise, and its performance may be somewhat inadequate for such scenarios. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise.
Signature Specimen: Elbaite from the Cruzeiro Pegmatite Minas Gerais, Brazil: At the Natural History Museum of Los Angeles County

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSWe are indebted to Editor-in-Chief Marie Huizing for the invitation to write this article, to William Besse for providing the location maps, to Daniel Trinchillo for the locality photo, to John S. White for the photo of Marion Stuart, and to Günther Neumeier for reviewing the manuscript.Additional informationNotes on contributorsCarl A. FrancisDr. Carl A. Francis is a curator at the Maine Mineral & Gem Museum and an executive editor of Rocks & Minerals.Aaron CelestianDr. Aaron Celestian is Curator of Mineral Sciences at the Natural History Museum of Los Angeles County.
An Image-Text Matching Method for Multi-Modal Robots

With the rapid development of artificial intelligence and deep learning, image-text matching has gradually become an important research topic in cross-modal fields. Achieving correct image-text matching requires a strong understanding of the correspondence between visual and textual information. In recent years, deep learning-based image-text matching methods have achieved significant success. However, image-text matching requires a deep understanding of intra-modal information and the exploration of fine-grained alignment between image regions and textual words. How to integrate these two aspects into a single model remains a challenge. Additionally, reducing the internal complexity of the model and effectively constructing and utilizing prior knowledge are also areas worth exploring, therefore addressing the issues of excessive computational complexity in existing fine-grained matching methods and the lack of multi-perspective matching.
Data extraction from scanned invoice documents in multiple languages

This work provides an open-source method for extracting rel- evant information from scanned documents, such as bills, bank accounts, and invoices. The solution supports documents in 10 different languages and can extract data from these documents irrespective of their template or structure. We have pre-existing solutions based on OpenCV and deep learning technologies, but none provide a generic solution with high accu- racy and support for multiple languages. The proposed method identifies the language of the input document using a pre-trained fast-text model. The document is segmented into different text regions using Run Length Smoothing Algorithm (RLSA). The output of RLSA is passed through a custom pattern recognition algorithm to filter out the regions having the possibility of relevant data based on invoices or account statements. The filtered segments are passed through the Tesseract OCR module for raw text extraction. Based on the identified language of the document, extracted raw text is mapped against the language-specific entity libraries, and final key-value pairs are stored in JSON or CSV files. After being tested on more than 1000 documents, our proposed solution had an average accuracy of 90.27% for all language documents.
Multi-document reading comprehension model based on electra and document sliding window

Multi-document reading comprehension is an important and difficult task in natural language processing. To address the issue that ELECTRA pre-training model has length limitation and cannot be directly adapt to multi-document reading comprehension task, this paper proposes a novel model based on ELECTRA and document sliding windows. In the model multiple documents are split and merged through document sliding windows, new segmentation embedding is introduced, answer position in documents is modelled as a learning target, and ELECTRA is used for joint training in each window. After obtaining all prediction outcomes of each window, the results are comprehensively sorted to achieve the optimal answer. The experiments show that Rouge-L of this model reaches 51.28% on the multi-document reading comprehension dataset MS-MARCO, ranking the current best result.
The Pedagogical Shift in the Emergence of Digital Technology: Transforming Teaching Practices

The rapid advancement in digital technology has necessitated a fundamental transformation in the approach to teaching and learning in the classroom. Consequently, teachers must recognize and adapt to this shift by employing appropriate and effective pedagogical methods. However, the lack of comprehensive literature and well-defined policies pertaining to pedagogical adjustments in response to the emergence of technology has left numerous issues unresolved. Therefore, this research investigated the pedagogical approaches adopted by teachers in light of technological advancements in this digital age. The pedagogical shift accompanying the emergence of technology holds significant importance for effective teaching and learning. The research question that was asked to address this pedagogical shift was: In what ways are teachers applying pedagogical approaches in the context of digital technology emergence? To address the research question, a combination of quantitative and qualitative data was collected from high school teachers in the Pinetown district located in Durban KwaZulu-Natal Province through questionnaires and interviews. The findings revealed that teachers are not adequately equipped to implement the pedagogical approaches required for the integration of technology. Furthermore, teachers often perceive technology as a mere tool for occasional use in teaching and learning. Relying solely on laptops and electronic gadgets, without effectively integrating technology into the curriculum and pedagogical approaches, as well as fostering authentic teaching and learning experiences, does not ensure that learners will be prepared to thrive in the era of technology. Additionally, the research discovered that only 31.1% of teachers employed problem-based learning, while 21.7% employed project-based learning as their pedagogical approach. Consequently, there is a need for well-structured policies that can support teachers in making the necessary pedagogical shift to accommodate the emergence of technology. Keywords: Teaching and learning, Pedagogical approaches, Technology integration, Digital technology, Digital skills.