Reviewerly/exHarmony huggingface dataset:
Geology, fluid inclusions, mineral and (S-O) isotope chemistry of the Badran orogenic Au deposit, Yana-Kolyma belt, eastern Siberia: implications for ore genesis

Authors:
raw_author_name: Valery Fridovsky
raw_affiliation_string: Diamond and Precious Metal Geology Institute, Russia
raw_author_name: Sergey Kryazhev
raw_affiliation_string: Central Research Institute of Geological Prospecting for Base and Precious Metals, Russia; Diamond and Precious Metal Geology Institute, Russia
raw_author_name: Lena Polufuntikova
raw_affiliation_string: Diamond and Precious Metal Geology Institute, Russia
raw_author_name: Maxim Kudrin
raw_affiliation_string: Diamond and Precious Metal Geology Institute, Russia
raw_author_name: Galina Anisimova
raw_affiliation_string: Diamond and Precious Metal Geology Institute, Russia

Abstract: The Badran orogenic gold deposit is located in the Yana-Kolyma belt, Eastern Siberia; it has proven reserves of ∼9.3 t of gold and an average grade of 7.8 ppm. The total gold production at the Badran deposit since 1984 amounts to ∼34 t. Despite many years of study, the origin of the gold deposits of the Yana-Kolyma metallogenic belt, one of the world’s largest belts, and the Badran deposit is controversial. Synthesis of regional geology and geology of the Badran deposit, fluid inclusion analysis, mineral and (S-O) isotope chemistry defines the genetic model, origin of fluids, and source of metals in the evolution of the ore-forming system, equivalent to other orogenic gold deposits on the margin of the Siberian craton. The deposit is localized in the Upper Triassic clastic rocks and is controlled by the NW-trending thrust. Polyphase mineralization occurs as disseminated arsenian pyrite and arsenopyrite ores with invisible gold, quartz veins with native gold and Fe, Pb, Zn, Cu sulfides and sulfosalts of orogenic type, and locally post-ore Ag, Sb-bearing minerals and Hg epithermal features. The quartz veins with native gold were formed from low-medium saline (1.5–10 wt% NaCl eq.) aqueous-carbonic fluids boiling at temperatures of 290°C to 210 °C and pressures of 300–250 to 125 bar. The δ 34 S values of pyrite and arsenopyrite vary from −1.1‰ to +2.4‰, with an average of +0.4‰; the δ 18 О of quartz from +15.1‰ to +17.5‰ at constant δ 18 О H2O about +7.5‰ (±1.0‰). High contents of As (up to 2.4 wt%) and Co/Ni ∼ from 0.3 to 9.9 in pyrite of proximal alteration are typical for hydrothermal systems. The results obtained confirm that the ore-forming fluids did not have a single origin, but were formed from a mixture of subcontinental lithospheric mantle and metamorphic sources. The subcrustal lithospheric mantle was fertilized in the time preceding mineralization (Late Jurassic) and was derived directly from the down-going subduction slab and overlying sediment wedge at the closure of the Oymyakon Ocean.
Consumer’s acceptance of retail service robots: mediating role of pleasure and arousal

Authors:
raw_author_name: Siti Abdullah
raw_affiliation_string: Nottingham Business School, Nottingham Trent University, Nottingham, UK
raw_author_name: Husam Kokash
raw_affiliation_string: Department of Marketing, College of Business Administration, Prince Sultan University, Riyadh, Saudi Arabia
raw_author_name: Saif Ahmed
raw_affiliation_string: Graduate School of Business, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

Abstract: This study explores the influence of consumer emotions, specifically pleasure and arousal, on the intention to use retail service robots. Through a survey of 323 people using an online snowball sampling technique and analysis via the PLS-SEM method, it finds that subjective norms, technological self-efficacy, habitual behavior, and attitudes significantly affect users’ intentions. The research notably reveals that pleasure plays a more crucial role than arousal in mediating how consumers’ perceptions impact their willingness to interact with these robots. By integrating the Theory of Reasoned Action, self-efficacy theory, and the PAD model (pleasure, arousal, dominance), the study enhances the understanding of human-robot interaction. It provides actionable insights for businesses, suggesting that emphasizing emotional aspects, particularly pleasure, can be a strategic approach to encourage the adoption of service robots. These insights are valuable for designing marketing strategies and service experiences that effectively increase consumer engagement with robotic technology. Top of Form.
Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

Authors:
raw_author_name: Gouenou Coatrieux
raw_affiliation_string: IMT Atlantique, INSERM, LATIM - UMR 1101, Brest, F-29238, France
raw_author_name: Reda Bellafqira
raw_affiliation_string: IMT Atlantique, INSERM, LATIM - UMR 1101, Brest, F-29238, France
raw_author_name: Naima Oubenali
raw_affiliation_string: Univ Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France
raw_author_name: Marc Cuggia
raw_affiliation_string: Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France
raw_author_name: Guillaume Bouzillé
raw_affiliation_string: Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France

Abstract: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers.We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods.A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information.This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.
Pengembangan media pembelajaran berbasis android menggunakan articulate storyline 3 materi program linear di sekolah menengah atas

Authors:
raw_author_name: None Muhammad Irfan
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: None Dede Suratman
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: None Dona Fitriawan
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: Ade Mirza
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: Revi Lestari Pasaribu
raw_affiliation_string: Universitas Tanjungpura

Abstract: The purpose of this research to create learning media for Android using Articulate Storyline 3 (AS3) as well as identifying validity and practicality of this Android-based learning tool developed with AS3. This research uses the ADDIE model, encompassing five stages: analysis, design, development, implementation, and evaluation. The participants in this study consist of two subject matter experts, two media experts, and 32 respondents from the ninth-grade students of SMA Negeri 3 Pontianak. The focus of the research is on the Android-based linear program learning media developed during the study. The data collection instrument is a questionnaire with descriptive statistical analysis techniques. The findings indicate that the validation analysis, based on assessments from material and media experts, yields an average score of 4.18, categorizing as 'good.' Furthermore, the practicality analysis, gauged through student responses, averages at 4.33, indicating a 'very good' category. Consequently, the Android based learning media employing AS3 is deemed both valid and practical for utilization.
A Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory for Multi-label Text Classification

Authors:
raw_author_name: Hamed Khataei Maragheh
raw_affiliation_string: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
raw_author_name: Farhad Soleimanian Gharehchopogh
raw_affiliation_string: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
raw_author_name: Kambiz Majidzadeh
raw_affiliation_string: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
raw_author_name: Amin Babazadeh Sangar
raw_affiliation_string: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

Abstract: Abstract Multi-label text classification (MLTC) is a popular method for organizing electronic documents, which is crucial for accessing and processing data. As the number of classes increases, learning multi-label data will be challenging. The number of possible states for various labels increases exponentially, and learning algorithms in single-label data cannot be used to solve these problems. In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning neural networks that can learn intricate patterns are used in many real-world problems because of their high power and accuracy. This paper proposed a hybridization of the long short-term memory (LSTM) neural network and the convolutional neural network (CNN) method for MLTC. The proposed model uses LSTM to enhance CNN to improve the proposed model’s accuracy. Also, the competitive search algorithm (CSA) is used to improve the LSTM hyperparameters. The LSTM hyperparameters play an important role in increasing the detection accuracy. The CSA algorithm finds the best values for the hyperparameters by searching the problem space. It was tested on four different datasets of multi-label texts: Reuters-21578, RCV1-v2, EUR-Lex, and Bookmarks. The result showed that the proposed model performed better than CNN and LSTM-CSA in terms of accuracy percentage and that it has improved by an average of more than 10%. Also, the results show that the LSTM-CSA model has higher detection accuracy compared to LSTM—Gradient-based optimizer (GBO) and LSTM—whale optimization algorithm (WOA).
Study on the correlation between real-time GNSS landslide acceleration monitoring and earthquake response: a case of May 2, 2023, MW = 5.2 Baoshan earthquake, Yunnan

Authors:
raw_author_name: Qiru Sui
raw_affiliation_string: State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
raw_author_name: Yuting Mao
raw_affiliation_string: State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China

Abstract: Abstract Background Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Purpose Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. Methods This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. Results GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. Conclusion A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.
Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks

Authors:
raw_author_name: C. Christodoulou
raw_affiliation_string: Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico USA
raw_author_name: Z. Peng
raw_affiliation_string: Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA
raw_author_name: S. K. Goudos
raw_affiliation_string: Department of Physics ELEDIA@AUTH Aristotle University of Thessaloniki Thessaloniki Greece

Abstract: Abstract A novel use case for two data‐driven models, namely, a Transformer and a convolutional graph neural network (CGNN) is proposed. The authors propose to use these models for emulating the dynamics of electromagnetic (EM) propagation and scattering. The Transformer translates a past sequence into a future sequence by constructing representations from the past and using it to predict the future, taking all of its own previous predictions as input at each step of prediction. The CGNN updates the current state of attribute vectors of each node by passing it information (messages) from all of its neighbouring nodes. We train these models with FDTD simulations of plane waves propagating and scattering from PEC objects. The authors demonstrate that, within the bounds of computational resources, the Transformer can be utilised as a surrogate for EM dynamics, providing 14× speed‐up, while the CGNN can be utilised as a next‐frame predictor, providing 9× speed‐up. When comparing the accuracy of these two models with the authors’ previously developed Encoder‐Recurrent‐Decoder (ERD) model, it is observed that the error for both the Transformer and the CGNN remains within the same bound for the ERD model. To the best of the authors’ knowledge, this work is the first to utilise the Transformer as a surrogate for EM dynamics.
Exploring biometric domain adaptation in human action recognition models for unconstrained environments

Authors:
raw_author_name: David Freire-Obregón
raw_affiliation_string: SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
raw_author_name: Paola Barra
raw_affiliation_string: Università di Napoli Parthenope, Naples, Italy
raw_author_name: Modesto Castrillón-Santana
raw_affiliation_string: SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
raw_author_name: Maria De Marsico
raw_affiliation_string: Sapienza Università di Roma, Rome, Italy

Abstract: Abstract In conventional machine learning (ML), a fundamental assumption is that the training and test sets share identical feature distributions, a reasonable premise drawn from the same dataset. However, real-world scenarios often defy this assumption, as data may originate from diverse sources, causing disparities between training and test data distributions. This leads to a domain shift, where variations emerge between the source and target domains. This study delves into human action recognition (HAR) models within an unconstrained, real-world setting, scrutinizing the impact of input data variations related to contextual information and video encoding. The objective is to highlight the intricacies of model performance and interpretability in this context. Additionally, the study explores the domain adaptability of HAR models, specifically focusing on their potential for re-identifying individuals within uncontrolled environments. The experiments involve seven pre-trained backbone models and introduce a novel analytical approach by linking domain-related (HAR) and domain-unrelated (re-identification (re-ID)) tasks. Two key analyses addressing contextual information and encoding strategies reveal that maintaining the same encoding approach during training results in high task correlation while incorporating richer contextual information enhances performance. A notable outcome of this study is the comprehensive evaluation of a novel transformer-based architecture driven by a HAR backbone, which achieves a robust re-ID performance superior to state-of-the-art (SOTA). However, it faces challenges when other encoding schemes are applied, highlighting the role of the HAR classifier in performance variations.
Compression of Medical images using SPIHT Algorithm for Telemedicine Application

Authors:
raw_author_name: Deny J
raw_affiliation_string: Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
raw_author_name: S. N Kumar
raw_affiliation_string: Department of Electrical and Electronic Engineering, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India

Abstract: Image compression plays a pivotal role in the medical field for the storage and transfer of DICOM images. This research work focuses on the compression of medical images using Set Partitioning in Hierarchy Trees (SPIHT) algorithm. The CT/MR images are used as input, the images are subjected to filtering by a median filter. The CT images in general are corrupted by Gaussian noise and MR images are corrupted by rician noise. The SPIHT algorithm comprises of following phases; transformation into wavelet domain, refinement pass and sorting pass. The Haar wavelet transform is employed and the wavelet coefficients are subjected to sorting and refinement pass. The Haar wavelet transform generates LL, HL, HL and HH sub-bands. In the sorting pass, the coefficients are classified into significant and insignificant. The refinement pass creates the precision bits for the significant coefficients. The main characteristic of the SPIHT algorithm is that it does not use an entropy coder. The reconstructed image in the decoding stage was validated by performance metrics. The SPIHT algorithm generates proficient results, when compared with the classical algorithms like wavelet and embedded zero tree wavelet (EZW) algorithms.
How much freedom does an effectiveness metric really have?

Authors:
raw_author_name: Alistair Moffat
raw_affiliation_string: School of Computing and Information Systems The University of Melbourne Melbourne Australia
raw_author_name: Joel Mackenzie
raw_affiliation_string: School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia

Abstract: Abstract It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings . Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome.