Reviewerly/exHarmony huggingface dataset:
Factors affecting Thai EFL students’ behavioral intentions toward mobile-assisted language learning

Authors:
raw_author_name: Li Pan
raw_affiliation_string: Graduate School of Business and Advanced Technology Management, Assumption University, Thailand
raw_author_name: Yan Ye
raw_affiliation_string: Graduate School of Education, Stamford International University, Thailand
raw_author_name: Xinyi Li
raw_affiliation_string: Graduate School of Education, Stamford International University, Thailand

Abstract: Introduction Recently, researchers have begun to pay more attention to topics related to the adoption of mobile devices for supporting second or foreign language learning. Mobile-assisted language learning (MALL) is now prevalent among language learners and educators because of its convenient and enjoyable features. This study combined and extended the Technology Acceptance Model (TAM) and Expectation Confirmation Theory (ECT) to investigate the factors influencing English as a Foreign Language (EFL) students’ behavioral intentions to use MALL at two universities in Bangkok, Thailand. Methods Quantitative methods were utilized in this study and the researchers obtained a total of 507 valid responses by using three-step sampling. After using confirmatory factor analysis (CFA) to determine that the study had enough construct validity, structural equation modeling (SEM) was applied to test the research’s hypotheses. Results The findings revealed that all 15 hypotheses were supported, except that social influence cannot significantly influence behavioral intention. Discussion and implication By acquiring a deeper understanding of the factors that impact the behavioral intentions of language learners to utilize MALL, developers and providers can improve their capacity to design more enjoyable and effective applications that align with customer expectations and enhance financial gains. By understanding students’ behavioral intentions towards MALL, educators can efficiently raise awareness of its benefits and provide effective training, enabling students to utilize available resources and enhance their language learning experience.
Breast Tumor Classification with Enhanced Transfer Learning Features and Selection Using Chaotic Map-Based Optimization

Authors:
raw_author_name: Sannasi Chakravarthy
raw_affiliation_string: Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, 638401, India
raw_author_name: Bharanidharan Nagarajan
raw_affiliation_string: School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, 632014, India
raw_author_name: V. Vinoth Kumar
raw_affiliation_string: School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, 632014, India
raw_author_name: T. R. Mahesh
raw_affiliation_string: Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
raw_author_name: R. Sivakami
raw_affiliation_string: Department of Computer Science and Engineering, Sona College of Technology, Salem, 636005, India
raw_author_name: Jonnakuti Rajkumar Annand
raw_affiliation_string: Department of Electromechanical Engineering, Sawla Campus, Arba Minch University, 4400, Arba Minch, Ethiopia

Abstract: Abstract Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.
Immune subtyping of melanoma whole slide images using multiple instance learning

Authors:
raw_author_name: Darren Treanor
raw_affiliation_string: Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, LS2 9JT, United Kingdom; Leeds Teaching Hospitals Trust, Leeds, LS2 9JT, United Kingdom
raw_author_name: Ali Gooya
raw_affiliation_string: School of Computing, University of Glasgow, Glasgow G12 8QQ, United Kingdom
raw_author_name: Derek Magee
raw_affiliation_string: School of Computing, University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom

Abstract: Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into ‘high’ or ‘low immune’ subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into ‘high’ and ‘low immune’ subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.
Unravelling the global landscape of Bitcoin research: insights from bibliometric analysis

Authors:
raw_author_name: Guizhou Wang
raw_affiliation_string: Faculty of Science and Technology, University of Stavanger, Stavanger, Norway
raw_author_name: Kjell Hausken
raw_affiliation_string: Faculty of Science and Technology, University of Stavanger, Stavanger, Norway

Abstract: Bitcoin has been gaining increasing attention in academia and industry. This article investigates Bitcoin’s research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection. The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market. The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research. The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012–2016), cryptocurrency and market efficiency (2017–2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019–2022). The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.
Regularization and optimization in model-based clustering

Authors:
raw_author_name: Joaquim Dias Garcia
raw_affiliation_string: LAMPS, Departamento de Engenharia Elétrica, PUC-Rio, Brazil; PSR, Rio de Janeiro, Brazil
raw_author_name: Marcus Poggi
raw_affiliation_string: Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Brazil
raw_author_name: Thibaut Vidal
raw_affiliation_string: CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematical and Industrial Engineering, Polytechnique Montréal, Canada; Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Brazil

Abstract: Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is that they essentially fit a mixture of identical spherical Gaussians to data that vastly deviates from such a distribution. In comparison, general Gaussian Mixture Models (GMMs) can fit richer structures but require estimating a quadratic number of parameters per cluster to represent the covariance matrices. This poses two main issues: (i) the underlying optimization problems are challenging due to their larger number of local minima, and (ii) their solutions can overfit the data. In this work, we design search strategies that circumvent both issues. We develop more effective optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting. Through extensive computational analyses, we observe that optimization or regularization in isolation does not substantially improve cluster recovery. However, combining these techniques permits a completely new level of performance previously unachieved by k-means algorithm variants, unraveling vastly different cluster structures. These results shed new light on the current status quo between GMM and k-means methods and suggest the more frequent use of general GMMs for data exploration. To facilitate such applications, we provide open-source code as well as Julia packages (UnsupervisedClustering.jl and RegularizedCovarianceMatrices.jl) implementing the proposed techniques.
Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

Authors:
raw_author_name: Xiwei Wu
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
raw_author_name: Bing Xiao
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
raw_author_name: Lu Cao
raw_affiliation_string: National Innovation Institute of Defense Technology, Chinese Academy of Military Science, Beijing, 100071, China
raw_author_name: Haibin Huang
raw_affiliation_string: School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China

Abstract: Abstract This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.
Mixed reality and digital twins for astronaut training

Authors:
raw_author_name: Amadeo Arguelles
raw_affiliation_string: Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz s/n, Col. Nueva Industrial Vallejo, Mexico City, 07738, CDMX, Mexico

Abstract: Astronaut training is a complex activity given its nature; the infrastructure and equipment required are very specific and expensive. Immersive technologies have the capability to simulate complete environments and allow users to interact with them. The effectiveness of immersive technologies for training and learning activities has already been addressed in various works. However, merely simulating scenarios is not enough; participants need to interact with vehicles, machines, and spacecraft systems to see how they affect the environment and surrounding objects. This study develops four scenarios across three modules, inspired by NASA and ESA astronaut training programs. The first module introduces a theoretical scenario visualized through mixed reality (MR) with topics from ESA’s training module. The second module presents two practical scenarios: an ISS emergency simulation involving Hohmann transfer and circular motion concepts, and a spacewalk for repair and maintenance tasks, in these scenarios, the digital twins of the ISS propulsion and navigation systems were developed as well as the digital twin of a space suit. The third module simulates a spaceship launch, utilizing 3D models from SpaceX’s Falcon Heavy and digital twins of the propulsion and navigation systems. During the interaction of the participants with the digital twins and the scenarios, the data generated is stored in a database so it can be analyzed and compared against the ISS dataset from the Jet Propulsion Laboratory (JPL) and the telemetry from SpaceX Falcon heavy launch. In the first phase of this work, the main goal was to check if digital twins can be implemented in conjunction with immersive technologies and therefore be suitable for training activities. We used a mean squared error metric between the digital twins and the physic object data, demonstrating that developed digital twins closely align with physical systems, validating their efficacy for astronaut training.
UoloNet: based on multi-tasking enhanced small target medical segmentation model

Authors:
raw_author_name: Kejia Zhang
raw_affiliation_string: Computer Science and Technology, Harbin Engineering University, Nantong Street, Harbin, Heilongjiang, China
raw_author_name: Lan Zhang
raw_affiliation_string: Computer Science and Technology, Harbin Engineering University, Nantong Street, Harbin, Heilongjiang, China
raw_author_name: Haiwei Pan
raw_affiliation_string: Computer Science and Technology, Harbin Engineering University, Nantong Street, Harbin, Heilongjiang, China

Abstract: Abstract In recent years, UNET (Ronneberger et al. 2015) and its derivative models have been widely used in medical image segmentation with more superficial structures and excellent segmentation results. Due to the lack of modeling for the overall characteristics of the target, the division tasks of minor marks will produce some discrete noise points, resulting in a decline in model accuracy and application effects. We propose a multi-tasking medical image analysis model UoloNet, a YOLO-based (Redmon et al. 2016; Shafiee et al. 2017) object detection branch is added based on UNET. The shared learning of the two tasks through semantic segmentation and object detection has promoted the model’s mastery of the overall characteristics of the target. In the reasoning stage, merging the two functions of object detection and semantic segmentation can effectively remove discrete noise points in the division and enhance the accuracy of semantic segmentation. In the future, the object detection task will be the problem of excessive convergence of semantic segmentation tasks. The model uses CIOU (Zheng et al. 2020) losses instead of IOU losses in YOLO, which further improves the model’s overall accuracy. The effectiveness of the proposed model is verified both in the MRI dataset SEHPI, which we posted and in the public dataset LITS (Christ 2017).
Quantum two-block group algebra codes

Authors:
raw_author_name: Hsiang-Ku Lin
raw_affiliation_string: Department of Physics & Astronomy, University of California, Riverside, California 92521, USA
raw_author_name: Leonid P. Pryadko
raw_affiliation_string: Department of Physics & Astronomy, University of California, Riverside, California 92521, USA

Abstract: We consider quantum two-block group algebra (2BGA) codes, a family of smallest lifted-product (LP) codes. These codes are related to generalized-bicycle codes, except a cyclic group is replaced with an arbitrary finite group, generally non-Abelian. As special cases, 2BGA codes include a subset of square-matrix LP codes over Abelian groups, including quasicyclic codes, and all square-matrix hypergraph-product codes constructed from a pair of classical group codes. We establish criteria for permutation equivalence of 2BGA codes and give bounds for their parameters, both explicit and in relation to other quantum and classical codes. We also enumerate the optimal parameters of all inequivalent binary connected 2BGA codes with stabilizer generator weights $W\ensuremath{\le}8$, of length $n\ensuremath{\le}100$ for Abelian groups, and $n\ensuremath{\le}200$ for non-Abelian groups.
Pyrite geochemistry reveals the key controlling factors of large gold deposit formation in Jiaodong Peninsula: A comparative study

Authors:
raw_author_name: Yuanlin Chen
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
raw_author_name: Huan Li
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
raw_author_name: Majid Ghaderi
raw_affiliation_string: Department of Economic Geology, Tarbiat Modares University, Tehran 14115-175, Iran
raw_author_name: Yunfeng Wang
raw_affiliation_string: Key Laboratory of Karst Georesources and Environment, Ministry of Education, College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China

Abstract: With an estimated gold reserve of more than 5000 t, the Jiaodong Peninsula is incontestably the largest gold ore belt in China. Dozens of gold deposits developed in the area with variable deposit types, grades, and reserves, and the key controlling factors for these differences are not clear. In this study, the representative Jiuqu (JQ), Canzhuang (CZ), and Sanshandao Northern Sea (SNS) gold deposits located in the NW portion of the Jiaodong district have been comparatively investigated using pyrite geochemistry, to constrain the mineralization sources and grade differences of each deposit. The pyrite from ore samples in JQ, CZ, and SNS deposits display euhedral to sub-euhedral geometries and do not show obvious zonation. The SNS pyrites present higher values for most trace elements (e.g., Cu, As, Ni) than the JQ and CZ pyrites. Moreover, the SNS deposit has the highest δ34S value (+11.70 ‰), followed by the CZ deposit (+10.43 ‰) and the JQ deposit (+7.67 ‰). This is positively correlated to their ore grade in order. The presence of relatively high contents of Cu, As, Ni as well as δ34S values in the SNS deposit pyrites may suggest that more mantle materials have been contributed to the gold ores. It can be argued that these high values are associated with high oxygen fugacity in the subducting and stagnant slabs related to the lithospheric mantle metasomatism, which is responsible for the δ34S enrichment and formation of Au and S reservoirs, serving as the key controlling factor for the formation of large gold deposits in Jiaodong. This study enables us to better describe the evolution of the mineralizing fluids and understand the mechanisms of ore genesis in gold mineralization systems by using pyrite geochemistry.
Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain

Authors:
raw_author_name: David García
raw_affiliation_string: Department of Energy, University of Oviedo, c/Wifredo Ricart, s/n, 33204 Gijón, Spain
raw_author_name: Jesús-Ignacio Prieto
raw_affiliation_string: Department of Physics, University of Oviedo, c/Federico García Lorca, nº18, 33007 Oviedo, Spain

Abstract: Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.