Reviewerly/exHarmony huggingface dataset:
RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning

Abstract Random reconstruction of three-dimensional (3D) digital rocks from two-dimensional (2D) slices is crucial for elucidating the microstructure of rocks and its effects on pore-scale flow in terms of numerical modeling, since massive samples are usually required to handle intrinsic uncertainties. Despite remarkable advances achieved by traditional process-based methods, statistical approaches and recently famous deep learning-based models, few works have focused on producing several kinds of rocks with one trained model and allowing the reconstructed samples to approximately satisfy certain given properties, such as porosity. To fill this gap, we propose a new framework with deep learning, named RockGPT, which is composed of VQ-VAE and conditional GPT, to synthesize 3D samples based on a single 2D slice from the perspective of video generation. The VQ-VAE is utilized to compress high-dimensional input video, i.e., the sequence of continuous rock slices, to discrete latent codes and reconstruct them. In order to obtain diverse reconstructions, the discrete latent codes are modeled using conditional GPT in an autoregressive manner, while incorporating conditional information from a given slice, rock type, and porosity. We conduct two experiments on five kinds of rocks, and the results demonstrate that RockGPT can produce different kinds of rocks with a single model, and the porosities of reconstructed samples can distribute around specified targets with a narrow range. In a broader sense, through leveraging the proposed conditioning scheme, RockGPT constitutes an effective way to build a general model to produce multiple kinds of rocks simultaneously that also satisfy user-defined properties.
MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network

Influence maximization (IM), as an essential problem in social network analysis, can identify a minimum group of the influential nodes to maximize the spread of information on the network. The majority of IM studies focus on homogeneous networks, whereas in real world, heterogeneous networks are ubiquitous. The existing IM methods based on homogeneous networks do not consider a variety of complex heterogeneous relationships and the attribute of different types of nodes, which don’t accommodate most real scenario. It is of great significance to study IM methods based on heterogeneous networks, where efficient integrating complex multiple semantic relationships and structures embedded in the heterogeneous network is the key breakthrough point. In this paper, a novel deep learning algorithm for influence maximization on heterogeneous networks based on a multiple aggregation of heterogeneous relation embedding named MAHE-IM is proposed, which can capture the heterogeneous high-order structure and semantic features of heterogeneous information networks. For more comprehensive and systematical evaluation of MAHE-IM, we further extend fourteen state-of-the-art homogeneous and heterogeneous network embedding and graph neural network methods for IM problem and propose fourteen extented IM algorithms. Four popular IM algorithms and our extended fourteen IM algorithms are taken as eighteen baseline algorithms, which can be categorized three types: greedy-based, network embedding-based and GNN-based IM algorithms. Compared with eighteen baseline algorithms, the experimental results illustrate that MAHE-IM is significantly efficient and effective. MAHE-IM is more practical on heterogeneous networks. In addition, in order to maximize the convenience for users, a webserver (https://mahe-im.com/) is developed. The users can obtain the IM results by submitting their heterogeneous networks to the webserver. The corresponding source code and used data in this paper can also be available at https://mahe-im.com/ and https://codeocean.com/capsule/4091031/tree/v1.
Beyond Being Real: A Sensorimotor Control Perspective on Interactions in Virtual Reality

We can create Virtual Reality (VR) interactions that have no equivalent in the real world by remapping spacetime or altering users' body representation, such as stretching the user's virtual arm for manipulation of distant objects or scaling up the user's avatar to enable rapid locomotion. Prior research has leveraged such approaches, what we call beyond-real techniques, to make interactions in VR more practical, efficient, ergonomic, and accessible. We present a survey categorizing prior movement-based VR interaction literature as reality-based, illusory, or beyond-real interactions. We survey relevant conferences (CHI, IEEE VR, VRST, UIST, and DIS) while focusing on selection, manipulation, locomotion, and navigation in VR. For beyond-real interactions, we describe the transformations that have been used by prior works to create novel remappings. We discuss open research questions through the lens of the human sensorimotor control system and highlight challenges that need to be addressed for effective utilization of beyond-real interactions in future VR applications, including plausibility, control, long-term adaptation, and individual differences.
Investigating Learners' Experience of Autonomous Learning in E-learning Context

Autonomous learning has been identified as an effective mechanism for learning. Its importance in learning has mainly been studied by placing its diverse elements and functions in supporting and encouraging learner autonomy. The Outbreak of COVID-19 has signified its significance in the learning process. This study aims to investigate the learners' potential ability of the practices of autonomous learning during the implementation of E-learning. The research was carried out using a quantitative technique and a questionnaire design. An online questionnaire was used to collect data. Data were analyzed using descriptive statistics. The results of the data analysis suggested that the learners who took part in the research appeared to have a clear viewpoint on the potential ability of autonomous learning.
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition

Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention.Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representations.However, it is difficult to model such interactions as image and text representations
Code search based on context-aware code translation

Code search is a widely used technique by developers during software development. It provides semantically similar implementations from a large code corpus to developers based on their queries. Existing techniques leverage deep learning models to construct embedding representations for code snippets and queries, respectively. Features such as abstract syntactic trees, control flow graphs, etc., are commonly employed for representing the semantics of code snippets. However, the same structure of these features does not necessarily denote the same semantics of code snippets, and vice versa. In addition, these techniques utilize multiple different word mapping functions that map query words/code tokens to embedding representations. This causes diverged embeddings of the same word/token in queries and code snippets. We propose a novel context-aware code translation technique that translates code snippets into natural language descriptions (called translations). The code translation is conducted on machine instructions, where the context information is collected by simulating the execution of instructions. We further design a shared word mapping function using one single vocabulary for generating embeddings for both translations and queries. We evaluate the effectiveness of our technique, called TranCS, on the CodeSearchNet corpus with 1,000 queries. Experimental results show that TranCS significantly outperforms state-of-the-art techniques by 49.31% to 66.50% in terms of MRR (mean reciprocal rank).
Blockchain adoption from the Shipping industry: An empirical study

Blockchain technology can have numerous applications in the shipping industry (although it is a newcomer in blockchain technology) as it provides transparency, security, and cost reduction in financial transactions. Although at its early steps, this technology has been applied in the shipping industry. The majority of the related literature deals with the applicability of blockchain technology in the various processes of a shipping company and the related supply chains. Instead, this paper focuses on the willingness of shipping companies to adopt blockchain technology through an exploratory survey. It develops a framework based on a slightly altered version of the classic unified theory of acceptance using the technology (UTAUT) model and linear regression analysis. The outcome is a set of linear equations of behavioral intention and expectation of the Greek shipping industry adopting blockchain technology, highlighting the most influential factors. This paper contributes to the related discussion by unveiling the intention of Greek bulk shipping companies to adopt blockchain technology in their daily operations.
A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.
The Advancement of Virtual Reality in Automotive Market Research: Challenges and Opportunities

Virtual Reality (VR) can play a key role in automotive marketing research, lowering costs and shortening the time to launch a new product. However, few VR applications support automotive customers’ experiences during the early stages of product design. This study aims to identify and characterize into attributes the challenges and opportunities for the application of Virtual Reality in car clinics through a systematic review of the literature and patents. We searched PatentScout, ScienceDirect, Springer, and IEEEXplore for studies published between the databases’ inception and July 2020. Of the 77,383 patents and 336,785 articles identified, 72 and 13 were eligible, respectively. We discovered that patents are strongly concentrated by a few inventors, that the United States has the most records, and that the most prevalent applications relate to devices for automatically reading responders’ emotions in virtual environments. The articles revealed sixteen categories of challenges and opportunities: cost, location to customers, flexibility in interactions, model transportation, depth perception, haptic perception, motion, movement perception/physical collision, color and texture, sound feedback, product interaction/manipulation, visual–spatial, graphic quality, intuitiveness, cybersecurity, and cybersickness. Virtual Reality may be used for automotive marketing research but key factors such as hardware and software specification, stimulus quality, and survey objectives must be considered.
Blockchain technology adoption for sustainable learning

Sustainable Learning and Education (SLE) is a recent emerging philosophy founded on sustainability principles and in response to the UN announced Sustainable Development Goals (SDGs). Therefore, technologies should be implemented to empower educational institutions to achieve SLE. This study aims to investigate the factors impacting the intentions of using blockchain technology for SLE in Jordanian universities. Accordingly, an extended Technology Acceptance Model (TAM) is proposed where five more factors are integrated. To this end, an extended model was proposed and validated using structural equation modeling based on 407 responses collected using an online survey. The results showed that adopted factors significantly impact blockchain use in SLE. We believe that the study finding would assist decision-makers in building systems for sustainable learning and education for the Jordanian higher educational institutes.
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that the samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-LT-100, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.
The analysis of financial market risk based on machine learning and particle swarm optimization algorithm

Abstract The financial industry is a key to promoting the development of the national economy, and the risk it takes is also the largest hidden risk in the financial market. Therefore, the risk existing in the current financial market should be deeply explored under blockchain technology (BT) to ensure the functions of financial markets. The risk of financial markets is analyzed using machine learning (ML) and random forest (RF). First, the clustering method is introduced, and an example is given to illustrate the RF classification model. The collected data sets are divided into test sets and training sets, the corresponding rules are formulated and generated, and the branches of the decision tree (DT) are constructed according to the optimization principle. Finally, the steps of constructing the branches of DT are repeated until they are not continued. The results show that the three major industries of the regional economy account for 3.5%, 51.8%, 3.2%, 3.4%, and 3.8% of the regional GDP, respectively, the secondary industry makes up 44.5%, 43%, 45.1%, 44.8%, and 43.6%, respectively, and the tertiary industry occupies 20%, 3.7%, 52.3%, 52.9%, 54%, and 54.6%, respectively. This shows that with the development of the industrial structure under BT, the economic subject gradually shifts from the primary industry to the tertiary industry; BT can improve the efficiency of the financial industry and reduce operating costs and dependence on media. Meanwhile, the financial features of BT can provide a good platform for business expansion. The application of BT to the supply chain gives a theoretical reference for promoting the synergy between companies.
Optical Transparency Induced by a Largely Purcell Enhanced Quantum Dot in a Polarization-Degenerate Cavity

Optically active spin systems coupled to photonic cavities with high cooperativity can generate strong light-matter interactions, a key ingredient in quantum networks. However, obtaining high cooperativities for quantum information processing often involves the use of photonic crystal cavities that feature a poor optical access from the free space, especially to circularly polarized light required for the coherent control of the spin. Here, we demonstrate coupling with a cooperativity as high as 8 of an InAs/GaAs quantum dot to a fabricated bullseye cavity that provides nearly degenerate and Gaussian polarization modes for efficient optical accessing. We observe spontaneous emission lifetimes of the quantum dot as short as 80 ps (an ∼15 Purcell enhancement) and a ∼80% transparency of light reflected from the cavity. Leveraging the induced transparency for photon switching while coherently controlling the quantum dot spin could contribute to ongoing efforts of establishing quantum networks.
Preparing teachers for emergency remote teaching: A professional development framework for teachers in higher education

COVID-19 has significantly impacted teaching and learning in higher education, leading institutions to embrace Emergency Remote Teaching (ERT) in response to school and university closure. A systematic review research methodology was used to identify, analyse and synthesise literature on professional development in higher education published between 2010 and 2020. Following an inductive thematic analysis, the authors identified four themes that represent the literature: learning approaches, delivery modes, design features and institutional support. Based on the emerging themes and the analysis of the selection of studies, a framework for professional development is proposed to prepare teachers in higher education for ERT. The use of the framework is recommended to guide higher education institutions in best assisting their academic staff during an ERT context.
Segmentation mask and feature similarity loss guided GAN for object-oriented image-to-image translation

While image-to-image translation has been extensively studied, there are a number of limitations in existing methods designed for transformation between instances of different shapes from different domains. In this paper, a novel approach was proposed (hereafter referred to as ObjectVariedGAN) to handle geometric translation. One may encounter large and significant shape changes during image-to-image translation, especially object transfiguration. Thus, we focus on synthesizing the desired results to maintain the shape of the foreground object without requiring paired training data. Specifically, our proposed approach learns the mapping between source domains and target domains, where the shapes of objects differ significantly. Feature similarity loss is introduced to encourage generative adversarial networks (GANs) to obtain the structure attribute of objects (e.g., object segmentation masks). Additionally, to satisfy the requirement of utilizing unaligned datasets, cycle-consistency loss is combined with context-preserving loss. Our approach feeds the generator with source image(s), incorporated with the instance segmentation mask, and guides the network to generate the desired target domain output. To verify the effectiveness of proposed approach, extensive experiments are conducted on pre-processed examples from the MS-COCO datasets. A comparative summary of the findings demonstrates that ObjectVariedGAN outperforms other competing approaches, in the terms of Inception Score, Frechet Inception Distance, and human cognitive preference.
Implications of Blended Teaching Based on Theory of Semantic Wave for Teaching English Writing in High School

In recent years, information technology has been widely used in foreign language teaching. As a new English teaching mode, the blended teaching, integrates the advantages of online teaching and traditional face-to-face class. Based on the theory of semantic wave, teachers can play the role of blended teaching in enhancing the writing interest and writing ability of high school students. The research makes a blended teaching design paradigm based on previous research and give some suggestions to teachers, which will better serve the teaching and learning.
Mining multilingual and multiscript Twitter data: unleashing the language and script barrier

Micro-blogging sites like Twitter have become an opinion hub where views on diverse topics are expressed. Interpreting, comprehending and analysing this emotion-rich information can unearth many valuable insights. The job is trivial if the tweets are in English. But lately, increase in native languages for communication has imposed a great challenge in social media mining. Things become more complicated when people use Roman scripts to write non-English languages. India, being a country with a diverse collection of scripts and languages, encounters the problem severely. We have developed a system that automatically identifies and classifies native tweets, irrespective of the script used. Converting all tweets to English, we get rid of the 'script vs language' problem. The new approach we formulated consists of Script Identification, Language analysis, and Clustered mining. Considering English and the top two Indian languages, we found that the proposed framework gives better precision than the prevailing approaches.
A novel multi-class ensemble model based on feature selection using Hadoop framework for classifying imbalanced biomedical data

Due to the exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Traditional decision tree models such as multivariate Bernoulli model, random forest and multinominal naïve Bayesian tree use attribute selection measures to decide best split at each node of the decision tree. Also, the efficiency of document analysis in Hadoop framework is limited mainly due to the class imbalance problem and large candidate sets. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a map-reduce based multi-class ensemble decision tree model was designed and implemented on the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.
Binary tree-seed algorithmswith S-shaped and V-shaped transfer functions

Tree-seed algorithm (TSA) is a nature-inspired metaheuristic optimization algorithm.TSA was originally designed and introduced for solving continuous optimization problems.In this study, TSA was modified with transfer functions so as to solve binary optimization problems.Continuous search space was mapped to binary search space with transfer functions.Four S-shaped and four Vshaped transfer functions were used for discretization.Uncapacitated facility location problem (UFLP) is a pure binary optimization problem.In order to measure the performance, 15 different sized (small, medium, large and extra-large) UFLPs were solved with eight different binary TSAs in this study.Experimental results has shown that S-shaped transfer functions are better than V-shaped transfer functions on these problem sets.