Reviewerly/exHarmony huggingface dataset:
Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks.
Blockchain in Education: A Systematic Review and Practical Case Studies

The advent of blockchain technology over the last decade has led to the development of multiple use-cases of decentralization in various fields including education.This paper presents a unique bibliometric and qualitative analysis of the blockchain in education with novel contributions on temporal development, emerging themes and practical case studies on adoption and integration with existing educational technologies.We focus on identifying the major actors in the space, demographic participation and adoption, current hot topics, grey areas, and potential areas for innovation.Our analysis shows that while the blockchain has been around for about 13 years, blockchain in education only became prominent 5 years ago.This research also reveals that most of the efforts have been focused on reporting and verifying academic certificates and transcripts: only very few research focused on reporting and connecting in-depth academic records such as learning behaviour logs, learning contents and assessment data.This calls for concern as current education blockchain systems do not consider interoperability at the blockchain level and the heterogeneous nature in which institutes create and consume academic data.Finally, we present discussions on the implications of our findings, potential solutions and aspects of education blockchain research that can help to improve educational outcomes for various stakeholders.INDEX TERMS Academic records, analysis, application programming interface (API), artificial intelligence (AI), bibliometric, blockchain of learning logs (BOLL), education, experience API (xAPI), information and communication technology (ICT), latent dirichlet allocation (LDA), learning management system (LMS), learning record store (LRS), learning tools interoperability (LTI), quick response (QR), sharable content object reference model (SCORM), smart ecosystem for learning and inclusion (SELI)
Do We Still Need Human Assessors? Prompt-Based GPT-3 User Simulation in Conversational AI

Scarcity of user data continues to be a problem in research on conversational user interfaces and often hinders or slows down technical innovation. In the past, different ways of synthetically generating data, such as data augmentation techniques have been explored. With the rise of ever improving pre-trained language models, we ask if we can go beyond such methods by simply providing appropriate prompts to these general purpose models to generate data. We explore the feasibility and cost-benefit trade-offs of using non fine-tuned synthetic data to train classification algorithms for conversational agents. We compare this synthetically generated data with real user data and evaluate the performance of classifiers trained on different combinations of synthetic and real data. We come to the conclusion that, although classifiers trained on such synthetic data perform much better than random baselines, they do not compare to the performance of classifiers trained on even very small amounts of real user data, largely because such data is lacking much of the variability found in user generated data. Nevertheless, we show that in situations where very little data and resources are available, classifiers trained on such synthetically generated data might be preferable to the collection and annotation of naturalistic data.
Ensemble learning based multi-modal intra-hour irradiance forecasting

Accurate intra-hour irradiance forecasting plays an important role in improving the effectiveness of photovoltaic power management. More and more sensors, for example, total sky imager and cloud sensors, are widely used in photovoltaic plants, so that various factors could be comprehensively concerned to improve the accuracy of forecast. However, the existing methods have difficulty in encoding heterogeneous multi-source data dynamically. To mitigate these problems and improve the practicality of the model, an ensemble learning based multi-modal intra-hour irradiance forecasting method is proposed to forecast the global horizontal solar irradiance in the next 10 min. First, six base learners are built by selecting different combinations of multi-modal data as inputs for considering various factors. Then, a linear ridge regressor is used to integrate the pre-trained base learners to obtain outputs. Furthermore, to improve the robustness of the ensemble models, a dynamical fine-tuning scheme based on clear sky index is proposed. The weather states are predicted by a hidden Markov model based on the clear sky index. Experiments show that the proposed forecasting method effectively improves the accuracy of intra-hour irradiance forecasting by 11.6% when compared with commonly used models. • Multi-modal data features are effectively incorporated through ensemble learning. • The applicability for data with different sampling rates makes the method practical. • Meta-learner parameters are fine-tuned using clear sky index for the robustness. • Valid information is captured by different base learners to mitigate overfitting.
DIC-Net: Upgrade the performance of traditional DIC with Hermite dataset and convolution neural network

Digital image correlation (DIC) is a non-contact optical method that tracks the speckle pattern on specimen surface to calculate the displacement and strain by image correlation algorithm. Although the traditional DIC method can conveniently measure surface deformation, it still has many limitations: (1) the accuracy of displacement and strain calculation needs to be improved in the case of high deformation gradient; (2) under match or over-match can hardly be avoided when the filters are used to reconstruct smooth displacement or strain field, and (3) boundary effect remains unresolved in computing the deformation near the boundary of region of interest or the discontinuous area (e.g. area near crack tip or crack face). Recently, the deep learning based DIC (Deep-DIC) has revealed its attractive ability in handling above issues in traditional DIC, and impressive results have been achieved. The mean value of the absolute error (MAE) on the test set has been optimized to 0.0361 pixels using existing Deep-DIC approaches, which are accompanied by a real-time measurement speed. The network structure and training dataset are two key factors for the deep learning method. However, the current working networks have been modified from other image tasks and cannot fully adapt to the demands of the DIC tasks, and the dataset they generated still has evident flaws, limiting the method's accuracy and generalization performance which is utilized to assess performance on samples outside the training set. In this paper, we firstly proposed a new Hermite dataset that is created by using the high-order Hermite element to take account more complex deformation, then a new network architecture designed for the DIC task has been developed to extract richer deformation features. A test set of 3216 examples containing six different modes of displacement is used to compare the performance of our network with others. The proposed DIC-Net-d achieves the lowest MAE in the test set. Meanwhile, in the Star5 image sets from DIC-Challenge, the proposed DIC-Net-d achieves a spatial resolution of 17.25 pixels and a noise level of 0.0136 which outperforms existing traditional and non-traditional methods. Finally, the strain network trained by our Hermite dataset is also successful in predicting the strain field of Star6 in the DIC challenge. The experiment results show the superiority of the proposed Hermite dataset and new network with respect to other Deep-DIC methods.
Improving RGB-D Salient Object Detection via Modality-Aware Decoder

Most existing RGB-D salient object detection (SOD) methods are primarily focusing on cross-modal and cross-level saliency fusion, which has been proved to be efficient and effective. However, these methods still have a critical limitation, i.e., their fusion patterns - typically the combination of selective characteristics and its variations, are too highly dependent on the network's non-linear adaptability. In such methods, the balances between RGB and D (Depth) are formulated individually considering the intermediate feature slices, but the relation at the modality level may not be learned properly. The optimal RGB-D combinations differ depending on the RGB-D scenarios, and the exact complementary status is frequently determined by multiple modality-level factors, such as D quality, the complexity of the RGB scene, and degree of harmony between them. Therefore, given the existing approaches, it may be difficult for them to achieve further performance breakthroughs, as their methodologies belong to some methods that are somewhat less modality sensitive. To conquer this problem, this paper presents the Modality-aware Decoder (MaD). The critical technical innovations include a series of feature embedding, modality reasoning, and feature back-projecting and collecting strategies, all of which upgrade the widely-used multi-scale and multi-level decoding process to be modality-aware. Our MaD achieves competitive performance over other state-of-the-art (SOTA) models without using any fancy tricks in the decoder's design. Codes and results will be publicly available at https://github.com/MengkeSong/MaD.
A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO′s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments.
Using mobile applications with augmented reality elements in the self-study process of prospective translators

The paper deals with the use of mobile applications for language learning in the self-study process of prospective translators. A comparative analysis was made of some mobile language learning applications. The capabilities of mobile language learning applications are summarised, which include learning vocabulary grammar, pronunciation training, and practice listening and speaking. Particular attention is paid to interactive tasks and gamification of learning. An experiment was conducted with students using the Duolingo mobile app. The advantages of using mobile applications with augmented reality elements in the self-study process of future translators are identified. The main ones are interactivity, accessibility, individual schedules, and the ability to learn anytime, anywhere. There has been a significant increase in motivation to learn a foreign language independently thanks to mobile apps. It is proposed that mobile applications should only be seen as additional opportunities for independent and autonomous learning.
Mobile-assisted language learning (MALL) research trends and patterns through bibliometric analysis: Empowering language learners through ubiquitous educational technologies

Mobile devices and technologies have proliferated extensively and become an integral part of life and learning. Mobile-assisted language learning (MALL) has progressed as an emerging area of research corresponding to recent advances in mobile technologies and the proliferation of smartphones and tablet computers. Accordingly, this study examined MALL research between 2008 and 2020 through a bibliometric analysis using social network analysis (SNA) and text mining techniques. The SNA and text mining analysis suggest five broad research themes: (1) self-regulated language learning by defining one's own learning objectives, (2) providing learner agency and motivation by empowering autonomy , (3) personalizing learning through artificial intelligence (AI)-supported mobile learning (m-learning), (4) MALL for learning in the wild, and (5) MALL to support higher education. The findings show that while MALL research has been considerably operationalized around linguistic factors, nonlinguistic factors relating to learners' interactions with mobile devices or applications have been largely overlooked. It was found that MALL scholarship has recently tended to incorporate the use of mobile devices in informal learning contexts and outside the classroom due to the flexibility and anytime anywhere functionality of m-learning. The study concludes with several suggestions and highlights the areas that need more attention in MALL research.
Analysis of Polkadot: Architecture, Internals, and Contradictions

Polkadot is a network protocol launched in 2020 with the ambition of unlocking the full potential of blockchain technologies. Its novel multi-chain protocol allows arbitrary data to be transferred across heterogeneous blockchains, enabling the implementation of a wide range of novel use cases. The Polka-dot architecture is based on the principles of sharding, which promises to solve scalability and interoperability shortcomings that encumber many existing blockchain-based systems. Lured by these impressive features, investors immediately appreciated the Polkadot project, which is firmly ranked among the top 10 cryptocurrencies by capitalization (around 10 Billions USD). However, Polkadot has not received the same level of attention from academia that other proposals in the crypto domain have received so far, like Bitcoin, Ethereum, and Algorand, to cite a few. Polkadot architecture is described and discussed only in the grey literature, and very little is known about its internals. In this paper, we provide the first systematic study on the Polka-dot environment, detailing its protocols, governance, and economic model. Then, we identify several limitations-supported by an empirical analysis of its ledger-that could severely affect the scalability and overall security of the network. Finally, based on our analysis, we provide future directions to inspire researchers to investigate further the Polkadot ecosystem and its pitfalls in terms of performance, security, and network aspects.
The Changing Tendency and Association Analysis of Intelligent Coal Mines in China: A Policy Text Mining Study

The intellectualization of coal mines provides core technical support for the high-quality development of the coal industry. Intelligent texts, especially intelligent policy documents, play an extremely important role in analyzing the trend of intelligent policies in coal mines. This paper collects more than 50 central and local intelligent coal mine policy texts from recent years. The method of text analysis is a tool used for text mining, and semantic networks are generated; it reflects that the policy mainly focuses on promoting large-scale equipment and platform integration, to promote the overall goal of safe, efficient, and intelligent development of coal mining. By analyzing the high-frequency words of the policy from 2016 to 2022, it reflects that the policy trend mainly goes through the following three stages: firstly, eliminate backward enterprises, encourage coal mine automation and mechanization; then, standardize the basic concept of coal mine intellectualization, carry out the transformation of coal mine intellectualization; and the third stage is to promote the application of key technologies of intellectualization, build intelligent demonstration coal mines and reach the acceptance stage, and promote the further development trend of coal mine intellectualization.
Forecasting the COVID-19 with Interval Type-3 Fuzzy Logic and the Fractal Dimension

In this article, the prediction of COVID-19 based on a combination of fractal theory and interval type-3 fuzzy logic is put forward. The fractal dimension is utilized to estimate the time series geometrical complexity level, which in this case is applied to the COVID-19 problem. The main aim of utilizing interval type-3 fuzzy logic is for handling uncertainty in the decision-making occurring in forecasting. The hybrid approach is formed by an interval type-3 fuzzy model structured by fuzzy if then rules that utilize as inputs the linear and non-linear values of the dimension, and the forecasts of COVID-19 cases are the outputs. The contribution is the new scheme based on the fractal dimension and interval type-3 fuzzy logic, which has not been proposed before, aimed at achieving an accurate forecasting of complex time series, in particular for the COVID-19 case. Publicly available data sets are utilized to construct the interval type-3 fuzzy system for a time series. The hybrid approach can be a helpful tool for decision maker in fighting the pandemic, as they could use the forecasts to decide immediate actions. The proposed method has been compared with previous works to show that interval type-3 fuzzy systems outperform previous methods in prediction.
A Multi-Objective Optimization Framework for Multi-Stakeholder Fairness-Aware Recommendation

Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, however, mainly focus on maximizing consumers’ satisfaction by recommending the most relevant items to each individual. This may result in unfair exposure of items, thus jeopardizing producer benefits. Additionally, they do not care whether consumers from diverse demographic groups are equally satisfied. To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR , that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee. We first propose four fairness constraints on consumers and producers. In order to train the whole framework in an end-to-end way, we utilize the smooth rank and stochastic ranking policy to make these fairness criteria differentiable and friendly to back-propagation. Then, we adopt the multiple gradient descent algorithm to generate a Pareto set of solutions, from which the most appropriate one is selected by the Least Misery Strategy. The experimental results demonstrate that Multi-FR largely improves recommendation fairness on multiple stakeholders over the state-of-the-art approaches while maintaining almost the same recommendation accuracy. The training efficiency study confirms our model’s ability to simultaneously optimize different fairness constraints for many stakeholders efficiently.
Deep active learning models for imbalanced image classification

Active learning can query valuable samples in an unlabeled sample pool for annotation, thus building a more informative labeled dataset and reducing the annotation cost. However, traditional active learning methods are not effective in the task of imbalanced image classification for ignoring the distribution bias. In this study, we propose a Balanced Active Learning (BAL) method for imbalanced image classification. BAL estimates the probability of a sample belonging to minority or majority classes and compensates the annotation query for the minority classes, thus alleviating the class imbalance in the selected training samples. Experiments on three imbalanced image classification datasets, imbalanced CIFAR-10, ISIC2020, and Caltech256, showed that BAL achieved new state-of-the-art performance of active learning in a variety of classification tasks and different types of imbalance.
Enhanced graph neural network for session-based recommendation

A key problem of session-based recommendation is to predict items of interest based on anonymous user’s current interaction session. To improve the prediction accuracy, existing methods mainly adopted two paradigms, i.e., sequence pattern and co-occurrence pattern. Though decent results were achieved, these methods still could not better simulate the real intention of users, or consider the complex pattern transformation between interacted items. To address these limitations, we propose a novel framework called Enhanced Graph Neural Network (E-GNN) for session-based recommendation, where the interaction sequence of all anonymous users is modeled as a Weighted Global Item Graph (WGIG) and the current interaction session of a target user is modeled as a Local Session Graph (LSG). Specifically, the WGIG is first constructed to produce a weight matrix effectively revealing the connection patterns of all intereacted items. The LSG is then constructed to explicitly model the initial interaction patterns of items in the target user’s current session. Finally, the weighting matrix and LSG are input into our proposed fusion algorithm, and the E-GNN representing the target user’s real interaction pattern is the output. The main contributions of this paper are: (1) Multiple interaction patterns are explicitly modelled in one interaction session graph; and (2) A new fusion algorithm is proposed to reconstruct current session graph by integrating the extracted weights representing the directivity of interacted items from the WGIG. A large number of comparative experiments are conducted on two public datasets, and the results show that E-GNN has distinct advantages over other baseline models.