Reviewerly/exHarmony huggingface dataset:
Interplaying Reading and Writing in ESL/EFL: A Literature Review of Strategies for Indonesian Teachers

The importance of integrating reading and writing activities in ESL/EFL classes cannot be overstated, especially in Indonesia where low literacy levels and reading interest present a challenge for teachers. This study conducted a literature review of 120 articles and books over the last 10 years taken from ZLibrary and Google Scholar's free database to identify effective strategies for incorporating reading and writing activities into the classroom that can be applied in many contexts and countries, including Indonesia. It was found that exposing students to various English text structures through these activities can greatly enhance their writing skills. By examining this literature, the study identifies 6 effective strategies for integrating reading and writing activities in ESL/EFL classes, namely summarizing, paraphrasing, short story writing, comparing and contrasting, discovery learning, and project-based learning. Teachers can employ these strategies to interplay reading and writing activities, helping students develop critical thinking skills, vocabulary, and writing abilities. In conclusion, it is clear that integrating reading and writing activities in ESL/EFL classes can provide a comprehensive approach to teaching and nurture students' writing skills, even in countries with low literacy levels and limited reading interest like Indonesia. Future research should explore additional effective strategies for integrating these activities.
Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods’ use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.
Objective Assessment During a Pandemic

Life during the pandemic tested the objective assessment of learners' knowledge. By using criteria and grammatical rules for decision-making, the informal evaluation problem is resolved in a variety of unstructured situations.The current study's objective is to evaluate and compare the knowledge that has been acquired and competencies by the learners on the basis of online presented learning material and subsequent online assessment. The achieved results obtained during the evaluation of a test and exclude a constructed course project on the teaching material in the discipline "Web technologies" are compared.A formalized approach based on non-standard application of the one-parameter Rush model with fuzzy evaluations of the criteria was used for the two applied evaluation methods. The model enables program implementation and can be embedded in e-learning platforms.
Virtual and Augmented Reality Training on Digital Mirror D-Wall. Case Study: Long Covid-19 Rehabilitation Protocol

Clinicians agree it's effective for interdisciplinary communication (80%) and interventions (65%).Conclusions: The OBS-INS is a valid behavioral outcome measure for assessing CB in ABI with high intra-rater reliability.The OBS-INS is best administered to individuals with ABI by the same clinician to ensure reliability of results.
An improved generative adversarial network for remote sensing image super‐resolution

Abstract Spatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation.
Genetic Algorithm Based Over-Sampling with DNN in Classifying the Imbalanced Data Distribution Problem

Objective: Data imbalance exists in many real-life applications.In the imbalanced datasets, the minority class data creates a wrong inference during the classification that leads to more misclassification.More research has been done in the past to solve this issue, but as of now there is no global working solution found to do efficient classification.After analyzing various existing literatures, it is proposed to minimize the misclassification through genetic based oversampling and deep neural network (DNN) classifier.Method: In the proposed oversampling method synthetic samples are generated based on genetic algorithm.Initial populations for the genetic algorithm are generated using Gaussian weight initialization technique and the fittest individual from the population are selected by Euclidean distance for further processing to generate synthetic data in double the minority class size and the dataset is classified with the DNN.Findings: The performance of the oversampled training data with DNN Classifier is compared with C4.5 and Support Vector Machine (SVM) classifiers and found that the DNN classifier outperforms the other two classifiers.The data generated using SMOTE and ADASYN are considered for comparison.It is found that the proposed approach outperforms the other approaches.It is also proved from the experiment that misclassification is reduced and the proposed method is statistically significant and is comparatively better.Novelty: Initial population generation by Gaussian weight initialization, the fittest sample selection by Euclidean distance measure, synthetic samples with double the minority class size and DNN for classification to reduce the misclassification is novelty in this work.
A personalised recommendation of mobile learning model based on content awareness

In order to overcome the problems of traditional recommendation methods such as large error in recommendation results and long time-consuming process of recommendation results generation, the paper proposes a personalised recommendation method based on content-aware mobile learning mode. First, the recommendation process architecture is designed, which mainly includes a user demand analysis module, a user preference analysis module, and a mobile learning model resource library decision module. Then, the energy function is used, and the dataset is inserted to design the content perception process. Finally, according to the perceptual results, a user emotional topic model with a supervision mechanism is used to complete personalised recommendation. The experimental results show that the average absolute error value of the recommendation results obtained by the method in this paper is between 0.06-0.15, the maximum recommendation result generation process takes only 4.5 s, and the clustering effect of different mobile learning modes is better.
Post-Pandemic Sentiment Analysis Based on Twitter Data Using Deep Learning

Since January 2020, COVID-19 has been spreading over the world and has been declared a pandemic. Nation and society are growing scared of it as fresh instances and mass deaths increase daily. India was one of the major countries to suffer the consequences of COVID-19 during that phase as multiple waves hit India. Many social media channels were being used by people from all over the country to discuss this pandemic and its aftereffects. One of the most popular ways to share opinions or judgments today is through social media. Therefore, machines are continuously being developed to analyze what people post on social networking sites like Twitter, Facebook, Instagram, and other platforms thanks to advancements in current computing technology. Based on their mood, these ideas or points of view can be grouped and examined. In this paper, we used tweets collected from Twitter to analyze the sentiment that people conveyed on social media after the second wave of Corona Virus. The sentiment of the tweets has been divided into five categories: "Strongly Negative", "Negative", "Neutral", "Positive" and "Strongly Positive". First, we classify data using Python’s Vader. We have trained a model using our own labeled dataset and evaluated its performance using Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN).
Few-Shot Semantic Relation Prediction Across Heterogeneous Graphs

Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of links. In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data. Since a variety of semantic relations exist in multiple heterogeneous graphs, the transferable knowledge can be mined from some existing semantic relations to help predict the new semantic relations with few labeled data. This inspires a novel problem of few-shot semantic relation prediction across heterogeneous graphs. However, the existing methods cannot solve this problem because they not only require a large number of labeled samples as input, but also focus on a single graph with a fixed heterogeneity. Targeting this novel and challenging problem, in this paper, we propose a Meta-learning based Graph neural network for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the graph structure between objects into multiple normalized subgraphs, then adopts a two-view graph neural network to capture local heterogeneous information and global structure information of these subgraphs. Secondly, MetaGS aggregates the information of these subgraphs with a hyper-prototypical network, which can learn from existing semantic relations and adapt to new semantic relations. Thirdly, using the well-initialized two-view graph neural network and hyper-prototypical network, MetaGS can effectively learn new semantic relations from different graphs while overcoming the limitation of few labeled data. Extensive experiments on three real-world datasets have demonstrated the superior performance of MetaGS over the state-of-the-art methods.
A New Privacy-Preserving Protocol for Academic Certificates on Hyperledger Fabric

Academic certificates are integral to an individual's education and career prospects, yet conventional paper-based certificates pose challenges with their transport and vulnerability to forgery.In response to this predicament, institutions have taken measures to release e-certificates, though ensuring authenticity remains a pressing concern.Blockchain technology, recognised for its attributes of security, transparency, and decentralisation, presents a resolution to this problem and has garnered attention from various sectors.While blockchain-based academic certificate management systems have been proposed, current systems exhibit some security and privacy limitations.To address these issues, this research proposes a new Decentralised Control Verification Privacy-Centered (DCVPC) protocol based on Hyperledger Fabric blockchain for preserving the privacy of academic certificates.The proposed protocol aims to protect academic certificates' privacy by granting complete authority over all network nodes, creating channels for universities to have their private environment, and limiting access to the ledger.The protocol is highly secure, resistant to attacks, and allows improved interoperability and automation of the certificate verification process.A proof-of-concept was developed to demonstrate the protocol's functionality and performance.The proposed protocol presents a promising solution for enhancing security, transparency, and privacy of academic certificates.It guarantees that the certificate's rightful owner is correctly identified, and the issuer is widely recognised.This research makes a valuable contribution to the area of blockchain-based academic certificate management systems by introducing a new protocol that addresses the present security and privacy limitations.
Comprehensive Test and Evaluation Path of College Teachers' Professional Development Based on a Cloud Education Big Data Platform

Teachers serve as carriers who shoulder the important task of improving the quality of education, and their professional level affects the quality of vocational and technical personnel training. At present, the professional growth of teachers in vocational colleges has attracted more and more attention from the society. This article studies the comprehensive test and evaluation path of college teachers' professional development based on cloud education big data platform. This article constructs a "supply-demand relationship" model between cloud education big data platform and college teachers' professional development, and expounds the comprehensive test and evaluation strategy of college teachers' professional development based on cloud education big data platform. This article constructs a comprehensive measure index system of influencing factors of college teachers' professional development, and weights the evaluation indexes based on AHM attribute hierarchy model. By optimizing the super-efficiency SBM model, it proposes a comprehensive evaluation method considering the temporal and spatial features of college teachers' professional development level, which is suitable for different professional teachers' professional development features. The experimental results verify the effectiveness of the evaluation index system and evaluation model.
Using word embeddings to investigate human psychology: Methods and applications

( 5 天津大学智能与计算学部, 天津 300350)
Enhancing Federated Learning Robustness Through Clustering Non-IID Features

Federated learning (FL) enables many clients to train a joint model without sharing the raw data. While many byzantine-robust FL methods have been proposed, FL remains vulnerable to security attacks (such as poisoning attacks and evasion attacks) because of its distributed nature. Additionally, real-world training data used in FL are usually Non-Independent and Identically Distributed (Non-IID), which further weakens the robustness of the existing FL methods (such as Krum, Median, Trimmed-Mean, etc.), thereby making it possible for a global model in FL to be broken in extreme Non-IID scenarios. In this work, we mitigate the vulnerability of existing FL methods in Non-IID scenarios by proposing a new FL framework called Mini-Federated Learning (Mini-FL). Mini-FL follows the general FL approach but considers the Non-IID sources of FL and aggregates the gradients by groups. Specifically, Mini-FL first performs unsupervised learning for the gradients received to define the grouping policy. Then, the server divides the gradients received into different groups according to the grouping policy defined and performs byzantine-robust aggregation. Finally, the server calculates the weighted mean of gradients from each group to update the global model. Owning the strong generality, Mini-FL can utilize the most existing byzantine-robust method. We demonstrate that Mini-FL effectively enhances FL robustness and achieves greater global accuracy than existing FL methods when against the security attacks and in Non-IID settings.
Research on tree image retrieval method based on twin network multi feature fusion

In this paper, an image retrieval method based on twin network multi feature fusion is proposed to improve the retrieval accuracy of tree images to meet the detection requirements of power grid line tree obstacles. In the twin network, the color features, texture features and shape features of the reference image and test image are included in the SiamRPN module for convolution processing, and then the similarity is judged through UM module fusion. The experimental results show that this method can achieve accurate retrieval of coconut, banyan and other tree images, and the retrieval accuracy and robustness are significantly higher than other image retrieval methods.
A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face class biases due to class variants and background confusion. In this paper, we propose a joint framework that combines more valuable class-aware and class-agnostic alignment guidance to facilitate the segmentation. Specifically, we design a hybrid alignment module which establishes multi-scale query-support correspondences to mine the most relevant class-aware information for each query image from the corresponding support features. In addition, we explore utilizing base-classes knowledge to generate class-agnostic prior mask which makes a distinction between real background and foreground by highlighting all object regions, especially those of unseen classes. By jointly aggregating class-aware and class-agnostic alignment guidance, better segmentation performances are obtained on query images. Extensive experiments on PASCAL- $$5^i$$ and COCO- $$20^i$$ datasets demonstrate that our proposed joint framework performs better, especially on the 1-shot setting.
An automatic text generation algorithm of technical disclosure for catenary construction based on knowledge element model

Preparing technical disclosure documents has always been a labor-intensive task in the site management practice of current OCS (Overhead Contact System) project construction, and the overall information level of construction management is not high. As a result, the technical disclosure documents are of poor quality, which significantly affects the effective play of technical documents in guiding construction activities. In view of the above limitations, this paper proposes a text-generation method for OCS engineering technical disclosure based on a knowledge element model. By investigating the characteristics of OCS engineering technical knowledge, a representation model is established to model the technical knowledge from multiple sources, such as case data, standard specifications, and design data. As OCS engineering is highly standardized, we propose a case-rule hybrid reasoning model for the reuse of OCS engineering case knowledge. The mining and utilization of earlier engineering technical knowledge are made possible by similar retrieval of precedent situations and adaptive rules modification. Finally, we suggest an automatic text-generating technique based on a configuration template for new building projects. The knowledge element model is converted into a technical disclosure document expressed in natural language using a two-level mapping process. The cantilever installation project is used as an illustration for empirical research, and relevant practitioners are invited to carry out a manual review by questionnaire from the perspectives of topic relevance, topic integrity, topic word implication, sentence smoothness, sentence continuity, and information volume. At the same time, standard assessment metrics such as BLEU and ROUGE are employed to compare with the neural network-based text generation approach. The outcomes demonstrate that the strategy suggested in this paper can generate technical disclosure text that performs well. Overall, its text integrity and readability may satisfy on-site management's demands and help on-site management lessen the workload of technical management staff.
Fluent APIs in Functional Languages

Fluent API is an object-oriented pattern for elegant APIs and embedded DSLs. A smart fluent API can enforce the API protocol or DSL syntax at compile time. Since fluent API implementations typically rely on overloading function names, they are hard to realize in functional programming languages. This work shows how functional fluent APIs can be implemented in the absence of name overloading, by relying on parametric polymorphism and Hindley-Milner type inference. The implementation supports fluent API protocols in the regular- and deterministic context-free language classes, and even beyond.
Exploring Entity-level User Preference on the Knowledge Graph for Recommender System

Knowledge graphs (KG) have attracted extensive attention in recommender systems since they contain rich external knowledge. The recent trend in KG-enhanced recommender systems is to employ graph neural networks (GNN) to learn the node representations of the involved graph structures in the recommender system to speculate user preferences. However, existing KG-enhanced recommendation models face two major issues: i) User preferences are mainly built at the relation level and fail to model preferences at the attribute entity level; ii) Implicit feedback lacks accurate user rating information and the data may contain noisy interactions. Such inaccurate preference modeling and imperfect interaction data hinder the capture of users’ actual preferences. To this end, we propose an Entity-level user Preference-aware model on Knowledge Graph (EPKG), which models user preferences at the attribute entity level. Specifically, we introduce the number of connections between attribute entities and user interaction items in the knowledge graph and establish a weight distribution on the number of connections to speculate user preferences for attribute entities. Furthermore, we devise user preference learning to model user preferences to the finer attribute entity level. Afterward, we design a preference-aware aggregation strategy that uses entity-level user preferences to guide the learning of item weights in user interaction history, which in turn alleviates the effects of lack of user rating information and noisy interactions. Experimental results on the three datasets show that EPKG achieves significant improvement compared to the state-of-the-art models. Especially for the Last-FM dataset, EPKG improves [email protected] and Re[email protected] by 31.5% and 18.4%, respectively.