Reviewerly/exHarmony huggingface dataset:
Automation of the generation of scientific statistics and the development of monitoring publishing activity dashboards in real time

One of the components that creates a burden on scientists is work with documents. Since this is manual work, the typical process involves spending a significant amount of time on agreeing to correct errors. An approach is proposed, which involves the use of a database with information about scientists and document templates to simplify such activities of scientists.
Interpretable User Behavioral Analysis and Personalized Recommendation with Side Information

Due to information overload, recommender system is developed and widely applied to better predict customer behaviors empowered by interaction data. In this paper, we develop an interpretable recommender system to improve click behavior prediction accuracy. Our proposed recommender system consists of i) designing a user-item interaction rating system defined as the weighted-sum of the multiple interaction variables, where the weight vector is regarded as hyperparameter, ii) using matrix factorization technique to capture the latent structure from the observable user-item interaction, and predict personalized ratings for items not shown to users, and iii) determining the weight vector by optimizing AUC which evaluates click behavior prediction accuracy on a validation set. To be specific, an algorithm with radial basis function surrogates is applied for hyperparameter optimization. We also develop a case study with user behavioral data from Netease music and observe a 15.5% improvement in AUC when incorporating multiple information sources.
Evaluation of Artificial Neural Network, using Evaluation based on Distance from Average Solution

Computer systems based on artificial neural networks, often known as neural networks or neural nets, are modeled after the organic neural networks seen in animal brains. Artificial neurons are a group of interconnected units or nodes that serve as the foundation of an ANN and are meant to approximate the function of biological brain neurons. This article contains a survey of practical uses for neural networks. It offers a taxonomy of Artificial Neural Networks (ANNs), in-forms the reader of recent and upcoming developments in ANN applications research, and highlights research areas of interest. This paper also discusses the difficulties, contributions, comparative effectiveness, and important methodologies in ANN applications. The study examines a wide range of ANN applications in numerous disciplines, including computing, science, engineering, medicine, the environment, agriculture, mining, technology, climate, business, and the arts similar to nanotechnology. This study analyses performance assesses ANN contributions and criticizes techniques. The study discovered that artificial neural networks with feedforward and feedback propagation do well when applied to solving human problems. Therefore, based on data analysis characteristics such as accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and efficiency, we presented feed-forward and feedback propagation ANN models for research centers. A computational model known as an artificial neural network (ANN) is composed of many processing elements that accept inputs and produce outputs by their specified activation functions. This article will make other articles in this computer magazine easier to grasp for those who know little or nothing about ANNs. We go over the reasons for creating ANNs, the fundamentals of a biological neuron and an artificial computer model, network designs, learning mechanisms, and some of the most widely used ANN models. A successful ANN character recognition application brings us to a close. EDAS Evaluation Based on Distance from Average Solution method for Notebook(n1), Notebook(n2), Notebook(n3), Notebook(n4), Notebook(n5). Notebook (n1), Notebook (n2), Notebook (n3), Notebook (n4), Notebook (n5). Speed (MHz), RAM (Mbytes), Display (inches), Price (Euro). Notebook (n5) has the highest rank whereas Notebook (n1) has the lowest rank. The need for hybrid systems, Optical Neural Networks, EDAS Method.
An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models.
FedFR: Evaluation and Selection of Loss Functions for Federated Face Recognition

With growing concerns about data privacy and the boom in mobile and ubiquitous computing, federated learning, as an emerging privacy-preserving collaborative computing approach, has been receiving widespread attention recently. In this context, many clients collaboratively train a shared global model under the orchestration of a remote server, while keeping the training data localized. To achieve better federated learning performance, the majority of existing works have focused on designing advanced learning algorithms, such as server-side parameter aggregation policies. However, the local optimization on client devices, especially selecting an appropriate loss function for local training, has not been well studied. To fill this gap, we construct a federated face recognition prototype system and test five classical metric learning methods(i.e. loss functions) in this system, comparing their practical performance in terms of the global model accuracy, communication cost, convergence rate, and resource occupancy. Extensive empirical studies demonstrate that the relative performance between these approaches varies greatly in different federated scenarios. Specifically, when the number of categories to recognize on each client is large, using the classification-based loss function can make a better global model faster with less communication cost; while when there are only a few classes on each client, using the pair-based method can be more communication-efficient and obtain higher accuracy. Finally, we interpret this phenomenon from the perspective of similarity optimization and offer some suggestions on making suitable choices amongst various loss functions.
Analysis of Speaking Skills with Storytelling Method in Indonesian Language Learning in Elementary Schools

This literature study, entitled "Analysis of Speaking Skills with Storytelling Methods in Indonesian Language Learning in Elementary Schools," describes the literature on speaking skills using the storytelling method with the narrative literature review (NLR) model. The technique employed is the comparison method. The purpose of this study is based on the needs in primary school environments, especially in high school, because knowing the problems and difficulties in applying Indonesian language learning to speaking skills for elementary school students is very much needed. It has been proven that it takes work to maximize students' speaking skills in front of an audience. Researchers identified the subject matter discussed in this study as 40 articles published in national journals within the last ten years. The article Speaking Skills with a Storytelling Approach consists of 20 pieces on speaking skills by various methods, as many as nine pieces for speaking skills in high school, which in general is as much as two articles; additionally, to speak with storytelling skills using a variety of models, as many as four articles; three articles for speaking skills using image media and comics; and the other two articles discuss speaking skills in law school in general. The novelty of the research obtained in the literature review of speaking skills and the theories presented by previous experts The impact of research that uses the study of literature is more significant when other researchers pay attention to the suggestions given by researchers when researching speaking skills.
InterCriteria Analysis of the Financial Data for Selected 8 EU Countries

In the member countries of the European Union, the activities of their banks are controlled by the supervisory institution of the given country and by the European Central Bank (ECB). This is the so-called Single Supervisory Mechanism. The European Central Bank is an independent institution of the European Council. Every year, the European Central Bank reviews the significance criteria and the institutions that are included, as well as whether there are any new credit institutions that should be monitored. The paper presents an analysis of the results of the significant banks of the 8 EU member countries. In this paper, for the purposes of analysis, an InterCriteria Analysis of the data from the ECB report - Supervisory Banking Statistics for 2020 has been carried out. Interrelationships that may not be visible at first glance are shown, as well as expected interrelationships are confirmed.
Universal Algebraic Logic - Dedicated to the Unity of Science

This is the presentation of a book that gives a comprehensive introduction to Universal Algebraic Logic. The three main themes are (i) universal logic and the question of what logic is, (ii) duality theories between the world of logics and the world of algebra, and (iii) Tarskian algebraic logic proper including algebras of relations of various ranks, cylindric algebras, relation algebras, polyadic algebras and other kinds of algebras of logic. One of the strengths of our approach is that it is directly applicable to a wide range of logics including not only propositional logics but also e.g. classical first order logic and other quantifier logics. Following the Tarskian tradition, besides the connections between logic and algebra, related logical connections with geometry and eventually spacetime geometry leading up to relativity are also part of the perspective of the book. Besides Tarskian algebraizations of logics, category theoretical perspectives are also touched upon.
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization

Anomaly detection is fundamental yet, challenging problem with practical applications in industry. The current approaches neglect the higher-order dependencies within the networks of interconnected sensors in the high-dimensional time series(multisensor data) for anomaly detection. To this end, we present a self-adapting anomaly detection framework for joint learning of (a) discrete hypergraph structure and (b) modeling the temporal trends and spatial relations among the interdependent sensors using the hierarchical encoder-decoder architecture to overcome the challenges. The hypergraph representation learning-based framework exploits the relational inductive biases in the hypergraph-structured data to learn the pointwise single-step-ahead forecasts through the self-supervised autoregressive task and predicts the anomalies based on the forecast error. Furthermore, our framework incentivizes learning the anomaly-diagnosis ontology through a differentiable approach. It derives the anomaly information propagation-based computational hypergraphs for root cause analysis and provides recommendations through an offline, optimal predictive control policy to remedy an anomaly. We conduct extensive experiments to evaluate the proposed method on the benchmark datasets for fair and rigorous comparison with the popular baselines. The proposed method outperforms the baseline models and achieves SOTA performance. We report the ablation studies to support the efficacy of the framework.
SciFoodNER: Food Named Entity Recognition for Scientific Text

Named Entity Recognition (NER) and Named Entity Linking (NEL) are key tasks in Information Extraction, addressing the identification and normalization of entity mentions from raw text. In the domain of food and nutrition, there have been several NER methods already developed, however, when applied to scientific text, they fail to generalize and produce large performance degradation. This introduces the need for new food NER and NEL models, developed specifically for extracting food entities from scientific text. In this paper, we present a scientific food NER and NEL model, SciFoodNER, obtained by fine-tuning transformer models on a corpus of scientific abstracts annotated with food entities. The models can identify mentions of food entites from raw text, and link the food entities to the Hansard Taxonomy, the FoodOn ontology and the Systematised Nomenclature of Medicine Clinical Terms (SNOMEDCT). Out of the evaluated models, the BioBERT model achieves the best results, reaching a median macro-averaged F1 score of 0.90 for the NER task, 0.66 for the NEL task linking to the Hansard Taxonomy, 0.43 for the NEL task linking to the FoodOn ontology and 0.58 for the NEL task linking to the SNOMEDCT ontology.
Design of Automatic Bass Playing Robot

In this paper, the bass automatic playing robot was designed with the analysis of the human movement characteristics when playing Musical Instruments, the robot completed the kinematics and dynamics analysis of each performance mechanism accorded to the principle of bionics. The playing mechanism was divided into a string pressing mechanism, a plucking mechanism and a muffler mechanism. After 3D simulation by Pro/ E, the playing mechanism was printed by 3D printer. Accorded to system design requirements, the pneumatic system was selected as the driving device of the strumming mechanism and the strumming mechanism, the stepper motor and miniature servo motor (steering gear) were selected as the driving device of the translation mechanism. After the test, the designed robot could accurately complete the performance requirements.
An Inspection Mode Based on Unmanned Aerial Vehicle for Photovoltaic System

Due to the limitations of the low efficiency of human inspection affected by geographical environment, and the difficulties in locating failure position caused by the lack of data support in management, inefficient inspection has become the biggest difficulty in the operation and maintenance of photovoltaic power plants. Thus, finding a new type of intelligent operation and maintenance mode is meaningful to help improve the efficiency of photovoltaic power plants. With the help of unmanned aerial vehicle (UAVs), in this paper, an “artificial intelligence (AI) algorithm + cloud computing” mode is used to realized the unified modeling and access of unmanned inspection information in the whole photovoltaic power plant. By combining bit data, three-dimensional (3D) modeling and self-developed target fault point location algorithm, a new photovoltaic intelligent operation and maintenance mode is finally proposed. It can be proved that the intelligent cloud platform built under the proposed mode has improved the efficiency of photovoltaic inspection by more than 50%, which can greatly reduce the inspection time and improve the industrial efficiency.
Named Entity Recognition For Quranic Text Using Rule Based Approaches

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially in Named Entity Recognition where different domains contain several types of entities. The current NER model is deemed not fit to accurately extract entities from Quranic text due to its unique content. This paper describes the building of a rule-based Named Entity Recognition method to extract the entities that exist in the English translation to the meaning of the Quranic text and its performance evaluation. Named entity tagging, a common task in-text annotation, in which entities (nouns) in the unstructured text are identified and assigned a class. A few rules are built to extract several types of entities such as the name of prophets and people, creation, location, time, and the various names of God. The rules are built mainly using regular expressions and gazetteers. The rules that have been built result in high precision and recall as well as a satisfactory F-score of over 90%. The results from this experiment can be used as annotation in building a machine learning model to extract entities from the same type of domain specifically on the Quranic text or generally in the Islamic domain text.
NAMED ENTITY RECOGNITION FOR QURANIC TEXT USING RULE BASED APPROACHES

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially in Named Entity Recognition where different domains contain several types of entities. The current NER model is deemed not fit to accurately extract entities from Quranic text due to its unique content. This paper describes the building of a rule-based Named Entity Recognition method to extract the entities that exist in the English translation to the meaning of the Quranic text and its performance evaluation. Named entity tagging, a common task in-text annotation, in which entities (nouns) in the unstructured text are identified and assigned a class. A few rules are built to extract several types of entities such as the name of prophets and people, creation, location, time, and the various names of God. The rules are built mainly using regular expressions and gazetteers. The rules that have been built result in high precision and recall as well as a satisfactory F-score of over 90%. The results from this experiment can be used as annotation in building a machine learning model to extract entities from the same type of domain specifically on the Quranic text or generally in the Islamic domain text.
IGRP: Iterative Gradient Rank Pruning for Finding Graph Lottery Ticket

Graph Neural Networks (GNNs) have shown promising performance in many applications, yet remain extremely difficult to train over large-scale graph datasets. Existing weight pruning techniques can prune out the layer weights; however, they cannot fully address the high computation complexity of GNN inference, caused by large graph size and complicated node connections. In this paper, we propose an Iterative Gradient Rank Pruning (IGRP) algorithm to find graph lottery tickets (GLT) of GNNs where each GLT includes a pruned adjacency matrix and a sub-network. Our IGRP can avoid layer collapse and the winning ticket achieves Maximal critical compression. We evaluate the proposed method on small-scale (Cora and Citeseer), medium-scale (PubMed and Wiki-CS), and large-scale (Ogbn-ArXiv and Ogbn-Products) graph datasets. We demonstrate that both Single-shot and Multi-shot of IGRP outperform the state-of-the-art unified GNN sparsification (UGS) framework on node classification. The source code can be found in https://github.com/poweiharn/IGRP_GNN.
Evaluation of Smart Farming Agriculture Using COPRAS Method

Smart Farming Agriculture.Smart farming is the use of new technologies that emerged in the fields of agriculture and livestock production at the beginning of the Fourth Industrial Revolution to increase the quantity and quality of production and maximize the use of resources to minimize environmental impact.It is only through information management that crops can be profitably converted modern agricultural advances are causing smart agriculture to expand tremendously and become a vital component, which will be important for producers' decision-making.Smart farming is a management concept that focuses on providing the agricultural industry with the infrastructure to use advanced technology including big data, cloud, and the Internet of Things (IoT).Network of things a promising technology exists that offers effective and dependable solutions for upgrading numerous domains.Web-based solutions are being created primarily to monitor and autonomously maintain agricultural farms with the least amount of human intervention.The article discusses a number of Internet of Things applications in agriculture.This explains its key elements are smart farming.COPRAS (Complex Proportional Evaluation is proposed to evaluate the possible maintenance strategy.Linguistic terms are used to evaluate the ratings and weights.The rankings of the alternatives are COPRAS Name of Fruit or Vegetable, Water Content (g), Nitrogen (lb/ac), Phosphorous (mg), and Potassium (mg).Soybean, Apple, Rice, Corn, Cucumber.Water Content (g), Nitrogen (lb/ac), Phosphorous (mg), Potassium (mg).Soybean, Apple, Rice, Corn, Cucumber.Apple got the first rank whereas the Soybean has the lowest rank.
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels

Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization problem of this task is inherently challenging, requiring well-designed constraints. In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints. Firstly, we extend a conventional non-convex energy function as an intensity constraint and implement it with U-Net. For shape constraint, synthetic images are generated from anatomical labels via image-to-image translation, as shape supervision for the segmentation network. Moreover, augmentation invariance is applied to facilitate the segmentation network to learn the latent features in terms of shape. We evaluated the proposed framework using the public datasets from MICCAI2019 MSCMR Challenge, and achieved promising results on cardiac MRIs with Dice scores of 0.5737, 0.7796, and 0.6287 in Myo, LV, and RV, respectively.
STUDENTS READINESS FOR THE FACE-TO-FACE CLASSES IN JUNIOR AND SENIOR HIGH SCHOOL

Because of the decreasing prevalence of COVID-19, significant changes have been made to the educational system. The concern of face-to-face classes piques students interest in education. The studys goal was to assess students readiness for face-to-face classes. A total of 144 student respondents were chosen at random from one of the high schools in the Division of Gingoog City. The study used a survey research design with a demographic profile questionnaire and a 24-item survey questionnaire validated by experts. The data were treated using descriptive and inferential statistics. The findings revealed that students emotional, physical, and financial readiness was high. There are significant differences in emotional characteristics among students based on their age. Furthermore, physical health was significantly different when classified by gender. However, there are no significant emotional, physical, or financial differences between year levels. As a result, it was clear that students are generally enthusiastic about the face-to-face learning. This could be due to the time when schools were forced to close.