Reviewerly/exHarmony huggingface dataset:
Feature based Sentiment Analysis of Product Reviews using Deep Learning Methods

In web-based item audits clients examine about items and its highlights.An item might have hundreds or thousands of surveys, customers share their experience about items and remarks about items qualities.These item audits might have positive or negative opinions.A positive feeling contains great assessment on item and its elements correspondingly a pessimistic opinion tells disadvantages and issues of item and its highlights.Elements or angles are important for the item or its attributes.In this study we utilized highlight/viewpoint based opinion examination and a few strategies for breaking down the feelings communicated in web-based item surveys about the different elements of items.
Enhancing Response Relevance and Emotional Consistency for Dialogue Response Generation

VAE (Variational Autoencoder) and CVAE (Conditional V AE) encode the sentence with the latent variable to generate response in Dialogue. However, studies have shown that the latent variables obtained are more inclined to remember the first words and the length of the sentence, and only represents limited local features. In order to alleviate this problem, we propose to involve contrastive learning to generate positive and negative samples for training process, which enriches the latent variables representation with the global information of sentence and generates more relevant response. On the other hand, those generative models do not consider emotional information of dialogue, a sentiment discrimination module is introduced in our model to maintain the emotional consistency. Experiments on two public datasets - DailyDialog and PERSONA-CHAT demonstrate the effectiveness of our method, the evaluation results of BLEU and Rouge are both improved. The sentiment discrimination network also forces the model to generating emotional consistency response with share embedding.
Graph-based Dependency Parser Building for Myanmar Language

Examining the relationships between words in a sentence to determine its grammatical structure is known as dependency parsing (DP). Based on this, a sentence is broken down into several components. The process is based on the concept that every linguistic component of a sentence has a direct relationship to one another. These relationships are called dependencies. Dependency parsing is one of the key steps in natural language processing (NLP) for several text mining approaches. As the dominant formalism for dependency parsing in recent years, Universal Dependencies (UD) have emerged. The various UD corpus and dependency parsers are publicly accessible for resource-rich languages. However, there are no publicly available resources for dependency parsing, especially for the low-resource language, Myanmar. Thus, we manually extended the existing small Myanmar UD corpus (i.e., myPOS UD corpus) as myPOS version 3.0 UD corpus to publish the extended Myanmar UD corpus as the publicly available resource. To evaluate the effects of the extended UD corpus versus the original UD corpus, we utilized the graph-based neural dependency parsing models, namely, jPTDP (joint POS tagging and dependency parsing) and UniParse (universal graph-based parsing), and the evaluation scores are measured in terms of unlabeled and labeled attachment scores: (UAS) and (LAS). We compared the accuracies of graph-based neural models based on the original and extended UD corpora. The experimental results showed that, compared to the original myPOS UD corpus, the extended myPOS version 3.0 UD corpus enhanced the accuracy of dependency parsing models.
Syllable-to-Syllable and Word-to-Word Transducers for Burmese Dialect Translation

Weighted Finite State Transducers (WFST) can be very efficient to implement Burmese dialects translation. We illustrate this on two Burmese dialect language pairs, Burmese-Beik and Burmese-Rakhine. In this study, we examine syllable and word segmentation schemes and their effect on alignment and transducing between dialect language pairs. We performed alignments with Anymalign, fastalign, pialign, Hieralign, eflomal and GIZA ++ approaches and implemented WFST based machine translation system with OpenFst library. From the overall results, syllable segmentation achieved higher BLEU and chrF scores for Burmese-Rakhine and Rakhine-Burmese translations. However, word segmentation achieved better translation performance for Burmese-Beik and Beik-Burmese translation directions. Alignment techniques fast align, Hieralign, eflomal and GIZA ++ are working well for low-resource Burmese dialects.
Media Development Apedu Educational Applications Based On Android In Force Material

Learning media is one way to facilitate in conveying the content of material from educators to learners. In the era of technological development today there are new habits at the level of elementary school education, one of which is using digital media as a means to convey material. Therefore, there needs to be a learning media that is able to utilize technology, especially for elementary school. This research is included in research and development that later produces a teaching material in the form of learning media that utilizes based on android technology by using animation and backing sound features that are able to stimulate students' thinking systems. To obtain a research learning media also aims to know the process, implementation, feasibility, and effectiveness of the media. The research process is carried out using the ADDIE model which has five stages in the research. In the development stage researchers obtained a score from media experts, material experts, RPP experts, and design experts by 90%, while in the implementation stage researchers obtained a score of 79.15% which was obtained from the average score given by media users. Then for the eligibility score is taken at the stage of the media creation process to the expert validation stage, so as to obtain an average score of 90%. While the effectiveness of the learning media is taken from the results of the t test conducted after the use of learning media, obtained t count 7,970 and t table 2.144 so that Ho is rejected and Ha is accepted with a significance level of 0.00 with a correlation value of 0.872 which means there is a positive influence from the use of learning media. The score indicates that the media developed by researchers falls into a category that is validly feasible and effectively used by elementary school students.
Neural Network Classifier Improvements Based on Ant Colony Algorithm

Compared with other classifiers, neural network (NN) classifiers are faster and have more advantages.However, the NN classifier is unique, because the number of features often changes when selecting features and the classifier is classified by the number of hidden layer nodes, and a NN with a fixed number of hidden layer nodes is used to evaluate different types of feature subsets obviously inappropriate.Therefore, the NN classifier is optimized, and the ant colony optimization (ACO) algorithm is introduced to obtain a diverse solution, which will be faster than the NN classifier to avoid it falling into the problem of local solutions.As a classic feedforward NN, BP neural network (BPNN) is currently the most widely used NN for classification problems.Therefore, this paper constructs a BPNN classifier.By comparing the traditional BPNN classifier and the improved ACO-BPNN classifier on malicious web page detection and classification effects of different sizes of data sets, the results show that, compared with the traditional BPNN classification model, ACO-BPNN The classification accuracy of the BPNN classification model is higher and the classification effect is better.
Approaches to parallelise Eclat algorithm and analysing its performance for &lt;i&gt;K&lt;/i&gt; length prefix-based equivalence classes

Frequent item set mining (FIM), is one of the prevalent, well-known methods of data mining and is a topic of interest for researchers in the field of decision making. With the establishment of the period of big data where the data is continuously generated from multidimensional sources with enormous volume, and variety in an almost unrevealed way, transforming this data into valuable knowledge discovery which can add value to the organisations to make an efficient decision making poses a challenge in the present research. This leads to the problem of discovery of the maximum frequent patterns in vast datasets and to create a more generalised and interpretable representation of veracity. Targeting the problems stated above, this paper suggests a parallelisation method suitable for any type of parallel environment. The implemented algorithm can be run on a single computer with multi-core processor as well as on a cluster of such machines.
The Strategy Exploration on Information Technology and Curriculum Integration Ability Training of EFL Pre-Service Teachers

The core and foothold of the combination of information technology (IT) and education is the integration between IT and curriculum. Pre-service teachers' level of IT and English curriculum integration ability determines whether EFL pre-service teachers can become qualified teachers in the information age, and EFL pre-service teachers' cultivation must take the formation of this ability as an important educational objective throughout the whole training process. The main areas of EFL pre-service teachers' competence to integrate IT with the curriculum include technical literacy, IT support for English teaching, IT support for English learning, and IT support for English teachers' self-development. The training strategies include curriculum system construction, well-curriculum linkage, teaching demonstration and discipline penetration, hardware and software building, "blended" learning models, timely practice and training, multiple assessments, and activities inside and outside class.
IMPLEMENTASI CLOUD STORAGE MENGGUNAKAN OWNCOULD DI SD NEGERI LEMPUYANGAN 1 YOGYAKARTA

Cloud storage is a cloud-based data storage technology that makes it easy for us to do storage without the need for physical storage. At SD Negeri Lempuyangan 1 Yogyakarta the storage media still uses physical storage media such as flash drives and one of the cloud storages, namely Google Drive. Limitations of physical storage media such as physically damaged, bad sectors, exposed to viruses, lost and subscription prices or additional storage capacity on Google Drive are quite expensive. The way to overcome damage or obstacles in data storage, researchers use owncoud as a cloud computing technology with cloud storage services with several advantages, including being able to access data anytime and anywhere, the capacity is not limited (depending on hosting), can share data easily, quickly , and accurate, and most importantly free of charge. By having their own server, data security can be monitored in real time and data management and users can be carried out more freely. The results of the blackbox test conclude that the processes that run from the login process to the occurrence of business processes are in accordance with the design made. All forms and features run according to their functions.
KLASIFIKASI KABUPATEN/KOTA DI INDONESIA BERDASARKAN TINGKAT KEDALAMAN DAN KEPARAHAN KEMISKINAN MENGGUNAKAN NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBOR

Poverty is a complex problem, because it involves many other fields. Indonesia as a developing country with 514 districts/cities so far has not been able to solve the problem of poverty. The poverty level can be seen from the value of the poverty depth index and the poverty severity index. It is necessary to classify it, to find out which districts/cities have a high or low poverty rate seen from the two index values. The poverty rate can also be influenced by several factors such as the number of poor people, the percentage of poor people, adjusted per capita expenditure, and the average length of schooling. In this study, the classification is carried out using the Naïve Bayes Classifier and K-nearest Neighbor methods. Both methods are carried out by dividing the training data and testing data by 60%: 40%, 70%:30%, and 80%:20%. In addition, samples were randomized using the functions set.seed(6), set.seed(99), and set.seed(123). In K-nearest Neighbor using the number of neighbors k = 3, k = 5, k = 7, and k = 9 with Euclid distance and Manhattan distance. The results of the analysis with both methods obtained that the Naïve Bayes Classifier method is better used than the K-nearest Neighbor. The results of the highest average accuracy on the poverty depth index and poverty severity index occur in the data splitting of 60%: 40% with an average accuracy of 89.47% and 87.80%, respectively.
On the joint optimization of security, scalability and decentralization of real-time group communications

In this paper we consider the problem of how to optimize real-time communications for groups of any size assuming zero-trust in everything except the group members. The objective is to minimize the maximum load across group members while reducing transmission latency. These objectives are required to facilitate decentralization of the application deployment without Cloud assistance while guaranteeing high communications quality.
A comprehensive study on the security of medical information using encryption

The exponential rise in the use of the Internet and rapid progress in computer technology has revolutionised healthcare allowing medical information to be shared efficiently across networks. While these advancements make it convenient to store and share information, they also have their limitations. Medical data contain highly sensitive and confidential information about patients and can be subjected to various kinds of passive and active attacks and analyses if transferred over an insecure network. These data leakages can be catastrophic for the respective individual or organisation due to the undesirable scenarios which emanate from them. Hence, information security is a major concern and warrants developing techniques to securely transfer medical data.This chapter discusses information security techniques and their properties in detail, reviews the research related to cryptography-based medical information security by categorising the techniques into traditional, hybrid and deep learning-based approaches and highlights their limitations. Comparative studies based on the universality of the method and metric-wise performance are also presented. We further discuss the potential challenges and prospective future directions, enabling a path forward to advance the research in this area.
Image Features Fused with BP Neural Network

Because of the remarkable advantages of computer neural network(NN), it has been widely used in the field of image feature(IF) fusion recognition, and the research involves multiple research fields.In addition to image recognition or image classification, it has been studied in computer vision, industrial control and other aspects.This paper focuses on the analysis of the IF of the fusion BP NN.This paper briefly analyzes the fusion of IF, spectral features and texture features, proposes BP NN algorithm, and applies it to image fusion design; Finally, through experimental tests, the detection accuracy of fusion BP NN and traditional BP algorithm in IF fusion is compared and analyzed, which verifies the effectiveness and feasibility of the BP NN algorithm in this paper.
Deep Convolutional Neural Networks for Facial Expression Recognition

Facial expression recognition technology has been widely used in traffic, medical and criminal investigation and other fields.At present, facial expression recognition technology refers to feature extraction, classification and recognition through facial expression.In order to solve the shortcomings of the existing recognition technology research, this paper discusses the Softmax classifier function equation, facial expression and (DCNN), and briefly discusses the sample data and parameter configuration of the proposed facial expression recognition technology.Finally, the recognition process structure designed in this paper is applied to the five collected facial expressions (angry, sad, happy, afraid and calm) and the other two recognition models (GAN) and (LSTM) to compare the recognition rate.The experimental data show that the recognition rate of the proposed algorithm (CNN) is 95.2% and 94.9%, respectively, which is significantly better than that of the recognition model (GAN) and (LSTM).In the recognition of happiness, fear and calm, the average recognition rate of the proposed (CNN) reaches about 96.3%, while the highest recognition rate of the recognition model (GAN) and (LSTM) is only 92.7%.Therefore, it is verified that facial expression recognition based on depth (CNN) has a good performance effect.
Central bank digital currency

The widespread adoption of the cryptocurrencies and stable coins has enabled central banks across the world to research and roll out a central bank-issued digital currency, which could address different problems based on the type. Wholesale central bank digital currency (CBDC) – available only for commercial banks and clearing houses for use in the wholesale interbank market retail CBDC – is widely available for consumer use. CBDC would be fundamentally different to cryptocurrencies as they are issued by the central bank and backed by government securities. CBDC would be a risk-free currency and therefore perform all the essential functions of the money. Blockchain/distributed ledger technology (DLT) would form the backbone of CBDC by making it more decentralized and ensuring innovations in cross-border transactions, retail payments and gross settlements in digital money. Elements of DLT such as decentralization, sharing of data and use of cryptography and programmability would enhance the purpose and utilization of CBDC. This chapter gives a detailed overview on the implementation and design aspects that need to be considered before rolling out CBDC using DLT and the associated use cases.
SQL

This chapter introduces readers to SQL. It explains terminology (such as tables and queries), how to open and browse a database, how to examine a table’s structure and how to query a table to retrieve specific columns of specific records in a specific order. We also cover how to summarize data with GROUP BY queries and crosstab queries, which are similar to Pivot Tables. It explains how to join tables that share a variable, such as multiple years of Census data. We also emphasize the importance of shoe-leather reporting to verify matches. This chapter covers both “inner” joins and “outer” joins, nested queries (such as how to find records in Table A that are not in Table B) and how to improve joins by splitting, combining and manipulating the character strings in key fields. The exercises in the chapter include working with Census data on incomes and geographic areas.
Blockchain technology in disaster management

Natural hazards such as floods, storms, volcanic eruptions, earthquakes, and landslides have unpredictably affected the earth in a very tragic format. Proper management of disasters and pandemics is a difficult task. It consists of recording, processing, storing, and distributing information to the government and public to make effective decisions, such as allocating funds, initiating rescue operations, providing food and shelter, medical and rehabilitation centers, transportation facilities, water and power supplies, and other necessities. Many disaster relief agencies and plans are available but do not work properly because of these challenges. There is a huge scope for advanced technologies to overcome these difficulties and provide a secure way to store personal identification information and better security. One of the important requirements for the future system is to store information and data safely and make the transaction secure and anonymous because securing the information against unauthorized access points is much more important. Blockchain (BC) is the fastest-growing digital technology. It offers a robust self-regulating, self-monitoring, and cyber-resilient data transaction operation, assuring the facilitation and protection of a truly efficient data exchange system. BC is a shared-distributed database, and every node on a network can share, but no one controls it entirely. Encryption and public are two fundamental features of the BC, and it uses private and public keys to provide better security. This chapter critically evaluates the existing BC-oriented disaster management systems and their future research scope.
Nonparametric scanning tests of homogeneity for hierarchical models with continuous covariates

Abstract In many applications of hierarchical models, there is often interest in evaluating the inherent heterogeneity in view of observed data. When the underlying hypothesis involves parameters resting on the boundary of their support space such as variances and mixture proportions, it is a usual practice to entertain testing procedures that rely on common heterogeneity assumptions. Such procedures, albeit omnibus for general alternatives, may entail a substantial loss of power for specific alternatives such as heterogeneity varying with covariates. We introduce a novel and flexible approach that uses covariate information to improve the power to detect heterogeneity, without imposing unnecessary restrictions. With continuous covariates, the approach does not impose a regression model relating heterogeneity parameters to covariates or rely on arbitrary discretizations. Instead, a scanning approach requiring continuous dichotomizations of the covariates is proposed. Empirical processes resulting from these dichotomizations are then used to construct the test statistics, with limiting null distributions shown to be functionals of tight random processes. We illustrate our proposals and results on a popular class of two-component mixture models, followed by simulation studies and applications to two real datasets in cancer and caries research.
Research on intelligent safety monitoring system for railway passenger platform

The system is based on laser scanning technology to detect whether there is intrusion across the warning line or end of the train station; The trackside dynamic monitoring system can judge the train running direction, detect the train running speed, calculate the arrival time and transmit the data to the network control system; The control system can give different levels of audible and visual alarm according to the crossing or intrusion behavior and the arrival time of the train, and transmit the information to the staff for processing according to the situation. The designed platform intelligent monitoring system reduces the workload of operators, and improves the safety performance and accuracy.
Introduction

As the COVID-19 pandemic surged globally in 2020, pandemic surveillance was actively explored and debated as part of the prescription.Questions of data privacy, cybersecurity, and the ethics of the surveillance technologies centered an international conversation on the benefits and disadvantages of the appropriate uses and expansion of cybersurveillance and data tracking.In November 2020, Pennsylvania
Socialization of Education in City Ho Chi Minh City from 1997 to 2010

The socialization of education is a major policy of our Party and State.Initially, certain results have been achieved in mobilizing social resources to meet the increasing needs of all classes of people.Socialization is building a community with the responsibility of all classes of people for the creation and improvement of a healthy and favorable socio-economic environment for educational, medical and cultural activities in each country.This is the responsibility community of the party committee, the people's council, the people's committee, mass organizations, economic organizations, local businesses, and each citizen.In this article, the author focuses on clarifying the current situation and drawing some experiences on educational socialization in Ho Chi Minh City.