Reviewerly/exHarmony huggingface dataset:
A Study of Adolescents’ Anxiety and Achievement in English as a Foreign Language

The purpose of this study was to determine the anxiety of adolescents, especially in Mardi Yuana Junior High School students, Serang City, and their achievements in the process of learning English as a foreign language in foreign language classes. This research is qualitative research with a descriptive approach through questionnaire observations, interviews, and documentation. The results of this study indicate that 2 students feel anxious with a percentage of 5.88%, 20 students feel mildly anxious with a percentage of 58.82%, 12 students feel relaxed with a percentage of 35.29%, 0 students feel relaxed/very relaxed with a percentage of 0%. So it could be concluded that the factors that could affect the students' anxiety in learning English as a foreign language are internal and external. Internal factors include perception, personality, and intelligence. While external factors include motivation, classroom environment, and cognitive. As for what affects students' achievement in learning English as a foreign language, they are afraid of being given a negative evaluation, when they make mistakes and are afraid of being laughed at by their classmates, it makes them afraid to explore themselves further about foreign language learning. Therefore, anxiety in learning English should at least motivate students, especially in their teens so that they can learn foreign languages well.
TWITTER TEXT SENTIMENT ANALYSIS OF AMAZON UNLOCKED MOBILE REVIEWS USING SUPERVISED LEARNING TECHNIQUES

Due to the drastic increase in usage of Internet and e-commerce, online shopping becomes very common in day to day lives.One of the popular e-commerce websites is Amazon, which allows users to post Reviews and Ratings regarding the purchased products.It finds useful for other customers and companies to improve the product quality.Since the manual extraction of sentiments from User Product Reviews is a tedious process, accurate and Automated Sentiment Analysis (SA) tools are essential.This research work aims to examine the performance of different Machine Learning (ML) models in analyzing the sentiments of Amazon Product Reviews.Initially, the Product Reviews are pre-processed in different ways to transform the data into a useful format.Besides, Term Frequency -Inverse Document Frequency (TF-IDF) vectorizer is used to derive Feature Embeddings.Finally, three Machine Learning (ML) models namely Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Support Vector Machines (SVM) are used for Sentiment Analysis (SA).The Performance Validation of the Machine Learning (ML) models is performed using Benchmark Dataset from Kaggle Repository.The Experimental Results reported that the Support Vector Machine (SVM) model has resulted in better Performance over the other Machine Learning (ML) models interms of Different Measures.
Study of Surface Roughness of Cutting Isothermally Hardened Ductile Iron in Tosa Knife Forging Technique Based on Intelligent Calculation

With a long history of more than 600 years of development, the Achang Tosa knives have always been handmade and made in strict accordance with tradition and absorbing the essence of foreign cultures. It is not only a tool or a gift, but also a “knife culture” and a local “folk culture” with local ethnic characteristics in Yunnan. In this paper, we begin our research on the Achang Tosa knives by compiling literature and conducting field research to investigate the “knife culture” jointly created by the Achang and neighboring ethnic groups by studying the forging process of the Achang Tosa knives. Three sets of specimens were prepared and the mechanical properties were determined by combining the existing isothermal quenched ductile iron (ADI) material production, and a high-speed cutting test was carried out with a CC650 ceramic tool to investigate the relationship between the machined material, tool material, and cutting dosage-surface roughness during high-speed cutting of ADI; based on the particle swarm algorithm, a theoretical model between surface roughness and cutting parameters was established to provide theoretical guidance for the optimal production process of ADI in high-speed cutting. It provides more valuable experience for the protection of intangible cultural heritage of ethnic minorities and also provides reference for the development and protection of unique crafts of other ethnic minorities.
Form Specification of Smart Contract for Intellectual Property Transaction Based on Blockchain

In view of the current chaotic structure of smart contracts and no unified definition of smart contracts, the implementation of smart contracts for the same business in the same field is quite different due to different development institutions and operating platforms, resulting in a low level of smart contract sharing and high development costs, hindering the development of smart contracts. Combined with the intellectual property transaction scenario, this paper proposes a blockchain-based intellectual property transaction smart contract form specification, designs the overall structure and process specification of the smart contract in the intellectual property transaction process, formulates a standard for the standardization of smart contract writing in intellectual property transaction scenarios, and solves the current chaotic structure of intellectual property transaction smart contracts, which is conducive to the collaborative development of scholars in various fields.
Application of Adaptive Technologies in Teaching students with Special educational needs

The article deals with adaptive technologies, characterized as a set of technologies and teaching methods aimed at implementing the concept of personalized learning. Adaptive technologies are based on the principle of adaptability — the formation of the educational process, taking into account the individual characteristics of the student. The purpose of the study is to consider the essence and structure of the concept of “adaptive learning technology” and determine the effectiveness of its application. Methods used in the study: literature review, pedagogical experiment, comparative analysis. As part of the study, to assess the effectiveness of adaptive technology in the educational process of the university, the author conducted an experiment with the participation of two groups of students. The first group mastered the educational material according to the traditional system of education, the second group used one of the adaptive learning technologies — the project method. The study conducted by the author showed that the introduction of adaptive technology into the educational process of the university shows an effective result.
Global Issues Spanning Higher Education

We are pleased to publish the first regular issue (Volume 12, Issue 1) of &lt;em&gt;Higher Learning Research Communications (HLRC)&lt;/em&gt; for 2022. While the pandemic waxes and wanes, students in many parts of the world are returning or have returned to face-to-face instruction. It will take some time to fully understand the impact of the pandemic on higher education and what lasting changes will result from it. The Special Issue, Education Technologies and COVID-19: Experiences and Lessons learned, for which we are continuing to review manuscripts, should provide some insight into this question.This letter also contains article summaries.
Rainwater Infiltration Box to Prevent Flood Disaster

Flood disaster is a natural disaster that ranks the third cause of economic loss worldwide. As a tropical country that has a rainy season, every year some parts of Indonesia are prone for being affected by floods. The causative factors include high rainfall and poor surface water management, making rainwater directly converted into surface water (run-off). Efforts to reduce run-off water include making biopores infiltration wells from paralon pipe material with a diameter of 10 centimeters, but because of the small volume, the biopores cannot functioned properly, so it is necessary to build infiltration wells with a larger capacity. The purpose of this research is to make a model of infiltration wells that have a larger volume than stainless steel. This study used a pre-experimental design and is a development research of biopores infiltration wells. The stages in this research include design validation and absorption function testing. The results of the study were the design of a box-shaped absorption container with a height of 120 cm. 100 cm wide and 100 cm long, made of 304 stainless steel, with an inlet and outlet holes for rainwater, a bottom absorption area, and has a lid that is easy to open for cleaning. In the function test, it was obtained the ability to accommodate rainwater of 1.2 cubic meters and the ability to absorb water of 0.9 liters per second on the soil of the test location, so that this infiltration box is estimated to be able to accommodate rainfall of 120 mm a day. The conclusion is that this rainwater absorption container is able to accommodate rainwater while absorbing water so that it can be an alternative technology for preventing flood.
Learned Low Bitrate Video Compression with Space-Time Super-Resolution

This paper presents a learned low bitrate video compression framework that consists of pre-processing, compression and post-processing. In pre-processing stage, the source videos are optionally reduced to low-resolution or low-frame-rate ones to better meet with the limited band-width. In compression stage, inter-frame prediction is performed by deformable convolution (DCN). The predicted frame is then used as temporal conditions to compress the current frame. In post-processing stage, the decoded videos are fed into a Space-Time Super-Resolution module, in which the videos are restored to original spatial and temporal resolutions. Experimental results on CLIC22 video test conditions demonstrate that the proposed method shows better performance on both objective and subjective quality at low bitrate. Our team name is PKUSZ-LVC.
Design of College English News Listening Teaching System Based on Improved SPOC by using IoT

Due to the limitation of actual teaching conditions, limited resources, and unsatisfactory teaching effect, a hybrid teaching system of college English news listening course based on improved SPOC is proposed. The hardware environment is made up of 5 servers and 3 switches, and the hardware service resource is optimized by SPOC cloud deployment. The cough relieving mechanism based on evaluation level is built inside the system. The objective of this study designs and implements a research on the application of the improved SPOC hybrid teaching system in college English listening teaching. The test results show that the design system can effectively improve students’ learning performance and enthusiasm and initiative and, to some extent, improve students’ listening learning habits.
Reddit Dataset on Meme Stock: GameStop

This dataset includes one-year Reddit posts, post metadata, post sentiments, 57 post meta-features and post comments threads from several subreddits around a meme stock: GameStop. These subreddits are dedicated to the discussion of GameStop stock and the social movement of global wealth transfer that the event GameStop stock short squeeze initiated in January 2021. The subreddits included are r/GME, r/superstonk, r/DDintoGME, and r/GME Jungle. The whole dataset is stored in eight CSV files and four HTML files.
Feature Matching of Multimodal Images Based on Nonlinear Diffusion and Progressive Filtering

Traditional image feature matching methods cannot obtain satisfactory results for multi-modal images in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences and geometric distortion. The key to multi-modal image matching is trying to eliminate the nonlinear radiation distortion and extract more robust features. This paper proposes a new robust feature matching method for multi-modal images. Our method starts by detecting feature points on phase congruency maps in nonlinear scale space and then removing mismatches by progressive filtering. Specifically, the phase congruency maps are generated by the Log-Gabor filter. Then, the feature points on phase congruency maps are detected in nonlinear scale space constructed by the nonlinear diffusion filter. Subsequently, the structure descriptor is established by the Log-Gabor filter, and the initial correspondences are constructed by bilateral matching. Finally, an iterative strategy is used to remove mismatches by progressive filtering. We perform comparison experiments on our proposed method with the SIFT, RIFT, VFC, LLT, LPM, and mTopKPR methods using multi-modal images. The algorithms of each method are comprehensively evaluated both qualitatively and quantitatively. Our experimental results indicate the superiority of our method over the other six matching methods.
Exploiting lexical patterns for knowledge graph construction from unstructured text in Spanish

Abstract Knowledge graphs (KGs) are useful data structures for the integration, retrieval, dissemination, and inference of information in various information domains. One of the main challenges in building KGs is the extraction of named entities (nodes) and their relations (edges), particularly when processing unstructured text as it has no semantic descriptions. Generating KGs from texts written in Spanish represents a research challenge as the existing structures, models, and strategies designed for other languages are not compatible in this scenario. This paper proposes a method to design and construct KGs from unstructured text in Spanish. We defined lexical patterns to extract named entities and (non) taxonomic, equivalence, and composition relations. Next, named entities are linked and enriched with DBpedia resources through a strategy based on SPARQL queries. Finally, OWL properties are defined from the predicate relations for creating resource description framework (RDF) triples. We evaluated the performance of the proposed method to determine the degree of elements extracted from the input text and to assess their quality through standard information retrieval measures. The evaluation revealed the feasibility of the proposed method to extract RDF triples from datasets in general and computer science domains. Competitive results were observed by comparing our method regarding an existing approach from the literature.
MULTI-NETVIS: Visual Analytics for Multivariate Network

In the process of multivariate network exploration and analysis, it is important to consider network topology and attribute correlation analysis. In order to consider both in presentation and analysis, existing research focuses on visual design and multiple-view visualization. However, this multitudinous visual design makes network cognition and analysis difficult. In multi-view visualization, the associated information among attributes is rarely retained and is often accompanied by tedious interaction processes. In this paper, a layout scheme is proposed to balance attribute and topology analysis in multivariate network visual analysis and a multivariate network visual analytics system is implemented based on the layout scheme. The analysis scenarios of overall, community, and local multi-granularity are provided by the layout scheme, which combines 3D, 2.5D, and 2D layouts. According to the layout scheme, we propose a layout transformation method to maintain the relative position of the topological context layouts in three dimensions. Furthermore, we propose a Louvain-3D FDA layout algorithm for the 3D layout, and introduce an edge bundling algorithm in the 2.5D layout to achieve an attribute-oriented topology layout. Combining the principle of interaction from global to detail, we design a novel system, Multi-NetVis, which supports users in drilling exploration and analysis and takes both the network attribute correlations and topological structure into consideration. Finally, two datasets are selected to demonstrate the usage scenarios and an evaluation experiment is designed to verify the effectiveness of the layout scheme.
Multiview Representation Learning for Human Activity Recognition

Over the years, multi-view representation learning has become widespread in machine learning and deep learning due to the availability of extensive data and the contribution of great researchers from all over the world. Some works in activity recognition presume that the sensory data used to train the models are derived from the same body locations. These methods encounter challenges when the expected output user and the body location are unknown. However, to construct a model that can recognize human activity and transportation systems in a user-independent manner, we make use of Deep canonical correlation analysis (DCCA), which is a method that involves learning the representation of two views. It is a technique that helps multiple views of nonlinear correlated values to be linearly correlated. In addition, it can also be described as a surrogate to kernel canonical correlation analysis (KCCA). While KCCA works well with respect to the inner product, DCCA does not need the inner product. DCCA has many attributes, such as scaling time; it scales well with the training set. Moreover, we observed that the works that were done with an approach of DCCA performed better than jobs done by CCA and KCCA. This paper shows an implementation of representative learning: deep neural networks, deep canonical correlation analysis, and its several extensions. Our approach for this work is based on two parts: the reduction of impacts from each phone position and the recognition of the correct activity. Further, the architecture of our research is comprised of three different aspects: firstly, it allows the recognition of the input source; secondly, it enables the normalization of data to make training faster and also to stabilize the impact of the source on the activity learning process; and lastly, it allows the recognition of the correct activity.
Microservices Event Driven and Streaming Architectural Approach for Payments and Trade Settlement Services

In BFSI (Banking, Finance, Securities and Insurance) industry core day-to-day operations are payment, clearing and settlement (PCS) services are key functions. Having a good PCS system is a must to have a satisfied customers and achieve accelerated growth in revenues. Implementation of PCS transactions processed through a series of integrated systems and integrations were neither real-time nor near real-time. The evolution of Digital Transformation (DT), microservices, containerization, Cloud-native deployments and peer pressure in the BFSI sector has triggered the architecting and development of real-time or near real-time or asynchronous PCS systems against synchronous ones. In the current work, an effort has been made to architect a layered system with an in-built and Event Driven Architecture (EDA) that can handle asynchronous transactions. The intended system has multiple layers like integration, application, data, security, and infrastructure. The new architecture should address a) business concerns like usability, security management, functional coverage, flexibility, functional integration, automation, and reporting, b) technical concerns like modularity, extensibility, reusability, future-readiness, performance, adaptability and c) operational concerns like managibility, maintainability, scalability, availability, cloud and robust enough to handle the load. The goal of current work is achieve a multi-tier event driven and stream processing architecture that consists of front-end, API gateway, microservices, databases and flexible to deploy on a hybrid cloud environment. The system is capable of interfacing with industry standard third party systems.
Real-Time Facial Emotion Classification using Deep Convolution Neural Network

In the age of digitalization, machine learning researchers are fascinated by the identification of human emotions via their facial expressions or body movements. The emotions which one can see on a person's face reveal a lot about their personal thought processes and shows what's going on inside the mind. Facial expression recognition (FER) is an important and crucial task for the machines to understand the emotional changes in human beings. The main objective of FER is to train a particular model to recognise and analyse human emotions in the similar way that humans do. By constructing a multi-class classifier model, we aim to categorise a human facial image into one of the seven emotions under consideration in this study. To attain the optimum accuracy, we developed our model with varying depths, maximum pooling and dense layers, eventually achieving 86 percent accuracy on the test set. To address overfitting, we applied tactics such as dropout and maxpooling layers. We also demonstrate a real-time emotion recognition method utilising a webcam for users live video feed that gives correct results for user's faces in real time.
Analytical study for Price Prediction of Bitcoin using Machine Learning and Deep Learning

Referring to the recent price boom and bust of cryptocurrencies, Bitcoin is the world's most well-known Cryp-tocurrency, making it enticing to financial market participants. The significant volatility of the Bitcoin conversion scale makes it difficult to predict. As a result, forecasting its behavior is critical for monetary business sectors. However, machine learning based solutions have been proved to be more promising for providing much accurate Bitcoin price predictions. Thus, the aim of this work is to explore and critically analyze the various machine learning based approaches. Further, this work is focused to provide all essential details from the fundamental knowledge of crypto currency particularly Bitcoin to its architecture and price inflation with various factors which is useful for proceeding research and study in the field. The work covers analysis of different supervised machine learning methods assessed to acquire the most pertinent traits for the prediction. The prediction results are also used as inputs to enhance price direction predictions. The outcomes demonstrated that the chosen features and the efficient machine learning and deep learning technique improve accuracy.
Enhancing Supply Chain Management Using Blockchain Technology

A supply chain is a network of businesses and distribution choices that conducts the tasks of acquiring inputs, transforming these resources into variable and completed outputs, and distributing these manufactured goods to clients. The network is made up of many jobs at varying tiers of operation & distribution alternatives that carry out the work of procurement. It is necessary function in commercial and industrial sectors; nevertheless, the intricacy of the system differs significantly between industries. Blockchain is revolutionizing the modern landscape by providing a fresh viewpoint on access control, resilience, and overall performance. It provides a safe means of exchanging any type of item, service, or trade. Block chain technology would allow for further flexible production networks, quicker breakthroughs, and a somewhat intimate consumer interaction market. Such an article provides an outline of distributed ledger-tech as well as its possibilities for improving logistics operations.