Reviewerly/exHarmony huggingface dataset:
WEB SCRAPING OF DISEASE INFORMATION FROM SOCIAL MEDIA TWITTER

Environmental degradation caused by land conversion, trash (both domestic and industrial), and natural catastrophes is all variables that contribute to the establishment of disease susceptibility. Experts throughout the world suggest “ONE HEALTH” as a strategy for dealing with the threat of zoonoses. The One Health concept is a worldwide strategy to expand interdisciplinary collaboration and communication in all aspects of health care for humans, animals, and the environment. To overcome this disease of zoonoses, we developed a system of information zoonoses and Emerging Infectious Disease (SIZE). In this system of SIZE, we gather the disease information from social media. The disease information was collected from Twitter are Demam Berdarah Dengue (DBD), malaria disease, Antraks Disease, Canine Madness (Anjing Gila), Bird Flu (flu burung), and Ebola Disease. Twitter is a social media platform that has become a constant resource developing for data collectors. To perform this task to get the data of disease information, related tweets and Twitter user details the data collection using web scraping. Data Collection from Twitter was carried out by applying web scraping technology using python language. The scraping experiment from twitter in this study has succeeded in retrieving disease information from 2015-2020 using an advanced tool for Twitter scrapping called Twint using the python script. As the results lately have been increased number of tweets of diseases from canine madness (anjing gila) 34477, followed by Malaria Disease (28046) and Demam Berdarah Dengue (DBD) 11950 in 2020.
CITIZENAPP: YET ANOTHER SOCIAL MEDIA PLATFORM FOR PUBLIC FACILITY MONITORING IN SIDOARJO

Public facilities are one of the important assets that support citizens' daily activities. Its benefits can be shared by a large number of people in a given region. However, many public facilities found in the Sidoarjo Regency area are not maintained, such as roads, bus stops, trash cans, sidewalks, parks, street lighting, traffic lights, and traffic signs, which can cause discomfort when using public facilities. These conditions could be minimized when local governments involve societies to maintain or monitor them. The objective of this study is to create an integrated smart city mobile application that can be used to report damage to public facilities using the QR Code that has been attached to them. In this study, the waterfall method was used as a development method. The result of this study is a mobile application which is expected to be useful for monitoring public facilities in Sidoarjo. According to the results, the application looks helpful and compatible with all Android devices.
Pelatihan Peningkatan Kompetensi Pedagogik Guru Bahasa Inggris Pebelajar Muda di Kampung English Desa Temulus Kabupaten Kudus

Teaching English to young learners has been a concern of language teaching experts over the last decade in non-English speaking countries. Teaching English in this context should have a positive impact on young learners' English mastery according to the child's age development if teaching simple vocabulary and grammar are taught using songs and games. The use of these two media according to Chou's (2012) research has a positive impact because songs can make it easier for children to memorize vocabulary, while the use of games can increase positive attitudes to participate in learning. Therefore, teaching English with this media is trained for English teachers in Kampung English, Temulus Village, Kudus Regency with the aim of making English learning more fun, interesting, and motivating for students.
Experimental Demonstration of High-Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multi-Detection

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, we demonstrate reservoir computing that utilizes multidetected nonlinear spin wave interference in an yttrium iron garnet single crystal. The subject computing system achieved excellent performance when used for hand-written digit recognition, second-order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors (NMSEs) for NARMA2 and second-order nonlinear dynamical tasks were 1.81x10-2 and 8.37x10-5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance was achieved with higher nonlinearity and the large memory capacity of interfered spin wave multi-detection.
A Systematic Analysis on Blockchain Consensus Algorithms and Security Threats

Blockchain is a new technology which is basically used for store and verified transactions of cryptocurrency like Bitcoin, Ethereum etc. It is a digital, distributed, decentralized and immutable ledger. Transactions in Blockchain always store after verified by multiple validators in peer-to-peer network by using consensus algorithms like Proof of Work (PoW), Proof of Stake (PoS) etc. In blockchain there is no third party involved or managed the transactions. This paper elaborates the basics of Blockchain, its digital transactions in multiple areas like Internet of Things, Education, Manufacturing and financial services, characteristics and limitations of consensus algorithms, gave technical guidance for a suitable consensus algorithms and further areas of research.
Application of PageRank Model for Olympic Women�s Taekwondo Rankings: Comparison of PageRank and Accumulated Point Index System

Application of PageRank Model for Olympic Women’s Taekwondo Rankings: Comparison of PageRank and Accumulated Point Index System
News headline generation based on improved decoder from transformer

Most of the news headline generation models that use the sequence-to-sequence model or recurrent network have two shortcomings: the lack of parallel ability of the model and easily repeated generation of words. It is difficult to select the important words in news and reproduce these expressions, resulting in the headline that inaccurately summarizes the news. In this work, we propose a TD-NHG model, which stands for news headline generation based on an improved decoder from the transformer. The TD-NHG uses masked multi-head self-attention to learn the feature information of different representation subspaces of news texts and uses decoding selection strategy of top-k, top-p, and punishment mechanisms (repetition-penalty) in the decoding stage. We conducted a comparative experiment on the LCSTS dataset and CSTS dataset. Rouge-1, Rouge-2, and Rouge-L on the LCSTS dataset and CSTS dataset are 31.28/38.73, 12.68/24.97, and 28.31/37.47, respectively. The experimental results demonstrate that the proposed method can improve the accuracy and diversity of news headlines.
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors

Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.g., age, race or gender, may be considered anomalous by some operators while others may consider this attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection. Our approach then learns representations which do not contain information over the nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes that are relevant for detecting anomalies, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach.
Tracking Blobs in the Turbulent Edge Plasma of a Tokamak Fusion Device

Abstract The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, GMA, Flow Walk, and Mask R-CNN) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. High entry barriers traditionally limit tokamak plasma research to a small community of researchers in the field. By making a dataset and benchmark publicly available, we hope to open the field to a broad community in science and engineering.
Web Based Application Wet Cake Snack Product Distribution Using Concept Business To Business To Consumer

Business To Business To Customers is part of E-Commerce which is a process of buying and selling transactions and distribution to consumers. Distribution of wet cakes from producers on Jl. Panglima Minal Senggoro is still manual by recording and monitoring products that run out from partners. Distribution from producers to retailers uses a profit-sharing system that has been agreed upon by both parties. The design of this system is designed using the Waterfall method and the Codeigniter Framework (CI) as well as with the design of the E-commerce Framework, namely B2B2C which produces information about cake manufacturers, knows the available products, and also helps producers in recapitulating sales to partners. The features in this system are Approval in user registration, the input of cake products, selection of payment methods, uploading proof of payment, updating of remaining products on the partner side, and dynamic reviews and ratings. So that producers can compete with other wet cake.
Text Enriched Sparse Hyperbolic Graph Convolutional Networks

Heterogeneous networks, which connect informative nodes containing text with different edge types, are routinely used to store and process information in various real-world applications. Graph Neural Networks (GNNs) and their hyperbolic variants provide a promising approach to encode such networks in a low-dimensional latent space through neighborhood aggregation and hierarchical feature extraction, respectively. However, these approaches typically ignore metapath structures and the available semantic information. Furthermore, these approaches are sensitive to the noise present in the training data. To tackle these limitations, in this paper, we propose Text Enriched Sparse Hyperbolic Graph Convolution Network (TESH-GCN) to capture the graph's metapath structures using semantic signals and further improve prediction in large heterogeneous graphs. In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer. These extracted features in conjunction with semantic features from the language model (for robustness) are used for the final downstream task. Experiments on various heterogeneous graph datasets show that our model outperforms the current state-of-the-art approaches by a large margin on the task of link prediction. We also report a reduction in both the training time and model parameters compared to the existing hyperbolic approaches through a reformulated hyperbolic graph convolution. Furthermore, we illustrate the robustness of our model by experimenting with different levels of simulated noise in both the graph structure and text, and also, present a mechanism to explain TESH-GCN's prediction by analyzing the extracted metapaths.
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report

In this report, we present the technical details of our submission to the 2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDA methods align the global features extracted from the whole video clips across the source and target domains but suffer from the spatial redundancy of feature matching in video recognition. Motivated by the observation that in most cases a small image region in each video frame can be informative enough for the action recognition task, we propose to exploit informative image regions to perform efficient domain alignment. Specifically, we first use lightweight CNNs to extract the global information of the input two-stream video frames and select the informative image patches by a differentiable interpolation-based selection strategy. Then the global information from videos frames and local information from image patches are processed by an existing video adaptation method, i.e., TA3N, in order to perform feature alignment for the source domain and the target domain. Our method (without model ensemble) ranks 4th among this year's teams on the test set of EPIC-KITCHENS-100.
Perception of academics from the aesthetics and cosmetics course on fatphobia

Objective: Analyze the perception of academics of the Aesthetics and Cosmetics course about fatphobia. Methodology: This is a qualitative study carried out at a private university in the city of Fortaleza/CE with five academics from the Aesthetics and Cosmetics course. The collection was carried out through a semi-structured and individual interview, and after transcription and analysis of the speeches according to Minayo, the following themes emerged: Definition of fatphobia; Insertion in the academic space of the course; Fatphobic situations suffered in the academic environment, and Awareness in the fight against fatphobia. Results: The analysis of the speeches led to the definition of fatphobia as prejudice and the judgment of people in relation to the "fat" body, especially students with a background in Aesthetics and Cosmetics for being "models" for their clients, emphasizing this physical characteristic since the period of academic formation until the insertion in the job market. Final Considerations: The academics of the Aesthetics and Cosmetics Course participating in the research experienced and/or witnessed some forms of fatphobia, resulting in the importance of raising awareness of future aesthetic professionals and their clients about fatphobia because, often, academics feel pressure to adapt to the imposed standards to present themselves as a “model” for their clients.
Analisis Toko dan Asal Toko Fashion Pria di Shopee Menggunakan Data Scrapping dan Exploratory Data Analysis

In the current era of technological development, e-commerce is required to be more developed, effective, efficient and become the main choice for buyers and sellers. The problem formulation of this journal is the most trending store and the city of origin of the store. This is evidenced by the best-selling products sold based on categories and types of men's fashion products. The purpose of this research is to find out the type of fashion that is loved by male buyers in the Shopee marketplace and get discounts/free shipping and can be a recommendation for what fashion products are suitable for newcomers to Shopee Indonesia. This research was conducted on April 4, 2021, which coincided with the Covid-19 pandemic. This research was carried out in 4 steps in applying Web Scrapping and Exploratory Data Analysis techniques: Observation, Data Collection, Information Extraction and Analysis. this research: (1) from the buyer's point of view it can be a reference for the goods to be purchased and get a discount/free shipping because the city of origin of the shop is the same or closest; (2) from the side of new sellers (new traders) in the Indonesian shopee marketplace, it can be a reference for any men's fashion that will sell well in the future based on the store that sells the most products.
An analysis of performance of combined aerobic biofilter – granular adsorption – nano adsorption process in seaweed industrial wastewater treatment

Wastewater of Seaweed industry contains macronutrients, alkalis, moderately biodegradable organics, and has a large volume, which it is treated using a biological process (activated sludge) followed by an adsorption process. The results of this treatment are quite effective in reducing the concentration of pollutants to reach the quality standard. However, the reuse of seaweed industrial wastewater into drinking water or process water is still not effective when using a combination of these processes. On the other hand, biological treatment such as aerobic fixed film biofilter (AF2B) and nano adsorption has high effectiveness in reducing biodegradable organic matter (BOD, COD), bacteria, and other metals. Therefore, the reuse of seaweed industrial wastewater into drinking water can be done by combining the two types of processes. The aim of the research was to study the ability of the AF2B – Granular adsorption – nano adsorption process in reducing pollutants in seaweed industrial wastewater. The experiment is conducted by flowing wastewater continuously into a series of process an AF2B reactor, GAC, and CNTs column. By making changes to the factors that affect the performance of each process, and then measuring the pollutant concentration at the input and output of each process, so the performance of each process can be known. Data is analysed to obtain optimum condition in each process that used for design process in wastewater treatment plant. The results showed that of the AF2B/GAC process was able to reduce contaminants such as TSS, BOD, COD, NH3‑N and Chlorine by 98 %, 99 %, 97.3 %, 97.8 %, and 100 % respectively. Furthermore, in the CNTs process, all pollutants not detected until they meet drinking water quality standards
Similar model assisted phase unwrapping for three-dimensional measurement based on fringe projection

A novel algorithm for phase unwrapping with the assistance of similar model of scene is proposed. The major steps of proposed algorithm are as follows. (1) Obtain wrapped phase and modulation of fringe by phase shift method. (2) Extract feature points of scene from background of fringe. (3) Obtain scaling and motion parameters of model from 2D and 3D coordinates of feature points. (4) Calculate reference phase from model by motion parameter and shape of model according to camera model. (5) Retrieve absolute phase by wrapped phase of scene and reference phase. The experimental results are demonstrated. If the similar model of scene is available, the proposed algorithm is efficient for phase unwrapping in phase measurement profilometry without additional patterns, even though the scene contains isolated parts.
Building Experience and Confidence in HPC Practitioners through the Project-Based, Hands-On Practical HPC Course

The MIT SuperCloud and Lincoln Laboratory Supercomputing Center have been introducing High Performance Computing (HPC) to a new audience through the ”Practical High Performance Computing: Scaling Beyond your Laptop” class for the past four years. This informal class, open to the entire MIT community, introduces HPC, identifies canonical HPC workflows, and provides hands-on activities to explore the challenges encountered in the HPC environment. The students use their own research applications as project work to apply the class concepts to gain experience and confidence in using an HPC system and throughout the scaling process. Survey data collected before and after each class demonstrate that students feel they gain familiarity and experience in the concepts taught in the course and confidence in their own ability to apply those concepts.
Revisiting Pretraining Objectives for Tabular Deep Learning

Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from pretraining, which is a workhorse of DL for vision and NLP. For tabular problems, several pretraining methods were proposed, but it is not entirely clear if pretraining provides consistent noticeable improvements and what method should be used, since the methods are often not compared to each other or comparison is limited to the simplest MLP architectures. In this work, we aim to identify the best practices to pretrain tabular DL models that can be universally applied to different datasets and architectures. Among our findings, we show that using the object target labels during the pretraining stage is beneficial for the downstream performance and advocate several target-aware pretraining objectives. Overall, our experiments demonstrate that properly performed pretraining significantly increases the performance of tabular DL models, which often leads to their superiority over GBDTs.
EFL Instructors’ Perceptions of Utilizing Mobile-Assisted Language Learning in Higher Education

Abstract Introduction: One of the prominent approaches in language education is Mobile-Assisted Language Learning (MALL) due to recent advances in technology. To benefit from MALL effectively and develop it when it is required, it is important to know the perceptions of the ones who use it. Although much research related to students’ and teachers’ perceptions of MALL in primary and secondary education is present in the literature, research related to perceptions of instructors in higher education is limited. Thus, this study aimed at identifying the instructors’ perceptions of utilizing MALL in higher education. Methods: As the perceptions of the participants were aimed to be explored in detail, the qualitative research design was adopted. An open-ended questionnaire was developed by the researchers and administered to 24 instructors working at the English Language Teaching (ELT) departments of different universities in Turkey. The data were analyzed through content analysis. Results: It was found that the instructors defined MALL as using mobile technologies for language learning. Moreover, the participants had positive perceptions about MALL as it had some benefits, such as anytime, anywhere learning, stronger students’ interest, higher motivation and autonomy, individualized and collaborative learning. However, MALL users faced some challenges, such as weak internet connection and battery, time consumption in terms of preparing content for the class, classroom management problems and low digital literacy of both students and instructors. Discussion: The results of the study were parallel to the findings of the previous studies in the literature. This study found that MALL contributed to learning independent of time and place, high motivation and interest, saving time and energy, individualized learning, collaborative learning, autonomous learning, and learning with fun. Limitations: This study is not without limitations. Triangulation of data with in-depth interviews could be implemented to increase detailed information and reliability. Conclusions: The results of the study showed that instructors who had benefitted from technology in their teaching process had positive perceptions despite some challenges. To this end, it is suggested that MALL be utilized in language teaching by minimizing its challenges.