Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM)

Authors:
raw_author_name: Budi Warsito
raw_affiliation_string: Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
raw_author_name: Rukun Santoso
raw_affiliation_string: Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia

Abstract: The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.
Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Authors:
raw_author_name: Wei Li
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
raw_author_name: Megha Chakraborty
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany; Institute of Geosciences, Goethe University, 60438 Frankfurt am Main, Germany
raw_author_name: Jonas Köhler
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany; Institute of Geosciences, Goethe University, 60438 Frankfurt am Main, Germany
raw_author_name: Claudia Quinteros-Cartaya
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
raw_author_name: Georg Rümpker
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany; Institute of Geosciences, Goethe University, 60438 Frankfurt am Main, Germany
raw_author_name: Nishtha Srivastava
raw_affiliation_string: Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany; Institute of Geosciences, Goethe University, 60438 Frankfurt am Main, Germany

Abstract: Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.
All-optical image denoising using a diffractive visual processor

Authors:
raw_author_name: Çağatay Işıl
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Tianyi Gan
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Huseyin Karaca
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Jingxi Li
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Deniz Mengu
raw_affiliation_string: Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Mona Jarrahi
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Kaan Akşit
raw_affiliation_string: University College London, Department of Computer Science, London, United Kingdom
raw_author_name: Aydogan Ozcan
raw_affiliation_string: Bioengineering Department, University of California, Los Angeles, CA, 90095, USA

Abstract: Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.
A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

Authors:
raw_author_name: Yue Yang
raw_affiliation_string: School of Cyberspace Security, Hainan University, Haikou, 570228, China
raw_author_name: Jieren Cheng
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Zhaowu Liu
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Huimin Li
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Ganglou Xu
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China

Abstract: Abstract With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge.
Recommendation systems-based software requirements elicitation process—a systematic literature review

Authors:
raw_author_name: Faiz Akram
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, A Central University, New Delhi, 110025, India
raw_author_name: Tanvir Ahmad
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, A Central University, New Delhi, 110025, India
raw_author_name: Mohd. Sadiq
raw_affiliation_string: Software Engineering Lab., Computer Engineering Section, UPFET, Jamia Millia Islamia, A Central University, New Delhi, 110025, India

Abstract: Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.
A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and <scp>BiLSTM</scp>

Authors:
raw_author_name: Wenshuai Lin
raw_affiliation_string: Pazhou Laboratory Guangzhou 510006 China; School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou 510006 China
raw_author_name: Bin Zhang
raw_affiliation_string: School of Automation and the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou 510006 Guangdong China
raw_author_name: Renquan Lu
raw_affiliation_string: School of Automation and the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou 510006 Guangdong China

Abstract: In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Effects of Multimodal Exercise With Augmented Reality on Cognition in Community-Dwelling Older Adults

Authors:
raw_author_name: Jesus del Pozo-Cruz
raw_affiliation_string: Department of Physical Education and Sports, University of Seville, Sevilla, Spain
raw_author_name: Alexandre Bernardino
raw_affiliation_string: Instituto de Sistemas e Robótica - Instituto Superior Técnico, Portugal

Abstract: This study aims to investigate the effects of an exercise intervention using multimodal exercise with augmented reality and multimodal exercise-only on cognitive function in older adults living in a community dwelling.Quasi-experimental research study.In this control study, 78 participants were divided into 2 experimental groups (with sessions 3 times a week for 12 weeks) and a control group (CG).EG1 participated in a multimodal exercise-only intervention program, EG2 participated in a multimodal exercise program with augmented reality exergames, and CG continued its usual activities. Participants were assessed at baseline and postintervention after 12 weeks.Comparison between baseline and postintervention at 12 weeks showed significant improvements in executive functions, verbal fluency, choice reaction time, and dual task in EG1, whereas there were improvements in general cognition, executive functions, verbal fluency, discrimination reaction time, and depression in EG2 (P ≤ .05). The clinical effect sizes of the interventions were large for overall cognition, executive functions, and reaction time on single- and dual-task reaction time in EG1 and for overall cognition, executive functions, and verbal fluency in EG2.The intervention programs showed significant improvements in several cognitive domains. The multimodal exercise-only showed improvements in more variables than the multimodal exercise with augmented reality, but the augmented reality group showed greater changes between baseline and postintervention.
Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction

Authors:
raw_author_name: Pengpeng Shao
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Jianhua Tao
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China; Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Dawei Zhang
raw_affiliation_string: The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Abstract: A Temporal Knowledge Graph (TKG) is a sequence of Knowledge Graphs (KGs) attached with time information, in which each KG contains the facts that co-occur at the same timestamp. Temporal knowledge prediction (TKP) aims to predict future events given observed historical KGs in TKGs, which is essential for many applications to provide intelligent analysis services. However, most existing TKP methods focus on entity and relation prediction tasks but ignore the importance of time prediction tasks. Furthermore, there is uncertainty in time prediction, and it is difficult for prediction models to model it completely. In this work, we propose a collaboration framework with Bayesian Hypernetwork and Time-Difference Evolutional Network (BH-TDEN) to address these problems. First, we begin with the time prediction task, and we present a Bayesian hypernetwork to model the uncertainty of events time. For the input of Bayesian hypernetwork, we design a novel time-difference evolutional network to obtain the entities and relations embedding. Specifically, we propose an auto-regressive time gate parameterized by the time difference of adjacent KGs in entity and relation encoder to learn the time-sensitive TKG embedding, which not only learns the relationship between the given time information and TKG embedding but also provides more expressive TKG embedding for Bayesian hypernetwork to accurately predict the time of future events. Furthermore, we also present a novel relation updating mechanism that employs the neighbor relations of the subject corresponding to the current relation to learn more adaptive relation embedding. Extensive experiments demonstrate that the proposed method obtains considerable time prediction and link prediction performance on four TKG benchmark datasets.
Enhancing <scp>scene‐text</scp> visual question answering with relational reasoning, attention and dynamic vocabulary integration

Authors:
raw_author_name: Mayank Agrawal
raw_affiliation_string: Department of Computer Engineering and Applications GLA University Mathura India
raw_author_name: Anand Singh Jalal
raw_affiliation_string: Department of Computer Engineering and Applications GLA University Mathura India
raw_author_name: Himanshu Sharma
raw_affiliation_string: Department of Computer Engineering and Applications GLA University Mathura India

Abstract: Abstract Visual question answering (VQA) is a challenging task in computer vision. Recently, there has been a growing interest in text‐based VQA tasks, emphasizing the important role of textual information for better understanding of images. Effectively utilizing text information within the image is crucial for achieving success in this task. However, existing approaches often overlook the contextual information and neglect to utilize the relationships between scene‐text tokens and image objects. They simply incorporate the scene‐text tokens mined from the image into the VQA model without considering these important factors. In this paper, the proposed model initially analyzes the image to extract text and identify scene objects. It then comprehends the question and mines relationships among the question, OCRed text, and scene objects, ultimately generating an answer through relational reasoning by conducting semantic and positional attention. Our decoder with attention map loss enables prediction of complex answers and handles dynamic vocabularies, reducing decoding space. It outperforms softmax‐based cross entropy loss in accuracy and efficiency by accommodating varying vocabulary sizes. We evaluated our model's performance on the TextVQA dataset and achieved an accuracy of 53.91% on the validation set and 53.98% on the test set. Moreover, on the ST‐VQA dataset, our model obtained ANLS scores of 0.699 on the validation set and 0.692 on the test set.
Exploring variables explaining elementary school student’s cognitive, social and emotional competencies using random forest

Authors:
raw_author_name: Hyewon Chung

Abstract: This study was designed to explore major predictors related to cognitive, social and emotional competencies of elementary school students to discuss the implication for competencies development. To do this, random forest was applied to 1st wave data(4th graders) of the Seoul Educational Longitudinal Study of Students(SELS) 2020, and Shapley addictive explanations (SHAP) analysis was conducted to explore the importance of major predictors and the relationship between competencies and the predictors. The main result are as follows. First, the predictors commonly derived from three competencies were 'class attitude' and 'ICT competency'. The more positive class attitude and higher ICT competency, the higher cognitive and social-emotional competencies. Second, 'Anxiety' was found to major predictor of three competencies, but it was found to have positive relationship with cognitive competency and negative relationship with social-emotional competency. Third, there were the most common predictors explaining social and emotional competencies. Based on these findings, implications for developing competencies and suggestions for future research are discussed.
Potensi Konsep Matematika dalam Penggunaan Aperture di Fotografi

Authors:
raw_author_name: None Dona Fitriawan
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: Dede Suratman
raw_affiliation_string: Universitas Tanjungpura
raw_author_name: Agung Hartoyo
raw_affiliation_string: Universitas Tanjungpura

Abstract: This study aims to help understand how the use of aperture works with mathematical concepts so that the use of this method is applied to photography activities and as a medium for learning mathematics in schools. The method used in this study is descriptive with the form of action research, because this study describes the mathematical concepts contained in the use of aperture in photography. From the interview, researchers collected information on mathematical concepts contained in the sequence located behind the F-stop number and explored the relationship between the F-stop number and the diameter of the circular aperture opening area in the lens, there are several mathematical concepts such as fractional numbers, geometric series, scales, comparisons, magnitudes &amp; units, space builds, to the area of a circle. In this case, it proves that in the use of Aperture in photography, there are mathematical concepts related to school mathematics learning
Analysis of the Readability Characteristics of Educational Text: based on Texts in Korean Language, Social Studies, and Science

Authors:
raw_author_name: Soyoung Choi
raw_author_name: Eunah Park
raw_author_name: Taejin Byun
raw_author_name: Myeungjin Ha

Abstract: In this study, an educational text database of Korean language, social studies, and science departments was established, and a qualitative checklist for measuring readability was developed. After setting the level of readability through the teacher