Reviewerly/exHarmony huggingface dataset:
Time Series Prediction of Bitcoin Cryptocurrency Price Based on Machine Learning Approach

Over the past few years, Bitcoin has attracted the attention of numerous parties, ranging from academic researchers to institutional investors. Bitcoin is the first and most widely used cryptocurrency to date. Due to the significant volatility of the Bitcoin price and the fact that its trading method does not require a third party, it has gained great popularity since its inception in 2009 among a wide range of individuals. Given the previous difficulties in predicting the price of cryptocurrencies, this project will be developing and implementing a time series approach-based solution prediction model using machine learning algorithms which include Support Vector Machine Regression (SVR), K-Nearest Neighbor Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) to determine the trend of bitcoin price movement, and assessing the effectiveness of the machine learning models. The data that will be used is the close prices of Bitcoin from the year 2018 up to the year 2023. The performance of the machine learning models is evaluated by comparing the results of R-squared, mean absolute error (MAE), mean squared error (RMSE), and also through a visualization graph of the original close price and predicted close price of Bitcoin in a dashboard. Among the models compared, LSTM emerged as the most accurate, followed by SVR, while XGBoost and KNN exhibited comparatively lower performance.
OPTIMALISASI APLIKASI MENDELEY SEBAGAI SARANA PENELITIAN DI SEKOLAH TINGGI TEOLOGI TABGHA BATAM

Skills in using technology facilities and applications are indispensable in the preparation of research journals and dedication to lecturers at the Tabgha Theological Seminary of Batam, especially for journal publications that meet publication standards. The use of the right application is an effort made to meet the above standards, therefore Tabgha Theological Seminary Batam uses the tools available in the Mendeley application. In this service, the method applied is by holding Mendeley application training which contains an introduction to the Mendeley application and its benefits, installation steps on lecturer computers, installation on Microsoft Word, application of citations and bibliography. The result of this training is that lecturers can optimally use the Mendeley application so that they can present journal publications that meet predetermined standards and templates. The results of the Community Service questionnaire initially found that only 25% had Mendeley installed;&#x0D; 12.5% ​​already have a Mendeley account and 12.5% ​​are able to use Mendeley in various research reports, and after training 100% of the lecturers have installed, have an account and are able to use the Mendeley Application proficiently.
EFEKTIVITAS MODEL RECIPROCAL TEACHING DENGAN MODEL STUDENT TEAM ACHIEVEMENT DIVISION DALAM PEMBELAJARAN PUISI

This research is entitled "Effectiveness of the Reciprocal Teaching Model with the STAD Model in Learning Poetry for Class X SMA Negeri 11 Garut in the Academic Year 2022/2023". theoretical. This study aims to analyze differences in the effectiveness of learning by using the Student Team Achievement Division (STAD) and Reciprocal Teaching models in improving poetry learning in class X students of SMA Negeri 11 Garut. This study used a quasi-experimental method, namely by giving treatment to two different sample classes. The research population was students of class X MIPA SMA Negeri 11 Garut with a sample of class X MIPA 7 as the Student Team Achievement Division (STAD) class and class X MIPA 8 as the Reciprocal Teaching class. Data collection techniques in the form of tests carried out after being given treatment. The results showed that (1) an increase in students' poetry learning abilities using the Student Team Achievement Division (STAD) model obtained a high increase. (2) Increasing students' poetry learning ability using the Reciprocal Teaching model experienced a high increase. (3) Learning with the Reciprocal Teaching model is more effective than learning with the Student Team Achievement Division (STAD) model in improving students' poetry learning abilities. Based on the research findings above, the two learning models are appropriate for use by teachers as an alternative to improve poetry learning abilities.Keywords: Poetry Learning, STAD, Reciprocal Teaching.
TaLNA App: An Interactive Numeracy Mobile Learning Application for Children with Autism

In light of the COVID-19 pandemic’s disruptions to education, there is a growing need for alternative learning methods, particularly for children with autism who thrive on routine and familiarity. This study aims to enhance numeracy learning for children with autism by using the touchscreen-assistive learning numeracy application (TaLNA) during the post-pandemic period, aligning with Sustainable Development Goal (SDG) 4 (quality education). Our primary focus is to leverage mobile learning through the TaLNA application to provide specialized educational content and remote progress monitoring. Combining mobile learning with SDG 4 principles ensures continuous access to education, promoting inclusivity and equity, even in challenging circumstances. Mobile learning offers personalized numeracy experiences tailored to individual needs, maintaining educational consistency and structure amidst disrupted routines and environments. This research has significant implications for instructors, teachers, and parents of children with autism, providing valuable insights for their pedagogical approaches.
Effective Backdoor Attack on Graph Neural Networks in Spectral Domain

The susceptibility of Graph Neural Networks (GNNs) to backdoor attacks poses a significant potential threat to GNN-based Internet of Things (IoT) systems. In such attacks, GNNs are manipulated to behave abnormally when presented with maliciously crafted samples known as trigger samples, which can be exploited by attackers for their malicious intent. To address this issue, this paper studies the vulnerability of GNNs from the attacker’s angle by proposing a novel backdoor attack on GNNs. Our technincal motivation is that most existing backdoor attacks only treat GNNs as black box and define trigger samples in the spatial domain, which lack deeper insights and further exploitation of GNNs, limiting the attacking effectiveness. Inspired by frequency-based backdoor attacks in vision domain and spectral graph theory, we propose an effective graph backdoor attack based on the spectral domain of graph data, which injects trigger signals into the frequency spectrum of normal attributed graphs. By injecting subtle trigger signal into the important frequency band of important node features, the learning of backdoor and clean data are entangled. As a result, it becomes hard for the defender to remove the backdoor without degrading the model’s normal performance, improving the robustness of the attack. Experiments on both homophilic graphs and heterophilic graphs, with commonly used GNN architectures show that our proposed method achieves high attack success rate without significantly degrading the normal performance of the victim GNN. The attack is also shown to be robust against various processing towards graph data due to the entanglement strategy.
Pengaruh Intensitas Belajar di Rumah terhadap Hasil Belajar Matematika Siswa Kelas V SDN Gedanganak 03 Ungaran

This research was motivated by the lack of maximum learning outcomes in Mathematics and the low intensity of learning at home by fifth grade students at SDN Gedanganak 03 Ungaran. The purpose of this study was to determine whether or not the intensity of studying at home had an effect on the mathematics learning outcomes of fifth grade students at SDN Gedanganak 03 Ungaran. The type of research used is ex post facto quantitative research. The population in this study amounted to 35 students of class V. The sampling technique used saturated sampling because all the populations in this study were used as samples. The variables studied included: the intensity of studying at home and learning outcomes of Mathematics. Data collection techniques using a questionnaire. Then the results of the data obtained were analyzed using a simple linear regression test. Based on the results of the research and after the data was analyzed, there was an effect of the intensity of studying at home on learning outcomes in Mathematics. This is shown by the results of simple linear regression, namely the value of Sign. smaller than 0.05 (0.00 &lt;0.05) or, tcount &gt; ttable (5.279 &gt; 2.035). Then Ha is accepted and H0 is rejected. This means that there is a significant influence between the intensity of studying at home on the mathematics learning outcomes of fifth grade students at SDN Gedanganak 03 Ungaran.Keywords: learning intensity, home, learning outcomes, mathematics
Proposal of a Home Monitoring System Using a High-Resolution Vibration Analysis

This paper proposes a new home monitoring system observing house vibrations using a high-resolution vibration analysis termed as the Geometric Mean for Real-time Serial-to-parallel converter (GMRS). This system can be applicable to not only the elderly monitoring, but also crime prevention system. We verify the performance of the proposed system by experiments. It is shown that the proposed system is superior to a conventional system based on Fourier transform.
A fuzzy cognitive map approach to understand agricultural system and food prices in Türkiye: Policy recommendations for national food security

Abstract Once one of the few self‐sufficient food countries in the world, Turkey has become dependent on imports to feed its population. Food prices have climbed to among the highest in the world, severely threatening the food security of the country. Most researchers generally attributed the high prices to the increased input costs of agriculture. Although the role of input prices cannot be denied, this paper focuses on a neglected problem that can account for food price inflation: the attitudes and behaviours of farming communities towards agriculture. Through fuzzy cognitive map methodology, known to be very effective in understanding complex networks of problems, we identify and map the relationships among the factors affecting the agriculture system, develop interview and literature‐driven scenarios, and test these scenarios to demonstrate their role in explaining the relationship between attitudes and behaviours of farming communities and food prices in Turkey. Our findings provide recommendations to policymakers.
Facilitating Self-regulated Learning Using Mobile Learning Games

Facilitating Self-regulated learning for students has become a concern recently. This is important for students because this mental activity increases a sense of control and confidence in fulfilling learning tasks, thus increasing self-efficacy, motivation, and autonomy in learning. Self-Regulated Learning (SRL) helps students progress and develop personally and socially. It is known that educators usually train self-regulated using learning media such as Massive Open Online Courses (MOOCs), games, augmented reality, virtual reality, and others. However, games are the most easiest and enjoyable learning media because games can promote independent learning in a constructivist, social problem-solving context. Some games would be better if they were developed with learning in mind. Therefore, the research objective of this paper is to develop games that can enhance self-regulatory learning. The type of study used was a 4D modeling application development study with the participation of 120 vocational high school students. The results showed that students who used game-based mobile learning improved in three aspects of self-regulated learning: metacognition, motivation, and behavior.
Obstacle Avoidance for Automated Guided Vehicles Based on Deep Reinforcement Learning

Automated Guided Vehicles AGVs play a vital role in enhancing productivity and efficiency within factory environments. However, their safe and effective operation heavily relies on the ability to navigate through complex spaces while avoiding obstacles. The significance of obstacle avoidance in AGV systems is emphasized, considering its impact on ensuring smooth material flow, minimizing collision risks, and optimizing production processes. The existing state of obstacle avoidance applications in factory settings reveals certain limitations and challenges. Current research and industrial implementations often rely on rule-based approaches or predefined paths, which may not adequately adapt to dynamic environments or unexpected obstacles. Additionally, some methods lack the ability to handle diverse obstacle types or efficiently plan optimal paths, leading to sub-optimal navigation or reduced throughput. In response to these challenges, this study proposes a novel approach for dynamic obstacle avoidance of AGVs based on deep reinforcement learning. By leveraging the Deep Deterministic Policy Gradient (DDPG) model, the AGV learns to make real-time decisions and navigate through dynamic obstacles effectively. The integration of deep neural networks with the actor-critic framework enables the AGV to learn and adapt optimal policies for obstacle avoidance in real-time, overcoming the limitations of rule-based methods. Simulation experiments are conducted to validate the performance and feasibility of the proposed approach. The results demonstrate that the DDPG-based method allows the AGV to successfully navigate through both dynamic and static obstacles in a dynamic environment, improving safety and efficiency in intelligent manufacturing applications.
Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills.
3D Separable Winograd Convolutions with an Efficient Pipeline

As time goes on, a neural network architecture becomes more and more complicated. Among of layers of the neural networks, the convolution layer accounts for a considerable proportion of the overall neural network operation, demonstrated by many pieces of literature. Therefore, this paper will focus on computing efficiency to improve the convolution layer, further optimizing the neural network.
Bi-Directional Hybrid Deep Learning model for Speaker Iden-tification

Speaker identification is the process of automatically determining who is speaking from the known speakers by the model. It is crucial in voice-based authentication, forensic investigations, security and surveillance. In recent studies, the combination of convolutional neural network (CNN) and recurrent neural network (RNN) variants performed better than separate models of both. However, only limited studies are conducted in speaker identification using a combination of CNN and RNN variants. In this study, we proposed speaker identification using hybrid two-dimensional CNN (2DCNN) and bidirectional gated recurrent unit (BiGRU) to improve performance. The proposed model integrates the advantage of 2DCNN and BiGRU layers to improve the performance of the model. 2DCNN layers have the advantage of extracting short-term spatial features from input data and it has a limited number of parameters for computation. BiGRU layers have an advantage in extracting long-term temporal dependency between the features in both directions (i.e. backward and forward) and it is efficient in achieving convergence during training. Spectrograms of the speech were used as input in our proposed model because of the rich acoustic features of the speaker. To compare the performance of the proposed model, additional experiments are conducted using the models 2DCNN, CNN-LSTM, CNN-BiLSTM and CNN-GRU. The experiments were conducted on the VoxCeleb1 audio dataset, which consists of 153,516 utterances collected from the 1251 speakers. The accuracy, precision, recall and f1 score of the proposed model are 98.28%, 99.08%, 98.92% and 98.97% respectively. The proposed model was compared with the existing works to show the effectiveness of the proposed model. The experiment results and the comparison with the existing works show that the proposed model has higher performance than both existing works and other models experimented in this study.
Systematic Review from Different Encryption Techniques Perspectives

The volume of Big Data produced from various sources is always growing. Such vast amounts of data cannot be stored using conventional techniques. As a result, the majority of businesses have switched to using cloud storage as a substitute for storing Big Data. Cloud storage has made great progress, but there are still several problems, such security worries. This article examines Big Data, its difficulties, and many security concerns from the perspectives of various encryption approaches. Additionally, it presents various views to address these issues and suggests a new classification of Big Data security difficulties
Korean named entity recognition based on language-specific features – CORRIGENDUM

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Quality framework for explainable artificial intelligence (XAI) and machine learning applications

Artificial intelligence (AI) and machine learning (ML) applications are applied in many applications and devices, and it is expected to grow by 15 trillion dollars by 2030. There are more demands for explainable AI (XAI) for its improvement in explainability attributes of the AI quality. Software quality is defined as the product meets its required product specification and is expected to behave as it is expected by the stakeholders. Furthermore, we need a systematic approach to the design, development, implementation, and testing of AI products. Therefore, this chapter proposes a software engineering framework for AI and ML applications (SEF - AI and ML) supporting the complete XAI application development phases including a reference architecture to standardize across XAI applications. The framework has been validated through a case study involving an explainable Chatbot using business process modeling notations (BPMN), modeling, and simulation. The results demonstrate a 98% utilization rate and improved time efficiency, confirming the validation of performance and resource requirements for cloud-driven AI Chatbot services. Therefore, SEF - AI and ML has the potential to be a standard framework for AI and ML applications to achieve the desired quality and certainty of AI products and services.
Science and Technology for Advanced Manufacturing Projects (DOD)

Federal Grants & ContractsVolume 47, Issue 24 p. 4-4 Grant Alerts Science and Technology for Advanced Manufacturing Projects (DOD) First published: 15 November 2023 https://doi.org/10.1002/fgc.33364Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue2416 November 2023Pages 4-4 RelatedInformation
Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.
Effect of Tranexamic Acid in Prevention of Post-Partum Haemorrhage: A Prospective Observational Study

Background: Treatment of Post partum haemorrhage relies primarily on uterotonics, but early use of Tranexamic Acid (TXA) has become part of several recommended algorithms. Recent data has demonstrated that Tranexamic Acid (TXA), an antifibrinolytic agent, reduces death due to bleeding when used as a treatment for PPH. This study was conducted with an objective to see the role of tranexamic acid along with uterotonic agent (oxytocin) in prevention of postpartum hemorrhage. Materials and Methods: The present prospective observational study carried out at the Maternal and Child Health (MCH) wing Department of a rural tertiary care hospital and medical college situated in the central India during January 2021 to December 2022. A total sample size of 1640 patients attending labour room for vaginal birth or caesarean section in 3rd stage of labour were included in the study. Total 1640 women were further divided into two groups, Group A (receiving both tranexamic acid along with oxytocin) and Group B (receiving only oxytocin). Blood loss in each group was measured by visual method and Gravimetric (measurement by weight) method. They were followed till delivery; maternal and neonatal outcome was studied. Statistical analysis was done by using SPSS 27.0 version and GraphPad Prism 7.0 version and p&lt;0.05 considered as level of significance. Results: The majority of patients in Group A and Group B were between 26-30 years of age group. There was no significant difference between the groups in terms of maternal age, gestational age, gravida and booking status. The mean foetal birth weight among Group A was 2421.28 ± 626.91 and Group B was 2381.38 ± 721.20 with no significant difference between the groups. There was significant reduction in blood loss in study group A as compared to control group in both vaginal and LSCS birth with statistically significant difference. Conclusion: Tranexamic acid injection, an antifibrinolytic agent when given prophylactically after delivery of placenta along with oxytocin appears to reduce the blood loss during normal labour as well as caesarean sections effectively.